diff --git a/.github/labeler.yml b/.github/labeler.yml new file mode 100644 index 000000000..fdb0e58b6 --- /dev/null +++ b/.github/labeler.yml @@ -0,0 +1,63 @@ +version: 1 +labels: + - label: "size-XS" + size: + exclude-files: ["poetry.lock"] + below: 10 + - label: "size-S" + size: + exclude-files: ["poetry.lock"] + above: 9 + below: 100 + - label: "size-M" + size: + exclude-files: ["poetry.lock"] + above: 100 + below: 500 + - label: "size-L" + size: + exclude-files: ["poetry.lock"] + above: 499 + below: 1000 + - label: "size-XL" + size: + exclude-files: ["poetry.lock"] + above: 999 + - label: "airflow" + files: + - "^src/airflow/.*" + - label: "Documentation" + files: + - "^docs/.*" + - label: "Dataset" + files: + - "^src/gentropy/dataset/.*" + - label: "Method" + files: + - "^src/gentropy/method/.*" + - label: "Datasource" + files: + - "^src/gentropy/datasource/.*" + - label: "Step" + files: + - "^src/gentropy/[a-zA-Z]\\w+\\.py" + - label: "Feature" + title: "^feat.*" + - label: "Bug" + title: "^fix.*" + - label: "Refactor" + title: "^refactor.*" + - label: "Chore" + title: "^chore.*" + - label: "CI" + title: "^ci.*" + - label: "Test" + title: "^test.*" + - label: "Documentation" + title: "^docs.*" + - label: "Performance" + title: "^perf.*" + - label: "Build" + title: "^build.*" + - label: "Revert" + title: "^revert.*" diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 000000000..aca51c6ea --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,28 @@ +## ✨ Context + + + +## πŸ›  What does this PR implement + + + +## πŸ™ˆ Missing + + + +## 🚦 Before submitting + +- [ ] Do these changes cover one single feature (one change at a time)? +- [ ] Did you read the [contributor guideline](https://opentargets.github.io/gentropy/development/contributing/#contributing-checklist)? +- [ ] Did you make sure to update the documentation with your changes? +- [ ] Did you make sure there is no commented out code in this PR? +- [ ] Did you follow [conventional commits](https://www.conventionalcommits.org/en/v1.0.0/) standards in PR title and commit messages? +- [ ] Did you make sure the branch is up-to-date with the `dev` branch? +- [ ] Did you write any new necessary tests? +- [ ] Did you make sure the changes pass local tests (`make test`)? +- [ ] Did you make sure the changes pass pre-commit rules (e.g `poetry run pre-commit run --all-files`)? diff --git a/.github/workflows/artifact.yml b/.github/workflows/artifact.yml new file mode 100644 index 000000000..2aa634a7e --- /dev/null +++ b/.github/workflows/artifact.yml @@ -0,0 +1,85 @@ +name: Build and Push to Artifact Registry + +"on": + push: + branches: ["*"] + tags: ["*"] + +env: + PROJECT_ID: open-targets-genetics-dev + REGION: europe-west1 + GAR_LOCATION: europe-west1-docker.pkg.dev/open-targets-genetics-dev + REPOSITORY: gentropy-app + PYTHON_VERSION_DEFAULT: "3.10.8" + +jobs: + build-push-artifact: + runs-on: ubuntu-latest + steps: + - name: "Checkout" + uses: "actions/checkout@v3" + + - name: "auth" + uses: "google-github-actions/auth@v2" + with: + credentials_json: "${{ secrets.SERVICE_ACCOUNT_KEY }}" + + - name: "Set up Cloud SDK" + uses: "google-github-actions/setup-gcloud@v2" + + - name: "Use gcloud CLI" + run: "gcloud info" + + - name: Set up QEMU + uses: docker/setup-qemu-action@v3 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: "Docker auth" + run: |- + gcloud auth configure-docker ${{ env.REGION }}-docker.pkg.dev --quiet + + - name: Quick Docker build (gentropy only, AMD64 only, with layer cache) + uses: docker/build-push-action@v6 + with: + platforms: linux/amd64 + push: true + tags: "${{ env.GAR_LOCATION }}/${{ env.REPOSITORY }}/gentropy:${{ github.ref_name }}" + context: . + cache-from: type=gha + cache-to: type=gha,mode=max + + - name: Build and push gentropy image + if: github.ref == 'refs/heads/dev' || startsWith(github.ref, 'refs/tags/v') + uses: docker/build-push-action@v6 + with: + platforms: linux/amd64,linux/arm64 + push: true + tags: "${{ env.GAR_LOCATION }}/${{ env.REPOSITORY }}/gentropy:${{ github.ref_name }}" + context: . + + - name: Build and push VEP image + if: github.ref == 'refs/heads/dev' || startsWith(github.ref, 'refs/tags/v') + uses: docker/build-push-action@v6 + with: + platforms: linux/amd64 + push: true + tags: "${{ env.GAR_LOCATION }}/${{ env.REPOSITORY }}/custom_ensembl_vep:${{ github.ref_name }}" + context: . + file: "src/vep/Dockerfile" + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ env.PYTHON_VERSION_DEFAULT }} + - name: Install and configure Poetry + uses: snok/install-poetry@v1 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + + - name: Build and push spark cluster dependencies + run: | + make build diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml new file mode 100644 index 000000000..8e9e6f92e --- /dev/null +++ b/.github/workflows/labeler.yml @@ -0,0 +1,14 @@ +name: Label PRs + +"on": + - pull_request + - issues + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - uses: srvaroa/labeler@master + env: + GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}" diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index 5fb541572..f27ae499e 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -17,6 +17,11 @@ jobs: uses: actions/setup-python@v5 with: python-version: 3.10.8 + - name: Set up Java + uses: actions/setup-java@v4 + with: + java-version: "8" + distribution: "temurin" - name: Install and configure Poetry uses: snok/install-poetry@v1 with: diff --git a/.github/workflows/pr_release_trigger.yaml b/.github/workflows/pr_release_trigger.yaml index 27c4d7dec..d97828754 100644 --- a/.github/workflows/pr_release_trigger.yaml +++ b/.github/workflows/pr_release_trigger.yaml @@ -15,7 +15,7 @@ jobs: source_branch: "dev" destination_branch: "main" pr_title: "chore: trigger release process" - pr_body: ":warning: *This PR requires a MERGE or REBASE COMMIT (Don't squash!)*" + pr_body: ":warning: *This PR requires a MERGE COMMIT (Don't squash!)*" pr_label: "auto-pr" pr_draft: false pr_allow_empty: true diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 385e0d9ec..4eb48b992 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -23,22 +23,35 @@ jobs: contents: write steps: + # NOTE: commits using GITHUB_TOKEN does not trigger workflows + - uses: actions/create-github-app-token@v1 + id: trigger-token + with: + app-id: ${{ vars.TRIGGER_WORKFLOW_GH_APP_ID}} + private-key: ${{ secrets.TRIGGER_WORKFLOW_GH_APP_KEY }} - uses: actions/checkout@v4 with: fetch-depth: 0 repository: opentargets/gentropy token: ${{ secrets.GITHUB_TOKEN }} + persist-credentials: false - name: Python Semantic Release id: semrelease - uses: python-semantic-release/python-semantic-release@master + # v9.6.0 is required due to the python v3.12 in the newer version of semantic release action which + # breaks the poetry build command. + uses: python-semantic-release/python-semantic-release@v9.6.0 with: - github_token: ${{ secrets.GITHUB_TOKEN }} + github_token: ${{ steps.trigger-token.outputs.token }} - name: Publish package to GitHub Release uses: python-semantic-release/upload-to-gh-release@main if: ${{ steps.semrelease.outputs.released }} == 'true' with: + # NOTE: allow to start the workflow when push action on tag gets executed + # requires using GH_APP to authenitcate, otherwise push authorised with + # the GITHUB_TOKEN does not trigger the tag artifact workflow. + # see https://github.com/actions/create-github-app-token github_token: ${{ secrets.GITHUB_TOKEN }} tag: ${{ steps.semrelease.outputs.tag }} diff --git a/.gitignore b/.gitignore index 07c8bef82..b62418654 100644 --- a/.gitignore +++ b/.gitignore @@ -11,3 +11,6 @@ src/airflow/logs/* !src/airflow/logs/.gitkeep site/ .env +.coverage* +wandb/ +hail*.log diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 45736c8af..e62a9d790 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,9 +1,12 @@ +default_language_version: + python: python3.10 ci: autoupdate_commit_msg: "chore: pre-commit autoupdate" autofix_commit_msg: "chore: pre-commit auto fixes [...]" + skip: [poetry-lock] repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.2.2 + rev: v0.7.1 hooks: - id: ruff args: @@ -12,13 +15,12 @@ repos: files: ^((gentropy|utils|tests)/.+)?[^/]+\.py$ - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.5.0 + rev: v5.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer exclude: "CHANGELOG.md" - id: debug-statements - - id: check-merge-conflict - id: check-case-conflict - id: check-json exclude: (.vscode|.devcontainer) @@ -56,14 +58,14 @@ repos: exclude: "CHANGELOG.md" - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook - rev: v9.11.0 + rev: v9.18.0 hooks: - id: commitlint - additional_dependencies: ["@commitlint/config-conventional"] + additional_dependencies: ["@commitlint/config-conventional@18.6.3"] stages: [commit-msg] - repo: https://github.com/pre-commit/mirrors-mypy - rev: "v1.8.0" + rev: "v1.13.0" hooks: - id: mypy args: @@ -79,7 +81,7 @@ repos: - "--disallow-untyped-defs" - repo: https://github.com/econchick/interrogate - rev: 1.5.0 + rev: 1.7.0 hooks: - id: interrogate args: [--verbose] @@ -96,6 +98,13 @@ repos: - id: beautysh - repo: https://github.com/jsh9/pydoclint - rev: 0.4.1 + rev: 0.5.9 hooks: - id: pydoclint + + - repo: https://github.com/python-poetry/poetry + rev: "1.8.0" + hooks: + - id: poetry-check + - id: poetry-lock + args: ["--no-update"] diff --git a/.vscode/settings.json b/.vscode/settings.json index aaafda97f..ecc456889 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -25,5 +25,8 @@ "python.testing.pytestEnabled": true, "mypy-type-checker.severity": { "error": "Information" - } + }, + "yaml.extension.recommendations": false, + "workbench.remoteIndicator.showExtensionRecommendations": false, + "extensions.ignoreRecommendations": true } diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 000000000..ff8706b62 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,27 @@ +FROM python:3.10-bullseye + +RUN apt-get update \ + && apt-get clean \ + && apt-get install -y openjdk-11-jdk \ + && rm -rf /var/lib/apt/lists/* + +ENV POETRY_NO_INTERACTION=1 \ + POETRY_VIRTUALENVS_IN_PROJECT=1 \ + POETRY_VIRTUALENVS_CREATE=1 \ + POETRY_CACHE_DIR=/tmp/poetry_cache \ + JAVA_HOME=/usr + +RUN pip install poetry==1.7.1 +WORKDIR /app + +COPY pyproject.toml poetry.lock ./ +RUN touch README.md + +RUN poetry config installer.max-workers 10 +RUN poetry install --without dev,docs,tests --no-root --no-interaction --no-ansi -vvv && rm -rf $POETRY_CACHE_DIR + +COPY src ./src + +RUN poetry install --without dev,docs,tests + +ENTRYPOINT ["poetry", "run", "gentropy"] diff --git a/Makefile b/Makefile index 3057f95ce..1d79d35fd 100644 --- a/Makefile +++ b/Makefile @@ -1,10 +1,10 @@ PROJECT_ID ?= open-targets-genetics-dev REGION ?= europe-west1 -APP_NAME ?= $$(cat pyproject.toml| grep -m 1 "name" | cut -d" " -f3 | sed 's/"//g') -VERSION_NO ?= $$(poetry version --short) -CLEAN_VERSION_NO := $(shell echo "$(VERSION_NO)" | tr -cd '[:alnum:]') -BUCKET_NAME=gs://genetics_etl_python_playground/initialisation/${VERSION_NO}/ -BUCKET_COMPOSER_DAGS=gs://europe-west1-ot-workflows-fe147745-bucket/dags/ +APP_NAME ?= $$(cat pyproject.toml | grep -m 1 "name" | cut -d" " -f3 | sed 's/"//g') +REF ?= $$(git rev-parse --abbrev-ref HEAD) +PACKAGE_VERSION ?= $$(poetry version --short) +CLEAN_PACKAGE_VERSION := $(shell echo "$(PACKAGE_VERSION)" | tr -cd '[:alnum:]') +BUCKET_NAME=gs://genetics_etl_python_playground/initialisation/${APP_NAME}/${REF} .PHONY: $(shell sed -n -e '/^$$/ { n ; /^[^ .\#][^ ]*:/ { s/:.*$$// ; p ; } ; }' $(MAKEFILE_LIST)) @@ -22,7 +22,7 @@ setup-dev: ## Setup development environment check: ## Lint and format code @echo "Linting API..." - @poetry run ruff src/gentropy . + @poetry run ruff check src/gentropy . @echo "Linting docstrings..." @poetry run pydoclint --config=pyproject.toml src @poetry run pydoclint --config=pyproject.toml --skip-checking-short-docstrings=true tests @@ -38,31 +38,33 @@ build-documentation: ## Create local server with documentation create-dev-cluster: build ## Spin up a simple dataproc cluster with all dependencies for development purposes @echo "Creating Dataproc Dev Cluster" @gcloud config set project ${PROJECT_ID} - @gcloud dataproc clusters create "ot-genetics-dev-${CLEAN_VERSION_NO}" \ + @gcloud dataproc clusters create "ot-genetics-dev-${CLEAN_PACKAGE_VERSION}-$(USER)" \ --image-version 2.1 \ --region ${REGION} \ --master-machine-type n1-standard-16 \ - --initialization-actions=gs://genetics_etl_python_playground/initialisation/${VERSION_NO}/install_dependencies_on_cluster.sh \ - --metadata="PACKAGE=gs://genetics_etl_python_playground/initialisation/${VERSION_NO}/gentropy-${VERSION_NO}-py3-none-any.whl,CONFIGTAR=gs://genetics_etl_python_playground/initialisation/${VERSION_NO}/config.tar.gz" \ - --single-node \ + --initialization-actions=$(BUCKET_NAME)/install_dependencies_on_cluster.sh \ + --metadata="PACKAGE=$(BUCKET_NAME)/${APP_NAME}-${PACKAGE_VERSION}-py3-none-any.whl" \ + --secondary-worker-type spot \ + --worker-machine-type n1-standard-4 \ + --worker-boot-disk-size 500 \ + --autoscaling-policy="projects/${PROJECT_ID}/regions/${REGION}/autoscalingPolicies/otg-etl" \ --optional-components=JUPYTER \ - --enable-component-gateway + --enable-component-gateway \ + --max-idle=60m make update-dev-cluster: build ## Reinstalls the package on the dev-cluster @echo "Updating Dataproc Dev Cluster" @gcloud config set project ${PROJECT_ID} - gcloud dataproc jobs submit pig --cluster="ot-genetics-dev-${CLEAN_VERSION_NO}" \ + gcloud dataproc jobs submit pig --cluster="ot-genetics-dev-${CLEAN_PACKAGE_VERSION}" \ --region ${REGION} \ --jars=${BUCKET_NAME}/install_dependencies_on_cluster.sh \ -e='sh chmod 750 $${PWD}/install_dependencies_on_cluster.sh; sh $${PWD}/install_dependencies_on_cluster.sh' build: clean ## Build Python package with dependencies @gcloud config set project ${PROJECT_ID} - @echo "Packaging Code and Dependencies for ${APP_NAME}-${VERSION_NO}" + @echo "Packaging Code and Dependencies for ${APP_NAME}-${PACKAGE_VERSION}" @poetry build - @tar -czf dist/config.tar.gz config/ - @echo "Uploading to Dataproc" - @gsutil cp src/gentropy/cli.py ${BUCKET_NAME} - @gsutil cp ./dist/${APP_NAME}-${VERSION_NO}-py3-none-any.whl ${BUCKET_NAME} - @gsutil cp ./dist/config.tar.gz ${BUCKET_NAME} - @gsutil cp ./utils/install_dependencies_on_cluster.sh ${BUCKET_NAME} + @echo "Uploading to ${BUCKET_NAME}" + @gsutil cp src/${APP_NAME}/cli.py ${BUCKET_NAME}/ + @gsutil cp ./dist/${APP_NAME}-${PACKAGE_VERSION}-py3-none-any.whl ${BUCKET_NAME}/ + @gsutil cp ./utils/install_dependencies_on_cluster.sh ${BUCKET_NAME}/ diff --git a/config/__init__.py b/config/__init__.py deleted file mode 100644 index 31939863f..000000000 --- a/config/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -"""Reference configuration yamls.""" - -from __future__ import annotations diff --git a/config/datasets/ot_gcp.yaml b/config/datasets/ot_gcp.yaml deleted file mode 100644 index 54c7dd2fe..000000000 --- a/config/datasets/ot_gcp.yaml +++ /dev/null @@ -1,69 +0,0 @@ -# Release specific configuration: -release_version: "24.01" -dev_version: XX.XX -release_folder: gs://genetics_etl_python_playground/releases/${datasets.release_version} - -inputs: gs://genetics_etl_python_playground/input -static_assets: gs://genetics_etl_python_playground/static_assetss -outputs: gs://genetics_etl_python_playground/output/python_etl/parquet/${datasets.dev_version} - -## Datasets: -gwas_catalog_dataset: gs://gwas_catalog_data -# Ingestion input files: -gwas_catalog_associations: ${datasets.gwas_catalog_dataset}/curated_inputs/gwas_catalog_associations_ontology_annotated.tsv -gwas_catalog_studies: - - ${datasets.gwas_catalog_dataset}/curated_inputs/gwas_catalog_download_studies.tsv - - ${datasets.gwas_catalog_dataset}/curated_inputs/gwas_catalog_unpublished_studies.tsv -gwas_catalog_ancestries: - - ${datasets.gwas_catalog_dataset}/curated_inputs/gwas_catalog_download_ancestries.tsv - - ${datasets.gwas_catalog_dataset}/curated_inputs/gwas_catalog_unpublished_ancestries.tsv -gwas_catalog_sumstats_lut: ${datasets.gwas_catalog_dataset}/curated_inputs/harmonised_list.txt -gwas_catalog_study_curation: ${datasets.gwas_catalog_dataset}/manifests/gwas_catalog_study_curation.tsv -# Harmonised summary statistics list: -gwas_catalog_summary_stats_list: ${datasets.gwas_catalog_dataset}/manifests/gwas_catalog_harmonised_summary_statistics_list.txt -# Inclusion lists: -gwas_catalog_curated_inclusion_list: ${datasets.gwas_catalog_dataset}/manifests/gwas_catalog_curation_included_studies -gwas_catalog_summary_statistics_inclusion_list: ${datasets.gwas_catalog_dataset}/manifests/gwas_catalog_summary_statistics_included_studies -# Ingestion output folders: -gwas_catalog_study_index: ${datasets.gwas_catalog_dataset}/study_index -gwas_catalog_study_locus_folder: ${datasets.gwas_catalog_dataset}/study_locus_datasets -gwas_catalog_credible_set_folder: ${datasets.gwas_catalog_dataset}/credible_set_datasets - -# Input datasets -chain_37_38: ${datasets.static_assets}/grch37_to_grch38.over.chain -vep_consequences: ${datasets.static_assets}/vep_consequences.tsv -anderson: ${datasets.static_assets}/andersson2014/enhancer_tss_associations.bed -javierre: ${datasets.static_assets}/javierre_2016_preprocessed -jung: ${datasets.static_assets}/jung2019_pchic_tableS3.csv -thurman: ${datasets.static_assets}/thurman2012/genomewideCorrs_above0.7_promoterPlusMinus500kb_withGeneNames_32celltypeCategories.bed8.gz -target_index: ${datasets.release_folder}/targets # OTP 23.12 data - -gene_interactions: ${datasets.release_folder}/interaction # OTP 23.12 data -eqtl_catalogue_paths_imported: ${datasets.inputs}/preprocess/eqtl_catalogue/tabix_ftp_paths_imported.tsv -finngen_finemapping_results_path: ${datasets.inputs}/Finngen_susie_finemapping_r10/full -finngen_finemapping_summaries_path: ${datasets.inputs}/Finngen_susie_finemapping_r10/Finngen_susie_credset_summary_r10.tsv - -# Dev output datasets -variant_annotation: ${datasets.outputs}/variant_annotation -study_locus: ${datasets.outputs}/study_locus -summary_statistics: ${datasets.outputs}/summary_statistics -study_locus_overlap: ${datasets.outputs}/study_locus_overlap -susie_finemapping: ${datasets.outputs}/finngen_susie_finemapping - -ld_index: ${datasets.outputs}/ld_index -catalog_study_index: ${datasets.study_index}/catalog -catalog_study_locus: ${datasets.study_locus}/catalog_study_locus - -from_sumstats_study_locus: ${datasets.study_locus}/from_sumstats -from_sumstats_pics: ${datasets.credible_set}/from_sumstats - -# ETL output datasets: -l2g_gold_standard_curation: ${datasets.release_folder}/locus_to_gene_gold_standard.json -l2g_model: ${datasets.release_folder}/locus_to_gene_model -l2g_predictions: ${datasets.release_folder}/locus_to_gene_predictions -colocalisation: ${datasets.release_folder}/colocalisation -study_index: ${datasets.release_folder}/study_index -variant_index: ${datasets.release_folder}/variant_index -credible_set: ${datasets.release_folder}/credible_set -gene_index: ${datasets.release_folder}/gene_index -v2g: ${datasets.release_folder}/variant_to_gene diff --git a/config/ot_config.yaml b/config/ot_config.yaml deleted file mode 100644 index 7f28a58d6..000000000 --- a/config/ot_config.yaml +++ /dev/null @@ -1,5 +0,0 @@ -defaults: - - config - - datasets: ot_gcp - - _self_ - - override step/session: dataproc diff --git a/config/step/ot_colocalisation.yaml b/config/step/ot_colocalisation.yaml deleted file mode 100644 index 4433595ef..000000000 --- a/config/step/ot_colocalisation.yaml +++ /dev/null @@ -1,6 +0,0 @@ -defaults: - - colocalisation - -credible_set_path: ${datasets.credible_set} -study_index_path: ${datasets.study_index} -coloc_path: ${datasets.colocalisation} diff --git a/config/step/ot_eqtl_catalogue.yaml b/config/step/ot_eqtl_catalogue.yaml deleted file mode 100644 index 141c94ee3..000000000 --- a/config/step/ot_eqtl_catalogue.yaml +++ /dev/null @@ -1,6 +0,0 @@ -defaults: - - eqtl_catalogue - -eqtl_catalogue_paths_imported: ${datasets.eqtl_catalogue_paths_imported} -eqtl_catalogue_study_index_out: ${datasets.eqtl_catalogue_study_index_out} -eqtl_catalogue_summary_stats_out: ${datasets.eqtl_catalogue_summary_stats_out} diff --git a/config/step/ot_finngen_finemapping_ingestion.yaml b/config/step/ot_finngen_finemapping_ingestion.yaml deleted file mode 100644 index 46aa497fa..000000000 --- a/config/step/ot_finngen_finemapping_ingestion.yaml +++ /dev/null @@ -1,7 +0,0 @@ -defaults: - - finngen_finemapping_ingestion - -finngen_finemapping_results_path: ${datasets.finngen_finemapping_results_path} -finngen_finemapping_summaries_path: ${datasets.finngen_finemapping_summaries_path} -finngen_release_prefix: ${datasets.finngen_release_prefix} -finngen_finemapping_out: ${datasets.finngen_finemapping_out} diff --git a/config/step/ot_finngen_studies.yaml b/config/step/ot_finngen_studies.yaml deleted file mode 100644 index c2657bbf5..000000000 --- a/config/step/ot_finngen_studies.yaml +++ /dev/null @@ -1,4 +0,0 @@ -defaults: - - finngen_studies - -finngen_study_index_out: ??? diff --git a/config/step/ot_finngen_sumstat_preprocess.yaml b/config/step/ot_finngen_sumstat_preprocess.yaml deleted file mode 100644 index ad0e93a09..000000000 --- a/config/step/ot_finngen_sumstat_preprocess.yaml +++ /dev/null @@ -1,5 +0,0 @@ -defaults: - - finngen_sumstat_preprocess - -raw_sumstats_path: ??? -out_sumstats_path: ??? diff --git a/config/step/ot_gene_index.yaml b/config/step/ot_gene_index.yaml deleted file mode 100644 index ce5971bf9..000000000 --- a/config/step/ot_gene_index.yaml +++ /dev/null @@ -1,5 +0,0 @@ -defaults: - - gene_index - -target_path: ${datasets.target_index} -gene_index_path: ${datasets.gene_index} diff --git a/config/step/ot_gwas_catalog_ingestion.yaml b/config/step/ot_gwas_catalog_ingestion.yaml deleted file mode 100644 index fc82b82c2..000000000 --- a/config/step/ot_gwas_catalog_ingestion.yaml +++ /dev/null @@ -1,12 +0,0 @@ -defaults: - - gwas_catalog_ingestion - -catalog_study_files: ${datasets.gwas_catalog_studies} -catalog_ancestry_files: ${datasets.gwas_catalog_ancestries} -catalog_associations_file: ${datasets.gwas_catalog_associations} -catalog_sumstats_lut: ${datasets.gwas_catalog_sumstats_lut} -variant_annotation_path: ${datasets.variant_annotation} -catalog_studies_out: ${datasets.gwas_catalog_study_index} -catalog_associations_out: ${datasets.gwas_catalog_study_locus_folder}/gwas_catalog_curated_associations -gwas_catalog_study_curation_file: ${datasets.gwas_catalog_study_curation} -inclusion_list_path: ${datasets.gwas_catalog_curated_inclusion_list} diff --git a/config/step/ot_gwas_catalog_study_curation.yaml b/config/step/ot_gwas_catalog_study_curation.yaml deleted file mode 100644 index 77c1d7834..000000000 --- a/config/step/ot_gwas_catalog_study_curation.yaml +++ /dev/null @@ -1,8 +0,0 @@ -defaults: - - gwas_catalog_study_curation - -catalog_study_files: ${datasets.gwas_catalog_studies} -catalog_ancestry_files: ${datasets.gwas_catalog_ancestries} -catalog_sumstats_lut: ${datasets.gwas_catalog_sumstats_lut} -gwas_catalog_study_curation_file: ${datasets.gwas_catalog_study_curation} -gwas_catalog_study_curation_out: ??? diff --git a/config/step/ot_gwas_catalog_study_inclusion.yaml b/config/step/ot_gwas_catalog_study_inclusion.yaml deleted file mode 100644 index 7f3bf80b3..000000000 --- a/config/step/ot_gwas_catalog_study_inclusion.yaml +++ /dev/null @@ -1,12 +0,0 @@ -defaults: - - gwas_catalog_study_inclusion - -catalog_study_files: ${datasets.gwas_catalog_studies} -catalog_ancestry_files: ${datasets.gwas_catalog_ancestries} -catalog_associations_file: ${datasets.gwas_catalog_associations} -variant_annotation_path: ${datasets.variant_annotation} -gwas_catalog_study_curation_file: ${datasets.gwas_catalog_study_curation} -harmonised_study_file: ${datasets.gwas_catalog_summary_stats_list} -criteria: ??? -inclusion_list_path: ??? -exclusion_list_path: ??? diff --git a/config/step/ot_gwas_catalog_sumstat_preprocess.yaml b/config/step/ot_gwas_catalog_sumstat_preprocess.yaml deleted file mode 100644 index d0c936807..000000000 --- a/config/step/ot_gwas_catalog_sumstat_preprocess.yaml +++ /dev/null @@ -1,5 +0,0 @@ -defaults: - - gwas_catalog_sumstat_preprocess - -raw_sumstats_path: ??? -out_sumstats_path: ??? diff --git a/config/step/ot_ld_based_clumping.yaml b/config/step/ot_ld_based_clumping.yaml deleted file mode 100644 index d02c0acdd..000000000 --- a/config/step/ot_ld_based_clumping.yaml +++ /dev/null @@ -1,7 +0,0 @@ -defaults: - - ld_based_clumping - -ld_index_path: ${datasets.ld_index} -study_locus_input_path: ??? -study_index_path: ??? -clumped_study_locus_output_path: ??? diff --git a/config/step/ot_ld_index.yaml b/config/step/ot_ld_index.yaml deleted file mode 100644 index 70dc6b5ee..000000000 --- a/config/step/ot_ld_index.yaml +++ /dev/null @@ -1,4 +0,0 @@ -defaults: - - ld_index - -ld_index_out: ${datasets.ld_index} diff --git a/config/step/ot_locus_to_gene_predict.yaml b/config/step/ot_locus_to_gene_predict.yaml deleted file mode 100644 index bd5c31b25..000000000 --- a/config/step/ot_locus_to_gene_predict.yaml +++ /dev/null @@ -1,10 +0,0 @@ -defaults: - - locus_to_gene - -run_mode: predict -model_path: ${datasets.l2g_model} -predictions_path: ${datasets.l2g_predictions} -credible_set_path: ${datasets.credible_set} -variant_gene_path: ${datasets.v2g} -colocalisation_path: ${datasets.colocalisation} -study_index_path: ${datasets.study_index} diff --git a/config/step/ot_locus_to_gene_train.yaml b/config/step/ot_locus_to_gene_train.yaml deleted file mode 100644 index d055621ca..000000000 --- a/config/step/ot_locus_to_gene_train.yaml +++ /dev/null @@ -1,17 +0,0 @@ -defaults: - - locus_to_gene - -run_mode: train -wandb_run_name: null -perform_cross_validation: false -model_path: ${datasets.l2g_model} -predictions_path: ${datasets.l2g_predictions} -credible_set_path: ${datasets.credible_set} -variant_gene_path: ${datasets.v2g} -colocalisation_path: ${datasets.colocalisation} -study_index_path: ${datasets.study_index} -gold_standard_curation_path: ${datasets.l2g_gold_standard_curation} -gene_interactions_path: ${datasets.gene_interactions} -hyperparameters: - max_depth: 5 - loss_function: binary:logistic diff --git a/config/step/ot_pics.yaml b/config/step/ot_pics.yaml deleted file mode 100644 index 851c4ca06..000000000 --- a/config/step/ot_pics.yaml +++ /dev/null @@ -1,5 +0,0 @@ -defaults: - - pics - -study_locus_ld_annotated_in: ??? -picsed_study_locus_out: ??? diff --git a/config/step/ot_variant_annotation.yaml b/config/step/ot_variant_annotation.yaml deleted file mode 100644 index 5da76b398..000000000 --- a/config/step/ot_variant_annotation.yaml +++ /dev/null @@ -1,4 +0,0 @@ -defaults: - - variant_annotation - -variant_annotation_path: ${datasets.variant_annotation} diff --git a/config/step/ot_variant_index.yaml b/config/step/ot_variant_index.yaml deleted file mode 100644 index 1625c7126..000000000 --- a/config/step/ot_variant_index.yaml +++ /dev/null @@ -1,6 +0,0 @@ -defaults: - - variant_index - -variant_annotation_path: ${datasets.variant_annotation} -credible_set_path: ${datasets.study_locus} -variant_index_path: ${datasets.variant_index} diff --git a/config/step/ot_variant_to_gene.yaml b/config/step/ot_variant_to_gene.yaml deleted file mode 100644 index 1ac6d2fbe..000000000 --- a/config/step/ot_variant_to_gene.yaml +++ /dev/null @@ -1,14 +0,0 @@ -defaults: - - variant_to_gene - -variant_index_path: ${datasets.variant_index} -variant_annotation_path: ${datasets.variant_annotation} -gene_index_path: ${datasets.gene_index} -vep_consequences_path: ${datasets.vep_consequences} -liftover_chain_file_path: ${datasets.chain_37_38} -interval_sources: - andersson: ${datasets.anderson} - javierre: ${datasets.javierre} - jung: ${datasets.jung} - thurman: ${datasets.thurman} -v2g_path: ${datasets.v2g} diff --git a/config/step/ot_window_based_clumping.yaml b/config/step/ot_window_based_clumping.yaml deleted file mode 100644 index 0e7e0e39b..000000000 --- a/config/step/ot_window_based_clumping.yaml +++ /dev/null @@ -1,6 +0,0 @@ -defaults: - - window_based_clumping - -summary_statistics_input_path: ??? -study_locus_output_path: ??? -inclusion_list_path: ??? diff --git a/config/step/session/dataproc.yaml b/config/step/session/dataproc.yaml deleted file mode 100644 index 6ac641718..000000000 --- a/config/step/session/dataproc.yaml +++ /dev/null @@ -1,5 +0,0 @@ -defaults: - - base_session - -spark_uri: yarn -write_mode: errorifexists diff --git a/docs/assets/imgs/ensembl_logo.png b/docs/assets/imgs/ensembl_logo.png new file mode 100644 index 000000000..83fd25751 Binary files /dev/null and b/docs/assets/imgs/ensembl_logo.png differ diff --git a/docs/development/airflow.md b/docs/development/airflow.md deleted file mode 100644 index ff5f7906c..000000000 --- a/docs/development/airflow.md +++ /dev/null @@ -1,124 +0,0 @@ -# Airflow configuration - -This section describes how to set up a local Airflow server which will orchestrate running workflows in Google Cloud Platform. This is useful for testing and debugging, but for production use, it is recommended to run Airflow on a dedicated server. - -## Install pre-requisites - -- [Docker](https://docs.docker.com/get-docker/) -- [Google Cloud SDK](https://cloud.google.com/sdk/docs/install) - -!!! warning macOS Docker memory allocation - - On macOS, the default amount of memory available for Docker might not be enough to get Airflow up and running. Allocate at least 4GB of memory for the Docker Engine (ideally 8GB). [More info](https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#) - -## Configure Airflow access to Google Cloud Platform - -!!! warning Specifying Google Cloud parameters - - Run the next two command with the appropriate Google Cloud project ID and service account name to ensure the correct Google default application credentials are set up. - -Authenticate to Google Cloud: - -```bash -gcloud auth application-default login --project= -``` - -Create the service account key file that will be used by Airflow to access Google Cloud Platform resources: - -```bash -gcloud iam service-accounts keys create ~/.config/gcloud/service_account_credentials.json --iam-account=@appspot.gserviceaccount.com -``` - -## Set up Airflow - -Change the working directory so that all subsequent commands will work: - -```bash -cd src/airflow -``` - -### Build Docker image - -!!! note Custom Docker image for Airflow - - The custom Dockerfile built by the command below extends the official [Airflow Docker Compose YAML](https://airflow.apache.org/docs/apache-airflow/stable/docker-compose.yaml). We add support for Google Cloud SDK, Google Dataproc operators, and access to GCP credentials. - -```bash -docker build . --tag extending_airflow:latest -``` - -### Set Airflow user ID - -!!! note Setting Airflow user ID - - These commands allow Airflow running inside Docker to access the credentials file which was generated earlier. - -```bash -# If any user ID is already specified in .env, remove it. -grep -v "AIRFLOW_UID" .env > .env.tmp -# Add the correct user ID. -echo "AIRFLOW_UID=$(id -u)" >> .env.tmp -# Move the file. -mv .env.tmp .env -``` - -### Initialise - -Before starting Airflow, initialise the database: - -```bash -docker compose up airflow-init -``` - -Now start all services: - -```bash -docker compose up -d -``` - -Airflow UI will now be available at `http://localhost:8080/`. Default username and password are both `airflow`. - -For additional information on how to use Airflow visit the [official documentation](https://airflow.apache.org/docs/apache-airflow/stable/index.html). - -### Cleaning up - -At any time, you can check the status of your containers with: - -```bash -docker ps -``` - -To stop Airflow, run: - -```bash -docker compose down -``` - -To cleanup the Airflow database, run: - -```bash -docker compose down --volumes --remove-orphans -``` - -### Advanced configuration - -More information on running Airflow with Docker Compose can be found in the [official docs](https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html). - -1. **Increase Airflow concurrency**. Modify the `docker-compose.yaml` and add the following to the x-airflow-common β†’ environment section: - - ```yaml - AIRFLOW__CORE__PARALLELISM: 32 - AIRFLOW__CORE__MAX_ACTIVE_TASKS_PER_DAG: 32 - AIRFLOW__SCHEDULER__MAX_TIS_PER_QUERY: 16 - AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG: 1 - # Also add the following line if you are using CeleryExecutor (by default, LocalExecutor is used). - AIRFLOW__CELERY__WORKER_CONCURRENCY: 32 - ``` - -1. **Additional pip packages**. They can be added to the `requirements.txt` file. - -## Troubleshooting - -Note that when you a a new workflow under `dags/`, Airflow will not pick that up immediately. By default the filesystem is only scanned for new DAGs every 300s. However, once the DAG is added, updates are applied nearly instantaneously. - -Also, if you edit the DAG while an instance of it is running, it might cause problems with the run, as Airflow will try to update the tasks and their properties in DAG according to the file changes. diff --git a/docs/development/contributing.md b/docs/development/contributing.md index 9742d3742..acbb8f2a7 100644 --- a/docs/development/contributing.md +++ b/docs/development/contributing.md @@ -18,7 +18,7 @@ For Google Cloud configuration: Check that you have the `make` utility installed, and if not (which is unlikely), install it using your system package manager. -Check that you have `java` installed. +Check that you have `java` installed. To be able to use all features including hail support use java 11. ## Environment configuration @@ -26,30 +26,19 @@ Run `make setup-dev` to install/update the necessary packages and activate the d It is recommended to use VS Code as an IDE for development. -## How to run the code +## How to create gentropy step -All pipelines in this repository are intended to be run in Google Dataproc. Running them locally is not currently supported. +All gentropy steps can be invoked after successful environment configuration by running -In order to run the code: +```python +poetry run gentropy step= +``` -1. Manually edit your local `src/airflow/dags/*` file and comment out the steps you do not want to run. +1. Create a new step config in the `src/gentropy/config.py` that inherits from `StepConfig` class. -2. Manually edit your local `pyproject.toml` file and modify the version of the code. +2. Register new step configuration to `ConfigStore`. - - This must be different from the version used by any other people working on the repository to avoid any deployment conflicts, so it's a good idea to use your name, for example: `1.2.3+jdoe`. - - You can also add a brief branch description, for example: `1.2.3+jdoe.myfeature`. - - Note that the version must comply with [PEP440 conventions](https://peps.python.org/pep-0440/#normalization), otherwise Poetry will not allow it to be deployed. - - Do not use underscores or hyphens in your version name. When building the WHL file, they will be automatically converted to dots, which means the file name will no longer match the version and the build will fail. Use dots instead. - -3. Manually edit your local `src/airflow/dags/common_airflow.py` and set `OTG_VERSION` to the same version as you did in the previous step. - -4. Run `make build`. - - - This will create a bundle containing the neccessary code, configuration and dependencies to run the ETL pipeline, and then upload this bundle to Google Cloud. - - A version specific subpath is used, so uploading the code will not affect any branches but your own. - - If there was already a code bundle uploaded with the same version number, it will be replaced. - -5. Open Airflow UI and run the DAG. +3. Create a step class that holds the business logic in new file in the `src/gentropy`. ## Contributing checklist @@ -66,21 +55,26 @@ For more details on each of these steps, see the sections below. - If during development you had a question which wasn't covered in the documentation, and someone explained it to you, add it to the documentation. The same applies if you encountered any instructions in the documentation which were obsolete or incorrect. - Documentation autogeneration expressions start with `:::`. They will automatically generate sections of the documentation based on class and method docstrings. Be sure to update them for: - - Dataset definitions in `docs/python_api/datasource/STEP` (example: `docs/python_api/datasource/finngen/study_index.md`) - - Step definition in `docs/python_api/step/STEP.md` (example: `docs/python_api/step/finngen.md`) + - Datasource main page, for example: `docs/python_api/datasources/finngen/_finngen.md` + - Dataset definitions, for example: `docs/python_api/datasources/finngen/study_index.md` + - Step definition, for example: `docs/python_api/steps/finngen_sumstat_preprocess.md` ### Configuration -- Input and output paths in `config/datasets/gcp.yaml` -- Step configuration in `config/step/STEP.yaml` (example: `config/step/finngen.yaml`) +- step default configuration in the `src/gentropy/config/` `StepConfig` derived classes. ### Classes -- Dataset class in `src/gentropy/datasource/STEP` (example: `src/gentropy/datasource/finngen/study_index.py` β†’ `FinnGenStudyIndex`) -- Step main running class in `src/gentropy/STEP.py` (example: `src/gentropy/finngen.py`) +- Datasource init, for example: `src/gentropy/datasource/finngen/__init__.py` +- Dataset classes, for example: `src/gentropy/datasource/finngen/study_index.py` β†’ `FinnGenStudyIndex` +- Step main running class, for example: `src/gentropy/finngen_sumstat_preprocess.py` ### Tests -- Test study fixture in `tests/conftest.py` (example: `mock_study_index_finngen` in that module) -- Test sample data in `tests/data_samples` (example: `tests/gentropy/data_samples/finngen_studies_sample.json`) -- Test definition in `tests/` (example: `tests/dataset/test_study_index.py` β†’ `test_study_index_finngen_creation`) +- Test study fixture in `tests/conftest.py`, for example: `mock_study_index_finngen` in that module +- Test sample data, for example: `tests/gentropy/data_samples/finngen_studies_sample.json` +- Test definition, for example: `tests/dataset/test_study_index.py` β†’ `test_study_index_finngen_creation`) + +### Airflow dags + +- Upstream of version 2.0.0 airflow orchestration layer was moved to the [orchestration repository](https://github.com/opentargets/orchestration) diff --git a/docs/development/troubleshooting.md b/docs/development/troubleshooting.md index b9f7385da..498ee3b86 100644 --- a/docs/development/troubleshooting.md +++ b/docs/development/troubleshooting.md @@ -39,3 +39,29 @@ Another solution which helps is to remove Node, NodeJS, and npm from your system On Ubuntu, this can be done using `sudo apt remove node nodejs npm`, followed by `sudo apt autoremove`. But in some cases, depending on your existing installation, you may need to also manually remove some files. See [this StackOverflow answer](https://stackoverflow.com/a/41057802) for guidance. After running these commands, you are advised to open a fresh shell, and then also reinstall Pyenv and Poetry to make sure they pick up the changes (see relevant section above). + +## MacOS + +Some functions on MacOS may throw a java error: + +`python3.10/site-packages/py4j/protocol.py:326: Py4JJavaError` + +This can be resolved by adding the follow line to your `~/.zshrc`: + +`export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES` + +## Creating development dataproc cluster (OT users only) + +To start dataproc cluster in the development mode run + +``` +make create-dev-cluster +``` + +The command above will prepare 3 different resources: + +- gentropy package +- cli script +- cluster setup script + +and based on the branch ref (for example `dev`) will create a namespaced folder under GCS (`gs://genetics_etl_python_playground/initialisation/gentropy/dev`) with the three files described above. These files will be then used to create the cluster environment. diff --git a/docs/development/workflows.md b/docs/development/workflows.md deleted file mode 100644 index 2269041d8..000000000 --- a/docs/development/workflows.md +++ /dev/null @@ -1,27 +0,0 @@ -# Pipeline workflows - -This page describes the high level components of the pipeline, which are organised as Airflow DAGs (directed acyclic graphs). - -## Note on DAGs and Dataproc clusters - -Each DAG consists of the following general stages: - -1. Create cluster (if it already exists, this step is skipped) - -1. Install dependencies on the cluster - -1. Run data processing steps for this DAG - -1. Delete the cluster - -Within a DAG, all data processing steps run on the same Dataproc cluster as separate jobs. - -There is no need to configure DAGs or steps depending on the size of the input data. Clusters have autoscaling enabled, which means they will increase or decrease the number of worker VMs to accommodate the load. - -## DAG 1: Preprocess - -This DAG contains steps which are only supposed to be run once, or very rarely. They ingest external data and apply bespoke transformations specific for each particular data source. The output is normalised according to the data schemas used by the pipeline. - -## DAG 2: ETL - -The ETL DAG takes the inputs of the previous step and performs the main algorithmic processing. This processing is supposed to be data source agnostic. diff --git a/docs/howto/command_line/run_step_in_cli.md b/docs/howto/command_line/run_step_in_cli.md index ac7d55ff9..b935d84fb 100644 --- a/docs/howto/command_line/run_step_in_cli.md +++ b/docs/howto/command_line/run_step_in_cli.md @@ -24,7 +24,6 @@ Available options: ukbiobank variant_annotation variant_index - variant_to_gene Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace. ``` diff --git a/docs/python_api/_python_api.md b/docs/python_api/_python_api.md index 9952aa56f..0e1b51480 100644 --- a/docs/python_api/_python_api.md +++ b/docs/python_api/_python_api.md @@ -10,3 +10,4 @@ The overall architecture of the package distinguishes between: - [**Datasets**](datasets/_datasets.md): data model - [**Methods**](methods/_methods.md): statistical analysis tools - [**Steps**](steps/_steps.md): pipeline steps +- [**Common**](common/_common.md): Common classes diff --git a/docs/python_api/common/_common.md b/docs/python_api/common/_common.md new file mode 100644 index 000000000..5957ec3bd --- /dev/null +++ b/docs/python_api/common/_common.md @@ -0,0 +1,9 @@ +--- +title: Common +--- + +Common utilities used in gentropy package. + +- [**Genomic Region**](genomic_region.md): class to represent genomic regions +- [**Version Engine**](version_engine.md): class to extract version from datasource input paths +- [**Types**](types.md): Literal types used in the gentropy diff --git a/docs/python_api/common/genomic_region.md b/docs/python_api/common/genomic_region.md new file mode 100644 index 000000000..c0841d299 --- /dev/null +++ b/docs/python_api/common/genomic_region.md @@ -0,0 +1,6 @@ +--- +title: Genomic Region +--- + +:::gentropy.common.genomic_region.KnownGenomicRegions +:::gentropy.common.genomic_region.GenomicRegion diff --git a/docs/python_api/common/types.md b/docs/python_api/common/types.md new file mode 100644 index 000000000..ffb460a3a --- /dev/null +++ b/docs/python_api/common/types.md @@ -0,0 +1,8 @@ +--- +title: Literal Types +--- + +:::gentropy.common.types +:::gentropy.common.types.LD_Population +:::gentropy.common.types.VariantPopulation +:::gentropy.common.types.DataSourceType diff --git a/docs/python_api/common/version_engine.md b/docs/python_api/common/version_engine.md new file mode 100644 index 000000000..28d9b4b2e --- /dev/null +++ b/docs/python_api/common/version_engine.md @@ -0,0 +1,12 @@ +--- +title: VersionEngine +--- + +**VersionEngine**: + +Version engine allows for registering datasource specific version seeker class to retrieve datasource version used as input to gentropy steps. Currently implemented only for GnomAD datasource. + +This class can be then used to produce automation over output directory versioning. + +:::gentropy.common.version_engine.VersionEngine +:::gentropy.common.version_engine.GnomADVersionSeeker diff --git a/docs/python_api/datasets/biosample_index.md b/docs/python_api/datasets/biosample_index.md new file mode 100644 index 000000000..d3e4ee2c8 --- /dev/null +++ b/docs/python_api/datasets/biosample_index.md @@ -0,0 +1,9 @@ +--- +title: Biosample index +--- + +::: gentropy.dataset.biosample_index.BiosampleIndex + +## Schema + +--8<-- "assets/schemas/biosample_index.md" diff --git a/docs/python_api/datasets/l2g_feature.md b/docs/python_api/datasets/l2g_features/_l2g_feature.md similarity index 50% rename from docs/python_api/datasets/l2g_feature.md rename to docs/python_api/datasets/l2g_features/_l2g_feature.md index cf8c3dcf1..b2f6f8187 100644 --- a/docs/python_api/datasets/l2g_feature.md +++ b/docs/python_api/datasets/l2g_features/_l2g_feature.md @@ -2,7 +2,9 @@ title: L2G Feature --- -::: gentropy.method.l2g.feature_factory.L2GFeature +## Abstract Class + +::: gentropy.dataset.l2g_features.l2g_feature.L2GFeature ## Schema diff --git a/docs/python_api/datasets/l2g_features/colocalisation.md b/docs/python_api/datasets/l2g_features/colocalisation.md new file mode 100644 index 000000000..2b2680e66 --- /dev/null +++ b/docs/python_api/datasets/l2g_features/colocalisation.md @@ -0,0 +1,24 @@ +--- +title: From colocalisation +--- + +## List of features + +::: gentropy.dataset.l2g_features.colocalisation.EQtlColocClppMaximumFeature +::: gentropy.dataset.l2g_features.colocalisation.PQtlColocClppMaximumFeature +::: gentropy.dataset.l2g_features.colocalisation.SQtlColocClppMaximumFeature +::: gentropy.dataset.l2g_features.colocalisation.EQtlColocH4MaximumFeature +::: gentropy.dataset.l2g_features.colocalisation.PQtlColocH4MaximumFeature +::: gentropy.dataset.l2g_features.colocalisation.SQtlColocH4MaximumFeature +::: gentropy.dataset.l2g_features.colocalisation.EQtlColocClppMaximumNeighbourhoodFeature +::: gentropy.dataset.l2g_features.colocalisation.PQtlColocClppMaximumNeighbourhoodFeature +::: gentropy.dataset.l2g_features.colocalisation.SQtlColocClppMaximumNeighbourhoodFeature +::: gentropy.dataset.l2g_features.colocalisation.EQtlColocH4MaximumNeighbourhoodFeature +::: gentropy.dataset.l2g_features.colocalisation.PQtlColocH4MaximumNeighbourhoodFeature +::: gentropy.dataset.l2g_features.colocalisation.SQtlColocH4MaximumNeighbourhoodFeature + +## Common logic + +::: gentropy.dataset.l2g_features.colocalisation.common_colocalisation_feature_logic +::: gentropy.dataset.l2g_features.colocalisation.extend_missing_colocalisation_to_neighbourhood_genes +::: gentropy.dataset.l2g_features.colocalisation.common_neighbourhood_colocalisation_feature_logic diff --git a/docs/python_api/datasets/l2g_features/distance.md b/docs/python_api/datasets/l2g_features/distance.md new file mode 100644 index 000000000..e426b2952 --- /dev/null +++ b/docs/python_api/datasets/l2g_features/distance.md @@ -0,0 +1,19 @@ +--- +title: From distance +--- + +## List of features + +::: gentropy.dataset.l2g_features.distance.DistanceSentinelTssFeature +::: gentropy.dataset.l2g_features.distance.DistanceSentinelTssNeighbourhoodFeature +::: gentropy.dataset.l2g_features.distance.DistanceTssMeanFeature +::: gentropy.dataset.l2g_features.distance.DistanceTssMeanNeighbourhoodFeature +::: gentropy.dataset.l2g_features.distance.DistanceSentinelFootprintFeature +::: gentropy.dataset.l2g_features.distance.DistanceSentinelFootprintNeighbourhoodFeature +::: gentropy.dataset.l2g_features.distance.DistanceFootprintMeanFeature +::: gentropy.dataset.l2g_features.distance.DistanceFootprintMeanNeighbourhoodFeature + +## Common logic + +::: gentropy.dataset.l2g_features.distance.common_distance_feature_logic +::: gentropy.dataset.l2g_features.distance.common_neighbourhood_distance_feature_logic diff --git a/docs/python_api/datasets/l2g_features/other.md b/docs/python_api/datasets/l2g_features/other.md new file mode 100644 index 000000000..a3d89c13b --- /dev/null +++ b/docs/python_api/datasets/l2g_features/other.md @@ -0,0 +1,15 @@ +--- +title: Other features +--- + +## List of features + +::: gentropy.dataset.l2g_features.other.GeneCountFeature +::: gentropy.dataset.l2g_features.other.ProteinGeneCountFeature +::: gentropy.dataset.l2g_features.other.ProteinCodingFeature +::: gentropy.dataset.l2g_features.other.CredibleSetConfidenceFeature + +## Common logic + +::: gentropy.dataset.l2g_features.other.common_genecount_feature_logic +::: gentropy.dataset.l2g_features.other.is_protein_coding_feature_logic diff --git a/docs/python_api/datasets/l2g_features/vep.md b/docs/python_api/datasets/l2g_features/vep.md new file mode 100644 index 000000000..d2715e977 --- /dev/null +++ b/docs/python_api/datasets/l2g_features/vep.md @@ -0,0 +1,15 @@ +--- +title: From VEP +--- + +## List of features + +::: gentropy.dataset.l2g_features.vep.VepMeanFeature +::: gentropy.dataset.l2g_features.vep.VepMeanNeighbourhoodFeature +::: gentropy.dataset.l2g_features.vep.VepMaximumFeature +::: gentropy.dataset.l2g_features.vep.VepMaximumNeighbourhoodFeature + +## Common logic + +::: gentropy.dataset.l2g_features.vep.common_vep_feature_logic +::: gentropy.dataset.l2g_features.vep.common_neighbourhood_vep_feature_logic diff --git a/docs/python_api/datasets/l2g_prediction.md b/docs/python_api/datasets/l2g_prediction.md index 5434ad398..7606c7243 100644 --- a/docs/python_api/datasets/l2g_prediction.md +++ b/docs/python_api/datasets/l2g_prediction.md @@ -6,4 +6,4 @@ title: L2G Prediction ## Schema ---8<-- "assets/schemas/l2g_prediction.md" +--8<-- "assets/schemas/l2g_predictions.md" diff --git a/docs/python_api/datasets/variant_annotation.md b/docs/python_api/datasets/variant_annotation.md deleted file mode 100644 index f82210b6e..000000000 --- a/docs/python_api/datasets/variant_annotation.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -title: Variant annotation ---- - -::: gentropy.dataset.variant_annotation.VariantAnnotation - -## Schema - ---8<-- "assets/schemas/variant_annotation.md" diff --git a/docs/python_api/datasets/variant_to_gene.md b/docs/python_api/datasets/variant_to_gene.md deleted file mode 100644 index 2af67df92..000000000 --- a/docs/python_api/datasets/variant_to_gene.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -title: Variant-to-gene ---- - -::: gentropy.dataset.v2g.V2G - -## Schema - ---8<-- "assets/schemas/v2g.md" diff --git a/docs/python_api/datasources/_datasources.md b/docs/python_api/datasources/_datasources.md index 036dbcb37..43b212e50 100644 --- a/docs/python_api/datasources/_datasources.md +++ b/docs/python_api/datasources/_datasources.md @@ -10,6 +10,7 @@ This section contains information about the data source harmonisation tools avai 1. [GWAS Catalog](gwas_catalog/_gwas_catalog.md) (with or without full summary statistics) 1. [FinnGen](finngen/_finngen.md) +1. [UKB PPP (EUR)](ukb_ppp_eur/_ukb_ppp_eur.md) ## Molecular QTLs @@ -22,9 +23,10 @@ This section contains information about the data source harmonisation tools avai ## Variant annotation/validation 1. [GnomAD](gnomad/_gnomad.md) v4.0 -1. GWAS catalog harmonisation pipeline [more info](https://www.ebi.ac.uk/gwas/docs/methods/summary-statistics#_harmonised_summary_statistics_data) +2. GWAS catalog's [harmonisation pipeline](https://www.ebi.ac.uk/gwas/docs/methods/summary-statistics#_harmonised_summary_statistics_data) +3. Ensembl's [Variant Effect Predictor](https://www.ensembl.org/info/docs/tools/vep/index.html) -## Linkage desiquilibrium +## Linkage disequilibrium 1. [GnomAD](gnomad/_gnomad.md) v2.1.1 LD matrixes (7 ancestries) @@ -35,3 +37,8 @@ This section contains information about the data source harmonisation tools avai ## Gene annotation 1. [Open Targets Platform Target Dataset](open_targets/target.md) (derived from Ensembl) + +## Biological samples + +1. [Uberon](biosample_ontologies/_uberon.md) +2. [Cell Ontology](biosample_ontologies/_cell_ontology.md) diff --git a/docs/python_api/datasources/biosample_ontologies/_cell_ontology.md b/docs/python_api/datasources/biosample_ontologies/_cell_ontology.md new file mode 100644 index 000000000..5798e032b --- /dev/null +++ b/docs/python_api/datasources/biosample_ontologies/_cell_ontology.md @@ -0,0 +1,5 @@ +--- +title: Cell Ontology +--- + +The [Cell Ontology](http://www.obofoundry.org/ontology/cl.html) is a structured controlled vocabulary for cell types. It is used to annotate cell types in single-cell RNA-seq data and other omics data. diff --git a/docs/python_api/datasources/biosample_ontologies/_uberon.md b/docs/python_api/datasources/biosample_ontologies/_uberon.md new file mode 100644 index 000000000..4bb47305a --- /dev/null +++ b/docs/python_api/datasources/biosample_ontologies/_uberon.md @@ -0,0 +1,5 @@ +--- +title: Uberon +--- + +The [Uberon](http://uberon.github.io/) ontology is a multi-species anatomy ontology that integrates cross-species ontologies into a single ontology. diff --git a/docs/python_api/datasources/ensembl/_ensembl.md b/docs/python_api/datasources/ensembl/_ensembl.md new file mode 100644 index 000000000..56c49870a --- /dev/null +++ b/docs/python_api/datasources/ensembl/_ensembl.md @@ -0,0 +1,10 @@ +--- +title: Ensembl annotations +--- + +
+ +

Ensembl

+
+ +[Ensembl](https://www.ensembl.org/index.html) provides a diverse set of genetic data Gentropy takes advantage of including gene set, and variant annotations. diff --git a/docs/python_api/datasources/ensembl/variant_effect_predictor_parser.md b/docs/python_api/datasources/ensembl/variant_effect_predictor_parser.md new file mode 100644 index 000000000..c956ed3e1 --- /dev/null +++ b/docs/python_api/datasources/ensembl/variant_effect_predictor_parser.md @@ -0,0 +1,5 @@ +--- +title: Variant effector parser +--- + +::: gentropy.datasource.ensembl.vep_parser.VariantEffectPredictorParser diff --git a/docs/python_api/datasources/eqtl_catalogue/_eqtl_catalogue.md b/docs/python_api/datasources/eqtl_catalogue/_eqtl_catalogue.md index 5382d9d57..2e95f561c 100644 --- a/docs/python_api/datasources/eqtl_catalogue/_eqtl_catalogue.md +++ b/docs/python_api/datasources/eqtl_catalogue/_eqtl_catalogue.md @@ -2,10 +2,6 @@ title: eQTL Catalogue --- -The [eQTL Catalogue](https://www.ebi.ac.uk/eqtl/) aims to provide uniformly processed gene expression and splicing Quantitative Trait Loci (QTLs) from all available public studies on humans. +The [eQTL Catalogue](https://www.ebi.ac.uk/eqtl/) aims to provide unified gene, protein expression and splicing Quantitative Trait Loci (QTLs) from all available human public studies. -It serves as the ultimate resource of eQTLs that we use for colocalization and target prioritization. - -We utilize data from the following study within the eQTL Catalogue: - -1. **GTEx v8**, 49 tissues +It serves as the ultimate resource of molecular QTLs that we use for colocalization and target prioritization. diff --git a/docs/python_api/datasources/eqtl_catalogue/finemapping.md b/docs/python_api/datasources/eqtl_catalogue/finemapping.md new file mode 100644 index 000000000..c272de266 --- /dev/null +++ b/docs/python_api/datasources/eqtl_catalogue/finemapping.md @@ -0,0 +1,5 @@ +--- +title: Fine mapping results +--- + +::: gentropy.datasource.eqtl_catalogue.finemapping.EqtlCatalogueFinemapping diff --git a/docs/python_api/datasources/ukb_ppp_eur/_ukb_ppp_eur.md b/docs/python_api/datasources/ukb_ppp_eur/_ukb_ppp_eur.md new file mode 100644 index 000000000..e416e1f32 --- /dev/null +++ b/docs/python_api/datasources/ukb_ppp_eur/_ukb_ppp_eur.md @@ -0,0 +1,7 @@ +--- +title: UKB-PPP (EUR) +--- + +The UKB-PPP is a collaboration between the UK Biobank (UKB) and thirteen biopharmaceutical companies characterising the plasma proteomic profiles of 54,219 UKB participants. + +The original data is available at https://www.synapse.org/#!Synapse:syn51364943/. The associated paper is https://www.nature.com/articles/s41586-023-06592-6. diff --git a/docs/python_api/datasources/ukb_ppp_eur/study_index.md b/docs/python_api/datasources/ukb_ppp_eur/study_index.md new file mode 100644 index 000000000..b7f0d91a0 --- /dev/null +++ b/docs/python_api/datasources/ukb_ppp_eur/study_index.md @@ -0,0 +1,5 @@ +--- +title: Study Index +--- + +::: gentropy.datasource.ukb_ppp_eur.study_index.UkbPppEurStudyIndex diff --git a/docs/python_api/datasources/ukb_ppp_eur/summary_stats.md b/docs/python_api/datasources/ukb_ppp_eur/summary_stats.md new file mode 100644 index 000000000..e2db55c25 --- /dev/null +++ b/docs/python_api/datasources/ukb_ppp_eur/summary_stats.md @@ -0,0 +1,5 @@ +--- +title: Summary Statistics +--- + +::: gentropy.datasource.ukb_ppp_eur.summary_stats.UkbPppEurSummaryStats diff --git a/docs/python_api/methods/clumping.md b/docs/python_api/methods/clumping.md index 6cc368013..86968dfe0 100644 --- a/docs/python_api/methods/clumping.md +++ b/docs/python_api/methods/clumping.md @@ -10,6 +10,7 @@ We have implemented two clumping methods: 1. **Distance-based clumping:** Uses genomic window to clump the significant SNPs into one hit. 2. **LD-based clumping:** Uses genomic window and LD to clump the significant SNPs into one hit. +3. **Locus-breaker clumping:** Applies a distance cutoff between baseline significant SNPs. Returns the start and end position of the locus as well. The algorithmic logic is similar to classic clumping approaches from PLINK (Reference: [PLINK Clump Documentation](https://zzz.bwh.harvard.edu/plink/clump.shtml)). See details below: @@ -20,3 +21,7 @@ The algorithmic logic is similar to classic clumping approaches from PLINK (Refe # LD-based clumping: ::: gentropy.method.clump.LDclumping + +# Locus-breaker clumping + +::: gentropy.method.locus_breaker_clumping.LocusBreakerClumping diff --git a/docs/python_api/methods/l2g/_l2g.md b/docs/python_api/methods/l2g/_l2g.md index fca3ba79d..bbd7dad66 100644 --- a/docs/python_api/methods/l2g/_l2g.md +++ b/docs/python_api/methods/l2g/_l2g.md @@ -9,13 +9,10 @@ The **β€œlocus-to-gene” (L2G)** model derives features to prioritize likely ca - **Chromatin Interaction:** (e.g., promoter-capture Hi-C) - **Variant Pathogenicity:** (from VEP) -The L2G model is distinct from the variant-to-gene (V2G) pipeline in that it: - -- Uses a machine-learning model to learn the weights of each evidence source based on a gold standard of previously identified causal genes. -- Relies upon fine-mapping and colocalization data. - Some of the predictive features weight variant-to-gene (or genomic region-to-gene) evidence based on the posterior probability that the variant is causal, determined through fine-mapping of the GWAS association. +For a more detailed description of how each feature is computed, see [the L2G Feature documentation](../../datasets/l2g_features/_l2g_feature.md). + Details of the L2G model are provided in our Nature Genetics publication (ref - [Nature Genetics Publication](https://www.nature.com/articles/s41588-021-00945-5)): - **Title:** An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. diff --git a/docs/python_api/methods/l2g/evaluator.md b/docs/python_api/methods/l2g/evaluator.md deleted file mode 100644 index 4b389e8c0..000000000 --- a/docs/python_api/methods/l2g/evaluator.md +++ /dev/null @@ -1,5 +0,0 @@ ---- -title: W&B evaluator ---- - -::: gentropy.method.l2g.evaluator.WandbEvaluator diff --git a/docs/python_api/methods/l2g/feature_factory.md b/docs/python_api/methods/l2g/feature_factory.md index 35b4ed710..ec812d2da 100644 --- a/docs/python_api/methods/l2g/feature_factory.md +++ b/docs/python_api/methods/l2g/feature_factory.md @@ -2,6 +2,6 @@ title: L2G Feature Factory --- -::: gentropy.method.l2g.feature_factory.ColocalisationFactory +::: gentropy.method.l2g.feature_factory.FeatureFactory -::: gentropy.method.l2g.feature_factory.StudyLocusFactory +::: gentropy.method.l2g.feature_factory.L2GFeatureInputLoader diff --git a/docs/python_api/methods/sumstat_imputation.md b/docs/python_api/methods/sumstat_imputation.md new file mode 100644 index 000000000..6e64d35b1 --- /dev/null +++ b/docs/python_api/methods/sumstat_imputation.md @@ -0,0 +1,28 @@ +--- +title: Summary Statistics Imputation +--- + +Summary statistics imputation leverages linkage disequilibrium (LD) information to compute Z-scores of missing SNPs from neighbouring observed SNPs +SNPs by taking advantage of the Linkage Disequilibrium. + +We implemented the basic model from RAISS (Robust and Accurate Imputation from Summary Statistics) package (see the original [paper](https://academic.oup.com/bioinformatics/article/35/22/4837/5512360)). + +The full repository for the RAISS package can be found [here](https://gitlab.pasteur.fr/statistical-genetics/raiss). + +The original model was suggested in 2014 by Bogdan Pasaniuc et al. [here](https://pubmed.ncbi.nlm.nih.gov/24990607/). + +It represents the following formula: + +E(z*i|z_t) = M*{i,t} \cdot (M\_{t,t})^{-1} \cdot z_t + +Where: + +- E(z_i|z_t) represents the expected z-score of SNP 'i' given the observed z-scores at known SNP indexes 't'. + +- M\_{i,t} represents the LD (Linkage Disequilibrium) matrix between SNP 'i' and the known SNPs at indexes 't'. + +- (M\_{t,t})^{-1} represents the inverse of the LD matrix of the known SNPs at indexes 't'. + +- z_t represents the vector of observed z-scores at the known SNP indexes 't'. + +:::gentropy.method.sumstat_imputation.SummaryStatisticsImputation diff --git a/docs/python_api/methods/sumstat_quality_controls.md b/docs/python_api/methods/sumstat_quality_controls.md new file mode 100644 index 000000000..dfc5c9d16 --- /dev/null +++ b/docs/python_api/methods/sumstat_quality_controls.md @@ -0,0 +1,18 @@ +--- +title: QC of GWAS Summary Statistics +--- + +This class consists of several general quality control checks for GWAS with full summary statistics. +There are several checks included: + +1. Genomic control lambda (median of the distribution of Chi2 statistics divided by expected for Chi2 with df=1). Lambda should be reasonably close to 1. Ideally not bigger than 2. + +2. P-Z check: the linear regression between log10 of reported p-values and log10 of p-values inferred from betas and standard errors. Intercept of the regression should be close to 0, slope close to 1. + +3. Mean beta check: mean of beta. Should be close to 0. + +4. The N_eff check: It estimates the ratio between effective sample size and the expected one and checks its distribution. It is possible to conduct only if the effective allele frequency is provided in the study. The median ratio is always close to 1, standard error should be close to 0. + +5. Number of SNPs and number of significant SNPs. + +:::gentropy.method.sumstat_quality_controls.SummaryStatisticsQC diff --git a/docs/python_api/steps/biosample_index_step.md b/docs/python_api/steps/biosample_index_step.md new file mode 100644 index 000000000..d8f7abbb4 --- /dev/null +++ b/docs/python_api/steps/biosample_index_step.md @@ -0,0 +1,5 @@ +--- +title: biosample_index +--- + +::: gentropy.biosample_index.BiosampleIndexStep diff --git a/docs/python_api/steps/credible_set_qc_step.md b/docs/python_api/steps/credible_set_qc_step.md new file mode 100644 index 000000000..2999115e7 --- /dev/null +++ b/docs/python_api/steps/credible_set_qc_step.md @@ -0,0 +1,7 @@ +--- +title: credible_set_qc +--- + +::: gentropy.credible_set_qc.CredibleSetQCStep + +::: gentropy.config.CredibleSetQCStepConfig diff --git a/docs/python_api/steps/eqtl_catalogue.md b/docs/python_api/steps/eqtl_catalogue.md index 17ea6a6f6..32f2aa257 100644 --- a/docs/python_api/steps/eqtl_catalogue.md +++ b/docs/python_api/steps/eqtl_catalogue.md @@ -1,5 +1,5 @@ --- -title: eqtl_catalogue +title: eQTL Catalogue --- ::: gentropy.eqtl_catalogue.EqtlCatalogueStep diff --git a/docs/python_api/steps/gwas_catalog_inclusion.md b/docs/python_api/steps/gwas_catalog_inclusion.md deleted file mode 100644 index e9ede6dd6..000000000 --- a/docs/python_api/steps/gwas_catalog_inclusion.md +++ /dev/null @@ -1,5 +0,0 @@ ---- -title: gwas_catalog_study_inclusion ---- - -::: gentropy.gwas_catalog_study_inclusion.GWASCatalogStudyInclusionGenerator diff --git a/docs/python_api/steps/gwas_catalog_ingestion.md b/docs/python_api/steps/gwas_catalog_ingestion.md deleted file mode 100644 index 69ea92479..000000000 --- a/docs/python_api/steps/gwas_catalog_ingestion.md +++ /dev/null @@ -1,5 +0,0 @@ ---- -title: gwas_catalog_ingestion ---- - -::: gentropy.gwas_catalog_ingestion.GWASCatalogIngestionStep diff --git a/docs/python_api/steps/gwas_catalog_study_index.md b/docs/python_api/steps/gwas_catalog_study_index.md new file mode 100644 index 000000000..4984de2a0 --- /dev/null +++ b/docs/python_api/steps/gwas_catalog_study_index.md @@ -0,0 +1,5 @@ +--- +title: gwas_catalog_study_inclusion +--- + +::: gentropy.gwas_catalog_study_index.GWASCatalogStudyIndexGenerationStep diff --git a/docs/python_api/steps/gwas_catalog_top_hits.md b/docs/python_api/steps/gwas_catalog_top_hits.md new file mode 100644 index 000000000..03d81eafb --- /dev/null +++ b/docs/python_api/steps/gwas_catalog_top_hits.md @@ -0,0 +1,5 @@ +--- +title: GWAS Catalog Top Hits Ingestion Step +--- + +::: gentropy.gwas_catalog_top_hits.GWASCatalogTopHitIngestionStep diff --git a/docs/python_api/steps/l2g.md b/docs/python_api/steps/l2g.md index 847569e36..e6aeb0ebb 100644 --- a/docs/python_api/steps/l2g.md +++ b/docs/python_api/steps/l2g.md @@ -1,5 +1,11 @@ --- -title: locus_to_gene +title: Locus to Gene (L2G) --- +::: gentropy.l2g.LocusToGeneFeatureMatrixStep + ::: gentropy.l2g.LocusToGeneStep + +::: gentropy.l2g.LocusToGeneEvidenceStep + +::: gentropy.l2g.LocusToGeneAssociationsStep diff --git a/docs/python_api/steps/ld_index.md b/docs/python_api/steps/ld_index.md index bf8b9b58e..d8f61a528 100644 --- a/docs/python_api/steps/ld_index.md +++ b/docs/python_api/steps/ld_index.md @@ -1,5 +1,5 @@ --- -title: ld_index +title: GnomAD Linkage data ingestion --- -::: gentropy.ld_index.LDIndexStep +::: gentropy.gnomad_ingestion.LDIndexStep diff --git a/docs/python_api/steps/locus_breaker_clumping.md b/docs/python_api/steps/locus_breaker_clumping.md new file mode 100644 index 000000000..1e49234e3 --- /dev/null +++ b/docs/python_api/steps/locus_breaker_clumping.md @@ -0,0 +1,5 @@ +--- +title: locus_breaker_clumping +--- + +::: gentropy.locus_breaker_clumping.LocusBreakerClumpingStep diff --git a/docs/python_api/steps/study_locus_validation.md b/docs/python_api/steps/study_locus_validation.md new file mode 100644 index 000000000..350558806 --- /dev/null +++ b/docs/python_api/steps/study_locus_validation.md @@ -0,0 +1,5 @@ +--- +title: Study-Locus Validation +--- + +::: gentropy.study_locus_validation.StudyLocusValidationStep diff --git a/docs/python_api/steps/study_validation.md b/docs/python_api/steps/study_validation.md new file mode 100644 index 000000000..8e2b68099 --- /dev/null +++ b/docs/python_api/steps/study_validation.md @@ -0,0 +1,5 @@ +--- +title: Study Validation +--- + +::: gentropy.study_validation.StudyValidationStep diff --git a/docs/python_api/steps/summary_statistics_qc.md b/docs/python_api/steps/summary_statistics_qc.md new file mode 100644 index 000000000..a9fea12b9 --- /dev/null +++ b/docs/python_api/steps/summary_statistics_qc.md @@ -0,0 +1,5 @@ +--- +title: summary_statistics_qc +--- + +::: gentropy.sumstat_qc_step.SummaryStatisticsQCStep diff --git a/docs/python_api/steps/ukb_ppp_eur_sumstat_preprocess.md b/docs/python_api/steps/ukb_ppp_eur_sumstat_preprocess.md new file mode 100644 index 000000000..4dd74f1df --- /dev/null +++ b/docs/python_api/steps/ukb_ppp_eur_sumstat_preprocess.md @@ -0,0 +1,5 @@ +--- +title: ukb_ppp_eur_sumstat_preprocess +--- + +::: gentropy.ukb_ppp_eur_sumstat_preprocess.UkbPppEurStep diff --git a/docs/python_api/steps/variant_annotation_step.md b/docs/python_api/steps/variant_annotation_step.md index e65a071b2..2b5582df4 100644 --- a/docs/python_api/steps/variant_annotation_step.md +++ b/docs/python_api/steps/variant_annotation_step.md @@ -1,5 +1,5 @@ --- -title: variant_annotation +title: GnomAD variant data ingestion --- -::: gentropy.variant_annotation.VariantAnnotationStep +::: gentropy.gnomad_ingestion.GnomadVariantIndexStep diff --git a/docs/python_api/steps/variant_to_gene_step.md b/docs/python_api/steps/variant_to_gene_step.md deleted file mode 100644 index 1a3e56af8..000000000 --- a/docs/python_api/steps/variant_to_gene_step.md +++ /dev/null @@ -1,5 +0,0 @@ ---- -title: variant_to_gene ---- - -::: gentropy.v2g.V2GStep diff --git a/docs/roadmap.md b/docs/roadmap.md index 33ddf5eb2..51d4636f2 100644 --- a/docs/roadmap.md +++ b/docs/roadmap.md @@ -14,7 +14,7 @@ The Open Targets core team is working on refactoring Open Targets Genetics, aimi - Faster/robust addition of new datasets and datatypes - Reduce computational and financial cost -See [here](https://github.com/opentargets/issues/issues?q=is%3Aissue+is%3Aopen+label%3AGenetics_ETL_refactoring) for a list of open issues for this project. +See [here](https://github.com/opentargets/issues/issues?q=is%3Aissue+is%3Aopen+label%3Agentropy) for a list of open issues for this project. Schematic diagram representing the drafted process: diff --git a/docs/src_snippets/howto/python_api/b_create_dataset.py b/docs/src_snippets/howto/python_api/b_create_dataset.py index 1a006c97a..61945862e 100644 --- a/docs/src_snippets/howto/python_api/b_create_dataset.py +++ b/docs/src_snippets/howto/python_api/b_create_dataset.py @@ -42,6 +42,7 @@ def create_from_pandas() -> SummaryStatistics: """Create a dataset from a path with Pandas files.""" # --8<-- [start:create_from_pandas_import] import pyspark.pandas as ps + from gentropy.dataset.summary_statistics import SummaryStatistics # --8<-- [end:create_from_pandas_import] diff --git a/docs/src_snippets/howto/python_api/c_applying_methods.py b/docs/src_snippets/howto/python_api/c_applying_methods.py index 12eaf61ac..d0bec9edc 100644 --- a/docs/src_snippets/howto/python_api/c_applying_methods.py +++ b/docs/src_snippets/howto/python_api/c_applying_methods.py @@ -23,7 +23,7 @@ def apply_class_method_clumping(summary_stats: SummaryStatistics) -> StudyLocus: from gentropy.method.window_based_clumping import WindowBasedClumping clumped_summary_statistics = WindowBasedClumping.clump( - summary_stats, window_length=500_000 + summary_stats, distance=250_000 ) # --8<-- [end:apply_class_method_clumping] return clumped_summary_statistics diff --git a/mkdocs.yml b/mkdocs.yml index 180c6fbe1..3704ed274 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -48,7 +48,8 @@ markdown_extensions: - pymdownx.tabbed: alternate_style: true combine_header_slug: true - + - pymdownx.tasklist: + custom_checkbox: true hooks: - src/utils/schemadocs.py diff --git a/notebooks/FineMappingSimmuations.ipynb b/notebooks/FineMappingSimmuations.ipynb new file mode 100644 index 000000000..fcec9bbe9 --- /dev/null +++ b/notebooks/FineMappingSimmuations.ipynb @@ -0,0 +1,407 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulations to benchmark the fine-mapping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The simulations are based on the specific GnomAD LD matrix from the 1Mb region on chromosome 7.\n", + "\n", + "At each iteration of the simulation we randomly select n_causal causal variants and generate Z-scores. We then perform fine mapping using GentroPy functions and examine the output.\n", + "\n", + "We expect all selected variants to be presented in detected credible sets." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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For SparkR, use setLogLevel(newLevel).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/05/21 18:05:22 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from gentropy.common.session import Session\n", + "from gentropy.finemapping_simulations import FineMappingSimulations\n", + "\n", + "session = Session()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "ld_matrix = np.load(\"/Users/yt4/Projects/ot_data/tmp/ld_matrix.npy\")\n", + "ld_index=session.spark.read.parquet(\"/Users/yt4/Projects/ot_data/tmp/ld_index\")\n", + "ld_matrix_for_sim=ld_matrix[0:500,:][:,0:500]\n", + "ld_index_for_sim=ld_index.limit(500)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of causal variants = 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_causal=1\n", + "x1=FineMappingSimulations.SimulationLoop(\n", + " n_iter=100,\n", + " n_causal=n_causal,\n", + " session=session,\n", + " he2_reggen=0.003,\n", + " sample_size=100_000,\n", + " ld_matrix_for_sim=ld_matrix_for_sim,\n", + " ld_index=ld_index_for_sim\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'successful_runs': 76, 'number_of_cs': 76, 'expected_results': 76, 'false_positives': 0.013157894736842105, 'accuracy': 0.9868421052631579, 'accuracy_lead': 0.6447368421052632, 'sensitivity': 0.9868421052631579}\n", + "{'successful_runs': 76, 'number_of_cs': 76, 'expected_results': 76, 'false_positives': 0.013157894736842105, 'accuracy': 0.9868421052631579, 'accuracy_lead': 0.6447368421052632, 'sensitivity': 0.9868421052631579}\n", + "{'successful_runs': 76, 'number_of_cs': 76, 'expected_results': 76, 'false_positives': 0.013157894736842105, 'accuracy': 0.9868421052631579, 'accuracy_lead': 0.6447368421052632, 'sensitivity': 0.9868421052631579}\n" + ] + } + ], + "source": [ + "x2=x1[(x1[\"pValueExponent\"]<=-6) | (x1[\"credibleSetIndex\"]==1)]\n", + "x3=x2[(x2[\"purityMinR2\"]>=0.25) | (x2[\"credibleSetIndex\"]==1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of causal variants = 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_causal=3\n", + "x1=FineMappingSimulations.SimulationLoop(\n", + " n_iter=100,\n", + " n_causal=n_causal,\n", + " session=session,\n", + " he2_reggen=0.003,\n", + " sample_size=100_000,\n", + " ld_matrix_for_sim=ld_matrix_for_sim,\n", + " ld_index=ld_index_for_sim\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'successful_runs': 89, 'number_of_cs': 177, 'expected_results': 267, 'false_positives': 0.062146892655367235, 'accuracy': 0.9378531073446328, 'accuracy_lead': 0.6666666666666666, 'sensitivity': 0.6217228464419475}\n", + "{'successful_runs': 89, 'number_of_cs': 172, 'expected_results': 267, 'false_positives': 0.05232558139534884, 'accuracy': 0.9476744186046512, 'accuracy_lead': 0.6802325581395349, 'sensitivity': 0.6104868913857678}\n", + "{'successful_runs': 89, 'number_of_cs': 161, 'expected_results': 267, 'false_positives': 0.049689440993788817, 'accuracy': 0.9503105590062112, 'accuracy_lead': 0.6832298136645962, 'sensitivity': 0.5730337078651685}\n" + ] + } + ], + "source": [ + "x2=x1[(x1[\"pValueExponent\"]<=-6) | (x1[\"credibleSetIndex\"]==1)]\n", + "x3=x2[(x2[\"purityMinR2\"]>=0.25) | (x2[\"credibleSetIndex\"]==1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CARMA without noise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_causal=1\n", + "x1=FineMappingSimulations.SimulationLoop(\n", + " n_iter=100,\n", + " n_causal=n_causal,\n", + " session=session,\n", + " he2_reggen=0.003,\n", + " sample_size=100_000,\n", + " ld_matrix_for_sim=ld_matrix_for_sim,\n", + " ld_index=ld_index_for_sim,\n", + " noise=False,\n", + " run_carma=True\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'successful_runs': 74, 'number_of_cs': 74, 'expected_results': 74, 'false_positives': 0.04054054054054054, 'accuracy': 0.9594594594594594, 'accuracy_lead': 0.7027027027027027, 'sensitivity': 0.9594594594594594}\n", + "{'successful_runs': 74, 'number_of_cs': 74, 'expected_results': 74, 'false_positives': 0.04054054054054054, 'accuracy': 0.9594594594594594, 'accuracy_lead': 0.7027027027027027, 'sensitivity': 0.9594594594594594}\n", + "{'successful_runs': 74, 'number_of_cs': 74, 'expected_results': 74, 'false_positives': 0.04054054054054054, 'accuracy': 0.9594594594594594, 'accuracy_lead': 0.7027027027027027, 'sensitivity': 0.9594594594594594}\n" + ] + } + ], + "source": [ + "x2=x1[(x1[\"pValueExponent\"]<=-6) | (x1[\"credibleSetIndex\"]==1)]\n", + "x3=x2[(x2[\"purityMinR2\"]>=0.25) | (x2[\"credibleSetIndex\"]==1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## No noise, but with CARMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_causal=3\n", + "x1=FineMappingSimulations.SimulationLoop(\n", + " n_iter=100,\n", + " n_causal=n_causal,\n", + " session=session,\n", + " he2_reggen=0.003,\n", + " sample_size=100_000,\n", + " ld_matrix_for_sim=ld_matrix_for_sim,\n", + " ld_index=ld_index_for_sim,\n", + " noise=False,\n", + " run_carma=True\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'successful_runs': 91, 'number_of_cs': 172, 'expected_results': 273, 'false_positives': 0.10465116279069768, 'accuracy': 0.8953488372093024, 'accuracy_lead': 0.6453488372093024, 'sensitivity': 0.5641025641025641}\n", + "{'successful_runs': 91, 'number_of_cs': 162, 'expected_results': 273, 'false_positives': 0.09259259259259259, 'accuracy': 0.9074074074074074, 'accuracy_lead': 0.6666666666666666, 'sensitivity': 0.5384615384615384}\n", + "{'successful_runs': 91, 'number_of_cs': 150, 'expected_results': 273, 'false_positives': 0.07333333333333333, 'accuracy': 0.9266666666666666, 'accuracy_lead': 0.6933333333333334, 'sensitivity': 0.5091575091575091}\n" + ] + } + ], + "source": [ + "x2=x1[(x1[\"pValueExponent\"]<=-6) | (x1[\"credibleSetIndex\"]==1)]\n", + "x3=x2[(x2[\"purityMinR2\"]>=0.25) | (x2[\"credibleSetIndex\"]==1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding noise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One varaint, noise, no CARMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_causal=1\n", + "x1=FineMappingSimulations.SimulationLoop(\n", + " n_iter=100,\n", + " n_causal=n_causal,\n", + " session=session,\n", + " he2_reggen=0.005,\n", + " sample_size=100_000,\n", + " ld_matrix_for_sim=ld_matrix_for_sim,\n", + " ld_index=ld_index_for_sim,\n", + " noise=True,\n", + " run_carma=False,\n", + " scale_noise=2,\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'successful_runs': 76, 'number_of_cs': 115, 'expected_results': 76, 'false_positives': 0.48695652173913045, 'accuracy': 0.5130434782608696, 'accuracy_lead': 0.4, 'sensitivity': 0.7763157894736842}\n", + "{'successful_runs': 76, 'number_of_cs': 112, 'expected_results': 76, 'false_positives': 0.4732142857142857, 'accuracy': 0.5267857142857143, 'accuracy_lead': 0.4107142857142857, 'sensitivity': 0.7763157894736842}\n", + "{'successful_runs': 76, 'number_of_cs': 111, 'expected_results': 76, 'false_positives': 0.46846846846846846, 'accuracy': 0.5315315315315315, 'accuracy_lead': 0.4144144144144144, 'sensitivity': 0.7763157894736842}\n" + ] + } + ], + "source": [ + "x2=x1[(x1[\"pValueExponent\"]<=-6) | (x1[\"credibleSetIndex\"]==1)]\n", + "x3=x2[(x2[\"purityMinR2\"]>=0.25) | (x2[\"credibleSetIndex\"]==1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One varaint, noise and CARMA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_causal=1\n", + "x1=FineMappingSimulations.SimulationLoop(\n", + " n_iter=100,\n", + " n_causal=n_causal,\n", + " session=session,\n", + " he2_reggen=0.005,\n", + " sample_size=100_000,\n", + " ld_matrix_for_sim=ld_matrix_for_sim,\n", + " ld_index=ld_index_for_sim,\n", + " noise=True,\n", + " run_carma=True,\n", + " scale_noise=2,\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'successful_runs': 86, 'number_of_cs': 99, 'expected_results': 86, 'false_positives': 0.30303030303030304, 'accuracy': 0.696969696969697, 'accuracy_lead': 0.5353535353535354, 'sensitivity': 0.8023255813953488}\n", + "{'successful_runs': 86, 'number_of_cs': 95, 'expected_results': 86, 'false_positives': 0.2736842105263158, 'accuracy': 0.7263157894736842, 'accuracy_lead': 0.5578947368421052, 'sensitivity': 0.8023255813953488}\n", + "{'successful_runs': 86, 'number_of_cs': 93, 'expected_results': 86, 'false_positives': 0.26881720430107525, 'accuracy': 0.7311827956989247, 'accuracy_lead': 0.5698924731182796, 'sensitivity': 0.7906976744186046}\n" + ] + } + ], + "source": [ + "x2=x1[(x1[\"pValueExponent\"]<=-6) | (x1[\"credibleSetIndex\"]==1)]\n", + "x3=x2[(x2[\"purityMinR2\"]>=0.25) | (x2[\"credibleSetIndex\"]==1)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gentropy-krNFZEZg-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/FineMapping_AlzheimierDisease.ipynb b/notebooks/FineMapping_AlzheimierDisease.ipynb new file mode 100644 index 000000000..bdb37e354 --- /dev/null +++ b/notebooks/FineMapping_AlzheimierDisease.ipynb @@ -0,0 +1,1714 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fine-mapping of Alzheimer's disease GWAS summary statistics using GentroPy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook presents an example of fine-mapping of the GWAS catalog study for Alzheimer's disease ([link to study](https://genetics.opentargets.org/Study/GCST90012877/associations)). The study itself is a good benchmarking example for fine-mapping - relatively large number of SNPs, very strong signal on the 19th chromosome (APOE). It's worth noting that usually very strong signals are excluded from fine-mapping due to instability.\n", + "\n", + "Also, we excluded MHC region (6:28M-34M) from fine-mapping because it has a huge density of the variants.\n", + "\n", + "To execute it on your local machine (not dataproc) you need to install https://github.com/broadinstitute/install-gcs-connector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your browser has been opened to visit:\n", + "\n", + " https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=764086051850-6qr4p6gpi6hn506pt8ejuq83di341hur.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8085%2F&scope=openid+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcloud-platform+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fsqlservice.login+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Faccounts.reauth&state=2Jvk4c7unAsigRvEKhceIxcrpGmeK8&access_type=offline&code_challenge=84guS6MmOY7qgvNpHLxoJbhRDBUAEUS93teMwQboD3Q&code_challenge_method=S256\n", + "\n", + "\n", + "Credentials saved to file: [/Users/yt4/.config/gcloud/application_default_credentials.json]\n", + "\n", + "These credentials will be used by any library that requests Application Default Credentials (ADC).\n", + "\n", + "Quota project \"open-targets-genetics-dev\" was added to ADC which can be used by Google client libraries for billing and quota. Note that some services may still bill the project owning the resource.\n", + "\n", + "\n", + "Updates are available for some Google Cloud CLI components. To install them,\n", + "please run:\n", + " $ gcloud components update\n", + "\n" + ] + } + ], + "source": [ + "!gcloud auth application-default login" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n function drop(id) {\n const view = Bokeh.index.get_by_id(id)\n if (view != null) {\n view.model.document.clear()\n Bokeh.index.delete(view)\n }\n }\n\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n\n // Clean up Bokeh references\n if (id != null) {\n drop(id)\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim()\n drop(id)\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"a8e9a6a6-96f7-4efd-a426-b2299499ef03\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.0.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"a8e9a6a6-96f7-4efd-a426-b2299499ef03\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", + "application/vnd.bokehjs_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/04/09 10:40:45 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", + "pip-installed Hail requires additional configuration options in Spark referring\n", + " to the path to the Hail Python module directory HAIL_DIR,\n", + " e.g. /path/to/python/site-packages/hail:\n", + " spark.jars=HAIL_DIR/backend/hail-all-spark.jar\n", + " spark.driver.extraClassPath=HAIL_DIR/backend/hail-all-spark.jar\n", + " spark.executor.extraClassPath=./hail-all-spark.jarRunning on Apache Spark version 3.3.4\n", + "SparkUI available at http://192.168.0.232:4040\n", + "Welcome to\n", + " __ __ <>__\n", + " / /_/ /__ __/ /\n", + " / __ / _ `/ / /\n", + " /_/ /_/\\_,_/_/_/ version 0.2.127-bb535cd096c5\n", + "LOGGING: writing to /dev/null\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "import hail as hl\n", + "import pandas as pd\n", + "import pyspark.sql.functions as f\n", + "\n", + "from gentropy.common.session import Session\n", + "from gentropy.dataset.study_index import StudyIndex\n", + "from gentropy.dataset.summary_statistics import SummaryStatistics\n", + "from gentropy.method.window_based_clumping import WindowBasedClumping\n", + "from gentropy.susie_finemapper import SusieFineMapperStep\n", + "\n", + "pd.set_option(\"display.max_colwidth\", None)\n", + "pd.set_option(\"display.expand_frame_repr\", False)\n", + "\n", + "hail_dir = os.path.dirname(hl.__file__)\n", + "session = Session(hail_home=hail_dir, start_hail=True, extended_spark_conf={\"spark.driver.memory\": \"12g\",\n", + " \"spark.kryoserializer.buffer.max\": \"500m\",\"spark.driver.maxResultSize\":\"3g\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the data and clumping" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of SNPs in GWAS: 10607272\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 7:> (0 + 1) / 1]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clumps: 33\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "path_gwas1=\"gs://gwas_catalog_data/harmonised_summary_statistics/GCST90012877.parquet\"\n", + "path_si=\"gs://gwas_catalog_data/study_index\"\n", + "\n", + "gwas1 = SummaryStatistics.from_parquet(session, path_gwas1)\n", + "study_index = StudyIndex.from_parquet(session, path_si)\n", + "\n", + "slt=WindowBasedClumping.clump(gwas1,gwas_significance=5e-8,distance=1e6)\n", + "slt_df=slt._df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 15:===================================================> (9 + 1) / 10]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+----------------+----------+---------+----------------+----------+--------------+--------------+-------------------------------+----------------+--------------------+---------------+\n", + "| studyId| variantId|chromosome| position| beta|sampleSize|pValueMantissa|pValueExponent|effectAlleleFrequencyFromSource| standardError| studyLocusId|qualityControls|\n", + "+------------+----------------+----------+---------+----------------+----------+--------------+--------------+-------------------------------+----------------+--------------------+---------------+\n", + "|GCST90012877| 1_161185602_G_A| 1|161185602| 0.0609052805639| null| 4.302| -8| 0.23499| 0.0111181765833| 6360456299763482946| []|\n", + "|GCST90012877| 1_207577223_T_C| 1|207577223| -0.122752564739| null| 1.403| -23| 0.822818| 0.0122652043685|-6742466305250328444| []|\n", + "|GCST90012877| 10_11678309_A_G| 10| 11678309| 0.0668997305692| null| 1.085| -11| 0.380517|0.00984571382836| 3672202482976347473| []|\n", + "|GCST90012877| 10_59886075_G_T| 10| 59886075|-0.0523916765294| null| 3.802| -8| 0.480668|0.00952612570169| 760299597568413738| []|\n", + "|GCST90012877| 10_80520381_T_G| 10| 80520381| 0.0701098772587| null| 2.736| -9| 0.793475| 0.0117897597766|-6168361428432361140| []|\n", + "|GCST90012877|11_121564878_T_C| 11|121564878| -0.186386086749| null| 5.586| -14| 0.037005| 0.0247938672944|-7548659272243096830| []|\n", + "|GCST90012877| 11_47370397_G_A| 11| 47370397| 0.0634588530202| null| 6.911| -11| 0.387521| 0.0097291000298| 1916491992423016132| []|\n", + "|GCST90012877| 11_60328267_T_C| 11| 60328267|-0.0892048800109| null| 9.335| -20| 0.371215|0.00980658024905| 3318332793803757311| []|\n", + "|GCST90012877| 11_86156833_A_G| 11| 86156833| 0.103281644827| null| 5.214| -26| 0.629462|0.00979200684254| 3806751464721795080| []|\n", + "|GCST90012877| 14_52924962_A_G| 14| 52924962| 0.102404628268| null| 3.69| -10| 0.092233| 0.0163413709974|-8640267085448358001| []|\n", + "|GCST90012877| 14_92472511_G_A| 14| 92472511|-0.0762776811698| null| 7.454| -14| 0.339674| 0.0101980809801| 8895835730818824947| []|\n", + "|GCST90012877| 15_50707194_C_G| 15| 50707194|-0.0722934881552| null| 1.639| -9| 0.197469| 0.0119888249532|-4585712009512019667| []|\n", + "|GCST90012877| 15_58730416_T_C| 15| 58730416|-0.0675867539589| null| 2.674| -11| 0.319058| 0.010142839928|-9173595866829505633| []|\n", + "|GCST90012877| 15_63277703_C_T| 15| 63277703| 0.0849598934189| null| 1.052| -8| 0.139487| 0.0148475601067|-6181511576673508209| []|\n", + "|GCST90012877| 16_31115000_C_A| 16| 31115000|-0.0620662164665| null| 4.466| -9| 0.281382| 0.0105807514538|-3612515273077152914| []|\n", + "|GCST90012877| 17_5229833_T_C| 17| 5229833|-0.0849787931131| null| 1.352| -9| 0.875068| 0.0140203927902|-7070596043624425654| []|\n", + "|GCST90012877| 17_63483402_T_C| 17| 63483402| 0.0542810764988| null| 1.215| -8| 0.529632|0.00952697587266| 7171154626284587162| []|\n", + "|GCST90012877| 19_1050875_A_G| 19| 1050875|-0.0772974277902| null| 2.415| -13| 0.674169| 0.0105546077307| 6109438569946056978| []|\n", + "|GCST90012877| 19_44892009_G_A| 19| 44892009| 0.352722374032| null| 1.995| -277| 0.605067|0.00991069396551| 6814727764900576662| []|\n", + "|GCST90012877| 19_51224706_C_A| 19| 51224706|-0.0582180344342| null| 1.295| -8| 0.325551| 0.010237506551|-8288099943480320096| []|\n", + "+------------+----------------+----------+---------+----------------+----------+--------------+--------------+-------------------------------+----------------+--------------------+---------------+\n", + "only showing top 20 rows\n", + "\n", + "None\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fine-mapping without outliers detection and imputation using 2M as window size" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-09 10:41:57.354 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 10:42:15.499 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:42:28.284 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:44:41.305 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 10:44:51.854 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:45:03.059 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:47:04.871 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 10:47:17.310 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:47:29.113 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:50:32.790 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 10:50:46.191 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:50:57.958 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:53:22.698 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 10:53:34.535 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:53:45.816 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:57:23.189 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 10:57:34.563 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:57:43.988 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 10:59:39.834 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 10:59:52.878 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:00:06.629 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:02:11.433 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:02:22.101 Hail: INFO: Ordering unsorted dataset with network shuffle\n", + "2024-04-09 11:02:32.320 Hail: INFO: wrote table with 175330 rows in 8 partitions to /tmp/__iruid_20813-EyC6kjgQ1hAjFSiH1Xp7sB\n", + "2024-04-09 11:02:35.350 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:04:16.225 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:04:27.837 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:04:41.879 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:07:09.950 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:07:21.139 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:07:33.197 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:09:56.240 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:10:08.288 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:10:20.802 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:14:07.114 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:14:20.204 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:14:32.464 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:16:41.133 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:16:53.191 Hail: INFO: Ordering unsorted dataset with network shuffle\n", + "2024-04-09 11:17:03.328 Hail: INFO: wrote table with 211068 rows in 9 partitions to /tmp/__iruid_35318-By6CsozcY2JvH6dhwjdBPU\n", + "2024-04-09 11:17:10.133 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:19:10.141 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:19:21.964 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:19:34.636 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:21:47.445 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:21:59.127 Hail: INFO: Ordering unsorted dataset with network shuffle\n", + "2024-04-09 11:22:15.902 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:23:18.294 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:23:32.131 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:23:48.719 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:26:51.703 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:27:02.820 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:27:18.412 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:29:02.997 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:29:19.516 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:29:41.224 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:33:19.553 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:33:32.903 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:33:49.144 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:36:16.552 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:36:28.952 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:36:46.964 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:40:40.837 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:40:51.976 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:41:04.014 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:43:54.259 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:44:05.534 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:44:20.087 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:46:45.605 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:46:59.301 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:47:13.181 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:49:50.219 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:50:02.311 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:50:16.072 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:52:34.864 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:52:46.513 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:53:00.919 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:55:50.580 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:56:02.124 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:56:16.907 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:59:15.457 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 11:59:27.380 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 11:59:40.184 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:03:31.055 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 12:03:42.554 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:03:53.915 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:06:33.123 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 12:06:45.813 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:06:58.340 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:09:23.153 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 12:09:33.531 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:09:43.693 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:11:13.739 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 12:11:26.087 Hail: INFO: Ordering unsorted dataset with network shuffle\n", + "2024-04-09 12:11:38.950 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:13:17.020 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 12:13:29.727 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:13:45.213 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:20:03.844 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-09 12:20:13.834 Hail: INFO: Coerced sorted dataset\n", + "2024-04-09 12:20:24.282 Hail: INFO: Coerced sorted dataset\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 1:160185602-162185602 ; number of CSs: 1 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/p5/4t9crp1563l792qz8xz_3x5h0000gq/T/ipykernel_46905/1319014212.py:29: FutureWarning:\n", + "\n", + "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + "\n", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 1:206577223-208577223 ; number of CSs: 1 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 10:10678309-12678309 ; number of CSs: 1 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 10:58886075-60886075 ; number of CSs: 1 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 10:79520381-81520381 ; number of CSs: 1 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + 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"output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 20:55423488-57423488 ; number of CSs: 1 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 1199:=====================> (3 + 5) / 8]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 21:25775872-27775872 ; number of CSs: 2 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 4:10025995-12025995 ; number of CSs: 1 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 6:39974457-41974457 ; number of CSs: 2 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 6:46627419-48627419 ; number of CSs: 1 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 1403:===================================> (5 + 3) / 8]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 7:142410495-144410495 ; number of CSs: 2 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 7:99374211-101374211 ; number of CSs: 1 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 1505:> (0 + 8) / 8]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Region: 8:26610986-28610986 ; number of CSs: 3 ; log:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "df = slt_df.withColumn(\"row_index\", f.monotonically_increasing_id())\n", + "\n", + "columns = [\"N_gwas\", \"N_ld\", \"N_overlap\", \"N_outliers\", \"N_imputed\", \"N_final_to_fm\", \"eleapsed_time\"]\n", + "logs = pd.DataFrame(columns=columns)\n", + "\n", + "for i in range(0,df.count()):\n", + " if i!=27:\n", + " one_row = df.filter(df.row_index == i).first()\n", + "\n", + " res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n", + " GWAS=gwas1,\n", + " session=session,\n", + " study_locus_row=one_row,\n", + " study_index=study_index,\n", + " window= 2_000_000,\n", + " L=10,\n", + " susie_est_tausq=False,\n", + " run_carma=False,\n", + " run_sumstat_imputation=False,\n", + " carma_time_limit=600,\n", + " imputed_r2_threshold=0.8,\n", + " ld_score_threshold=4\n", + " )\n", + "\n", + " sl=res[\"study_locus\"]\n", + " #print(sl._df.withColumn(\"size\", f.size(sl._df[\"locus\"])).show())\n", + " #print(res[\"log\"])\n", + " logs=pd.concat([logs,res[\"log\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " N_gwas N_ld N_overlap N_outliers N_imputed N_final_to_fm eleapsed_time\n", + "0 7120 10431 6456 0 0 6456 56.839336\n", + "0 7128 8657 5769 0 0 5769 46.149004\n", + "0 9203 12106 7930 0 0 7930 93.531924\n", + "0 8351 10014 6995 0 0 6995 74.174323\n", + "0 9388 12551 8337 0 0 8337 120.602071\n", + "0 6560 8729 5758 0 0 5758 45.064894\n", + "0 5005 7701 3954 0 0 3954 55.229344\n", + "0 7012 8940 5815 0 0 5815 38.824251\n", + "0 8661 10303 7291 0 0 7291 68.802810\n", + "0 8081 9966 6771 0 0 6771 64.327746\n", + "0 8375 11213 7467 0 0 7467 141.808555\n", + "0 7377 9622 6369 0 0 6369 51.198955\n", + "0 8181 10864 7116 0 0 7116 49.033224\n", + "0 7976 10135 6704 0 0 6704 58.357743\n", + "0 3369 6542 2972 0 0 2972 17.138722\n", + "0 9006 12776 7969 0 0 7969 83.551872\n", + "0 4565 7018 3887 0 0 3887 37.801989\n", + "0 8278 13741 7852 0 0 7852 105.623508\n", + "0 7582 10448 6100 0 0 6100 58.572944\n", + "0 9145 12706 8242 0 0 8242 129.442009\n", + "0 8795 11311 7584 0 0 7584 86.336201\n", + "0 7852 10028 7041 0 0 7041 67.886754\n", + "0 8393 10850 7195 0 0 7195 72.375809\n", + "0 7639 10031 6520 0 0 6520 61.900982\n", + "0 8899 11509 7922 0 0 7922 86.535298\n", + "0 8908 11309 7889 0 0 7889 93.595320\n", + "0 10654 12663 8990 0 0 8990 133.390712\n", + "0 9073 10228 7398 0 0 7398 79.774280\n", + "0 8033 9785 6822 0 0 6822 63.950340\n", + "0 4570 5516 3162 0 0 3162 27.943313\n", + "0 5716 8785 4760 0 0 4760 30.024706\n", + "0 9243 10989 7869 0 0 7869 108.898056\n" + ] + } + ], + "source": [ + "pd.set_option(\"display.max_rows\", None)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6653.3125\n" + ] + } + ], + "source": [ + "summary = logs[\"N_overlap\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fine-mapping of APOE locus" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "Row(studyId='GCST90012877', variantId='19_44892009_G_A', chromosome='19', position=44892009, beta=0.352722374032, sampleSize=None, pValueMantissa=1.9950000047683716, pValueExponent=-277, effectAlleleFrequencyFromSource=0.6050670146942139, standardError=0.00991069396551, studyLocusId=6814727764900576662, qualityControls=[], row_index=18)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = slt_df.withColumn(\"row_index\", f.monotonically_increasing_id())\n", + "one_row = df.filter(df.row_index == 18).first()\n", + "one_row" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Without CARMA, without imputation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-08 21:34:03.208 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + "2024-04-08 21:34:19.253 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 21:34:34.941 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 21:37:16.576 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + "2024-04-08 21:37:28.867 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 21:37:44.733 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 21:52:03.198 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + "2024-04-08 21:52:15.100 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 21:52:30.553 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 22:21:27.877 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + "2024-04-08 22:21:40.137 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 22:21:55.249 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 22:23:21.795 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + "2024-04-08 22:23:35.031 Hail: INFO: Coerced sorted dataset\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "| studyLocusId| studyId| region|credibleSetIndex| locus| variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n", + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "|-6417720984991662128|GCST90012877|19:43892009-45892009| 1|[{19_44908684_T_C...|19_44908684_T_C| 19|44908684| SuSiE-inf| 2135.710824756712| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 2|[{19_44921094_A_T...|19_44921094_A_T| 19|44921094| SuSiE-inf| 955.4948390766739| 1|\n", + "| 8324745608044585165|GCST90012877|19:43892009-45892009| 3|[{19_44917947_C_T...|19_44917947_C_T| 19|44917947| SuSiE-inf| 690.0307437138443| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 4|[{19_44921094_A_T...|19_44921094_A_T| 19|44921094| SuSiE-inf|425.33378303492805| 1|\n", + "| 2240477384494621278|GCST90012877|19:43892009-45892009| 5|[{19_44891079_T_C...|19_44891079_T_C| 19|44891079| SuSiE-inf|395.31055398960274| 1|\n", + "| 1029535804909934921|GCST90012877|19:43892009-45892009| 6|[{19_44894695_T_C...|19_44894695_T_C| 19|44894695| SuSiE-inf| 333.9497424582455| 1|\n", + "|-6417720984991662128|GCST90012877|19:43892009-45892009| 7|[{19_44908684_T_C...|19_44908684_T_C| 19|44908684| SuSiE-inf| 261.573648706883| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 8|[{19_44921094_A_T...|19_44921094_A_T| 19|44921094| SuSiE-inf|186.66554412409607| 1|\n", + "|-7370952295217410456|GCST90012877|19:43892009-45892009| 9|[{19_44922505_T_G...|19_44922505_T_G| 19|44922505| SuSiE-inf| 78.06352464083552| 1|\n", + "| 3925446284512644964|GCST90012877|19:43892009-45892009| 10|[{19_44913574_T_G...|19_44913574_T_G| 19|44913574| SuSiE-inf|55.346197523194675| 1|\n", + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "\n", + "None\n", + " N_gwas N_ld N_overlap N_outliers N_imputed N_final_to_fm eleapsed_time\n", + "0 7582 10448 6100 0 0 6100 66.112839\n" + ] + } + ], + "source": [ + "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n", + " GWAS=gwas1,\n", + " session=session,\n", + " study_locus_row=one_row,\n", + " study_index=study_index,\n", + " window= 2_000_000,\n", + " L=10,\n", + " susie_est_tausq=False,\n", + " run_carma=False,\n", + " run_sumstat_imputation=False,\n", + " carma_time_limit=1000,\n", + " imputed_r2_threshold=0.8,\n", + " ld_score_threshold=4\n", + ")\n", + "sl=res[\"study_locus\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### With CARMA, without imputation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "| studyLocusId| studyId| region|credibleSetIndex| locus| variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n", + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "|-6417720984991662128|GCST90012877|19:43892009-45892009| 1|[{19_44908684_T_C...|19_44908684_T_C| 19|44908684| SuSiE-inf|1995.6574121818223| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 2|[{19_44921094_A_T...|19_44921094_A_T| 19|44921094| SuSiE-inf| 721.2637360279233| 1|\n", + "| 7760477027903907683|GCST90012877|19:43892009-45892009| 3|[{19_44911142_C_A...|19_44911142_C_A| 19|44911142| SuSiE-inf|248.39159334060017| 1|\n", + "|-1172224975892516254|GCST90012877|19:43892009-45892009| 4|[{19_44894255_A_C...|19_44894255_A_C| 19|44894255| SuSiE-inf| 96.16160678286879| 1|\n", + "| 8852802213660052283|GCST90012877|19:43892009-45892009| 5|[{19_44862190_G_A...|19_44862190_G_A| 19|44862190| SuSiE-inf| 55.80518621838019| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 6|[{19_44921094_A_T...|19_44921094_A_T| 19|44921094| SuSiE-inf| 53.24772075097935| 1|\n", + "|-6417720984991662128|GCST90012877|19:43892009-45892009| 7|[{19_44908684_T_C...|19_44908684_T_C| 19|44908684| SuSiE-inf| 45.65754067281976| 1|\n", + "|-6417720984991662128|GCST90012877|19:43892009-45892009| 8|[{19_44908684_T_C...|19_44908684_T_C| 19|44908684| SuSiE-inf| 39.3840804563262| 1|\n", + "| 6986973025714240626|GCST90012877|19:43892009-45892009| 9|[{19_44873060_C_G...|19_44873060_C_G| 19|44873060| SuSiE-inf| 38.54912041595975| 1|\n", + "| 3640651426400620880|GCST90012877|19:43892009-45892009| 10|[{19_44845920_G_C...|19_44845920_G_C| 19|44845920| SuSiE-inf|35.378479810047224| 2|\n", + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "\n", + "None\n", + " N_gwas N_ld N_overlap N_outliers N_imputed N_final_to_fm eleapsed_time\n", + "0 7582 10448 6100 151 0 5949 783.939477\n" + ] + } + ], + "source": [ + "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n", + " GWAS=gwas1,\n", + " session=session,\n", + " study_locus_row=one_row,\n", + " study_index=study_index,\n", + " window= 2_000_000,\n", + " L=10,\n", + " susie_est_tausq=False,\n", + " run_carma=True,\n", + " run_sumstat_imputation=False,\n", + " carma_time_limit=1000,\n", + " imputed_r2_threshold=0.8,\n", + " ld_score_threshold=4\n", + ")\n", + "sl=res[\"study_locus\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Without CARMA, with imputation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-08 22:25:15.739 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-08 22:25:30.625 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 22:25:46.020 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 22:32:35.094 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-08 22:32:47.616 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 22:33:02.484 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 22:51:33.149 Hail: INFO: Table.join: renamed the following fields on the right to avoid name conflicts:\n", + " 'age_index_dict' -> 'age_index_dict_1'\n", + " 'freq_index_dict' -> 'freq_index_dict_1'\n", + " 'faf_index_dict' -> 'faf_index_dict_1'\n", + " 'freq_meta' -> 'freq_meta_1'\n", + " 'rf' -> 'rf_1'\n", + " 'age_distribution' -> 'age_distribution_1'\n", + " 'popmax_index_dict' -> 'popmax_index_dict_1'\n", + "2024-04-08 22:51:45.708 Hail: INFO: Coerced sorted dataset\n", + "2024-04-08 22:52:00.731 Hail: INFO: Coerced sorted dataset\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+------------+--------------------+----------------+--------------------+-----------------+----------+--------+-----------------+------------------+----+\n", + "| studyLocusId| studyId| region|credibleSetIndex| locus| variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n", + "+--------------------+------------+--------------------+----------------+--------------------+-----------------+----------+--------+-----------------+------------------+----+\n", + "|-1350283509846281677|GCST90012877|19:43892009-45892009| 1|[{19_44909967_TGG...|19_44909967_TGG_T| 19|44909967| SuSiE-inf| 2310.665662473933| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 2|[{19_44921094_A_T...| 19_44921094_A_T| 19|44921094| SuSiE-inf| 903.6138342773536| 1|\n", + "| 8324745608044585165|GCST90012877|19:43892009-45892009| 3|[{19_44917947_C_T...| 19_44917947_C_T| 19|44917947| SuSiE-inf| 700.3080514793324| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 4|[{19_44921094_A_T...| 19_44921094_A_T| 19|44921094| SuSiE-inf|431.79459690536703| 1|\n", + "| 1029535804909934921|GCST90012877|19:43892009-45892009| 5|[{19_44894695_T_C...| 19_44894695_T_C| 19|44894695| SuSiE-inf|402.50010763388156| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 6|[{19_44921094_A_T...| 19_44921094_A_T| 19|44921094| SuSiE-inf|225.93101254172214| 1|\n", + "| -60207296485035224|GCST90012877|19:43892009-45892009| 7|[{19_44888997_C_T...| 19_44888997_C_T| 19|44888997| SuSiE-inf| 191.4947272198485| 1|\n", + "|-1350283509846281677|GCST90012877|19:43892009-45892009| 8|[{19_44909967_TGG...|19_44909967_TGG_T| 19|44909967| SuSiE-inf|105.04460057482835| 1|\n", + "|-4078755027603845519|GCST90012877|19:43892009-45892009| 9|[{19_44918393_G_A...| 19_44918393_G_A| 19|44918393| SuSiE-inf| 63.30243818120949| 1|\n", + "| 3925446284512644964|GCST90012877|19:43892009-45892009| 10|[{19_44913574_T_G...| 19_44913574_T_G| 19|44913574| SuSiE-inf|54.079307276192694| 1|\n", + "+--------------------+------------+--------------------+----------------+--------------------+-----------------+----------+--------+-----------------+------------------+----+\n", + "\n", + "None\n", + " N_gwas N_ld N_overlap N_outliers N_imputed N_final_to_fm eleapsed_time\n", + "0 7582 10448 6100 0 681 6781 334.328722\n" + ] + } + ], + "source": [ + "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n", + " GWAS=gwas1,\n", + " session=session,\n", + " study_locus_row=one_row,\n", + " study_index=study_index,\n", + " window= 2_000_000,\n", + " L=10,\n", + " susie_est_tausq=False,\n", + " run_carma=False,\n", + " run_sumstat_imputation=True,\n", + " carma_time_limit=10000,\n", + " imputed_r2_threshold=0.8,\n", + " ld_score_threshold=4\n", + ")\n", + "sl=res[\"study_locus\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### With CARMA, with imputation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "| studyLocusId| studyId| region|credibleSetIndex| locus| variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n", + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "| 3030414938485808431|GCST90012877|19:43892009-45892009| 1|[{19_44895007_C_T...|19_44895007_C_T| 19|44895007| SuSiE-inf|2680.9099711333456| 1|\n", + "|-2201142982564351776|GCST90012877|19:43892009-45892009| 2|[{19_44900601_A_G...|19_44900601_A_G| 19|44900601| SuSiE-inf| 2103.873956796136| 1|\n", + "|-6417720984991662128|GCST90012877|19:43892009-45892009| 3|[{19_44908684_T_C...|19_44908684_T_C| 19|44908684| SuSiE-inf|1968.8126348567705| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 4|[{19_44921094_A_T...|19_44921094_A_T| 19|44921094| SuSiE-inf|1089.9033376410644| 1|\n", + "| 7760477027903907683|GCST90012877|19:43892009-45892009| 5|[{19_44911142_C_A...|19_44911142_C_A| 19|44911142| SuSiE-inf|188.55568384844716| 1|\n", + "|-6417720984991662128|GCST90012877|19:43892009-45892009| 6|[{19_44908684_T_C...|19_44908684_T_C| 19|44908684| SuSiE-inf| 83.57344085238768| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 7|[{19_44921094_A_T...|19_44921094_A_T| 19|44921094| SuSiE-inf| 82.01732099119907| 1|\n", + "| 8852802213660052283|GCST90012877|19:43892009-45892009| 8|[{19_44862190_G_A...|19_44862190_G_A| 19|44862190| SuSiE-inf| 45.92126992319222| 1|\n", + "|-1611304699666037367|GCST90012877|19:43892009-45892009| 9|[{19_44821259_C_T...|19_44821259_C_T| 19|44821259| SuSiE-inf|37.363613067645254| 1|\n", + "| 3556335645959991344|GCST90012877|19:43892009-45892009| 10|[{19_45017701_G_T...|19_45017701_G_T| 19|45017701| SuSiE-inf|30.736039473626658| 4|\n", + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "\n", + "None\n", + " N_gwas N_ld N_overlap N_outliers N_imputed N_final_to_fm eleapsed_time\n", + "0 7582 10448 6100 152 715 6663 1036.467428\n" + ] + } + ], + "source": [ + "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n", + " GWAS=gwas1,\n", + " session=session,\n", + " study_locus_row=one_row,\n", + " study_index=study_index,\n", + " window= 2_000_000,\n", + " L=10,\n", + " susie_est_tausq=False,\n", + " run_carma=True,\n", + " run_sumstat_imputation=True,\n", + " carma_time_limit=10000,\n", + " imputed_r2_threshold=0.8,\n", + " ld_score_threshold=4\n", + ")\n", + "sl=res[\"study_locus\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### With CARMA, with imputation, with estimation of infinitisimal effects (susie_est_tausq=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "| studyLocusId| studyId| region|credibleSetIndex| locus| variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n", + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "|-6417720984991662128|GCST90012877|19:43892009-45892009| 1|[{19_44908684_T_C...|19_44908684_T_C| 19|44908684| SuSiE-inf| 1105.297844890198| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 2|[{19_44921094_A_T...|19_44921094_A_T| 19|44921094| SuSiE-inf|1042.0949995382389| 1|\n", + "|-2201142982564351776|GCST90012877|19:43892009-45892009| 3|[{19_44900601_A_G...|19_44900601_A_G| 19|44900601| SuSiE-inf| 760.0654878716481| 1|\n", + "| 3030414938485808431|GCST90012877|19:43892009-45892009| 4|[{19_44895007_C_T...|19_44895007_C_T| 19|44895007| SuSiE-inf| 388.8928142354868| 1|\n", + "| -251577639520141451|GCST90012877|19:43892009-45892009| 5|[{19_44899220_C_T...|19_44899220_C_T| 19|44899220| SuSiE-inf| 259.5645544847559| 1|\n", + "| 7760477027903907683|GCST90012877|19:43892009-45892009| 6|[{19_44911142_C_A...|19_44911142_C_A| 19|44911142| SuSiE-inf|231.66277856324325| 1|\n", + "| 4133344777320628094|GCST90012877|19:43892009-45892009| 7|[{19_44904531_G_A...|19_44904531_G_A| 19|44904531| SuSiE-inf|143.22657752219786| 1|\n", + "|-1764089385585984368|GCST90012877|19:43892009-45892009| 8|[{19_44893642_T_C...|19_44893642_T_C| 19|44893642| SuSiE-inf| 87.72507299242906| 1|\n", + "|-1158278093713046158|GCST90012877|19:43892009-45892009| 9|[{19_44921094_A_T...|19_44921094_A_T| 19|44921094| SuSiE-inf| 71.4171763690986| 1|\n", + "|-6417720984991662128|GCST90012877|19:43892009-45892009| 10|[{19_44908684_T_C...|19_44908684_T_C| 19|44908684| SuSiE-inf| 43.36071977593145| 1|\n", + "+--------------------+------------+--------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "\n", + "None\n", + " N_gwas N_ld N_overlap N_outliers N_imputed N_final_to_fm eleapsed_time\n", + "0 7582 10448 6100 151 720 6669 1229.515921\n" + ] + } + ], + "source": [ + "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n", + " GWAS=gwas1,\n", + " session=session,\n", + " study_locus_row=one_row,\n", + " study_index=study_index,\n", + " window= 2_000_000,\n", + " L=10,\n", + " susie_est_tausq=True,\n", + " run_carma=True,\n", + " run_sumstat_imputation=True,\n", + " carma_time_limit=10000,\n", + " imputed_r2_threshold=0.8,\n", + " ld_score_threshold=4\n", + ")\n", + "sl=res[\"study_locus\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fine-mapping of MHC region using 1Mb window" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "Row(studyId='GCST90012877', variantId='6_32592248_A_G', chromosome='6', position=32592248, beta=-0.103604380043, sampleSize=None, pValueMantissa=2.877000093460083, pValueExponent=-15, effectAlleleFrequencyFromSource=0.21086899936199188, standardError=0.0131209374957, studyLocusId=5718491981995302674, qualityControls=[], row_index=27)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = slt_df.withColumn(\"row_index\", f.monotonically_increasing_id())\n", + "one_row = df.filter(df.row_index == 27).first()\n", + "one_row" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 1541:==========================================> (6 + 2) / 8]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+------------+-------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "| studyLocusId| studyId| region|credibleSetIndex| locus| variantId|chromosome|position|finemappingMethod|credibleSetlog10BF|size|\n", + "+--------------------+------------+-------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "|-3446214959021623473|GCST90012877|6:32092248-33092248| 1|[{6_32557997_G_A,...| 6_32557997_G_A| 6|32557997| SuSiE-inf| 4323.908142062261| 1|\n", + "| -439738150050389281|GCST90012877|6:32092248-33092248| 2|[{6_32558002_G_T,...| 6_32558002_G_T| 6|32558002| SuSiE-inf|3428.8321277074765| 1|\n", + "| 5831857384024844796|GCST90012877|6:32092248-33092248| 3|[{6_32557987_C_A,...| 6_32557987_C_A| 6|32557987| SuSiE-inf|1699.8680349563335| 1|\n", + "|-1087057043201011402|GCST90012877|6:32092248-33092248| 4|[{6_32557977_T_C,...| 6_32557977_T_C| 6|32557977| SuSiE-inf| 965.9753305300063| 1|\n", + "| 6919234179916081233|GCST90012877|6:32092248-33092248| 5|[{6_32649735_C_T,...| 6_32649735_C_T| 6|32649735| SuSiE-inf| 369.9698233117616| 1|\n", + "| 7781006900918060896|GCST90012877|6:32092248-33092248| 6|[{6_32652962_C_T,...| 6_32652962_C_T| 6|32652962| SuSiE-inf| 328.6834447478274| 1|\n", + "|-7512794333418509403|GCST90012877|6:32092248-33092248| 7|[{6_32591896_T_G,...| 6_32591896_T_G| 6|32591896| SuSiE-inf|326.52393082050276| 1|\n", + "| 4056478719932360430|GCST90012877|6:32092248-33092248| 8|[{6_32621456_GC_G...|6_32621456_GC_G| 6|32621456| SuSiE-inf|263.48518383939836| 1|\n", + "| 8380896542014789747|GCST90012877|6:32092248-33092248| 9|[{6_32648039_G_A,...| 6_32648039_G_A| 6|32648039| SuSiE-inf|176.62947310155317| 1|\n", + "| 9053545161380162736|GCST90012877|6:32092248-33092248| 10|[{6_32700030_C_T,...| 6_32700030_C_T| 6|32700030| SuSiE-inf| 69.83226092797517| 2|\n", + "+--------------------+------------+-------------------+----------------+--------------------+---------------+----------+--------+-----------------+------------------+----+\n", + "\n", + "None\n", + " N_gwas N_ld N_overlap N_outliers N_imputed N_final_to_fm eleapsed_time\n", + "0 19311 22318 13188 0 0 13188 298.784288\n" + ] + } + ], + "source": [ + "res=SusieFineMapperStep.susie_finemapper_one_studylocus_row_v2_dev(\n", + " GWAS=gwas1,\n", + " session=session,\n", + " study_locus_row=one_row,\n", + " study_index=study_index,\n", + " window= 1_000_000,\n", + " L=10,\n", + " susie_est_tausq=False,\n", + " run_carma=False,\n", + " run_sumstat_imputation=False,\n", + " carma_time_limit=10000,\n", + " imputed_r2_threshold=0.8,\n", + " ld_score_threshold=4\n", + ")\n", + "sl=res[\"study_locus\"]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gentropy-krNFZEZg-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/Finngen_PICS_run.ipynb b/notebooks/Finngen_PICS_run.ipynb index 21f29db2f..614f07ba5 100644 --- a/notebooks/Finngen_PICS_run.ipynb +++ b/notebooks/Finngen_PICS_run.ipynb @@ -1,1293 +1,1284 @@ { - "cells": [ + "cells": [ + { + "cell_type": "markdown", + "id": "2dd3bf11", + "metadata": {}, + "source": [ + "# Running PICS finemapping on Finngen summary statistics\n", + "\n", + "1. Read summary stats.\n", + "2. Apply window based clumping.\n", + "3. LD expansion.\n", + "4. lD clumping.\n", + "5. PICS." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0deeb686", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-13T09:53:43.429135Z", + "start_time": "2023-10-13T09:53:23.566141Z" + } + }, + "outputs": [ { - "cell_type": "markdown", - "id": "2dd3bf11", - "metadata": {}, - "source": [ - "# Running PICS finemapping on Finngen summary statistics\n", - "\n", - "1. Read summary stats.\n", - "2. Apply window based clumping.\n", - "3. LD expansion.\n", - "4. lD clumping.\n", - "5. PICS." + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"cb01f13c-6396-4b85-99db-68f8056c07dd\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"cb01f13c-6396-4b85-99db-68f8056c07dd\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Setting default log level to \"WARN\".\n", - "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", - "23/10/13 09:53:40 INFO SparkEnv: Registering MapOutputTracker\n", - "23/10/13 09:53:40 INFO SparkEnv: Registering BlockManagerMaster\n", - "23/10/13 09:53:40 INFO SparkEnv: Registering BlockManagerMasterHeartbeat\n", - "23/10/13 09:53:40 INFO SparkEnv: Registering OutputCommitCoordinator\n", - "23/10/13 09:53:42 WARN GhfsStorageStatistics: Detected potential high latency for operation op_get_file_status. latencyMs=198; previousMaxLatencyMs=0; operationCount=1; context=gs://dataproc-temp-europe-west1-426265110888-ymkbpaze/64dcfdf8-46d3-4b5c-aad4-0a12ee0ba91a/spark-job-history\n", - "23/10/13 09:53:42 WARN GhfsStorageStatistics: Detected potential high latency for operation op_mkdirs. latencyMs=166; previousMaxLatencyMs=0; operationCount=1; context=gs://dataproc-temp-europe-west1-426265110888-ymkbpaze/64dcfdf8-46d3-4b5c-aad4-0a12ee0ba91a/spark-job-history\n" - ] - } - ], - "source": [ - "# Import:\n", - "from pyspark.sql import functions as f, types as t\n", - "\n", - "from gentropy.common.session import Session\n", - "\n", - "from gentropy.dataset.summary_statistics import SummaryStatistics\n", - "from gentropy.dataset.study_locus import StudyLocus\n", - "from gentropy.dataset.study_index import StudyIndex\n", - "from gentropy.dataset.ld_index import LDIndex\n", - "from gentropy.method.ld import LDAnnotator\n", - "\n", - "from gentropy.method.pics import PICS\n", - "\n", - "# Initialize session:\n", - "session = Session()\n", - "\n", - "# Input:\n", - "sumstats = 'gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/*'\n", - "ld_index_path = 'gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/ld_index/'\n", - "\n", - "# Parameters:\n", - "clump_window_length = 500_000 # Distance between semi-indices.\n", - "locus_window_length = 250_000 # Distance around semi-indices from where the tags are collected.\n", - "\n", - "# Output:\n", - "window_based_clumped_output = 'gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus'\n", - "ld_clumped_output = 'gs://ot-team/dsuveges/finngen/2023.10.13_ld_clumped_w_locus'\n", - "picsed_output = 'gs://ot-team/dsuveges/finngen/2023.10.06_PICSed'\n" - ] + "data": { + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"cb01f13c-6396-4b85-99db-68f8056c07dd\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"cb01f13c-6396-4b85-99db-68f8056c07dd\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", + "application/vnd.bokehjs_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "id": "3bdbcfbf", - "metadata": {}, - "source": [ - "## 1. Read summary statistics" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", + "23/10/13 09:53:40 INFO SparkEnv: Registering MapOutputTracker\n", + "23/10/13 09:53:40 INFO SparkEnv: Registering BlockManagerMaster\n", + "23/10/13 09:53:40 INFO SparkEnv: Registering BlockManagerMasterHeartbeat\n", + "23/10/13 09:53:40 INFO SparkEnv: Registering OutputCommitCoordinator\n", + "23/10/13 09:53:42 WARN GhfsStorageStatistics: Detected potential high latency for operation op_get_file_status. latencyMs=198; previousMaxLatencyMs=0; operationCount=1; context=gs://dataproc-temp-europe-west1-426265110888-ymkbpaze/64dcfdf8-46d3-4b5c-aad4-0a12ee0ba91a/spark-job-history\n", + "23/10/13 09:53:42 WARN GhfsStorageStatistics: Detected potential high latency for operation op_mkdirs. latencyMs=166; previousMaxLatencyMs=0; operationCount=1; context=gs://dataproc-temp-europe-west1-426265110888-ymkbpaze/64dcfdf8-46d3-4b5c-aad4-0a12ee0ba91a/spark-job-history\n" + ] + } + ], + "source": [ + "# Import:\n", + "from pyspark.sql import functions as f\n", + "\n", + "from gentropy.common.session import Session\n", + "from gentropy.dataset.ld_index import LDIndex\n", + "from gentropy.dataset.study_index import StudyIndex\n", + "from gentropy.dataset.study_locus import StudyLocus\n", + "from gentropy.dataset.summary_statistics import SummaryStatistics\n", + "from gentropy.method.ld import LDAnnotator\n", + "from gentropy.method.pics import PICS\n", + "\n", + "# Initialize session:\n", + "session = Session()\n", + "\n", + "# Input:\n", + "sumstats = \"gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/*\"\n", + "ld_index_path = \"gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/ld_index/\"\n", + "\n", + "# Parameters:\n", + "clump_window_length = 500_000 # Distance between semi-indices.\n", + "locus_window_length = 250_000 # Distance around semi-indices from where the tags are collected.\n", + "\n", + "# Output:\n", + "window_based_clumped_output = \"gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus\"\n", + "ld_clumped_output = \"gs://ot-team/dsuveges/finngen/2023.10.13_ld_clumped_w_locus\"\n", + "picsed_output = \"gs://ot-team/dsuveges/finngen/2023.10.06_PICSed\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "3bdbcfbf", + "metadata": {}, + "source": [ + "## 1. Read summary statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c893433c", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T08:46:39.186806Z", + "start_time": "2023-10-06T08:46:04.601886Z" + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "id": "c893433c", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T08:46:39.186806Z", - "start_time": "2023-10-06T08:46:04.601886Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23/10/06 08:46:37 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=236; previousMaxLatencyMs=147; operationCount=276; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_AB1_ACTINOMYCOSIS/chromosome=1/part-00011-33e31f88-435f-4200-9adc-3e909d706910.c000.snappy.parquet\n", - "23/10/06 08:46:37 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=269; previousMaxLatencyMs=236; operationCount=282; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_HEIGHT_IRN/chromosome=2/part-00020-774f0990-ad3d-46ae-9648-f39c18fae314.c000.snappy.parquet\n", - "23/10/06 08:46:38 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=417; previousMaxLatencyMs=269; operationCount=288; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_HEIGHT_IRN/chromosome=2/part-00020-774f0990-ad3d-46ae-9648-f39c18fae314.c000.snappy.parquet\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------+-----------+--------+--------------+--------------+-----------+-------------+-------------------------------+---------------------------+---------------------------+----------+\n", - "| studyId| variantId|position|pValueMantissa|pValueExponent| beta|standardError|effectAlleleFrequencyFromSource|betaConfidenceIntervalLower|betaConfidenceIntervalUpper|chromosome|\n", - "+--------------------+-----------+--------+--------------+--------------+-----------+-------------+-------------------------------+---------------------------+---------------------------+----------+\n", - "|FINNGEN_R9_HEIGHT...|2_10603_A_C| 10603| 8.515| -1| 0.0117813| 0.0629271| 2.30629E-4| -0.0511458| 0.0747084| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_10610_G_A| 10610| 8.814| -3| 0.0187529| 0.00715985| 0.0194828| 0.01159305| 0.02591275| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_10659_G_A| 10659| 9.292| -1|-0.00289323| 0.0325443| 8.5694E-4| -0.03543752999999...| 0.029651069999999998| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_10756_T_C| 10756| 2.368| -1| -0.180083| 0.15223| 5.62468E-5| -0.33231299999999997| -0.02785299999999999| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_10797_C_T| 10797| 8.506| -1|-0.00190634| 0.01012| 0.00847354| -0.01202634| 0.008213660000000001| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_10823_G_C| 10823| 6.834| -2| -0.259102| 0.142146| 1.48813E-4| -0.401248| -0.116956| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_10847_C_T| 10847| 5.97| -1| 0.0487634| 0.0922252| 1.03478E-4| -0.04346179999999...| 0.1409886| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11305_G_C| 11305| 6.992| -1|-0.00333638| 0.00863326| 0.013513| -0.01196964| 0.00529688| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11311_C_G| 11311| 1.676| -1| 0.112938| 0.0818501| 1.30145E-4| 0.0310879| 0.1947881| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11320_G_A| 11320| 2.484| -7| -0.0106449| 0.0020634| 0.302909| -0.0127083| -0.0085815| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11486_A_G| 11486| 6.829| -3| 0.0135765| 0.00501887| 0.035261| 0.00855763| 0.01859537| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11573_A_C| 11573| 7.428| -1| 0.00483333| 0.0147283| 0.00424829| -0.00989497| 0.01956163| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11594_G_T| 11594| 5.291| -1|-0.00908649| 0.014438| 0.0045864| -0.02352449| 0.00535151| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11607_T_C| 11607| 5.666| -3| 0.0139369| 0.00503765| 0.0350636| 0.008899250000000001| 0.01897455| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11609_T_G| 11609| 8.231| -2| -0.065766| 0.0378528| 7.11595E-4| -0.10361880000000001| -0.02791320000000...| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11620_G_C| 11620| 5.866| -1| -0.0235368| 0.0432809| 5.48483E-4| -0.0668177| 0.019744099999999997| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11677_C_G| 11677| 3.944| -1| -0.0888112| 0.104287| 1.01977E-4| -0.1930982| 0.015475799999999998| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11694_C_T| 11694| 8.855| -2| -0.0468924| 0.0275339| 0.00127759| -0.0744263| -0.0193585| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11696_C_T| 11696| 2.446| -1| -0.0384871| 0.0330789| 8.17298E-4| -0.071566| -0.00540820000000...| 2|\n", - "|FINNGEN_R9_HEIGHT...|2_11834_A_G| 11834| 5.664| -3| 0.0139372| 0.00503766| 0.0350634| 0.008899540000000001| 0.01897486| 2|\n", - "+--------------------+-----------+--------+--------------+--------------+-----------+-------------+-------------------------------+---------------------------+---------------------------+----------+\n", - "only showing top 20 rows\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "finngen_sumstats = SummaryStatistics(\n", - " _df=(\n", - " session.spark.read.parquet(sumstats, recursiveFileLookup=True)\n", - " # We need to add chromosome column as this is a partition column:\n", - " .withColumn('chromosome',f.split(f.col('variantId'), '_')[0])\n", - " ),\n", - " _schema=SummaryStatistics.get_schema()\n", - ")\n", - "finngen_sumstats.df.show()\n", - "print(finngen_sumstats.df.count())" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "23/10/06 08:46:37 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=236; previousMaxLatencyMs=147; operationCount=276; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_AB1_ACTINOMYCOSIS/chromosome=1/part-00011-33e31f88-435f-4200-9adc-3e909d706910.c000.snappy.parquet\n", + "23/10/06 08:46:37 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=269; previousMaxLatencyMs=236; operationCount=282; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_HEIGHT_IRN/chromosome=2/part-00020-774f0990-ad3d-46ae-9648-f39c18fae314.c000.snappy.parquet\n", + "23/10/06 08:46:38 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=417; previousMaxLatencyMs=269; operationCount=288; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_HEIGHT_IRN/chromosome=2/part-00020-774f0990-ad3d-46ae-9648-f39c18fae314.c000.snappy.parquet\n" + ] }, { - "cell_type": "markdown", - "id": "a76b27ce", - "metadata": {}, - "source": [ - "## 2. Apply window based clumping.\n", - "\n", - "- Clumping distance: +/-500kbp\n", - "- Locus collected: +/-250kbp" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+-----------+--------+--------------+--------------+-----------+-------------+-------------------------------+---------------------------+---------------------------+----------+\n", + "| studyId| variantId|position|pValueMantissa|pValueExponent| beta|standardError|effectAlleleFrequencyFromSource|betaConfidenceIntervalLower|betaConfidenceIntervalUpper|chromosome|\n", + "+--------------------+-----------+--------+--------------+--------------+-----------+-------------+-------------------------------+---------------------------+---------------------------+----------+\n", + "|FINNGEN_R9_HEIGHT...|2_10603_A_C| 10603| 8.515| -1| 0.0117813| 0.0629271| 2.30629E-4| -0.0511458| 0.0747084| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_10610_G_A| 10610| 8.814| -3| 0.0187529| 0.00715985| 0.0194828| 0.01159305| 0.02591275| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_10659_G_A| 10659| 9.292| -1|-0.00289323| 0.0325443| 8.5694E-4| -0.03543752999999...| 0.029651069999999998| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_10756_T_C| 10756| 2.368| -1| -0.180083| 0.15223| 5.62468E-5| -0.33231299999999997| -0.02785299999999999| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_10797_C_T| 10797| 8.506| -1|-0.00190634| 0.01012| 0.00847354| -0.01202634| 0.008213660000000001| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_10823_G_C| 10823| 6.834| -2| -0.259102| 0.142146| 1.48813E-4| -0.401248| -0.116956| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_10847_C_T| 10847| 5.97| -1| 0.0487634| 0.0922252| 1.03478E-4| -0.04346179999999...| 0.1409886| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11305_G_C| 11305| 6.992| -1|-0.00333638| 0.00863326| 0.013513| -0.01196964| 0.00529688| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11311_C_G| 11311| 1.676| -1| 0.112938| 0.0818501| 1.30145E-4| 0.0310879| 0.1947881| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11320_G_A| 11320| 2.484| -7| -0.0106449| 0.0020634| 0.302909| -0.0127083| -0.0085815| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11486_A_G| 11486| 6.829| -3| 0.0135765| 0.00501887| 0.035261| 0.00855763| 0.01859537| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11573_A_C| 11573| 7.428| -1| 0.00483333| 0.0147283| 0.00424829| -0.00989497| 0.01956163| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11594_G_T| 11594| 5.291| -1|-0.00908649| 0.014438| 0.0045864| -0.02352449| 0.00535151| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11607_T_C| 11607| 5.666| -3| 0.0139369| 0.00503765| 0.0350636| 0.008899250000000001| 0.01897455| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11609_T_G| 11609| 8.231| -2| -0.065766| 0.0378528| 7.11595E-4| -0.10361880000000001| -0.02791320000000...| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11620_G_C| 11620| 5.866| -1| -0.0235368| 0.0432809| 5.48483E-4| -0.0668177| 0.019744099999999997| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11677_C_G| 11677| 3.944| -1| -0.0888112| 0.104287| 1.01977E-4| -0.1930982| 0.015475799999999998| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11694_C_T| 11694| 8.855| -2| -0.0468924| 0.0275339| 0.00127759| -0.0744263| -0.0193585| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11696_C_T| 11696| 2.446| -1| -0.0384871| 0.0330789| 8.17298E-4| -0.071566| -0.00540820000000...| 2|\n", + "|FINNGEN_R9_HEIGHT...|2_11834_A_G| 11834| 5.664| -3| 0.0139372| 0.00503766| 0.0350634| 0.008899540000000001| 0.01897486| 2|\n", + "+--------------------+-----------+--------+--------------+--------------+-----------+-------------+-------------------------------+---------------------------+---------------------------+----------+\n", + "only showing top 20 rows\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": 34, - "id": "fc0dd4f0", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T10:22:38.264681Z", - "start_time": "2023-10-06T10:10:29.768171Z" - }, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23/10/06 10:22:32 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=1812; previousMaxLatencyMs=253; operationCount=415; context=gs://ot-team/dsuveges/finngen/2023.10.06_window_clumped/_temporary/0/_temporary/attempt_202310061022254599137247441201519_0051_m_000011_106753/part-00011-53e48f6d-d432-40c5-9201-d4c8f03e6dee-c000.snappy.parquet\n", - " \r" - ] - } - ], - "source": [ - "# This process takes ~1h on a 32 cores:\n", - "(\n", - " SummaryStatistics(\n", - " _df=(\n", - " session.spark.read.parquet(sumstats, recursiveFileLookup=True)\n", - " .withColumn(\n", - " 'chromosome',\n", - " f.split(f.col('variantId'), '_')[0]\n", - " )\n", - " ),\n", - " _schema=SummaryStatistics.get_schema()\n", - " )\n", - " .window_based_clumping(\n", - " distance=clump_window_length,\n", - " locus_collect_distance=locus_window_length,\n", - " with_locus=True\n", - " )\n", - " .df.write.mode('overwrite')\n", - " .parquet(window_based_clumped_output)\n", - ")" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "finngen_sumstats = SummaryStatistics(\n", + " _df=(\n", + " session.spark.read.parquet(sumstats, recursiveFileLookup=True)\n", + " # We need to add chromosome column as this is a partition column:\n", + " .withColumn(\"chromosome\",f.split(f.col(\"variantId\"), \"_\")[0])\n", + " ),\n", + " _schema=SummaryStatistics.get_schema()\n", + ")\n", + "finngen_sumstats.df.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a76b27ce", + "metadata": {}, + "source": [ + "## 2. Apply window based clumping.\n", + "\n", + "- Clumping distance: +/-500kbp\n", + "- Locus collected: +/-250kbp" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "fc0dd4f0", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T10:22:38.264681Z", + "start_time": "2023-10-06T10:10:29.768171Z" }, + "scrolled": false + }, + "outputs": [ { - "cell_type": "markdown", - "id": "a1cd0172", - "metadata": {}, - "source": [ - "## 3. LD expansion\n", - "\n", - "- For FINNGEN, study table needs to be mocked." - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "23/10/06 10:22:32 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=1812; previousMaxLatencyMs=253; operationCount=415; context=gs://ot-team/dsuveges/finngen/2023.10.06_window_clumped/_temporary/0/_temporary/attempt_202310061022254599137247441201519_0051_m_000011_106753/part-00011-53e48f6d-d432-40c5-9201-d4c8f03e6dee-c000.snappy.parquet\n", + " \r" + ] + } + ], + "source": [ + "# This process takes ~1h on a 32 cores:\n", + "(\n", + " SummaryStatistics(\n", + " _df=(\n", + " session.spark.read.parquet(sumstats, recursiveFileLookup=True)\n", + " .withColumn(\n", + " \"chromosome\",\n", + " f.split(f.col(\"variantId\"), \"_\")[0]\n", + " )\n", + " ),\n", + " _schema=SummaryStatistics.get_schema()\n", + " )\n", + " .window_based_clumping(\n", + " distance=clump_window_length,\n", + " locus_collect_distance=locus_window_length,\n", + " with_locus=True\n", + " )\n", + " .df.write.mode(\"overwrite\")\n", + " .parquet(window_based_clumped_output)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a1cd0172", + "metadata": {}, + "source": [ + "## 3. LD expansion\n", + "\n", + "- For FINNGEN, study table needs to be mocked." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1305f6c3", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T08:13:10.050554Z", + "start_time": "2023-10-06T08:13:05.901762Z" + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "id": "1305f6c3", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T08:13:10.050554Z", - "start_time": "2023-10-06T08:13:05.901762Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 5:===============================================> (15 + 3) / 18]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------+---------------------+---------+---------+---------------+\n", - "| studyId|ldPopulationStructure|projectId|studyType|traitFromSource|\n", - "+--------------------+---------------------+---------+---------+---------------+\n", - "|FINNGEN_R9_K11_EN...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_P16_IN...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "| FINNGEN_R9_C_STROKE| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_G6_HER...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_RHEUMA...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_N14_EN...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "| FINNGEN_R9_PAIN| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_D3_COA...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_F5_UNSORG| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_HEIGHT...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_M13_SY...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_APPEND...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_ALLERG...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_M13_FI...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_E4_DM2...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_AUTOIM...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_CD2_BE...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_F5_DIS...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_E4_DMN...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_E4_FH_IHD| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "+--------------------+---------------------+---------+---------+---------------+\n", - "only showing top 20 rows\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "# Generating a \"fake\" study index, just for providing ld_population structure for each finngen study:\n", - "studies_df = (\n", - " session.spark.read.parquet('gs://ot-team/dsuveges/finngen_semi_indices_250kbp')\n", - " # Generating a list of study identifiers:\n", - " .select('studyId')\n", - " .distinct()\n", - " # Adding fabricated values required to parse as gwas catalog study:\n", - " .select(\n", - " 'studyId',\n", - " StudyIndex.aggregate_and_map_ancestries(\n", - " f.array(\n", - " f.struct(\n", - " f.lit('Finnish').alias('ancestry'),\n", - " f.lit(100).cast('long').alias('sampleSize')\n", - " )\n", - " )\n", - " ).alias('ldPopulationStructure'),\n", - " f.lit('FINNGEN').alias('projectId'),\n", - " f.lit('gwas').alias('studyType'),\n", - " f.lit('cicaful').alias('traitFromSource')\n", - " )\n", - ")\n", - "\n", - "study_index = (\n", - " StudyIndex(\n", - " _df=studies_df,\n", - " _schema=StudyIndex.get_schema()\n", - " )\n", - ")\n", - "\n", - "study_index.df.show()" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 5:===============================================> (15 + 3) / 18]\r" + ] }, { - "cell_type": "code", - "execution_count": 19, - "id": "cb2d5030", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T09:09:22.063715Z", - "start_time": "2023-10-06T09:09:21.359206Z" - } - }, - "outputs": [], - "source": [ - "# Loading ld index:\n", - "ld_index = LDIndex.from_parquet(session, ld_index_path)\n", - "\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+---------------------+---------+---------+---------------+\n", + "| studyId|ldPopulationStructure|projectId|studyType|traitFromSource|\n", + "+--------------------+---------------------+---------+---------+---------------+\n", + "|FINNGEN_R9_K11_EN...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_P16_IN...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "| FINNGEN_R9_C_STROKE| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_G6_HER...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_RHEUMA...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_N14_EN...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "| FINNGEN_R9_PAIN| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_D3_COA...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_F5_UNSORG| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_HEIGHT...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_M13_SY...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_APPEND...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_ALLERG...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_M13_FI...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_E4_DM2...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_AUTOIM...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_CD2_BE...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_F5_DIS...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_E4_DMN...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_E4_FH_IHD| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "+--------------------+---------------------+---------+---------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] }, { - "cell_type": "markdown", - "id": "3d13ae41", - "metadata": {}, - "source": [ - "## 4. LD based clumping.\n", - "\n", - "- Persist resulting dataset.\n", - "- Save dataset." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "# Generating a \"fake\" study index, just for providing ld_population structure for each finngen study:\n", + "studies_df = (\n", + " session.spark.read.parquet(\"gs://ot-team/dsuveges/finngen_semi_indices_250kbp\")\n", + " # Generating a list of study identifiers:\n", + " .select(\"studyId\")\n", + " .distinct()\n", + " # Adding fabricated values required to parse as gwas catalog study:\n", + " .select(\n", + " \"studyId\",\n", + " StudyIndex.aggregate_and_map_ancestries(\n", + " f.array(\n", + " f.struct(\n", + " f.lit(\"Finnish\").alias(\"ancestry\"),\n", + " f.lit(100).cast(\"long\").alias(\"sampleSize\")\n", + " )\n", + " )\n", + " ).alias(\"ldPopulationStructure\"),\n", + " f.lit(\"FINNGEN\").alias(\"projectId\"),\n", + " f.lit(\"gwas\").alias(\"studyType\"),\n", + " f.lit(\"cicaful\").alias(\"traitFromSource\")\n", + " )\n", + ")\n", + "\n", + "study_index = (\n", + " StudyIndex(\n", + " _df=studies_df,\n", + " _schema=StudyIndex.get_schema()\n", + " )\n", + ")\n", + "\n", + "study_index.df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cb2d5030", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T09:09:22.063715Z", + "start_time": "2023-10-06T09:09:21.359206Z" + } + }, + "outputs": [], + "source": [ + "# Loading ld index:\n", + "ld_index = LDIndex.from_parquet(session, ld_index_path)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d13ae41", + "metadata": {}, + "source": [ + "## 4. LD based clumping.\n", + "\n", + "- Persist resulting dataset.\n", + "- Save dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "20449e76", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T09:42:11.040193Z", + "start_time": "2023-10-06T09:09:52.968139Z" }, + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "id": "20449e76", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T09:42:11.040193Z", - "start_time": "2023-10-06T09:09:52.968139Z" - }, - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23/10/06 09:11:42 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1312; previousMaxLatencyMs=1306; operationCount=59583672; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_RX_N05C/chromosome=1/part-00012-a13aba14-79f4-45c0-9af0-937d599fa3f0.c000.snappy.parquet\n", - "23/10/06 09:11:43 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1316; previousMaxLatencyMs=1312; operationCount=59631953; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_RX_CODEINE_TRAMADOL/chromosome=1/part-00012-36e099c5-3f60-4091-bf73-6fa15d48af6c.c000.snappy.parquet\n", - "23/10/06 09:16:19 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1489; previousMaxLatencyMs=1316; operationCount=76331604; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/ld_index/chromosome=7/part-00010-ff42773a-494c-46d2-bc22-322062b5e715.c000.snappy.parquet\n", - "23/10/06 09:16:21 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1735; previousMaxLatencyMs=1489; operationCount=76482674; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/ld_index/chromosome=7/part-00010-ff42773a-494c-46d2-bc22-322062b5e715.c000.snappy.parquet\n", - "23/10/06 09:16:23 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_operations. latencyMs=460; previousMaxLatencyMs=337; operationCount=17471; context=gs://dataproc-temp-europe-west1-426265110888-ymkbpaze/64dcfdf8-46d3-4b5c-aad4-0a12ee0ba91a/spark-job-history/local-1696579691428.inprogress\n", - "23/10/06 09:27:35 WARN GhfsStorageStatistics: Detected potential high latency for operation op_open. latencyMs=1041; previousMaxLatencyMs=1034; operationCount=215062; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_M13_DORSOPATHYNAS/chromosome=19/part-00009-09e75b10-c64d-4b79-9c84-6b0ca039c439.c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=126; previousMaxLatencyMs=93; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552056325521918358154_0027_m_000008_57347/part-00008-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=134; previousMaxLatencyMs=126; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555844252084106444775_0027_m_000028_57367/part-00028-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=134; previousMaxLatencyMs=126; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556216099889261123757_0027_m_000015_57354/part-00015-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=137; previousMaxLatencyMs=134; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909553770650668910223592_0027_m_000020_57359/part-00020-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=134; previousMaxLatencyMs=126; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552371970894888794726_0027_m_000025_57364/part-00025-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=136; previousMaxLatencyMs=134; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955320032532820679068_0027_m_000009_57348/part-00009-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=139; previousMaxLatencyMs=136; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554704118087127617221_0027_m_000031_57370/part-00031-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=139; previousMaxLatencyMs=136; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955146935648450954616_0027_m_000021_57360/part-00021-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=140; previousMaxLatencyMs=139; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557457999139377546046_0027_m_000026_57365/part-00026-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=141; previousMaxLatencyMs=140; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557648490159907156305_0027_m_000017_57356/part-00017-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=144; previousMaxLatencyMs=141; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556136722862237844168_0027_m_000013_57352/part-00013-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=143; previousMaxLatencyMs=141; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955993438161489409499_0027_m_000016_57355/part-00016-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=144; previousMaxLatencyMs=141; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552708616931813844767_0027_m_000006_57345/part-00006-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=145; previousMaxLatencyMs=144; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555919710644676214353_0027_m_000004_57343/part-00004-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=145; previousMaxLatencyMs=144; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551292246187607294419_0027_m_000002_57341/part-00002-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=146; previousMaxLatencyMs=145; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551853556311935071964_0027_m_000030_57369/part-00030-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=147; previousMaxLatencyMs=146; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555640079838863826112_0027_m_000022_57361/part-00022-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=147; previousMaxLatencyMs=146; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955962380753726438814_0027_m_000000_57339/part-00000-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=149; previousMaxLatencyMs=147; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551603246466691698658_0027_m_000001_57340/part-00001-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=150; previousMaxLatencyMs=149; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554079176040000118228_0027_m_000027_57366/part-00027-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=154; previousMaxLatencyMs=150; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909553545408253172771512_0027_m_000005_57344/part-00005-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=156; previousMaxLatencyMs=154; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556087040172921209608_0027_m_000018_57357/part-00018-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=160; previousMaxLatencyMs=156; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955995307818437095879_0027_m_000024_57363/part-00024-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=161; previousMaxLatencyMs=160; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552743075067405201713_0027_m_000014_57353/part-00014-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=106; previousMaxLatencyMs=95; operationCount=30; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551603246466691698658_0027_m_000001_57340/part-00001-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=138; previousMaxLatencyMs=106; operationCount=31; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552371970894888794726_0027_m_000025_57364/part-00025-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=165; previousMaxLatencyMs=0; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955320032532820679068_0027_m_000009_57348/part-00009-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00009-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=183; previousMaxLatencyMs=165; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955962380753726438814_0027_m_000000_57339/part-00000-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00000-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=186; previousMaxLatencyMs=183; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552708616931813844767_0027_m_000006_57345/part-00006-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00006-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=191; previousMaxLatencyMs=186; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555919710644676214353_0027_m_000004_57343/part-00004-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00004-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=227; previousMaxLatencyMs=186; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554079176040000118228_0027_m_000027_57366/part-00027-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00027-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=233; previousMaxLatencyMs=227; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556216099889261123757_0027_m_000015_57354/part-00015-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00015-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=239; previousMaxLatencyMs=233; operationCount=31; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554704118087127617221_0027_m_000031_57370/part-00031-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00031-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=284; previousMaxLatencyMs=239; operationCount=31; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551853556311935071964_0027_m_000030_57369/part-00030-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00030-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=128; previousMaxLatencyMs=0; operationCount=28; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555919710644676214353_0027_m_000004_57343\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=318; previousMaxLatencyMs=284; operationCount=31; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955146935648450954616_0027_m_000021_57360/part-00021-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00021-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=150; previousMaxLatencyMs=128; operationCount=29; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555920613268130744433_0027_m_000010_57349\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=190; previousMaxLatencyMs=150; operationCount=29; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955320032532820679068_0027_m_000009_57348\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=226; previousMaxLatencyMs=190; operationCount=30; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554079176040000118228_0027_m_000027_57366\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=172; previousMaxLatencyMs=161; operationCount=53; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557479860471944928338_0027_m_000035_57374/part-00035-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=146; previousMaxLatencyMs=138; operationCount=53; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557479860471944928338_0027_m_000035_57374/part-00035-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:58 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=150; previousMaxLatencyMs=146; operationCount=60; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551578112291921242039_0027_m_000054_57393/part-00054-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:58 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=1259; previousMaxLatencyMs=226; operationCount=53; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551853556311935071964_0027_m_000030_57369\n", - "23/10/06 09:41:58 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=1497; previousMaxLatencyMs=1259; operationCount=56; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557355876953011052025_0027_m_000019_57358\n", - "23/10/06 09:41:58 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=711; previousMaxLatencyMs=318; operationCount=75; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557693125728422191174_0027_m_000050_57389/part-00050-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00050-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:59 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=157; previousMaxLatencyMs=150; operationCount=93; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551476648569527086024_0027_m_000080_57419/part-00080-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:41:59 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=752; previousMaxLatencyMs=711; operationCount=91; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554631887941475021153_0027_m_000057_57396/part-00057-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00057-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:41:59 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=182; previousMaxLatencyMs=157; operationCount=96; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556932256815778386100_0027_m_000093_57432/part-00093-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:42:00 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=201; previousMaxLatencyMs=182; operationCount=128; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554714655772639032866_0027_m_000122_57461/part-00122-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:42:01 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=253; previousMaxLatencyMs=201; operationCount=187; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555299227751328654817_0027_m_000175_57513/part-00175-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:42:02 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=1159; previousMaxLatencyMs=752; operationCount=198; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556191692784728530413_0027_m_000172_57510/part-00172-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00172-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", - "23/10/06 09:42:02 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=1051; previousMaxLatencyMs=172; operationCount=200; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955452778920582844510_0027_m_000198_57536/part-00198-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", - "23/10/06 09:42:08 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=11057; previousMaxLatencyMs=1497; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557457999139377546046_0027_m_000026_57365\n", - "23/10/06 09:42:08 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=11233; previousMaxLatencyMs=11057; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551292246187607294419_0027_m_000002_57341\n", - "23/10/06 09:42:08 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=11402; previousMaxLatencyMs=11233; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552371970894888794726_0027_m_000025_57364\n", - "23/10/06 09:42:08 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=11583; previousMaxLatencyMs=11402; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556136722862237844168_0027_m_000013_57352\n", - "23/10/06 09:42:09 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=12651; previousMaxLatencyMs=11583; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909553722108484298157195_0027_m_000023_57362\n", - "23/10/06 09:42:10 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=13120; previousMaxLatencyMs=12651; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554704118087127617221_0027_m_000031_57370\n", - "23/10/06 09:42:10 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=13440; previousMaxLatencyMs=13120; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909553545408253172771512_0027_m_000005_57344\n", - " \r" - ] - } - ], - "source": [ - "(\n", - " # To annotate study/locus, study level info and ld panel is needed:\n", - " LDAnnotator.ld_annotate(\n", - " StudyLocus.from_parquet(session, window_based_clumped_output),\n", - " study_index, \n", - " ld_index\n", - " )\n", - " # Clumping linked study-loci together:\n", - " .clump()\n", - " .df.write.mode('overwrite').parquet(ld_clumped_output)\n", - ")\n", - "\n" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "23/10/06 09:11:42 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1312; previousMaxLatencyMs=1306; operationCount=59583672; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_RX_N05C/chromosome=1/part-00012-a13aba14-79f4-45c0-9af0-937d599fa3f0.c000.snappy.parquet\n", + "23/10/06 09:11:43 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1316; previousMaxLatencyMs=1312; operationCount=59631953; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_RX_CODEINE_TRAMADOL/chromosome=1/part-00012-36e099c5-3f60-4091-bf73-6fa15d48af6c.c000.snappy.parquet\n", + "23/10/06 09:16:19 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1489; previousMaxLatencyMs=1316; operationCount=76331604; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/ld_index/chromosome=7/part-00010-ff42773a-494c-46d2-bc22-322062b5e715.c000.snappy.parquet\n", + "23/10/06 09:16:21 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1735; previousMaxLatencyMs=1489; operationCount=76482674; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/ld_index/chromosome=7/part-00010-ff42773a-494c-46d2-bc22-322062b5e715.c000.snappy.parquet\n", + "23/10/06 09:16:23 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_operations. latencyMs=460; previousMaxLatencyMs=337; operationCount=17471; context=gs://dataproc-temp-europe-west1-426265110888-ymkbpaze/64dcfdf8-46d3-4b5c-aad4-0a12ee0ba91a/spark-job-history/local-1696579691428.inprogress\n", + "23/10/06 09:27:35 WARN GhfsStorageStatistics: Detected potential high latency for operation op_open. latencyMs=1041; previousMaxLatencyMs=1034; operationCount=215062; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_M13_DORSOPATHYNAS/chromosome=19/part-00009-09e75b10-c64d-4b79-9c84-6b0ca039c439.c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=126; previousMaxLatencyMs=93; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552056325521918358154_0027_m_000008_57347/part-00008-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=134; previousMaxLatencyMs=126; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555844252084106444775_0027_m_000028_57367/part-00028-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=134; previousMaxLatencyMs=126; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556216099889261123757_0027_m_000015_57354/part-00015-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=137; previousMaxLatencyMs=134; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909553770650668910223592_0027_m_000020_57359/part-00020-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=134; previousMaxLatencyMs=126; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552371970894888794726_0027_m_000025_57364/part-00025-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=136; previousMaxLatencyMs=134; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955320032532820679068_0027_m_000009_57348/part-00009-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=139; previousMaxLatencyMs=136; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554704118087127617221_0027_m_000031_57370/part-00031-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=139; previousMaxLatencyMs=136; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955146935648450954616_0027_m_000021_57360/part-00021-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=140; previousMaxLatencyMs=139; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557457999139377546046_0027_m_000026_57365/part-00026-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=141; previousMaxLatencyMs=140; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557648490159907156305_0027_m_000017_57356/part-00017-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=144; previousMaxLatencyMs=141; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556136722862237844168_0027_m_000013_57352/part-00013-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=143; previousMaxLatencyMs=141; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955993438161489409499_0027_m_000016_57355/part-00016-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=144; previousMaxLatencyMs=141; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552708616931813844767_0027_m_000006_57345/part-00006-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=145; previousMaxLatencyMs=144; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555919710644676214353_0027_m_000004_57343/part-00004-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=145; previousMaxLatencyMs=144; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551292246187607294419_0027_m_000002_57341/part-00002-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=146; previousMaxLatencyMs=145; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551853556311935071964_0027_m_000030_57369/part-00030-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=147; previousMaxLatencyMs=146; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555640079838863826112_0027_m_000022_57361/part-00022-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=147; previousMaxLatencyMs=146; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955962380753726438814_0027_m_000000_57339/part-00000-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=149; previousMaxLatencyMs=147; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551603246466691698658_0027_m_000001_57340/part-00001-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=150; previousMaxLatencyMs=149; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554079176040000118228_0027_m_000027_57366/part-00027-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=154; previousMaxLatencyMs=150; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909553545408253172771512_0027_m_000005_57344/part-00005-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=156; previousMaxLatencyMs=154; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556087040172921209608_0027_m_000018_57357/part-00018-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=160; previousMaxLatencyMs=156; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955995307818437095879_0027_m_000024_57363/part-00024-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=161; previousMaxLatencyMs=160; operationCount=32; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552743075067405201713_0027_m_000014_57353/part-00014-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:56 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=106; previousMaxLatencyMs=95; operationCount=30; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551603246466691698658_0027_m_000001_57340/part-00001-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=138; previousMaxLatencyMs=106; operationCount=31; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552371970894888794726_0027_m_000025_57364/part-00025-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=165; previousMaxLatencyMs=0; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955320032532820679068_0027_m_000009_57348/part-00009-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00009-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=183; previousMaxLatencyMs=165; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955962380753726438814_0027_m_000000_57339/part-00000-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00000-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=186; previousMaxLatencyMs=183; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552708616931813844767_0027_m_000006_57345/part-00006-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00006-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=191; previousMaxLatencyMs=186; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555919710644676214353_0027_m_000004_57343/part-00004-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00004-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=227; previousMaxLatencyMs=186; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554079176040000118228_0027_m_000027_57366/part-00027-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00027-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=233; previousMaxLatencyMs=227; operationCount=30; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556216099889261123757_0027_m_000015_57354/part-00015-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00015-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=239; previousMaxLatencyMs=233; operationCount=31; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554704118087127617221_0027_m_000031_57370/part-00031-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00031-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=284; previousMaxLatencyMs=239; operationCount=31; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551853556311935071964_0027_m_000030_57369/part-00030-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00030-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=128; previousMaxLatencyMs=0; operationCount=28; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555919710644676214353_0027_m_000004_57343\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=318; previousMaxLatencyMs=284; operationCount=31; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955146935648450954616_0027_m_000021_57360/part-00021-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00021-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=150; previousMaxLatencyMs=128; operationCount=29; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555920613268130744433_0027_m_000010_57349\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=190; previousMaxLatencyMs=150; operationCount=29; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955320032532820679068_0027_m_000009_57348\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=226; previousMaxLatencyMs=190; operationCount=30; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554079176040000118228_0027_m_000027_57366\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=172; previousMaxLatencyMs=161; operationCount=53; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557479860471944928338_0027_m_000035_57374/part-00035-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:57 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=146; previousMaxLatencyMs=138; operationCount=53; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557479860471944928338_0027_m_000035_57374/part-00035-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:58 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=150; previousMaxLatencyMs=146; operationCount=60; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551578112291921242039_0027_m_000054_57393/part-00054-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:58 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=1259; previousMaxLatencyMs=226; operationCount=53; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551853556311935071964_0027_m_000030_57369\n", + "23/10/06 09:41:58 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=1497; previousMaxLatencyMs=1259; operationCount=56; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557355876953011052025_0027_m_000019_57358\n", + "23/10/06 09:41:58 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=711; previousMaxLatencyMs=318; operationCount=75; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557693125728422191174_0027_m_000050_57389/part-00050-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00050-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:59 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=157; previousMaxLatencyMs=150; operationCount=93; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551476648569527086024_0027_m_000080_57419/part-00080-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:41:59 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=752; previousMaxLatencyMs=711; operationCount=91; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554631887941475021153_0027_m_000057_57396/part-00057-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00057-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:41:59 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=182; previousMaxLatencyMs=157; operationCount=96; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556932256815778386100_0027_m_000093_57432/part-00093-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:42:00 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=201; previousMaxLatencyMs=182; operationCount=128; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554714655772639032866_0027_m_000122_57461/part-00122-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:42:01 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=253; previousMaxLatencyMs=201; operationCount=187; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909555299227751328654817_0027_m_000175_57513/part-00175-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:42:02 WARN GhfsStorageStatistics: Detected potential high latency for operation op_rename. latencyMs=1159; previousMaxLatencyMs=752; operationCount=198; context=rename(gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556191692784728530413_0027_m_000172_57510/part-00172-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet -> gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/part-00172-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet)\n", + "23/10/06 09:42:02 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=1051; previousMaxLatencyMs=172; operationCount=200; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_20231006090955452778920582844510_0027_m_000198_57536/part-00198-a0adc7ff-71a9-4671-ad1e-0093f9d7d260-c000.snappy.parquet\n", + "23/10/06 09:42:08 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=11057; previousMaxLatencyMs=1497; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909557457999139377546046_0027_m_000026_57365\n", + "23/10/06 09:42:08 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=11233; previousMaxLatencyMs=11057; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909551292246187607294419_0027_m_000002_57341\n", + "23/10/06 09:42:08 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=11402; previousMaxLatencyMs=11233; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909552371970894888794726_0027_m_000025_57364\n", + "23/10/06 09:42:08 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=11583; previousMaxLatencyMs=11402; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909556136722862237844168_0027_m_000013_57352\n", + "23/10/06 09:42:09 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=12651; previousMaxLatencyMs=11583; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909553722108484298157195_0027_m_000023_57362\n", + "23/10/06 09:42:10 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=13120; previousMaxLatencyMs=12651; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909554704118087127617221_0027_m_000031_57370\n", + "23/10/06 09:42:10 WARN GhfsStorageStatistics: Detected potential high latency for operation op_delete. latencyMs=13440; previousMaxLatencyMs=13120; operationCount=199; context=gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped/_temporary/0/_temporary/attempt_202310060909553545408253172771512_0027_m_000005_57344\n", + " \r" + ] + } + ], + "source": [ + "(\n", + " # To annotate study/locus, study level info and ld panel is needed:\n", + " LDAnnotator.ld_annotate(\n", + " StudyLocus.from_parquet(session, window_based_clumped_output),\n", + " study_index,\n", + " ld_index\n", + " )\n", + " # Clumping linked study-loci together:\n", + " .clump()\n", + " .df.write.mode(\"overwrite\").parquet(ld_clumped_output)\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3ec08141", + "metadata": {}, + "source": [ + "## 5. PICS - finemapping." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "aab9d72e", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T09:50:46.179833Z", + "start_time": "2023-10-06T09:49:25.408253Z" + } + }, + "outputs": [ { - "cell_type": "markdown", - "id": "3ec08141", - "metadata": {}, - "source": [ - "## 5. PICS - finemapping." - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "23/10/06 09:50:16 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=1081; previousMaxLatencyMs=1051; operationCount=396; context=gs://ot-team/dsuveges/finngen/2023.10.06_PICSed/_temporary/0/_temporary/attempt_202310060949259143728196112407547_0033_m_000193_57732/part-00193-9d4ae5b4-a18c-4406-bd26-9387e62477de-c000.snappy.parquet\n", + " \r" + ] + } + ], + "source": [ + "(\n", + " # The previously generated LD clumped dataset is read as StudyLocus:\n", + " PICS.finemap(StudyLocus.from_parquet(session, ld_clumped_output))\n", + " .annotate_credible_sets()\n", + " .df.write.mode(\"overwrite\").parquet(picsed_output)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0bc2cb6e", + "metadata": {}, + "source": [ + "## QC results - Sumstats" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "7cbeab8b", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T10:40:53.295506Z", + "start_time": "2023-10-06T10:26:04.284592Z" + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 21, - "id": "aab9d72e", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T09:50:46.179833Z", - "start_time": "2023-10-06T09:49:25.408253Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23/10/06 09:50:16 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=1081; previousMaxLatencyMs=1051; operationCount=396; context=gs://ot-team/dsuveges/finngen/2023.10.06_PICSed/_temporary/0/_temporary/attempt_202310060949259143728196112407547_0033_m_000193_57732/part-00193-9d4ae5b4-a18c-4406-bd26-9387e62477de-c000.snappy.parquet\n", - " \r" - ] - } - ], - "source": [ - "(\n", - " # The previously generated LD clumped dataset is read as StudyLocus:\n", - " PICS.finemap(StudyLocus.from_parquet(session, ld_clumped_output))\n", - " .annotate_credible_sets()\n", - " .df.write.mode('overwrite').parquet(picsed_output)\n", - ")" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "23/10/06 10:26:41 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_operations. latencyMs=572; previousMaxLatencyMs=460; operationCount=925353; context=gs://dataproc-temp-europe-west1-426265110888-ymkbpaze/64dcfdf8-46d3-4b5c-aad4-0a12ee0ba91a/spark-job-history/local-1696579691428.inprogress\n", + "[Stage 57:===========================================> (3 + 1) / 4]\r" + ] }, { - "cell_type": "markdown", - "id": "0bc2cb6e", - "metadata": {}, - "source": [ - "## QC results - Sumstats" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "In the Finngen dataset, there are 45,820,490,260 associations from 2,272 studies.\n" + ] }, { - "cell_type": "code", - "execution_count": 35, - "id": "7cbeab8b", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T10:40:53.295506Z", - "start_time": "2023-10-06T10:26:04.284592Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23/10/06 10:26:41 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_operations. latencyMs=572; previousMaxLatencyMs=460; operationCount=925353; context=gs://dataproc-temp-europe-west1-426265110888-ymkbpaze/64dcfdf8-46d3-4b5c-aad4-0a12ee0ba91a/spark-job-history/local-1696579691428.inprogress\n", - "[Stage 57:===========================================> (3 + 1) / 4]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "In the Finngen dataset, there are 45,820,490,260 associations from 2,272 studies.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "assoc_count = finngen_sumstats.df.count()\n", - "study_count = finngen_sumstats.df.select('studyId').distinct().count()\n", - "\n", - "print(f'In the Finngen dataset, there are {assoc_count:,} associations from {study_count:,} studies.')" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "assoc_count = finngen_sumstats.df.count()\n", + "study_count = finngen_sumstats.df.select(\"studyId\").distinct().count()\n" + ] + }, + { + "cell_type": "markdown", + "id": "19f94399", + "metadata": {}, + "source": [ + "## QC results - Window based clumping" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "5ff794d4", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T10:41:39.724996Z", + "start_time": "2023-10-06T10:41:38.322389Z" + } + }, + "outputs": [ { - "cell_type": "markdown", - "id": "19f94399", - "metadata": {}, - "source": [ - "## QC results - Window based clumping" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "In the window based cluped dataset, there are 19,005 semi-indices, from 1,387 studies.\n" + ] + } + ], + "source": [ + "windowed_count = session.spark.read.parquet(window_based_clumped_output).count()\n", + "windowed_studies = session.spark.read.parquet(window_based_clumped_output).select(\"studyId\").distinct().count()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d88b0d87", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T10:08:22.129308Z", + "start_time": "2023-10-06T10:08:18.802493Z" + } + }, + "source": [ + "## QC results - LD clumping" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "3cd88471", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T10:43:29.370733Z", + "start_time": "2023-10-06T10:43:26.646342Z" + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "id": "5ff794d4", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T10:41:39.724996Z", - "start_time": "2023-10-06T10:41:38.322389Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "In the window based cluped dataset, there are 19,005 semi-indices, from 1,387 studies.\n" - ] - } - ], - "source": [ - "windowed_count = session.spark.read.parquet(window_based_clumped_output).count()\n", - "windowed_studies = session.spark.read.parquet(window_based_clumped_output).select('studyId').distinct().count()\n", - "\n", - "print(f'In the window based cluped dataset, there are {windowed_count:,} semi-indices, from {windowed_studies:,} studies.')\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 87:======================================================> (28 + 1) / 29]\r" + ] }, { - "cell_type": "markdown", - "id": "d88b0d87", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T10:08:22.129308Z", - "start_time": "2023-10-06T10:08:18.802493Z" - } - }, - "source": [ - "## QC results - LD clumping" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "In the LD based cluped dataset, there are 19,005 semi-indices, from 1,387 studies.\n" + ] }, { - "cell_type": "code", - "execution_count": 38, - "id": "3cd88471", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T10:43:29.370733Z", - "start_time": "2023-10-06T10:43:26.646342Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 87:======================================================> (28 + 1) / 29]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "In the LD based cluped dataset, there are 19,005 semi-indices, from 1,387 studies.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "ld_clumped_df = session.spark.read.parquet(ld_clumped_output)\n", - "\n", - "windowed_count = ld_clumped_df.count()\n", - "windowed_studies = ld_clumped_df.select('studyId').distinct().count()\n", - "\n", - "print(f'In the LD based cluped dataset, there are {windowed_count:,} semi-indices, from {windowed_studies:,} studies.')\n" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "ld_clumped_df = session.spark.read.parquet(ld_clumped_output)\n", + "\n", + "windowed_count = ld_clumped_df.count()\n", + "windowed_studies = ld_clumped_df.select(\"studyId\").distinct().count()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "d9d002d0", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T10:44:36.008520Z", + "start_time": "2023-10-06T10:44:34.483964Z" + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 40, - "id": "d9d002d0", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T10:44:36.008520Z", - "start_time": "2023-10-06T10:44:34.483964Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 93:====================================================> (27 + 2) / 29]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------------------------------------------------+-----+\n", - "|qualityControls |count|\n", - "+--------------------------------------------------------------+-----+\n", - "|[Variant not found in LD reference] |4706 |\n", - "|[] |13714|\n", - "|[Explained by a more significant variant in high LD (clumped)]|585 |\n", - "+--------------------------------------------------------------+-----+\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "ld_clumped_df.groupBy('qualityControls').count().show(truncate=False)" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 93:====================================================> (27 + 2) / 29]\r" + ] }, { - "cell_type": "markdown", - "id": "7a3be915", - "metadata": {}, - "source": [ - "## QC results - PICS" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------------------------------------------------+-----+\n", + "|qualityControls |count|\n", + "+--------------------------------------------------------------+-----+\n", + "|[Variant not found in LD reference] |4706 |\n", + "|[] |13714|\n", + "|[Explained by a more significant variant in high LD (clumped)]|585 |\n", + "+--------------------------------------------------------------+-----+\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": 43, - "id": "b1ddd53c", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T10:59:16.339543Z", - "start_time": "2023-10-06T10:59:15.930105Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-RECORD 0-----------------------------------------------\n", - " variantId | 20_35437976_G_A \n", - " chromosome | 20 \n", - " studyId | FINNGEN_R9_HEIGHT... \n", - " position | 35437976 \n", - " pValueMantissa | 3.811 \n", - " pValueExponent | -193 \n", - " beta | -0.0551669 \n", - " standardError | 0.00186085 \n", - " effectAlleleFrequencyFromSource | 0.5599 \n", - " betaConfidenceIntervalLower | -0.05702774999999... \n", - " betaConfidenceIntervalUpper | -0.05330605 \n", - " studyLocusId | 5242723067793949472 \n", - " qualityControls | [Variant not foun... \n", - " ldSet | null \n", - " locus | null \n", - "only showing top 1 row\n", - "\n", - "root\n", - " |-- variantId: string (nullable = true)\n", - " |-- chromosome: string (nullable = true)\n", - " |-- studyId: string (nullable = true)\n", - " |-- position: integer (nullable = true)\n", - " |-- pValueMantissa: float (nullable = true)\n", - " |-- pValueExponent: integer (nullable = true)\n", - " |-- beta: double (nullable = true)\n", - " |-- standardError: double (nullable = true)\n", - " |-- effectAlleleFrequencyFromSource: float (nullable = true)\n", - " |-- betaConfidenceIntervalLower: double (nullable = true)\n", - " |-- betaConfidenceIntervalUpper: double (nullable = true)\n", - " |-- studyLocusId: long (nullable = true)\n", - " |-- qualityControls: array (nullable = true)\n", - " | |-- element: string (containsNull = true)\n", - " |-- ldSet: array (nullable = true)\n", - " | |-- element: struct (containsNull = true)\n", - " | | |-- tagVariantId: string (nullable = true)\n", - " | | |-- r2Overall: double (nullable = true)\n", - " |-- locus: array (nullable = true)\n", - " | |-- element: struct (containsNull = true)\n", - " | | |-- variantId: string (nullable = true)\n", - " | | |-- r2Overall: double (nullable = true)\n", - " | | |-- posteriorProbability: double (nullable = true)\n", - " | | |-- standardError: double (nullable = true)\n", - " | | |-- is95CredibleSet: boolean (nullable = true)\n", - " | | |-- is99CredibleSet: boolean (nullable = true)\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23/10/06 10:59:16 WARN CacheManager: Asked to cache already cached data.\n" - ] - }, - { - "data": { - "text/plain": [ - "19005" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "picsed_df = session.spark.read.parquet(picsed_output).persist()\n", - "picsed_df.show(1, vertical=True)\n", - "picsed_df.printSchema()\n", - "picsed_df.count()\n" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "ld_clumped_df.groupBy(\"qualityControls\").count().show(truncate=False)" + ] + }, + { + "cell_type": "markdown", + "id": "7a3be915", + "metadata": {}, + "source": [ + "## QC results - PICS" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b1ddd53c", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T10:59:16.339543Z", + "start_time": "2023-10-06T10:59:15.930105Z" + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 59, - "id": "937e1eb0", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-06T11:11:55.487431Z", - "start_time": "2023-10-06T11:11:54.898030Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(\n", - " picsed_df\n", - " .filter(f.col('locus').isNotNull())\n", - " .select(\n", - " 'studyId',\n", - " 'variantId',\n", - " f.aggregate(\n", - " f.transform(\n", - " f.col('locus'),\n", - " lambda locus: f.when(locus.is99CredibleSet, f.lit(1.0)).otherwise( f.lit(0.0))\n", - " ),\n", - " f.lit(0.0),\n", - " lambda summa, value: summa + value\n", - " ).alias('99CredCount'),\n", - " f.aggregate(\n", - " f.transform(\n", - " f.col('locus'),\n", - " lambda locus: f.when(locus.is95CredibleSet, f.lit(1.0)).otherwise( f.lit(0.0))\n", - " ),\n", - " f.lit(0.0),\n", - " lambda summa, value: summa + value\n", - " ).alias('95CredCount'),\n", - " )\n", - "# .orderBy(f.col('99CredCount').desc())\n", - "# .show()\n", - " .filter(f.col('99CredCount') < 100)\n", - "# .count()\n", - " .toPandas()\n", - " [['99CredCount', '95CredCount']]\n", - " .plot.hist(bins=25, alpha=0.5, label='Credible set size')\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0-----------------------------------------------\n", + " variantId | 20_35437976_G_A \n", + " chromosome | 20 \n", + " studyId | FINNGEN_R9_HEIGHT... \n", + " position | 35437976 \n", + " pValueMantissa | 3.811 \n", + " pValueExponent | -193 \n", + " beta | -0.0551669 \n", + " standardError | 0.00186085 \n", + " effectAlleleFrequencyFromSource | 0.5599 \n", + " betaConfidenceIntervalLower | -0.05702774999999... \n", + " betaConfidenceIntervalUpper | -0.05330605 \n", + " studyLocusId | 5242723067793949472 \n", + " qualityControls | [Variant not foun... \n", + " ldSet | null \n", + " locus | null \n", + "only showing top 1 row\n", + "\n", + "root\n", + " |-- variantId: string (nullable = true)\n", + " |-- chromosome: string (nullable = true)\n", + " |-- studyId: string (nullable = true)\n", + " |-- position: integer (nullable = true)\n", + " |-- pValueMantissa: float (nullable = true)\n", + " |-- pValueExponent: integer (nullable = true)\n", + " |-- beta: double (nullable = true)\n", + " |-- standardError: double (nullable = true)\n", + " |-- effectAlleleFrequencyFromSource: float (nullable = true)\n", + " |-- betaConfidenceIntervalLower: double (nullable = true)\n", + " |-- betaConfidenceIntervalUpper: double (nullable = true)\n", + " |-- studyLocusId: long (nullable = true)\n", + " |-- qualityControls: array (nullable = true)\n", + " | |-- element: string (containsNull = true)\n", + " |-- ldSet: array (nullable = true)\n", + " | |-- element: struct (containsNull = true)\n", + " | | |-- tagVariantId: string (nullable = true)\n", + " | | |-- r2Overall: double (nullable = true)\n", + " |-- locus: array (nullable = true)\n", + " | |-- element: struct (containsNull = true)\n", + " | | |-- variantId: string (nullable = true)\n", + " | | |-- r2Overall: double (nullable = true)\n", + " | | |-- posteriorProbability: double (nullable = true)\n", + " | | |-- standardError: double (nullable = true)\n", + " | | |-- is95CredibleSet: boolean (nullable = true)\n", + " | | |-- is99CredibleSet: boolean (nullable = true)\n", + "\n" + ] }, { - "cell_type": "markdown", - "id": "a8b99c39", - "metadata": {}, - "source": [ - "## Finngen clumping with locus\n", - "\n", - "- Window width: 500kbp\n", - "- Locus width: 250kbp\n", - "- LD threshold: 0.5\n", - "\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "23/10/06 10:59:16 WARN CacheManager: Asked to cache already cached data.\n" + ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "09f14d49", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-13T09:54:35.337711Z", - "start_time": "2023-10-13T09:54:34.446422Z" - } - }, - "outputs": [], - "source": [ - "# Import:\n", - "from pyspark.sql import functions as f, types as t\n", - "\n", - "from gentropy.common.session import Session\n", - "\n", - "from gentropy.dataset.summary_statistics import SummaryStatistics\n", - "from gentropy.dataset.study_locus import StudyLocus\n", - "from gentropy.dataset.study_index import StudyIndex\n", - "from gentropy.dataset.ld_index import LDIndex\n", - "from gentropy.method.ld import LDAnnotator\n", - "\n", - "from gentropy.method.pics import PICS\n", - "\n", - "# Initialize session:\n", - "session = Session()\n", - "\n", - "# Input:\n", - "sumstats = 'gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/*'\n", - "ld_index_path = 'gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/ld_index/'\n", - "\n", - "# Output:\n", - "ld_clumped_output = 'gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped'\n", - "picsed_output = 'gs://ot-team/dsuveges/finngen/2023.10.06_PICSed'\n", - "window_based_clumped_output = 'gs://ot-team/dsuveges/finngen/2023.10.06_window_clumped'" + "data": { + "text/plain": [ + "19005" ] - }, + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "picsed_df = session.spark.read.parquet(picsed_output).persist()\n", + "picsed_df.show(1, vertical=True)\n", + "picsed_df.printSchema()\n", + "picsed_df.count()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "937e1eb0", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-06T11:11:55.487431Z", + "start_time": "2023-10-06T11:11:54.898030Z" + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "id": "52861491", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-13T11:39:14.790777Z", - "start_time": "2023-10-13T10:47:20.349271Z" - }, - "code_folding": [], - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23/10/13 10:47:53 WARN HintErrorLogger: Hint (strategy=broadcast) is not supported in the query: build right for right outer join.\n", - "23/10/13 10:47:53 WARN package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.\n", - "23/10/13 10:48:19 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1248; previousMaxLatencyMs=1230; operationCount=59116581; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_OTHER_SYSTCON_FG/chromosome=2/part-00019-1d683f00-9247-401b-846c-8b2498bc68bf.c000.snappy.parquet\n", - "23/10/13 10:49:02 WARN GhfsStorageStatistics: Detected potential high latency for operation op_get_file_status. latencyMs=1039; previousMaxLatencyMs=1037; operationCount=126124; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_K11_SCISS_BITE_INCLAVO/chromosome=2/part-00018-e37f4a01-bdb4-442f-8dcd-681da303e876.c000.snappy.parquet\n", - "23/10/13 10:52:37 WARN GhfsStorageStatistics: Detected potential high latency for operation op_get_file_status. latencyMs=1917; previousMaxLatencyMs=1039; operationCount=144264; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_AUTOIMMUNE_NONTHYROID_STRICT/chromosome=5/part-00006-02537c5f-2dea-47c5-8655-b0d84827115a.c000.snappy.parquet\n", - "23/10/13 10:52:38 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1942; previousMaxLatencyMs=1248; operationCount=94444057; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_K11_PULPITIS_1_ONLYAVO/chromosome=5/part-00006-de277609-305a-4cec-8bb1-5b37a70be48b.c000.snappy.parquet\n", - "23/10/13 11:07:41 WARN HintErrorLogger: Hint (strategy=broadcast) is not supported in the query: build right for right outer join.\n", - "23/10/13 11:12:32 WARN HintErrorLogger: Hint (strategy=broadcast) is not supported in the query: build right for right outer join.\n", - "23/10/13 11:12:48 WARN HintErrorLogger: Hint (strategy=broadcast) is not supported in the query: build right for right outer join.\n", - "23/10/13 11:21:40 WARN GoogleCloudStorageReadChannel: Failed read retry #1/10 for 'gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_H7_CONJUHAEMOR/chromosome=12/part-00016-03e2c019-d707-414b-b4e1-8b6474ccfb26.c000.snappy.parquet'. Sleeping...\n", - "java.net.SocketException: Connection reset\n", - "\tat java.net.SocketInputStream.read(SocketInputStream.java:186) ~[?:?]\n", - "\tat java.net.SocketInputStream.read(SocketInputStream.java:140) ~[?:?]\n", - "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.readFromSocket(ConscryptEngineSocket.java:920) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", - "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.processDataFromSocket(ConscryptEngineSocket.java:884) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", - "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.readUntilDataAvailable(ConscryptEngineSocket.java:799) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", - "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.read(ConscryptEngineSocket.java:772) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", - "\tat java.io.BufferedInputStream.read1(BufferedInputStream.java:290) ~[?:?]\n", - "\tat java.io.BufferedInputStream.read(BufferedInputStream.java:351) ~[?:?]\n", - "\tat sun.net.www.MeteredStream.read(MeteredStream.java:134) ~[?:?]\n", - "\tat java.io.FilterInputStream.read(FilterInputStream.java:133) ~[?:?]\n", - "\tat sun.net.www.protocol.http.HttpURLConnection$HttpInputStream.read(HttpURLConnection.java:3529) ~[?:?]\n", - "\tat com.google.cloud.hadoop.repackaged.gcs.com.google.api.client.http.javanet.NetHttpResponse$SizeValidatingInputStream.read(NetHttpResponse.java:164) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", - "\tat java.io.BufferedInputStream.read1(BufferedInputStream.java:290) ~[?:?]\n", - "\tat java.io.BufferedInputStream.read(BufferedInputStream.java:351) ~[?:?]\n", - "\tat java.nio.channels.Channels$ReadableByteChannelImpl.read(Channels.java:388) ~[?:?]\n", - "\tat com.google.cloud.hadoop.repackaged.gcs.com.google.cloud.hadoop.gcsio.GoogleCloudStorageReadChannel.read(GoogleCloudStorageReadChannel.java:315) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", - "\tat com.google.cloud.hadoop.fs.gcs.GoogleHadoopFSInputStream.lambda$read$1(GoogleHadoopFSInputStream.java:170) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", - "\tat com.google.cloud.hadoop.fs.gcs.GhfsStorageStatistics.trackDuration(GhfsStorageStatistics.java:77) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", - "\tat com.google.cloud.hadoop.fs.gcs.GoogleHadoopFSInputStream.read(GoogleHadoopFSInputStream.java:159) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", - "\tat java.io.DataInputStream.read(DataInputStream.java:149) ~[?:?]\n", - "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:102) ~[parquet-common-1.12.2.jar:1.12.2]\n", - "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFullyHeapBuffer(DelegatingSeekableInputStream.java:127) ~[parquet-common-1.12.2.jar:1.12.2]\n", - "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:91) ~[parquet-common-1.12.2.jar:1.12.2]\n", - "\tat org.apache.parquet.hadoop.ParquetFileReader$ConsecutivePartList.readAll(ParquetFileReader.java:1704) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", - "\tat org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:925) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", - "\tat org.apache.parquet.hadoop.ParquetFileReader.readNextFilteredRowGroup(ParquetFileReader.java:972) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", - "\tat org.apache.spark.sql.execution.datasources.parquet.SpecificParquetRecordReaderBase$ParquetRowGroupReaderImpl.readNextRowGroup(SpecificParquetRecordReaderBase.java:320) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.checkEndOfRowGroup(VectorizedParquetRecordReader.java:403) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:324) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:227) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:116) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:274) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:116) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:565) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_0$(Unknown Source) ~[?:?]\n", - "\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) ~[?:?]\n", - "\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460) ~[scala-library-2.12.14.jar:?]\n", - "\tat org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:142) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.scheduler.Task.run(Task.scala:136) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) ~[?:?]\n", - "\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) ~[?:?]\n", - "\tat java.lang.Thread.run(Thread.java:829) ~[?:?]\n", - "23/10/13 11:21:41 WARN GoogleCloudStorageReadChannel: Failed read retry #1/10 for 'gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_AMN2/chromosome=12/part-00016-6c463da6-b063-495d-8b62-8ad766491cfb.c000.snappy.parquet'. Sleeping...\n", - "java.net.SocketException: Connection reset\n", - "\tat java.net.SocketInputStream.read(SocketInputStream.java:186) ~[?:?]\n", - "\tat java.net.SocketInputStream.read(SocketInputStream.java:140) ~[?:?]\n", - "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.readFromSocket(ConscryptEngineSocket.java:920) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", - "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.processDataFromSocket(ConscryptEngineSocket.java:884) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", - "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.readUntilDataAvailable(ConscryptEngineSocket.java:799) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", - "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.read(ConscryptEngineSocket.java:772) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", - "\tat java.io.BufferedInputStream.read1(BufferedInputStream.java:290) ~[?:?]\n", - "\tat java.io.BufferedInputStream.read(BufferedInputStream.java:351) ~[?:?]\n", - "\tat sun.net.www.MeteredStream.read(MeteredStream.java:134) ~[?:?]\n", - "\tat java.io.FilterInputStream.read(FilterInputStream.java:133) ~[?:?]\n", - "\tat sun.net.www.protocol.http.HttpURLConnection$HttpInputStream.read(HttpURLConnection.java:3529) ~[?:?]\n", - "\tat com.google.cloud.hadoop.repackaged.gcs.com.google.api.client.http.javanet.NetHttpResponse$SizeValidatingInputStream.read(NetHttpResponse.java:164) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", - "\tat java.io.BufferedInputStream.read1(BufferedInputStream.java:290) ~[?:?]\n", - "\tat java.io.BufferedInputStream.read(BufferedInputStream.java:351) ~[?:?]\n", - "\tat java.nio.channels.Channels$ReadableByteChannelImpl.read(Channels.java:388) ~[?:?]\n", - "\tat com.google.cloud.hadoop.repackaged.gcs.com.google.cloud.hadoop.gcsio.GoogleCloudStorageReadChannel.read(GoogleCloudStorageReadChannel.java:315) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", - "\tat com.google.cloud.hadoop.fs.gcs.GoogleHadoopFSInputStream.lambda$read$1(GoogleHadoopFSInputStream.java:170) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", - "\tat com.google.cloud.hadoop.fs.gcs.GhfsStorageStatistics.trackDuration(GhfsStorageStatistics.java:77) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", - "\tat com.google.cloud.hadoop.fs.gcs.GoogleHadoopFSInputStream.read(GoogleHadoopFSInputStream.java:159) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", - "\tat java.io.DataInputStream.read(DataInputStream.java:149) ~[?:?]\n", - "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:102) ~[parquet-common-1.12.2.jar:1.12.2]\n", - "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFullyHeapBuffer(DelegatingSeekableInputStream.java:127) ~[parquet-common-1.12.2.jar:1.12.2]\n", - "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:91) ~[parquet-common-1.12.2.jar:1.12.2]\n", - "\tat org.apache.parquet.hadoop.ParquetFileReader$ConsecutivePartList.readAll(ParquetFileReader.java:1704) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", - "\tat org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:925) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", - "\tat org.apache.parquet.hadoop.ParquetFileReader.readNextFilteredRowGroup(ParquetFileReader.java:972) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", - "\tat org.apache.spark.sql.execution.datasources.parquet.SpecificParquetRecordReaderBase$ParquetRowGroupReaderImpl.readNextRowGroup(SpecificParquetRecordReaderBase.java:320) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.checkEndOfRowGroup(VectorizedParquetRecordReader.java:403) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:324) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:227) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:116) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:274) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:116) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:565) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_0$(Unknown Source) ~[?:?]\n", - "\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) ~[?:?]\n", - "\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", - "\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460) ~[scala-library-2.12.14.jar:?]\n", - "\tat org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:170) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.scheduler.Task.run(Task.scala:136) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", - "\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) ~[?:?]\n", - "\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) ~[?:?]\n", - "\tat java.lang.Thread.run(Thread.java:829) ~[?:?]\n", - "23/10/13 11:36:03 WARN HintErrorLogger: Hint (strategy=broadcast) is not supported in the query: build right for right outer join.\n", - "23/10/13 11:39:00 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=202; previousMaxLatencyMs=190; operationCount=105; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_20231013113900962145652452525416_0067_m_000009_79286/part-00009-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", - "23/10/13 11:39:00 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=216; previousMaxLatencyMs=202; operationCount=105; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139003257422348970599913_0067_m_000028_79305/part-00028-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", - "23/10/13 11:39:00 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=218; previousMaxLatencyMs=216; operationCount=105; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139006484121277706965747_0067_m_000003_79280/part-00003-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", - "23/10/13 11:39:01 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=243; previousMaxLatencyMs=218; operationCount=105; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139004953018478188900860_0067_m_000023_79300/part-00023-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", - "23/10/13 11:39:02 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=165; previousMaxLatencyMs=153; operationCount=92; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139003598197994288369319_0067_m_000026_79303/part-00026-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", - "23/10/13 11:39:02 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=193; previousMaxLatencyMs=165; operationCount=99; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139004279983947938826985_0067_m_000025_79302/part-00025-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", - "23/10/13 11:39:02 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=243; previousMaxLatencyMs=193; operationCount=103; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139004602145763603687891_0067_m_000030_79307/part-00030-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", - "23/10/13 11:39:02 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=329; previousMaxLatencyMs=243; operationCount=104; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_20231013113900353649088592248874_0067_m_000005_79282/part-00005-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", - " \r" - ] - } - ], - "source": [ - "window_based_clumped_output = 'gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus'\n", - "\n", - "clump_window_length = 500_000\n", - "locus_window_length = 250_000\n", - "\n", - "(\n", - " SummaryStatistics(\n", - " _df=(\n", - " session.spark.read.parquet(sumstats, recursiveFileLookup=True)\n", - " .withColumn(\n", - " 'chromosome',\n", - " f.split(f.col('variantId'), '_')[0]\n", - " )\n", - " ),\n", - " _schema=SummaryStatistics.get_schema()\n", - " )\n", - " .window_based_clumping(\n", - " distance=clump_window_length,\n", - " locus_collect_distance=locus_window_length,\n", - " with_locus=True\n", - " )\n", - " .df.write.mode('overwrite')\n", - " .parquet(window_based_clumped_output)\n", - ")\n" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 18, - "id": "6b234362", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-13T12:39:15.023402Z", - "start_time": "2023-10-13T11:50:42.603425Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------+---------------------+---------+---------+---------------+\n", - "| studyId|ldPopulationStructure|projectId|studyType|traitFromSource|\n", - "+--------------------+---------------------+---------+---------+---------------+\n", - "|FINNGEN_R9_K11_EN...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_H7_KER...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_H8_EXT...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_H7_RET...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_RHEUMA...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_H7_KER...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_HEIGHT...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_M13_SY...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_M13_DO...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_M13_PY...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "| FINNGEN_R9_GOUT_NOS| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_M13_FI...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_ALLERG...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_E4_DM2...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_G6_CER...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_L12_UR...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_AUTOIM...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_I9_HYP...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_M13_AR...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "|FINNGEN_R9_K11_AP...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", - "+--------------------+---------------------+---------+---------+---------------+\n", - "only showing top 20 rows\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23/10/13 11:55:34 WARN GhfsStorageStatistics: Detected potential high latency for operation op_open. latencyMs=676; previousMaxLatencyMs=470; operationCount=333148; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/ld_index/chromosome=5/part-00076-ff42773a-494c-46d2-bc22-322062b5e715.c000.snappy.parquet\n", - "23/10/13 12:39:11 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=279; previousMaxLatencyMs=243; operationCount=140; context=gs://ot-team/dsuveges/finngen/2023.10.13_ld_clumped_w_locus/_temporary/0/_temporary/attempt_202310131239103300432709600941830_0086_m_000000_80535/part-00000-17d449c4-f0c3-4617-b378-b74b864ab64a-c000.snappy.parquet\n", - "23/10/13 12:39:11 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=335; previousMaxLatencyMs=279; operationCount=140; context=gs://ot-team/dsuveges/finngen/2023.10.13_ld_clumped_w_locus/_temporary/0/_temporary/attempt_202310131239106417331548307580589_0086_m_000027_80562/part-00027-17d449c4-f0c3-4617-b378-b74b864ab64a-c000.snappy.parquet\n", - " \r" - ] - } - ], - "source": [ - "ld_clumped_output = 'gs://ot-team/dsuveges/finngen/2023.10.13_ld_clumped_w_locus'\n", - "\n", - "studies_df = (\n", - " session.spark.read.parquet(window_based_clumped_output)\n", - " # Generating a list of study identifiers:\n", - " .select('studyId')\n", - " .distinct()\n", - " # Adding fabricated values required to parse as gwas catalog study:\n", - " .select(\n", - " 'studyId',\n", - " StudyIndex.aggregate_and_map_ancestries(\n", - " f.array(\n", - " f.struct(\n", - " f.lit('Finnish').alias('ancestry'),\n", - " f.lit(100).cast('long').alias('sampleSize')\n", - " )\n", - " )\n", - " ).alias('ldPopulationStructure'),\n", - " f.lit('FINNGEN').alias('projectId'),\n", - " f.lit('gwas').alias('studyType'),\n", - " f.lit('cicaful').alias('traitFromSource')\n", - " )\n", - ")\n", - "\n", - "study_index = (\n", - " StudyIndex(\n", - " _df=studies_df,\n", - " _schema=StudyIndex.get_schema()\n", - " )\n", - ")\n", - "\n", - "study_index.df.show()\n", - "\n", - "# Loading ld index:\n", - "ld_index = LDIndex.from_parquet(session, ld_index_path)\n", - "\n", - "(\n", - " # To annotate study/locus, study level info and ld panel is needed:\n", - " LDAnnotator.ld_annotate(\n", - " StudyLocus.from_parquet(session, window_based_clumped_output),\n", - " study_index, \n", - " ld_index\n", - " )\n", - " # Clumping linked study-loci together:\n", - " .clump()\n", - " .df.write.mode('overwrite').parquet(ld_clumped_output)\n", - ")\n", - "\n", - "\n" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(\n", + " picsed_df\n", + " .filter(f.col(\"locus\").isNotNull())\n", + " .select(\n", + " \"studyId\",\n", + " \"variantId\",\n", + " f.aggregate(\n", + " f.transform(\n", + " f.col(\"locus\"),\n", + " lambda locus: f.when(locus.is99CredibleSet, f.lit(1.0)).otherwise( f.lit(0.0))\n", + " ),\n", + " f.lit(0.0),\n", + " lambda summa, value: summa + value\n", + " ).alias(\"99CredCount\"),\n", + " f.aggregate(\n", + " f.transform(\n", + " f.col(\"locus\"),\n", + " lambda locus: f.when(locus.is95CredibleSet, f.lit(1.0)).otherwise( f.lit(0.0))\n", + " ),\n", + " f.lit(0.0),\n", + " lambda summa, value: summa + value\n", + " ).alias(\"95CredCount\"),\n", + " )\n", + "# .orderBy(f.col('99CredCount').desc())\n", + "# .show()\n", + " .filter(f.col(\"99CredCount\") < 100)\n", + "# .count()\n", + " .toPandas()\n", + " [[\"99CredCount\", \"95CredCount\"]]\n", + " .plot.hist(bins=25, alpha=0.5, label=\"Credible set size\")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a8b99c39", + "metadata": {}, + "source": [ + "## Finngen clumping with locus\n", + "\n", + "- Window width: 500kbp\n", + "- Locus width: 250kbp\n", + "- LD threshold: 0.5\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "09f14d49", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-13T09:54:35.337711Z", + "start_time": "2023-10-13T09:54:34.446422Z" + } + }, + "outputs": [], + "source": [ + "# Import:\n", + "from pyspark.sql import functions as f\n", + "\n", + "from gentropy.common.session import Session\n", + "from gentropy.dataset.ld_index import LDIndex\n", + "from gentropy.dataset.study_index import StudyIndex\n", + "from gentropy.dataset.study_locus import StudyLocus\n", + "from gentropy.dataset.summary_statistics import SummaryStatistics\n", + "from gentropy.method.ld import LDAnnotator\n", + "from gentropy.method.pics import PICS\n", + "\n", + "# Initialize session:\n", + "session = Session()\n", + "\n", + "# Input:\n", + "sumstats = \"gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/*\"\n", + "ld_index_path = \"gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/ld_index/\"\n", + "\n", + "# Output:\n", + "ld_clumped_output = \"gs://ot-team/dsuveges/finngen/2023.10.06_LD_clumped\"\n", + "picsed_output = \"gs://ot-team/dsuveges/finngen/2023.10.06_PICSed\"\n", + "window_based_clumped_output = \"gs://ot-team/dsuveges/finngen/2023.10.06_window_clumped\"" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "52861491", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-13T11:39:14.790777Z", + "start_time": "2023-10-13T10:47:20.349271Z" }, + "code_folding": [], + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "id": "f637e3c0", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-13T10:43:26.766382Z", - "start_time": "2023-10-13T10:43:25.980232Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------------------------------------------------+-----+\n", - "|qualityControls |count|\n", - "+--------------------------------------------------------------+-----+\n", - "|[Variant not found in LD reference] |4607 |\n", - "|[] |13813|\n", - "|[Explained by a more significant variant in high LD (clumped)]|585 |\n", - "+--------------------------------------------------------------+-----+\n", - "\n" - ] - } - ], - "source": [ - "(\n", - " session.spark.read.parquet(ld_clumped_output)\n", - " .groupBy('qualityControls')\n", - " .count()\n", - "# .show(1, False, True)\n", - " .show(truncate=False)\n", - ")" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "23/10/13 10:47:53 WARN HintErrorLogger: Hint (strategy=broadcast) is not supported in the query: build right for right outer join.\n", + "23/10/13 10:47:53 WARN package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.\n", + "23/10/13 10:48:19 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1248; previousMaxLatencyMs=1230; operationCount=59116581; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_OTHER_SYSTCON_FG/chromosome=2/part-00019-1d683f00-9247-401b-846c-8b2498bc68bf.c000.snappy.parquet\n", + "23/10/13 10:49:02 WARN GhfsStorageStatistics: Detected potential high latency for operation op_get_file_status. latencyMs=1039; previousMaxLatencyMs=1037; operationCount=126124; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_K11_SCISS_BITE_INCLAVO/chromosome=2/part-00018-e37f4a01-bdb4-442f-8dcd-681da303e876.c000.snappy.parquet\n", + "23/10/13 10:52:37 WARN GhfsStorageStatistics: Detected potential high latency for operation op_get_file_status. latencyMs=1917; previousMaxLatencyMs=1039; operationCount=144264; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_AUTOIMMUNE_NONTHYROID_STRICT/chromosome=5/part-00006-02537c5f-2dea-47c5-8655-b0d84827115a.c000.snappy.parquet\n", + "23/10/13 10:52:38 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_read_operations. latencyMs=1942; previousMaxLatencyMs=1248; operationCount=94444057; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_K11_PULPITIS_1_ONLYAVO/chromosome=5/part-00006-de277609-305a-4cec-8bb1-5b37a70be48b.c000.snappy.parquet\n", + "23/10/13 11:07:41 WARN HintErrorLogger: Hint (strategy=broadcast) is not supported in the query: build right for right outer join.\n", + "23/10/13 11:12:32 WARN HintErrorLogger: Hint (strategy=broadcast) is not supported in the query: build right for right outer join.\n", + "23/10/13 11:12:48 WARN HintErrorLogger: Hint (strategy=broadcast) is not supported in the query: build right for right outer join.\n", + "23/10/13 11:21:40 WARN GoogleCloudStorageReadChannel: Failed read retry #1/10 for 'gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_H7_CONJUHAEMOR/chromosome=12/part-00016-03e2c019-d707-414b-b4e1-8b6474ccfb26.c000.snappy.parquet'. Sleeping...\n", + "java.net.SocketException: Connection reset\n", + "\tat java.net.SocketInputStream.read(SocketInputStream.java:186) ~[?:?]\n", + "\tat java.net.SocketInputStream.read(SocketInputStream.java:140) ~[?:?]\n", + "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.readFromSocket(ConscryptEngineSocket.java:920) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", + "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.processDataFromSocket(ConscryptEngineSocket.java:884) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", + "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.readUntilDataAvailable(ConscryptEngineSocket.java:799) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", + "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.read(ConscryptEngineSocket.java:772) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", + "\tat java.io.BufferedInputStream.read1(BufferedInputStream.java:290) ~[?:?]\n", + "\tat java.io.BufferedInputStream.read(BufferedInputStream.java:351) ~[?:?]\n", + "\tat sun.net.www.MeteredStream.read(MeteredStream.java:134) ~[?:?]\n", + "\tat java.io.FilterInputStream.read(FilterInputStream.java:133) ~[?:?]\n", + "\tat sun.net.www.protocol.http.HttpURLConnection$HttpInputStream.read(HttpURLConnection.java:3529) ~[?:?]\n", + "\tat com.google.cloud.hadoop.repackaged.gcs.com.google.api.client.http.javanet.NetHttpResponse$SizeValidatingInputStream.read(NetHttpResponse.java:164) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", + "\tat java.io.BufferedInputStream.read1(BufferedInputStream.java:290) ~[?:?]\n", + "\tat java.io.BufferedInputStream.read(BufferedInputStream.java:351) ~[?:?]\n", + "\tat java.nio.channels.Channels$ReadableByteChannelImpl.read(Channels.java:388) ~[?:?]\n", + "\tat com.google.cloud.hadoop.repackaged.gcs.com.google.cloud.hadoop.gcsio.GoogleCloudStorageReadChannel.read(GoogleCloudStorageReadChannel.java:315) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", + "\tat com.google.cloud.hadoop.fs.gcs.GoogleHadoopFSInputStream.lambda$read$1(GoogleHadoopFSInputStream.java:170) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", + "\tat com.google.cloud.hadoop.fs.gcs.GhfsStorageStatistics.trackDuration(GhfsStorageStatistics.java:77) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", + "\tat com.google.cloud.hadoop.fs.gcs.GoogleHadoopFSInputStream.read(GoogleHadoopFSInputStream.java:159) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", + "\tat java.io.DataInputStream.read(DataInputStream.java:149) ~[?:?]\n", + "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:102) ~[parquet-common-1.12.2.jar:1.12.2]\n", + "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFullyHeapBuffer(DelegatingSeekableInputStream.java:127) ~[parquet-common-1.12.2.jar:1.12.2]\n", + "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:91) ~[parquet-common-1.12.2.jar:1.12.2]\n", + "\tat org.apache.parquet.hadoop.ParquetFileReader$ConsecutivePartList.readAll(ParquetFileReader.java:1704) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", + "\tat org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:925) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", + "\tat org.apache.parquet.hadoop.ParquetFileReader.readNextFilteredRowGroup(ParquetFileReader.java:972) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", + "\tat org.apache.spark.sql.execution.datasources.parquet.SpecificParquetRecordReaderBase$ParquetRowGroupReaderImpl.readNextRowGroup(SpecificParquetRecordReaderBase.java:320) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.checkEndOfRowGroup(VectorizedParquetRecordReader.java:403) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:324) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:227) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:116) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:274) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:116) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:565) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_0$(Unknown Source) ~[?:?]\n", + "\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) ~[?:?]\n", + "\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460) ~[scala-library-2.12.14.jar:?]\n", + "\tat org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:142) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.scheduler.Task.run(Task.scala:136) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) ~[?:?]\n", + "\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) ~[?:?]\n", + "\tat java.lang.Thread.run(Thread.java:829) ~[?:?]\n", + "23/10/13 11:21:41 WARN GoogleCloudStorageReadChannel: Failed read retry #1/10 for 'gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/preprocess/finngen/summary_stats/FINNGEN_R9_AMN2/chromosome=12/part-00016-6c463da6-b063-495d-8b62-8ad766491cfb.c000.snappy.parquet'. Sleeping...\n", + "java.net.SocketException: Connection reset\n", + "\tat java.net.SocketInputStream.read(SocketInputStream.java:186) ~[?:?]\n", + "\tat java.net.SocketInputStream.read(SocketInputStream.java:140) ~[?:?]\n", + "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.readFromSocket(ConscryptEngineSocket.java:920) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", + "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.processDataFromSocket(ConscryptEngineSocket.java:884) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", + "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.readUntilDataAvailable(ConscryptEngineSocket.java:799) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", + "\tat org.conscrypt.ConscryptEngineSocket$SSLInputStream.read(ConscryptEngineSocket.java:772) ~[conscrypt-openjdk-2.5.2-linux-x86_64.jar:2.5.2]\n", + "\tat java.io.BufferedInputStream.read1(BufferedInputStream.java:290) ~[?:?]\n", + "\tat java.io.BufferedInputStream.read(BufferedInputStream.java:351) ~[?:?]\n", + "\tat sun.net.www.MeteredStream.read(MeteredStream.java:134) ~[?:?]\n", + "\tat java.io.FilterInputStream.read(FilterInputStream.java:133) ~[?:?]\n", + "\tat sun.net.www.protocol.http.HttpURLConnection$HttpInputStream.read(HttpURLConnection.java:3529) ~[?:?]\n", + "\tat com.google.cloud.hadoop.repackaged.gcs.com.google.api.client.http.javanet.NetHttpResponse$SizeValidatingInputStream.read(NetHttpResponse.java:164) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", + "\tat java.io.BufferedInputStream.read1(BufferedInputStream.java:290) ~[?:?]\n", + "\tat java.io.BufferedInputStream.read(BufferedInputStream.java:351) ~[?:?]\n", + "\tat java.nio.channels.Channels$ReadableByteChannelImpl.read(Channels.java:388) ~[?:?]\n", + "\tat com.google.cloud.hadoop.repackaged.gcs.com.google.cloud.hadoop.gcsio.GoogleCloudStorageReadChannel.read(GoogleCloudStorageReadChannel.java:315) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", + "\tat com.google.cloud.hadoop.fs.gcs.GoogleHadoopFSInputStream.lambda$read$1(GoogleHadoopFSInputStream.java:170) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", + "\tat com.google.cloud.hadoop.fs.gcs.GhfsStorageStatistics.trackDuration(GhfsStorageStatistics.java:77) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", + "\tat com.google.cloud.hadoop.fs.gcs.GoogleHadoopFSInputStream.read(GoogleHadoopFSInputStream.java:159) ~[gcs-connector-hadoop3-2.2.16.jar:?]\n", + "\tat java.io.DataInputStream.read(DataInputStream.java:149) ~[?:?]\n", + "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:102) ~[parquet-common-1.12.2.jar:1.12.2]\n", + "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFullyHeapBuffer(DelegatingSeekableInputStream.java:127) ~[parquet-common-1.12.2.jar:1.12.2]\n", + "\tat org.apache.parquet.io.DelegatingSeekableInputStream.readFully(DelegatingSeekableInputStream.java:91) ~[parquet-common-1.12.2.jar:1.12.2]\n", + "\tat org.apache.parquet.hadoop.ParquetFileReader$ConsecutivePartList.readAll(ParquetFileReader.java:1704) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", + "\tat org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:925) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", + "\tat org.apache.parquet.hadoop.ParquetFileReader.readNextFilteredRowGroup(ParquetFileReader.java:972) ~[parquet-hadoop-1.12.2.jar:1.12.2]\n", + "\tat org.apache.spark.sql.execution.datasources.parquet.SpecificParquetRecordReaderBase$ParquetRowGroupReaderImpl.readNextRowGroup(SpecificParquetRecordReaderBase.java:320) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.checkEndOfRowGroup(VectorizedParquetRecordReader.java:403) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:324) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:227) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:116) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:274) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:116) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.FileSourceScanExec$$anon$1.hasNext(DataSourceScanExec.scala:565) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_0$(Unknown Source) ~[?:?]\n", + "\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) ~[?:?]\n", + "\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:760) ~[spark-sql_2.12-3.3.0.jar:3.3.0]\n", + "\tat scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460) ~[scala-library-2.12.14.jar:?]\n", + "\tat org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:170) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.scheduler.Task.run(Task.scala:136) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551) ~[spark-core_2.12-3.3.0.jar:3.3.0]\n", + "\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) ~[?:?]\n", + "\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) ~[?:?]\n", + "\tat java.lang.Thread.run(Thread.java:829) ~[?:?]\n", + "23/10/13 11:36:03 WARN HintErrorLogger: Hint (strategy=broadcast) is not supported in the query: build right for right outer join.\n", + "23/10/13 11:39:00 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=202; previousMaxLatencyMs=190; operationCount=105; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_20231013113900962145652452525416_0067_m_000009_79286/part-00009-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", + "23/10/13 11:39:00 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=216; previousMaxLatencyMs=202; operationCount=105; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139003257422348970599913_0067_m_000028_79305/part-00028-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", + "23/10/13 11:39:00 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=218; previousMaxLatencyMs=216; operationCount=105; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139006484121277706965747_0067_m_000003_79280/part-00003-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", + "23/10/13 11:39:01 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=243; previousMaxLatencyMs=218; operationCount=105; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139004953018478188900860_0067_m_000023_79300/part-00023-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", + "23/10/13 11:39:02 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=165; previousMaxLatencyMs=153; operationCount=92; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139003598197994288369319_0067_m_000026_79303/part-00026-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", + "23/10/13 11:39:02 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=193; previousMaxLatencyMs=165; operationCount=99; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139004279983947938826985_0067_m_000025_79302/part-00025-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", + "23/10/13 11:39:02 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=243; previousMaxLatencyMs=193; operationCount=103; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_202310131139004602145763603687891_0067_m_000030_79307/part-00030-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", + "23/10/13 11:39:02 WARN GhfsStorageStatistics: Detected potential high latency for operation stream_write_close_operations. latencyMs=329; previousMaxLatencyMs=243; operationCount=104; context=gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus/_temporary/0/_temporary/attempt_20231013113900353649088592248874_0067_m_000005_79282/part-00005-65e9ae61-ec7d-4672-a525-8de91e583465-c000.snappy.parquet\n", + " \r" + ] + } + ], + "source": [ + "window_based_clumped_output = \"gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus\"\n", + "\n", + "clump_window_length = 500_000\n", + "locus_window_length = 250_000\n", + "\n", + "(\n", + " SummaryStatistics(\n", + " _df=(\n", + " session.spark.read.parquet(sumstats, recursiveFileLookup=True)\n", + " .withColumn(\n", + " \"chromosome\",\n", + " f.split(f.col(\"variantId\"), \"_\")[0]\n", + " )\n", + " ),\n", + " _schema=SummaryStatistics.get_schema()\n", + " )\n", + " .window_based_clumping(\n", + " distance=clump_window_length,\n", + " locus_collect_distance=locus_window_length,\n", + " with_locus=True\n", + " )\n", + " .df.write.mode(\"overwrite\")\n", + " .parquet(window_based_clumped_output)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6b234362", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-13T12:39:15.023402Z", + "start_time": "2023-10-13T11:50:42.603425Z" + } + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "id": "5bf42196", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-13T12:55:39.212186Z", - "start_time": "2023-10-13T12:55:38.131092Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "text/plain": [ - "[Row(locus=[Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99764322_T_C', pValueMantissa=2.3949999809265137, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0201963, standardError=0.00665169, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99764860_G_A', pValueMantissa=1.0499999523162842, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00813303, standardError=0.00248194, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99765280_C_T', pValueMantissa=7.499000072479248, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0147615, standardError=0.00437943, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99765335_A_G', pValueMantissa=1.2640000581741333, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107816, standardError=0.00189456, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99766311_C_T', pValueMantissa=1.0579999685287476, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.010837, standardError=0.00189422, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99766702_A_G', pValueMantissa=1.2059999704360962, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107966, standardError=0.00189453, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99766923_T_C', pValueMantissa=1.3300000429153442, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010765, standardError=0.00189451, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99767090_TTTG_T', pValueMantissa=1.2740000486373901, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107778, standardError=0.00189432, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99768341_T_C', pValueMantissa=1.2289999723434448, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107887, standardError=0.00189422, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99768718_A_G', pValueMantissa=1.2649999856948853, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107795, standardError=0.0018942, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99768993_G_A', pValueMantissa=1.1629999876022339, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108066, standardError=0.00189423, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99769297_T_C', pValueMantissa=1.2929999828338623, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0113037, standardError=0.00186275, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99769386_C_T', pValueMantissa=1.7280000448226929, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00670209, standardError=0.00281514, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99769548_A_G', pValueMantissa=1.7280000448226929, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0067021, standardError=0.00281514, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99769607_C_T', pValueMantissa=1.2710000276565552, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107775, standardError=0.00189416, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99770233_A_AT', pValueMantissa=1.1360000371932983, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108149, standardError=0.00189434, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99771038_C_A', pValueMantissa=1.1039999723434448, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0108218, standardError=0.00189392, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99771332_T_C', pValueMantissa=1.284999966621399, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00972726, standardError=0.00390986, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99771548_C_T', pValueMantissa=1.1369999647140503, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108168, standardError=0.00189473, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99774249_G_A', pValueMantissa=1.0410000085830688, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00813435, standardError=0.00248058, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99774476_AT_A', pValueMantissa=1.1540000438690186, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0108061, standardError=0.0018937, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99777761_G_A', pValueMantissa=2.9170000553131104, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0857961, standardError=0.0393337, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99777984_T_G', pValueMantissa=9.097999572753906, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108749, standardError=0.00189236, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99778063_G_A', pValueMantissa=1.2730000019073486, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00887632, standardError=0.00203368, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99778821_G_A', pValueMantissa=1.6679999828338623, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00682868, standardError=0.00285271, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99779613_C_G', pValueMantissa=7.630000114440918, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0109299, standardError=0.00189215, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99781296_A_G', pValueMantissa=8.560999870300293, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.010892, standardError=0.00189195, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99781822_G_A', pValueMantissa=1.4390000104904175, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00689344, standardError=0.00281655, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99781826_A_G', pValueMantissa=7.65500020980835, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0109284, standardError=0.00189207, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99782821_C_T', pValueMantissa=1.3680000305175781, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00573303, standardError=0.00232523, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99783206_A_G', pValueMantissa=7.632999897003174, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0109293, standardError=0.00189206, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99783290_A_C', pValueMantissa=8.821999549865723, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108841, standardError=0.00189224, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99784269_C_T', pValueMantissa=9.149999618530273, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108721, standardError=0.00189218, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99784331_G_A', pValueMantissa=4.435999870300293, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0327234, standardError=0.0162747, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99784751_C_A', pValueMantissa=3.0409998893737793, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.149822, standardError=0.0692119, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99785581_A_G', pValueMantissa=7.164000034332275, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0113888, standardError=0.00423514, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99786875_C_T', pValueMantissa=5.257999897003174, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0110515, standardError=0.00189278, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99787050_C_G', pValueMantissa=6.671999931335449, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0109731, standardError=0.00189226, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99787264_C_A', pValueMantissa=7.834000110626221, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0109198, standardError=0.00189186, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99787285_C_T', pValueMantissa=9.189000129699707, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108723, standardError=0.00189246, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99787359_T_G', pValueMantissa=7.504000186920166, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0109356, standardError=0.00189222, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99787460_G_T', pValueMantissa=6.499000072479248, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.010984, standardError=0.00189269, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99787461_G_T', pValueMantissa=6.499000072479248, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.010984, standardError=0.00189269, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99787483_G_A', pValueMantissa=7.631999969482422, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0109293, standardError=0.00189206, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99787568_C_T', pValueMantissa=7.631999969482422, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0109293, standardError=0.00189206, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99787571_C_T', pValueMantissa=7.631999969482422, pValueExponent=-9, pValueMantissaConditioned=None, 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posteriorProbability=None, variantId='10_99859558_G_A', pValueMantissa=6.2779998779296875, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0321283, standardError=0.00593792, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99859674_G_A', pValueMantissa=1.1050000190734863, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100907, standardError=0.00190082, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99861014_C_G', pValueMantissa=1.350000023841858, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0080568, standardError=0.00185135, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), 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standardError=0.00896575, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99871164_A_T', pValueMantissa=9.444999694824219, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0101485, standardError=0.0019015, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99871248_A_C', pValueMantissa=4.265999794006348, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011332, standardError=0.00192934, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99871256_C_T', pValueMantissa=1.0980000495910645, pValueExponent=-7, pValueMantissaConditioned=None, 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variantId='10_99872802_A_G', pValueMantissa=2.828000068664551, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0103237, standardError=0.00185953, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873074_C_T', pValueMantissa=4.716000080108643, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112996, standardError=0.00192927, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873158_C_T', pValueMantissa=1.1759999990463257, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010069, standardError=0.00190084, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873263_C_T', pValueMantissa=1.1440000534057617, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010071, standardError=0.00189938, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873471_G_A', pValueMantissa=6.283999919891357, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0723658, standardError=0.0264824, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873553_C_T', pValueMantissa=1.8109999895095825, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0122764, standardError=0.00286365, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), 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standardError=0.00190119, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875381_T_C', pValueMantissa=1.2410000562667847, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100525, standardError=0.00190121, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875452_C_T', pValueMantissa=2.5290000438690186, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0135056, standardError=0.00447237, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875704_T_A', pValueMantissa=2.9600000381469727, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00410906, standardError=0.00188882, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875760_A_AG', pValueMantissa=1.2430000305175781, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010052, standardError=0.00190125, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99876031_G_GT', pValueMantissa=1.3940000534057617, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0240895, standardError=0.00753787, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99876891_T_C', pValueMantissa=3.986999988555908, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102146, standardError=0.0018601, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99878758_G_A', pValueMantissa=2.763000011444092, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.151851, standardError=0.0689453, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99878850_C_G', pValueMantissa=4.1519999504089355, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102024, standardError=0.0018603, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99880039_G_A', pValueMantissa=4.0960001945495605, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102077, standardError=0.00186045, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99880120_A_C', pValueMantissa=4.75, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0113026, standardError=0.00193019, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99880736_G_C', pValueMantissa=2.4639999866485596, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.11912, standardError=0.039343, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99882938_G_A', pValueMantissa=4.218999862670898, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102002, standardError=0.00186086, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99883489_C_T', pValueMantissa=7.486000061035156, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0140267, standardError=0.00524475, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99884754_C_T', pValueMantissa=2.5510001182556152, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011665, standardError=0.00195783, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99885306_C_T', pValueMantissa=7.618000030517578, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0122854, standardError=0.00460381, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99885741_T_C', pValueMantissa=4.251999855041504, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0101963, standardError=0.00186062, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99887218_A_G', pValueMantissa=1.3589999675750732, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100249, standardError=0.00190197, betaConditioned=None, 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variantId='10_99908813_T_C', pValueMantissa=5.124000072479248, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112768, standardError=0.00192994, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99909229_T_C', pValueMantissa=8.24899959564209, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00998922, standardError=0.0018631, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99909454_A_C', pValueMantissa=7.598999977111816, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0109359, standardError=0.00189297, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, 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variantId='10_99966874_G_A', pValueMantissa=9.85099983215332, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133219, standardError=0.00195661, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99967655_A_C', pValueMantissa=4.085999965667725, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0132548, standardError=0.00191151, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99968174_T_C', pValueMantissa=6.03000020980835, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131184, standardError=0.00200495, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, 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variantId='10_99979375_G_A', pValueMantissa=3.937000036239624, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.13666, standardError=0.0663293, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99979565_C_T', pValueMantissa=4.479000091552734, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0275515, standardError=0.0137302, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99979592_G_A', pValueMantissa=3.3989999294281006, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133022, standardError=0.00191119, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99980479_A_C', pValueMantissa=7.7230000495910645, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0930047, standardError=0.0349125, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99980767_T_TG', pValueMantissa=3.1600000858306885, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133205, standardError=0.001911, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99981303_T_C', pValueMantissa=1.0260000228881836, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0129016, standardError=0.0019962, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99982408_C_T', pValueMantissa=4.807000160217285, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131845, standardError=0.00200471, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99982941_G_A', pValueMantissa=3.236999988555908, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133135, standardError=0.00191092, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99983547_G_T', pValueMantissa=1.378000020980835, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0138444, standardError=0.00204793, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99983965_C_T', pValueMantissa=1.3339999914169312, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.276188, standardError=0.0722979, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99984671_A_G', pValueMantissa=1.090999960899353, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.012885, standardError=0.00199651, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99984912_C_T', pValueMantissa=3.328000068664551, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133085, standardError=0.00191128, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99986015_T_C', pValueMantissa=1.0609999895095825, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.012891, standardError=0.00199615, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988026_G_T', pValueMantissa=7.709000110626221, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0122774, standardError=0.0046077, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988174_G_A', pValueMantissa=3.121999979019165, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133243, standardError=0.0019111, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988593_A_C', pValueMantissa=4.09499979019165, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0833533, standardError=0.0407789, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988730_T_C', pValueMantissa=3.1760001182556152, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133191, standardError=0.00191101, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988731_G_A', pValueMantissa=5.669000148773193, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0144037, standardError=0.00520681, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988980_A_G', pValueMantissa=4.874000072479248, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131807, standardError=0.00200475, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99989148_T_C', pValueMantissa=4.875, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131806, standardError=0.00200475, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99989685_T_C', pValueMantissa=4.556000232696533, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0132006, standardError=0.00200472, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99989906_C_T', pValueMantissa=3.2639999389648438, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133133, standardError=0.00191121, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99990445_G_A', pValueMantissa=6.452000141143799, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.090219, standardError=0.0331213, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99990521_G_A', pValueMantissa=2.9019999504089355, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.013345, standardError=0.00191124, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99990640_T_A', pValueMantissa=1.5579999685287476, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0139907, standardError=0.00442234, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99992006_G_GA', pValueMantissa=3.0260000228881836, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133329, standardError=0.00191111, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99992015_T_C', pValueMantissa=3.5850000381469727, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.013291, standardError=0.00191165, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99992806_A_G', pValueMantissa=3.0290000438690186, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133327, standardError=0.00191112, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99993453_A_G', pValueMantissa=3.0329999923706055, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133323, standardError=0.00191113, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99993852_T_C', pValueMantissa=3.0360000133514404, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133322, standardError=0.00191113, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99994177_C_T', pValueMantissa=4.533999919891357, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0132032, standardError=0.00200489, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99994349_A_T', pValueMantissa=1.0379999876022339, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0287633, standardError=0.00422921, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99995001_T_C', pValueMantissa=3.2860000133514404, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133152, standardError=0.00191176, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99995032_G_C', pValueMantissa=4.89900016784668, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131854, standardError=0.00200569, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99995714_T_C', pValueMantissa=3.374000072479248, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133048, standardError=0.00191128, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99995753_C_CTT', pValueMantissa=3.372999906539917, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133049, standardError=0.00191128, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99996207_C_G', pValueMantissa=3.384000062942505, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133041, standardError=0.00191129, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99997268_A_G', pValueMantissa=3.378999948501587, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133047, standardError=0.00191132, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99997438_CCA_C', pValueMantissa=6.603000164031982, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.109829, standardError=0.0404341, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99997720_A_G', pValueMantissa=2.815999984741211, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133548, standardError=0.00191149, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99998079_C_CA', pValueMantissa=4.61299991607666, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0922906, standardError=0.0462786, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99998104_G_A', pValueMantissa=1.2979999780654907, pValueExponent=-6, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00911911, standardError=0.0018841, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99999971_A_G', pValueMantissa=2.9570000171661377, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.030431, standardError=0.0102384, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100000235_C_T', pValueMantissa=5.6519999504089355, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.014492, standardError=0.00201036, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100000486_G_A', pValueMantissa=4.172999858856201, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.305198, standardError=0.149885, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100002038_G_A', pValueMantissa=3.7739999294281006, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0211624, standardError=0.0073064, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100002628_A_C', pValueMantissa=2.953000068664551, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0138093, standardError=0.00189258, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100004140_G_A', pValueMantissa=2.6459999084472656, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0994314, standardError=0.0448022, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100004827_A_C', pValueMantissa=6.945000171661377, pValueExponent=-14, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0142296, standardError=0.0019001, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100005233_T_C', pValueMantissa=3.111999988555908, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0216712, standardError=0.00733013, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100005358_G_C', pValueMantissa=4.872000217437744, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0145472, standardError=0.00201239, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100005438_A_G', pValueMantissa=1.093000054359436, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0533162, standardError=0.02095, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100006780_C_T', pValueMantissa=5.067999839782715, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0145408, standardError=0.002013, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100007241_C_T', pValueMantissa=6.783999919891357, pValueExponent=-14, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0142505, standardError=0.0019021, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100007404_G_GC', pValueMantissa=1.3420000076293945, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0198944, standardError=0.00377285, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100008640_A_G', pValueMantissa=5.019999980926514, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0145435, standardError=0.002013, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100009013_G_A', pValueMantissa=6.611999988555908, pValueExponent=-14, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0142475, standardError=0.00190084, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100009635_T_G', pValueMantissa=1.0549999475479126, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0134968, standardError=0.0018948, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100011477_C_G', pValueMantissa=2.444999933242798, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0148805, standardError=0.00203232, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100011685_CAAAT_C', pValueMantissa=4.139999866485596, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0101715, standardError=0.00185449, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100011812_A_T', pValueMantissa=1.2029999494552612, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0141145, standardError=0.00190314, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100013275_A_T', pValueMantissa=2.174999952316284, pValueExponent=-14, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0145924, standardError=0.00191005, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100013744_G_A', pValueMantissa=4.76800012588501, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.51719, standardError=0.261177, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100014566_A_G', pValueMantissa=8.550999641418457, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0119513, standardError=0.00358432, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100014970_T_C', pValueMantissa=3.993000030517578, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0146099, standardError=0.00201355, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100015400_G_A', pValueMantissa=5.646999835968018, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0160357, standardError=0.00222448, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100015645_T_C', pValueMantissa=2.236999988555908, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0145559, standardError=0.00207384, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100015854_C_T', pValueMantissa=6.158999919891357, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0160044, standardError=0.0022238, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100016635_T_G', pValueMantissa=1.718999981880188, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0147181, standardError=0.00218755, betaConditioned=None, 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pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0159723, standardError=0.00222609, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100022313_C_CT', pValueMantissa=6.171999931335449, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0504229, standardError=0.0184125, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100023134_T_G', pValueMantissa=1.3980000019073486, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0268617, standardError=0.0109298, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100024168_G_A', pValueMantissa=2.3359999656677246, pValueExponent=-6, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00880598, standardError=0.00186491, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100024869_C_T', pValueMantissa=3.556999921798706, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0162101, standardError=0.00222929, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100025927_T_TA', pValueMantissa=3.621999979019165, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.014426, standardError=0.00207534, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, 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betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100030853_G_A', pValueMantissa=7.5980000495910645, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0158245, standardError=0.00220759, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100030993_G_A', pValueMantissa=3.437000036239624, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0224965, standardError=0.00768921, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100031808_C_T', pValueMantissa=5.959000110626221, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0212214, standardError=0.00771671, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100032684_A_C', pValueMantissa=5.459000110626221, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.014926, standardError=0.00206922, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100033239_T_A', pValueMantissa=1.3669999837875366, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0463194, standardError=0.0187842, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100033337_C_T', pValueMantissa=9.138999938964844, pValueExponent=-14, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0165264, standardError=0.00221749, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100033635_G_GT', pValueMantissa=1.0110000371932983, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0164902, standardError=0.00221658, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100035604_T_A', pValueMantissa=3.4110000133514404, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.01433, standardError=0.002059, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100037190_G_A', pValueMantissa=3.2070000171661377, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0120692, standardError=0.00203849, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100037544_C_T', pValueMantissa=1.6990000009536743, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00996527, standardError=0.00417483, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100037655_A_G', pValueMantissa=6.13100004196167, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0127317, standardError=0.00219016, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100038188_C_T', pValueMantissa=7.738999843597412, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0126728, standardError=0.00219478, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100038589_T_C', pValueMantissa=3.734999895095825, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010628, standardError=0.00193133, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100038648_G_A', pValueMantissa=1.7230000495910645, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00994355, standardError=0.00417478, betaConditioned=None, 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standardError=0.00417473, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100039997_T_C', pValueMantissa=5.01800012588501, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0126197, standardError=0.00202937, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100040770_T_G', pValueMantissa=3.059999942779541, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.120454, standardError=0.0557085, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100042338_T_C', pValueMantissa=2.61899995803833, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.189007, standardError=0.085007, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100042505_G_A', pValueMantissa=3.4860000610351562, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0101879, standardError=0.00482832, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100042848_C_CT', pValueMantissa=4.413000106811523, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0093599, standardError=0.00185336, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100043308_C_T', pValueMantissa=5.076000213623047, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0126162, standardError=0.00202939, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100044357_C_T', pValueMantissa=5.36299991607666, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0127797, standardError=0.00218999, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100044501_A_G', pValueMantissa=1.7130000591278076, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00995148, standardError=0.00417441, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100044818_C_T', pValueMantissa=4.927000045776367, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0204248, standardError=0.00726406, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100044839_G_T', pValueMantissa=4.875, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0126274, standardError=0.00202913, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100045047_A_C', pValueMantissa=4.922999858856201, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0204266, standardError=0.00726404, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100045603_T_A', pValueMantissa=1.6740000247955322, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00998325, standardError=0.00417297, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100045685_C_T', pValueMantissa=1.3309999704360962, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131383, standardError=0.00204533, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100045864_G_A', pValueMantissa=1.6770000457763672, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00998092, standardError=0.00417295, betaConditioned=None, standardErrorConditioned=None, 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posteriorProbability=None, variantId='10_100162586_C_T', pValueMantissa=1.5570000410079956, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0117095, standardError=0.00370097, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100162599_A_G', pValueMantissa=1.7910000085830688, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0115545, standardError=0.0037, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100164449_A_T', pValueMantissa=8.289999961853027, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0343476, standardError=0.0102747, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), 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pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0162704, standardError=0.00614785, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100170743_T_C', pValueMantissa=1.819000005722046, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0115379, standardError=0.00370004, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100171278_T_G', pValueMantissa=1.965999960899353, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0114445, standardError=0.00369739, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100171408_C_CTATT', pValueMantissa=1.8220000267028809, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0115357, standardError=0.00370003, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100171892_C_A', pValueMantissa=4.783999919891357, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0290312, standardError=0.0146713, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100171937_A_T', pValueMantissa=3.7090001106262207, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0264529, standardError=0.0126887, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, 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posteriorProbability=None, variantId='10_100178873_G_C', pValueMantissa=3.313999891281128, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00845972, standardError=0.00288043, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100178957_A_G', pValueMantissa=1.8370000123977661, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011527, standardError=0.00370001, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100181244_T_A', pValueMantissa=9.291000366210938, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0116765, standardError=0.00352639, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), 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standardError=0.00369993, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100184256_T_C', pValueMantissa=4.7220001220703125, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.029124, standardError=0.0146772, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100184474_G_A', pValueMantissa=3.7109999656677246, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00543283, standardError=0.00187227, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100184568_G_A', pValueMantissa=2.1500000953674316, pValueExponent=-4, pValueMantissaConditioned=None, 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pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00813035, standardError=0.00287118, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100189674_T_G', pValueMantissa=9.279999732971191, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.126539, standardError=0.0486397, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100190351_G_A', pValueMantissa=4.619999885559082, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00813223, standardError=0.00287113, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100190395_G_A', pValueMantissa=2.0, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0760922, standardError=0.024623, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100192759_T_C', pValueMantissa=1.9559999704360962, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0114555, standardError=0.00369915, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100193524_C_T', pValueMantissa=9.555000305175781, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0122529, standardError=0.0037093, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100193948_A_C', pValueMantissa=3.0179998874664307, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0115477, standardError=0.00194714, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100195245_A_T', pValueMantissa=4.635000228881836, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0081285, standardError=0.00287091, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100195684_C_CA', pValueMantissa=4.2270002365112305, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.137066, standardError=0.0674911, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), 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standardError=0.0175348, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100197920_A_G', pValueMantissa=2.384999990463257, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0167093, standardError=0.00739523, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100198475_T_C', pValueMantissa=1.9639999866485596, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0114509, standardError=0.00369901, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100199282_C_G', pValueMantissa=1.1059999465942383, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0127328, standardError=0.00390313, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100199476_G_A', pValueMantissa=1.319000005722046, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0118973, standardError=0.00370432, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100200167_G_A', pValueMantissa=3.1589999198913574, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0376207, standardError=0.012745, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100201209_C_T', pValueMantissa=1.097000002861023, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00602792, standardError=0.0018465, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100201414_G_A', pValueMantissa=4.788000106811523, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0068162, standardError=0.00344526, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100201633_T_A', pValueMantissa=1.3179999589920044, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0118983, standardError=0.00370432, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100202455_T_C', pValueMantissa=1.9600000381469727, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0114531, standardError=0.00369901, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100202467_G_A', pValueMantissa=3.875999927520752, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0277856, standardError=0.00962082, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100202772_A_T', pValueMantissa=1.9600000381469727, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0114531, standardError=0.00369901, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100203494_T_C', pValueMantissa=1.9589999914169312, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0114534, standardError=0.00369901, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100203542_G_T', pValueMantissa=1.6130000352859497, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00697446, standardError=0.00184854, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100203788_C_A', pValueMantissa=4.110000133514404, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.113144, standardError=0.0394286, betaConditioned=None, standardErrorConditioned=None, 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betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100206333_A_G', pValueMantissa=4.160999774932861, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0263098, standardError=0.0129133, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100206660_A_G', pValueMantissa=2.4539999961853027, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.113651, standardError=0.0505422, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100206733_C_T', pValueMantissa=1.315999984741211, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0119003, standardError=0.00370432, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100207009_TA_T', pValueMantissa=1.9559999704360962, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0114549, standardError=0.003699, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100207455_C_G', pValueMantissa=1.305999994277954, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00593547, standardError=0.00184639, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100207653_G_A', pValueMantissa=3.6470000743865967, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0114874, standardError=0.00194719, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100207898_CAAGTT_C', pValueMantissa=3.384999990463257, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0921644, standardError=0.0434369, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100208164_G_C', pValueMantissa=3.874000072479248, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0277875, standardError=0.0096208, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100209200_T_C', 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posteriorProbability=None, variantId='10_100211240_A_G', pValueMantissa=7.230000019073486, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00932586, standardError=0.00188208, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100212531_T_C', pValueMantissa=3.874000072479248, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0277872, standardError=0.00962078, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100212621_T_C', pValueMantissa=1.6109999418258667, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00697491, standardError=0.00184852, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), 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pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00604851, standardError=0.00184672, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100218867_A_G', pValueMantissa=3.3550000190734863, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.205837, standardError=0.0968422, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100218929_A_C', pValueMantissa=1.9490000009536743, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011459, standardError=0.003699, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100219439_G_A', pValueMantissa=1.3109999895095825, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0119039, standardError=0.00370431, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100219496_T_C', pValueMantissa=1.9490000009536743, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011459, standardError=0.003699, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100219653_G_A', pValueMantissa=8.52400016784668, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0210222, standardError=0.00799145, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100219661_T_C', pValueMantissa=7.915999889373779, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0163115, standardError=0.00614227, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100220164_T_C', pValueMantissa=1.3109999895095825, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0119039, standardError=0.00370431, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100220814_T_TA', pValueMantissa=4.504000186920166, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00815468, standardError=0.00287084, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100220831_C_A', pValueMantissa=4.275000095367432, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.136751, standardError=0.0674941, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100221396_C_T', pValueMantissa=2.944000005722046, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0115551, standardError=0.00194705, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100221486_T_G', pValueMantissa=1.3890000581741333, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0160699, standardError=0.00653242, betaConditioned=None, 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pValueExponentConditioned=None, beta=0.00473991, standardError=0.00185855, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100224086_A_ACTCTCTCT', pValueMantissa=9.140999794006348, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0123865, standardError=0.00373569, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100224729_G_A', pValueMantissa=9.512999534606934, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0122608, standardError=0.00371028, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100224788_C_T', pValueMantissa=4.552000045776367, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00814496, standardError=0.00287083, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100224846_CA_C', pValueMantissa=4.505000114440918, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00815457, standardError=0.00287084, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100225330_C_T', pValueMantissa=4.679999828338623, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00811972, standardError=0.0028709, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100225460_G_A', pValueMantissa=1.4520000219345093, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0138989, standardError=0.00568655, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100227298_T_G', pValueMantissa=4.505000114440918, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00815457, standardError=0.00287084, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100227436_A_C', pValueMantissa=2.1089999675750732, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0204774, standardError=0.00887858, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100227710_T_C', pValueMantissa=2.624000072479248, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0115932, standardError=0.00194728, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100228161_T_C', pValueMantissa=1.1180000305175781, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0127208, standardError=0.00390316, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100228188_C_G', pValueMantissa=1.3109999895095825, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0119039, standardError=0.00370431, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100228581_G_GGGA', pValueMantissa=1.9490000009536743, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011459, standardError=0.003699, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100228620_A_G', pValueMantissa=1.9490000009536743, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011459, standardError=0.003699, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100229692_G_T', pValueMantissa=1.9490000009536743, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011459, standardError=0.003699, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100229731_G_C', pValueMantissa=4.275000095367432, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.136751, standardError=0.0674941, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100230118_T_C', pValueMantissa=1.9490000009536743, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011459, standardError=0.003699, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100230958_C_A', pValueMantissa=3.694000005722046, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, 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pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00815129, standardError=0.00287266, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100231791_G_C', pValueMantissa=1.3109999895095825, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0119039, standardError=0.00370431, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100233804_CAG_C', pValueMantissa=3.674999952316284, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0114848, standardError=0.00194718, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100235720_T_G', pValueMantissa=4.255000114440918, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0683751, standardError=0.0337137, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100236254_A_G', pValueMantissa=1.8240000009536743, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.621107, standardError=0.263096, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100236529_C_T', pValueMantissa=1.5670000314712524, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00698791, standardError=0.00184852, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, 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is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100260869_C_T', pValueMantissa=4.205999851226807, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0082302, standardError=0.00287541, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100262992_C_T', pValueMantissa=1.5770000219345093, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011701, standardError=0.00370274, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100263055_G_C', pValueMantissa=1.3109999895095825, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0119039, standardError=0.00370431, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100263140_C_T', pValueMantissa=1.5770000219345093, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011701, standardError=0.00370274, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100263249_C_T', pValueMantissa=1.1790000200271606, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00711917, standardError=0.00184892, betaConditioned=None, standardErrorConditioned=None, r2Overall=None)], size=1001)]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "window_based_clumped_output = 'gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus'\n", - "\n", - "(\n", - " session.spark.read.parquet(ld_clumped_output)\n", - " .filter(f.size(f.col('qualityControls')) == 0)\n", - "# .show(1, False, True)\n", - " .select('locus', f.size(f.col('locus')).alias('size'))\n", - " .limit(1)\n", - " .collect()\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+---------------------+---------+---------+---------------+\n", + "| studyId|ldPopulationStructure|projectId|studyType|traitFromSource|\n", + "+--------------------+---------------------+---------+---------+---------------+\n", + "|FINNGEN_R9_K11_EN...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_H7_KER...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_H8_EXT...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_H7_RET...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_RHEUMA...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_H7_KER...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_HEIGHT...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_M13_SY...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_M13_DO...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_M13_PY...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "| FINNGEN_R9_GOUT_NOS| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_M13_FI...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_ALLERG...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_E4_DM2...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_G6_CER...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_L12_UR...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_AUTOIM...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_I9_HYP...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_M13_AR...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "|FINNGEN_R9_K11_AP...| [{fin, 1.0}]| FINNGEN| gwas| cicaful|\n", + "+--------------------+---------------------+---------+---------+---------------+\n", + "only showing top 20 rows\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "id": "e242acdd", - "metadata": {}, - "outputs": [], - "source": [] + "name": "stderr", + "output_type": "stream", + "text": [ + "23/10/13 11:55:34 WARN GhfsStorageStatistics: Detected potential high latency for operation op_open. latencyMs=676; previousMaxLatencyMs=470; operationCount=333148; context=gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/ld_index/chromosome=5/part-00076-ff42773a-494c-46d2-bc22-322062b5e715.c000.snappy.parquet\n", + "23/10/13 12:39:11 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=279; previousMaxLatencyMs=243; operationCount=140; context=gs://ot-team/dsuveges/finngen/2023.10.13_ld_clumped_w_locus/_temporary/0/_temporary/attempt_202310131239103300432709600941830_0086_m_000000_80535/part-00000-17d449c4-f0c3-4617-b378-b74b864ab64a-c000.snappy.parquet\n", + "23/10/13 12:39:11 WARN GhfsStorageStatistics: Detected potential high latency for operation op_create. latencyMs=335; previousMaxLatencyMs=279; operationCount=140; context=gs://ot-team/dsuveges/finngen/2023.10.13_ld_clumped_w_locus/_temporary/0/_temporary/attempt_202310131239106417331548307580589_0086_m_000027_80562/part-00027-17d449c4-f0c3-4617-b378-b74b864ab64a-c000.snappy.parquet\n", + " \r" + ] } - ], - "metadata": { - "gist": { - "data": { - "description": "GCS/dsuveges/PICS/2023.10.06 - PICS FINNGEN from top to bottom.ipynb", - "public": false - }, - "id": "" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + ], + "source": [ + "ld_clumped_output = \"gs://ot-team/dsuveges/finngen/2023.10.13_ld_clumped_w_locus\"\n", + "\n", + "studies_df = (\n", + " session.spark.read.parquet(window_based_clumped_output)\n", + " # Generating a list of study identifiers:\n", + " .select(\"studyId\")\n", + " .distinct()\n", + " # Adding fabricated values required to parse as gwas catalog study:\n", + " .select(\n", + " \"studyId\",\n", + " StudyIndex.aggregate_and_map_ancestries(\n", + " f.array(\n", + " f.struct(\n", + " f.lit(\"Finnish\").alias(\"ancestry\"),\n", + " f.lit(100).cast(\"long\").alias(\"sampleSize\")\n", + " )\n", + " )\n", + " ).alias(\"ldPopulationStructure\"),\n", + " f.lit(\"FINNGEN\").alias(\"projectId\"),\n", + " f.lit(\"gwas\").alias(\"studyType\"),\n", + " f.lit(\"cicaful\").alias(\"traitFromSource\")\n", + " )\n", + ")\n", + "\n", + "study_index = (\n", + " StudyIndex(\n", + " _df=studies_df,\n", + " _schema=StudyIndex.get_schema()\n", + " )\n", + ")\n", + "\n", + "study_index.df.show()\n", + "\n", + "# Loading ld index:\n", + "ld_index = LDIndex.from_parquet(session, ld_index_path)\n", + "\n", + "(\n", + " # To annotate study/locus, study level info and ld panel is needed:\n", + " LDAnnotator.ld_annotate(\n", + " StudyLocus.from_parquet(session, window_based_clumped_output),\n", + " study_index,\n", + " ld_index\n", + " )\n", + " # Clumping linked study-loci together:\n", + " .clump()\n", + " .df.write.mode(\"overwrite\").parquet(ld_clumped_output)\n", + ")\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f637e3c0", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-13T10:43:26.766382Z", + "start_time": "2023-10-13T10:43:25.980232Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------------------------------------------------+-----+\n", + "|qualityControls |count|\n", + "+--------------------------------------------------------------+-----+\n", + "|[Variant not found in LD reference] |4607 |\n", + "|[] |13813|\n", + "|[Explained by a more significant variant in high LD (clumped)]|585 |\n", + "+--------------------------------------------------------------+-----+\n", + "\n" + ] + } + ], + "source": [ + "(\n", + " session.spark.read.parquet(ld_clumped_output)\n", + " .groupBy(\"qualityControls\")\n", + " .count()\n", + "# .show(1, False, True)\n", + " .show(truncate=False)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "5bf42196", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-13T12:55:39.212186Z", + "start_time": "2023-10-13T12:55:38.131092Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" + { + "data": { + "text/plain": [ + "[Row(locus=[Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99764322_T_C', pValueMantissa=2.3949999809265137, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0201963, standardError=0.00665169, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99764860_G_A', pValueMantissa=1.0499999523162842, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00813303, standardError=0.00248194, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99765280_C_T', pValueMantissa=7.499000072479248, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0147615, standardError=0.00437943, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99765335_A_G', pValueMantissa=1.2640000581741333, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107816, standardError=0.00189456, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99766311_C_T', pValueMantissa=1.0579999685287476, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.010837, standardError=0.00189422, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99766702_A_G', pValueMantissa=1.2059999704360962, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107966, standardError=0.00189453, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99766923_T_C', pValueMantissa=1.3300000429153442, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010765, standardError=0.00189451, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99767090_TTTG_T', pValueMantissa=1.2740000486373901, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107778, standardError=0.00189432, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99768341_T_C', pValueMantissa=1.2289999723434448, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107887, standardError=0.00189422, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99768718_A_G', pValueMantissa=1.2649999856948853, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107795, standardError=0.0018942, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99768993_G_A', pValueMantissa=1.1629999876022339, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108066, standardError=0.00189423, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99769297_T_C', pValueMantissa=1.2929999828338623, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0113037, standardError=0.00186275, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99769386_C_T', pValueMantissa=1.7280000448226929, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00670209, standardError=0.00281514, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99769548_A_G', pValueMantissa=1.7280000448226929, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0067021, standardError=0.00281514, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99769607_C_T', pValueMantissa=1.2710000276565552, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107775, standardError=0.00189416, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99770233_A_AT', pValueMantissa=1.1360000371932983, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108149, standardError=0.00189434, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99771038_C_A', pValueMantissa=1.1039999723434448, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0108218, standardError=0.00189392, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99771332_T_C', pValueMantissa=1.284999966621399, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00972726, standardError=0.00390986, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99771548_C_T', pValueMantissa=1.1369999647140503, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108168, standardError=0.00189473, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99774249_G_A', pValueMantissa=1.0410000085830688, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00813435, standardError=0.00248058, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99774476_AT_A', pValueMantissa=1.1540000438690186, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0108061, standardError=0.0018937, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99777761_G_A', pValueMantissa=2.9170000553131104, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0857961, standardError=0.0393337, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99777984_T_G', pValueMantissa=9.097999572753906, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108749, standardError=0.00189236, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99778063_G_A', pValueMantissa=1.2730000019073486, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00887632, standardError=0.00203368, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99778821_G_A', pValueMantissa=1.6679999828338623, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00682868, standardError=0.00285271, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99779613_C_G', pValueMantissa=7.630000114440918, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0109299, standardError=0.00189215, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99781296_A_G', pValueMantissa=8.560999870300293, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.010892, standardError=0.00189195, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99781822_G_A', pValueMantissa=1.4390000104904175, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00689344, standardError=0.00281655, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99781826_A_G', pValueMantissa=7.65500020980835, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0109284, standardError=0.00189207, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99782821_C_T', pValueMantissa=1.3680000305175781, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00573303, standardError=0.00232523, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99783206_A_G', pValueMantissa=7.632999897003174, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0109293, standardError=0.00189206, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99783290_A_C', pValueMantissa=8.821999549865723, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108841, standardError=0.00189224, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99784269_C_T', pValueMantissa=9.149999618530273, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0108721, standardError=0.00189218, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99784331_G_A', pValueMantissa=4.435999870300293, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0327234, standardError=0.0162747, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99784751_C_A', pValueMantissa=3.0409998893737793, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.149822, standardError=0.0692119, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99785581_A_G', pValueMantissa=7.164000034332275, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0113888, standardError=0.00423514, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99786875_C_T', pValueMantissa=5.257999897003174, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0110515, standardError=0.00189278, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), 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standardError=0.0076615, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99819195_T_G', pValueMantissa=6.382999897003174, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0124767, standardError=0.0045745, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99820908_A_G', pValueMantissa=1.4980000257492065, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00696602, standardError=0.0028633, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99821086_GA_G', pValueMantissa=3.259999990463257, pValueExponent=-5, pValueMantissaConditioned=None, 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pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0115151, standardError=0.00192136, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99822696_T_C', pValueMantissa=7.763000011444092, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.162918, standardError=0.0611962, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99823047_A_G', pValueMantissa=7.198999881744385, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0110742, standardError=0.0019139, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99823280_T_C', 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posteriorProbability=None, variantId='10_99825835_C_T', pValueMantissa=6.0320000648498535, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0111299, standardError=0.00191372, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99827260_C_T', pValueMantissa=3.6500000953674316, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0489284, standardError=0.0233964, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99828083_C_T', pValueMantissa=3.938999891281128, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0959414, standardError=0.0465714, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99828402_T_A', pValueMantissa=9.142000198364258, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0211518, standardError=0.00637931, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99828407_A_T', pValueMantissa=9.133999824523926, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0211533, standardError=0.0063793, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99829453_A_AT', pValueMantissa=6.083000183105469, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0111253, standardError=0.00191338, betaConditioned=None, 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standardError=0.00753787, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99832187_T_C', pValueMantissa=4.421000003814697, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0112213, standardError=0.0019124, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99832400_AAC_A', pValueMantissa=6.706999778747559, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0110021, standardError=0.00189753, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99832402_C_CTGTGTGT', pValueMantissa=6.710999965667725, pValueExponent=-9, pValueMantissaConditioned=None, 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pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.260334, standardError=0.0961939, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99834214_T_C', pValueMantissa=1.784000039100647, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0474718, standardError=0.0200386, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99835361_G_A', pValueMantissa=2.0769999027252197, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0595141, standardError=0.0257408, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99841479_C_T', pValueMantissa=6.188000202178955, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0104861, standardError=0.00193711, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99842816_A_G', pValueMantissa=8.645000457763672, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00723726, standardError=0.00184352, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99844024_C_G', pValueMantissa=2.3450000286102295, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107969, standardError=0.00193341, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99844450_C_T', pValueMantissa=2.874000072479248, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0107269, standardError=0.00193316, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99845178_C_T', pValueMantissa=4.0320000648498535, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0656125, standardError=0.0319983, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99845936_A_G', pValueMantissa=3.069999933242798, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00675859, standardError=0.00312767, betaConditioned=None, standardErrorConditioned=None, 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betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99853411_T_TG', pValueMantissa=9.414999961853027, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011087, standardError=0.00193121, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99856822_A_G', pValueMantissa=2.7119998931884766, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0114876, standardError=0.00193129, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99856987_G_C', pValueMantissa=5.239999771118164, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010358, standardError=0.001903, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99857429_C_T', pValueMantissa=1.718000054359436, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0116617, standardError=0.00193637, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99857502_A_G', pValueMantissa=6.296000003814697, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010296, standardError=0.00190308, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99857670_G_A', pValueMantissa=2.7939999103546143, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011478, standardError=0.00193126, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99857979_C_T', pValueMantissa=2.5840001106262207, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00775769, standardError=0.00184386, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99858035_C_T', pValueMantissa=2.7939999103546143, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011478, standardError=0.00193127, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99858181_G_C', pValueMantissa=2.7920000553131104, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0114783, standardError=0.00193127, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99858624_T_C', pValueMantissa=6.314000129699707, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0103011, standardError=0.0019042, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99859267_C_A', pValueMantissa=1.5019999742507935, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0239274, standardError=0.00753783, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99859558_G_A', pValueMantissa=6.2779998779296875, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0321283, standardError=0.00593792, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99859674_G_A', pValueMantissa=1.1050000190734863, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100907, standardError=0.00190082, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99861014_C_G', pValueMantissa=1.350000023841858, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0080568, standardError=0.00185135, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99861191_A_G', pValueMantissa=2.062000036239624, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00788332, standardError=0.00185141, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99861743_A_C', pValueMantissa=1.621999979019165, pValueExponent=-6, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00893977, standardError=0.00186417, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99861975_TTGG_T', pValueMantissa=1.5509999990463257, pValueExponent=-6, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00895366, standardError=0.00186357, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99862766_C_T', pValueMantissa=1.7289999723434448, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00993376, standardError=0.0019007, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99863009_T_C', pValueMantissa=1.3009999990463257, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0535743, standardError=0.0215732, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99863999_T_C', pValueMantissa=1.4730000495910645, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0137073, standardError=0.00361145, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99864093_A_C', pValueMantissa=5.741000175476074, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0322238, standardError=0.00593803, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99864115_G_T', pValueMantissa=7.295000076293945, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.150642, standardError=0.0445916, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99865012_T_C', pValueMantissa=2.450000047683716, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00781456, standardError=0.00185205, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99865013_G_A', pValueMantissa=1.7259999513626099, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0159887, standardError=0.00510203, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99865261_T_C', pValueMantissa=1.4459999799728394, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00999453, standardError=0.00190036, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99865337_G_GAAGA', pValueMantissa=1.3170000314712524, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00806712, standardError=0.00185143, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99865513_C_G', pValueMantissa=1.3250000476837158, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0080656, standardError=0.00185165, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99866792_C_T', pValueMantissa=1.4500000476837158, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00803164, standardError=0.00185225, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99867170_C_T', pValueMantissa=8.121000289916992, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0111182, standardError=0.00192825, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99867197_T_C', pValueMantissa=2.680999994277954, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0171717, standardError=0.00775509, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99867487_C_G', pValueMantissa=2.8310000896453857, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.359987, standardError=0.164156, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99867528_G_T', pValueMantissa=2.8389999866485596, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0077156, standardError=0.0018432, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99868259_G_A', pValueMantissa=2.265000104904175, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.119984, standardError=0.0392983, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99868412_T_G', pValueMantissa=1.6779999732971191, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00994383, standardError=0.00190064, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99868443_A_G', pValueMantissa=1.4809999465942383, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00801978, standardError=0.0018515, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99868585_TC_T', pValueMantissa=1.437999963760376, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0080333, standardError=0.00185188, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99868691_T_G', pValueMantissa=1.4730000495910645, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00802444, standardError=0.00185208, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99869742_G_A', pValueMantissa=4.677000045776367, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0101608, standardError=0.00185986, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99870037_G_A', pValueMantissa=3.996999979019165, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0939625, standardError=0.0326443, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99870234_G_A', pValueMantissa=2.5759999752044678, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0270256, standardError=0.00896575, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99871164_A_T', pValueMantissa=9.444999694824219, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0101485, standardError=0.0019015, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99871248_A_C', pValueMantissa=4.265999794006348, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011332, standardError=0.00192934, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99871256_C_T', pValueMantissa=1.0980000495910645, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100932, standardError=0.00190087, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99871596_T_C', pValueMantissa=1.097000002861023, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100928, standardError=0.00190078, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99871838_G_C', pValueMantissa=4.270999908447266, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0113316, standardError=0.00192933, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99871912_T_C', pValueMantissa=1.0989999771118164, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100928, standardError=0.00190087, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99872008_T_C', pValueMantissa=1.0989999771118164, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100927, standardError=0.00190087, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99872112_C_T', pValueMantissa=1.093000054359436, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100944, standardError=0.00190084, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99872802_A_G', pValueMantissa=2.828000068664551, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0103237, standardError=0.00185953, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873074_C_T', pValueMantissa=4.716000080108643, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112996, standardError=0.00192927, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873158_C_T', pValueMantissa=1.1759999990463257, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010069, standardError=0.00190084, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873263_C_T', pValueMantissa=1.1440000534057617, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010071, standardError=0.00189938, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873471_G_A', pValueMantissa=6.283999919891357, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0723658, standardError=0.0264824, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873553_C_T', pValueMantissa=1.8109999895095825, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0122764, standardError=0.00286365, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99873602_C_T', pValueMantissa=3.7880001068115234, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102286, standardError=0.00185958, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99874022_C_T', pValueMantissa=3.7920000553131104, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102286, standardError=0.00185966, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99874186_C_CT', pValueMantissa=1.2230000495910645, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100561, standardError=0.00190097, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875195_T_C', pValueMantissa=4.964000225067139, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112867, standardError=0.00192988, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875202_G_T', pValueMantissa=1.312999963760376, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100339, standardError=0.00190142, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875320_C_A', pValueMantissa=1.2369999885559082, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100535, standardError=0.00190119, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875381_T_C', pValueMantissa=1.2410000562667847, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100525, standardError=0.00190121, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875452_C_T', pValueMantissa=2.5290000438690186, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0135056, standardError=0.00447237, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875704_T_A', pValueMantissa=2.9600000381469727, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00410906, standardError=0.00188882, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99875760_A_AG', pValueMantissa=1.2430000305175781, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010052, standardError=0.00190125, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99876031_G_GT', pValueMantissa=1.3940000534057617, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0240895, standardError=0.00753787, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99876891_T_C', pValueMantissa=3.986999988555908, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102146, standardError=0.0018601, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99878758_G_A', pValueMantissa=2.763000011444092, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.151851, standardError=0.0689453, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99878850_C_G', pValueMantissa=4.1519999504089355, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102024, standardError=0.0018603, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99880039_G_A', pValueMantissa=4.0960001945495605, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102077, standardError=0.00186045, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99880120_A_C', pValueMantissa=4.75, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0113026, standardError=0.00193019, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99880736_G_C', pValueMantissa=2.4639999866485596, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.11912, standardError=0.039343, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99882938_G_A', pValueMantissa=4.218999862670898, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102002, standardError=0.00186086, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99883489_C_T', pValueMantissa=7.486000061035156, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0140267, standardError=0.00524475, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99884754_C_T', pValueMantissa=2.5510001182556152, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011665, standardError=0.00195783, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99885306_C_T', pValueMantissa=7.618000030517578, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0122854, standardError=0.00460381, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99885741_T_C', pValueMantissa=4.251999855041504, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0101963, standardError=0.00186062, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99887218_A_G', pValueMantissa=1.3589999675750732, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0100249, standardError=0.00190197, betaConditioned=None, 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standardError=0.00190207, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99890983_C_T', pValueMantissa=1.3830000162124634, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.01002, standardError=0.00190223, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99891336_A_G', pValueMantissa=3.815000057220459, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102374, standardError=0.00186162, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99891408_C_T', pValueMantissa=1.6360000371932983, pValueExponent=-7, pValueMantissaConditioned=None, 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pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.177641, standardError=0.083791, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99893179_G_C', pValueMantissa=5.1570000648498535, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112781, standardError=0.00193051, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99893428_C_T', pValueMantissa=4.14300012588501, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0102059, standardError=0.00186081, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99894094_G_C', pValueMantissa=4.321000099182129, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0249459, standardError=0.00708754, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99894492_G_T', pValueMantissa=8.531999588012695, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0101123, standardError=0.00188821, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99894565_A_G', pValueMantissa=4.785999774932861, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00954874, standardError=0.00273411, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99894939_T_C', pValueMantissa=8.916999816894531, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00988844, standardError=0.00184916, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99895167_T_C', pValueMantissa=4.169000148773193, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00948941, standardError=0.00187497, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99895555_C_T', pValueMantissa=4.659999847412109, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0340557, standardError=0.0120355, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99896225_G_A', pValueMantissa=1.0920000076293945, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110344, standardError=0.0019305, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99896692_A_G', pValueMantissa=3.4130001068115234, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110917, standardError=0.00187665, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99896979_T_G', pValueMantissa=3.2119998931884766, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0401381, standardError=0.0187308, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99897831_T_C', pValueMantissa=1.9040000438690186, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.14616, standardError=0.0623359, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99898123_G_A', pValueMantissa=1.1069999933242798, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110304, standardError=0.00193059, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99898484_T_A', pValueMantissa=1.1139999628067017, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110285, standardError=0.00193061, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99898828_T_C', pValueMantissa=3.2699999809265137, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0111045, standardError=0.00187657, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99899677_C_CTTGT', pValueMantissa=3.51200008392334, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110858, standardError=0.00187714, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99900290_T_G', pValueMantissa=7.426000118255615, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0109405, standardError=0.00189249, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99900292_A_AT', pValueMantissa=1.1360000371932983, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00988613, standardError=0.00390553, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99900500_A_C', pValueMantissa=2.2009999752044678, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0412365, standardError=0.0180062, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99901642_T_C', pValueMantissa=5.086999893188477, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.113465, standardError=0.0405014, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99901731_C_T', pValueMantissa=1.5379999876022339, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0140283, standardError=0.00442901, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99902496_G_A', pValueMantissa=1.2289999723434448, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0224604, standardError=0.00897057, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99902603_C_T', pValueMantissa=4.140999794006348, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0854585, standardError=0.0419028, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99902712_G_C', pValueMantissa=1.1929999589920044, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110082, standardError=0.00193099, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99902720_C_G', pValueMantissa=3.492000102996826, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011087, standardError=0.00187705, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99902757_G_A', pValueMantissa=4.366000175476074, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110198, standardError=0.00187741, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99903008_A_C', pValueMantissa=3.4590001106262207, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.01109, standardError=0.00187705, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99903292_G_A', pValueMantissa=3.4660000801086426, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110895, standardError=0.00187709, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99903681_G_T', pValueMantissa=6.072000026702881, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110979, standardError=0.00190858, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99904413_A_G', pValueMantissa=3.4719998836517334, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110894, standardError=0.00187714, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99904499_G_A', pValueMantissa=1.1369999647140503, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110237, standardError=0.00193097, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99904588_G_A', pValueMantissa=9.54800033569336, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0142134, standardError=0.00430252, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99905340_C_T', pValueMantissa=1.184000015258789, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110128, standardError=0.00193138, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99905534_G_C', pValueMantissa=1.2209999561309814, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0379664, standardError=0.0151507, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99905541_A_G', pValueMantissa=3.575000047683716, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110805, standardError=0.00187717, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99905693_A_G', pValueMantissa=3.490999937057495, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110881, standardError=0.00187721, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99906210_T_C', pValueMantissa=3.5, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110874, standardError=0.00187724, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99906644_G_A', pValueMantissa=1.0570000410079956, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110495, standardError=0.00193126, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99906718_T_C', pValueMantissa=3.4709999561309814, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110901, standardError=0.00187726, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99908035_C_T', pValueMantissa=7.296000003814697, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0148557, standardError=0.00439751, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99908057_A_G', pValueMantissa=7.160999774932861, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0149937, standardError=0.00443164, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99908221_T_C', pValueMantissa=3.878000020980835, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110578, standardError=0.0018776, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99908813_T_C', pValueMantissa=5.124000072479248, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112768, standardError=0.00192994, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99909229_T_C', pValueMantissa=8.24899959564209, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00998922, standardError=0.0018631, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99909454_A_C', pValueMantissa=7.598999977111816, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0109359, standardError=0.00189297, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99910263_C_T', pValueMantissa=5.330999851226807, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0111417, standardError=0.00190898, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99910368_T_C', pValueMantissa=3.302999973297119, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011106, standardError=0.00187736, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99910580_T_G', pValueMantissa=1.1369999647140503, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0110251, standardError=0.00193122, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99911338_T_C', pValueMantissa=3.4579999446868896, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011092, standardError=0.00187738, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99911811_G_A', pValueMantissa=2.575000047683716, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.118741, standardError=0.0393917, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99912354_A_G', pValueMantissa=5.099999904632568, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112784, standardError=0.00192997, betaConditioned=None, 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pValueExponentConditioned=None, beta=-0.0111042, standardError=0.00187747, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99915884_T_C', pValueMantissa=5.684999942779541, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0109318, standardError=0.00187645, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99915918_T_G', pValueMantissa=1.281000018119812, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00983696, standardError=0.00186253, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99916127_T_C', pValueMantissa=5.081999778747559, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112798, standardError=0.00193, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99916503_A_T', pValueMantissa=6.974999904632568, pValueExponent=-5, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00734427, standardError=0.00184662, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99916552_T_C', pValueMantissa=2.9839999675750732, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0122137, standardError=0.00562257, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99917731_C_T', pValueMantissa=5.0289998054504395, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112837, standardError=0.0019301, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99919106_C_T', pValueMantissa=2.8299999237060547, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0131403, standardError=0.00440112, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99920364_C_T', pValueMantissa=4.159999847412109, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0219416, standardError=0.0076565, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99921641_GC_G', pValueMantissa=5.14900016784668, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112777, standardError=0.00193036, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99922010_GAAGA_G', pValueMantissa=3.134999990463257, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0255599, standardError=0.00709237, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99922205_A_G', pValueMantissa=8.65999984741211, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00997687, standardError=0.00186386, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99922419_C_T', pValueMantissa=5.785999774932861, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112417, standardError=0.00193063, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99923392_A_AAAT', pValueMantissa=7.868000030517578, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010012, standardError=0.00186439, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99924208_C_T', pValueMantissa=6.610000133514404, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0112121, standardError=0.00193293, betaConditioned=None, 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standardError=0.0664409, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99925030_G_A', pValueMantissa=7.132999897003174, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0123943, standardError=0.00460661, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99925171_T_C', pValueMantissa=2.7660000324249268, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0131669, standardError=0.00439981, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99925527_C_T', pValueMantissa=3.3429999351501465, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.011112, standardError=0.00187899, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99926307_T_C', pValueMantissa=1.2309999465942383, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00985993, standardError=0.0018643, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99926344_G_A', pValueMantissa=7.146999835968018, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0123917, standardError=0.00460668, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99928121_C_T', pValueMantissa=2.6080000400543213, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0237748, standardError=0.0106854, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99928961_T_A', pValueMantissa=7.409999847412109, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0092492, standardError=0.00274137, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99929621_G_C', pValueMantissa=2.4159998893737793, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00965232, standardError=0.00186913, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99930293_C_T', pValueMantissa=2.371000051498413, pValueExponent=-6, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00891412, standardError=0.00188903, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99930622_A_G', pValueMantissa=2.9200000762939453, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0122713, standardError=0.0019469, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99930880_C_G', pValueMantissa=6.189000129699707, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0126631, standardError=0.00193652, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99931871_G_T', pValueMantissa=1.0870000123977661, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0132779, standardError=0.00195421, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99932501_G_T', pValueMantissa=7.144999980926514, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0130514, standardError=0.0020025, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99932525_C_G', pValueMantissa=3.86299991607666, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0132541, standardError=0.00190924, betaConditioned=None, standardErrorConditioned=None, 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betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99933398_T_G', pValueMantissa=1.1490000486373901, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0132623, standardError=0.00195424, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99933499_T_C', pValueMantissa=7.710999965667725, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0130536, standardError=0.00190735, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99933515_AG_A', pValueMantissa=6.0929999351501465, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0130982, standardError=0.00200234, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99933811_A_C', pValueMantissa=3.0450000762939453, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0271544, standardError=0.009164, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99933841_A_G', pValueMantissa=7.820000171661377, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0130497, standardError=0.00190735, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99935601_G_A', pValueMantissa=6.01200008392334, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131034, standardError=0.00200253, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99935839_G_A', pValueMantissa=1.9040000438690186, pValueExponent=-6, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0088457, standardError=0.00185702, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99936985_A_C', pValueMantissa=8.661999702453613, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0130204, standardError=0.00190715, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99937543_C_A', pValueMantissa=6.139999866485596, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0130966, standardError=0.00200245, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99938738_G_A', pValueMantissa=1.0329999923706055, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0132869, standardError=0.00195344, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99939547_T_C', pValueMantissa=6.979000091552734, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0130948, standardError=0.00190939, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99939652_G_A', pValueMantissa=4.034999847412109, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0132546, standardError=0.00191099, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99940864_C_T', pValueMantissa=5.973999977111816, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131208, standardError=0.0020049, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99941429_ACCTCAGGGTTACC_A', pValueMantissa=4.370999813079834, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.187402, standardError=0.0929166, betaConditioned=None, 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variantId='10_99979375_G_A', pValueMantissa=3.937000036239624, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.13666, standardError=0.0663293, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99979565_C_T', pValueMantissa=4.479000091552734, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0275515, standardError=0.0137302, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99979592_G_A', pValueMantissa=3.3989999294281006, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133022, standardError=0.00191119, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99980479_A_C', pValueMantissa=7.7230000495910645, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0930047, standardError=0.0349125, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99980767_T_TG', pValueMantissa=3.1600000858306885, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133205, standardError=0.001911, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99981303_T_C', pValueMantissa=1.0260000228881836, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0129016, standardError=0.0019962, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), 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standardError=0.00191128, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99986015_T_C', pValueMantissa=1.0609999895095825, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.012891, standardError=0.00199615, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988026_G_T', pValueMantissa=7.709000110626221, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0122774, standardError=0.0046077, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988174_G_A', pValueMantissa=3.121999979019165, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133243, standardError=0.0019111, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988593_A_C', pValueMantissa=4.09499979019165, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0833533, standardError=0.0407789, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988730_T_C', pValueMantissa=3.1760001182556152, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133191, standardError=0.00191101, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988731_G_A', pValueMantissa=5.669000148773193, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0144037, standardError=0.00520681, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99988980_A_G', pValueMantissa=4.874000072479248, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131807, standardError=0.00200475, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99989148_T_C', pValueMantissa=4.875, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131806, standardError=0.00200475, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99989685_T_C', pValueMantissa=4.556000232696533, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0132006, standardError=0.00200472, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99989906_C_T', pValueMantissa=3.2639999389648438, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133133, standardError=0.00191121, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99990445_G_A', pValueMantissa=6.452000141143799, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.090219, standardError=0.0331213, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99990521_G_A', pValueMantissa=2.9019999504089355, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.013345, standardError=0.00191124, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99990640_T_A', pValueMantissa=1.5579999685287476, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0139907, standardError=0.00442234, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99992006_G_GA', pValueMantissa=3.0260000228881836, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133329, standardError=0.00191111, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99992015_T_C', pValueMantissa=3.5850000381469727, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.013291, standardError=0.00191165, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99992806_A_G', pValueMantissa=3.0290000438690186, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133327, standardError=0.00191112, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99993453_A_G', pValueMantissa=3.0329999923706055, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133323, standardError=0.00191113, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99993852_T_C', pValueMantissa=3.0360000133514404, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133322, standardError=0.00191113, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99994177_C_T', pValueMantissa=4.533999919891357, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0132032, standardError=0.00200489, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99994349_A_T', pValueMantissa=1.0379999876022339, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0287633, standardError=0.00422921, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99995001_T_C', pValueMantissa=3.2860000133514404, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133152, standardError=0.00191176, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99995032_G_C', pValueMantissa=4.89900016784668, pValueExponent=-11, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0131854, standardError=0.00200569, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99995714_T_C', pValueMantissa=3.374000072479248, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133048, standardError=0.00191128, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99995753_C_CTT', pValueMantissa=3.372999906539917, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133049, standardError=0.00191128, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99996207_C_G', pValueMantissa=3.384000062942505, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133041, standardError=0.00191129, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99997268_A_G', pValueMantissa=3.378999948501587, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133047, standardError=0.00191132, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99997438_CCA_C', pValueMantissa=6.603000164031982, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.109829, standardError=0.0404341, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99997720_A_G', pValueMantissa=2.815999984741211, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0133548, standardError=0.00191149, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99998079_C_CA', pValueMantissa=4.61299991607666, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0922906, standardError=0.0462786, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99998104_G_A', pValueMantissa=1.2979999780654907, pValueExponent=-6, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00911911, standardError=0.0018841, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_99999971_A_G', pValueMantissa=2.9570000171661377, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.030431, standardError=0.0102384, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100000235_C_T', pValueMantissa=5.6519999504089355, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.014492, standardError=0.00201036, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100000486_G_A', pValueMantissa=4.172999858856201, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.305198, standardError=0.149885, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100002038_G_A', pValueMantissa=3.7739999294281006, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0211624, standardError=0.0073064, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100002628_A_C', pValueMantissa=2.953000068664551, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0138093, standardError=0.00189258, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100004140_G_A', pValueMantissa=2.6459999084472656, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0994314, standardError=0.0448022, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100004827_A_C', pValueMantissa=6.945000171661377, pValueExponent=-14, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0142296, standardError=0.0019001, betaConditioned=None, 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standardError=0.02095, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100006780_C_T', pValueMantissa=5.067999839782715, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0145408, standardError=0.002013, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100007241_C_T', pValueMantissa=6.783999919891357, pValueExponent=-14, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0142505, standardError=0.0019021, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100007404_G_GC', pValueMantissa=1.3420000076293945, pValueExponent=-7, pValueMantissaConditioned=None, 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pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0134968, standardError=0.0018948, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100011477_C_G', pValueMantissa=2.444999933242798, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0148805, standardError=0.00203232, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100011685_CAAAT_C', pValueMantissa=4.139999866485596, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0101715, standardError=0.00185449, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100011812_A_T', pValueMantissa=1.2029999494552612, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0141145, standardError=0.00190314, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100013275_A_T', pValueMantissa=2.174999952316284, pValueExponent=-14, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0145924, standardError=0.00191005, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100013744_G_A', pValueMantissa=4.76800012588501, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.51719, standardError=0.261177, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, 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standardError=0.00218782, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100018116_C_T', pValueMantissa=2.197000026702881, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0145668, standardError=0.00207465, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100018135_G_C', pValueMantissa=1.4559999704360962, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.275534, standardError=0.0725377, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100018265_A_T', pValueMantissa=2.7190001010894775, pValueExponent=-6, pValueMantissaConditioned=None, 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pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0159723, standardError=0.00222609, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100022313_C_CT', pValueMantissa=6.171999931335449, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0504229, standardError=0.0184125, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100023134_T_G', pValueMantissa=1.3980000019073486, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0268617, standardError=0.0109298, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100024168_G_A', pValueMantissa=2.3359999656677246, pValueExponent=-6, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00880598, standardError=0.00186491, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100024869_C_T', pValueMantissa=3.556999921798706, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0162101, standardError=0.00222929, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100025927_T_TA', pValueMantissa=3.621999979019165, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.014426, standardError=0.00207534, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100026878_C_T', pValueMantissa=9.258999824523926, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0185461, standardError=0.00712673, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100027354_G_A', pValueMantissa=1.968000054359436, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.103617, standardError=0.0444235, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100027592_G_C', pValueMantissa=1.965999960899353, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.103629, standardError=0.0444216, betaConditioned=None, standardErrorConditioned=None, 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betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100030853_G_A', pValueMantissa=7.5980000495910645, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0158245, standardError=0.00220759, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100030993_G_A', pValueMantissa=3.437000036239624, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0224965, standardError=0.00768921, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100031808_C_T', pValueMantissa=5.959000110626221, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0212214, standardError=0.00771671, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100032684_A_C', pValueMantissa=5.459000110626221, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.014926, standardError=0.00206922, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100033239_T_A', pValueMantissa=1.3669999837875366, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0463194, standardError=0.0187842, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100033337_C_T', pValueMantissa=9.138999938964844, pValueExponent=-14, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0165264, standardError=0.00221749, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100033635_G_GT', pValueMantissa=1.0110000371932983, pValueExponent=-13, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0164902, standardError=0.00221658, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100035604_T_A', pValueMantissa=3.4110000133514404, pValueExponent=-12, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.01433, standardError=0.002059, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100037190_G_A', pValueMantissa=3.2070000171661377, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0120692, standardError=0.00203849, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100037544_C_T', pValueMantissa=1.6990000009536743, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00996527, standardError=0.00417483, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100037655_A_G', pValueMantissa=6.13100004196167, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0127317, standardError=0.00219016, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100038188_C_T', pValueMantissa=7.738999843597412, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0126728, standardError=0.00219478, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100038589_T_C', pValueMantissa=3.734999895095825, pValueExponent=-8, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.010628, standardError=0.00193133, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100038648_G_A', pValueMantissa=1.7230000495910645, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00994355, standardError=0.00417478, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100039544_T_C', pValueMantissa=5.021999835968018, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0126227, standardError=0.00202988, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100039545_G_T', pValueMantissa=1.5509999990463257, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0130939, standardError=0.00204582, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100039976_C_T', pValueMantissa=1.7209999561309814, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00994495, standardError=0.00417473, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100039997_T_C', pValueMantissa=5.01800012588501, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0126197, standardError=0.00202937, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100040770_T_G', pValueMantissa=3.059999942779541, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.120454, standardError=0.0557085, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100042338_T_C', pValueMantissa=2.61899995803833, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.189007, standardError=0.085007, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100042505_G_A', pValueMantissa=3.4860000610351562, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0101879, standardError=0.00482832, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100042848_C_CT', pValueMantissa=4.413000106811523, pValueExponent=-7, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0093599, standardError=0.00185336, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100043308_C_T', pValueMantissa=5.076000213623047, pValueExponent=-10, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0126162, standardError=0.00202939, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100044357_C_T', pValueMantissa=5.36299991607666, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0127797, standardError=0.00218999, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100044501_A_G', pValueMantissa=1.7130000591278076, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00995148, standardError=0.00417441, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, 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variantId='10_100136849_G_A', pValueMantissa=2.4240000247955322, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00682879, standardError=0.00186061, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100137626_G_C', pValueMantissa=4.564000129699707, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.133964, standardError=0.067025, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100139939_C_T', pValueMantissa=2.2850000858306885, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0168361, standardError=0.00739727, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100140109_A_G', pValueMantissa=8.161999702453613, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0116036, standardError=0.00201274, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100140433_T_C', pValueMantissa=8.460000038146973, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00745523, standardError=0.00283129, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100140822_C_T', pValueMantissa=4.0370001792907715, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00653853, standardError=0.00184827, betaConditioned=None, 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posteriorProbability=None, variantId='10_100162586_C_T', pValueMantissa=1.5570000410079956, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0117095, standardError=0.00370097, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100162599_A_G', pValueMantissa=1.7910000085830688, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0115545, standardError=0.0037, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100164449_A_T', pValueMantissa=8.289999961853027, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0343476, standardError=0.0102747, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), 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variantId='10_100171408_C_CTATT', pValueMantissa=1.8220000267028809, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0115357, standardError=0.00370003, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100171892_C_A', pValueMantissa=4.783999919891357, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0290312, standardError=0.0146713, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100171937_A_T', pValueMantissa=3.7090001106262207, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0264529, standardError=0.0126887, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100172580_C_A', pValueMantissa=1.8209999799728394, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0115368, standardError=0.00370004, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100172766_C_T', pValueMantissa=1.3320000171661377, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0118891, standardError=0.00370488, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100173406_G_C', pValueMantissa=3.993000030517578, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0276948, standardError=0.00962055, betaConditioned=None, standardErrorConditioned=None, 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variantId='10_100219439_G_A', pValueMantissa=1.3109999895095825, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0119039, standardError=0.00370431, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100219496_T_C', pValueMantissa=1.9490000009536743, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011459, standardError=0.003699, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100219653_G_A', pValueMantissa=8.52400016784668, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0210222, standardError=0.00799145, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, 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pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00814496, standardError=0.00287083, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100224846_CA_C', pValueMantissa=4.505000114440918, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00815457, standardError=0.00287084, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100225330_C_T', pValueMantissa=4.679999828338623, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00811972, standardError=0.0028709, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100225460_G_A', pValueMantissa=1.4520000219345093, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0138989, standardError=0.00568655, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100227298_T_G', pValueMantissa=4.505000114440918, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00815457, standardError=0.00287084, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100227436_A_C', pValueMantissa=2.1089999675750732, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.0204774, standardError=0.00887858, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, 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variantId='10_100242630_C_T', pValueMantissa=1.253999948501587, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011921, standardError=0.00369498, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100242920_TGA_T', pValueMantissa=4.275000095367432, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.136751, standardError=0.0674941, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100243390_G_A', pValueMantissa=8.79800033569336, pValueExponent=-9, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0252612, standardError=0.00439142, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, 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pValueExponentConditioned=None, beta=0.00834089, standardError=0.00287304, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100248412_C_G', pValueMantissa=1.4470000267028809, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00702484, standardError=0.00184863, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100248726_A_G', pValueMantissa=3.694999933242798, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00834082, standardError=0.00287304, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100249239_A_C', pValueMantissa=2.0339999198913574, 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variantId='10_100249705_T_C', pValueMantissa=1.472000002861023, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0117796, standardError=0.00370405, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100250941_T_C', pValueMantissa=1.8240000009536743, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.621107, standardError=0.263096, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100250976_T_A', pValueMantissa=6.709000110626221, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0690304, standardError=0.0254633, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100251390_T_A', pValueMantissa=2.9049999713897705, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0055723, standardError=0.00187139, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100251454_A_G', pValueMantissa=1.1790000200271606, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00712036, standardError=0.00184919, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100251790_C_T', pValueMantissa=3.497999906539917, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00593413, standardError=0.00281426, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), 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pValueExponentConditioned=None, beta=0.011701, standardError=0.00370274, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100256287_C_T', pValueMantissa=4.053999900817871, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00825485, standardError=0.00287235, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100256511_G_A', pValueMantissa=1.0700000524520874, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00604016, standardError=0.00184634, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100257612_C_T', pValueMantissa=1.9170000553131104, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0082026, standardError=0.00350204, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100258083_A_G', pValueMantissa=2.7039999961853027, pValueExponent=-2, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0635849, standardError=0.0287601, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100258094_C_T', pValueMantissa=1.5770000219345093, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011701, standardError=0.00370274, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100258332_C_T', pValueMantissa=3.9760000705718994, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0082729, standardError=0.00287251, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100260000_A_G', pValueMantissa=1.6239999532699585, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0116706, standardError=0.00370306, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100260586_A_G', pValueMantissa=3.8919999599456787, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.00829222, standardError=0.00287251, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100260869_C_T', pValueMantissa=4.205999851226807, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0082302, standardError=0.00287541, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100262992_C_T', pValueMantissa=1.5770000219345093, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011701, standardError=0.00370274, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100263055_G_C', pValueMantissa=1.3109999895095825, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.0119039, standardError=0.00370431, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100263140_C_T', pValueMantissa=1.5770000219345093, pValueExponent=-3, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=0.011701, standardError=0.00370274, betaConditioned=None, standardErrorConditioned=None, r2Overall=None), Row(is95CredibleSet=None, is99CredibleSet=None, logABF=None, posteriorProbability=None, variantId='10_100263249_C_T', pValueMantissa=1.1790000200271606, pValueExponent=-4, pValueMantissaConditioned=None, pValueExponentConditioned=None, beta=-0.00711917, standardError=0.00184892, betaConditioned=None, standardErrorConditioned=None, r2Overall=None)], size=1001)]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "window_based_clumped_output = \"gs://ot-team/dsuveges/finngen/2023.10.13_window_clumped_w_locus\"\n", + "\n", + "(\n", + " session.spark.read.parquet(ld_clumped_output)\n", + " .filter(f.size(f.col(\"qualityControls\")) == 0)\n", + "# .show(1, False, True)\n", + " .select(\"locus\", f.size(f.col(\"locus\")).alias(\"size\"))\n", + " .limit(1)\n", + " .collect()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e242acdd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "gist": { + "data": { + "description": "GCS/dsuveges/PICS/2023.10.06 - PICS FINNGEN from top to bottom.ipynb", + "public": false + }, + "id": "" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/notebooks/Mapping_EFO_finngen.ipynb b/notebooks/Mapping_EFO_finngen.ipynb new file mode 100644 index 000000000..1feaf890c --- /dev/null +++ b/notebooks/Mapping_EFO_finngen.ipynb @@ -0,0 +1,761 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mapping EFOs for the FinnGen study index using old study index from the previos prod" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook adds EFOs from previos prod version of study_index to the new FinnGen study_index using trait name as a matching key.\n", + "\n", + "The rsulting study index has 1542 rows with not null EFOs (out of 2408 rows).\n", + "\n", + "The new study index is saved here:\n", + "\"gs://genetics-portal-dev-analysis/yt4/study_index_finngen_with_efo\"" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your browser has been opened to visit:\n", + "\n", + " https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=764086051850-6qr4p6gpi6hn506pt8ejuq83di341hur.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8085%2F&scope=openid+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcloud-platform+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fsqlservice.login+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Faccounts.reauth&state=XHb8Uk43SsVjvFRqwgrX4Tgg2tTOHS&access_type=offline&code_challenge=OkiqDAkHXDGEgJQbX8r0ZYKfZ7gcgfXS8mfZc5a913Y&code_challenge_method=S256\n", + "\n", + "\n", + "Credentials saved to file: [/Users/yt4/.config/gcloud/application_default_credentials.json]\n", + "\n", + "These credentials will be used by any library that requests Application Default Credentials (ADC).\n", + "\n", + "Quota project \"open-targets-genetics-dev\" was added to ADC which can be used by Google client libraries for billing and quota. Note that some services may still bill the project owning the resource.\n" + ] + } + ], + "source": [ + "!gcloud auth application-default login" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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For SparkR, use setLogLevel(newLevel).\n", + "pip-installed Hail requires additional configuration options in Spark referring\n", + " to the path to the Hail Python module directory HAIL_DIR,\n", + " e.g. /path/to/python/site-packages/hail:\n", + " spark.jars=HAIL_DIR/backend/hail-all-spark.jar\n", + " spark.driver.extraClassPath=HAIL_DIR/backend/hail-all-spark.jar\n", + " spark.executor.extraClassPath=./hail-all-spark.jarRunning on Apache Spark version 3.3.4\n", + "SparkUI available at http://192.168.0.232:4040\n", + "Welcome to\n", + " __ __ <>__\n", + " / /_/ /__ __/ /\n", + " / __ / _ `/ / /\n", + " /_/ /_/\\_,_/_/_/ version 0.2.127-bb535cd096c5\n", + "LOGGING: writing to /dev/null\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "import hail as hl\n", + "import pandas as pd\n", + "\n", + "from gentropy.common.session import Session\n", + "from gentropy.dataset.study_index import StudyIndex\n", + "\n", + "pd.set_option(\"display.max_colwidth\", None)\n", + "pd.set_option(\"display.expand_frame_repr\", False)\n", + "\n", + "hail_dir = os.path.dirname(hl.__file__)\n", + "session = Session(hail_home=hail_dir, start_hail=True, extended_spark_conf={\"spark.driver.memory\": \"12g\",\n", + " \"spark.kryoserializer.buffer.max\": \"500m\",\"spark.driver.maxResultSize\":\"3g\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "path_si=\"gs://genetics_etl_python_playground/releases/24.03/study_index/finngen/study_index\"\n", + "path_si_old=\"gs://genetics-portal-dev-analysis/yt4/study_index.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "si_old=session.spark.read.csv(path_si_old, header=True,sep=\"\\t\")\n", + "si_new=StudyIndex.from_parquet(session=session, path=path_si)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+-------------------+--------------------+-------+---------+-------------+----+----------+----------+-----------+---------+------------+--------------+-------+--------------------+-----------------+--------------------+\n", + "| study_id| ancestry_initial|ancestry_replication|n_cases|n_initial|n_replication|pmid|pub_author| pub_date|pub_journal|pub_title|has_sumstats|num_assoc_loci| source| trait_reported| trait_efos| trait_category|\n", + "+--------------------+-------------------+--------------------+-------+---------+-------------+----+----------+----------+-----------+---------+------------+--------------+-------+--------------------+-----------------+--------------------+\n", + "|FINNGEN_R6_M13_MU...|['European=253458']| []| 108.0| 253458| 0.0|null|FINNGEN_R6|2022-01-24| null| null| True| 0|FINNGEN|Multifocal fibros...|['MONDO_0009230']|immune system dis...|\n", + "|FINNGEN_R6_M13_MU...|['European=199528']| []| 1804.0| 199528| 0.0|null|FINNGEN_R6|2022-01-24| null| null| True| 0|FINNGEN|Disorders of muscles| ['EFO_0002970']|musculoskeletal o...|\n", + "|FINNGEN_R6_M13_MU...|['European=197821']| []| 97.0| 197821| 0.0|null|FINNGEN_R6|2022-01-24| null| null| True| 0|FINNGEN|\"\"\"Muscle wasting...| ['EFO_0009851']| biological process|\n", + "|FINNGEN_R6_M13_MU...|['European=198253']| []| 529.0| 198253| 0.0|null|FINNGEN_R6|2022-01-24| null| null| True| 0|FINNGEN|Other specified d...| ['EFO_0002970']|musculoskeletal o...|\n", + "|FINNGEN_R6_M13_MU...|['European=198179']| []| 455.0| 198179| 0.0|null|FINNGEN_R6|2022-01-24| null| null| True| 1|FINNGEN| Muscle strain| ['EFO_0010686']|injury, poisoning...|\n", + "+--------------------+-------------------+--------------------+-------+---------+-------------+----+----------+----------+-----------+---------+------------+--------------+-------+--------------------+-----------------+--------------------+\n", + "only showing top 5 rows\n", + "\n" + ] + } + ], + "source": [ + "si_old.show(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "| studyId| projectId|studyType| traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|publicationTitle|publicationFirstAuthor|publicationDate|publicationJournal|backgroundTraitFromSourceMappedIds| initialSampleSize|nCases|nControls|nSamples| cohorts|ldPopulationStructure| discoverySamples|replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|\n", + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Actinomycosis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 101| 363227| 363328|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Amoebiasis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 160| 367214| 367374|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas|Anogenital herpes...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 1986| 400197| 402183|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Aspergillosis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 211| 403213| 403424|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas|Atypical virus in...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 282| 409849| 410131|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "only showing top 5 rows\n", + "\n" + ] + } + ], + "source": [ + "si_new_df=si_new.df\n", + "si_new_df.show(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "57246\n", + "2408\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+-----------------+\n", + "| trait_reported| trait_efos|\n", + "+--------------------+-----------------+\n", + "|Multifocal fibros...|['MONDO_0009230']|\n", + "|Disorders of muscles| ['EFO_0002970']|\n", + "|\"\"\"Muscle wasting...| ['EFO_0009851']|\n", + "|Other specified d...| ['EFO_0002970']|\n", + "| Muscle strain| ['EFO_0010686']|\n", + "+--------------------+-----------------+\n", + "only showing top 5 rows\n", + "\n" + ] + } + ], + "source": [ + "si_old=si_old.select(\"trait_reported\",\"trait_efos\")\n", + "si_old.show(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql.functions import lower\n", + "\n", + "si_old = si_old.withColumn(\"trait_reported_low\", lower(si_old[\"trait_reported\"])).select(\"trait_reported_low\",\"trait_efos\")\n", + "si_new_df= si_new_df.withColumn(\"trait_reported_low\", lower(si_new_df[\"traitFromSource\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "2408" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "si_old = si_old.dropDuplicates([\"trait_reported_low\"])\n", + "joined_df = si_new_df.join(si_old, \"trait_reported_low\", how=\"left\")\n", + "joined_df.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+--------------------+\n", + "| trait_reported_low| studyId| projectId|studyType| traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|publicationTitle|publicationFirstAuthor|publicationDate|publicationJournal|backgroundTraitFromSourceMappedIds| initialSampleSize|nCases|nControls|nSamples| cohorts|ldPopulationStructure| discoverySamples|replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats| trait_efos|\n", + "+--------------------+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+--------------------+\n", + "| actinomycosis|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Actinomycosis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 101| 363227| 363328|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| null|\n", + "| amoebiasis|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Amoebiasis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 160| 367214| 367374|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['EFO_0007144']|\n", + "|anogenital herpes...|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas|Anogenital herpes...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 1986| 400197| 402183|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['EFO_0007282']|\n", + "| aspergillosis|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Aspergillosis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 211| 403213| 403424|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['EFO_0007157']|\n", + "|atypical virus in...|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas|Atypical virus in...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 282| 409849| 410131|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['MONDO_0024318']|\n", + "|bacterial infecti...|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Bacterial infecti...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 20226| 363227| 383453|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| null|\n", + "|bacterial, viral ...|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Bacterial, viral ...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2852| 409329| 412181|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| null|\n", + "|other bacterial i...|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Other bacterial i...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 6145| 367214| 373359|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['EFO_0000771']|\n", + "| candidiasis|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas| Candidiasis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 4306| 403213| 407519|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['MONDO_0002026']|\n", + "|other sexually tr...|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas|Other sexually tr...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2186| 400197| 402383|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|['MONDO_0021681',...|\n", + "| cholera|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas| Cholera| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 1385| 367214| 368599|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['EFO_1001235']|\n", + "|dengue fever [cla...|FINNGEN_R10_AB1_D...|FINNGEN_R10| gwas|Dengue fever [cla...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 53| 409137| 409190|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| null|\n", + "| dermatophytosis|FINNGEN_R10_AB1_D...|FINNGEN_R10| gwas| Dermatophytosis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 3921| 403213| 407134|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['MONDO_0004678']|\n", + "| early syphilis|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Early syphilis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 308| 400197| 400505|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['EFO_0007504']|\n", + "|infectious mononu...| FINNGEN_R10_AB1_EBV|FINNGEN_R10| gwas|Infectious mononu...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2979| 400974| 403953|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['EFO_0007326']|\n", + "| enterobiasis|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Enterobiasis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 112| 411658| 411770|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| null|\n", + "| erysipelas|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Erysipelas| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 22261| 363227| 385488|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['EFO_1001462']|\n", + "|diarrhoea and gas...|FINNGEN_R10_AB1_G...|FINNGEN_R10| gwas|Diarrhoea and gas...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 32210| 367214| 399424|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['MONDO_0045031']|\n", + "|gonococcal infection|FINNGEN_R10_AB1_G...|FINNGEN_R10| gwas|Gonococcal infection| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 954| 400197| 401151|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true| ['DOID_7551']|\n", + "| helminthiases|FINNGEN_R10_AB1_H...|FINNGEN_R10| gwas| Helminthiases| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 523| 411658| 412181|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|['EFO_0007245', '...|\n", + "+--------------------+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+--------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "joined_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1542\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 22:===========================================> (3 + 1) / 4]\r" + ] + } + ], + "source": [ + "num_non_null_rows = joined_df.filter(joined_df.trait_efos.isNotNull()).count()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+---------+---------+--------------------+------------------------+------+------------------+--------+--------------------+----------------------+---------------+--------------------+----------------------------------+--------------------+------+---------+--------+--------------------+---------------------+--------------------+--------------------+---------------+-------------+--------------------+-----------+\n", + "| studyId|projectId|studyType| traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId| publicationTitle|publicationFirstAuthor|publicationDate| publicationJournal|backgroundTraitFromSourceMappedIds| initialSampleSize|nCases|nControls|nSamples| cohorts|ldPopulationStructure| discoverySamples| replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|\n", + "+------------+---------+---------+--------------------+------------------------+------+------------------+--------+--------------------+----------------------+---------------+--------------------+----------------------------------+--------------------+------+---------+--------+--------------------+---------------------+--------------------+--------------------+---------------+-------------+--------------------+-----------+\n", + "| GCST000102| GCST| gwas|Endothelial funct...| [EFO_0004298]| null| null|17903301|Genome-wide assoc...| Vasan RS| 2007-09-19| BMC Med Genet| null|Up to 1,238 Europ...| 0| 0| 1238| [null]| [{nfe, 1.0}]| [{1238, European}]| []| null| null| null| false|\n", + "| GCST000272| GCST| gwas| Height| [EFO_0004339]| null| null|19030899|Genome-wide assoc...| Lei SF| 2008-11-23| Hum Genet| null|618 Chinese ances...| 0| 0| 618| [null]| [{eas, 1.0}]| [{618, East Asian}]|[{2953, East Asian}]| null| null| null| false|\n", + "| GCST000436| GCST| gwas|Acenocoumarol mai...| [GO_0061476]| null| null|19578179|A genome-wide ass...| Teichert M| 2009-07-04| Hum Mol Genet| null|1,451 European an...| 0| 0| 1451| [null]| [{nfe, 1.0}]| [{1451, European}]| [{287, NR}]| null| null| null| false|\n", + "| GCST000514| GCST| gwas|Response to antip...| [GO_0097332]| null| null|19875103|Genomewide associ...| Aberg K| 2009-10-27| Biol Psychiatry| null|421 European ance...| 738| 0| 738| [null]| [{afr, 0.28997289...|[{214, African Am...| []| null| null| null| false|\n", + "| GCST000550| GCST| gwas| Metabolite levels| [EFO_0004725]| null| null|20037589|A genome-wide per...| Illig T| 2009-12-27| Nat Genet| null|1,029 European an...| 0| 0| 1029| [null]| [{nfe, 1.0}]| [{1029, European}]| [{1202, European}]| null| null| null| false|\n", + "| GCST000708| GCST| gwas| Freckling| [EFO_0003963]| null| null|20585627|Web-based, partic...| Eriksson N| 2010-06-24| PLoS Genet| null|9,126 European an...| 0| 0| 9126| [null]| [{nfe, 1.0}]| [{9126, European}]| []| null| null| null| false|\n", + "| GCST000754| GCST| gwas|Personality dimen...| [EFO_0004365]| null| null|20691247|A genome-wide ass...| Verweij KJ| 2010-08-04| Biol Psychol| null|5,117 European an...| 0| 0| 5117| [null]| [{nfe, 1.0}]| [{5117, European}]| []| null| null| null| false|\n", + "| GCST000880| GCST| gwas|Menarche (age at ...| [EFO_0004703]| null| null|21102462|Thirty new loci f...| Elks CE| 2010-11-21| Nat Genet| null|86,142 European a...| 0| 0| 87802| [null]| [{nfe, 1.0}]| [{87802, European}]| [{14731, European}]| null| null| null| false|\n", + "| GCST001031| GCST| gwas|Large B-cell lymp...| [EFO_0000403]| null| null|21471979|Common variants o...| Kumar V| 2011-04-07| J Hum Genet| null|74 Japanese ances...| 74| 934| 1008| [null]| [{eas, 1.0}]|[{1008, East Asian}]|[{3634, East Asian}]| null| null| null| false|\n", + "| GCST001032| GCST| gwas|Caffeine consumption| [EFO_0004330]| null| null|21490707|Genome-wide meta-...| Cornelis MC| 2011-04-07| PLoS Genet| null|47,431 European a...| 0| 0| 47431| [null]| [{nfe, 1.0}]| [{47431, European}]| []| null| null| null| false|\n", + "| GCST001059| GCST| gwas| Neutrophil count| [EFO_0004833]| null| null|21507922|Duffy-null-associ...| Ramsuran V| 2011-05-01| Clin Infect Dis| null|115 African ances...| 0| 0| 115| [null]| [{afr, 1.0}]|[{115, Sub-Sahara...| []| null| null| null| false|\n", + "| GCST002187| GCST| gwas|Systolic blood pr...| [EFO_0006335]| null| null|24058526|Genome-wide meta-...| Bhatnagar P| 2013-09-13| PLoS One| [MONDO_0011382]|1617 African Amer...| 1617| 0| 1617| [null]| [{afr, 1.0}]|[{1617, African A...| []| null| null| null| false|\n", + "| GCST002623| GCST| gwas| L-arginine levels| [EFO_0006524]| null| null|25245031|Genome-wide assoc...| Luneburg N| 2014-09-21|Circ Cardiovasc G...| null|3,747 European an...| 0| 0| 6739| [null]| [{nfe, 1.0}]|[{3747, European}...| [{1159, European}]| null| null| null| false|\n", + "| GCST003261| GCST| gwas|Ischemic stroke (...| [HP_0002140]| null| null|26708676|Loci associated w...| Pulit SL| 2015-12-18| Lancet Neurol| null|up to 8,062 Europ...| 9510| 32473| 41983| [null]| [{amr, 0.06647928...|[{2791, Hispanic ...|[{256, African Am...| null| null| null| false|\n", + "| GCST003427| GCST| gwas|Alzheimer disease...| [EFO_0004847, MON...| null| null|26830138|Family-based asso...| Herold C| 2016-02-02| Mol Psychiatry| null|2,478 European an...| 2478| 979| 3457| [null]| [{nfe, 1.0}]| [{3524, European}]| []| null| null| null| false|\n", + "| GCST003665| GCST| gwas|Free cholesterol ...| [EFO_0004611, EFO...| null| null|27005778|Genome-wide study...| Kettunen J| 2016-03-23| Nat Commun| null|21,555 European a...| 0| 0| 21555|[EGCUT, ERF, FTC,...| [{nfe, 1.0}]| [{21555, European}]| []| null| null| null| false|\n", + "| GCST003773| GCST| gwas|Loneliness (multi...| [EFO_0007865]| null| null|27629369|Genome-Wide Assoc...| Gao J| 2016-09-15|Neuropsychopharma...| null|8,490 European an...| 0| 0| 10760| [null]| [{nfe, 0.80529739...|[{8490, European}...| []| null| null| null| false|\n", + "| GCST003791| GCST| gwas|Response to metfo...| [EFO_0006952, GO_...| null| null|28173075|Metformin pharmac...| Niu N| 2016-09-11| Hum Mol Genet| null|up to 96 African ...| 0| 0| 288| [null]| [{afr, 0.33333333...|[{96, African Ame...| []| null| null| null| false|\n", + "| GCST003824| GCST| gwas|Depression in res...| [EFO_0007006, EFO...| null| null|27723809|Genome-Wide Assoc...| Matsunami K| 2016-10-10| PLoS One| [EFO_0004220]|45 Japanese ances...| 45| 179| 224| [null]| [{eas, 1.0}]| [{224, East Asian}]| [{160, East Asian}]| null| null| null| false|\n", + "| GCST003837| GCST| gwas| Chronotype| [EFO_0004354]| null| null|27494321|Genome-Wide Assoc...| Jones SE| 2016-08-05| PLoS Genet| null|127,898 British i...| 0| 0| 127898| [null]| [{nfe, 1.0}]|[{127898, European}]| [{89283, NR}]| []| []|ftp://ftp.ebi.ac....| true|\n", + "| GCST004678| GCST| gwas|Psychosis pronene...| [EFO_0008337]| null| null|28525603|Genome-Wide Assoc...| Ortega-Alonso A| 2017-05-19| Schizophr Bull| null|3,967 Finnish anc...| 0| 0| 3967| [null]| [{nfe, 1.0}]| [{3967, European}]| []| null| null| null| false|\n", + "| GCST005189| GCST| gwas| Tanning| [EFO_0004279]| null| null|29195075|An Unexpectedly C...| Martin AR| 2017-11-30| Cell| null|216 Sub-Saharan A...| 0| 0| 216| [null]| [{afr, 1.0}]|[{216, Sub-Sahara...|[{240, Sub-Sahara...| null| null| null| false|\n", + "| GCST005437| GCST| gwas|Random C-peptide ...| [EFO_0005187]| null| null|29404672|Meta-genome-wide ...| Roshandel D| 2018-02-05| Diabetologia| [MONDO_0005147]|1,497 European an...| 0| 0| 1497| [null]| [{nfe, 1.0}]| [{1497, European}]| []| null| null| null| false|\n", + "| GCST005503| GCST| gwas|Medium HDL partic...| [EFO_0004612]| null| null|29084231|Common, low-frequ...| Davis JP| 2017-10-30| PLoS Genet| null|8,372 Finnish anc...| 0| 0| 8372| [null]| [{nfe, 1.0}]| [{8372, European}]| []| null| null| null| false|\n", + "| GCST005669| GCST| gwas|Delta-6 desaturas...| [EFO_0007765, EFO...| null| null|29246731|A common variant ...| de Toro-Martin J| 2017-11-02| J Clin Lipidol| null|81 extreme respon...| 0| 0| 141| [null]| [{nfe, 1.0}]| [{141, NR}]| []| null| null| null| false|\n", + "| GCST005749| GCST| gwas|Digit length rati...| [EFO_0004841]| null| null|29659830|Genome-wide assoc...| Warrington NM| 2018-04-12| Hum Mol Genet| null|14,382 European a...| 0| 0| 15661| [null]| [{nfe, 1.0}]|[{14382, European...| []| null| null| null| false|\n", + "| GCST006420| GCST| gwas|Affective disorde...| [EFO_0004247, EFO...| null| null|30116032|Genetics of suici...| Erlangsen A| 2018-08-16| Mol Psychiatry| null|4,302 European an...| 4302| 13294| 17596| [null]| [{nfe, 1.0}]| [{17596, European}]| []| null| null| null| false|\n", + "| GCST006484| GCST| gwas| Type 2 diabetes| [MONDO_0005148]| null| null|30130595|Pilot genome-wide...| Dominguez-Cruz MG| 2018-08-18| Gene| null|45 Maya ancestry ...| 45| 47| 92| [null]| [{amr, 1.0}]|[{92, Native Amer...| []| null| null| null| false|\n", + "| GCST006496| GCST| gwas|Glomerular filtra...| [EFO_0006829, EFO...| null| null|30160337|Genome Wide Assoc...| Asleh R| 2018-08-30| Clin Transplant| null|243 European ance...| 0| 0| 251| [null]| [{nfe, 0.99601593...|[{243, European},...| []| null| null| null| false|\n", + "| GCST006739| GCST| gwas|Proportion of mis...| [EFO_0006923]| null| null|30188897|Detecting past an...| Jeong C| 2018-09-06| PLoS Genet| null|981 Tibetan ances...| 0| 0| 981| [null]| [{nfe, 1.0}]| [{981, NR}]| []| null| null| null| false|\n", + "| GCST006907| GCST| gwas|Ischemic stroke (...| [EFO_0005524]| null| null|29531354|Multiancestry gen...| Malik R| 2018-03-12| Nat Genet| null|4,373 European an...| 4373| 406111| 410484| [null]| [{nfe, 1.0}]|[{150765, European}]| []| []| []|ftp://ftp.ebi.ac....| true|\n", + "| GCST006960| GCST| gwas|Inflammatory bowe...| [EFO_0003767]| null| null|26490195|Inherited determi...| Cleynen I| 2015-10-18| Lancet| null|16,902 European a...| 29838| 0| 29838| [null]| [{nfe, 1.0}]| [{29838, European}]| [{6182, European}]| null| null| null| false|\n", + "| GCST007217| GCST| gwas|RR interval (hear...| [EFO_0004831]| null| null|30679814|Genome-wide assoc...| van Setten J| 2019-01-24| Eur J Hum Genet| null|2,006 Erasmus Ruc...| 0| 0| 28698| [null]| [{nfe, 1.0}]| [{28698, European}]| []| null| null| null| false|\n", + "| GCST008154| GCST| gwas| Trunk fat mass| [EFO_0005409]| null| null|28552196|Whole-Genome Sequ...| Tachmazidou I| 2017-06-01| Am J Hum Genet| null|3,399 whole genom...| 0| 0| 16237| [null]| [{nfe, 1.0}]|[{3538, NR}, {128...| [{10667, European}]| null| null| null| false|\n", + "| GCST008483| GCST| gwas| Ulcerative colitis| [EFO_0000729]| null| null|26398853|Identification of...| Ye BD| 2016-01-01| Inflamm Bowel Dis| null|705 Korean ancest...| 705| 1178| 1883| [null]| [{eas, 1.0}]|[{1883, South Asi...|[{3674, South Asi...| null| null| null| false|\n", + "| GCST008671| GCST| gwas|Phlegm x occupati...| [EFO_0007939, EFO...| null| null|30449631|Genome-wide inter...| Zeng X| 2018-11-15| Environ Int| null|1,702 Dutch ances...| 1702| 6274| 7976| [null]| [{nfe, 1.0}]| [{7976, European}]| [{6789, European}]| null| null| null| false|\n", + "| GCST008675| GCST| gwas|Maximum habitual ...| [EFO_0007878]| null| null|31151762|Genome-wide Assoc...| Gelernter J| 2019-04-08| Biol Psychiatry| null|126,936 European ...| 0| 0| 143965| [null]| [{afr, 0.11828569...|[{17029, African ...| []| null| null| null| false|\n", + "| GCST008775| GCST| gwas|Birth weight or w...| [EFO_0004342, EFO...| null| null|30858448|Genetic overlap b...| Tekola-Ayele F| 2019-03-11| Sci Rep| null|153,781 European ...| 0| 0| 378240| [null]| [{nfe, 1.0}]|[{246502, Europea...| []| null| null| null| false|\n", + "| GCST008870| GCST| gwas|Keratinocyte canc...| [EFO_0010176]| null| null|31174203|Combined analysis...| Liyanage UE| 2019-06-07| Hum Mol Genet| null|at least 18,538 E...| 18538| 340302| 358840| [null]| [{nfe, 1.0}]|[{358840, European}]| []| null| null| null| false|\n", + "| GCST009173| GCST| gwas|Response to (pegy...| [EFO_0007859]| null| null|30715261|Genome Wide Assoc...| Brouwer WP| 2019-02-02| Clin Infect Dis| [EFO_0004239]|121 Asian, Europe...| 0| 0| 509| [null]| [{nfe, 0.5}, {afr...|[{127, European},...| []| null| null| null| false|\n", + "| GCST009364| GCST| gwas|Triglyceride leve...| [EFO_0004530, EFO...| null| null|31719535|Multi-ancestry sl...| Noordam R| 2019-11-12| Nat Commun| null|at least 2,926 Af...| 0| 49886| 61990| [null]| [{eas, 0.03837715...|[{2096, East Asia...|[{12579, Hispanic...| null| null| null| false|\n", + "| GCST009391| GCST| gwas|Metabolite levels...| [EFO_0005132]| null| null|23823483|A genome-wide ass...| Rhee EP| 2013-07-02| Cell Metab| null|2,076 European an...| 0| 0| 2076| [null]| [{nfe, 1.0}]| [{2076, European}]| []| null| null| null| false|\n", + "|GCST009391_2| GCST| gwas| Metabolite levels| []| null| null|23823483|A genome-wide ass...| Rhee EP| 2013-07-02| Cell Metab| null|2,076 European an...| 0| 0| 2076| [null]| [{nfe, 1.0}]| [{2076, European}]| []| null| null| null| false|\n", + "|GCST009391_3| GCST| gwas| Metabolite levels| [EFO_0004468, EFO...| null| null|23823483|A genome-wide ass...| Rhee EP| 2013-07-02| Cell Metab| null|2,076 European an...| 0| 0| 2076| [null]| [{nfe, 1.0}]| [{2076, European}]| []| null| null| null| false|\n", + "|GCST009391_4| GCST| gwas| Metabolite levels| [EFO_0004518]| null| null|23823483|A genome-wide ass...| Rhee EP| 2013-07-02| Cell Metab| null|2,076 European an...| 0| 0| 2076| [null]| [{nfe, 1.0}]| [{2076, European}]| []| null| null| null| false|\n", + "|GCST009391_5| GCST| gwas| Metabolite levels| [EFO_0004761]| null| null|23823483|A genome-wide ass...| Rhee EP| 2013-07-02| Cell Metab| null|2,076 European an...| 0| 0| 2076| [null]| [{nfe, 1.0}]| [{2076, European}]| []| null| null| null| false|\n", + "|GCST009391_6| GCST| gwas| Metabolite levels| [EFO_0004846]| null| null|23823483|A genome-wide ass...| Rhee EP| 2013-07-02| Cell Metab| null|2,076 European an...| 0| 0| 2076| [null]| [{nfe, 1.0}]| [{2076, European}]| []| null| null| null| false|\n", + "|GCST009391_7| GCST| gwas| Metabolite levels| [EFO_0005001]| null| null|23823483|A genome-wide ass...| Rhee EP| 2013-07-02| Cell Metab| null|2,076 European an...| 0| 0| 2076| [null]| [{nfe, 1.0}]| [{2076, European}]| []| null| null| null| false|\n", + "|GCST009391_8| GCST| gwas| Metabolite levels| [EFO_0005002]| null| null|23823483|A genome-wide ass...| Rhee EP| 2013-07-02| Cell Metab| null|2,076 European an...| 0| 0| 2076| [null]| [{nfe, 1.0}]| [{2076, European}]| []| null| null| null| false|\n", + "|GCST009391_9| GCST| gwas| Metabolite levels| [EFO_0005058]| null| null|23823483|A genome-wide ass...| Rhee EP| 2013-07-02| Cell Metab| null|2,076 European an...| 0| 0| 2076| [null]| [{nfe, 1.0}]| [{2076, European}]| []| null| null| null| false|\n", + "+------------+---------+---------+--------------------+------------------------+------+------------------+--------+--------------------+----------------------+---------------+--------------------+----------------------------------+--------------------+------+---------+--------+--------------------+---------------------+--------------------+--------------------+---------------+-------------+--------------------+-----------+\n", + "only showing top 50 rows\n", + "\n" + ] + } + ], + "source": [ + "path_tmp=\"gs://gwas_catalog_data/study_index\"\n", + "tmp=StudyIndex.from_parquet(session=session, path=path_tmp)\n", + "tmp.df.show(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "joined_df=joined_df.withColumn(\"traitFromSourceMappedIds\",joined_df[\"trait_efos\"]).drop(\"trait_efos\",\"trait_reported_low\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "| studyId| projectId|studyType| traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|publicationTitle|publicationFirstAuthor|publicationDate|publicationJournal|backgroundTraitFromSourceMappedIds| initialSampleSize|nCases|nControls|nSamples| cohorts|ldPopulationStructure| discoverySamples|replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|\n", + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Actinomycosis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 101| 363227| 363328|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Amoebiasis| ['EFO_0007144']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 160| 367214| 367374|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas|Anogenital herpes...| ['EFO_0007282']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 1986| 400197| 402183|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Aspergillosis| ['EFO_0007157']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 211| 403213| 403424|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas|Atypical virus in...| ['MONDO_0024318']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 282| 409849| 410131|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Bacterial infecti...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 20226| 363227| 383453|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Bacterial, viral ...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2852| 409329| 412181|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Other bacterial i...| ['EFO_0000771']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 6145| 367214| 373359|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas| Candidiasis| ['MONDO_0002026']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 4306| 403213| 407519|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas|Other sexually tr...| ['MONDO_0021681',...| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2186| 400197| 402383|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas| Cholera| ['EFO_1001235']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 1385| 367214| 368599|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_D...|FINNGEN_R10| gwas|Dengue fever [cla...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 53| 409137| 409190|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_D...|FINNGEN_R10| gwas| Dermatophytosis| ['MONDO_0004678']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 3921| 403213| 407134|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Early syphilis| ['EFO_0007504']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 308| 400197| 400505|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "| FINNGEN_R10_AB1_EBV|FINNGEN_R10| gwas|Infectious mononu...| ['EFO_0007326']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2979| 400974| 403953|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Enterobiasis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 112| 411658| 411770|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Erysipelas| ['EFO_1001462']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 22261| 363227| 385488|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_G...|FINNGEN_R10| gwas|Diarrhoea and gas...| ['MONDO_0045031']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 32210| 367214| 399424|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_G...|FINNGEN_R10| gwas|Gonococcal infection| ['DOID_7551']| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 954| 400197| 401151|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_H...|FINNGEN_R10| gwas| Helminthiases| ['EFO_0007245', '...| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 523| 411658| 412181|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "joined_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "string\n" + ] + } + ], + "source": [ + "column_type = dict(joined_df.dtypes)[\"traitFromSourceMappedIds\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql.functions import from_json\n", + "from pyspark.sql.types import ArrayType, StringType\n", + "\n", + "# Assuming joined_df is your DataFrame\n", + "joined_df = joined_df.withColumn(\n", + " \"traitFromSourceMappedIds\",\n", + " from_json(\"traitFromSourceMappedIds\", ArrayType(StringType()))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "| studyId| projectId|studyType| traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|publicationTitle|publicationFirstAuthor|publicationDate|publicationJournal|backgroundTraitFromSourceMappedIds| initialSampleSize|nCases|nControls|nSamples| cohorts|ldPopulationStructure| discoverySamples|replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|\n", + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Actinomycosis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 101| 363227| 363328|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Amoebiasis| [EFO_0007144]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 160| 367214| 367374|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas|Anogenital herpes...| [EFO_0007282]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 1986| 400197| 402183|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Aspergillosis| [EFO_0007157]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 211| 403213| 403424|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas|Atypical virus in...| [MONDO_0024318]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 282| 409849| 410131|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Bacterial infecti...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 20226| 363227| 383453|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Bacterial, viral ...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2852| 409329| 412181|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Other bacterial i...| [EFO_0000771]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 6145| 367214| 373359|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas| Candidiasis| [MONDO_0002026]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 4306| 403213| 407519|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas|Other sexually tr...| [MONDO_0021681, E...| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2186| 400197| 402383|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas| Cholera| [EFO_1001235]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 1385| 367214| 368599|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_D...|FINNGEN_R10| gwas|Dengue fever [cla...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 53| 409137| 409190|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_D...|FINNGEN_R10| gwas| Dermatophytosis| [MONDO_0004678]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 3921| 403213| 407134|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Early syphilis| [EFO_0007504]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 308| 400197| 400505|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "| FINNGEN_R10_AB1_EBV|FINNGEN_R10| gwas|Infectious mononu...| [EFO_0007326]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2979| 400974| 403953|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Enterobiasis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 112| 411658| 411770|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Erysipelas| [EFO_1001462]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 22261| 363227| 385488|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_G...|FINNGEN_R10| gwas|Diarrhoea and gas...| [MONDO_0045031]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 32210| 367214| 399424|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_G...|FINNGEN_R10| gwas|Gonococcal infection| [DOID_7551]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 954| 400197| 401151|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_H...|FINNGEN_R10| gwas| Helminthiases| [EFO_0007245, EFO...| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 523| 411658| 412181|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "joined_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "array\n" + ] + } + ], + "source": [ + "column_type = dict(joined_df.dtypes)[\"traitFromSourceMappedIds\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "si=StudyIndex(_df=joined_df, _schema=StudyIndex.get_schema())" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "| studyId| projectId|studyType| traitFromSource|traitFromSourceMappedIds|geneId|tissueFromSourceId|pubmedId|publicationTitle|publicationFirstAuthor|publicationDate|publicationJournal|backgroundTraitFromSourceMappedIds| initialSampleSize|nCases|nControls|nSamples| cohorts|ldPopulationStructure| discoverySamples|replicationSamples|qualityControls|analysisFlags|summarystatsLocation|hasSumstats|\n", + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Actinomycosis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 101| 363227| 363328|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Amoebiasis| [EFO_0007144]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 160| 367214| 367374|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas|Anogenital herpes...| [EFO_0007282]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 1986| 400197| 402183|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas| Aspergillosis| [EFO_0007157]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 211| 403213| 403424|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_A...|FINNGEN_R10| gwas|Atypical virus in...| [MONDO_0024318]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 282| 409849| 410131|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Bacterial infecti...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 20226| 363227| 383453|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Bacterial, viral ...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2852| 409329| 412181|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_B...|FINNGEN_R10| gwas|Other bacterial i...| [EFO_0000771]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 6145| 367214| 373359|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas| Candidiasis| [MONDO_0002026]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 4306| 403213| 407519|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas|Other sexually tr...| [MONDO_0021681, E...| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2186| 400197| 402383|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_C...|FINNGEN_R10| gwas| Cholera| [EFO_1001235]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 1385| 367214| 368599|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_D...|FINNGEN_R10| gwas|Dengue fever [cla...| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 53| 409137| 409190|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_D...|FINNGEN_R10| gwas| Dermatophytosis| [MONDO_0004678]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 3921| 403213| 407134|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Early syphilis| [EFO_0007504]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 308| 400197| 400505|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "| FINNGEN_R10_AB1_EBV|FINNGEN_R10| gwas|Infectious mononu...| [EFO_0007326]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 2979| 400974| 403953|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Enterobiasis| null| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 112| 411658| 411770|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_E...|FINNGEN_R10| gwas| Erysipelas| [EFO_1001462]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 22261| 363227| 385488|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_G...|FINNGEN_R10| gwas|Diarrhoea and gas...| [MONDO_0045031]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 32210| 367214| 399424|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_G...|FINNGEN_R10| gwas|Gonococcal infection| [DOID_7551]| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 954| 400197| 401151|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "|FINNGEN_R10_AB1_H...|FINNGEN_R10| gwas| Helminthiases| [EFO_0007245, EFO...| null| null| null| null| null| null| null| null|377,277 (210,870 ...| 523| 411658| 412181|[FinnGen]| [{fin, 1.0}]|[{377277, Finnish}]| null| null| null|gs://finngen-publ...| true|\n", + "+--------------------+-----------+---------+--------------------+------------------------+------+------------------+--------+----------------+----------------------+---------------+------------------+----------------------------------+--------------------+------+---------+--------+---------+---------------------+-------------------+------------------+---------------+-------------+--------------------+-----------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "si.df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "2408" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "si.df.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "si.df.write.parquet(path=\"gs://genetics-portal-dev-analysis/yt4/study_index_finngen_with_efo\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "path_to_study_index=\"gs://genetics-portal-dev-analysis/yt4/study_index_finngen_with_efo\"\n", + "si=StudyIndex.from_parquet(session=session, path=path_to_study_index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gentropy-krNFZEZg-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/Productionizing_LD_matrix.ipynb b/notebooks/Productionizing_LD_matrix.ipynb deleted file mode 100644 index 3ba4dc06b..000000000 --- a/notebooks/Productionizing_LD_matrix.ipynb +++ /dev/null @@ -1,1505 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "baf71347", - "metadata": {}, - "source": [ - "# Extracting square sub-matrices from reference\n", - "\n", - "\n", - "### Conceptual considerations:\n", - "\n", - "```\n", - "Summary statistics\n", - "↓\n", - "[window based clumping]\n", - "[ld based clumping]\n", - "↓\n", - "StudyLocus\n", - "↓\n", - "[ld matrix extraction] <- StudyIndex (studyId, ldPopulations), ldIndex, ldMatrix\n", - "↓\n", - "\n", - "```\n", - "\n", - "\n", - "- Assume we have only one ancestry" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d75ac655", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-25T11:18:41.257715Z", - "start_time": "2023-10-25T11:18:14.728571Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "SLF4J: No SLF4J providers were found.\n", - "SLF4J: Defaulting to no-operation (NOP) logger implementation\n", - "SLF4J: See https://www.slf4j.org/codes.html#noProviders for further details.\n", - "SLF4J: Class path contains SLF4J bindings targeting slf4j-api versions 1.7.x or earlier.\n", - "SLF4J: Ignoring binding found at [jar:file:/usr/lib/spark/jars/log4j-slf4j-impl-2.18.0.jar!/org/slf4j/impl/StaticLoggerBinder.class]\n", - "SLF4J: See https://www.slf4j.org/codes.html#ignoredBindings for an explanation.\n", - "Setting default log level to \"WARN\".\n", - "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", - "pip-installed Hail requires additional configuration options in Spark referring\n", - " to the path to the Hail Python module directory HAIL_DIR,\n", - " e.g. /path/to/python/site-packages/hail:\n", - " spark.jars=HAIL_DIR/backend/hail-all-spark.jar\n", - " spark.driver.extraClassPath=HAIL_DIR/backend/hail-all-spark.jar\n", - " spark.executor.extraClassPath=./hail-all-spark.jarRunning on Apache Spark version 3.3.0\n", - "SparkUI available at http://ds-genetics-etl-test-m.c.open-targets-eu-dev.internal:37397\n", - "Welcome to\n", - " __ __ <>__\n", - " / /_/ /__ __/ /\n", - " / __ / _ `/ / /\n", - " /_/ /_/\\_,_/_/_/ version 0.2.122-be9d88a80695\n", - "LOGGING: writing to /dev/null\n" - ] - } - ], - "source": [ - "from gentropy.common.session import Session\n", - "from gentropy.dataset.study_locus import StudyLocus\n", - "from gentropy.dataset.study_index import StudyIndex\n", - "from gentropy.datasource.gnomad.ld import GnomADLDMatrix\n", - "from gentropy.datasource.finngen.study_index import FinnGenStudyIndex\n", - "\n", - "import hail as hl\n", - "from hail.linalg import BlockMatrix\n", - "\n", - "from pyspark.sql import Window\n", - "import pyspark.sql.functions as f\n", - "from pyspark.sql import Column, DataFrame\n", - "\n", - "from urllib.request import urlopen\n", - "\n", - "\n", - "session = Session(hail_home='/opt/conda/miniconda3/lib/python3.10/site-packages/hail')\n", - "hl.init(sc=session.spark.sparkContext, log=\"/dev/null\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "9eac3419", - "metadata": {}, - "source": [ - "## Generate FINNGEN Study table\n", - "\n", - "- Executed in 2.22s\n", - "- Building a Finngen study table from source." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "51b4cd9c", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-25T11:19:13.439965Z", - "start_time": "2023-10-25T11:19:11.603603Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-RECORD 0--------------------------------------------------------------------------------------------------------------------\n", - " studyId | FINNGEN_R9_AB1_ACTINOMYCOSIS \n", - " traitFromSource | Actinomycosis \n", - " nCases | 93 \n", - " nControls | 332343 \n", - " nSamples | 332436 \n", - " projectId | FINNGEN_R9 \n", - " studyType | gwas \n", - " hasSumstats | true \n", - " initialSampleSize | 377,277 (210,870 females and 166,407 males) \n", - " discoverySamples | [{377277, Finnish}] \n", - " summarystatsLocation | https://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_AB1_ACTINOMYCOSIS.gz \n", - " ldPopulationStructure | [{fin, 1.0}] \n", - "only showing top 1 row\n", - "\n", - "+--------------------+------------+\n", - "| studyId|ldPopulation|\n", - "+--------------------+------------+\n", - "|FINNGEN_R9_ASTHMA...| fin|\n", - "|FINNGEN_R9_E4_PIT...| fin|\n", - "|FINNGEN_R9_E4_VIT...| fin|\n", - "|FINNGEN_R9_H8_MID...| fin|\n", - "|FINNGEN_R9_L12_PI...| fin|\n", - "|FINNGEN_R9_M13_HA...| fin|\n", - "|FINNGEN_R9_M13_SP...| fin|\n", - "|FINNGEN_R9_Q17_LV...| fin|\n", - "|FINNGEN_R9_ALCOHO...| fin|\n", - "|FINNGEN_R9_CHRONL...| fin|\n", - "|FINNGEN_R9_C_DIFF...| fin|\n", - "|FINNGEN_R9_E4_OBE...| fin|\n", - "|FINNGEN_R9_GOUT_S...| fin|\n", - "|FINNGEN_R9_I9_LYM...| fin|\n", - "|FINNGEN_R9_I9_VAR...| fin|\n", - "|FINNGEN_R9_J10_CH...| fin|\n", - "|FINNGEN_R9_K11_HY...| fin|\n", - "|FINNGEN_R9_K11_RE...| fin|\n", - "|FINNGEN_R9_M13_OS...| fin|\n", - "|FINNGEN_R9_O15_PR...| fin|\n", - "+--------------------+------------+\n", - "only showing top 20 rows\n", - "\n" - ] - } - ], - "source": [ - "# Processing studies:\n", - "def update_population(ld_pop: Column) -> Column:\n", - " return f.when(\n", - " f.size(ld_pop) == 1,\n", - " ld_pop.getItem(0).ldPopulation\n", - " )\n", - "\n", - "\n", - "# Read the JSON data from the URL.\n", - "json_data = urlopen('https://r9.finngen.fi/api/phenos').read().decode(\"utf-8\")\n", - "rdd = session.spark.sparkContext.parallelize([json_data])\n", - "df = session.spark.read.json(rdd)\n", - "\n", - "\n", - "finngen_release_prefix = 'FINNGEN_R9'\n", - "finngen_summary_stats_url_prefix = 'https://storage.googleapis.com/finngen-public-data-r9/summary_stats/finngen_R9_'\n", - "finngen_summary_stats_url_suffix = '.gz'\n", - "\n", - "# Parse the study index data.\n", - "studies = FinnGenStudyIndex.from_source(\n", - " df, \n", - " finngen_release_prefix, \n", - " finngen_summary_stats_url_prefix, \n", - " finngen_summary_stats_url_suffix\n", - ")\n", - "\n", - "studies.df.show(1, False, True)\n", - "\n", - "# From the study table we only need study identifier and the ld population\n", - "# We only need ld population if the study is based on single ancestry.\n", - "studies = (\n", - " studies.df\n", - " .select(\n", - " 'studyId',\n", - " update_population(f.col('ldPopulationStructure')).alias('ldPopulation'), \n", - " )\n", - " .distinct()\n", - " .persist()\n", - ")\n", - "\n", - "studies.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6452e680", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-24T10:24:17.267975Z", - "start_time": "2023-10-24T10:24:11.432842Z" - } - }, - "source": [ - "## Reading distance and ld clumped Finngen dataset\n", - "\n", - "- Extract two random loci for prototyping purposes.\n", - "- Input dataset: 19,005 study locus from 1,387 studies." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "46e851ca", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-25T11:25:04.824517Z", - "start_time": "2023-10-25T11:25:00.848659Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 63:===================================================> (33 + 3) / 36]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+-----------------------------------------+---------------+--------------+--------------+\n", - "|studyId |variantId |pValueMantissa|pValueExponent|\n", - "+-----------------------------------------+---------------+--------------+--------------+\n", - "|FINNGEN_R9_C3_BASAL_CELL_CARCINOMA_EXALLC|15_29035680_G_A|2.076 |-21 |\n", - "|FINNGEN_R9_G6_SLEEPAPNO |16_53771295_C_A|2.149 |-26 |\n", - "+-----------------------------------------+---------------+--------------+--------------+\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "sl = StudyLocus(\n", - " _df= (\n", - " session.spark.read\n", - " .parquet('gs://genetics_etl_python_playground/XX.XX/output/python_etl/parquet/finngen/2023.10.13_ld_clumped_w_locus/')\n", - " .filter(\n", - " (f.size(f.col('qualityControls')) == 0) &\n", - " (f.size(f.col('locus')) > 100)\n", - " )\n", - " .orderBy(f.rand(seed=23))\n", - " .limit(2)\n", - " ),\n", - " _schema=StudyLocus.get_schema()\n", - ").persist()\n", - "\n", - "# Get list of leads and studies:\n", - "sl.df.select('studyId', 'variantId', 'pValueMantissa', 'pValueExponent', ).show(truncate=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "id": "661d9633", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:16:21.754731Z", - "start_time": "2023-10-27T11:16:20.553762Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------+---------------+---------+---------+---------+-----------------------------------+\n", - "|studyLocusId |variantId |pValue |locusSize|ldSetSize|qualityControls |\n", - "+--------------------+---------------+---------+---------+---------+-----------------------------------+\n", - "|-6067699795164145074|15_27427129_A_G|9.592E-13|738 |35 |[] |\n", - "|-8027743839728879857|15_27983407_C_T|4.618E-61|837 |78 |[] |\n", - "|4889075444063922922 |15_28519016_G_A|2.786E-33|277 |-1 |[Variant not found in LD reference]|\n", - "|2224601896262245870 |15_29035680_G_A|2.076E-21|647 |5 |[] |\n", - "|-7676564604508510836|15_48134287_A_G|3.572E-12|442 |34 |[] |\n", - "+--------------------+---------------+---------+---------+---------+-----------------------------------+\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "# Let's get all top-loci from this study on chromosome 15:\n", - "(\n", - " session.spark.read\n", - " .parquet('gs://genetics_etl_python_playground/XX.XX/output/python_etl/parquet/finngen/2023.10.13_ld_clumped_w_locus/')\n", - " .filter(\n", - " (f.col('studyId') == 'FINNGEN_R9_C3_BASAL_CELL_CARCINOMA_EXALLC') &\n", - " (f.col('chromosome') == '15') \n", - "# f.col('ldSet').isNotNull()\n", - " )\n", - " .orderBy(f.col('position'))\n", - " .select(\n", - " 'studyLocusId',\n", - " 'variantId', \n", - " f.concat_ws('E','pValueMantissa', 'pValueExponent').alias('pValue'), \n", - "# 'studyLocusId', \n", - " f.size(f.col('locus')).alias('locusSize'),\n", - " f.size(f.col('ldSet')).alias('ldSetSize'),\n", - " f.col('qualityControls')\n", - " )\n", - "# .count()\n", - " .show(20, truncate=False)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d101a072", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "attachments": { - "image.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "9db8b8ab", - "metadata": {}, - "source": [ - "## Comparing with Finngen finemapping.\n", - "\n", - "The same [trait](https://r9.finngen.fi/pheno/C3_BASAL_CELL_CARCINOMA_EXALLC) has 3 finemapped credible sets.\n", - "\n", - "![image.png](attachment:image.png)\n", - "\n", - "- `15:27983407:C:T` and `15:27985172:C:T` are very close, I could find `27983407`.\n", - "- Also `15_48134287_A_G` is found by both approach.\n", - "- One of the other three snps was not found in the LD set (`15_28519016_G_A`). \n", - "- `15_27427129_A_G` and `15_29035680_G_A` were resolved in the LD matrix, and were found to be not linked. But they are not in the.\n", - "\n", - "What are the r between these variants?\n", - "\n", - "- 15_27983407_C_T vs 15_27427129_A_G -> r = \n", - "- 15_27983407_C_T vs 15_29035680_G_A -> r =" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "d8da5802", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T10:19:25.494510Z", - "start_time": "2023-10-27T10:19:23.020596Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+---------------+---------+---------+---------+---------------+----------+--------+--------+------------+\n", - "| variantId| pValue|locusSize|ldSetSize|qualityControls|chromosome|position| idx|ldPopulation|\n", - "+---------------+---------+---------+---------+---------------+----------+--------+--------+------------+\n", - "|15_27427129_A_G|9.592E-13| 738| 35| []| 15|27427129|11130387| fin|\n", - "|15_27983407_C_T|4.618E-61| 837| 78| []| 15|27983407|11133253| fin|\n", - "|15_29035680_G_A|2.076E-21| 647| 5| []| 15|29035680|11136878| fin|\n", - "+---------------+---------+---------+---------+---------------+----------+--------+--------+------------+\n", - "\n" - ] - } - ], - "source": [ - "# Extracting and resolving ld:\n", - "resolved_ld_index = (\n", - " session.spark.read\n", - " .parquet('gs://genetics_etl_python_playground/XX.XX/output/python_etl/parquet/finngen/2023.10.13_ld_clumped_w_locus/')\n", - " .filter(\n", - " (f.col('studyId') == 'FINNGEN_R9_C3_BASAL_CELL_CARCINOMA_EXALLC') &\n", - " f.col('variantId').isin(['15_27983407_C_T', '15_27427129_A_G', '15_29035680_G_A'])\n", - " )\n", - " .orderBy(f.col('position'))\n", - " .select(\n", - " 'variantId', \n", - " f.concat_ws('E','pValueMantissa', 'pValueExponent').alias('pValue'), \n", - "# 'studyLocusId', \n", - " f.size(f.col('locus')).alias('locusSize'),\n", - " f.size(f.col('ldSet')).alias('ldSetSize'),\n", - " f.col('qualityControls')\n", - " )\n", - "# .count()\n", - " .join(ld_index, on = 'variantId', how='left')\n", - " .persist()\n", - ")\n", - "\n", - "resolved_ld_index.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "id": "722eb21d", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T10:24:03.123125Z", - "start_time": "2023-10-27T10:23:32.607310Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-10-27 10:23:48.619 Hail: INFO: Coerced sorted dataset \n", - "[Stage 942:> (0 + 1) / 1]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+---+---+------------------+\n", - "|i |j |entry |\n", - "+---+---+------------------+\n", - "|0 |0 |2.0000000000000084|\n", - "|0 |1 |0.4201370447477838|\n", - "|0 |2 |0.0 |\n", - "|1 |0 |0.4201370447477838|\n", - "|1 |1 |2.0000000000000004|\n", - "|1 |2 |0.0 |\n", - "|2 |0 |0.0 |\n", - "|2 |1 |0.0 |\n", - "|2 |2 |1.9999999999999996|\n", - "+---+---+------------------+\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "# Accessing r data from the LD matrix:\n", - "half_matrix = (\n", - " BlockMatrix\n", - " .read(ld_matrix_template.format(POP=ld_population))\n", - " .filter(\n", - " [row['idx'] for row in resolved_ld_index.select('idx').collect()],\n", - " [row['idx'] for row in resolved_ld_index.select('idx').collect()]\n", - " )\n", - ")\n", - "\n", - "# matrix = half_matrix + half_matrix.T\n", - "matrix.entries().to_spark().show(100, truncate=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "id": "c316e0d0", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:18:11.801945Z", - "start_time": "2023-10-27T11:18:06.718163Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+---------------+----------+--------------------+--------------------+--------+--------+---------+--------------------------------+--------------------------------+---------------------------+---------------------------+--------------+--------------+-------------------------------+-------------+-------------------+---------------+-----------------+--------------------+--------------------+\n", - "| variantId|chromosome| studyId| studyLocusId|position| beta|oddsRatio|oddsRatioConfidenceIntervalLower|oddsRatioConfidenceIntervalUpper|betaConfidenceIntervalLower|betaConfidenceIntervalUpper|pValueMantissa|pValueExponent|effectAlleleFrequencyFromSource|standardError|subStudyDescription|qualityControls|finemappingMethod| locus| ldSet|\n", - "+---------------+----------+--------------------+--------------------+--------+--------+---------+--------------------------------+--------------------------------+---------------------------+---------------------------+--------------+--------------+-------------------------------+-------------+-------------------+---------------+-----------------+--------------------+--------------------+\n", - "|15_27983407_C_T| 15|FINNGEN_R9_C3_BAS...|-8027743839728879857|27983407|0.413866| null| null| null| 0.388762| 0.43897| 4.618| -61| 0.0443583| 0.025104| null| []| null|[{null, null, nul...|[{15_28482553_T_C...|\n", - "+---------------+----------+--------------------+--------------------+--------+--------+---------+--------------------------------+--------------------------------+---------------------------+---------------------------+--------------+--------------+-------------------------------+-------------+-------------------+---------------+-----------------+--------------------+--------------------+\n", - "\n" - ] - } - ], - "source": [ - "study_locus_id = -8027743839728879857\n", - "\n", - "# Running susie for a specific study locus:\n", - "sl = StudyLocus(\n", - " _df= (\n", - " session.spark.read\n", - " .parquet('gs://genetics_etl_python_playground/XX.XX/output/python_etl/parquet/finngen/2023.10.13_ld_clumped_w_locus/')\n", - " .filter(f.col('studyLocusId') == study_locus_id)\n", - " ),\n", - " _schema=StudyLocus.get_schema()\n", - ").persist()\n", - "\n", - "sl.df.show()" - ] - }, - { - "cell_type": "markdown", - "id": "947d112a", - "metadata": {}, - "source": [ - "## Annotating study locus with ld population + Get list of ancestires" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "id": "8be0fd89", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:20:59.482466Z", - "start_time": "2023-10-27T11:20:59.062418Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-RECORD 0------------------------------------------------\n", - " studyId | FINNGEN_R9_C3_BAS... \n", - " variantId | 15_27983407_C_T \n", - " chromosome | 15 \n", - " studyLocusId | -8027743839728879857 \n", - " position | 27983407 \n", - " beta | 0.413866 \n", - " oddsRatio | null \n", - " oddsRatioConfidenceIntervalLower | null \n", - " oddsRatioConfidenceIntervalUpper | null \n", - " betaConfidenceIntervalLower | 0.388762 \n", - " betaConfidenceIntervalUpper | 0.43897 \n", - " pValueMantissa | 4.618 \n", - " pValueExponent | -61 \n", - " effectAlleleFrequencyFromSource | 0.0443583 \n", - " standardError | 0.025104 \n", - " subStudyDescription | null \n", - " qualityControls | [] \n", - " finemappingMethod | null \n", - " locus | [{null, null, nul... \n", - " ldSet | [{15_28482553_T_C... \n", - " ldPopulation | fin \n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "['fin']" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Adding ld population to study locus:\n", - "dl_df = (\n", - " sl.df\n", - " .join(studies, on='studyId', how='left')\n", - " .persist()\n", - ")\n", - "\n", - "dl_df.show(1, vertical=True)\n", - "\n", - "# Extract the required ld populations:\n", - "ld_populations = [row['ldPopulation'] for row in dl_df.select('ldPopulation').distinct().collect()]\n", - "\n", - "ld_populations" - ] - }, - { - "cell_type": "markdown", - "id": "b69aad94", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-24T10:34:37.443692Z", - "start_time": "2023-10-24T10:34:37.436917Z" - } - }, - "source": [ - "## Read ld index" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "id": "5465b9d3", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:21:03.778148Z", - "start_time": "2023-10-27T11:21:03.770967Z" - }, - "code_folding": [ - 2 - ] - }, - "outputs": [], - "source": [ - "from gentropy.common.utils import _liftover_loci, convert_gnomad_position_to_ensembl\n", - "\n", - "def _process_ld_indices(\n", - " ld_index_raw: hl.Table, \n", - " grch37_to_grch38_chain_path: str\n", - ") -> DataFrame:\n", - " \"\"\"Creates a look up table between variants and their coordinates in the LD Matrix.\n", - "\n", - " !!! info \"Gnomad's LD Matrix and Index are based on GRCh37 coordinates. This function will lift over the coordinates to GRCh38 to build the lookup table.\"\n", - "\n", - " Args:\n", - " ld_index_raw (hl.Table): LD index table from GnomAD\n", - " grch37_to_grch38_chain_path (str): Path to the chain file used to lift over the coordinates\n", - "\n", - " Returns:\n", - " DataFrame: Look up table between variants in build hg38 and their coordinates in the LD Matrix\n", - " \"\"\"\n", - " ld_index_38 = _liftover_loci(\n", - " ld_index_raw, grch37_to_grch38_chain_path, \"GRCh38\"\n", - " )\n", - "\n", - " return (\n", - " ld_index_38.to_spark()\n", - " # Filter out variants where the liftover failed\n", - " .filter(f.col(\"`locus_GRCh38.position`\").isNotNull())\n", - " .withColumn(\n", - " \"chromosome\", f.regexp_replace(\"`locus_GRCh38.contig`\", \"chr\", \"\")\n", - " )\n", - " # Temporary filter:\n", - " .filter(f.col('chromosome').isin(['16', '15']))\n", - " .withColumn(\n", - " \"position\",\n", - " convert_gnomad_position_to_ensembl(\n", - " f.col(\"`locus_GRCh38.position`\"),\n", - " f.col(\"`alleles`\").getItem(0),\n", - " f.col(\"`alleles`\").getItem(1),\n", - " ),\n", - " )\n", - " .select(\n", - " \"chromosome\",\n", - " \"position\",\n", - " f.concat_ws(\n", - " \"_\",\n", - " f.col(\"chromosome\"),\n", - " f.col(\"position\"),\n", - " f.col(\"`alleles`\").getItem(0),\n", - " f.col(\"`alleles`\").getItem(1),\n", - " ).alias(\"variantId\"),\n", - " f.col(\"idx\"),\n", - " )\n", - " # Filter out ambiguous liftover results: multiple indices for the same variant\n", - " .withColumn(\"count\", f.count(\"*\").over(Window.partitionBy([\"variantId\"])))\n", - " .filter(f.col(\"count\") == 1)\n", - " .drop(\"count\")\n", - " )\n" - ] - }, - { - "cell_type": "markdown", - "id": "15eab28a", - "metadata": {}, - "source": [ - "- Full genome: 5min" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "id": "e231cc9a", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:25:47.523343Z", - "start_time": "2023-10-27T11:21:07.729708Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 975:=================================================>(9996 + 4) / 10000]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+----------+---------+--------------------+--------+------------+\n", - "|chromosome| position| variantId| idx|ldPopulation|\n", - "+----------+---------+--------------------+--------+------------+\n", - "| 15|100068435| 15_100068435_C_T|11488485| fin|\n", - "| 15|100074914| 15_100074914_T_C|11488528| fin|\n", - "| 15|100082112| 15_100082112_C_G|11488600| fin|\n", - "| 15|100084933| 15_100084933_C_T|11488622| fin|\n", - "| 15|100086055| 15_100086055_C_T|11488643| fin|\n", - "| 15|100102338|15_100102338_CGAA...|11488802| fin|\n", - "| 15|100102474| 15_100102474_A_G|11488806| fin|\n", - "| 15|100140453| 15_100140453_G_GAC|11489087| fin|\n", - "| 15|100141050| 15_100141050_G_A|11489092| fin|\n", - "| 15|100149133| 15_100149133_G_T|11489141| fin|\n", - "| 15|100270659| 15_100270659_G_A|11490053| fin|\n", - "| 15|100317282| 15_100317282_T_C|11490464| fin|\n", - "| 15|100376040| 15_100376040_ACTT_A|11490958| fin|\n", - "| 15|100391254| 15_100391254_CA_C|11491027| fin|\n", - "| 15|100394474| 15_100394474_T_C|11491043| fin|\n", - "| 15|100466861| 15_100466861_T_C|11491614| fin|\n", - "| 15|100516200| 15_100516200_T_C|11492028| fin|\n", - "| 15|100525848| 15_100525848_T_G|11492086| fin|\n", - "| 15|100588385| 15_100588385_A_T|11492713| fin|\n", - "| 15|100605649| 15_100605649_A_T|11492813| fin|\n", - "+----------+---------+--------------------+--------+------------+\n", - "only showing top 20 rows\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "grch37_to_grch38_chain_path = 'gs://hail-common/references/grch37_to_grch38.over.chain.gz'\n", - "ld_index_raw_template = 'gs://gcp-public-data--gnomad/release/2.1.1/ld/gnomad.genomes.r2.1.1.{POP}.common.ld.variant_indices.ht'\n", - "ld_matrix_template ='gs://gcp-public-data--gnomad/release/2.1.1/ld/gnomad.genomes.r2.1.1.{POP}.common.adj.ld.bm'\n", - "\n", - "\n", - "ld_population = 'fin'\n", - "ld_index_raw_path = ld_index_raw_template.format(POP=ld_population)\n", - "\n", - "ld_index = (\n", - " _process_ld_indices(\n", - " hl.read_table(ld_index_raw_path),\n", - " grch37_to_grch38_chain_path,\n", - " )\n", - " .withColumn('ldPopulation', f.lit(ld_population))\n", - " .persist()\n", - ")\n", - "\n", - "ld_index.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "id": "e43d0dbf", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:26:04.794300Z", - "start_time": "2023-10-27T11:26:04.691498Z" - } - }, - "outputs": [], - "source": [ - "# how to do the join?\n", - "window_size = 250_000\n", - "tags = (\n", - " # Pre-process study locus:\n", - " f.broadcast(dl_df)\n", - " .select('studyLocusId', 'chromosome', 'position', 'ldPopulation')\n", - " .alias('study_locus')\n", - " .join(\n", - " (\n", - " ld_index\n", - " .selectExpr(*[f\"{col} as ld_index_{col}\" for col in ld_index.columns])\n", - " .alias('ld_index')\n", - " ),\n", - " on = [\n", - " (f.col('ld_index.ld_index_chromosome') == f.col('study_locus.chromosome')) & \n", - " (f.col('ld_index.ld_index_ldPopulation') == f.col('study_locus.ldPopulation')) &\n", - " (f.col('ld_index.ld_index_position') >= f.col('study_locus.position') - window_size) & \n", - " (f.col('ld_index.ld_index_position') <= f.col('study_locus.position') + window_size)\n", - " ],\n", - " how='left'\n", - " )\n", - " .select(\n", - " 'studyLocusId',\n", - " 'chromosome',\n", - " f.col('ld_index_variantId').alias('variantId'),\n", - " f.col('ld_index_idx').alias('idx'),\n", - " f.col('ld_index_position'),\n", - " f.col('ld_index_ldPopulation').alias('ldPopulation')\n", - " )\n", - " .persist()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "id": "45fb0df5", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:26:08.409160Z", - "start_time": "2023-10-27T11:26:06.312358Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 979:============================================> (173 + 27) / 200]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------+-----+\n", - "| studyLocusId|count|\n", - "+--------------------+-----+\n", - "|-8027743839728879857| 2079|\n", - "+--------------------+-----+\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "tags.groupby('studyLocusId').count().show()" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "id": "858894fc", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:26:14.204291Z", - "start_time": "2023-10-27T11:26:12.440937Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------+----------+--------------+-------------+-------------+---------+--------+--------------+\n", - "|studyLocusId |chromosome|first_position|last_position|window_length|first_idx|last_idx|ldVariantCount|\n", - "+--------------------+----------+--------------+-------------+-------------+---------+--------+--------------+\n", - "|-8027743839728879857|15 |27733460 |28232552 |499092 |11131979 |11134057|2079 |\n", - "+--------------------+----------+--------------+-------------+-------------+---------+--------+--------------+\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 159, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Collecting region:\n", - "ld_windows = (\n", - " tags\n", - " .orderBy('studyLocusId', 'ld_index_position')\n", - " .groupBy('studyLocusId', 'chromosome', 'ldPopulation')\n", - " .agg(\n", - " f.first(f.col('ld_index_position')).alias('first_position'),\n", - " f.first(f.col('variantId')).alias('first_variantId'),\n", - " f.first(f.col('idx')).alias('first_idx'),\n", - " f.last(f.col('ld_index_position')).alias('last_position'),\n", - " f.last(f.col('variantId')).alias('last_variantId'),\n", - " f.last(f.col('idx')).alias('last_idx'),\n", - " f.size(f.collect_list(f.col('variantId'))).alias('ldVariantCount')\n", - " )\n", - " .withColumn('window_length', f.col('last_position') - f.col('first_position'))\n", - " .persist()\n", - ")\n", - "\n", - "(\n", - " ld_windows\n", - " .select(\n", - " 'studyLocusId',\n", - " 'chromosome',\n", - " 'first_position',\n", - " 'last_position',\n", - " 'window_length',\n", - " 'first_idx',\n", - " 'last_idx',\n", - " 'ldVariantCount'\n", - " )\n", - " .show(truncate=False)\n", - ")\n", - "\n", - "\n", - "ld_windows.count()" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "id": "61d51efe", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:26:22.608155Z", - "start_time": "2023-10-27T11:26:22.421535Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Row(studyLocusId=-8027743839728879857, chromosome='15', ldPopulation='fin', first_position=27733460, first_variantId='15_27733460_AGCCAAACTGGCTCATGGCC_A', first_idx=11131979, last_position=28232552, last_variantId='15_28232552_G_GA', last_idx=11134057, ldVariantCount=2079, window_length=499092)" - ] - }, - "execution_count": 160, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "row = ld_windows.collect()[0]\n", - "row" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "id": "d4d006bd", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:26:31.400420Z", - "start_time": "2023-10-27T11:26:30.851547Z" - } - }, - "outputs": [], - "source": [ - "ld_population = row['ldPopulation']\n", - "study_locus_id = row['studyLocusId']\n", - "chromosome = row['chromosome']\n", - "first_index = row['first_idx']\n", - "last_index = row['last_idx']\n", - "\n", - "# For each row, we need to open the gnomad \n", - "half_matrix = (\n", - " BlockMatrix\n", - " .read(ld_matrix_template.format(POP=ld_population))\n", - " .filter(\n", - " range(first_index, last_index),\n", - " range(first_index, last_index)\n", - " )\n", - ")\n", - "\n", - "matrix = half_matrix + half_matrix.T\n" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "id": "e24da4ca", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:27:24.435664Z", - "start_time": "2023-10-27T11:26:35.945855Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-10-27 11:26:58.169 Hail: INFO: Coerced sorted dataset \n", - " \r" - ] - } - ], - "source": [ - "(\n", - " matrix.entries().to_spark()\n", - " .select(\n", - " (f.col('i')+first_index).alias('idx_i'),\n", - " (f.col('j')+first_index).alias('idx_j'),\n", - " f.when(f.col('i') == f.col('j'), f.col('entry')/2).otherwise(f.col('entry')).alias('r'),\n", - " f.lit(study_locus_id).alias('study_locus_ids')\n", - " )\n", - " # Joining with i:\n", - " .join(\n", - " (\n", - " tags\n", - " .select(\n", - " f.col('variantId').alias('variantIdLeft'), \n", - " f.col('idx').alias('idx_i')\n", - " )\n", - " ), \n", - " on='idx_i', how='outer'\n", - " )\n", - " # Joining with i:\n", - " .join(\n", - " (\n", - " tags\n", - " .select(\n", - " 'chromosome', \n", - " f.col('variantId').alias('variantIdRight'), \n", - " f.col('idx').alias('idx_j')\n", - " )\n", - " ), \n", - " on='idx_j', how='outer'\n", - " )\n", - " .select(\n", - " 'variantIdLeft',\n", - " 'variantIdRight',\n", - " 'study_locus_ids',\n", - " 'r'\n", - " )\n", - " .distinct()\n", - " .write.mode('overwrite').parquet(f'gs://ot-team/dsuveges/ld_matrix-{study_locus_id}')\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "id": "3cb94848", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:28:28.344710Z", - "start_time": "2023-10-27T11:28:27.670811Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-RECORD 0---------------------------------------------------\n", - " studyId | FINNGEN_R9_C3_BASAL_CELL_CARCINOMA_EXALLC \n", - " variantIdLead | 15_27983407_C_T \n", - " studyLocusId | -8027743839728879857 \n", - " variantId | 15_27733459_AGCCAAACTGGCTCATGGCC_A \n", - " pValueMantissa | 1.937 \n", - " pValueExponent | -2 \n", - " beta | 0.0267247 \n", - " standardError | 0.011429 \n", - "only showing top 1 row\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "837" - ] - }, - "execution_count": 164, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "study_locus_id = -8027743839728879857\n", - "\n", - "# Get data:\n", - "ld_matrix = session.spark.read.parquet(f'gs://ot-team/dsuveges/ld_matrix-{study_locus_id}')\n", - "\n", - "# Select one study locus:\n", - "selected_locus = (\n", - " dl_df\n", - " .filter(f.col('studyLocusId') == study_locus_id)\n", - " .select(\n", - " '*',\n", - " f.explode_outer('locus').alias('exploded_locus')\n", - " )\n", - " .select(\n", - " 'studyId',\n", - " f.col('variantId').alias('variantIdLead'),\n", - " f.col('studyLocusId'),\n", - " *[f'exploded_locus.{col}' for col in ['variantId', 'pValueMantissa', 'pValueExponent', 'beta', 'standardError']]\n", - " )\n", - " .persist()\n", - ")\n", - "\n", - "selected_locus.show(1, False, True)\n", - "selected_locus.count()" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "id": "0e571433", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T13:49:41.356331Z", - "start_time": "2023-10-27T13:49:40.463119Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------+--------------------+---------------+---------------+---------------+-------------------+-------------------+----------+------------------+--------------------+\n", - "| studyId| studyLocusId| variantIdLead| variantIdLeft| variantIdRight|pValueMantissaRight|pValueExponentRight| betaRight|standardErrorRight| r|\n", - "+--------------------+--------------------+---------------+---------------+---------------+-------------------+-------------------+----------+------------------+--------------------+\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27792793_C_T|15_27772290_T_C| 9.979| -6| 0.0730063| 0.0165261| 0.3188276023578284|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27950388_C_T|15_27778556_C_T| 1.142| -2| 0.119731| 0.0473309| -0.1845689041174883|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_28104601_A_T|15_27809200_G_A| 1.397| -2|-0.0286999| 0.0116763| 0.01919714154020592|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27970325_G_A|15_27809200_G_A| 1.397| -2|-0.0286999| 0.0116763|-0.12413267238343029|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27899878_T_C|15_27851307_A_G| 5.606| -4|-0.0424098| 0.0122927|-0.45516734696600447|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27914364_C_T|15_27851307_A_G| 5.606| -4|-0.0424098| 0.0122927| -0.4029686644067302|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27828335_G_A|15_27858692_G_A| 3.599| -4| 0.0427448| 0.0119804| 0.09141477739652863|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27845815_A_G|15_27858692_G_A| 3.599| -4| 0.0427448| 0.0119804| 0.7803430284267874|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27820229_G_A|15_27858692_G_A| 3.599| -4| 0.0427448| 0.0119804|-0.08881336794530735|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27825631_G_C|15_27915426_C_A| 1.001| -6| 0.0654293| 0.0133765| 0.13879209804155437|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_28211160_A_T|15_27915426_C_A| 1.001| -6| 0.0654293| 0.0133765| 0.05996001018362227|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_28019021_G_T|15_27915426_C_A| 1.001| -6| 0.0654293| 0.0133765| 0.03843623265438152|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_28081390_A_T|15_27938863_T_A| 2.109| -3| 0.157002| 0.0510665|-0.04189151880774...|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27907664_A_T|15_27938914_G_T| 2.725| -2| 0.0355862| 0.0161172|-0.16859090364294946|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27819940_C_T|15_27938914_G_T| 2.725| -2| 0.0355862| 0.0161172| 0.11697321459267764|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27853556_T_G|15_27938914_G_T| 2.725| -2| 0.0355862| 0.0161172| -0.1404576556679461|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_28052911_T_C|15_27951206_G_A| 1.437| -2| 0.0695541| 0.0284131| 0.04461220378655805|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27790520_G_A|15_27951206_G_A| 1.437| -2| 0.0695541| 0.0284131| 0.04420892575950266|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27879868_C_A|15_27989264_G_T| 2.454| -17| 0.147291| 0.0173896| 0.2404542405419156|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27822551_G_A|15_27989264_G_T| 2.454| -17| 0.147291| 0.0173896|-0.04613729472121665|\n", - "+--------------------+--------------------+---------------+---------------+---------------+-------------------+-------------------+----------+------------------+--------------------+\n", - "only showing top 20 rows\n", - "\n", - "385641\n", - "621.0\n" - ] - }, - { - "data": { - "text/plain": [ - "621" - ] - }, - "execution_count": 189, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Filtering matrix:\n", - "processed_matrix = (\n", - " ld_matrix\n", - " .join(\n", - " selected_locus.select(f.col('variantId').alias('variantIdLeft')),\n", - " on='variantIdLeft', how='inner'\n", - " )\n", - " .join(\n", - " selected_locus.withColumnRenamed('variantId', 'variantIdRight'), \n", - " on='variantIdRight', how='inner'\n", - " )\n", - " .select(\n", - " 'studyId',\n", - " 'studyLocusId',\n", - " 'variantIdLead',\n", - " 'variantIdLeft',\n", - " 'variantIdRight',\n", - " f.col('pValueMantissa').alias('pValueMantissaRight'),\n", - " f.col('pValueExponent').alias('pValueExponentRight'),\n", - " f.col('beta').alias('betaRight'),\n", - " f.col('standardError').alias('standardErrorRight'),\n", - " 'r'\n", - " )\n", - " .persist()\n", - ")\n", - "\n", - "processed_matrix.show()\n", - "print(processed_matrix.count())\n", - "print(sqrt(processed_matrix.count()))\n", - "processed_matrix.select('variantIdRight').distinct().count()" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "id": "381f093b", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:29:01.779404Z", - "start_time": "2023-10-27T11:28:57.285168Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "text/plain": [ - "array([[1. , 0.88019644, 0.28333008, 0.25666969, 0.26345801],\n", - " [0.88019644, 1. , 0.26027515, 0.24115952, 0.2442993 ],\n", - " [0.28333008, 0.26027515, 1. , 0.85898573, 0.89168227],\n", - " [0.25666969, 0.24115952, 0.85898573, 1. , 0.96268566],\n", - " [0.26345801, 0.2442993 , 0.89168227, 0.96268566, 1. ]])" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "pivoted_matrix = (\n", - " processed_matrix\n", - " .orderBy(f.col('variantIdLeft'), f.col('variantIdRight'))\n", - " .groupBy('variantIdLeft')\n", - " .pivot(\"variantIdRight\")\n", - " .agg(f.first('r'))\n", - " .orderBy('variantIdLeft')\n", - " # Convert to numpy array ingested by SuSie:\n", - " .toPandas().set_index('variantIdLeft').to_numpy()\n", - ")\n", - "\n", - "pivoted_matrix[0:5, 0:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "444e2ca2", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-26T08:49:13.002374Z", - "start_time": "2023-10-26T08:49:12.604741Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , 0.99851182, 0.85089485, ..., 0.04532387,\n", - " 0.02258468, 0.02476075],\n", - " [ 0.99851182, 1. , 0.85111292, ..., 0.0465783 ,\n", - " 0.02253374, 0.02470884],\n", - " [ 0.85089485, 0.85111292, 1. , ..., 0.04748182,\n", - " 0.01191697, 0.01652519],\n", - " ...,\n", - " [ 0.04532387, 0.0465783 , 0.04748182, ..., 1. ,\n", - " -0.05908013, -0.05417476],\n", - " [ 0.02258468, 0.02253374, 0.01191697, ..., -0.05908013,\n", - " 1. , 0.96019982],\n", - " [ 0.02476075, 0.02470884, 0.01652519, ..., -0.05417476,\n", - " 0.96019982, 1. ]])" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pivoted_matrix.toPandas().set_index('variantIdLeft').to_numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "id": "8ed590f1", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T13:50:01.812451Z", - "start_time": "2023-10-27T13:49:58.985895Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "(\n", - " processed_matrix\n", - " # Convert to numpy array ingested by SuSie:\n", - " .write.mode('overwrite').parquet(f'gs://ot-team/dsuveges/processed-ld-matrix-{study_locus_id}')\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "id": "5fc2f7ac", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T11:30:11.297300Z", - "start_time": "2023-10-27T11:30:09.400659Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "# selected_locus.write.mode('overwrite').parquet('gs://ot-team/dsuveges/selected_studyLocus_2224601896262245870')\n", - "(\n", - " processed_matrix\n", - " .select(\n", - " 'studyId',\n", - " f.col('variantIdRight').alias('variantId'),\n", - " f.col('pValueMantissaRight').alias('pValueMantissa'),\n", - " f.col('pValueExponentRight').alias('pValueExponent'),\n", - " f.col('betaRight').alias('beta'),\n", - " f.col('standardErrorRight').alias('standardError'),\n", - " )\n", - " .distinct()\n", - " .write.mode('overwrite').parquet(f'gs://ot-team/dsuveges/selected_studyLocus_{study_locus_id}')\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "id": "a4d47348", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T13:33:39.686914Z", - "start_time": "2023-10-27T13:33:39.682284Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.00, 0.18, 0.18, 0.97, 0.97, 0.15, 0.18, 0.11, 0.13, 0.12\n", - "0.18, 1.00, 0.99, 0.15, 0.15, 0.03, 0.03, 0.02, 0.01, 0.02\n", - "0.18, 0.99, 1.00, 0.15, 0.15, 0.03, 0.03, 0.02, 0.01, 0.02\n", - "0.97, 0.15, 0.15, 1.00, 1.00, 0.14, 0.17, 0.11, 0.12, 0.12\n", - "0.97, 0.15, 0.15, 1.00, 1.00, 0.14, 0.17, 0.11, 0.12, 0.12\n", - "0.15, 0.03, 0.03, 0.14, 0.14, 1.00, 0.32, 0.45, 0.50, 0.25\n", - "0.18, 0.03, 0.03, 0.17, 0.17, 0.32, 1.00, 0.29, 0.33, 0.52\n", - "0.11, 0.02, 0.02, 0.11, 0.11, 0.45, 0.29, 1.00, 0.91, 0.29\n", - "0.13, 0.01, 0.01, 0.12, 0.12, 0.50, 0.33, 0.91, 1.00, 0.33\n", - "0.12, 0.02, 0.02, 0.12, 0.12, 0.25, 0.52, 0.29, 0.33, 1.00\n" - ] - } - ], - "source": [ - "print(\n", - " '\\n'.join(\n", - " [\n", - " ', '.join([f'{value**2:.2f}' for value in row]) \n", - " for row in\n", - " (\n", - " pivoted_matrix\n", - " [[291, 293, 295, 322, 323, 508, 514, 529, 534, 515]]\n", - " [:,[291, 293, 295, 322, 323, 508, 514, 529, 534, 515]]\n", - " )\n", - " ]\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "id": "f3910534", - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-27T13:45:22.792801Z", - "start_time": "2023-10-27T13:45:22.721043Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------+--------------------+---------------+---------------+---------------+-------------------+-------------------+----------+------------------+--------------------+\n", - "| studyId| studyLocusId| variantIdLead| variantIdLeft| variantIdRight|pValueMantissaRight|pValueExponentRight| betaRight|standardErrorRight| r|\n", - "+--------------------+--------------------+---------------+---------------+---------------+-------------------+-------------------+----------+------------------+--------------------+\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27792793_C_T|15_27772290_T_C| 9.979| -6| 0.0730063| 0.0165261| 0.3188276023578284|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27950388_C_T|15_27778556_C_T| 1.142| -2| 0.119731| 0.0473309| -0.1845689041174883|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_28104601_A_T|15_27809200_G_A| 1.397| -2|-0.0286999| 0.0116763| 0.01919714154020592|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27970325_G_A|15_27809200_G_A| 1.397| -2|-0.0286999| 0.0116763|-0.12413267238343029|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27899878_T_C|15_27851307_A_G| 5.606| -4|-0.0424098| 0.0122927|-0.45516734696600447|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27914364_C_T|15_27851307_A_G| 5.606| -4|-0.0424098| 0.0122927| -0.4029686644067302|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27828335_G_A|15_27858692_G_A| 3.599| -4| 0.0427448| 0.0119804| 0.09141477739652863|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27845815_A_G|15_27858692_G_A| 3.599| -4| 0.0427448| 0.0119804| 0.7803430284267874|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27820229_G_A|15_27858692_G_A| 3.599| -4| 0.0427448| 0.0119804|-0.08881336794530735|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27825631_G_C|15_27915426_C_A| 1.001| -6| 0.0654293| 0.0133765| 0.13879209804155437|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_28211160_A_T|15_27915426_C_A| 1.001| -6| 0.0654293| 0.0133765| 0.05996001018362227|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_28019021_G_T|15_27915426_C_A| 1.001| -6| 0.0654293| 0.0133765| 0.03843623265438152|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_28081390_A_T|15_27938863_T_A| 2.109| -3| 0.157002| 0.0510665|-0.04189151880774...|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27907664_A_T|15_27938914_G_T| 2.725| -2| 0.0355862| 0.0161172|-0.16859090364294946|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27819940_C_T|15_27938914_G_T| 2.725| -2| 0.0355862| 0.0161172| 0.11697321459267764|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27853556_T_G|15_27938914_G_T| 2.725| -2| 0.0355862| 0.0161172| -0.1404576556679461|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_28052911_T_C|15_27951206_G_A| 1.437| -2| 0.0695541| 0.0284131| 0.04461220378655805|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27790520_G_A|15_27951206_G_A| 1.437| -2| 0.0695541| 0.0284131| 0.04420892575950266|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27879868_C_A|15_27989264_G_T| 2.454| -17| 0.147291| 0.0173896| 0.2404542405419156|\n", - "|FINNGEN_R9_C3_BAS...|-8027743839728879857|15_27983407_C_T|15_27822551_G_A|15_27989264_G_T| 2.454| -17| 0.147291| 0.0173896|-0.04613729472121665|\n", - "+--------------------+--------------------+---------------+---------------+---------------+-------------------+-------------------+----------+------------------+--------------------+\n", - "only showing top 20 rows\n", - "\n" - ] - } - ], - "source": [ - "processed_matrix.show()" - ] - } - ], - "metadata": { - "_draft": { - "nbviewer_url": "https://gist.github.com/DSuveges/df6768b7d5637842e009aa945f3ea062" - }, - "gist": { - "data": { - "description": "Issue-3131-Productionizing_LD_matrix.ipynb", - "public": false - }, - "id": "df6768b7d5637842e009aa945f3ea062" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/README.md b/notebooks/README.md new file mode 100644 index 000000000..35132b6bf --- /dev/null +++ b/notebooks/README.md @@ -0,0 +1,3 @@ +# Notebooks + +The notebooks in listed in this directory are not actively maintained and updated. diff --git a/notebooks/Release_QC_metrics.ipynb b/notebooks/Release_QC_metrics.ipynb new file mode 100644 index 000000000..5f9bf77c0 --- /dev/null +++ b/notebooks/Release_QC_metrics.ipynb @@ -0,0 +1,984 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook Title: Exploratory Data Analysis for Release QC Metrics\n", + "\n", + "## Description:\n", + "This notebook provides an exploratory data analysis for release quality control (QC) metrics. The notebook utilizes PySpark and pandas for data processing and visualization.\n", + "\n", + "## Notebook Workflow:\n", + "1. Import necessary modules and set up the release path and version.\n", + "2. Load and analyze the variant index data:\n", + " - Count the number of unique variants.\n", + "3. Load and analyze the study index data for different data sources (FinnGen, GWASCat, eQTLcat):\n", + " - Count the number of unique studies for each data source.\n", + "4. Analyze the credible sets for each datasource (Finngen, gwascat, eqtlcat):\n", + " - Analyze the credible sets:\n", + " - Count the number of unique credible sets and unique study IDs.\n", + " - Plot a scatter plot of the credible set size vs. the top posterior probability.\n", + " - Count the number of credible sets with a top SNP posterior probability > 0.9..\n", + "5. Analyze colocalization data:\n", + " - Count the total number of colocalizations and the number with clpp > 0.8.\n", + " - Calculate the average number of overlaps per credible set.\n", + "6. Analyze locus-to-gene (L2G) predictions:\n", + " - Load the locus-to-gene predictions data.\n", + " - How many Studylocus contains a \"good\" l2g prediction? (l2g_score > 0.5)\n", + " - How does l2g perform based on different datasource inputs? (impossible to tell)\n", + " \n", + "Note: This notebook assumes that the necessary data files are available in the specified release path and version.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup steps\n", + "##### 1. Import necessary modules and set up the release path and version." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/03/20 13:24:56 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n" + ] + } + ], + "source": [ + "\"\"\"notebook for release qc metrics.\"\"\"\n", + "\n", + "import sys\n", + "\n", + "from pyspark.sql import functions as f\n", + "\n", + "from gentropy.common.session import Session\n", + "\n", + "sys.path.append(\"../../gentropy/src/\")\n", + "release_path=\"../../otg_releases\"\n", + "release_ver=\"2403\"\n", + "\n", + "session=Session()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Load and analyze the variant index data:\n", + " - Count the number of unique variants." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 3:> (0 + 8) / 9]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variant index contains 5468737 unique variants.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "\n", + "variant_index_path = f\"{release_path}/{release_ver}/variant_index\"\n", + "variant_index=session.spark.read.parquet(variant_index_path, recursiveFileLookup=True)\n", + "\n", + "# How many variants?\n", + "\n", + "# How many variants with MAF>=0.01 for EUR population?\n", + "#variant_index.filter(variant_index[\"alleleFrequencies.populationName\"] > 0.05).show(10, False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 5. Analyze the credible sets:\n", + "For each datasource and finemapping methods:\n", + "- Count the number of unique credible sets and unique study IDs.\n", + "- Plot a scatter plot of the credible set size vs. the top posterior probability.\n", + "- Count the number of credible sets with a top SNP posterior probability > 0.9.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unique finngen susie CSs: 13966\n", + "Ingested 13966 Credible sets from 2408 finngen studies\n", + "Summary of finngen sample sizes: Mean: 355917.896179402 L.quart: 316100.0 Median: 392089.0 U.quart: 409683.0\n", + "Number of finngen studies with at least one CS: 1194\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of CS with top SNP PP > 0.9: 1898\n", + "Summary of finngen credible set sizes: L.quart: 4.0 Median: 14.0 U.quart: 41.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# study_index='gs://genetics_etl_python_playground/releases/24.03/study_index'\n", + "# Study Index. It has different files for different datasource (FinnGen, GWASCat, eQTLcat).\n", + "# Credible_set. Please use Daniels’ notebook as a reference. For each subfolder:\n", + "\n", + "finngen_index_path=f\"{release_path}/{release_ver}/study_index/finngen\"\n", + "# finngen susie:\n", + "finngen_susie_path=f\"{release_path}/{release_ver}/credible_set/finngen_susie\"\n", + "\n", + "finngen_susie=session.spark.read.parquet(finngen_susie_path, recursiveFileLookup=True)\n", + "\n", + "# FinnGen:\n", + "finngen_index=session.spark.read.parquet(finngen_index_path, recursiveFileLookup=True)\n", + "# Number of CSs, studies.\n", + "sample_size_quartiles = finngen_index.stat.approxQuantile(\"nSamples\", [0.25, 0.5, 0.75], 0.01)\n", + "# Number of unique studyids with at leas one CS.\n", + "# Number of CSs with at leas one SNP with PIP>0.9\n", + "# The descriptive summary of 99% CS size and histogram/density plot\n", + "\n", + "credset_size_quartiles = finngen_susie.select(\"studyId\", \"region\", \"credibleSetIndex\", \"locus.posteriorProbability\").withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\"))).stat.approxQuantile(\"credset_size\", [0.25, 0.5, 0.75], 0.01)\n", + "\n", + "finngen_susie.select(\"studyId\", \"region\", \"credibleSetIndex\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\"))).toPandas().plot.scatter(x=\"credset_size\", y=\"top_PP\", xlim=[0, 500], alpha=0.05, label=\"finngen susie CSs\", title=\"finngen susie credsets\")\n", + "\n", + "finngen_susie_fm=finngen_susie.select(\"studyId\", \"studyLocusId\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\")))\n", + "# The histogram/density plot for total sample size\n", + "#finngen_index.select(f.col(\"nSamples\")).toPandas().plot.hist(bins=10, alpha=0.5, label=\"FinnGen sample size\", title=\"FinnGen sample sizes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unique finngen pics CSs: 19967 in 1342 studies.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of finngen_pics CS with top SNP PP > 0.9: 7239\n", + "Summary of finngen_pics sample sizes: L.quart: 316100.0 Median: 392089.0 U.quart: 409683.0\n", + "Summary of finngen_pics credset sizes: Mean: 36.04337156307908 L.quart: 1.0 Median: 7.0 U.quart: 30.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# finngen (PICs):\n", + "finngen_index_path=f\"{release_path}/{release_ver}/study_index/finngen\"\n", + "finngen_index=session.spark.read.parquet(finngen_index_path, recursiveFileLookup=True)\n", + "finngen_pics_path=f\"{release_path}/{release_ver}/credible_set/finngen_pics\"\n", + "# Number of CSs.\n", + "finngen_pics=session.spark.read.parquet(finngen_pics_path, recursiveFileLookup=True)\n", + "#gwascat_sumstats.printSchema()\n", + "# keep only credible sets snps\n", + "\n", + "finngen_pics_fm=finngen_pics.select(\"studyId\", \"studyLocusId\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\")))\n", + "finngen_pics_fm.select(\"credset_size\", \"top_PP\").toPandas().plot.scatter(x=\"credset_size\", y=\"top_PP\", alpha=0.05, xlim=[0, 500], label=\"finngen PICS CS\", title=\"finngen_pics CS\")\n", + "sample_size_quartiles = finngen_index.stat.approxQuantile(\"nSamples\", [0.25, 0.5, 0.75], 0.01)\n", + "\n", + "sample_size_quartiles = finngen_pics_fm.stat.approxQuantile(\"credset_size\", [0.25, 0.5, 0.75], 0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 9966 common loci between finngen susie and finngen pics\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "correlation coef: 0.6244595901320829\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "finngen_matching=finngen_pics_fm.withColumnRenamed(\"top_PP\", \"pics_PP\").join(finngen_susie_fm.withColumnRenamed(\"top_PP\", \"susie_PP\"), on=[\"studyId\", \"studyLocusId\"], how=\"inner\")\n", + "finngen_matching.select(\"pics_PP\", \"susie_PP\").toPandas().plot.scatter(x=\"susie_PP\", y=\"pics_PP\", alpha=0.05, title=\"finngen_pics vs finngen_susie CS\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unique eQTLcat studies: 1801315\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unqiue eQTLcat tissues: 76\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ingested 2055350 Credible sets from 1801315 eQTL catalog studies\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary of eQTL catalog sample sizes: Mean: 336.1455965527037 L.quart: 190.0 Median: 322.0 U.quart: 483.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of eQTL catalog studies with at least one CS: 1801315\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of CS with top SNP PP > 0.9: 386227\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 146:===============================================> (32 + 5) / 37]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary of eQTL credible set sizes: L.quart: 3.0 Median: 10.0 U.quart: 27.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "eqtl_index_path=f\"{release_path}/{release_ver}/study_index/eqtl_catalogue\"\n", + "# eQTLcat:\n", + "# Number of studies\n", + "eqtl_index=session.spark.read.parquet(eqtl_index_path, recursiveFileLookup=True)\n", + "# Number of tissues, list of tissues\n", + "#eqtl_index.select(f.col(\"biosampleFromSourceId\")).distinct().show(truncate=False)\n", + "\n", + "# Credible_set. Please use Daniels’ notebook as a reference. For each subfolder:\n", + "# eqtl catalog susie:\n", + "eqtlcat_susie_path=f\"{release_path}/{release_ver}/credible_set/eqtl_catalogue_susie\"\n", + "# Number of CSs.\n", + "eqtlcat_susie=session.spark.read.parquet(eqtlcat_susie_path, recursiveFileLookup=True)\n", + "\n", + "\n", + "# Number of CSs, studies.\n", + "sample_size_quartiles = eqtl_index.stat.approxQuantile(\"nSamples\", [0.25, 0.5, 0.75], 0.01)\n", + "# Number of unique studyids with at leas one CS.\n", + "# Number of CSs with at leas one SNP with PIP>0.9\n", + "# The descriptive summary of 99% CS size and histogram/density plot\n", + "\n", + "credset_size_quartiles = eqtlcat_susie.select(\"studyId\", \"region\", \"credibleSetIndex\", \"locus.posteriorProbability\").withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\"))).stat.approxQuantile(\"credset_size\", [0.25, 0.5, 0.75], 0.01)\n", + "\n", + "# Out of mem error:\n", + "#eqtlcat_susie.select(\"studyId\", \"region\", \"credibleSetIndex\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\"))).toPandas().plot.scatter(x=\"credset_size\", y=\"top_PP\", xlim=[0, 500], alpha=0.05, label=\"finngen susie CSs\", title=\"finngen susie credsets\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unique gwascat studies: 79858\n", + "Number of unique SUMSTATS gwascat studies: 18435\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unique gwas catalog sumstats CSs: 247782 in 9679 studies.\n", + "Summary of SUMSTATS gwas sample sizes: L.quart: 88329.0 Median: 357580.0 U.quart: 445573.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of SUMSTATS CS with top SNP PP > 0.9: 77721\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary of SUMSTATS gwascat pics credset sizes: Mean: 39.7659733230078 L.quart: 1.0 Median: 8.0 U.quart: 35.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unique gwas catalog curated CSs: 531198 in 35662 studies.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of CURATED CS with top SNP PP > 0.9: 92000\n", + "Summary of CURATED gwas sample sizes: L.quart: 687.0 Median: 4960.0 U.quart: 21282.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary of CURATED gwascat pics credset sizes: Mean: 35.42119007174911 L.quart: 1.0 Median: 8.0 U.quart: 34.0\n" + ] + }, + { + "data": { + "image/png": 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t93PdddcB8KIXvWidj1WtVjnzzDO5//77OfPMM9daxPC73/0ud955J1Au0eZ5vlGPJcTGkq3gQqzHeeedxy9/+UsOO+ww3vOe92Ct5ctf/jL77rvvGqX7q9UqBx54ILfffjsLFizoXNkfeeSRNBoNGo3GGsHN8ccfz/nnn8+pp57KoYceyh//+Ee+973vsdtuu40679WvfjWzZ8/msMMOY9asWdx///18+ctf5rjjjuskdl544YX88pe/5KijjuJd73oXe+21F4sWLeKKK67glltuobe3d8yPt/vuu9Pb28ull15KV1cXtVqNgw8+mF133XWN12hwcJCddtqJv/u7v2O//fajXq9zww03cNddd3HxxRd3znvHO97BlVdeyWte8xr+4R/+gUceeYTvfve77L777hv997M2559/Pr/97W857rjj2GWXXXj22Wf56le/yk477cThhx++3ue35557svvuu/PhD3+Yv/71r3R3d/Pf//3frFy5co3HOfDAAwH4l3/5F4455hiMMbz5zW/mHe94BytWrOBv/uZv2GmnnXjiiSf40pe+xP77789ee+01rs91Vd/5znd49atfzZve9CYWLFjA0UcfTa1W4y9/+Qs//OEPWbRoEZ/97GeBMq9n1113ZcGCBey+++40Gg1uuOEG/ud//oeXv/zlnZmgdfnIRz7Cn//8Zy6++GJ+/etfdyoUL168mGuuuYY777yzk5j86U9/mrvvvps3velNnSDvnnvu4Tvf+Q5Tp05db6K0EBttgnZpCbHVuPHGG/0BBxzgoyjyu+++u//GN77hP/ShD/kkSdY49yMf+YgH/Kc//elRx1/4whd6wD/yyCOjjrfbbf+hD33Iz5kzx1cqFX/YYYf52267bY3t0f/xH//hjzzySD9t2jQfx7Hffffd/Uc+8hHf398/6v6eeOIJf9JJJ/kZM2Z0ig6edtppnSJ+Y308773/8Y9/7Pfee+/Otvd1bQtP09R/5CMf8fvtt5/v6urytVrN77fffv6rX/3qGudefPHFfscdd/RxHPvDDjvM//73v1/nVvArrrhi1G0vv/xyD6yx1X2koOLI1u8bb7zRv/71r/c77LCDj6LI77DDDv4tb3mLf+ihh8b0/O677z4/f/58X6/X/fTp0/073/lO/7//+79rvAZFUfj3v//9fsaMGV4p1dkWfuWVV/pXv/rVfubMmT6KIv+CF7zA//M//7NftGjRWl+/VbGOIn6rW30r+Ihms+k/+9nP+pe//OW+Xq/7KIr8vHnz/Pvf/37/8MMPd877wQ9+4N/85jf73Xff3VcqFZ8kid9777392Wef7QcGBp738UeMPNepU6f6IAj8nDlz/AknnOBvuummzjm33nqrP+200/y+++7re3p6fBiG/gUveIE/5ZRT1vh9EGK8SG8pITbCG97wBv785z/zl7/8ZaKHIoQQYjWScyPE82i1WqO+/8tf/sJ1113HK17xiokZkBBCiPWSmRshnsecOXM45ZRT2G233XjiiSf42te+Rpqm3HvvvcybN2+ihyeEEGI1klAsxPN4zWteww9+8AMWL15MHMcccsghXHjhhRLYCCHEJCUzN0IIIYTYpkjOjRBCCCG2KRLcCCGEEGKbst3l3DjneOaZZ+jq6tqoEvNCCCGE2PK89wwODrLDDjust5EsbIfBzTPPPLNGV2EhhBBCbB2eeuopdtppp/Wes90FNyOl6p966im6u7sneDRCCCGEGIuBgQF23nnnzuf4+mx3wc3IUlR3d7cEN0IIIcRWZiwpJZJQLIQQQohtigQ3QgghhNimSHAjhBBCiG3KdpdzI4QQANZa8jyf6GEIIVYRRdHzbvMeCwluhBDbFe89ixcvpq+vb6KHIoRYjdaaXXfdlSiKNul+JLgRQmxXRgKbmTNnUq1WpZinEJPESJHdRYsW8YIXvGCTfjcluBFCbDestZ3AZtq0aRM9HCHEambMmMEzzzxDURSEYbjR9yMJxUKI7cZIjk21Wp3gkQgh1mZkOcpau0n3I8GNEGK7I0tRQkxO4/W7KcGNEEIIIbYpExrc/Pa3v2XBggXssMMOKKW45pprnvc2N910Ey996UuJ45gXvvCFfOtb39rs4xyruR+7tvMlhBBCiIkxocFNo9Fgv/324ytf+cqYzn/sscc47rjjeOUrX8nChQv5wAc+wDve8Q5+8YtfbOaRrt/aAhoJcoQQYu3OO+889t9//4kexgZ7/PHHUUqxcOHCDb7tWC/g12fu3LkopVBKUavVeOlLX8oVV1zR+fnaXteBgQHOPvts9txzT5IkYfbs2cyfP5+rrroK7z1Qfra+9a1vZYcddiBJEnbaaSde//rX88ADD6x3PIsXL+b9738/u+22G3Ecs/POO7NgwQJuvPHGzjn/+7//y+te9zpmzpxJkiTMnTuXE044gWeffXaTXovnM6G7pY499liOPfbYMZ9/6aWXsuuuu3LxxRcDsNdee3HLLbfwuc99jmOOOWZzDVMIIYSYFM4//3ze+c53MjAwwMUXX8wJJ5zAjjvuyKGHHrrGuX19fRx++OH09/fzb//2b7z85S8nCAJ+85vf8NGPfpS/+Zu/oVar8apXvYoXvehFXHXVVcyZM4enn36an/3sZ+utBfX4449z2GGH0dvby2c+8xle/OIXk+c5v/jFLzjttNN44IEHWLp0KUcffTTHH388v/jFL+jt7eXxxx/nJz/5CY1GYzO+SoCfJAB/9dVXr/ecI444wp9++umjjn3zm9/03d3d67xNu932/f39na+nnnrKA76/v38cRu39Lmf+9Hm/hBCTQ6vV8vfdd59vtVqbfF/WOp8X1lvrxmFk6zcwMODf+ta3+mq16mfPnu0vueQSf9RRR3XeD7/0pS/5ffbZp3P+1Vdf7QH/ta99rXPs6KOP9meffbb33vuHH37Yv+51r/MzZ870tVrNv+xlL/PXX3/9qMf8yle+4l/4whf6OI79zJkz/d/+7d92fmat9Z/+9Kf97rvv7qMo8jvvvLP/t3/7t87PP/rRj/p58+b5SqXid911V//xj3/cZ1nmvff+8ssv98Cor8svv3ytz/vXv/61f/nLX+6r1arv6enxhx56qH/88ce9996ffPLJ/vWvf/2o808//XR/1FFHdb4/6qij/Pve9z5/+umn+97eXj9z5kz/n//5n35oaMifcsopvl6v+913391fd911ndusWLHCv/Wtb/XTp0/3SZL4F77whf6b3/ym996vMe6Rx7rzzjv9/Pnz/bRp03x3d7c/8sgj/d133925z1122WXU7XbZZRfvvfcLFy70r3jFK3y9XvddXV3+pS99qb/rrrvW+lqM3M/nPve5zvd5nvtqteo/9rGPee+9P/fcc/1+++3X+fl73vMeX6vV/F//+tc17mtwcNDnee7vvfdeD3Re17E69thj/Y477uiHhobW+NnKlSu99+W/wyAIfJ7nY77f9f2O9vf3j/nze6tKKF68eDGzZs0adWzWrFkMDAzQarXWepuLLrqInp6eztfOO++8JYY6inN+iz/m+jjnKaybdOMSYmvgvaeVFfS3c/paOf3tnFZWdKb4N4czzjiDW2+9lZ/85Cdcf/313Hzzzdxzzz2dnx911FHcd999LF26FIDf/OY3TJ8+nZtuugkot8DfdtttvOIVrwBgaGiI1772tdx4443ce++9vOY1r2HBggU8+eSTAPz+97/nX/7lXzj//PN58MEH+fnPf86RRx7ZebyzzjqLT33qU3ziE5/gvvvu4/vf//6o9+auri6+9a1vcd999/GFL3yBr3/963zuc58D4IQTTuBDH/oQ++yzD4sWLWLRokWccMIJazznoih4wxvewFFHHcUf/vAHbrvtNt71rndt8G6ab3/720yfPp0777yT97///bznPe/h7//+7zn00EO55557ePWrX82JJ55Is9kE6Dynn/3sZ9x///187WtfY/r06QDceeedANxwww0sWrSIq666CoDBwUFOPvlkbrnlFm6//XbmzZvHa1/7WgYHBwG46667ALj88stZtGhR5/u3ve1t7LTTTtx1113cfffdfOxjH9ug2i5BEBCGIVmWrfEz5xw//OEPedvb3sYOO+ywxs/r9TpBEDBjxgy01lx55ZVj3n69YsUKfv7zn3PaaadRq9XW+Hlvby8As2fPpigKrr766s36+7FWYw6nNjPGMHMzb948f+GFF446du2113rAN5vNtd5mMszcrGikvpnm3rnNf4W3Ps4530xzv6KR+qWD7UkzLiG2lPGYuWmmuV86UP7+DLSy8vdpoO2b6divTjfEwMCAD8PQX3HFFZ1jfX19vlqtdmZunHN+2rRpnXP2339/f9FFF/nZs2d7772/5ZZbfBiGvtForPNx9tlnH/+lL33Je+/9f//3f/vu7m4/MDCw1vHEcey//vWvj/k5fOYzn/EHHnhg5/vVZxjWZvny5R7wN91001p/PtaZm8MPP7zzfVEUvlar+RNPPLFzbNGiRR7wt912m/fe+wULFvhTTz11rY/52GOPecDfe++96x27tdZ3dXX5//mf/+kcW9tnXFdXl//Wt7613vta1aozN2ma+gsvvNAD/qc/LVcIVn1dlyxZ4gF/ySWXPO/9fvnLX/bVatV3dXX5V77ylf7888/3jzzyyDrPv+OOOzzgr7rqque97//7f/+vD4LAT5061b/mNa/x//7v/+4XL168zvO3y5mb2bNns2TJklHHlixZQnd3N5VKZa23ieOY7u7uUV9b2tPL+ukbSmmmxYTOmLRzSyMtI/M4KP/qG6mlnW9asaTNQWaXxGTknKddOIxRhEajVPmnMYp2sXn+vT766KPkec5BBx3UOdbT08OLXvSizvdKKY488khuuukm+vr6uO+++3jve99LmqY88MAD/OY3v+HlL395p3jh0NAQH/7wh9lrr73o7e2lXq9z//33d2ZuXvWqV7HLLruw2267ceKJJ/K9732vM7Nx//33k6YpRx999DrH/KMf/YjDDjuM2bNnU6/X+fjHP96577GaOnUqp5xyCscccwwLFizgC1/4AosWLdqg+wB4yUte0vlvYwzTpk3jxS9+cefYyIzTSILre97zHn74wx+y//7789GPfpTf/e53z/sYS5Ys4Z3vfCfz5s2jp6eH7u5uhoaGnvc5n3HGGbzjHe9g/vz5fOpTn+KRRx553sc688wzqdfrVKtVPv3pT/OpT32K4447bo3z/AbMlJx22mksXryY733vexxyyCFcccUV7LPPPlx//fVrPX9D7vuCCy5g8eLFXHrppeyzzz5ceuml7Lnnnvzxj38c831sjK0quDnkkENGZWEDXH/99RxyyCETNKKx+eGtD3PHI8/y0OJ+lg21O9PY1rot9gE+EW/KG8NPwJS/EGPlvMc6T6BHL40EWmGdx03gv9NXvOIV3HTTTdx8880ccMABdHd3dwKe3/zmNxx11FGdcz/84Q9z9dVXc+GFF3LzzTezcOFCXvziF3eWN7q6urjnnnv4wQ9+wJw5czjnnHPYb7/96OvrW+eF5IjbbruNt73tbbz2ta/lpz/9Kffeey9nn332WpdOns/ll1/ObbfdxqGHHsqPfvQj9thjD26//XagbLC4+vvC2rq8r77Mo5QadWxkmcs5B5QbXZ544gk++MEP8swzz3D00Ufz4Q9/eL3jPPnkk1m4cCFf+MIX+N3vfsfChQuZNm3a8z7n8847jz//+c8cd9xx/OpXv2Lvvffm6quvXu9tPvKRj7Bw4UKefvppVq5cyZlnnrnW82bMmEFvb+/z7nga0dXVxYIFC7jgggv43//9X4444gj+7d/+ba3nzps3D6XUmO972rRp/P3f/z2f/exnuf/++9lhhx347Gc/O6bbbqwJDW6GhoZYuHBhZ1vdY489xsKFCzvR7llnncVJJ53UOf/d7343jz76KB/96Ed54IEH+OpXv8r/+3//jw9+8IMTMfwx++UfVnDTA3/lwUUr6W9mWOdYNpjy7GB7i32AT+Y35VVtTbNLYvxN9hk7rRRGK4rVxlc4j9EKvRkqH++2226EYdjJ0wDo7+/noYceGnXeSN7NFVdc0cmtecUrXsENN9zArbfe2jkGcOutt3LKKafwxje+kRe/+MXMnj2bxx9/fNT9BUHA/Pnz+fd//3f+8Ic/8Pjjj/OrX/2KefPmUalU1rjQHPG73/2OXXbZhbPPPpuXvexlzJs3jyeeeGLUOVEUjTm/44ADDuCss87id7/7Hfvuuy/f//73gfLDe/WZnI3Zor02M2bM4OSTT+a73/0un//85/nP//zPzrhhzdYAt956K//yL//Ca1/7WvbZZx/iOGbZsmWjzgnDcK3PeY899uCDH/wgv/zlL3nTm97E5Zdfvt6xTZ8+nRe+8IXMnj17vflHWmve/OY3873vfY9nnnlmjZ8PDQ1RFMVab6uUYs8991znjqapU6dyzDHH8JWvfGWt56xvl1UURey+++6bfbfUhAY3v//97znggAM44IADgHKK7oADDuCcc84BYNGiRaOm9XbddVeuvfZarr/+evbbbz8uvvhivvGNb0z6beBLPfzpmX4eXDTIE8sbLOlvsrKd0s4tBiisY7BVbNYP8Il4U95QW8vskhh/W8uMndaKJNBY68mtw/vyT2s9SaDRevx/j7q6ujj55JP5yEc+wq9//Wv+/Oc/80//9E9orUd9uL3kJS9hypQpfP/73x8V3FxzzTWkacphhx3WOXfevHlcddVVLFy4kP/93//lrW99a2fmAuCnP/0pX/ziF1m4cCFPPPEE3/nOd3DO8aIXvYgkSTjzzDP56Ec/yne+8x0eeeQRbr/9di677LLOfT/55JP88Ic/5JFHHuGLX/ziGrMRc+fO7VzMLlu2jDRN13jejz32GGeddRa33XYbTzzxBL/85S/5y1/+wl577QXA3/zN3/D73/+e73znO/zlL3/h3HPP5U9/+tMmv97nnHMOP/7xj3n44Yf585//zE9/+tPOY86cOZNKpcLPf/5zlixZQn9/f+c5/9d//Rf3338/d9xxB29729vWmOGaO3cuN954I4sXL2blypW0Wi3e9773cdNNN/HEE09w6623ctddd3UeazxccMEF7Lzzzhx88MF85zvf4b777uMvf/kL3/zmNznggAM6Ewyvf/3rufLKK7nvvvt4+OGHueyyy/jmN7/J61//+nXe91e+8hWstRx00EH893//N3/5y1+4//77+eIXv9hZTfnpT3/KP/7jP/LTn/6Uhx56iAcffJDPfvazXHfddeu97/EwoXVuXvGKV6z3zWtt1Ydf8YpXcO+9927GUW0ei1fCo0v7mTWlgveeSmhoBDnWOYwxKMB6R2Q0xox/zDnyplzOijiC4UDHWk8tNpvlTXlDjcwujczYjAi0Ii0czns0Ez9OMf5GZuyMUcSBpnC+M4NXicbnbWpkVmhT46UkNAC0C0daOIxW1GLTOb45XHLJJbz73e/m+OOPp7u7m49+9KM89dRTJEnSOUcpxRFHHMG1117L4YcfDpQBT3d3Ny960YtG7Wq55JJLePvb386hhx7K9OnTOfPMMxkYGOj8vLe3l6uuuorzzjuPdrvNvHnz+MEPfsA+++wDlDuKgiDgnHPO4ZlnnmHOnDm8+93vBuB1r3sdH/zgB3nf+95HmqYcd9xxfOITn+C8887r3P/f/u3fctVVV/HKV76Svr4+Lr/8ck455ZRRz7larfLAAw/w7W9/m+XLlzNnzhxOO+00/vmf/xmAY445hk984hN89KMfpd1u8/a3v52TTjppk3M5oijirLPO4vHHH6dSqXDEEUfwwx/+EChns774xS9y/vnnc84553DEEUdw0003cdlll/Gud72Ll770pey8885ceOGFayxlXXzxxZxxxhl8/etfZ8cdd+Shhx5i+fLlnHTSSSxZsoTp06fzpje9iU9+8pObNP5VTZ06ldtvv51PfepT/Nu//RtPPPEEU6ZM4cUvfjGf+cxn6OnpYaeddmLu3Ll88pOf7BQpHPl+fasiu+22G/fccw8XXHABH/rQh1i0aBEzZszgwAMP5Gtf+xoAe++9N9VqlQ996EM89dRTxHHMvHnz+MY3vsGJJ544bs9zbZSfbJdGm9nAwAA9PT309/ePS3LxhlQhnluHg144jX13mopRCqsUu0/vorcWEQxXnZzTW6GrEm3yuNbGe087t7QLhx2esUkCTRKaSdFI0DlPf7tcMw9XCfByW15R9iThpAjCxPja3H/vq/67b7farFj8NLvuuiuVSrJJ/+7d8HKuVmqL/7tsNBrsuOOOXHzxxfzTP/3TFn1sITandrvNY489xq677joqeIcN+/ye0Jmb7c3iIXhm5SC9lRi0phIF9MQhSis8UA81qY2pOb9Z3iyVUlSigDiYuDfl9dkaZpe2lIn84NzSNveM3aqzQlFQ3o/1Hu9hU2J6rdUWm0m89957eeCBBzjooIPo7+/n/PPPB9jsU/tCbK0kuNmC2sCiFRmBGWDH3jpaaZYPZUypRRhjSAtPKy1wlQhXeArnCLQmCMZ3mWpLvilvqImY8p9MJvvs2uawaj5YaJ57juORD7Z6HldaKCj/h/Me5dlqXtfPfvazPPjgg0RRxIEHHsjNN9/cKS4nhBhNgpstrJHDsoEWXUlIvWJIrWWgXbDztAjroJVbVgy2aRSO3DpCo+lJAnqrEVpvVTv3N8pkn13a3LZE7slkszln7NY1K6RUWQd/vKy6ur85gqUDDjiAu+++e9zvV4ht1bb/aTnJZDlkuaO/aelrFoSBppVZVgymZIWlmWYsb+YooCsOUMCyoYy+5obXiNiaaa0IzObZfTJZbc+7xZLQUIvL2bm0KHNtxmPGbl27BL1nXOYuvfc4VwZQxfCfzvlJt8tLiO3NtnkpOIm17XCvKW0ZaGU0mxm59RTe05UXdEUBO02NO1fplUgDBf3tgu7EjfsSlZg8tufdYptrxm71WaGR1oUjj7Gpsyzel/k7CtDque8NapPyeYTYXo3XhYF8Um5hOVAo0M7jnWcos/REhlldMYEOWNkqWH3CPA40uXUUq9ShENueraEW0ea2OWbsVp0Vsmg8kLZamxx8eF8GYoqRpagyWBrJ55HZGyE23EhVZ2M2bdZWZm62MAcoD43ckoQWbRQmUKAUlcCR59DXyqnGUecNPi3K3JtgO8i52Z7JbrHNY9SsUBxQTOll2bKlaK2oVqsbPXvjfbkUVf61rHofHufLGbetJVlZiMnAOcfSpUupVqsEwaaFJxLcbGEhYB30D6XU4ojIQFp4lg2mBEpROMtAI6M7CaknIWnhyArH9HokS1Lbge19t9jmNLJLcM6cOSilOo0SN5b3q0yhj45tykNKlqaE2FBaa17wghds8oWBBDdbmAXyAuIQGu2MFf056JTZXYpcKYz2KAVZYRlMy4TS6fWI3urmKewnJpftfbfYlqBUGeDMnDlzrY0WN0Q7K2jlDq3pzLQ5B5VQk2yju9uE2JyiaHx2Bstv3xZmgVYK3bXyTXZFK6VQoJyiFmmSOMB7CIxmWjWkO4kI13PVvj0Ve9ueTOZaRNsKY8wmr+vHsSdepS5RFG77dYmE2BpIcLOFOcAMv+ralEmIOE9/o00YJMyuhgRGkeWWwkHhPeFa7md7LPYmxGQzWWba5CJHiNEkuJkAUQgmDKgEAUmkqCUBqR3ZBmwoKIMaVJl7EQdrtmPYHou9CTFZTdRMm1zkCLF2kqE6ERzMriXsNKWK87BsKMU6SzUwtDLHUCtnqF3QzAqG2mXn8FE3346LvQkhnjNykQN06iM1Uks7txM5LCEmnAQ3EyCKYWZPhZ56jFGKxBimViNUoAiMohoGBEqR5pZWbsmK1YKb4WJvwWqzOYFWZYVUqa8hxDZPLnKEWDcJbiZA5KCwjuUDKVrBzJ6IKfWYEIVRkBaWJYMpy/pT8rxgoJ1j7XMBjhR7E2J8OOcp7NYZCMhFjhDrJsHNBFAG2rljKM3RSpM5TzO3ZM6XszWZw+NpW0crsywbatNIn9uyOlLszVpPbh3el39a60mC7asfkxAbw3tPKyvob+f0tXL62zmtrNiqqgrLRY4Q6ybBzQRop7Cy2SQOwHpHK3NgHYGBgSwnNIoZ3QnT6hGF92S5YzAtRl1dbq5Gg0JsD7aFXBW5yBFi3WRbzQQYyGHRyhTlA7qrITv2JFSikDS39PXnRNM103xMmlucgmpsaBeOwjoiXQYvk2ULqhBbm9VzVQBCowC3zt2Jk5VUtBZi7SS4mQCFLb8aaUEcBRQOGllG7jXGW9LCsbKZEQea3mpEdfictZFib0JsmG2p+7pc5AixdhLcTICyLSKYUAOeBxf1E0WaShjQm8QsHWrjvWJKNWBKNSS1nnocEBhZRRRiU62aq1LO2JS25lwVucgRYjQJbiaAA5yCIst5qpXirKcSxczp9VTCgKoNcN4xlDr6mznTuyr0VkK5IhNiHEj3dSG2fRLcTIAMaDdgpSpQWhFqj1GOQGs8ChT0VqPyasxoplQjqTosxDiSXBUhtm3yiTkBPND04FswpxtmTKnjvKa7EhEHBqU1rdwysythSi0i3ohS6tJrRoh1k1wVIbZtEtxMEAPEIVSTiN5qRKgCqkmMd5ah1DJHKUKj6IpDgjG0fx8JZhSQWSe9ZoQYA8lVEWLbJMHNBOgBahWYUtWYwKCB7kpAllsauWV2d8iUJCAOA7qSgCBYd3CzeuO8vLAU1lNLAmmoKYQQYrsk228mQGCgXlHoQOGsZzC1WFvgnKMSwfSaIQgCuhLD1Fq83vtatRhZOLyVNXVloCO9ZoQQQmyP5FJ+ArRt+cInQUQlUERGgwnYuTdhVndEvVY21DRal8mOw03xVrd6MbLCOpRSJMNBzkgxsq2xfocQQgixsWTmZgJYoCjKbafVyDClGjCrHtNVM3g0zpW1OFrtgqVDbVpZsdb7WbVxnnMe58qcG89zOTiwddfvEEIIITaUzNxMAANoA91JQKBDupKIKNAMNj3Lfcos5wi6FNoE5Lmnv5WThMEauzm0UmgFjXZO4cE6T1ZY8tySxAEKOr1mpH6HEEKI7YUENxMgAKpxiEWRhNDKHIPtHLRCoXl8eYv+dkGkNbUooJEVo/pKjdBagfesaGYYralGmkArWg6U9+TDMzZSv0MIMdlJ+QoxniS4mQAZ4DwMtHKMV/hIk4QFzQxqSYDRhnqgaReOFY2M7kq41vspCsvKRspQOyezHgN0VQKm1iOSKKB7uGWDvFEIISar1Xd8SvkKMR4k52YCBIBRniQwoCHUHmcVrcLSLiy1UFGvRjgUzbzAUS5BOecp7HO7nlY0UpY1cupRwMx6TD0JyGy5PAXl1Y8ENkKIyWzVHZ8jzUwbqaWd24kcltjKyczNBDBAZj2B8gQKcq8olKMSeNLcsryZ41WTmT0JcS2iYhStvOjk1RitCIDBdkEl1ASBITDln+2soK+VU01CSSAWQkxqq+/4BIabmZaFSEd2fAqxoSS4mQCVBJI4IHfgMwu5Y9mQxShFLQxIQk0r0+RFAcQ4oJFZjFFEWuOA/jSnlRd0xQHtwgNueEcUpLkjUMibghBiUhvZ8RmvVqhUyleITSXBzQRIQojDkMJBqKGnGtAuAtpZgVWe2GhCo8kKNTwTk6GMIdSaVDviQBMFGuvL3J1qZEgLR2YduXX0VALqUSgJekKISU0rhRnuyl7O2JSkfIXYVBLcTAAN4ByFgswawDAl0VAJUFoRhCHdlYBqbGjlluVDOVNrSRnQOE8zs1RCTTU0ZSKxgVpUnquMYXo9ovCeoXZeVioGIqOoRgHGSJqVEGJy0MPJw2XOjSMYDnSkfIXYVBLcTIDcQe4c1VDTXQmphpow0KA0SRxQTwJm91ZwDvpbBaZwDGUFWuvOlu5GZumtRSgPg1nZkyo0mqm1sqdUI7VoDd45hjJLZh31OGBKNZJdCEKISWPkPa1duLIiu5SvEONAgpsJ0M4hKyy1IGJqNWRKV0xaKJppzmCroDsJcN4zmBXERjGjFuJsmUDsvScwmrxwJLWIrkpEd2bJrCUyhiDQ9LdzjFFY62hmrkw2Hl7DHmyX1Y6liaYQYjJQSlGJAuJAltHF+JFPuAngAY8Gb0kLRzt3TK/FaOVJC4dC0TeU01uP6a0GdFcianFIK7MMpQW1uOwWXgkNrax4riO4t+i8IMstUWBG7ULw3uO8Q2lkF4IQYtLRWknysBg3EtxMAKPAW0vbhyhA46kl5XZupRW9lQCjNS+YVsUYAx7CwBAZTSMrSAJDbzUid55GOryLyiiG0oJGu6CwZXBTON8pAGhdGcxEWpOPJBrLG4kQQohtkAQ3EyBz0MgK4jCnXtHUk4AoDgkjS2wCKqEmd4oCz7RqWa8mtWU9iDAw9FRCIqMZSIvOzEyjndPKy0AHNIXzNPICpaAWhxTOU40MDmQXghBCiG2aBDcTYDCHSgr9KuW+vw4wqzsHYwiVIjCWWmSI4oCBRkE1zJnZXUFrT6gV9TigloQUtlyKioyikeYsb6Q45ztLUN2VEIVnsFVgjKYrDjBajWkXgmwhF0IIsTWT4GYCFEAjhcKD8w3QitntlGnVCo3U0sosMwJFK8t5fLklt45ZPVW6K2FnB8FIfYihtKCVW5z3JKEhs47CewKtmdlTIQ4zkjhAK41Silqs17kLQXq8CCGE2BZIcDMBPGXxvVCD1pqhRsqSFU1CE4H3ZIUlaRp0okB5lg6kTK1ETKlGnSCjzJ9RrEgtSkOkNVnhyp0HRtEsLEoruqsxXVEAiuediRnp8WKMIg6Gl7aGe77I7iohhBBbC6noNkGMAm/KOjQFmkbm8IVF4WjmFuscqLL6cGYtSwbbNNNi1H1EgSYJy2rGnrKqp3PlbZcPpqwcSvGu3DH1fN3BV+/xolT5pzGKdvFcs04hhBBispPgZoKkDvIUCld2CHfO02jnLG9alAfroFWUW8NBM5QWDGb5qCDDaE01CalGATO7k7I+DppW4YhDTT0OsI4xddcd6fESrBYABVphh3NwhBBCiK2BrDVMkDYQZdDS0JVYAuVJrSP1norxDLYzIq0ojCLFgzc02gWu9twW7lVLlzsFxmhqMXhv6IoDqklIbsfWXVd6vAghhNhWSHAzQRzQAHQBWW6xKGZ2x3QlAU+ubNPfdFRDi0kLwkDhPaR5USbsrGIkOXgoKxOLK6GhEhri4eNj7a4rPV6EEEJsKyS4mUABUA1haleVahJRT0Jm9lZY2S7ICk8QqOe6fztPmrs1lodGSpeHWqN8+X28ym6oDZl5kR4vQgghtgUS3EygQEG1oplSj5heD2nkluVDGUlg6Eo0ziu0MUyJTNkrCtaZ2Kt1ucOplTtyu3EzL9LjRYiti9SkEmLtJLiZQMZDEkBalLumPKrTvTuJQwKtqcaGSmSGC/atueNpVG0a67DOUWSOUCmUVlTCDZ95kR4vQkxuUpNKiPWT3VITxACVBCpRAniWNRzOWiqBxgQGhSIKNRpFMy3QWjGlFhGY0X9lI7VpAOLhXJuicLSsxQGZK98Evex2EmKbMer3PijfExqpHdPOSCG2BxLcTJAqgAbrLNo7+tMM5xxdSYjxEBhQ3pNZS1ZYeqKAKZWoM3PjnCfLLc3hflIjtWmcK7ePZ4UjHD5X3vSE2HZITSohnp8sS00QC2QFrGikxGHAzF5DvRISGs20uqarEpE6z2AzRxtNHGoy52mmOQCp9eS5ZTAt6EoCAl0GNiubGam12NwTGE0tKntKjWU7uBBi8hupSTUyYzNirDsjhdgeSHAzQVIgGt7ZXTjPlDigEoU0C8fs7oTZvTUGWhmRNtQjQyWO8B6WD2V476nEQXm1phUDaYFSCmsdjawgUoooMBilaGaWSqjRWsubnhDbAKlJJcTzm/Blqa985SvMnTuXJEk4+OCDufPOO9d7/uc//3le9KIXUalU2HnnnfngBz9Iu93eQqMdP46yOrFS0B1pkjAkNIbYGLRSpLb84dRaRG89ppxp9rSynJWtjKG0oJFbjAKsZ6CV08otGrAootAQBYZAKxqZRQ33lhJCbN1GalJZ68mtw/vyT2s9SbD+NitCbC8mNLj50Y9+xBlnnMG5557LPffcw3777ccxxxzDs88+u9bzv//97/Oxj32Mc889l/vvv5/LLruMH/3oR/zf//t/t/DIN10IKMAV0MgKlg61AEcUaPAKraASGmpJiPVgtCLNLY3MoRSdfBrrIAoU1pWViKuhIdLllZ335TbRvHBEWraKCrGtSEJDLS53QZYtWpCaVEKsYkKDm0suuYR3vvOdnHrqqey9995ceumlVKtVvvnNb671/N/97nccdthhvPWtb2Xu3Lm8+tWv5i1vecvzzvZMRgUQGsgcLB1K6Wu2cc5RCRVKQWodeWEZaufkeRmcFL7cMm4Lh/cQGk0QaByK3iRiar2c5ZnWFaNUuf5unacrCahKV28hthkjNal6kpDeSkhPElKJAtkGLsSwCQtusizj7rvvZv78+c8NRmvmz5/PbbfdttbbHHroodx9992dYObRRx/luuuu47Wvfe06HydNUwYGBkZ9TQYOyCzkFlCe0AQ8saJFfzNjej0C61nezFjRyGgXBVmRs7S/wYpWxrONlKf7GqwcauGspV1YwlBRDwPwCmN02VsqMlTDgN5qhDETvgIphBhnWiuCtdS/EmJ7N2GX88uWLcNay6xZs0YdnzVrFg888MBab/PWt76VZcuWcfjhh+O9pygK3v3ud693Weqiiy7ik5/85LiOfbxYoLsCU+sVIq1ptDxDaUFoNIWHnkpYdvb2imf6Wqxo5EyphVQjQyNzLO5PqYQZPbWYovB4HFp5vIfcQ2A0SawnzVS1VFMVQgixJWxVl/M33XQTF154IV/96le55557uOqqq7j22mv513/913Xe5qyzzqK/v7/z9dRTT23BEa9fomF6NWZmvYIymkroaKaWJ/sbNNKc3HoKB4GGwCh6aiFdSUwchkyrx8ShZihz1MOAJC6npJ1XxEZPqqlq7z2trKC/ndPXyulv57SyQgoLCiGE2CwmbOZm+vTpGGNYsmTJqONLlixh9uzZa73NJz7xCU488UTe8Y53APDiF7+YRqPBu971Ls4++2y0XjNWi+OYOI7H/wmMAw14rcitpRKGKK1pZo6nljeYVk3oroQMeE8797TSnBldEXGgsJ5O/6g40iSRGS7kpQBH5jyVSTQ7MlJN1Ziy/1XhfKe6akVygYQQQoyzCZu5iaKIAw88kBtvvLFzzDnHjTfeyCGHHLLW2zSbzTUCGGPKJZetcRbAhNAe7vRdizVRGBIZ6G/kPPbsEIv7miwdaDPQzHh2MGXZUEolCuiqhNSSAGM0vZWIwDy37BRohR1e/pkMpJqqEEKILW1CL5vPOOMMTj75ZF72spdx0EEH8fnPf55Go8Gpp54KwEknncSOO+7IRRddBMCCBQu45JJLOOCAAzj44IN5+OGH+cQnPsGCBQs6Qc7WIgRCBWFoUGgiY5haNSSBZjC1DKUFK5qKauGpJhqjPQPtgr5mypRajLUe5zy10BCsUql0PAt5jUeOjFRTFUIIsaVNaHBzwgknsHTpUs455xwWL17M/vvvz89//vNOkvGTTz45aqbm4x//OEopPv7xj/PXv/6VGTNmsGDBAi644IKJegobLQYqEXTHhkps6K0FaKMZaFty7zEarFM0spzcGub0xNSigHZh6W/nxIFhTk9CaExniapwHms9tdhs0pLUeHYclmqqQgghtjTlt8b1nE0wMDBAT08P/f39dHd3b/L9zf3YtRt1u6kGdp0ZMKe3RhhF7Da9TlcSsHIoo209XVHAztNrZIWjVVh2nlJl1+l1EmOoVwKSMCiXdsYpCFlVKys6OTKrB00bkyMz3vcnhBBi+7Mhn9/yyTJB6gl0VRLCICBUihUDbZpZwECrIA4VuVO0i4LYBFjvQEGgFHESUI3DzsxMJQqIg/HbYr16jgzQSVTe2OabI1vR24UjLRxGK6mmKoQQYrOR4GYCaKAaQ6A1oS4TqZc1LTMCTTXUdFdC0NBOLSSGehTSFZe5NWvrHaO1QlN2BS+s6yz1bEzAszlyZEaqqY5nECaEEEKsiwQ3E6AK4BXNdoaznkIraqEmd4C3KB9Rq4QYraknimlJxJRqQlcSrnW2Y1SOjHVlMz2G2zMYvUFLVZszR2YkCBNCCCE2p62qiN+2ognk1tNsZzw7lNFqZwRaoZxlRbNgyWCLyEN3ZJhdr9BdiwlDjfN+rVunR+rIQBnoNFNLM7ed7fGN1NLO7ZjGJh2HhRBCbO0kuJkADuhvwNK2Q+sMYzSN1LJsMCMtCpz3DOaW/nZBEChqkaGwnkX9bZ4dbI+q7rtqjoxRitR64shQCQyZ9RilNrimjHQcFkIIsTWTZakJklpwLfAJaO8ZaOd4pakEZffvwjm64hDlwVFW9g2NJy0cg60CgDgwZIUlt45KaLBudL5MNpwjs6H5MqvnyOABBd6D7NwWQggx2UlwM0EyQFtopB6jc5Q2eG9RKiIyhkpoSCLNylZBV8UTGI33HucdKE9fMyMKDd56GllBYR21KOjkyyjK5ODMWpQd7hy8gZGJUpAXbty3mgshhBCbkwQ3E6Q6vMKT5TCgC6phmS/TVQmY0xMRmoC+VkHhICtsOUszvBPKFo7+NKeHiCQ0xFox1B6ezTGKvkbGYDvHK8/yIY3SMKse05MEsAEJvdITSgghxNZIPqEmSBxBFEAlBKMNgTGkhUfjUFqTFxaUJzAhfY2MRlqgtMIoaGcW6zy588S6bJwZ6jIYCeKAwlmamaOaaKqhwQNDqaWvmTG1noxpfJuj3o0QQgixJUhwM0FaLaj0QGwMhVLUYk0QabzXKBQ91YgkCKiGmqG0IPKGKZWAZu5YPpTRlQR458lcWRTPaE2goBoYBqOQHaaGxIHp1JRpZQX97YLuxI3qRbUu0hNKCCHE1kqCmwmSAYWFovAEsUJrw4xqxPR6yLRaxPR6xLRajFdgtCK3ntyXOS9aQSO3VAqHV+AyTy0K8MMzLNZ5uuJgVF+uONAMpgWFcwRj2CQnPaGEEEJsrSS4mSAtoNmESrdnh64aU7tidptRZ2o1Zko9ZkotprsasbKV0x2FOMD5sppxHAaktqxjo5Wiv5UTKEVXaAiMJjSatHBUoueCmLRwZVE/Pbbd/yP1bsocm/FtzCmEGH/OSQVwIUZIcDOBMlcmEc/oCtlxSpWZXRWm1WMCo1FaUZQtpci8pycOSQvHQFYQh5rAKLQuk3wDo0FBJTREoaEnCVg2lAEFcVAGOlnhmF6PxrQkNUJ6Qgkx+Y2qUC67GoUAJLiZMHXKF99oGGpZVoQZlSSkJw4prEcpRag1XXFAaAxhoLGuTOY1SpVJxIEmBCKjUFoTD1cQ7q1GAPS3CwbTgtBoptejzvGxkp5QQkx+k31Xo8woiYkw8f/yt1MKCGKIo5Bm4RjKLcp5ljYzuqoBvWHElErI9K6EvLNzSVMJNSpQKKXwShHosoZNEhqqw29kWmum1hO6E1fm2Gi9QTM2q9teekLJm7DY2kzmXY2bOqMkv49iU0hwM0GaQJhDqCDAEw/ntPQ1Muqhph4YWrasSFyNAyKjaSrorUQMpQXGKKph2Skcr+hKAoxZbWdToMeUPLy9k2l9sbVa265G5zz4Mj9uInc1buyMkvw+ivEgwc0EsUDuYDDN6alXUCictygCGoUl9xZtDf2tnMKVfaFahSOODFGgaWSWocxSBXqSkMhIELOxJvu0vhDrsuquxkBDOhwUpLlFa0UlMhittnhQsCkzSvL7KMaD/EuZIDHl0tRQGxrtnKGsoL8ZEGqH155Hn20wu8fSrxXt3FO4sit3bzWktxrTWwkZaOekuSM1jsIXcnWzESbztL4Qz2fVXY1pVtDOy10IRiuiQNPKHVrbLR4UbGydLPl9FONFgpsJklO2ZC885M7SyizPDqVUQ0M1CelvFUQmwwwnEmutyQrLUGoZbFt6KgHh8K4lM1yHRq5uNpwUKxRbuyQ0OOd5Ns1xeGJjiIcvdIrhYGFLBwUbWydLfh/FeJFPwQniKF/8WghBYGjmOXlRkEYBXZHBKEhtQGwUDkWIQRswCjJreXbQMqUW0VONMVoPv3HJ1c2GkmKFYmunlCIODbU4IByuVj7y+x9oJiQo2Ng6WfL7KMaLBDcTLDLgUWX3bSDNHYuVovAerWB6V4UkDEhChQMK6zFAWniy3BFptcob2fNf3cgOhNGkWKHYFmj13DLOqv9mJzIo2Jg6WfL7KMaLBDcTKAUG2lBJLIUGZ6FaiwhiQ1F4+luOJMqpJCG1OKBVOHJb4BxUQkVoFOEq07freyNb1w6EyGg8bNfBjhQrFFu7yRgUbGydLPl9FONBgpsJVACZBect7TYUviCONKGGShwQRp52YXHO0sotinL7dxKWO6aC0FDu+vTP+0a2+g6E3DqWD6VorYgCs11vt5RihWJbMFmDgg2tkyW/j2I8SHAzgRIgCSEyBrBYIrxXDLULeivQHYVEoaErCmjlFuMhMND0nsxpuvC0c4PzZU+pWmyIjKawbtQbwtp2IDjnSXOHNopaFOCQhOTtpVih2DZta0GB/D6KTbF9fopNEooyWNFKE4QB3QGEJiB35RtTJQ7YaWqNF8/pZTDLeXYgJXOeWli2XtAorPUkiaYaBWTWMZAWaxS+Wn0HwkiwE4Ua78HDcNAjCclCbO1WDwokz05sjyS4mUBu+P+8L6iEEZnXKGfBGzLrUVYxvRYRRwEZ0FX1BKpcRtJakVtHYR2Z9ejc0srdWgtfxcPLTiM7EEaCHaNArZKjI9sthdh2SKVfsT2TsrYTqAUsb8CKwRytoB4Z6pGhuxoyqydmek9Md1I2u8ytIxje8rnq7iiAPLc0C9tZdlLDOyeMUbQLB0ASaKz15NahGH7js67TbBNku6UQ25KRPDugM2vbSC3t3E7ksITYImTmZhLIgLywTOuqMCWJ2WVGlZ2m1KklEXEwXKhPl1vBrfMERpU5M4XFOY8OFd4/F+yMWHUmZtVkw3x4icrYMpgZS0KyEGLrIZV+xfZOgpsJFAL1EKohNAtHO7cEXYop1QCrPJVQYYGBtKAoHFlW0FZggNx5WrmlEgZ0JwH6eQpfrZ5sqAjJrJt0OyuEEJtOKv2K7Z0ENxPIA7mCWhQxpStiai2kO4loZKBNQaA1KNB4CuconGOgldPOHUkcMK0aUktCPKrsAjycxLO+GherJhtWjN6onRWSoCjE5CaVfsX2ToKbCVQA3oFXikQHxCYgDAANzsHyZtoJItqFJ9RQTQKmdBmM1tSjgEoUYJ0HBZVAkzm/QTMxG7LdUhIUhdg6TMaifkJsSRLcTLAy9ih3PHnK2Rejyo6+znoW9beJtMKEBjw459hlekReeFY2M9pF+calga7uhIrWm21WZfVCgKvuyNpea+MIMVlN1qJ+QmwJ8ok0wZwDUGXvhaIg956BNCe35ZtR7sq8m1mhoZWXXcFXNlJCE2App5wL68msJysctcRslrV0SVAUYuuyrRX1E2JDyFbwCdQD9NagK4mw2vDwypRnlg/RauZkWUFfK8e7cgt4WljCoCzW199yZNYS67IIn1KKSmzInMc5v1nGOpKguLYdWXY4B0cIMflorQiMlsBGbFdk5mYCBQFYD81WSlKJaLQLnvFgvaIrCniBVyQ9BhQUvtxd1V3RrGwXFFZDWAY2SVA20czt5tsFIQmKo0lStRBCTF4S3EygZgE0wbqcSu7orhq6knIyrZ0XLGu0iSLNjK4YYxRDrQKlPHhFEpW9pIyC3HqG0gKtFLWwrEY83gm+mytBcWsLEkYlVReuTOQODdU4kKRqIYSYJCS4mUCKMtUmzcErS91FeKdxOKIwQClNQTk7005zBlNLrRIwpRqgvWJFKyPUZYKgoQw+WrlDa7tZEnzHM0Fxa9151c4tQ+0C68piiFnh6GvnTC1CptTiST12IYTYXkhwM4GaQOhBKSgKGMoLqlmGCSKs8SjlSQJFmlv62hatFZHW1JOQrPC0WgVF4KnGAdXQEIeGYjjxd3Mk+I5nguLWuPNqJKnaOkdaeIxR1OKAdm5Z2ciJA0MtCSd6mEIIsd2ThOIJ5nz5ZQxkWY7FUzhIC09fM6PRLsitK/tOBZqB1NLfylEKwlDTFQXUo4AkKpdFtkSC76YmKK6+82r1XlibKyl6UznvscPtK1YdexIa0NAcbochhBBiYklwM8EUZa0bBTivyPKCSHuM9hTWURSe/lZOO7d4paiFmsJ68sLTzixqONAYsTUk+G6tO6+0UqAgG64tNMI6T2TKnWuTdexCCLE9keBmgjWB3EESQGI8hQXryi6+kQlopjnLGymNrCB3EBg9XP/G4igL/+XW4X35p7WeJNi0WZXCbt7Zk1V3Xq1qsgdmWisqocGrclnN++EAdHjcodGTduxCCLE9mZzJDdsRCxQp2AAqlYDAGALt6UoigmA4h6ZdkEcBoS5nN9q5wyhITNmGYaCdE2lFJQ62igTfrbk0fDUOmFqErGzkNHxBZDShUYRab1JQKYQQYvxIcDMJ6OF+UoWCRMOKliPoazKllpCEChXGRIEmtZbAKiphQGI01SRkai2m8J68cMRGb3QybjMtGEoLAqO3SILv1loaXinFlFpMHBiahcV7CI3uBIJCCCEmngQ3E8wDgSqL+Rml6Y4DcudZ3syx3jO9FhMHBc/2O5YMpMypR1QrEVOrEdO6YozRKOdxukzSrbgN2yXlvaeZFjw7lOKcJw4N3pd/AlvFzqstTSlFLQmpuGCrG7sQQmwPJLiZBLICgrDcEq51QMU4Cq8IQk3uy+rDlTjEeU8BrGwWZNZRicrkYlSZiGu9JzKKehKOeSmpnVsG0wLnPdXIYD2dGZs4NKTF5qt6DBvWlXyy2ZrHLoQQ2zIJbiaYBpwuZ26stRjj0SrAFwWJ1sShxhjDlK4Eo6CdO7rjAK1geTMntYreakgSGryDVu4IzNiK+I1syQ4DTewMbniJZaQZphpO/JUkWSGEEFsT2S01CShfFvFrZw6Forsa0lWJ8BgqoSYMDNWg3IkTaE01NHgPQ5lFaUduy9or9TggDPSYa8WMbMmOVsmzKWyZrJzmlsI6SZIVQgix1ZGZmwnmGI4wPRTeM5SmzOyJqYURTnl6ahH1JKCVWVa2cowGlCPLLFFo0KrMi+muqE5C61iXklbdkr3qbZt5WQ25HgeSJCuEEGKrI8HNJKAVoCDPYHl/yk69UK+XCbd4yIuMgbYhLxzT6yHeK7zWGAVZ5qnE5bJWO7cYPfalpNFbsssdTFqXVY674kBaCQghhNgqSXAzCeS2rFLsFChlCQOIgpBKbKhEAYay7owLNc5DLTRMSQLa1jOUF/RWIzTQ386JtWZ6VzzmpaS1bcnuqYQyYyOEEGKrJcHNJJA6qFAGOOiQlY0Uow3VqMKMekQjdUShKROJjSJQmtx6AueoRGXDTOshUIrAKCIz9lSqkS3ZoXYUzhFoTRBIKpYQQoitlwQ3k4BWEIUQhZpqGOBUGWDk1jPULgvFxUF5rCuJyApH4XK6qyE91ZjuOISy7RG582xI44Q1KxM7Erd5KhMLIYQQW4Jcok8CkYYoMiRRSDUJqMUBkTFoo2iObNUONa3UYb1D42lmz/WRCgJNYDQONnjrdju3NFKL8x6jyh1UjdTSzu3me8JCCCHEZiQzN5NA4aDIy0aY1ke0MsuKoRaKGANMiQMY3to92M5RHrQqm2YOpQVQNtR0jg3qzeScp5VbMmtxRfm91gqtQOcQB5O7z5MQQgixNhLcTDADpB762mBx9OY5ESFah/Q1cxppQRwauioxvTUDXuFwTK8n5Rbx3NHXyqnHAVOq0VoTgZ1be4uDcpamwDpPaDRRoLHOkw0vUXUnoVTgFUIIsdWR4GaCWSAEtC+Xp7wu69V0JwGD7RytDG3nKLzFY0iLgtAE1JOw7G8UedLCopUiDkbnyYz0jWrlFjyYQI/u9O2hcB5FOfMDEBhFbh2F82xQ8o4QQggxSUx4zs1XvvIV5s6dS5IkHHzwwdx5553rPb+vr4/TTjuNOXPmEMcxe+yxB9ddd90WGu3mEQKVoAwsnNcsa1oaaUGoNdUIlHMMtC0rhzIG2wVRANHwjiY9HAx5ypmYEd57VjZSFg226W/ltApLmhUMtYvn8mkURLoMcnLr8N6TWwd++LhM2gghhNgKTejMzY9+9CPOOOMMLr30Ug4++GA+//nPc8wxx/Dggw8yc+bMNc7PsoxXvepVzJw5kyuvvJIdd9yRJ554gt7e3i0/+HGUBFCvBVSTiDjUgGdKPcYDg40cZSy9hcXHhlZakFs3aoamcH6NROJmWrCykRMYRRIZCudJC08cuOc6fStFJQ4wuaXwdOrcJKEern4s0Y0QYuuzrqV4sf2Y0ODmkksu4Z3vfCennnoqAJdeeinXXnst3/zmN/nYxz62xvnf/OY3WbFiBb/73e8Iw7J67ty5c7fkkDeLOIE4DKgGCuvLX8S8sDRSx1A7Q2kYamaAY6jp0KpNPYnoTsK1JhI752kWFjSdJajQKKDsQRUMt2cIjKYSGpyDUJVb0p0H76ESSjKxEGLrsmZpCzV6KV5sNyZsWSrLMu6++27mz5//3GC0Zv78+dx2221rvc1PfvITDjnkEE477TRmzZrFvvvuy4UXXoi16962nKYpAwMDo74mmywHm6c472i2cxrtgiV9LZY32+TO0k4Llg1ltHPHzJ4IEyha7YK+Vk47t1RCTaBVp1mm8x4/XNQvzW3neKAVWeFA0ZmVSULTCYysL5e5arGRCsVCiK3OSGkLKGuDAVLaYjs1YTM3y5Ytw1rLrFmzRh2fNWsWDzzwwFpv8+ijj/KrX/2Kt73tbVx33XU8/PDDvPe97yXPc84999y13uaiiy7ik5/85LiPf7yEgC2gbT0VB/UYqsZjvScwilgFBKFBKU8tCpjelZAVniQ21AKNLcplptbIklKgCbXCWkcrK2gXnsgUVKMyL8er52ZlRqZu48AQB0amcYUQWy3nPO3CYYwiHN4gMTJj3VmKl/e27caEJxRvCOccM2fO5D//8z858MADOeGEEzj77LO59NJL13mbs846i/7+/s7XU089tQVH/PwUUNiy1k1eWLIC2sVwPkygCQJFLTIEBlqZYygvMBraqaWvlbFkKKWV5kSm/KVtpJaBdk5hPSbQ1CONx7N0MGWwndEbB1QiQysr6G/n9LVy+ts5aVE23ZRffiHE1sh5j3WeYLX3sEAr7PCFnNh+TNjMzfTp0zHGsGTJklHHlyxZwuzZs9d6mzlz5hCGIcY8t2Sy1157sXjxYrIsI4qiNW4TxzFxHI/v4MeRBwogzaCrV5HEhnYBHkslTGi0M/pbOaoJ7UIRBopZ9QTiMj8mNIrcQVY4kijAOUt/u6ArCUi8oZ1bijTHGFWuOStFXzPDOggCTRxoCuc7U7mVSKoDCCG2PlopjFYUzg/P2JTWtuFCbPsmbOYmiiIOPPBAbrzxxs4x5xw33ngjhxxyyFpvc9hhh/Hwww/jnOsce+ihh5gzZ85aA5utQQ6klIX82lZhTEBeQCu3dCdlh2+UwuGJDHinaOYF3nkKoDsJCQNNu3BlhWFVbus2ww0xo0ATBobp9ZiuJMI6z4pWOVOjKJOHQ6MxRnXuQwghtjZ6eFneWj+qtMVImxqZld6+TOiy1BlnnMHXv/51vv3tb3P//ffznve8h0aj0dk9ddJJJ3HWWWd1zn/Pe97DihUrOP3003nooYe49tprufDCCznttNMm6imMCwXkGSwdbLN05SDLhho8vazBo0ub9LVyAhS9lZAZ1Ygk1gSmzJ+phobQ6FHTrm44WHGUa9C59SSBQeuy/1SgFVla0N/KWNnMGGjntLMCo5CpWyHEVm1kgwSUpS0A2SCxnZrQNYgTTjiBpUuXcs4557B48WL2339/fv7zn3eSjJ988km0fi7+2nnnnfnFL37BBz/4QV7ykpew4447cvrpp3PmmWdO1FMYFzmgPDRz8N7ihntHDbXaGCIqSUAcVqgkAV1xSKgV1TigHodk1lPkZb6MdeUuqZ4kwDlIvcVaN5w8XP6SW+cpnMcCPavk6RTWEUfBRk/dSl0JIcREU8Mz1nEg70fbO+X99nWpPjAwQE9PD/39/XR3d2/y/c392LXjMKqyx9TUCAIDSkFvopnVUyUKA7orIbN6Ynac2k1XJcBZeMHUhF2md9EqHK3UkoSaahKSDOfRpIWjmVv6GhlGK+pxQGg0g2nBYCujcJ7eakRodJmX4z1zuhJqSbhB45a6EkKsnwT+QoyPDfn8luzRScICjQx6qxAHoMKI/nZG7CAJFKlNSLOcrqTMoRnMHAOtnO5aTE8SEgUao59bV65EmjgwRFrRyhzGaJz3pLkliQxGlQnG2fAWcoPqtHTYECN1JYxRkpwsxCok8Bdi4sinzyTigSSC3lqF2CisN1jnsIC1briPpaYWGZT2tLKCqbWYahys8WY5crVYjQICU9Z5sLas8xAHmq4kxPvh7ZPDV5VmlSXAsVxtSl0JIdZNAn8hJo78hk0iHrAW8jwjDhKMV4QGcquoxKbsEWUdQ5mlx4Q0c8fyZooxiiQMcL7s8J1Zt8bVYncc4GPKGjd52fU70ArvwTtI4nLWZ0OuNkfqSsSrzfgEWpEOt3jQ0n1TbIck8BdiYklwM4kEgCsgt568cAQaUqdJvMcArbSgP7XM6oqpBDFJZMhyx+L+Fj21GO8hK8p2C9U4GH21GJdXi2WhPksztzQyS2j0qN0EG3K1KXUlhFg7CfyFmFhbVYXibVky/GfDQjN1ZHkb6zzVSDO9FpA6xyNLmvQPpmSZZSgrKKzDOsvywYx2Xm7nTgtHmpf1asqGmeuoYeNB+/LPEatfba739khdCSHWZdXAf1US+AuxZWzwzE2aphRFQa1W2xzj2W4pyr+MioZqBRyGOAqYVo2Y0pVgC4c2nlqsSAvLisGUoZalbQsibeiuBcMzPpYkNDRzi1aqU9smzSxZYbHO08rLACYKglEzM6HRG3y12ZnxKRzpcHKy1JUQ27uRwL/83XIEw4GOtb7TqFYIsfmMeeZm6dKlHHvssdTrdbq7u/k//+f/8PDDD2/OsW1XCkAr6K4FVMOYOAyZ0RVSjSPwUK/HzJ1WJQ5Dcq8IQgM4lg9mpHa4no1StDPLQDOlv5mVX62MZQMtBtKcvnbOs0MphbUEWq0xM4Nng682R+pK9CQhvZWQniSkEq2Z4CzE9kYKygkxccYc3Jx55pksXLiQ888/n89+9rP09fXxzne+c3OObbuSA4WnMy+iUDRTR18rY7BdEHuP0p5G26FxtNOCZlFQiw3dSUgzc8P5NLBsKMdaRxRomu2cZwcz8J54eDt4O3ekue089kiFYxQbvcykdTlLJFekQpQk8Bdi4ox5Wer666/nW9/6FscccwwAxx9/PHvttRdpmk7qxpRbC0WZ/jKUFygNcaAYTHMqYUB3YkidQrWhq6KJA8XKZo4y0BuVRff6mzlT6kW5vBUoqpEhLRyZdXQlhjAodzvFQbnjql04ouEdG6vOzMgykxDjS2slycNCbGFjnrl55pln2G+//Trfz5s3jziOWbRo0WYZ2PZmpDv4YAsG2wVZkaEx9FQC4jiguxrQVTMYY1DKlF2/A0MlCcrbesdQO6dVeKbVIrqSEKcgH65V08oKvH8unybNLda5NWZm5GpTCCHE1m6DEoqNMWt8v511b9isLGBzKALIckekyq7fGsX0WkzmylYLhYPuSohR5WufO8e0akhuPc5Cllv6fE4UaSqBIbMOZRW5dVSigNyWszLWekyw9pkZudoUQgixtRpzcOO9Z4899hh1BT80NMQBBxwwqrnlihUrxneE2xED9FTLKsVxFDG1HjOlHuOGE4hDFfCC3oChosyZKRxUA03hHUoZjFb0RJpnmxm2XdBVCfAOMuvpSYLhZSZHZAw9SUgcmjH1u5HeOEIIIbYmYw5uLr/88s05DgGElMtTqYXIFTSKgmVDGWFg6Gtm9FbKJSJ02YqhJzBEgaYoPF2VgEoUYvCYlkUbR5Z7qpHB6HIXVCu3VEJDLQ6et7+Ncx7rHFnhyJyX3jhCCCG2GmMObk4++eTNOQ4BoECZMhHKo0hzT547KnEE3uGdp5E7CuuIddn92zpIraPuy51O7cLRXQlQgPWenkpUbhEvLPUkoLcSEaynQeaq7Rea7Zx27qjEhnocYD3SG0cIIcSkt0GfUD/60Y/4yU9+QpZlHH300bz73e/eXOPaLoUGbAaEYLShK9QkoaYWapTRpNaTFTmRMYCimTm0gkCVQYnRCg9UQ8Ngu8CYskqqHS4eVg3MegMbeK79glLgAGMUufWkhRsOaKQ3jhBCiMltzMHN1772NU477TTmzZtHpVLhqquu4pFHHuEzn/nM5hzfdiMc/rMAQg1xoMkcLGumVOKQug0JAkVaAAqm1kLqcYh1nkaak7uy2J5W5X3EoUarMjDx3tOVBFSfZ7Zl1fYLCnC+LERmXRncxIGX3jhCCCEmvTFvBf/yl7/Mueeey4MPPsjChQv59re/zVe/+tXNObbtSgCEAUQhVIwiMorAgEETGNVpixAGZSE+UBitiQJTVhnWYJRCA85BPQmZXo+pRoZqGNBbjTDmub9u5zyFHd0vaqTZXzBc88asUgNnJKlYeuMIIYSY7MYc3Dz66KOj8m7e+ta3UhSF1LkZJw6IFFRCSMIQqzXWQ2IU1nu00kRGE5tyvqSV5TTSjOWNNn3tHIcnDBQzumJm9yRUooDCQ2A0XZWgs9Xbe08rK+hv5/S1cvrbOa2swA4HOorhGaBVmmK283KZynovTTGFEJvV2i68hNhQY16WStN0VLNMrTVRFNFqtTbLwLY3HmhaCCxEFUPVaDSwIitgMGdWV4JS5ZKQ946iUCwdTEEpAjxWK/qaOWGXppaExIWjcA6NQhuF96DUczk1xijiQJNbx/KhFK0VUWDICotznmoclDuxrGModxiGKxjHWqoVCyHG3aqbGWR3pthUG5RQ/IlPfIJqtdr5PssyLrjgAnp6ejrHLrnkkvEb3XbEAtZCZCAMPLUkoCsJAUUcaqJA44drzTinSJ0lDgMSU1Ytjo0i956VzQzvyx1UjbSgcJ5IKypxQGw0qS1zasLhJSrnPGnu0EZRiwJ0aGi0C9Lc4oKyM3lXEhIFGqNlxkYIsXmsfuFVOC+7M8VGG/O/mCOPPJIHH3xw1LFDDz2URx99tPO9RNcbT1MW8YuDgLRQNNqWniRiWldMJdK0c08tgR3qEc0s56kVbboroBJF3Wiqw8nFA82s8/dgh5eZcusxuSUrHNY6uqsR8FwCcRRqvC9nj6LAoCoK5z1dUSDNMIUQm92qmxlGLrxCo5DdmWJjjTm4uemmmzbjMISlLN7nfEEShqRZwaKBJm1necGUGgpHTxKAUhRW4VAEpvxyQFo4FNAsHHUc3pVvEoEpl54KD7FRtHJPVjji0HQSiI0CtUqScLkjqnwzWd8bilQuFkKMh5H3oni1UhWyO1NsrM0219fd3c3ChQvZbbfdNtdDbFM8ZYAzmIF1bXKgmsNQVoB1DKUZjbRgSneFamiohxrnIc3Llpu5dThrSXNHo2XICkdXEmK06rxBBMP/neUW5z2BUuU6t/P0JGEnQHm+HVGyNi6EGE+r7s4sZ2xKsjtTbKzNFtxIQ80N44HK8O9vI/UUrqwzk9ucx6xn8VDKkpVNdp/Ty/SeKhWt6CbEOU9/21FYj1GeShjQyi1pVrZOmFaHMDCdYn5GgcXTbOVlRWTvibQuCwAOb/W21lOLzTpnY2RtXAgxnkZ2Z5bvI+WF2Fjei4RYF/kkmiQU0PagWpCEgC5bMKAgyxxpWjDYzPFKk1tNJVSsbGZMrcVU45BAK6pxSKw13pXbwgfbObl1zOhKqMcB7dziPdQrIV0xZM7hbHmlNLITy+i1dwkfIWvjQojNYeQ9p124Mb0XCbE+EtxMEh5oAO0cej0YUxb108PF+ZxVFE4xlBd4Z3HeMNQuCMKAWhzQU4mYUo3Lxpt5gfWevLD0NwuSsJxh0VpRjYNOUFIxmlw7ALqiABTPmz8ja+NCiM1BKUUlCogDyeUTm06Cm0nGAo0CkgJqiScvHC1rMcpTjRMibcgdZO2izKupBoRBhTDQeMqifc3Mk9tyiSrUnmoQUBQOE+hOYDNiJChBlbd9PiNr45l1ZUXk4TegwvmyH5XzOCWzN0KIjaO1kgsksck2W3AjiaUbTlP2mFJABmQZ4B1ZAXEIgYZKqGllOa3ckReWnXoS7HAtm6xw5NbRaFu8KgstTqkYqklEq7C006Is8Bc8N827oQl7SoF3jr5GDhoio9EKrPWEgWbAIwnGQgghJtSY2y9sKEko3nAaSIB4+L+VLgOd2EDFGAKtyYcDGLyjKzIMZgXPDrRJ8wKNZygtGMoKPI5Qa2pxQBQaqqEB70lzS24d3nty6za4nUI7t1gHtdgQGU1WOFY2M1JriUPTWa5qpJZ2bjfTKyWEEEKs2ybN3IwEMGu7Ov/Zz37GjjvuuCl3v90p22FCJQHrIA6gEkeEWOI4phpoFJ688DigHgcExgCKpYM5O0zRxAaaaUajreiqeFDlDqhKaAhDQy00nbo4RisqoSYYboz5fAHOSDJxEGgqJih7wDgHDYa3nGuUUpJgLIQQYkJt1MzNZZddxr777kuSJCRJwr777ss3vvGNUeccfvjhxHE8LoPcXijKnJu2hXoCc2d088LpVaZ0JUSBIkkMmVPkeUEtNFQqITPrMXOnVykc5FZRjYJy11RgCLQizy3PDmYsG2hTiwK6KhE9SUhPEpRLWc7T3y46DTTXN+O2atdwGF4bV6rzvVvltsHw1nMnM3hCCCG2sA2euTnnnHO45JJLeP/7388hhxwCwG233cYHP/hBnnzySc4///xxH+T2pAAqGiqxZlo9oSvUDLQt2igqARQmIAkVc3qrRJGhqxoONzFV5N7SKjQzumLSolyCyhwExpF7TS18rl5EWnhaudugWjVrK7SlVVkhWQ3/d+d5SPEtIYQQE2SDg5uvfe1rfP3rX+ctb3lL59jrXvc6XvKSl/D+979fgptNEFAmFFdCCHVAM8tp5Yoo0CRhQHc1pl04cmtZ2czZOTF472llvmygqTXWObqTiNx6WqagXTi6E4NXinA4H6YoHENZgdYbVqtmbYW27HClY6XAeo/ySPEtIYQQE2qDg5s8z3nZy162xvEDDzyQoijGZVDbo4jh3VIGAgPaFww02hgd0l0NUUah8XQnhjwvu347PLnzKG+xgPKGMNDkzpNEQVnXxkMYKIzWBFrTygoaacHKZkYlNDjnO7uaxlKrZm2FtqbVy0acqfVSfEsIIcSE2+CcmxNPPJGvfe1raxz/z//8T972treNy6C2R54y0qxG5RJPYEI8mswVOFfucGrmAJooDJnZbZhajemtRkztTuiOQyJjCDWkuWWoneGcR+FxDnqSgMKXS09KKSqhwTpPM3tuV9NYlpJGCm31JCG9lZCeJKQal1+rHqtEgWwDF0IIMSE2arfUZZddxi9/+Uv+z//5PwDccccdPPnkk5x00kmcccYZnfMuueSS8RnldiAH6kBkIDIBXbWYWmRoZg6LpicJqUUBoVEMpAW9JARBQCUMSALDzFpMbh2pdRhT5tMU3lGPyoCjOwkZzGynbYIfDnRGmmBqrfDDW7zHspS0tkJbUnxLCCHEZLDBwc2f/vQnXvrSlwLwyCOPADB9+nSmT5/On/70p855ctW+4dLhP6vVAKUV3XFINQHQBBqGsozBVBMoh9aKKYlhWj2ksJ6hzBKocofS9HpMPQrxCgKtCQJNYd2otgnx8JJRK7e0cksSGepxIEtJQgghtnobHNz8+te/3hzjEJRLUy0H9azAJzFOQy0MKGy5fBSHhqndEdOqEZU4YiiDnsJReGhmBZEpdz0tb+UopZhSi1GqrGHjhtsjjOx08r5st5AAldDQm0QEwWar6SiEEEJsMZtUxO/pp58GYKeddhqXwWzvagashaFWQT3O6G8pisLTsmX37lpiqIYB3ZUQozWpzVnWgFoQ0LIOrTS91QilFCsbOZHRGKNpF+WsTV5Y8uGO3s5DZh04mFILMUZm2oQQQmwbNvhS3TnH+eefT09PD7vssgu77LILvb29/Ou//ivOuc0xxu1G5qBQMJRSNsv04HFkWUGaF+RZQSsrWDaU0t/MaKcFy4dylgy0sYWjJwmoRMNLSxpWtDIGW+UOtjjQxKEhtZaVzYyscERGU4sN1iGtEoQQQmwzNnjm5uyzz+ayyy7jU5/6FIcddhgAt9xyC+eddx7tdpsLLrhg3Ae5vVAetCtbL2R5hqNKTxJhUAyklqFCEbVSGq2CJNb0VCOmKIULAupxgDFl+4Mst2UfqdRRqQedWjaB1oTGoJVmSi0i0GVPqdxKqwQhhBDbjg0Obr797W/zjW98g9e97nWdYy95yUvYcccdee973yvBzSboB+pZ+ZfSymEgtXRlGVZrksigvKWwhpbLqVUrKKU7y0m5c7Sygiy3tK3He4f10D3cJFMp1WmFEAxv9x4JZMZS30YIIYTYWmzwstSKFSvYc8891zi+5557smLFinEZ1PYsBYIIIg2NtOCRpS0GGhmxMRhjsF7hnKESanqqIVOqEUbDymbOUysarGxmGOWpxQGVUDOUWtLhJaeR+jUOaZUghBBi27XBwc1+++3Hl7/85TWOf/nLX2a//fYbl0FtzyKgGkMSxaA8/a2cvmZBMy8o8Bjt6akosgKc9eQWwuGt3v2tnEZucUBoNFOqEV7DUFpgrStbJWhFoMqlqLwoAx9rPUmgZUlKCCHENmGDl6X+/d//neOOO44bbrhhVOPMp556iuuuu27cB7i9cYAf6UTpy3YMXkOoPaBptHOKSBEYg/OQW8dgKye3BdUkJNLgnKeVW+JA0x0HDLYL0twShoYp1ZB2bhlo5+TWERpNTxJ06t8IIYQQW7sN/kTbddddeeihh3jjG99IX18ffX19vOlNb+LBBx9kl1122Rxj3K60gBUNsHlKpBXTKiG9ccjUWpWdeit0JSFpXvaLqiUBmbXk1hMaQxIYtDaExpAVjsF2gTGa3lpEbzWiJyk7iHs03ZWQGfWY7kqI82XOjRBCCLEt2OCZm1133ZVFixatkTi8fPlydt55Z6yVLcWbqglkFlRm8YGip67prkVUI0NXYljUn9JbiQiUJgzBKEUtDkkLix0u1hdpRX87JwgUU5KIYHjHVLtwKF3eRiuFAVJnyyKBgXTxFkIIsfXb4ODGD++4Wd3Q0BBJkmzygEQpKyBJQGnN9FqAxjOUFhilmFoL6U4MjTyHQjHUymllBZUwoCcxeO9ZMdRmMC0IlC8rHOeWxCj62jlalYFOYR0eMFrhgMgo6kkorTOEEEJs1cYc3Iw0xFRKcc4551CtVjs/s9Zyxx13sP/++4/7ALdHFUAFMKMWkzvFypYjisuKw+2iXIYqrGdaLUIFmnbmGGhaduwF8DTaBcsbOWGoqAQBoVasHEppZAWpdXQlIbFRZIVHaU0lUJjA0ModgbFUok0qXC2EEEJMqDF/it17771AOXPzxz/+kSiKOj+Looj99tuPD3/4w+M/wu2QB3rigNm9McsGsjJBOPVkKid3niK3tFoFBYB35LnH4nl2yFIsVvTGAZVA0Z1EFF7hM0vuPGnhSAKFs54VbUst1gQKWoVjZjUiDLQU8xNCCLHVG3NwM9Iw89RTT+ULX/gC3d3dm21Q27sW4Jyir+VwytBbD+mKAwbTnDg0GBR9rZywldLOPThLEhoaLYCMdpoztRbTXVMMpRl54eiulv2oolCTBIZWbmnnjigwxEYTGb1GMT/nfPnfqxT8E0IIISa7DV5/uPzyyzfHOMQqNLB0MCeJG2hj6GpqQhXQzizVuqbZsDTbOShLajXGebwDbQK6taWZabzJ6Wrl1Kohg6klCcvaw1ppakmEBdKiDIrCwGC07hTzU0ArKzoNN41WJIEmCY3k4wghhJj0JLliEoqBAshtgSkcS1VZmVgbRX+r4PEVDQpXkLoKoS5oeUVqLbn3zOlNqMcGj2J5K0MZUNozmFp6KiHhcD2bUCmahadtHZXQYL3HWk8tNmTW0UgtxijioAx6Gmm5C07ycYQQQkx2UrltEtKALWCgZWnZHKUcQQCVwID3ZC5HaUOoy5O18jhbFu5rFw7nFFOqAUYpBtoFVWMIjWJKNaS3Um4Zb2QWo8FZx1Ba0EhzIgOBUrQLhzGKcLgRZ2jKHlblfa99t5wQQggxWchl+CSlNQw1II8gNjmz6hVqFU3mHJqAKNBEISinsKEizx1aK7zzDKQZldjQXQ2pRQEzu2OSMKAahXggCjw91ZBqFKAp2zO02hblIRsOknoq4ajxSHNNIYQQWwsJbiahHMhz0AEYA967srdUUxEoRxQqlHNkqStbM3hN7jxkljTN6KolFM5RMYpKZLBOUYsCeqsRhXXgPYVSZNbTaOdk1hNpsF7jPaTDszndled2xElzTSGEEFuLSbEs9ZWvfIW5c+eSJAkHH3wwd95555hu98Mf/hClFG94wxs27wC3sAzIHDgFSkEjdaxo5lib0y4c1cBAEKINRIGmFiumJIbplYgkiZgSB3gUy4YK0swSG4X1vgxsgHR4p5TzHgUYBdZDO7OEgaYeGlqpJS0s3nty66S5phBCiK3GhAc3P/rRjzjjjDM499xzueeee9hvv/045phjePbZZ9d7u8cff5wPf/jDHHHEEVtopFtWBmhDuSUbxVCe0Whb8Jruakhde1Lr6WsW9LU83ZWIF82p01uJ8aFmai1izpSESlwW53t2oE1fM6M/zRlKczxlYJO5snkmCnLvwUM1DkhCjfO+03OqFhuS0EzoayKEEEKMxYQHN5dccgnvfOc7OfXUU9l777259NJLqVarfPOb31znbay1vO1tb+OTn/wku+222xYc7ZbjAGfBKo/Ck2hD7jy99ZAp1ZBKEtCVxEypBkSRIokCnNKgHMorEqPRStPXyFnRaFNY19kpleaWgWZGfzNjqF2wfKhNmlsCBQzP4lSTkClJRHccUAsNcSDbwIUQQmwdJjS4ybKMu+++m/nz53eOaa2ZP38+t9122zpvd/755zNz5kz+6Z/+6XkfI01TBgYGRn1tDSwwmMLKIRhKHY08w/tyN1PuNBhDLQnYobfGjFpC7uGJFS0W97dZOZjR18xYOtSir5nRaBcoPEopYmOIA00zs6AgDhUWaOcWBVhXbgmPjSJ3jsGsYEUrY2Uro5UV6+wtJoQQQkwWE5pQvGzZMqy1zJo1a9TxWbNm8cADD6z1NrfccguXXXYZCxcuHNNjXHTRRXzyk5/c1KFOiBxIAK2gsJo0zxlqZ2S2LLSnTdn5O9Ia7y1pZmloRT3y5M6Rp55Al1u5i1WatWutCUNDLQoITEg7swxmRbksRbkE5Zxj+VBG4T0aaGFptgum1SOqcbiW0QohhBCTw4QvS22IwcFBTjzxRL7+9a8zffr0Md3mrLPOor+/v/P11FNPbeZRjh9D+RdUixXaFmS5ZdlgRjNzaKVQlJWKjfFMqcRM7UpIQgVaobVmRj2mmgRorSk8OF+2U1BK0ZMEKKXIrScMDDPrMb3ViK44IA4M/e2C1Dkio4lDQ2Q0qXP0tXKpdSOEEGJSm9CZm+nTp2OMYcmSJaOOL1myhNmzZ69x/iOPPMLjjz/OggULOsecKxNegyDgwQcfZPfddx91mziOieN4M4x+8/OUeTet1FP4stJwJQ5IAs9QK8O2yoClkVqsS5nTm9AVR1RCjVFlAk1ReKy2KDzNNCcJA7wCoxRKQZpaLB6loBIG5NbhvaeVWZJAE5gy/g2MIhk+XlhHpCW5WAghxOQ0ocFNFEUceOCB3HjjjZ3t3M45brzxRt73vvetcf6ee+7JH//4x1HHPv7xjzM4OMgXvvAFdt555y0x7C0mAyIHTQuBBodnai0i0IZ20SRLy5YLyxsZhgycxfd6MkKmGI33ZbE+oxVKKZqpRWtNNdAMpZasKMoAynms81QDTyt3ZeVjVQZXq/KUx4UQQojJbMKL+J1xxhmcfPLJvOxlL+Oggw7i85//PI1Gg1NPPRWAk046iR133JGLLrqIJEnYd999R92+t7cXYI3j2woLZG0wFVBakxUOwoBKFJIVDuMNPdUQ76BlPX1DBWFaEPUquisRXdWQehTSnQQordAodFQuSfW1cqz3JGGAURAEBo+n7TyRUmS5QylFoBWF82S5oxqbzmyOWDfpqC6EEBNnwoObE044gaVLl3LOOeewePFi9t9/f37+8593koyffPJJtN4+P0xjyrybpALdFU1XJWQoswRFiyxzFEoxJVbERlOpRWWysfYEaJLY0FUJmVKNMErRdg5voXCOuCjvq2rLhGOFwnlHs5VTWEfuPIlROBx5AcXwFvA41PRUQvmwXg/vPe3hHl/SUV0IISaG8tvZ3t6BgQF6enro7++nu7t7k+9v7seuHYdRrSmhDG6qCdRi6OlK6IpiQuOxhaNtoasSMKUaobXCoCnw9FQCanHMrtMr7DClQncS0co9Tnnw5YdtpA31JMB6yHKL9dDfSkmtZ1oSUquUfadamUUrhhtnaqqRkQ/p59HKik5H9ZEZr5Fu69JRXQghNt6GfH7Lu+0kpCn/YkxQFvPLPdgspwUMeAi0ZkpimFGL0IFhoJWT2Zw5XVXqSci0WoDSmmcHchqFo2oM3kPLurKFQgSNXGGcZ6BtUdpTWE+oIHWeOpBEAcaUVYq7ooDASOuF5+OcH9VRHSA0CnC0C0cceHkNhRBiC5DgZhIKgIgyyIkCiAx4HWK0QXvPTlMSZnVFNHPN0kZKIy2rC4ehItCaIAgItMJoT6NdYGJDFCimViPw/5+9P4u1Lc3uesHf185mNbs5XWRERrrDULcu2FZhnBduUbolpTBCSMUDKssqycgPSPVgXlJC2A/YWH5IGxAysi0sIfFWFi4eQEJILoELS8UlS0h23bol6xq4trOJzIyI0+xmrTWbr62Hb+59mjh9t/c5Z/6kFSfOOmvvNffac35zfGP8x3+AjxkhIkJKrBGkXF67rktAgxCklKdJ4HdvyCGmp9KQvKt6k5SLMLvS95dR54nqMzN3eVfXh5nXyxzcXEIcEIClhpVWVLVmWWv2WouWguurhmuHC4zMLI8kf3xrx+ASbgw4q9l0I1Zk2sZAFtRWsG4qpBDknDkdHFLKkuUxigyc9h4lBEbL8xtxjJmcEv0YCPBEDcm7rjeRQqCmUlTJ2BTmieozM/P6MPN6mYOby4qAugZTKxICIwU3VgafJDsPJzuHkoIYM1IUt+Ioy1yoEGGICR0zMk87pZQREsYQUQhqpVC6ZGmskiyqTOcigy86m27wdDEhYuJUSJpKsayKTmc3FrvjBzUkg4/nepNKS0LKj3zt24icFuvyM6fPaG7mXerMu8y7vj7MvF7mM+qSEjKEANvRoaUm1JqjLtAYxVE3crQTGAU5w+gTUgkGl4hNYgySmxvPwmiWC1t8bHJidJksYFVrVq2lUpLeJyBRaYmPid0QSTkBgsrIYrBDcTIeQ5oWoc9qSGa9SeFscvoQEmNIKCnmieoz7zzz+jDzupmDm0tKB5x0sGyhNYFbJyM7F/jewwaAMYGQgn4MbEaPUZKYBFYJrPYoAePa0lrFGDIiZlaVxiqJ0YrGlBuulPH8RgxQKcHREEhZ4JPAhcR+Y7FaMU6L0MM0JLPepCCEoLGaSs+6gpmZM+b1YeZ1Mwc3l5gxge7BCEjaUyXBLiRqa1gpGHziTudwMbKwGkng9hb2WktTGTZDQEpBayXGSFqjMVbdV+c+uxHvRk9OEGQiRkHOidMRhhjofeKwMbSVIeVMznxGQzLrTe5HToaJMzMzb8b6EEIipDQ1Zbyb3mpvE3Nwc4mJQEjQeVjL8svKKaNF5KTLnAye0QfIAucjIcCYM0JkFpVmNyY+PR25sVeThCBLsFI8VMAXMigtIAgyGY/AapBC4VPm5sZxQwqWubghP6ghmfUmMzMzj+Iyrw8pJY47x8kQ8DFhlGSv1uy39p01kH0bmIObS4wHdhnECJ0AqxxVbxhdwiMYQqLRisEndi5SGYnJ0AdIGQQJlzJKCJQou6OisQnndW89ednElFGipI+1FASfS7AjJBVwPITz0tWjNCSz3mRmZuZRXNb14bhz3No6rJasKs0YEre2DoDDZX2hxzbz/MzBzQtSAeMr/P4B2GaQI2wVyL5DC4XQCpdKu3hba7KLrCvJfmuojGbZKFatodYKn8vOySrJdvTc3Hi0EGQpWFjFXm2QohgGQkkhr2pJ54p7cW0VH+xpVrVhZTX2EYvRrDeZmZl5FJdxfQghcTIErJbnHVuNlUDgZAis6zSXqN5Q5uDmBXkde44AbFMJcFLMtHVgT0mEgDEJ3mtUmfkkFNYq9poKJTUhZnZj4KC1tEbhYuK0c9zeeda1AinY9Z5+DBwsLBmJloJMSRnXVqGFoLKaSkvqyan4Scx6k5mZmUdxmdaHkBI+JlbV/bfCSks2YygaHObg5k1k/q29IK/rEg2U1vBdD9sBQi5+NCLBUefpXSTlzNIarq0se7Vi9JneRVaVotKK3kc2Y0AIsFqzsIbKKDZjpHORxkjaSrMwkhgzQkBblcDGSFlGN8yZmJmZmbcELSVGyfOS+xljKNobPWtu3ljmzM0L0r2m90nAmKdo1MO3TyKrKnJ1WROyYAiBZS1pjGD0GZ8iVkkaCS4lfIx0LjC4xH57t7RUGUVIicFFzLqkZhslWXtfRMaiXPxnHVYzMzMzbwtaF/Fw0dgEqsmh3YXE1aWdS1JvMHNw84JoivD3dVCs9SAGGDIYBUJIDitFQJBRbMZAirBoDYtGg5L8r59uyTljtKY2kkrfDVJiyhQrrUwIkdPecTx4YspoKVlVsGoMWs+BzczMzOvnVc+i2m8tACdDYDOWZourS3v+/MybyRzcvCCvK7ABMJT2cKVhUYHVAglkKTmsFUYptNYordBW0tYGoyTbMXDn1PP+oUJJ2PrASggSmW0f8CmxEpo/ur3hpA80VtEajUuRo5jQSnK4nIObmZmZ18frmkUlpeRwWbOuZ5+bt4n5N/gGkYBGQmVBa9CimOwdd54AuATeBVLOLFTJ4owhs6w0UgmyECwrAxl2LrAZfAlsakNjJLe3gRgztVZIKUmAj5GjzhMeqEnPzMzMvErOZlEB587Gu7HMv3sV6LOmiTmweSuYMzdvEA7Yr2G/KcM0QeBDQslMDIn9ZYVPcNJ7pIS2trRaY7TGKIGUklVdRjBkMrsQabRm1Rg2vUdLQWMVOx9RPuJSJsXEdtLpHC7qeXrvzMzMK2eeRTXzoszBzQuiKZ1MrwMDiAwHywqrJaf9SO8FkkwX4JoUWFkM97pRcWWRkUoy+Miq0uSYOO5GjnYeJPiQMCtBSgohQCu4s3Nsek+E4n0TEwfLilsbg0BwsKguLMB51bX3mZmZy8E8i2rmRZmDmxfE8nqCG3P20GUSOFmxqjRaQlVpKlOmeucUIQaOe8F60Bjj2asV2zFS6YCLiZQyy1qzmdrIcxZYLfEh8slmIGVYWclx7xkD3FhXGC2503sqrVjU5jX8xHd5XbX3mZmZy8GbMItq5nIzFxdfkCuv4T0kJYhaVHB9VXFtodBaEDJIJVnXhquLMgclINhvLO/vVSxrxVl5OsTEznlihlVrWNUVy0oxxsjWBbbe0ftIqwSKYj/eh0wl4bQPdM4zjoHOBVLKr+Gnvsvz1t5TyoQpmJuZmXlzOJtFFWMum7Zc/iyawNlva+bJzJmbF6QWwCu+d55JeZWCptKMUZJzRiJxIRJCSeE2CnoyQhhu7Dd8br9ldBGfS5dVjNBUgsYoeleciU93ntunDilLCWpVaxKCnCOHVtMYyRAyuz5Q15phMgt8XSnh56m9z5memZk3n8s6i2rmzWAObl6Q/QPgzqt/H0HRxOzGgBZl9ELMGaUVkNiNiU5GllohFCwrw6LS1EZxtHO0WhW3YaXpfGQ3BHpfXDiVKkMyhVcIKVi3ktwJaquREnROBAEWUUpwrzER8jy197NMj1KCSktCyueZn7P5MTMzM5ebB2dRkQFRyvLzHmXmScwr/QvyuvYQEug87IbAolLkLIlIVlaxqCpSTmgJtiplqZBhM0SUhIPWIjVUqqwMx91I5yMxZUJKrGvDtWXNSec46j17jcYo6F0ABIetRouSKtbw+mZO8Oy197nLYmbm7UKI0vwwZ2JnnoU5uHlB8mv6BB2QBqhV5M52xBiD1QqrJAiBQNJUApJk1WiutLokWLJAKzBSc21VsR0DuyHwndMBUkZraLRiDJF1pc7F0VoqllbSWFk8b7IgxITPmdFHlBSvZWE5q72XzEtCT4FOjJlFpT4TqMxdFjMzbxdzJnbmeZjPjBfEvCZJ9pkTcga6EGmz5MrCTDfrzOFS02hNyBmlJWPIGJMxgItwfW1YtxVSafaXjs5HKisRuUwBv915llbx/roh5sy6jbRW40Oid5GFVTSVwWpJ7xNSxte2sDxL7X3uspiZeXuYM7Ezz8sc3Lwgr8u3VwOVhJUpYxeyiAwuIpQk4jAicUTgw4OKFDLb0XOgLE2jqa3m6rLCpYyLET9pbXISpJSIOaKEYhTQGImQkrUwhFSmii8qxbK21KYEFGFacF50YXla35oHa++Pe/2zZnpmZmYuL3MmduZ5mYObFyS41/M+GWgtLBpDyKL41SA5rMpF//HW0xqFlC21UXQuIYRj2RQ9TaUUH28HBh+RQnDQaG73ge3oIQturNT5TJX9tsKq4pvTaIXREiXvtl9qyQstLM/bzSSleKr3m7ssZmbeDuZM7MzzMgc3L4h4NWNOPoMGdILtUEYrWKUxVpAoC0BtBAuruLkNCOmotGQ7ZoY+oCTsXOS48+SU8DGRyFxb1ayswidYtJq9xnClrbFWkVLJkKhJ03NvxuNFF5ZXXUN/lkzPzMzM5WXOxM48L3Nw84KE16S5GYBjBy7Dsga7VMgEUoEREpFymc1AxsWElLDtAntVKSMpBY0SbGOmD5HRRwafSLm0VnYucdiAlKVL6iyr4kIkpUxbaYyS9y0sUMwBnyV4eJ019KfN9MzMzFxe5kzszPMwBzcvSGNfz/sk7oqKjQVjFEobtFTEnAhZ4LPkWmNY1boY/NnMEIsqKKeMz5nd4DnpHMe9h5SpK831peXaQiOl5E43IoU6z6pIUdrPRx9JGZQUtLaYCJ4M/plbM+ca+szMzLMwZ2Jnnod5/MILcv3w9byPpoxgWC+gUoZaSlIMxaG49wSfEDkjc2Z0mUjmsDFYrfEhlg4nIESBEgItBcvW0BqNVhKtFFpJNmNECDBKIoTAasWyMVijWFeavdoghKBzJWh6lnEIcH8N/V7mGvrMzLvJ045JkVKg1Tx6YebpmIObF2Rv2byW94mUAZ0pTqZWMbPpHR8f93Q5kwn4GLjTjQzOY4CFVbRG4kP5Gikl1pTAZowZLQWHC4OSittbT86lFPXg2qGlIOeyuKSU2bqAkHcDoOJyLBjC081x0pMp1zwzZmbm3SXnTO8CJ4PnuPecDJ7eBXKeZ8HNvDhzWeoFqXQF9K/lvSTQJ8g7T4qxBAcClLIYobEqUUlJZQU+Z06GyOf3FF2IaAXb0dO7iIuRShYvnDFkjM5UWhCzQCtByve3acdcAp7RR3ofOeocjSmi47NS1JPKSvd1SMVETIngSku6VnKuoc/MvGPM5nwzr5L5DHpBwmvqBc+UEEruYAec9AkULC0sm4zQipDhzrbnZPBcWVZcXVQIKnbO4YNk5yKd9yipOFhK+jETcsSK0nWQMyyNpBsDIWckReujRdHZ9GmqfxtFTJnO3V2InlRWum8hMwqlJN4nai1ZVGbO2MzMvEPM5nwzr5o5uHlBuvj6UqiRqY4owJfYBiFg5wRCBPSokUrS5EhIiZPe87Wjnr0uEEgcthXBJ27uPIoSjIybxEGbWdeKVaWLK/EQEHfn1JHJjCHRVKqY/+WywzrLxkgpyIlHtmY+eiGDMGegZ2beOebGgplXzRzcvCC1er0XoJIgNYTJGtkFEKMjBEEvBIeihaqi1mESE2c2eD7ZjPj9TAoRkTI+CZatwBhFoxQSWNYal6GyiqUsi44UgjFGTnvPoirvWU3lo34qU9VWsZwmkD+Mexeye8tdL7qQPa3D8czMzOViNuebedXMwc0L0tTmtb7faYLGTaLiDDFCnDQyQmWEGqiVZAwK7z2nufjQ9C7x6WbEp8zn1xXX9hrGCAtbXIkRAu8jJ2MsxjdAa3XplJISMriYaCYRcW31eYlqv7blezwCKcTUUu4JmfP2cS3AGvXMC9nzOhzPzMxcDmZzvplXzRzcvCA5vt6P0FNKRSKXP8cM0gESZIIhRI4HjzYK0WVcdtRSs2oVtRLshsCdnWdvWdEYzaoxNFrhfGLnIpt+pPOJmDNCCG4sLIermsYqcgIf7y5EOcOi0o8NbKAsZOTM6RCwWmKkoHeBEDPX18/eITULEWdm3nxmc76ZV8l8J3hB2sXrzdxACWrk9FBMLsUKalUEwRLox8DgAm2l0UYSE4wp01hF7yOfnA584cqChdVIIGrBEAI7l0rQYhVjSHx01JMEfHiwQAjxXAtRShlEeX03Bo58RAKVkgw+EmMqYx6egofpd5SAQKJzkUrPu76ZmTeB2Zxv5lUyBzcvyHpVvfb3zIABbAVWgtaw1AqpJSiB0RJEog+CkDw5gxoFAsH1pebUJQYXUAkGF0lkDJk7LiIApQQullp4UBIfMkZKjFHPtRCdaW5iLAGIECWwUVJw0nsaq9hrqqf6fvfqd3LOZYxESIRYsk1WCZaT0eDMzMzlZx6TMvMqmIObFyT41+uDKICakq1o7VSiEhBFJseMVeBDxArNQkuyEAwR9rQs3jJJlKGbQrELEeUCmozLpcR1ZWHJCHzMSJm5urIIKYk5Y3i+hUgKwegjt3eeSksqowgxcdJ5IhmtJSkLWquePBn8HiFijOm8PKWnjq3eJ7SKc3lqZmZm5h1mdih+QZx/PT43Z5y1Z0sJamqjDh6EhCxg8IHTwdOlzHsHlvf3LItK0liFkhJrFJ9b13xwpeHGsuaDVUNbGULK+JDoxoiSAkHGT9riSiu0fLFTJedMzEWvA+BCpA8JLco4iDS1lz9phMOZENH7xHYMSFk+j5hhWWmMlk/tlDwzM/Pm8rRjG2beTebt7Qti7cVobs5u6EqBUBBDQmvJqqoJOTGMkVunnmWtyDFx2mX2l6WUtGyKiLcPkU92A5udI1LKTbd2idEnVk0REINgr36yaPhxpJyptWJdG3zKhBAZY2JdK4Qsx1RpRcz5qQy86inzsxlz+SzU3awPMPtkzMy8xczdkjNPwxzcvCBGv/6LqQcWonRMLQ1EDRlFUxmsEeBhFyI3d47TXlJZTWUSJMHgPHd2kisLhROJ7RC4uXG0VnJlaVlUihBL4LOoDIdLw/qedvfn8ZaRQqCNYp0NvU9EEjkryJAytKaIgEUu3jkuROxjhMFCCBaVYX86lkrdfa2PafbJmJl5i5m7JWeehvlMeEFifP030RHQAfYaMFLR+0SjJSurcAmGGFFWEVNAKI0QuZj+5cRJ59j0ER8iB8sKn4oHjU8Jn6C2mrbS7FzgsLZopdm4SBWLa+AY8zPvls5KSSFIGgNjhE0oTe1Xl5bGanLObAbPGBIyg9Lpsd9fSkFrFLsxlrb1zOyTMTPzljOPbXg23mWj0zm4eUHaC9opjBSNTSBPIxLgdIyM44iRqohuo2SxsiwaTY6SptLURrIdPB/dgZQly0azbhTDJCDuxoTVCYnEKIHImZTgzlC6rpaNea7d0r2eFjok5BJSgsWUFdoMnu0QWNeayqqn+v6zT8bMzLvFPLbh6ZhLd3Nw88J84XBxYe89Ogi66FlcinQ7T0jQaqiAyhqu79UcLmpcjESfORoS3icqE4kxknKZ53Ct1ixqhVSC3id8SNwGGDyVksSYqKxGCYEQ4pl3Sw96WhxgcbF8fe8jY0isa81iauN+mu//LD4Z7/IOZmbmbWEe2/B0zKW7Obh5YVp9cR+hDzBEaEVAZEOlBSJEck5oa1k1Ch/hqPNlToOUGClRlWVpJX2MtFGTUsYo2I4RmSOdjyysZmEVVkq6MTKEwKEU9+2Mnme3dG8reaMklc64EJHTTKt7dxVP+/0f155+toPpfMTHhFGS1jy55XxmZubyMY9teDJz6a4wBzcvSLygFGgAxgBjKGMQpPDsLSxNpUkusF9VaCm5ebwjS0mtFYeLCltJ1rUCIdn2kVMXaUzkpAekYH9h2FMKaxQ+g85QW8XWeTp3f5v2y9gtSSmwWqF0eiW7sd4Fbp6O+FTKVr0QdEPgytLSVq+/021mZubFmMvRj2cu3RXm4OYFWV1gim8ArooyKfy4gyE49mqFkQqXI3WSrPZqZMzc6SOD8xy2BgG4kLFaYDIYpam04MqiYt1adi4QkyCn4gBcKTlpb/J5N9KL7pYeLBO9it1YjIlPTnqO+4BWAqskWgoGAce9pzb6ndjBzMy8Ci6q1DuPbXg8c+muMAc3L4hWF3eiaKALIAKMHrQCkTNVpdFCkzRcbWtaK+FkoFEaKeE7Jx5yBCVZRE1jBypTo5RkmMYYGCWICU77UITFQmCNxPmzEQ3yuXZLjxK6ne0yXuZubDt6bu8cjVHUVhNTLmMlJPQuEmLCynm3NzPzLFwWseo8tuHhzKW7whzcvCA7Fy7svT1wMpT/14BVoKwlSxh9wEdJSIneC1ZWQYLtmNgMHgnstYJrK0OScLTz7DWBQ1PTxUgiIVMmp4Swkr2qYn9h0bIEIovKPNdF8iSh28vajaWUGaaBnFpJhBBTIJoYY8S+I7uXmZmXzSxWvfzMpbs5uHlhcr64m+TZZPBAaQXPGSyJYUh0OVJbw+2tRytHJRVKgfOCSoNRkoDgpPMgFIMbSBmEFFQShiFx7ANGKJYozDS/SStBeE6386cVur2M3VjKGYFgaRVDSIgpVUvO9D6yrg36KSeRz8zMFGax6pvBXLqbg5sX5iIDYQkoyi9R6eL2qyvN2A04D4jIt482jDHRaMuNtWFRWWojEFrjfea4CyhZTP3GUMo4RkqkhLW1vLdf01aGnMtQygaQUj6XKO11Ct2kKItvW2kSoUwNT+BiojGag9a+cxf7zMyLMotVn42LtqB4l0t3c3DzglQX2QpOmTG1qsqMqZCh7waGkHAOWiOotSVJhY+JzmeWDdwZAo2RLCvDECMxCwSC3ZjQSiIFSAGVkQgE6nxoZmLnIutGPpcoTQqBEDCEeN/IhFchdLvriqxY1QIfSjmqlbqMmajnTqmZmWdlFqs+HZdFl/QuMwc3L8hFbv4l5Re4rMrfXYDjPpEEWA1VZVjWls81mpM+IoRgr1ZsfSKnjNGJkCCnRN1otJGkVJ5bVJplrdmFiPERq4t+Z3CRw/au3uZpdyY5Z8YQcT6yGQJGSxZWoZUsTsWvQOh2nyuySrRC02pFW+l5gZmZeQ5mserTMeuSLp75U35BpLg43Yaf/ux6qFvBXp1RWrPtA0KVdu/bgycDrRYopVjVFTeyIMeMyAotM42FkMC5yOCnBStEjBTILEgpcdIFxhDRSuBjphvLuz/trKmzi70yCi0FOxc57j3LqpSIXoXQba47z8y8fGax6uOZdUmXg0uhqPy1X/s1vvu7v5u6rvniF7/If/pP/+mRr/2n//Sf8hf/4l/k4OCAg4MDvvSlLz329a+adWUv7L2hBDh3POSUqaxCIosQJ0vGEDnZjnxyOtC5hNKCzRhLClkpVq3i/X3D3qICNaWbYxHfjjHzyakj54yRAgRUSnHQWIQQ3Nk5bm8dwHn9fTdGBh8/c4z3XuxWl+nlh4uKg9ZSaUWlX22qVkpRym3zgjIz88KcbRr2asN+Y9irDY2ds6FnnOmS9APrjZaCOGW6Z149Fx7c/OZv/iZf/vKX+bmf+zl+7/d+jx/8wR/kR3/0R/n0008f+vrf+Z3f4cd//Mf59//+3/PVr36VDz/8kL/0l/4S3/rWt17zkResuvjdigBShN0QCUQOWsNea1nWlkoLxhi4s3NkHwlZICKEFBh8ZjOU0QSVEigEmyEic6Y1kkzChcRR70gpo5TAxcxuDGwGX1x/z2dNSZQSDCGR0v0X78MudikFtVHk6d8fRUqZED/7PWdmZi6WedPwcO7VJd3LrEt6vYicLzaM/OIXv8if+3N/jl/91V8FIKXEhx9+yN/6W3+Ln/7pn37i18cYOTg44Fd/9Vf5iZ/4iSe+/vT0lL29PU5OTliv1y98/Ke7kR/4hX/3wt/nRZDANQPrJVRa8d0HC5pGM4TMre2Id56I5E/dWPE97+0jyfRjYG9RIzKgM7XWSCBLQWM0C6PYbwxXFpY7naO1hrY2xeHXR462A9ZIPre/wOoS4BVdTWK/ub/NOqXMyVDKWOae531MAOzVn/XMmQV5MzMzbyq9C+eamwd1SbPm5vl5lvv3hWZunHP87u/+Ll/60pfOn5NS8qUvfYmvfvWrT/U9uq7De8/h4eFD/30cR05PT+97vI2EDMmXOvi3d66MSQAkEi01tZHkDEYKri5rFm3FfqvZX1rWlaWSii4kRIJKCGJKHHWOWxvHd05GTnqPD5GcMzlltj5ye+M4HTyDC+ScH7kzORMhxljGN+Rc/owxU+uH7/zONDrw5LLXzMzMzGWiNopFVTZ9YyibuFmX9Hq50BDy1q1bxBi5cePGfc/fuHGDP/iDP3iq7/F3/s7f4f33378vQLqXr3zlK/z8z//8Cx/ro7gMRnAGaCwkwI2Zzg3cPhmotCSREEKyqDSf7DzVJ1uGg5p1YxhiZlFJiJExRqJPSCPZhYT3gWWl2enA4AO3d9D7wNIqtCrTwoPIjD6RoqeNCa3UIzsmnkWEOAvyZmZm3mTmZoaL5+LvzC/AL/7iL/LP//k/51/+y39JXdcPfc3P/MzPcHJycv745je/+VKPQcuL/wgd0LkyZ0oBycF2gKNdIkRIOdENjtubjq+dbPjozpY7W8fgIilGtmMkxMx+qzmzfMq5ZE98zFRKEmNiOwb+6HbHcT8WDx0lGULgZHAcdY5ai/NgJYTE4AJh2rU8iwhxFuTNzMy8Dcy6pIvjQjM3V69eRSnFJ598ct/zn3zyCe+9995jv/Yf/sN/yC/+4i/y7/7dv+MHfuAHHvm6qqqoquqlHO/DkBc4OPOMDBwHuC4hKogJzqo3lYVlJfExs+09OYNEoJVmf6G5sVpBHiHD4dIQMjiX6Mh4l2hsJOaMy3BoFTEm+jGiteL6sqa1GhciXUhTqSlxOnhOhoCPCaMke7Vmv7VIOV3kaRIRJx560T/OKEwweeuIOXszMzMzM/NwLjTtYK3lz/7ZP8tv//Zvnz+XUuK3f/u3+fN//s8/8uv+/t//+/zCL/wCv/Vbv8UP//APv45DfSSXQfmuKb/I0UOI4OJk8KeLFgchsXXFqqlYtZori5qrywolNBk4XFqurAwhZnY+4nKCnNnsHBvn8KkM0dy4QBIZnzNGCFqrkVLgU2Z0gZunA398c8NHRx3kzKrSCODW1nHclbby3gVOBs9x7zkZPP2k17mXh2l0XIjsBs8YIqdjeOTXzszMzFwW5m7Pi+PCZdtf/vKX+Rt/42/wwz/8w/zIj/wIv/zLv8xut+Mnf/InAfiJn/gJPvjgA77yla8A8Eu/9Ev87M/+LL/xG7/Bd3/3d/Pxxx8DsFwuWS6Xr/34L0P2IE0PlyF7EAIoiRF2HeQYaGxg0Vj2q4r39xoOVhW1VdRakmLm043jdPTEDC5EPj7ash08pz5yfWVpKkudBJWRGKmQShTH4jFytPMomTl1me0YqJVkVRuklDS2jPY8GQJGSsaYn8q180GNThEzl+eNuv9rK62eqa590fNeZmZm3m7mbs+L58KDmx/7sR/j5s2b/OzP/iwff/wxP/RDP8Rv/dZvnYuMv/GNbyDv0bX8k3/yT3DO8df/+l+/7/v83M/9HH/v7/2913noAJciIk/TnzugikxdUiBDeX5w4BJY7Vg3K5aVoRsDu9ETUianxCenI7WVXGkMdwbHzc6jo2c7WhY20FhDiBEhFJWFzRDIDDgX0FLRGE3MCSkEGdgOntpqpCiBzKb3bEOg1vqpRML3CvJCTGwE1PZ+gXHOkaPOUenil/OkBWRecGZmZl4H8/iFi+dSfMo/9VM/xU/91E899N9+53d+576/f+1rX3v1B/QMXDZxa+JucCMAa6BS5c/GWFLM3O56fACtJVoVAfHoIyFEBhfofMLFRG0qKpnxAcYQicAYEwdtCVCCj9zpPPstLKVloRQuZnxI9D7hfEQryehjESnHjHzgjHvSNGE5jYDIGfQD+qYwiZyNKgHKkxaQRy04KWUqo+ZMzswMc2bzRZm7PS8HlyK4eZO5DJqbezk7GiFASKg07DWwqixVVeESHO9GcpasasvoEpmM0Yp+dAxBogQsjWaMibWukCJz1AWcC6xazbis2G8kCMGtrWM7BA7bxKKu8SnxyTgwjp7GCELMdD6x1yiEKw7Hy0pTTdmSp3HtfJjAOKXMzkWskufjGx63gDxswdESdqPnuE+sK40xas7kzLyzzJnNl8NZt+eZP9cZT9rIzbxcLr6P+Q3nskXgDgjAkJnawKG2BmM1lSoloz5k6kpTV5KUE0bLqcTjOel6+pBwIZAQ+FCyHaMPIDOtlYwJTodIpRTv7zdYpYmpBBALq9ESyDCEXHQyWmCNxihByJmTrhj/PcnI74yHCYx7Hxh9oHngax/VLv5ge3nOmaPdyJ3tyEnn2LlA7wLbIcxGgTPvJLNx5sthHr9wOZgzN28ZZno4Sou4VuBzmRkVbBmuWVtIOXK6y5zqiBWl9LTtRqrKEF0kxkxlIlIYOh+4XlUsGsuqqsg5852TnpAye41iYSUuRXofQAj2m4ovHGoqq+lcLDqcKdhYGUXnS8fTvpRP7dp59preR047zxDL7rLzEaXCEzNBD2Z/ujFw0hdR0rLSaFna5efU8cy7yFxKeXmcbcZKoJg+M35h/hxfD3Nw84JcBkHxveTpoQAtytTwFD1SasYoSAm8FwwxYWRiJRURybYfiTlzUBuUNWSv0FqRiViluLFfs6otnY/4kEgJPj4dGKPmSmPZayxNpUsqW0kWtcbHRD8GlBBEysVtl5KDxjDExKrS2Ke0Iz8TGKeUcUqyX2m8VZwMgaOdY781aK0euYDcu+CkHOlcIKSSfaqNwhpFmAImH+fU8cy7xVxKebk8iyP7zKthDm5ekMsoKI7AQsGqKdqbLCRtbRBSoXXGaM3aSHwUjCGCzFirqbTic4ctGVFu9DljlcYqyXt7LUpJ/Gbg5taRUqKtNMErbifHXmO40rSQ4RM/cHvn8D5yp3M0WrKwmizK1PBMEfw+6+iKlDIuZayWUxCSEblkb9wmcmPdsKj0IxeQs+e3LuAmg8FKS6rpeSUFg4vUVs2p45l3iscZZ86llGdnHr9w8czBzQty2S56dfYoRsAYAY017FcKpEIojZCC1hiwgkpXxJTJouR8Vo3Faslp7yBLVlbQ1JbOeW5vR27vAqe7gSwk7y0t+41mO8Af3ekYYqIxmp0r2R1EppawGxIhB64tKwTQj/Ghk8CfxNnuMqXSjaWlYN1Yah3Z+oiR4rFtlmcLjpESkcH5iE9lAdeyaA5I0Oo5dTzzbjGXUl4NUoo543VBzMHNC3LZLvqK0jG1CxD6kr2ROSNlKb20VpJjaelmmv4tBQgp2WsM7++3pJxZVIaUEkYr9mvJ1251fHLiESKz11aMMbN1idM+8MF+i0+Z7xwPLCrDlaWl0pJPtiNLI1nUAsSkAZICIwVWy4e2nD6uDVUKgRCcd0mdZX6kkrRMk9HTk7UBWpdBojmDTAk/tYRnAYcLQ1vNl8XMu8dcSpl5m5hX8beMHSWIEECMIDqo5Ehdaw60Yqklmxg5GTwLowiUkQujT8j9mt3oEFIgcglCjBD0MREFvH+lxvmEC5mlyJyOiRMXuZ5L6lorScyJlGHdWHzM5Jw5WFRlB1iXQZlCgAsJl+J5y2k1pcLHmB/ZhiqlwEqBD2VnmXO+u7u0xcjvabUB9y7kOiQao2iMoq0ePsxzZuZtZy6lzLxNzMHNW8aZmPjs/4cExz3sD4H9A41SinWt0N7jQibKTGsVRip2PvKN21sOFw2VlmQhuWJAZEEKsK4Vo5AM3nHSO4beszOST61mUSkOFxVSTjoeYFFpTgY/dVtIhChmfIJM79N9Znp3do6cYdmYxzp6tlazqvUkcry7u5RSIMTTawPmhXxm5uHMpZSZt4HZ5+YtpFTN7+mcEjCGwFHXs+kjKQXS5Pq70IpaaRorCRE+3Yxsh5FIQms4HTxHO8fJ6Pl0V9yAry4NMcNpyDRGs6oVPsPGTU7ECcYYkQJqLckpl3EQUtCYYv531nIqhEBNLdwhZ5QQkyGfRKkiQL63I00pyX5raY2mtYpVpVFKkhJP9Mt5GHLKOM2BzczMzMzbw5y5eUvJlDZwAYwJ7nSR2jqkULgk2biMEpmIQkpFTgFSIg0VX7s9stwFrixKicYn6HtHlx0xVuy3ikWlib64DS9bS+0TuzHQK8HCKk46T0yJw9ZyfVVTGYWSkpQzvYsIUTqftJLnHWeS+8tKj2pDvbek5KcS1vNoA2ab+ZmZmZm3kzm4eQlMQ7gvJQnIxZuOSmukFuyGiI+CKBMnQ8RKQe8TwUc+uKo4MBpB4tPTkRgF1sJhW9GPkaPdyBA0V5c1f+JKg5iGmjaVBkrXUW2KgZ8SYLVCKYnRqti7j4Hbu4HOR7Qsmpp1pck5k+8pK6WUGUMxAHyw1PSiJaXZZn5mZuZd4l3cyM3BzUvge4E/vOiDeAQBSLHMmVJSYIUgIUjREQJsh4CUkFNmCOWmL4DRQec9ISQWWNRasF9VXMmZSiuurGqUEnifUVNWxWjF4bLiyqrGTO3UPt51OB1D5Lj3hJhRQqKAzgecj1gjqbUmpET0iZ2LuJhYVpoxxIcGHs+rDXjYAM3NEAgxsaievUV9ZmZm5nFcVHDxLm/k5uDmJXDtCvzh7Ys+iocjKQFOTtCNnryyDDmjlEbmNGVIoFICLTLee271nr1aoYSgT6UjKSXIshjmjSHiU8JFaI1k2Wi8L+ZfalWTudsif1ZaCjHRuUhImXVriSnjQiLF6Tmj2JsEyJ2LWKPYbwxaycdO+n5WHrSZzzkTYmLnApsxsx+LwPpduPhnZmZeLRcdXDxsI/cy19PLzNv9070mtLnoI3g0Z47FMRZR8ckQUGSCEGQE61aDkIw+oRMMEe6c7oixIgNGaw5by7JSdC5xczuiheALhxojBS4kbm0d5DJs77h3aCW5riRKSVxIkDMhJEYXIGWMUVgtqEwmJoULkZQyfUwMMaGUpLWKxk6an/jy5ts8aDM/+EjnigFgSOXf35WLf2Zm5tVykcHFuz4vbF69XwIHy4s+gscjKNPB+zFwe+OorGRRK/q+pElrrVEiMrrI6XZAKYOLI5WS7C8g58R3TkZCzFghqazi+qqmDzD4gFCSvVrRj4l+jHzKwMIqpJRsXECmzImSdKMnpswqZxa1QU4lshASUUhqC0ZKtBQMPiFFLGMaHiIsft40770286WLbPLMAbQSVFoRc34nLv6ZmZlXx0UHF+/6vLA5uHkJvH91D/7Xk4s+jEcigJggZjCVQMRMTOCjZ3CRwUZqVdq7kQJji4OvoJS0jjuHkprGgDGawSfGEElZEFJiaTVGKmQlqYzEh8Sn25GFNejSA17au6XE+cBR58g5U1td5kKlzOFC0xg9TeYuQz/HaQGI+e58mxdN895rMx9EIsaElGWg6LlfTuaduPhnZmZeHRcdXLzr88Lm4OYl0LSXuC4F9EAOEE6gNSNNbdExkynBSxg8owogJLVSHNSGw1WDyJl+DOzGyOFScWXVImXJtNzcOSqjGFyiMSXYEQIy6vziXTeglTr3rdGyXGxjjNzpPOuQaCpNXRuWlb4v8JBy6piKEZHvGvX1LrxwmvesZbzzRe+jgGWlzwdovisX/8ybw7OOKpm5eC46uHjX54XNwc1LQObLHdwADMAIyOPI/qJn6UpmxkoIUuFCIKbEalmzrBSizDIgUQZVaqmREtaNIafEUe/pXCLnROslxkAjDbUWKKEIuVzEPgT2WguUbq00lYMSmShAKIGeTPys5DzA2I6BmMrCfSbwfVlp3rut5Kq0wbui8wHwMb0zF//M5edhmcqnGVUyc/FchuDiXZ4XNgc3LwFTvxkLSgZigBCh9wmRM0YpjM1URtIHQYiRjUskPG2lqVFopThYqMnMJ6O1pq0yAtBoqlqx0Ia9hSFGQMDCKLSQDLm0dFda0Y2BzRAQomRKVpXBxTyVuErQoaUogmJTXJPvbc2OKb3UNK+UgmVt0Cq+kxf/zOXnYYLUpx1VMnPxXHRw8S6PmZmvhJeA1W/OxzgCJz0sY0IbiDmwrCuWVcW46zgZIiHtyMuakCWVSFRaUClJHxMfn4wsrGBVGZYLS6s1Wx+4sx2IOZEpn8cYEpUKaCVwXhFiYjN6Uk7UlWFZmWL0FxMjgIAQE2PIGCVZNfozO9FXkeZ9ly/+mcvNwzKVSpTzvYxVORtV8u50wLxpXJb15V2cF/bm3JUvMVq9Obv8AVARtj3knlJOMoF1W7HUmhFPZYq4eK9WSGA3RP7w5hYpBC5nNHBtVfMD64br65p1iPyX3QlHW8d7+y1GS1yInOwCh0vDuhGMMZd2caNYWX2+c1FSIAEXIjKXAOZRE89eZZr3Xbz4Z+7nsmlYHiZIDTGRYkJI8VSjSmYuB/P68vqZg5uXwAd7l7wX/AHO5k5Fyv+c9BHEllVTsde0iJQQQjD0PcdjZucijVY0lWJlFVnB6ZC4edpTGU2lBFlAnzJCgA+RzRDY+ogdJd9baa4bzUml2bqAUpKYyzDNGBNH3UjMgoMarFUQIttpWOaDafYz7U0XIj6CUXIuI828EBdttPYo7s1Uagmjj3Q+cjL4qbyhUbJkb2YR/MzM/czBzUvgw/3FRR/CM5EoyREJGA0hw+ATrfH0EVzKtDYzjpkjn5Exslg3HNSGMQZGJ6irzEkfaTY91mhyytRacvN0YOcjGagUHHeZ08FRW82y1pz2jts7h9JFSLx1HjdmriwtlVWkDGPIVPqzafazm5BLGZFACLBSPPQmdNl24TOXl8vq4npvpnJ0gcEnEOWYQkps+lB0b1rNIviZmQeYg5uXwKqxF30Iz8QAVEA7rYPBg1ewGzM+91RaMnrNtg8IIfFCcDoG9lOiDxkXAvvB0ofAZtCsEexcBBfRStEaxbo19GOkj8XYbwgJI0pZKqSMS4neR5zLKJkRUrAZA0oIlIA8pXCDUVg5ifLuvQnZ8n16n5Aynt+ELusufOZyctFGa0/iLFP56ehJZCqlOLTleti5WK5LOWcvZ2YeZA5uXgK1fPM+xkDJ2EigkmVyuBAJ58H5xAaHi2BUQms42kZqI8hZIgWMKZUsSvCcDDCETAyRRQWDELitIwvYs4o+Rnadw1SKZWPZaytCTISU+OSk47QLhJRpbBEenwwe5xOHqwqjFW3OWCWf6iZ0WXfhM5eTizZaexJCCCqjWFRl3ImS8jzYqrSiD5FVpbFzYDMzcx/zav8SKD1CbxaKorkRAUKCrQcrM6SiyZGUkQ1uah13ITHGHZUWHC5qBuc56cqU8fcONd99UPPRyUDvEyF7KqPZqzWVktw8GTFCUEXDXgPaarSS5XMTAqkFIIgpE1JiOwYygpUtxn67MRJ0ycQYKQgxnZeb7r0JkXjuXfhcxno3uWijtadBirvn873nZqIEOFo9QoE/89zM68GbzxzcvARCShd9CM9MQxEVA7g0TQ7fgdVl0WwViARSAxlCKI9GS9aVpjWGvVojlGRpDN91paUPcLsfMQKUEjRGMaYEUrCuLEHAnc5Ru4BWZWDmdghIIYqZYIzc2TqEgL1aU1caoyQpRboxMKZELLKD8jMYhdHy/Cb0PLvwuYz1bnMZjNbehmO87DxtsDKvB28Pc3DzEmgv81jwh9BQxL4xFv1NnB5VBEfJ3AwZlC7lqpRBKpAC1pVgYRWCzJ2Nw+rAFw5qOh9JKaJFLm7GZG5tR+pK88GyQhuJd5FbJwMuJt7fq6mtxhpJjhmfEmPKpAytUbS2OCgPLtD7yEk3klImxIQ1RXh8tINVpXj/oIyFIHHfUMyzxeze2VQP0o2BzRgwWs5lrHeUizZaexrehGO8jDxrsDKXtd8e5t/WS8DaN2uBSYCfsjVngY2kBDBCQEpQ27sBjfPlTyIcjwm58WxDyYJcXVl2Y+C4c0QErdY0tYWU6XxkaSXLurSAp5jIubRxf7IZOVxmri0qNoNn8In1wmClwE1BzmbwpCyIU2u6ltB5yDHRGk2mLPRpahuXkzX9nZ0jpLulQi0Fhwt7344t50w3Bj7djqScqZIi6XzfTeRRZaw5Zf12cRmM1p50Tr2sY3yd5+5FXSf3vu8Ynj5Yuezi8plnYw5uXgJvmjnTCMhcsjSJUuaRUATE03MxlSAnUQIbYYsAOYZEiJHRS2qtqJTi043Dp4yVilVtuLFfg4CbpwN3dh5BT2sVUihCghvLCqMlKU4mflKSRIIssFphKAHJyeBLECNAI0BJ1nUpQ7WVRglBH0pHVpvubRm/Rzc0/f1BBh/ZjoGUMu3Ugt65sujVRj20jDWnrAshFDG4lhKt3x69x0UYrT3rOfWkY3xUQPE6z92Luk4efF8BjCFSG/VUwcpZWftxur43ba1/l5mDm5dAypmaUuJ5U+inPyUluAnTo5me70bQCZoFaAFDAjJkCaeDQ0LpbkqZ3eBJQnKtFQiZGUPRIIWYi/BYCLYu0miJlIqMYlkZbm1Gvnm7owsBIyS9LtocKQUhJXZjpFZlvtQIbMZAM7WSSynIGaySU+msCIrHmFk2BjVpcM7KUmPM1NPXne3QtJJURhEz0+JXMkFyml7+YBnrXU9Zp5Q47hwnQ8DHhFGSvVqz31qkfHuCnNfJyzqnnhRQvM5z96Kuk3vf10hB7wKng0fLsmk641HBiqAYkG5DyRSffYaPWg9mLjdv/4r8Gkg584UG/kv/5NdeNh6UQp/9CFUs/586aBdTNiTDboDKJvoQWIVASJFGKBSJ4yEQEyAUWmZ8SKxqg1KS0yGQrOKgEXif+MbtLcedYzsGfMzstwatJApPbTWDD0DZSU2zOElktqPHKkkIxcXPTCnkBwXFQtzd4YrMfYvZva+LMbEZAymXHdt2DAgy+839Zaw5ZQ3HnePW1mG1ZFWV+WG3tg6Aw2V9wUf35vEyz6nHBRSVVq/t3H3wZ0pTBkWIx5d6X9b7SlkClM5HcsyMPnJnN1JphZp+9kd1wrmYCDETcqaWpRx+ZxuoreTaqn7rr++3jTm4eQloKXnvCvyXjy76SF4eQoDPsEmw3JQuKkpyhNqUTMfxkIkMfCg06xa0BJ8yO+cYXUIJwfdcK6MpfOzYjZ5KS06GgdPeo6cdVms0KSU+2fac9JJIJgZY1sXAzKdEoxXJB076iLGKTOmWarQ5313dKyh+WFtvipkhBiQCKWA3eHzKxJjofcC7gFASqyVjTEgXzne+l90P5VUTQuJkCFgtz3ffjZVA4GQIrOv0VpWoXgcv65x6UpCkhHht5+7Zz2SVYHDhPJMkp5FxS6uQ8uVrFFMuzQb96DnpA4myWREpcbSLWDNypa1Kyf0hXWZnn+Gi1lQxcdp7Bh/xKZMom6Cc8ztVfn7TmYObl4BUgs9da+Gj7qIP5aVxdI9OZQssQlmctIJNB1Y7Vq1EC4XIZc7TQaPRtmhhfIq0U9ZGCjHtHjPHgyPGjI8BLQwoyCRuno7c6Rw5Q2sVHxzUXGlbujHST0GGNZLPHzRILYsmSAi0Euci4Ee1zHof8TFynDgvp6QQ8Alqq1k1huPtyEmEG63mcFF9JpX+JvihvEpCSviYWFX3LxmVlmzGUDQ4j5p4OvNQXtY59aQgCR4f9L+sczelfJ6p6cZA7xJCltKxiwkXMy4kjH75wY0UgtFHbm0dYvLPOnWB3eDJKXNHnwU1mv3GfKbL7L5sbsooJVkpiZaCISR2LqKUfCfKz28L82/qJSCF4Luu7gNvT3DzIB5o5dRNFYvYuBIZlzJDTGx6z25MfG6liAiOu8AwenrnEdM8nEbD0Tbgc2JhDUOMZJ/Z+EgKERcyIDjtHY3VXF/D4dLSucDoI42RHK6aYgA4LUZi0t6crc9ni1bnIr0vC9IYApshUlvFqtL0LnK7DyytRCvJGBJZCq60hspo8j0anHtT6WeBU8rxrlg58U54jWgpMdNnVTI2hTGUYFHPmptn5mX51zwpSNJKUuf8ynxyHtT79M7z6clQMrNa0WXQqgz6dCnTpFdTmjo7DikElSnrwpigkpLWKJa1RlFK1g9mYM4+QxeL7s6qsjb4mKhMMUq86PLz3KX5bMzBzUtASsH7h2/WZPBnJVB0L8aAMLCsNBGwmeIsHCKBxDePiyYGSiDzyWYkTL42N9YNi1qXKcdkjl3C+8hJ5xhjEfi2RuCipPNlsvh+a0kJToaivNkbHOvKUBmFekxaPZEZU8Q5x6fHA3Zych1FwmqJkYKMmDqlJBmotcTHfP79HkzbV1rSu8BJHxhDREvJfqOp3jCfo+dB6yIeLhqbUOaPhYQLiatLO5eknpOX4V/zNEHSq/TJeVDv04+ZzkUaq7CmdC7mBEaW8tirKOGmnLFSYrUgJIg5E2LmoDHkBDELGqOLh9dDgpSzz/Ck94wh0hqFj+n8M7RaMrqICxGrX+9mZu7SfD7m4OYlcWXZXvQhvFISpWMq9dBWMPqIUJLDRmKsKnOqVGbwGaEktZKcDiMxC0KIfHziCCGwqA19gJvHHV2EWsHp6CEL6qbCKIWRpUPraDuyrjUng2eMiTFKTjvPOEaurqrSoXPW9z3Ru8DtrcOnxKYfublx3N46Pr9fYbUkJEWjJY1R7Hwsi6JSGFUWfT2V0aDsfIvvTyaJ0gUWp0UaVUSbJ11xWD5YVG/9QrPflgGxJ8NkfKgkV5f2/PmZZ+dl+dc8KXh5VV4+DxMQZyHZaw1SCJZVaRSIKdP5xEqpV1LClUKgtCwBjLjbAEHORFE2KvB4ndHZkNLeRzoXi7GnKaXv3eDpQyIJMCG91uDiXe/SfF7mT+Yl8eHB4qIP4ZXTT48wQltnbM70PhCOA0Zp6koRYqYPEaskIoOUGathSJGvHTmW1vP+YcuqrchjYNuP7MbAe+uGvYXGuUgWsgzvlIGPNz210qwrjZRwvBsZfOLbJztu7LesqqLrqZPEKslx7xlTIsVE7zNNpahGwdEQsMZQA14Kcs7IKS4SogRTXShZHSHAhchuCGglOJ2yOS4lBBAzWK1oZGmxvdN7Kq1Y1G93BkdKyeGyZl2/nT43F8mLeuw8bfDysr18HtT7pJzJGVaV5nSIRYOjgJxxMWHVywuq7v05pSzjXqxVUydl6X4SwLpWLOsSbD1OZySEYFEbruXiru5DIifBaecYQuLKsqKa9EMbX7RMrzq4mLs0n585uHlJvLd8u8tS9zIAfQ/gCaEIXpbKs1xYxpi4uqyRRhcdTJrKP0Zye3Ac+8iqrVg1lisLzX/+TiDGkRAiN487hlg6o5SWZBGpRMXVpSUDx9uR73SeT04Hti7x33yw4Ic+OChp+KCwWtC7iJWCXQJB6cTaqw23O1/ay4Wid4nGKg6XdnIxLSMdruoiKirllkgx9oE+RIYxcNR7GiM4WNRF95MyWpbW9t7HIjx+BxYareUsHr6kvG4jwgf1Pmd/T6lkjoQQuFDE6I0uxp8vwuNKNG2ludJajnaeJDJXFgaXMrXS1FoS89PpjOSkUyJlcsr0Zx1nk22Ej4mYEj5G3tuT5y3mr4J3vUvzRZiDm5fFO7TWR+DEQe9g1QaSgFwLxl1k9AKRYNlGam0QUuB86ZIoWeLMyc6VDqYQQST2KsNp79gFiSSgFyX4WRhDFyKZzKb33NqVOVNCQGUlmy7xzaOe71KaVSMIoWRcvMzsJhHyEAAhqLQghMS3d55aCfRejUSTp4WjtRohijNpSpkdRUQ9hoxSZUe3dYFPNw6rJEYX7dAYIwLox0BszCtpc52ZeVZel/hUSoGmlJatLmUwLaCLmWWtaa1iO3giGYRk6yN1zs9d0nlSieZgUVFpRRdK1ujMqfjsvZ6kM0qpGH4u6mIE6kIkS0EMkePOU9k0bXgCdzYOpeDGun1l5al3vUvzRZiDm5dFgu8D/vCij+M10TPNqOqKv40SGWsETSUZQkR5xeAD1xeKLDNhKgEta8N6UeFHx3EfWTUVe3XiqIuoGNCiIk++HD5kPjkZGXwgITEUD5qdE7Ra4nPm5tazbgZSiriU2fWB49HjQ2KpJSjJGIrvhjaSfaU4XBhWjcWnjHCBlIp4UMiiDchkhiGQBShVuoRyzlRa0PnIH93u2G8stS7Op1qKV9rmOjPztHxmBIEAKwWt1U+dYXjawOjMsfq492xHT86wNIq9heXq0oIQ7IbA6DPLSpcmhMxz60WetkSzqA1N0uc/A/DUgd6DRqBWK5SLdDExxoQIRdATfHnPj457tJBcWdXPFeA86bOeJ8I/P3Nw87KQ8N/90II//J92F30kr4XEXTfjOhVHY4Vg3Vhylhw0Gp8TPoFSisNacurLOIW9RtNLuNl5jMpYq6kDCA8ulYW584ExJnKC27syC+v6fo2UZU5MpSWNhjiZdG1Gz/6iYr819D7Rx8hNN54HH2PMZCF4f1Wz31riVG7a+YzKngzc2G+otMLFUmYKMXG4Ks67o4+ECOtKcdx7Oh8QaKzOVNZgdWkVNT6WlDZPv6DOzDwPD7sxnmU2pCzBx87FySm8jMl4XMbkWbty7nWsvras6X1k8CWzcbiqCSERc6auNNUU9E9XxnPpRZ6lRPNgee5pSzefKbNJgVWCnQvEkImyjHghCw4XBh8zd7piXfEsmrtn+aznifDPxxzcvCS0lPzIn/o8/7f/6T9f9KG8dgZA93Ba+XNjvc+vK4ytuL3tOdn2eK9JaIJKhFtbalNKQdeWFQgYXXEJ3g2hZEG8JybBeysLWXA6RoSEq+u2GMkJGFNmbQUueaKT7B0Y2towxoyW8OkuU2nFwdISfSbLohcJ0w7QTALDsyDMx0RtykLc1ppPT0e6sbjynnSOPkRao3Eh0565IouyKJEzt3cDQ9AIRGmbV6UeP7dtvhze1oGdz8qjbox28mJRqpRXez/5tUw3/00fgEdnTJ6lK+fMsVqfZ1EEi8ogRWDjIvuTqFcgzq0hznhevcjrKNE8LFOilaTSim30uMDdwb1SYFTZ1D2r5u5ZPuvLMLX+TWQObl4SWkv+m/euAu9ecAPFxfj2KaTo0Bq2a8d371X0o+DTIPj2JnBtKVlZQ+8jzgc+OGgxVnNr68uAzc5xux9RFBFiTIKVFRyuF3Rj5PbGo+lYXV2WVs8ISoI1moVWNJVGSUltFac97NXFlG9pLU5GlISQYDeG80F6QgqIglaL4nGTSl95axWtEWwHh4uZo86hgLbWXFtZtFJkBIJM7wLHnQcJOWXC1C6+ajRCirlt8wWZB3bez6NujEGn86nWY7h7Y845k3JxC35UxuRZu3J8jGwGh5USF8oEbaslVpUhuSGlUtKZghEl7mYyY36+YOR1lWg+kylRkg/2Gm5uJDsfWdcaEOdjJqxRZTTNSxqXce9n/WBA/7LFw2/zhmFebV8i15cV3yfhDx+cRvmOcJSADSxa+M5xRwzgzvxvxnKDSjHRRYDE+6uKpZXsZOJWyMicaZSmD5EUM5C4vQ20laOpLDJ5xhg57TzrheXDpeXz11pikmSRyWWrWIwB+0htBQsrkaJMKFeiTBsP4a7I0EhBEBKjBDlldqMnZhhDJMMkHobey0mYrKm0ACnxIbJzka0LGKnYqzWDj3SueN+MKbFXF3dW6csAw5c5oPBd2cXNAzvv8rgbo4tFY+NSEcXb6WblYiKT0eLRJnrP2pUTYiakDDmxqExxJvaR3mVqq9GyZDYrJbi9dYScz129tRBcWdrnOm9fdYnm7GZvpKSq1fk1JoQBAf1Rx24MtFZjlUDJYgiqtHzqYO1pPmtSfqUB/dmG4agvXaRGKQ5bw+Giems2DHNw8xKpreGv/oX3+Mf/4eOLPpQLY0OZJP5N7zkdT6mMReSMz5luSIQsaGQmC8XWZ3IWHCwtt7YjAo0cUgkaQkArSR8Dp0PgcKFobIVRkqY2vL9fcWVZ43zpXAghMAyBVWM47QMxBWIsaWKjJMtKcjx4gPPszH5rqCtNnTOjT3QuEHNZJMOkuamsYTEtnDExBRMQc2Lwgd0YWdWG/dZgtOR08NNk9BJA5ZxxoSxm69q88M7rXXMrnQd23s9jb4wpU0lB7zMxZ3wo58ngYhlYm8ssMMFntSHPUvJJKROAg9pw0kdGH4vmzEecz1xbVvf9ToQAceYpxd1RKc/DqzMjfFx2sHz/K8saMtzpHIqSsTGyBDhnw3ufZtPxNJ/1cTe+0oD+aDfy0Z2+jMBJGRcdN48Hvutq4IPD5VuxlszBzUtESsF/9yc/eKeDm0DR4GQPaZOxZiQLii26FHgSUQmsKQFC5z05S0IsYw0UGecDO59ILrGMmU8Zp4WmYlUpbhw0DC7xreOe2hj2aoUbM394tGNZKfaXFddWFSmVtL0i82nnudM5FlaTSISUubqytNOE8UzAj5F+9Oe7Qh8SrY/ESlMbTZimin964tBKkWIsE49lWZRKK3lGkAm5ZJGsVowhTrvcz35ez5qBedfcSueBnffzpBtjazValezDrc1IyIm11VRGMaaMisVMr3lAB3NW8tkM5TO1Uj56gvYUYB0uK7TybMYwZS8FCwtSZJyLSCmmMQy6uBdP5/kYIyejRwuJtZ/NuDzumrj33/RTdn89DU+THRRCcGVV01hN7yNkULoENmejWZ5m0/Gk8lpK+YUC+ietKSEkbm5HdiGgkFRGUpnSzv/N4569pmL1FriOv32r4QWileTDK0v++2vwP9686KO5OEaKR8zgwLqSioaMJNPahDKwVJ5vKIH3kYN1zaZzfHI6ogSgJbUVbPsy0Vu1pcQTciKgIUWOuoDREiMl37ozcNxHbm5Hgo8cLA1fuNKy19Tc2ox0o+ek9wwucofSirobPX/08TE/9OEVDvdaWiNxAo5caV2VStCPgeM+YpXmcGGQUnK6G9m6wLVVxXpp6cdIPyaOxMi6tYy++GEANFqwHT160iPcm7SJsWSKXCqOro9aDO9dqIB3zq10Hth5P0+6MSolaZQsJahpErdREiEFKy0QogyVfbBEmnMm50xMiW4sN+7GKtaVRk8ZibPXnxv1AQfLmlWTGL3n5knP7SFyMgRy3tBUkkprlrWlMQqjBMedYzN4di7StYGDhT0vtTwuKwm8sozls2QHz1yMG6vvCyB6F55p0/Gw8lpjivjbhfhcAf3TZnVdKHP7DAJj5HmQuM4lqNoMnkVt3vi1ZA5uXiJSCg6XFX/9//j9/I//9/960YdzoSTATY97GRysHAQD6c6Oo8HR3O452u047SAJ2K/BCEVjIkhIRDZ9wMiBEDIpl86mtlL0gys71JRJOfLN2x3/87cixii+56Dic4dLnEsIKVBKUcmip7l1OvD124EuBv7M+wccrBpub0bGkNmvzVRKg36MeJmwMhOF5PZ2QCvNdkg0JnNtVXPUO3aDZ+cDn2wHUoL9RuNjpvOBtdU0lUYKcb4AlUW+BGgLq5BC3rcY3rtQ+ZjO/UpizFQP6AveZrfSeWDnZ3ka3YmQgqYyrKUg5YyfAp0QEzEHrBIsa3N+0ytasURtNa3VjCEyuMgpARvvv1E+LMC6sx351ulIoyU+wZ3eE04yVxcGq8tYlsG5Yr0goDUlAPj2yUBKmavr5rFZSeCxwcPDshVPmxV9nuzgva3mzyIQvveYzsprMZXz2aVMPwQymZwSg4+01dMH9E+b1S0aInAxU1f3Zv+m434GcfRlZg5uXjKN1fz33/MBLf+V7qIP5hISKLoc6yF34KKH5NkNkFIpaW17WLSRpSkX3Gk30vkiOBYZrCkGgbteMgZP70oafjdGbnWB0QcYHNve88npWKzZrWJ/sWAnAqdD5LT33N45Pt0O/P43Nnz/9QX7y5pFrdl2jp33kCR9iISYySLTWIMQcKXVJAR3dgEjgVhKZDGDAlat5mprUVqjBEQhqJQ83+Ft+jBlIsoQwd4n2inNvnWBPDmrdj4yxkjOpeyQphZ3Kavzbi949lbYN02MPA/svJ+n0Z3cm12JKdO7cvPVUpAT9D6hVTwPDB68OcuY8DETSVMp937zvXsDrN3gubUt3YQhQucDMkOIiW8edfipc+rWdsBqPdkkCKzSpFy6rrQUJCEeGiB0Lpa28in5mTPTa4ofVUoZN10zahIxQ/GjeZosz8OygylldmNACfHE7ODTCIRFvpt5GifX9aXWLBtDSJnep/uCEolgOwSEEE8V0D9LgGWN4qAxfKPrkYOnrTQuJAaXWDeKatqIvenMwc1LRghBWxv+5v/+Gv/4P7zDtanHEKaH8yV4IZYyVrkUJ93OAG4sE8gTEGTizhAYA1Ra0NQV28Fzc+cQOTG4zJ2up3NlJykmJU0fIqMPnAyKTZcYcqIbHJ33pJDxMU5t6AMHSzsN4lRlsnFjWdWKnBI+ZJY1CCQJybLWbMfARycDfmqzNakM1bx16skJriwtldE0AqyW5wuQkFOnlpLTYpvoXSCmVCzep3balMtNoTHlZiKVYDsGch7ZW1ZoIXCpGB2u6id7bDyrGPmyBEHzwM6H87g5Uucamj7Q+VA0YUDIsKw0evLEOQuQ7ht+OZ2n1hTHbj995lmUgPuspFVphcgwkhh9Ga8ipl+LJ6MkHO08t5QGHDc3I4e1ImZAKKx21EZx2kdaq7jSVuwtqvt+Di0FnQt0IZZjyEzvXXQuJ0PAK4k16jwwuLNzxS25MY/MYNyn3bknO5izJ8XEyegZQ+bGqsKnhMrikeWvpxEIDz6ymZyc++kalBmuLi1tbc6d0KEEJYeripPOEVN+qoD+UQGWpBiQOjVdM5Oq+/qqpneBO53H9b7opSrBwaKitW+H8/Ec3LwCrFJ88U98nv/2/3OT3383DIufiwBsYplVdS+aksWJACNUBiyBmA0hR759OvIegtqUXeDt7YgPgZMu0PvSjeE87FURmcuuRCqFqzzHO0/nEqMvHRxCQpMC2y5wazfQKMHn9luapkblzDhqtAJbOZjKQi4FdiPkFLm5cZAyWmcEEqUlhsyt3TilnjONLcdgNZM3hmSU6XwxVFJwe1uyU62RSEpHxuAiN9YVy8rS+4TIpZNsmyJ9KJ9aZRSLqYyVp4nMjwpInjZt/WAQlHPJGC2twVygK+rLGth5WYK2V01tFCEmNuNkfaAErb1bvjovZT5wc065lK98iLiYGFwgAYLptZNPzVHnOB08J6Pno6OOLBLvrxeMMaGF5Lgf2Q6JvTYSyfTO86kLpJxpasMVUzOGSFsZuiFCHtBS0t6j9wiptJz7VIT6tVElq+niuVC/1uI8o6MmY86zzqyYigUEqmSZrErlZ3ogwN9rDDlnvnlnx51tQGs4aIvz+HZ4vPnhvWW6lON5y3uMmcbc3dRsR89xX7K9K6sZQuRbpwMHY+D6fnufrslqxbqtWBoFgicG9A/+DnPOjD6yGUpAddQ7ZM4orahU8QL73H7Lqgn0LiClZFUb9hrz1jgfz8HNK8BaxQeHS/5Pf+G7+P1/+/WLPpxLzYOBDZSg5wwXS3ZHKYVMgm4cOd6N7IbI91xtqWVi1w0cD6WcFZjaTYGNgLgNVBrWRjKMgdMhMfTQT51LChg8WAlaQy8ykZ56F1jWGms0K5PZjQ6hO6yA2yea2prJEDBRm0Rb1VQaZMzEVESVMcOV1rJqFN85gcPGIibTM3JxkA1akmLitA/UWhZn5a2jc4FNH0lk2kpDhq0rBmJaFJ8dRVkkrdXsxsjg4/l8LCUFVpYyhBDT7vERaevOxZJ+n0pn3RjYjgEpKAaFfekgW9ea69P4ijfRC+Nda6MXorgG70/BXKXu7sh9TOdZBTmdK9sxnE+6711g6yKtFiQEgw90LiFlZtN5xlgCoz5Eok8c73o+2Y58587I3kKRE+xcGbdChk3v2XSO497RB1hYxefWI/utZWEknQscdZnOJ/Yby5VFOcf8NHB3bTVjKIGOlgIfE7s+ImRmkGXmm5ICLSCnEsAc92XswtkIBSFKFuhsGO59AX6lqI2mrg0fVJbGlCyRTyDTkwX7Z91SJ33ATUM7Ky2RmOIOPTiOtiNjykQp8SlTKUGMkU9OA2jJwpZMU23UedbHThqnlErA+aQZVJs+MLhyDP0Y2flAjJntGPE+stdocmXJOWO15uqyLgFhTOgpA/Ys18Jl3ijMwc0r4vNXFvz5P3GD/+tmx6//v29d9OG8sXRAMwIpokTH8a5kXIa9HWPoCR6GAONYXhsoAYsEgoeth5WC1kZcyLgBTvPdoMpSnI5znh7ACQnRlldoF7jpI4317C0MOQm+eSRYGbi+12K1YjtEbp1uiKJ0QfU+YgR8cNiyVpLvnAz88a2eVSU5aA0uFlF0jGUkBDkz+MS6rjgdAlsfaa2k7zy3N0WQXFmFyKU7LFGCGiEFPkMzGQae9pF1axBkjnee096XG5zRKC0gZ/bbu2n/nDMhRE7HUHQRSpJS4nQI5JjZOkfnEuvasFdphpD4dDMCsN9Wl3ZRexRvQxv9s95MpBS0RrEbI3HSftzbXSWmAHaMJSDofKT472ZqJUAIfIzFjJPyOJ1av0mRIWSOt47dGBBJcNIPdE6V10pBsoJv3I5sx6JxG30gRBhE5tNNMb47aTxCaVZ1McLsfGB7K7BuNdaUobZLq8qMpxAIcRpsKYuj+Ogj7aQL6n3itHdEIWgrg1El2D8ePFYKEAb9QAnoLMCPKaEpup/OlY6lmBPt5LScsr5PRHzv76FkwQTr2jD6UlbOuRTHpRDc2Tq+dnvHXmOotAYJd1xAyzIMmFS6pAZfMlK11vf9fh4VkJ+ZDipRAqXb245PNiOnfSDniFWKa2tLiDDGyMebwNIHFtawqhObIaO1pHdF29daxUFjaaoyRgbB+c94FmBB6e58WAbsMm0U3owr+g1EKcV/++EV9pYVRvwBv/LVWX/zPGTg2MGRuxuQSKA7ga+fJAJguL8rK04PP/19jBBvllT1yP3ZorOOLpuKSZ8RUGloK01MTKMiEkIk1lmSBAwusB0TUkr2VxUf3+44HgNLq2hqy0nngYSPESFg39Uk4Os3PVdazfW9FqMlMWZ2Q0Ap6MfE7e1QHJNjOdbjwTOGxLdOO9a15fN7TZnsnEr3lAT8tOC4mBiD5842cdI7bm4HQki0lWZdaYbJf6f3iWuruvie+MhpH5BK0GjFzgWOpxuQMZKPT0cygtpIKmOphEBJ+OS0dIQhxaVc1B7Gs44XuGy8SNbpcd1V9wZ8+60tQ2NdceA1WvLpdqT3JWNQW8nWl7bo2EeOdo7KamLOaK05tApSKA7ho2c3ROwAJ33gtPf4EDHGICg6Kqs8J51A6pF1U9FWFXttjRwcx9HjUiS5xO3NyNddcQxP6SygKOdz21hAIeSARJDJbDrHXm2JMSNJbEbPpi+ZyO3gqJTEKEWlFUYrfIrspjlZPkZu7jzBlbJxJBNi5nuBq4sK7yNb5xldyVxpKTFaMMaE0QojJb0XpVQ8feaKxMebHV+/s2NZa26s6nO9UK0VH+zVlACrmInulOCDPUmlzSMD8pTK7/Ko9zgXCSnSu0iZ8SW5swl8dNzT9Z7KyLKZ8olNTNxYWr77+pLD1nK8C1hTruPdGOl9Yl1Jru/XLOsaLaAyJaTb+Ug3JowR1LK0kC/qkm1yMXHSe1IqZfizIOgsG3wRXIrg5td+7df4B//gH/Dxxx/zgz/4g/zKr/wKP/IjP/LI1/+Lf/Ev+Lt/9+/yta99je///u/nl37pl/grf+WvvMYjfjqklHz+YMX/5S/+b/nwyjf41d/6Y74Rnvx1M/fz4EeWKF1VZzzYbv4wjrnrkPqo99BAnNxUEQIXyo4WEmIyIVOqZEC8K7u9G0riEmxcQArFqs60VrELmU83Aa16OgdWCY66wOA9Oxe4cbAgxcwnJwNKSWLyfHR7BAkLnUEYpBFcazQ5C7rRo2SDUQIXijW7kGUoYbaKbe/ZDhFjS/Dlp5b5O9uxdMpoiU6Z23JECcFeY+hDIssiMBVCkDLTrKxEEtPgTyE46SIST1MZZM6cDoGDNrGw5o3JfjzreIHLxotknR7VXfWwgO9sevdp72mUYmk0wxhQStC7EvgUbUfpjGooZavFNEhycBqXQcVEGiKVNfjo6UaPD4kqCWprcDGw7QXeZ/oYudZaVpXmm2HLMHrubAc+3TkGnxl84GTXI4VgWVskcNR5ti5wo9VkUa6/wYNRZdbcn7q+4MbeomRcosDnwG5wfLz1HO9GtBDUpmR3pFKlPd1qlAKRJdYaWiPJgPeJO7sRc3vLZnR843ZXuptISFHOGmMln1u37C9KuVrJkgXd9CNH3cjXP91yuhs46iQf3e5oJm1vkpnbpw3XD5ZcX1SsG42glK12QyibqxDJudTRtSwbrJunI3d2no0b6VzktAt0o+faoqL3nv/y6ZabJwM3T3c4H6mtKnomW/Hto8xHtzesmhqrBfvLCp0Fx0PxIPIps98oPjhcYK2h60Y+3XiEyOw3NatW0xrN5/YrtFgwyjKXz8XI7U1fSuFSoqSksYr9pvgCve7Nz4WvRr/5m7/Jl7/8ZX7913+dL37xi/zyL/8yP/qjP8p//s//mevXr3/m9f/xP/5HfvzHf5yvfOUr/NW/+lf5jd/4Df7aX/tr/N7v/R5/+k//6Qv4CR6PlIK9Zc3/8Ge+h+9775D/5//8x/zGf7rD0UUf2BtKxZkp4N3MDEAz/T3d8+8KaCmBy1nW5kGTYDs9J4FKlsWxriRaKxYKzBgYU9mRCamKqFIKtFFkJJ4izruSLVJIpJIslCbuEj2+1L59ZIyCxgiCz3xzHLFWo6Ui5Mxu8DQKlo3i5mkRPN/Yq1hajbVlYVhViqwELmYqJel8IrqIttCPgS4kkGUWVkilk2UMka1LtJVgzyp6nyALXIqcjKCyYN2ac5FmiKl0cVmBEnIK8nI5Rp9Y1JOOwsgi5hbijcl+vI6J0q+Kl5V1erC76lEB39kk8ZQyYhpN4lPGSonVogTFU6DvYhm/ALDflvMIUWTfCsleqznuHENd07tIYyWHS4MPGWRmVdni81RJInDceb59tOPOzk0ZqjIr7nYX6MbIwhYfGETEe/iD0x6XJVJlai0xQlJVgm/c7tl5gU8JKWBwie8cbznuPF1MiFC6EZ0v3j7X99qi5QmZ77nW8H3X9whZEmNiYSW3NgPfOd7y8fHIzdORzeg42TqyEmghkFrw3qrlysJQ25IZ2vnE8WnPp9uB232kEeBz4nSMBB8RRFprOdpzfHo68v5+w/V1w8G6IeXER6Pj45OB21vHUTcSMlRKsLSKb9/puN05joaAdxEfEkMIaJVRSEKKnPaRO6dlwxbyJIoWjiRAmS1SwNrAupUkack5ETN4XzJKe43lyrLieHQcd2XY8OfXFfvLBgmcXG05vhJpjGJZa7yPfGc7oqXgysKyV1t2rpRAr67Ea9/8XHhw84/+0T/ib/7Nv8lP/uRPAvDrv/7r/Jt/82/4Z//sn/HTP/3Tn3n9P/7H/5i//Jf/Mn/7b/9tAH7hF36Bf/tv/y2/+qu/yq//+q+/1mN/Ws7Swt/zuQP+z3sLvvS/6/kP//Vb/Mv/17f5o6dJO7yDaEqQYihBSk/JuqjpIbgb3ChK0AN3AxwogUttS/uryCBD0eUY7oqOW02ZFeWhreGDfUPbVlhlqIzlWHvEzk03wKmbBIkgUFuBzGUC+MJojFE0lWXflnESvU+gi+mWNQIlSupXSck4ZnoVqI0iJRhz5r11Q0qZ0zHx3spitCJLyUFjWNUGRRERK61ohUCgyALGmGiMYAxy2uEVY7AhJIwSaCXOO0mWtWJhDcZIrJQYrSbBcfnMeh9ZWUOlFS5Ebm2KB4mRxbdk8In31vfPDnoTsh+va6L0q+BVZZ0eF/AtKo0Rgp2TLBrF6EoQuF8b1BBI0+DIrQvTtZRgml0lZelGvL42oDSLxpahnrLMkpPAwkiSElxdV1xf19TC0I0RpYsBoPORVWORStJHIJVsk5KZLAQJg4sDJz7SVGC1IU0C3NZKjseM2PYIVcS5XT+yGSftUM4EqdntOoaQ8UJyQ2Sq2nJ01PPto4H3DpZ8uG5IMdOHwMlQxkx858QxxlK+2fjI0EWWVmOM4Y7oOenGUsa1GiMk3zkd+HQ7AhLK8HBCiDjnycCqVSA1p2PCH41EJpfklPjjmztudZ5h8GVuV0yckvn66PnoToePEWsqSJmTMbAbyvesJOhKc9yBE6ByyW4HwGcwGaop8/bJAMdDYtEOaKUQCfqQCDGVhgnn6H1GK402kpMxI40nR8Ef397RLmuMluzGwM3tCFmwvzDkLBhjphaCkO+3EHhdXGhw45zjd3/3d/mZn/mZ8+eklHzpS1/iq1/96kO/5qtf/Spf/vKX73vuR3/0R/lX/+pfPfT14zgyjuP5309PT1/8wJ+Ru2lhxbo2fHCw4Puu7/E/fP+H/NdPT/h//H//K/+/P4p8nO/emN8FBCWzkqfHWbCiuSsKbm0JUI62k4mXgPhAi9USMDXICAuKkDgCywbWjWA7ZkKELEBOqWslIUlYVbDSkkTmcwcLPthf4FLGxYxSmWvC8OHKcjwUxbEQsDaCQWnaSmG05Oqy5mbnaHVZWKXRtJUlpkQ1eXJYo9ApcTskrjQKRGRwgnw2l8YHjJXstxUhO4IQyFRuAgetIYuSITpcVIh7Mg0xlRlYC6E53jnCZLefcpnYbCcNzyATlVasKkOilB9aPWVzppu9loIwiaqbSnND1sUOPiakkqjJa2NR3z948U3IfsCrnyj9qnhVWacnBXxGSZYxsd+Wjp8xJHLKLCpDU5WJ2b1LvL/ynPRFXEyOZBQ5C9a1JuVEbRSHC11E8i4hZBnF0NaG7zlcsF5acoadT1SiXJuNEWhJGbkSStegFIIsZLl2QyILgRASPZnriFQ+Jy0lMUcGL6llEe4nIdFSQUpYo1ExcYooDuGVwSXBjUrjWsMQE5uh+Py4nHABfEikmFBKolPxuqq1xiWB0IrGCoRUxZk9CSoE2qryvkJRG4nP0wKvFIHIcsqMnImei04wsR0jUYBLQM5Io6ikRIXEzkW2figGi1nSVBIfJW3OjDHgXWZMIF0gS6gyKAN+KNe1oXy+CwNJw2lfNn8kiCSs0lQaUirnhIuKlBJVrVhWmgjIVP5zPE7WFlpy0nlSKnpFqxUpl/MypIyVxSH7dW9+LjS4uXXrFjFGbty4cd/zN27c4A/+4A8e+jUff/zxQ1//8ccPH1b5la98hZ//+Z9/OQf8gtybFl4py/d9bo/DdcOf+twBtzYDnfPkDN+4c5vf//odPvp05OvHcOdiD/ulsw8s6pIpqZVAT22XMUYcCpnLxZWQNErRVAopBzZFkkJWEAOs05S1mSKkZQ1NbchhspKvJU1lGMNISFCpEsyEWIIkreCgrWgaw1pr/swXVrx/sGaMgdFlhrE4hB4uDXe2IztfIqSUI/0I19cV7x3UWCH5+tGW3RgZY8a7yOf3G6qrNXe6UMpESlBVmsOY2Ks166ZCeUeMGa0kjVAIStBzY1VSulrC1WWFkIpKCVbWgLx/YGDMAmMUVt7VL1zFICWE0zIZOqWElIqrSzNpLSiBWKWRMp7f7NtK8/66oneJnQtYJXlvv0UAtZGsKoufnKB9fLOyH/DqJkq/al5l1ulxAd9dJ+BiErmYOoRizuwvKrQo9gSdC1wNAZFKFtSHxMfHA3dGRxihVpBU6f5ZWUBoaiO5vqppFxWt0SwbVWa6kRnGxGnvSWT6ISJlKcP5yXMpTzofkQULLaiUKhoPJTBKEXNxP25qyX6jOBnKZLttn/FkbBYIVTJIkZJ5leLM4NAgnMOHSDeUbEmlM8ZoREgsjKAbEznGyagwE2NCK41QAiNAC0lbaWqrWVWSwU1u0RGyKIVxKWBZa1a1JZOJWdCokmUZY0J6gRJgpCTm0kAw5gwiIVI5Zu8TMRYPIJkFVgiQJbhx0+9CSUhTLf5s1UipPC8FaAlqCqQUGasylRZsx/J5LFTGZwmijKNRSuAmUffCFkF76SrL7NeaMWfGEKm0RqtiXqhkKae+7s3PhZelXjU/8zM/c1+m5/T0lA8//PACj6ggplbF2miurWq+/729MtODjOTz8H8oA92GELi16/lk03Nz17PrPFFkGqPJIXPr5IT/5Zs3+fSotDJrCR/dgo+e8jgWlGg+AU+b02opZaD4FF+jgBXQVHB1AQerhlWjMaoMnlsYTWMlmzGTBVQikWSZjEtOtJXFp1gcRDtHIqMoXQ1GQq0UQYBUir1Ks25qrraS2ki+c+y42Tn2Kk0IiVoXkV+fUrnBW82yshysK/7ElYb3D/doa82V1jDGzEkfWFWStrKcDiPHu4CSJTDwMTAkaLRi3Ri+69qCbkxsnCMkgZVFVHhnO3B752iqspB94TDhM1xb1lgFX7vVczr4aUpzZq9RNFqjlcKoYvhXGcnn1mUacefuZlruvcGVrhk47j1dhn0haK0kRrBGUGmNkkXXcLgwtJOI+MGb/UFr6cbiCHt2c7u3K0flskC9admPe3mcs+9l5VVlnR4X8AnBZ4KqKSHIotI0VrO+pz1YitKtNPrIlVXNN27tuNN5KgVDhEU1ZUlCAiE5qA1KC5QChWBVCfpYXHuhdOdYGamUoq3K+aeVQGTIItJWmhxL+UNSMgT96DFKc22hWDeW1mpqHfnoqGS5SIkhwUpnEoJEJMQyBV1kiDJzsKz5risNjVUIB3utZa8O/C8fBwZdbvQRMc2kKtdoLSVZiNIFZjVWl5Ev69qwGSIxJhB58hRSqKqisSUwMEpiRSYLhVaKvcrQtIqtD8jJK2j0mSoXv6tFbQkhEkTEx1y0MkBtDcsWUgwEn8kiFSPDWEr0ULLl1pbrWmjBPuU6L5s9QWMVMUGtS5ZQSk2riypR5Awx4QQsasP3Xl3yvVcW7HyxeLRSEIbA6BO1zjhfRteYStKa17/5udDg5urVqyil+OSTT+57/pNPPuG999576Ne89957z/T6qqqoquqh/3YZODvZH4Y1ihbD4arhT97z4z3osRBCOjeOSpOqPsTEHx2d8K2jnkVVyh3OZ/ro0GgqK6bShGVVl0Dj9m5k6zwxRk7Hkma0RuC9QOnEXl0hk0QoiZEgkSSR2PQDv//Hd7jVueKrYQ3L2rC30PRRoLLkg0PDtfUCIzRSZAQCDfic6V1k03mOpzp0iOWiUAKMMSgBtZUoASeD53gXaBrF1YVlv6nYb0s7og/QVoqltiBhcJ5vHe/Y9pExlUm4PiS0AqM0VkGtNPtLy0Fr6UOatAGClZVcXVZTy3TCaMm6jkhZbvZtpYnTjkUJeZ49UaI4v44x0Y+B9/Yats7hQnHYXVe6LBKyiBW/51ppo8w5E3IZjmm0QitBqzWLWrOsSjACIER86A3u3mD57GYjgCFEXMzk0gpFY9R5YHPvOXjvzX5RG5qkH5rdeFOzH286r/pzf1TA96Sg6mHrl9GKtjLcWLc4H4k5kXK5LsJ0M8+Uzczoy4DIlItgua0E7y0Nh8uBbx0NJeM5evbrFVYJrFV0LnG0c2yHQK1z0Yn4SJfKxudKa/nwSsuiMkhVxkbEkDEio0XiZIhkKbm+tAw+TmaXJXOx0Ibvu9byJz+3x8GyJqfSERViZDNGtm5DpSWNgRgkKkYqnchSUkuBVYrWSKwAkcvf15XGp0wd4yQKLi3SAsngIqYtQd9Bq/neKws+f6Wld4lj5dhpSdc7hjFM/liZhVHk2pKywwnAQY4BrUt2dm1bPt4G/NEGpGKxEqxs4KQvpfnWgJm0htqWlu5aq7L5kYLkEzeWLUqrkpUSU1lPS7SA/YXlw4MFf/LGispoyBFNZgjF1VkrgU9FCH5lYbm2ri5k8yPyWY/ZBfHFL36RH/mRH+FXfuVXAEgp8YUvfIGf+qmfeqig+Md+7Mfouo5//a//9flzf+Ev/AV+4Ad+4KkExaenp+zt7XFycsJ6vX55P8g7xIPB1dnf3RgZUsAKRV2Vm+yZF4RUd7UBD37t2c2YDG4aK6Bl0cGcWZLbSYxWdgPpfCbM0/gonBldkTiv3UshSEzHJsX5MaWU75tfdB4wnvWRT38++LM/eLN58OtSzOfvp7U8P6azoXwhFZny2f1FivsNtB73+T/r72tm5ll5GefQo9aNsynUwHnJNeVM8CVznSdB9c6VsSMpJYwoGR8fyxyrMmwysbaG/aYqHUIpc7xzbMcyTHdwkd4HtoNnTKWZIKXEcZ9IElpj+MJ+zf/mvTWHq6YIbEVpw+99ZNuPfPPWlo9PR076kZ1LhFi6kJSU7LeW/caURoCU6WPG+4DPpXQVYhmPUhmBFrIM/SSzsJarK8vn9moOl00R88cyA+87pz3futNzezeW7IwqWqacEje3I986HooPUS5mm4erumj0EvTecXtX5vHFHJEpsRscXci4JKiV4KBRrCpN0grnI+7/3969B0V13XEA/+4C+0BcHvJWQBCK+ALFStdE6RQUaJpgm45UaYOJJROLbVrjI5pWjUlDJtqMnUziJGYKHWuCTXyliZr4Qk2KJhgQUUsDgqQNqBV5qTz32z/I3rDyjqsLy+8zs+N6z7l3z/lxYH93z7172lTQOQKeBj1cdI5wVBNtbSo0trahjWqM1DpgjIceo131cNFpYFKpoVGroHHquJlBre74Th2C0Dmo4aLtWDfLWgby/m3zaally5YhLS0N06dPx4wZM7B582bcuHFDuXvqkUcewejRo5GZmQkAePLJJxEbG4s//elPeOCBB5CTk4P8/Hy88cYbtuzGsHL7WZ75/47Oajjjm4tNe1oL5fZ9O5/5aTS9Z/g67cCHbH/WJDK3Sa1WWdTtawqjp/Iu22/r1u1tGsiaSQOdVhmK0zBicLHGGOrp70a3ddFxbZmu01uUR6cTIfNJjambbWYmEzFqhE45sVCj4yTG9PWFvSYVoXHomAJvbG2Bo0oNF13XVbc73wzi7+bSkQS0tysnI+bETK36+iRM1TFlZTJ9s91kItrQseaWWqVS9jH/q3F0gObrTzfMCSBcnRHoaUBLQMfrAV+vMfX1CVlbuwk3W1vR1NYOjYMazo5OMH39ZRc6p44prKa2NjS1dSRgzo5OgAqob25GS3tH8qFzcoTGoWMNsqb2jm9MNp/8ab5erqPjzqlvph51To4dJ36dTt76OtmzBZsnNykpKbh69SrWrl2L6upqREVF4cCBA8pFw5WVlRbr2MycORNvvfUWfv/732PNmjUICwvDnj17BuV33AghhLCO7qbAepvWV5KnfsyI6HS9vxV2TsQcHS1P4rqj6c+L9vRanRI+jfqbpKc7Luh+lXAzXTdv8YYR1rlMo18ndTZk82mpe02mpYQQQoihZyDv30NvaV8hhBBCiF5IciOEEEIIuyLJjRBCCCHsiiQ3QgghhLArktwIIYQQwq5IciOEEEIIuyLJjRBCCCHsiiQ3QgghhLArktwIIYQQwq7YfPmFe838hcz19fU2bokQQggh+sv8vt2fhRWGXXLT0NAAAAgICLBxS4QQQggxUA0NDXB1de21zrBbW8pkMuGrr77CyJEjoVJZd4Gv+vp6BAQE4Msvvxy261ZJDCQGgMTATOIgMQAkBoB1YkASDQ0N8Pf3t1hQuzvD7pMbtVqNMWPG3NXXMBgMw3YAm0kMJAaAxMBM4iAxACQGwJ3HoK9PbMzkgmIhhBBC2BVJboQQQghhVyS5sSKtVot169ZBq9Xauik2IzGQGAASAzOJg8QAkBgA9z4Gw+6CYiGEEELYN/nkRgghhBB2RZIbIYQQQtgVSW6EEEIIYVckuRFCCCGEXZHkxkpeffVVjB07FjqdDjExMfj0009t3SSrWb9+PVQqlcVj/PjxSnlTUxMyMjIwatQouLi44OGHH8bly5ctjlFZWYkHHngAzs7O8Pb2xooVK9DW1navu9Jvx48fx4MPPgh/f3+oVCrs2bPHopwk1q5dCz8/P+j1esTHx+OLL76wqFNTU4PU1FQYDAa4ublh8eLFaGxstKhTVFSEWbNmQafTISAgAC+99NLd7lq/9RWDRYsWdRkXiYmJFnWGegwyMzPx3e9+FyNHjoS3tzfmzZuHkpISizrWGv+5ubmYNm0atFotQkNDkZ2dfbe71y/9icH3v//9LmPhiSeesKgzlGMAAFu2bMGUKVOUL6EzGo3Yv3+/Um7v4wDoOwaDahxQ3LGcnBxqNBr+5S9/4blz55ienk43NzdevnzZ1k2zinXr1nHixImsqqpSHlevXlXKn3jiCQYEBPDw4cPMz8/n9773Pc6cOVMpb2tr46RJkxgfH8+CggLu27ePnp6eXL16tS260y/79u3jM888w127dhEAd+/ebVH+4osv0tXVlXv27OGZM2f40EMPMTg4mLdu3VLqJCYmMjIykidPnuSJEycYGhrKBQsWKOV1dXX08fFhamoqi4uL+fbbb1Ov1/P111+/V93sVV8xSEtLY2JiosW4qKmpsagz1GOQkJDArKwsFhcXs7CwkD/84Q8ZGBjIxsZGpY41xv/Fixfp7OzMZcuW8fz583zllVfo4ODAAwcO3NP+dqc/MYiNjWV6errFWKirq1PKh3oMSPK9997jBx98wH//+98sKSnhmjVr6OTkxOLiYpL2Pw7IvmMwmMaBJDdWMGPGDGZkZCj/b29vp7+/PzMzM23YKutZt24dIyMjuy2rra2lk5MT33nnHWXbhQsXCIB5eXkkO94k1Wo1q6urlTpbtmyhwWBgc3PzXW27Ndz+xm4ymejr68uNGzcq22pra6nVavn222+TJM+fP08A/Oyzz5Q6+/fvp0ql4n//+1+S5GuvvUZ3d3eLGKxatYrh4eF3uUcD11Nyk5yc3OM+9hYDkrxy5QoB8NixYyStN/5XrlzJiRMnWrxWSkoKExIS7naXBuz2GJAdb2pPPvlkj/vYWwzM3N3d+eabbw7LcWBmjgE5uMaBTEvdoZaWFpw+fRrx8fHKNrVajfj4eOTl5dmwZdb1xRdfwN/fHyEhIUhNTUVlZSUA4PTp02htbbXo//jx4xEYGKj0Py8vD5MnT4aPj49SJyEhAfX19Th37ty97YgVlJeXo7q62qLPrq6uiImJseizm5sbpk+frtSJj4+HWq3GqVOnlDqzZ8+GRqNR6iQkJKCkpATXr1+/R725M7m5ufD29kZ4eDiWLFmCa9euKWX2GIO6ujoAgIeHBwDrjf+8vDyLY5jrDMa/IbfHwGz79u3w9PTEpEmTsHr1aty8eVMps7cYtLe3IycnBzdu3IDRaByW4+D2GJgNlnEw7BbOtLb//e9/aG9vt/hhAYCPjw/+9a9/2ahV1hUTE4Ps7GyEh4ejqqoKzz77LGbNmoXi4mJUV1dDo9HAzc3NYh8fHx9UV1cDAKqrq7uNj7lsqDG3ubs+de6zt7e3RbmjoyM8PDws6gQHB3c5hrnM3d39rrTfWhITE/GTn/wEwcHBKCsrw5o1a5CUlIS8vDw4ODjYXQxMJhN++9vf4r777sOkSZMAwGrjv6c69fX1uHXrFvR6/d3o0oB1FwMAWLhwIYKCguDv74+ioiKsWrUKJSUl2LVrFwD7icHZs2dhNBrR1NQEFxcX7N69GxMmTEBhYeGwGQc9xQAYXONAkhvRp6SkJOX5lClTEBMTg6CgIPz9738fFL9swjZ+9rOfKc8nT56MKVOmYNy4ccjNzUVcXJwNW3Z3ZGRkoLi4GB9//LGtm2IzPcXg8ccfV55PnjwZfn5+iIuLQ1lZGcaNG3evm3nXhIeHo7CwEHV1dXj33XeRlpaGY8eO2bpZ91RPMZgwYcKgGgcyLXWHPD094eDg0OWq+MuXL8PX19dGrbq73Nzc8J3vfAelpaXw9fVFS0sLamtrLep07r+vr2+38TGXDTXmNvf2M/f19cWVK1csytva2lBTU2O3cQkJCYGnpydKS0sB2FcMli5divfffx9Hjx7FmDFjlO3WGv891TEYDIPmBKKnGHQnJiYGACzGgj3EQKPRIDQ0FNHR0cjMzERkZCT+/Oc/D6tx0FMMumPLcSDJzR3SaDSIjo7G4cOHlW0mkwmHDx+2mIe0J42NjSgrK4Ofnx+io6Ph5ORk0f+SkhJUVlYq/TcajTh79qzFG93BgwdhMBiUjzOHkuDgYPj6+lr0ub6+HqdOnbLoc21tLU6fPq3UOXLkCEwmk/ILbzQacfz4cbS2tip1Dh48iPDw8EE1HdNf//nPf3Dt2jX4+fkBsI8YkMTSpUuxe/duHDlypMsUmrXGv9FotDiGuc5g+BvSVwy6U1hYCAAWY2Eox6AnJpMJzc3Nw2Ic9MQcg+7YdBwM6PJj0a2cnBxqtVpmZ2fz/PnzfPzxx+nm5mZxRfhQ9tRTTzE3N5fl5eX85JNPGB8fT09PT165coVkxy2QgYGBPHLkCPPz82k0Gmk0GpX9zbf/zZ07l4WFhTxw4AC9vLwG9a3gDQ0NLCgoYEFBAQHw5ZdfZkFBAS9dukSy41ZwNzc37t27l0VFRUxOTu72VvCpU6fy1KlT/PjjjxkWFmZxG3RtbS19fHz4i1/8gsXFxczJyaGzs/OguQ26txg0NDRw+fLlzMvLY3l5OQ8dOsRp06YxLCyMTU1NyjGGegyWLFlCV1dX5ubmWtzeevPmTaWONca/+fbXFStW8MKFC3z11VcHzS3AfcWgtLSUGzZsYH5+PsvLy7l3716GhIRw9uzZyjGGegxI8umnn+axY8dYXl7OoqIiPv3001SpVPzoo49I2v84IHuPwWAbB5LcWMkrr7zCwMBAajQazpgxgydPnrR1k6wmJSWFfn5+1Gg0HD16NFNSUlhaWqqU37p1i7/61a/o7u5OZ2dn/vjHP2ZVVZXFMSoqKpiUlES9Xk9PT08+9dRTbG1tvddd6bejR48SQJdHWloayY7bwf/whz/Qx8eHWq2WcXFxLCkpsTjGtWvXuGDBArq4uNBgMPDRRx9lQ0ODRZ0zZ87w/vvvp1ar5ejRo/niiy/eqy72qbcY3Lx5k3PnzqWXlxednJwYFBTE9PT0Lgn9UI9Bd/0HwKysLKWOtcb/0aNHGRUVRY1Gw5CQEIvXsKW+YlBZWcnZs2fTw8ODWq2WoaGhXLFihcX3m5BDOwYk+dhjjzEoKIgajYZeXl6Mi4tTEhvS/scB2XsMBts4UJHkwD7rEUIIIYQYvOSaGyGEEELYFUluhBBCCGFXJLkRQgghhF2R5EYIIYQQdkWSGyGEEELYFUluhBBCCGFXJLkRQgghhF2R5EYIMahUVFRApVIpX90+2KhUKuzZs8fWzRBC9EJWBRdC2LVFixahtrbWaglJVVXVoFj3SgjRM0luhBB3RWtrK5ycnGzdDKsbTKuVCyG6J9NSQoh+M5lMeOmllxAaGgqtVovAwED88Y9/VKaSduzYgdjYWOh0Omzfvh0A8OabbyIiIgI6nQ7jx4/Ha6+9ZnHMTz/9FFOnToVOp8P06dNRUFBgUX79+nWkpqbCy8sLer0eYWFhyMrKUsq//PJLzJ8/H25ubvDw8EBycjIqKioAAOvXr8df//pX7N27FyqVCiqVCrm5ub32saWlBUuXLoWfnx90Oh2CgoKQmZmplHeellq/fr1y3M6P7OxsJV6ZmZkIDg6GXq9HZGQk3n333W8ReSHEgHzL9bOEEMPQypUr6e7uzuzsbJaWlvLEiRPcunUry8vLCYBjx47lzp07efHiRX711Vf829/+Rj8/P2Xbzp076eHhwezsbJIdK497eXlx4cKFLC4u5j/+8Q+GhIQQAAsKCkiSGRkZjIqK4meffcby8nIePHiQ7733HkmypaWFERERfOyxx1hUVMTz589z4cKFDA8PZ3NzMxsaGjh//nwmJiYqq1k3Nzf32seNGzcyICCAx48fZ0VFBU+cOMG33npLKQfA3bt3K+3vvFL2pk2b6OzszLNnz5Ikn3/+eY4fP54HDhxgWVkZs7KyqNVqmZuba+WfjBCiM0luhBD9Ul9fT61Wy61bt3YpMyc3mzdvttg+btw4i8SAJJ977jkajUaS5Ouvv85Ro0bx1q1bSvmWLVsskpsHH3yQjz76aLdt2rZtG8PDw2kymZRtzc3N1Ov1/PDDD0mSaWlpTE5O7nc/f/3rX/MHP/iBxTE765zcdJaXl0edTscdO3aQJJuamujs7Mx//vOfFvUWL17MBQsW9Ls9QoiBk2tuhBD9cuHCBTQ3NyMuLq7HOtOnT1ee37hxA2VlZVi8eDHS09OV7W1tbXB1dVWOOWXKFOh0OqXcaDRaHHPJkiV4+OGH8fnnn2Pu3LmYN28eZs6cCQA4c+YMSktLMXLkSIt9mpqaUFZW9q36uWjRIsyZMwfh4eFITEzEj370I8ydO7fXfSorKzFv3jwsX74c8+fPBwCUlpbi5s2bmDNnjkXdlpYWTJ069Vu1TQjRP5LcCCH6Ra/X91lnxIgRyvPGxkYAwNatWxETE2NRz8HBod+vm5SUhEuXLmHfvn04ePAg4uLikJGRgU2bNqGxsRHR0dHK9T2deXl59fs1Ops2bRrKy8uxf/9+HDp0CPPnz0d8fHyP18rcuHEDDz30EIxGIzZs2KBsN/f/gw8+wOjRoy320Wq136ptQoj+keRGCNEvYWFh0Ov1OHz4MH75y1/2Wd/Hxwf+/v64ePEiUlNTu60TERGBbdu2oampSfn05uTJk13qeXl5IS0tDWlpaZg1axZWrFiBTZs2Ydq0adixYwe8vb1hMBi6fQ2NRoP29vYB9BQwGAxISUlBSkoKfvrTnyIxMRE1NTXw8PCwqEcSP//5z2EymbBt2zaoVCqlbMKECdBqtaisrERsbOyAXl8IcWckuRFC9ItOp8OqVauwcuVKaDQa3Hfffbh69SrOnTvX41TVs88+i9/85jdwdXVFYmIimpubkZ+fj+vXr2PZsmVYuHAhnnnmGaSnp2P16tWoqKjApk2bLI6xdu1aREdHY+LEiWhubsb777+PiIgIAEBqaio2btyI5ORkbNiwAWPGjMGlS5ewa9curFy5EmPGjMHYsWPx4YcfoqSkBKNGjYKrq2uvt6i//PLL8PPzw9SpU6FWq/HOO+/A19cXbm5uXequX78ehw4dwkcffYTGxkbl0xpXV1eMHDkSy5cvx+9+9zuYTCbcf//9qKurwyeffAKDwYC0tLRv+ZMQQvTJ1hf9CCGGjvb2dj7//PMMCgqik5MTAwMD+cILLygXFJsvAu5s+/btjIqKokajobu7O2fPns1du3Yp5Xl5eYyMjKRGo2FUVBR37txpcaznnnuOERER1Ov19PDwYHJyMi9evKjsX1VVxUceeYSenp7UarUMCQlheno66+rqSJJXrlzhnDlz6OLiQgA8evRor3184403GBUVxREjRtBgMDAuLo6ff/65Uo5OFxTHxsYSQJdHVlYWSdJkMnHz5s0MDw+nk5MTvby8mJCQwGPHjg08+EKIflORpM0yKyGEEEIIK5Mv8RNCCCGEXZHkRggxrLzwwgtwcXHp9pGUlGTr5gkhrECmpYQQw0pNTQ1qamq6LdPr9V1u2xZCDD2S3AghhBDCrsi0lBBCCCHsiiQ3QgghhLArktwIIYQQwq5IciOEEEIIuyLJjRBCCCHsiiQ3QgghhLArktwIIYQQwq5IciOEEEIIu/J/6dDScoO+KsQAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gwascat_path=f\"{release_path}/{release_ver}/study_index/gwas_catalog\"\n", + "# Gwas Catalog:\n", + "gwascat_index=session.spark.read.parquet(gwascat_path, recursiveFileLookup=True)\n", + "# Number of GWAS curated studies\n", + "# Number of studies with full GWAS sumstats\n", + "#gwascat_index\n", + "# The histogram/density plot for total sample size separately for curated studies and full GWAS\n", + "#gwascat_index.filter(f.col(\"hasSumstats\") == True).select(f.col(\"nSamples\")).toPandas().plot.hist(bins=25, alpha=0.5, label=\"Sumstats GWAScat sample size\", title=\"Sumstats GWAScat sample size\")\n", + "#gwascat_index.filter(f.col(\"hasSumstats\") == False).select(f.col(\"nSamples\")).toPandas().plot.hist(bins=25, alpha=0.5, label=\"Sumstats GWAScat sample size\", title=\"Curated GWAScat sample size\")\n", + "\n", + "# Credible_set. Please use Daniels’ notebook as a reference. For each subfolder:\n", + "# gwas catalog sumstats (PICs):\n", + "gwascat_sumstats_path=f\"{release_path}/{release_ver}/credible_set/gwas_catalog_PICSed_summary_statistics\"\n", + "# Number of CSs.\n", + "gwascat_sumstats=session.spark.read.parquet(gwascat_sumstats_path, recursiveFileLookup=True)\n", + "\n", + "\n", + "\n", + "sample_size_quartiles = gwascat_index.join(gwascat_sumstats, how=\"inner\", on=\"studyId\").stat.approxQuantile(\"nSamples\", [0.25, 0.5, 0.75], 0.01)\n", + "\n", + "\n", + "gwascat_sumstats_fm=gwascat_sumstats.select(\"studyId\", \"studyLocusId\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\")))\n", + "gwascat_sumstats_fm.select(\"credset_size\", \"top_PP\").toPandas().plot.scatter(x=\"credset_size\", y=\"top_PP\", alpha=0.05, label=\"gwascat sumstats PICS CS\", title=\"gwascat sumstats PICS CS\")\n", + "\n", + "sample_size_quartiles = gwascat_sumstats_fm.stat.approxQuantile(\"credset_size\", [0.25, 0.5, 0.75], 0.01)\n", + "\n", + "\n", + "# gwas catalog curated (PICs):\n", + "gwascat_curated_path=f\"{release_path}/{release_ver}/credible_set/gwas_catalog_PICSed_curated_associations\"\n", + "# Number of CSs.\n", + "gwascat_curated=session.spark.read.parquet(gwascat_curated_path, recursiveFileLookup=True)\n", + "#gwascat_sumstats.printSchema()\n", + "# keep only credible sets snps\n", + "\n", + "gwascat_curated_fm=gwascat_curated.select(\"studyId\", \"studyLocusId\", \"locus.posteriorProbability\").withColumn(\"top_PP\", f.col(\"posteriorProbability\").getItem(0)).withColumn(\"credset_size\", f.size(f.col(\"posteriorProbability\")))\n", + "gwascat_curated_fm.select(\"credset_size\", \"top_PP\").toPandas().plot.scatter(x=\"credset_size\", y=\"top_PP\", alpha=0.05, label=\"gwascat curated PICS CS\", title=\"gwascat curated PICS CS\")\n", + "sample_size_quartiles = gwascat_index.join(gwascat_sumstats, how=\"anti\", on=\"studyId\").stat.approxQuantile(\"nSamples\", [0.25, 0.5, 0.75], 0.01)\n", + "\n", + "sample_size_quartiles = gwascat_curated_fm.stat.approxQuantile(\"credset_size\", [0.25, 0.5, 0.75], 0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 6. Analyze colocalization data:\n", + " - Count the total number of colocalizations and the number with clpp > 0.8.\n", + " - Calculate the average number of overlaps per credible set." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of colocalisations: 46733065 , of which, 4031821 > 0.8 clpp ( 8.6 %)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 360:============================================> (12 + 3) / 15]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average number of overlaps per CS: 90.79159130338489\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "coloc_path=f\"{release_path}/{release_ver}/colocalisation\"\n", + "coloc=session.spark.read.parquet(coloc_path, recursiveFileLookup=True)\n", + "\n", + "Avg_overlaps=coloc.groupBy(\"leftStudyLocusId\").count().agg(f.avg(\"count\")).collect()[0][0]\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 7. Analyze locus-to-gene (L2G) predictions:\n", + " - Histogram of l2g score distribution.\n", + " - Number of CS with at least one gene with L2G>=0.5\n", + " - Number of CS with more than one gene with L2G>=0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A total of 10561869 l2g predictions were computed.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 8591772 UNIQUE locus to gene predictions for 607372 unique studyloci\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Where 357875 studyloci contains at least one gene with score > 0.5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of these, 122485 studyloci contains more than one gene with score > 0.5\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "l2g_path=f\"{release_path}/{release_ver}/locus_to_gene_predictions\"\n", + "l2g=session.spark.read.parquet(l2g_path, recursiveFileLookup=True)\n", + "l2g.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"l2g scores\")\n", + "\n", + "# There are duplicated l2g predictions studyLocusId with finngen pics and susie" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are definitely duplicated studylocusIDs in the l2g predictions, and still around 20% of studylocus contains more than 1 gene with score>0.5. It is not possible to separate out these predictions based on whether they came from pics or susie, as the l2g outputs only contains the studylocusID (duplicated between pics and susie). \n", + "\n", + "If 20% is too high then it implies finngen pics and susie l2g are pointing (confidently) at different genes for the same studylocus.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 7. Analyze locus-to-gene (L2G) predictions:\n", + " - Consider only the top gene assignments from l2g:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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h6PHn5+fnB6VSiYULFxr09M033yAjI6PQJ8xyc3MNVrUuWOW6Vq1a8PLyKrYvrVYL4OHSDY9atGiRwX1TU1P06tULP/74I06dOlVoP7du3Sr2GMXx8vJC/fr18eWXX+Lu3bul3mfBGZZH5ychIQHx8fEGdQU/vwImJibSWbWCn9vjNS+99BIaNGjwxJ9rSeeuIj3rnBbHysqq0O/oo44dO1bkUiz0/OCZKqIyuHPnDrRaLW7fvo2xY8cWuiC6fv368PHxAQAMHz4cixcvRr9+/TBy5Eg4OTlh7dq1sLCwAPDkf93euXMHtWvXxjvvvIPmzZvjpZdewp49e3DkyBHMmTMHwMMXv2XLlqFHjx7w9PTE4MGD4eTkhHPnzuH06dPYtWsXgIdvdXTr1g0+Pj4IDg6WllRQq9WF1lkqSv369TF9+nRMnDgRly9fRkBAAKytrZGcnIzNmzdj2LBhGDNmDPbu3YvQ0FC8++67aNiwIXJzc/Hf//5XChxP4u/vDzMzM+zZs0f6WDoAeHp6wtTUFLNmzUJGRgbMzc3RqVMn2NvbY+LEiZg2bRq6du2KN998E+fPn8fSpUvx6quv4r333jPYv0ajwaxZs3D58mU0bNgQGzZsQFJSEpYvX26w9MLjvLy80KtXL8yfPx///vuvtCxAwVmuR3+GM2fOxL59++Dt7Y2hQ4fC3d0daWlpOHbsGPbs2YO0tLSnzvWjTExMsGLFCnTr1g1NmzbF4MGD8Z///Ad///039u3bB5VKhZ9//rnYx7/xxhvYtGkT3n77bfj7+yM5ORnR0dFwd3c3CBTvv/8+0tLS0KlTJ9SuXRtXrlzBokWL4OnpKZ1dcXd3x+uvvy6tuXT06FFpuQ855q6iPOucFsfLywt79uzB3LlzodFo4OrqKl0kn5iYiLS0NLz11ltyPx2qSir+A4dExufxJRWSk5MFgGJvj3+s+tKlS8Lf319YWlqKWrVqidGjR4sff/xRABC///57scfNysoSY8eOFc2bNxfW1tbCyspKNG/eXCxdurRQ7W+//SY6d+4s1b388sti0aJFBjV79uwRbdu2FZaWlkKlUokePXqIM2fOGNQUfIT81q1bRfb0448/inbt2gkrKythZWUlGjduLEJCQsT58+el5zpkyBBRv359YWFhIWxtbUXHjh3Fnj17njbNQggh3nzzTeHr61to/Ouvvxb16tUTpqamhZZXWLx4sWjcuLGoVq2acHBwEB9++GGh5SVee+010bRpU3H06FHh4+MjLCwshIuLi1i8eHGJ+srMzBQhISHC1tZWvPTSSyIgIECcP39eABAzZ840qE1JSREhISHC2dlZVKtWTTg6OgpfX1+xfPlyqaZgSYXHl54o+N1atWqVwfjx48dFz549Rc2aNYW5ublwcXERvXv3FnFxcU/sOz8/X8yYMUO4uLgIc3Nz8corr4ht27aJoKAg4eLiItX98MMPokuXLsLe3l4olUpRp04dMXz4cHHz5k2pZvr06aJVq1bCxsZGWFpaisaNG4svvvjCYDmKx5dUKM3cFfe7V9SSJgAKLTVSMHezZ882GC9urksyp6Xp6dy5c6JDhw7C0tKy0N8D48ePF3Xq1DFYwoGePwoh+EVERJVh/vz5GDVqFK5fv47//Oc/ld1OlfHrr7/i9ddfx7lz5wyuwXlWr7/+Ov75558i35Yrq6SkJLzyyiv47rvvEBgYKNt+XwQv0txlZWWhbt26mDBhgrSQKj2feE0VUQV4/AtmHzx4gK+++gpubm4MVI9p3749unTpgqioqMpuxUBRXxI8f/58mJiYoEOHDpXQkfF40edu1apVqFatWqG1ruj5w2uqiCpAz549UadOHXh6eiIjIwPfffcdzp07h7Vr11Z2a1XSzp07K7uFQqKiopCYmIiOHTvCzMxMWnZg2LBhhdZFIkMv+tx98MEHDFQvCIYqogqg1WqxYsUKrF27Fnl5eXB3d8f69evRp0+fym6NSqhNmzaIjY3F559/jrt376JOnTqIiIjApEmTKru1Ko9zRy8KXlNFREREJANeU0VEREQkA4YqIiIiIhnwmqoKlJ+fjxs3bsDa2ppfqElERGQkhBC4c+cONBoNTEyKPx/FUFWBbty48UJ80oWIiOh5dO3atSd+KTxDVQWytrYG8PCHolKpKrkbIiIiKgm9Xg9nZ2fpdbw4DFUVqOAtP5VKxVBFRERkZJ526Q4vVCciIiKSAUMVERERkQwYqoiIiIhkwGuqqhghBHJzc5GXl1fZrTwXTE1NYWZmxiUsiIio3DFUVSHZ2dm4efMm7t27V9mtPFeqV68OJycnKJXKym6FiIieYwxVVUR+fj6Sk5NhamoKjUYDpVLJsyvPSAiB7Oxs3Lp1C8nJyXBzc3viom1ERETPgqGqisjOzkZ+fj6cnZ1RvXr1ym7nuWFpaYlq1arhypUryM7OhoWFRWW3REREzyn+s72K4ZkU+XFOiYioIvDVhoiIiEgGDFVEREREMuA1VVXcvNgLFXq8UZ0bVujxiIiInhc8U0VEREQkA4YqMhrZ2dmV3QIREVGxGKromf3www/w8PCApaUlatasCT8/P2RmZgIAVq5ciaZNm8Lc3BxOTk4IDQ2VHnf16lW89dZbeOmll6BSqdC7d2+kpKRI2yMiIuDp6YkVK1bA1dVVWg4hPT0d77//PmrVqgWVSoVOnTrhjz/+qNgnTURE9BheU0XP5ObNm+jXrx+ioqLw9ttv486dO/j1118hhMCyZcsQHh6OmTNnolu3bsjIyMDBgwcBPFzstCBQ/fLLL8jNzUVISAj69OmD/fv3S/v/888/8eOPP2LTpk0wNTUFALz77ruwtLTEzp07oVar8dVXX8HX1xcXLlyAra1tZUwDEdFzp6Kv6ZVDZV8XzFBFz+TmzZvIzc1Fz5494eLiAgDw8PAAAEyfPh2jR4/GyJEjpfpXX30VABAXF4eTJ08iOTkZzs7OAIBvv/0WTZs2xZEjR6S67OxsfPvtt6hVqxYA4LfffsPhw4eRmpoKc3NzAMCXX36JLVu24IcffsCwYcMq5okTERE9hqGKnknz5s3h6+sLDw8PaLVadOnSBe+88w5ycnJw48YN+Pr6Fvm4s2fPwtnZWQpUAODu7g4bGxucPXtWClUuLi5SoAKAP/74A3fv3kXNmjUN9nf//n389ddf5fAMiYiISoahip6JqakpYmNjcejQIezevRuLFi3CpEmTEBcXJ8v+raysDO7fvXsXTk5OBm8RFrCxsZHlmERERGVRqReqHzhwAD169IBGo4FCocCWLVuKrf3ggw+gUCgwf/58g/G0tDQEBgZCpVLBxsYGwcHBuHv3rkHNiRMn0L59e1hYWMDZ2RlRUVGF9r9x40Y0btwYFhYW8PDwwI4dOwy2CyEwZcoUODk5wdLSEn5+frh48WKZn/vzRKFQoG3btpg2bRqOHz8OpVKJ2NhY1K1bt9hw1aRJE1y7dg3Xrl2Txs6cOYP09HS4u7sXe6wWLVpAp9PBzMwMDRo0MLjZ2dnJ/tyIiIhKqlJDVWZmJpo3b44lS5Y8sW7z5s34/fffodFoCm0LDAzE6dOnERsbi23btuHAgQMG19Xo9Xp06dIFLi4uSExMxOzZsxEREYHly5dLNYcOHUK/fv0QHByM48ePIyAgAAEBATh16pRUExUVhYULFyI6OhoJCQmwsrKCVqvFgwcPZJgJ45WQkIAZM2bg6NGjuHr1KjZt2oRbt26hSZMmiIiIwJw5c7Bw4UJcvHgRx44dw6JFiwAAfn5+8PDwQGBgII4dO4bDhw9j4MCBeO2119CyZctij+fn5wcfHx8EBARg9+7duHz5Mg4dOoRJkybh6NGjFfW0iYiICqnUt/+6deuGbt26PbHm77//xkcffYRdu3bB39/fYNvZs2cRExODI0eOSC/EixYtQvfu3fHll19Co9Fg7dq1yM7OxsqVK6FUKtG0aVMkJSVh7ty5UvhasGABunbtirFjxwIAPv/8c8TGxmLx4sWIjo6GEALz58/H5MmT8dZbbwF4eFG1g4MDtmzZgr59+8o9NZLK/iTD06hUKhw4cADz58+HXq+Hi4sL5syZI/1cHzx4gHnz5mHMmDGws7PDO++8A+Dh2a2ffvoJH330ETp06AATExN07dpVCl3FUSgU2LFjByZNmoTBgwfj1q1bcHR0RIcOHeDg4FDuz5eIiKg4Vfqaqvz8fAwYMABjx45F06ZNC22Pj4+HjY2NwZkNPz8/mJiYICEhAW+//Tbi4+PRoUMHKJVKqUar1WLWrFm4ffs2atSogfj4eISHhxvsW6vVSm9HJicnQ6fTwc/PT9quVqvh7e2N+Pj4YkNVVlYWsrKypPt6vb5M81CVNWnSBDExMcVuHz58OIYPH17ktjp16uCnn34q9rERERGIiIgoNG5tbY2FCxdi4cKFpe6XiIiovFTpxT9nzZoFMzMzfPzxx0Vu1+l0sLe3NxgzMzODra0tdDqdVPP4GYyC+0+reXT7o48rqqYokZGRUKvV0u3RT7oRERHR86XKhqrExEQsWLAAq1evhkKhqOx2ymTixInIyMiQbo9elE1ERETPlyobqn799VekpqaiTp06MDMzg5mZGa5cuYLRo0ejbt26AABHR0ekpqYaPC43NxdpaWlwdHSUah796hMA0v2n1Ty6/dHHFVVTFHNzc6hUKoMbERERPZ+qbKgaMGAATpw4gaSkJOmm0WgwduxY7Nq1CwDg4+OD9PR0JCYmSo/bu3cv8vPz4e3tLdUcOHAAOTk5Uk1sbCwaNWqEGjVqSDWPf/Q/NjYWPj4+AABXV1c4Ojoa1Oj1eiQkJEg1RERE9GKr1AvV7969iz///FO6n5ycjKSkJNja2qJOnTqFVs2uVq0aHB0d0ahRIwAPL5Lu2rUrhg4diujoaOTk5CA0NBR9+/aVll/o378/pk2bhuDgYIwfPx6nTp3CggULMG/ePGm/I0eOxGuvvYY5c+bA398f69evx9GjR6VlFxQKBcLCwjB9+nS4ubnB1dUVn376KTQaDQICAmSdEyGErPsjzikREVWMSg1VR48eRceOHaX7BZ/ACwoKwurVq0u0j7Vr1yI0NBS+vr4wMTFBr169DD4VplarsXv3boSEhMDLywt2dnaYMmWKwVpWbdq0wbp16zB58mR88skncHNzw5YtW9CsWTOpZty4ccjMzMSwYcOQnp6Odu3aISYmBhYWFs84Cw9Vq1YNAHDv3j1YWlrKsk966N69ewD+N8dERETlQSH4z/gKo9froVarkZGRUeT1VTdv3kR6ejrs7e1RvXp1o71Av6oQQuDevXtITU2FjY0NnJycKrslIiKjMS/2QmW3UGrltbbj016/C1TpdapeNAUXvT9+8T09Gxsbmyd+oICIiEgODFVViEKhgJOTE+zt7Q0urKeyq1atGkxNTSu7DSIiegEwVFVBpqamDAJERERGpsouqUBERERkTBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCSDSg1VBw4cQI8ePaDRaKBQKLBlyxZpW05ODsaPHw8PDw9YWVlBo9Fg4MCBuHHjhsE+0tLSEBgYCJVKBRsbGwQHB+Pu3bsGNSdOnED79u1hYWEBZ2dnREVFFepl48aNaNy4MSwsLODh4YEdO3YYbBdCYMqUKXBycoKlpSX8/Pxw8eJF+SaDiIiIjFqlhqrMzEw0b94cS5YsKbTt3r17OHbsGD799FMcO3YMmzZtwvnz5/Hmm28a1AUGBuL06dOIjY3Ftm3bcODAAQwbNkzartfr0aVLF7i4uCAxMRGzZ89GREQEli9fLtUcOnQI/fr1Q3BwMI4fP46AgAAEBATg1KlTUk1UVBQWLlyI6OhoJCQkwMrKClqtFg8ePCiHmSEiIiJjoxBCiMpuAgAUCgU2b96MgICAYmuOHDmCVq1a4cqVK6hTpw7Onj0Ld3d3HDlyBC1btgQAxMTEoHv37rh+/To0Gg2WLVuGSZMmQafTQalUAgAmTJiALVu24Ny5cwCAPn36IDMzE9u2bZOO1bp1a3h6eiI6OhpCCGg0GowePRpjxowBAGRkZMDBwQGrV69G3759S/Qc9Xo91Go1MjIyoFKpyjJNREREFWJe7IXKbqHURnVuWC77Lenrt1FdU5WRkQGFQgEbGxsAQHx8PGxsbKRABQB+fn4wMTFBQkKCVNOhQwcpUAGAVqvF+fPncfv2banGz8/P4FharRbx8fEAgOTkZOh0OoMatVoNb29vqaYoWVlZ0Ov1BjciIiJ6PhlNqHrw4AHGjx+Pfv36SSlRp9PB3t7eoM7MzAy2trbQ6XRSjYODg0FNwf2n1Ty6/dHHFVVTlMjISKjVaunm7OxcqudMRERExsMoQlVOTg569+4NIQSWLVtW2e2U2MSJE5GRkSHdrl27VtktERERUTkxq+wGnqYgUF25cgV79+41eC/T0dERqampBvW5ublIS0uDo6OjVJOSkmJQU3D/aTWPbi8Yc3JyMqjx9PQstndzc3OYm5uX5ukSERGRkarSZ6oKAtXFixexZ88e1KxZ02C7j48P0tPTkZiYKI3t3bsX+fn58Pb2lmoOHDiAnJwcqSY2NhaNGjVCjRo1pJq4uDiDfcfGxsLHxwcA4OrqCkdHR4MavV6PhIQEqYaIiIhebJUaqu7evYukpCQkJSUBeHhBeFJSEq5evYqcnBy88847OHr0KNauXYu8vDzodDrodDpkZ2cDAJo0aYKuXbti6NChOHz4MA4ePIjQ0FD07dsXGo0GANC/f38olUoEBwfj9OnT2LBhAxYsWIDw8HCpj5EjRyImJgZz5szBuXPnEBERgaNHjyI0NBTAw08mhoWFYfr06di6dStOnjyJgQMHQqPRPPHTikRERPTiqNQlFfbv34+OHTsWGg8KCkJERARcXV2LfNy+ffvw+uuvA3i4+GdoaCh+/vlnmJiYoFevXli4cCFeeuklqf7EiRMICQnBkSNHYGdnh48++gjjx4832OfGjRsxefJkXL58GW5uboiKikL37t2l7UIITJ06FcuXL0d6ejratWuHpUuXomHDkn98k0sqEBGRseCSCv9T0tfvKrNO1YuAoYqIiIwFQ9X/PJfrVBERERFVVQxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkg0oNVQcOHECPHj2g0WigUCiwZcsWg+1CCEyZMgVOTk6wtLSEn58fLl68aFCTlpaGwMBAqFQq2NjYIDg4GHfv3jWoOXHiBNq3bw8LCws4OzsjKiqqUC8bN25E48aNYWFhAQ8PD+zYsaPUvRAREdGLq1JDVWZmJpo3b44lS5YUuT0qKgoLFy5EdHQ0EhISYGVlBa1WiwcPHkg1gYGBOH36NGJjY7Ft2zYcOHAAw4YNk7br9Xp06dIFLi4uSExMxOzZsxEREYHly5dLNYcOHUK/fv0QHByM48ePIyAgAAEBATh16lSpeiEiIqIXl0IIISq7CQBQKBTYvHkzAgICADw8M6TRaDB69GiMGTMGAJCRkQEHBwesXr0affv2xdmzZ+Hu7o4jR46gZcuWAICYmBh0794d169fh0ajwbJlyzBp0iTodDoolUoAwIQJE7BlyxacO3cOANCnTx9kZmZi27ZtUj+tW7eGp6cnoqOjS9RLSej1eqjVamRkZEClUskyb0REROVhXuyFym6h1EZ1blgu+y3p63eVvaYqOTkZOp0Ofn5+0pharYa3tzfi4+MBAPHx8bCxsZECFQD4+fnBxMQECQkJUk2HDh2kQAUAWq0W58+fx+3bt6WaR49TUFNwnJL0UpSsrCzo9XqDGxERET2fqmyo0ul0AAAHBweDcQcHB2mbTqeDvb29wXYzMzPY2toa1BS1j0ePUVzNo9uf1ktRIiMjoVarpZuzs/NTnjUREREZqyobqp4HEydOREZGhnS7du1aZbdERERE5aTKhipHR0cAQEpKisF4SkqKtM3R0RGpqakG23Nzc5GWlmZQU9Q+Hj1GcTWPbn9aL0UxNzeHSqUyuBEREdHzqcqGKldXVzg6OiIuLk4a0+v1SEhIgI+PDwDAx8cH6enpSExMlGr27t2L/Px8eHt7SzUHDhxATk6OVBMbG4tGjRqhRo0aUs2jxymoKThOSXohIiKiF1ulhqq7d+8iKSkJSUlJAB5eEJ6UlISrV69CoVAgLCwM06dPx9atW3Hy5EkMHDgQGo1G+oRgkyZN0LVrVwwdOhSHDx/GwYMHERoair59+0Kj0QAA+vfvD6VSieDgYJw+fRobNmzAggULEB4eLvUxcuRIxMTEYM6cOTh37hwiIiJw9OhRhIaGAkCJeiEiIqIXm1llHvzo0aPo2LGjdL8g6AQFBWH16tUYN24cMjMzMWzYMKSnp6Ndu3aIiYmBhYWF9Ji1a9ciNDQUvr6+MDExQa9evbBw4UJpu1qtxu7duxESEgIvLy/Y2dlhypQpBmtZtWnTBuvWrcPkyZPxySefwM3NDVu2bEGzZs2kmpL0QkRERC+uKrNO1YuA61QREZGx4DpV/2P061QRERERGROGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSQZlC1aVLl+Tug4iIiMiolSlUNWjQAB07dsR3332HBw8eyN0TERERkdEpU6g6duwYXn75ZYSHh8PR0RHDhw/H4cOH5e6NiIiIyGiUKVR5enpiwYIFuHHjBlauXImbN2+iXbt2aNasGebOnYtbt27J3ScRERFRlfZMF6qbmZmhZ8+e2LhxI2bNmoU///wTY8aMgbOzMwYOHIibN2/K1ScRERFRlfZMoero0aMYMWIEnJycMHfuXIwZMwZ//fUXYmNjcePGDbz11lty9UlERERUpZmV5UFz587FqlWrcP78eXTv3h3ffvstunfvDhOThxnN1dUVq1evRt26deXslYiIiKjKKlOoWrZsGYYMGYJBgwbBycmpyBp7e3t88803z9QcERERkbEoU6i6ePHiU2uUSiWCgoLKsnsiIiIio1Oma6pWrVqFjRs3FhrfuHEj1qxZ88xNERERERmbMoWqyMhI2NnZFRq3t7fHjBkznrkpIiIiImNTplB19epVuLq6Fhp3cXHB1atXn7kpIiIiImNTplBlb2+PEydOFBr/448/ULNmzWduioiIiMjYlClU9evXDx9//DH27duHvLw85OXlYe/evRg5ciT69u0rd49EREREVV6ZPv33+eef4/Lly/D19YWZ2cNd5OfnY+DAgbymioiIiF5IZQpVSqUSGzZswOeff44//vgDlpaW8PDwgIuLi9z9ERERERmFMoWqAg0bNkTDhg3l6oWIiIjIaJXpmqq8vDx888036N+/P/z8/NCpUyeDm1zy8vLw6aefwtXVFZaWlqhfvz4+//xzCCGkGiEEpkyZAicnJ1haWsLPz6/Q4qRpaWkIDAyESqWCjY0NgoODcffuXYOaEydOoH379rCwsICzszOioqIK9bNx40Y0btwYFhYW8PDwwI4dO2R7rkRERGTcyhSqRo4ciZEjRyIvLw/NmjVD8+bNDW5ymTVrFpYtW4bFixfj7NmzmDVrFqKiorBo0SKpJioqCgsXLkR0dDQSEhJgZWUFrVaLBw8eSDWBgYE4ffo0YmNjsW3bNhw4cADDhg2Ttuv1enTp0gUuLi5ITEzE7NmzERERgeXLl0s1hw4dQr9+/RAcHIzjx48jICAAAQEBOHXqlGzPl4iIiIyXQjx62qeE7OzspC9RLk9vvPEGHBwcDL5DsFevXrC0tMR3330HIQQ0Gg1Gjx6NMWPGAAAyMjLg4OCA1atXo2/fvjh79izc3d1x5MgRtGzZEgAQExOD7t274/r169BoNFi2bBkmTZoEnU4HpVIJAJgwYQK2bNmCc+fOAQD69OmDzMxMbNu2TeqldevW8PT0RHR0dJH9Z2VlISsrS7qv1+vh7OyMjIwMqFQqeSeLiIhIRvNiL1R2C6U2qnP5XJKk1+uhVquf+vpdpjNVSqUSDRo0KHNzJdWmTRvExcXhwoWHP9g//vgDv/32G7p16wYASE5Ohk6ng5+fn/QYtVoNb29vxMfHAwDi4+NhY2MjBSoA8PPzg4mJCRISEqSaDh06SIEKALRaLc6fP4/bt29LNY8ep6Cm4DhFiYyMhFqtlm7Ozs7PMh1ERERUhZUpVI0ePRoLFixAGU5ylcqECRPQt29fNG7cGNWqVcMrr7yCsLAwBAYGAgB0Oh0AwMHBweBxDg4O0jadTgd7e3uD7WZmZrC1tTWoKWofjx6juJqC7UWZOHEiMjIypNu1a9dK9fyJiIjIeJTp03+//fYb9u3bh507d6Jp06aoVq2awfZNmzbJ0tz333+PtWvXYt26dWjatCmSkpIQFhYGjUaDoKAgWY5RnszNzWFubl7ZbRAREVEFKFOosrGxwdtvvy13L4WMHTtWOlsFAB4eHrhy5QoiIyMRFBQER0dHAEBKSgqcnJykx6WkpMDT0xMA4OjoiNTUVIP95ubmIi0tTXq8o6MjUlJSDGoK7j+tpmA7ERERvdjKFKpWrVoldx9FunfvHkxMDN+hNDU1RX5+PgDA1dUVjo6OiIuLk0KUXq9HQkICPvzwQwCAj48P0tPTkZiYCC8vLwDA3r17kZ+fD29vb6lm0qRJyMnJkc66xcbGolGjRqhRo4ZUExcXh7CwMKmX2NhY+Pj4lNvzJyIiIuNRpmuqgIdne/bs2YOvvvoKd+7cAQDcuHGj0PpPz6JHjx744osvsH37dly+fBmbN2/G3LlzpbNkCoUCYWFhmD59OrZu3YqTJ09i4MCB0Gg0CAgIAAA0adIEXbt2xdChQ3H48GEcPHgQoaGh6Nu3LzQaDQCgf//+UCqVCA4OxunTp7FhwwYsWLAA4eHhUi8jR45ETEwM5syZg3PnziEiIgJHjx5FaGiobM+XiIiIjFeZzlRduXIFXbt2xdWrV5GVlYXOnTvD2toas2bNQlZWVrFLDJTWokWL8Omnn2LEiBFITU2FRqPB8OHDMWXKFKlm3LhxyMzMxLBhw5Ceno527dohJiYGFhYWUs3atWsRGhoKX19fmJiYoFevXli4cKG0Xa1WY/fu3QgJCYGXlxfs7OwwZcoUg7Ws2rRpg3Xr1mHy5Mn45JNP4Obmhi1btqBZs2ayPFciIiIybmVapyogIADW1tb45ptvULNmTfzxxx+oV68e9u/fj6FDhxZa0ZweKuk6F0RERJWN61T9T0lfv8t0purXX3/FoUOHDNZ1AoC6devi77//LssuiYiIiIxama6pys/PR15eXqHx69evw9ra+pmbIiIiIjI2ZQpVXbp0wfz586X7CoUCd+/exdSpU8v9q2uIiIiIqqIyvf03Z84caLVauLu748GDB+jfvz8uXrwIOzs7/N///Z/cPRIRERFVeWUKVbVr18Yff/yB9evX48SJE7h79y6Cg4MRGBgIS0tLuXskIiIiqvLKFKqAh9+f995778nZCxEREZHRKlOo+vbbb5+4feDAgWVqhoiIiMhYlSlUjRw50uB+Tk4O7t27B6VSierVqzNUERER0QunTKHq9u3bhcYuXryIDz/8EGPHjn3mpoiIiJ4nxriQJpVemb/773Fubm6YOXNmobNYRERERC8C2UIV8PDi9Rs3bsi5SyIiIiKjUKa3/7Zu3WpwXwiBmzdvYvHixWjbtq0sjREREREZkzKFqoCAAIP7CoUCtWrVQqdOnTBnzhw5+iIiIiIyKmUKVfn5+XL3Qc/IWC+CLK9vFCciIqposl5TRURERPSiKtOZqvDw8BLXzp07tyyHICIiIjIqZQpVx48fx/Hjx5GTk4NGjRoBAC5cuABTU1O0aNFCqlMoFPJ0SURERFTFlSlU9ejRA9bW1lizZg1q1KgB4OGCoIMHD0b79u0xevRoWZskIiIiqurKdE3VnDlzEBkZKQUqAKhRowamT5/OT/8RERHRC6lMoUqv1+PWrVuFxm/duoU7d+48c1NERERExqZMoertt9/G4MGDsWnTJly/fh3Xr1/Hjz/+iODgYPTs2VPuHomIiIiqvDJdUxUdHY0xY8agf//+yMnJebgjMzMEBwdj9uzZsjZIREREZAzKFKqqV6+OpUuXYvbs2fjrr78AAPXr14eVlZWszREREREZi2da/PPmzZu4efMm3NzcYGVlBSGEXH0RERERGZUyhap///0Xvr6+aNiwIbp3746bN28CAIKDg7mcAhEREb2QyhSqRo0ahWrVquHq1auoXr26NN6nTx/ExMTI1hwRERGRsSjTNVW7d+/Grl27ULt2bYNxNzc3XLlyRZbGiIiIiIxJmUJVZmamwRmqAmlpaTA3N3/mpujFMS/2QmW3UGqjOjes7BaIiKgKKtPbf+3bt8e3334r3VcoFMjPz0dUVBQ6duwoW3NERERExqJMZ6qioqLg6+uLo0ePIjs7G+PGjcPp06eRlpaGgwcPyt0jERERUZVXpjNVzZo1w4ULF9CuXTu89dZbyMzMRM+ePXH8+HHUr19f7h6JiIiIqrxSn6nKyclB165dER0djUmTJpVHT0RERERGp9RnqqpVq4YTJ06URy9ERERERqtMb/+99957+Oabb+TuhYiIiMholelC9dzcXKxcuRJ79uyBl5dXoe/8mzt3rizNERERERmLUoWqS5cuoW7dujh16hRatGgBALhwwXCdIYVCIV93REREREaiVKHKzc0NN2/exL59+wA8/FqahQsXwsHBoVyaIyIiIjIWpbqmSghhcH/nzp3IzMyUtSEiIiIiY1SmC9ULPB6yysPff/+N9957DzVr1oSlpSU8PDxw9OhRgx6mTJkCJycnWFpaws/PDxcvXjTYR1paGgIDA6FSqWBjY4Pg4GDcvXvXoObEiRNo3749LCws4OzsjKioqEK9bNy4EY0bN4aFhQU8PDywY8eO8nnSREREZHRKFaoUCkWha6bK8xqq27dvo23btqhWrRp27tyJM2fOYM6cOahRo4ZUExUVhYULFyI6OhoJCQmwsrKCVqvFgwcPpJrAwECcPn0asbGx2LZtGw4cOIBhw4ZJ2/V6Pbp06QIXFxckJiZi9uzZiIiIwPLly6WaQ4cOoV+/fggODsbx48cREBCAgIAAnDp1qtyePxERERkPhSjF6SYTExN069ZN+tLkn3/+GZ06dSr06b9NmzbJ0tyECRNw8OBB/Prrr0VuF0JAo9Fg9OjRGDNmDAAgIyMDDg4OWL16Nfr27YuzZ8/C3d0dR44cQcuWLQEAMTEx6N69O65fvw6NRoNly5Zh0qRJ0Ol0UCqV0rG3bNmCc+fOAXh4/VhmZia2bdsmHb9169bw9PREdHR0iZ6PXq+HWq1GRkYGVCpVmeelKMb4xcTGil+oTESlxb+jK0Z5/f1c0tfvUp2pCgoKgr29PdRqNdRqNd577z1oNBrpfsFNLlu3bkXLli3x7rvvwt7eHq+88gq+/vpraXtycjJ0Oh38/PykMbVaDW9vb8THxwMA4uPjYWNjIwUqAPDz84OJiQkSEhKkmg4dOkiBCgC0Wi3Onz+P27dvSzWPHqegpuA4RcnKyoJerze4ERER0fOpVJ/+W7VqVXn1UaRLly5h2bJlCA8PxyeffIIjR47g448/hlKpRFBQEHQ6HQAU+vShg4ODtE2n08He3t5gu5mZGWxtbQ1qXF1dC+2jYFuNGjWg0+meeJyiREZGYtq0aWV45kRERGRsnulC9fKWn5+PFi1aYMaMGXjllVcwbNgwDB06tMRvt1W2iRMnIiMjQ7pdu3atslsiIiKiclKlQ5WTkxPc3d0Nxpo0aYKrV68CABwdHQEAKSkpBjUpKSnSNkdHR6Smphpsz83NRVpamkFNUft49BjF1RRsL4q5uTlUKpXBjYiIiJ5PVTpUtW3bFufPnzcYu3DhAlxcXAAArq6ucHR0RFxcnLRdr9cjISEBPj4+AAAfHx+kp6cjMTFRqtm7dy/y8/Ph7e0t1Rw4cAA5OTlSTWxsLBo1aiR90tDHx8fgOAU1BcchIiKiF1uVDlWjRo3C77//jhkzZuDPP//EunXrsHz5coSEhAB4uJxDWFgYpk+fjq1bt+LkyZMYOHAgNBoNAgICADw8s9W1a1cMHToUhw8fxsGDBxEaGoq+fftCo9EAAPr37w+lUong4GCcPn0aGzZswIIFCxAeHi71MnLkSMTExGDOnDk4d+4cIiIicPToUYSGhlb4vBAREVHVU6YvVK4or776KjZv3oyJEyfis88+g6urK+bPn4/AwECpZty4ccjMzMSwYcOQnp6Odu3aISYmBhYWFlLN2rVrERoaCl9fX5iYmKBXr15YuHChtF2tVmP37t0ICQmBl5cX7OzsMGXKFIO1rNq0aYN169Zh8uTJ+OSTT+Dm5oYtW7agWbNmFTMZREREVKWVap0qejZcp+r5wHWqiKi0+Hd0xTCqdaqIiIiIqGgMVUREREQyqNLXVBERUfkyxrel+BY8VVU8U0VEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBvxCZSIiMirG+CXQ9GLgmSoiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikoFZZTdAZGzmxV6o7BZKbVTnhpXdAhHRc8+ozlTNnDkTCoUCYWFh0tiDBw8QEhKCmjVr4qWXXkKvXr2QkpJi8LirV6/C398f1atXh729PcaOHYvc3FyDmv3796NFixYwNzdHgwYNsHr16kLHX7JkCerWrQsLCwt4e3vj8OHD5fE0iYiIyAgZTag6cuQIvvrqK7z88ssG46NGjcLPP/+MjRs34pdffsGNGzfQs2dPaXteXh78/f2RnZ2NQ4cOYc2aNVi9ejWmTJki1SQnJ8Pf3x8dO3ZEUlISwsLC8P7772PXrl1SzYYNGxAeHo6pU6fi2LFjaN68ObRaLVJTU8v/yRMREVGVZxSh6u7duwgMDMTXX3+NGjVqSOMZGRn45ptvMHfuXHTq1AleXl5YtWoVDh06hN9//x0AsHv3bpw5cwbfffcdPD090a1bN3z++edYsmQJsrOzAQDR0dFwdXXFnDlz0KRJE4SGhuKdd97BvHnzpGPNnTsXQ4cOxeDBg+Hu7o7o6GhUr14dK1eurNjJICIioirJKEJVSEgI/P394efnZzCemJiInJwcg/HGjRujTp06iI+PBwDEx8fDw8MDDg4OUo1Wq4Ver8fp06elmsf3rdVqpX1kZ2cjMTHRoMbExAR+fn5STVGysrKg1+sNbkRERPR8qvIXqq9fvx7Hjh3DkSNHCm3T6XRQKpWwsbExGHdwcIBOp5NqHg1UBdsLtj2pRq/X4/79+7h9+zby8vKKrDl37lyxvUdGRmLatGkle6JERERk1Kr0mapr165h5MiRWLt2LSwsLCq7nVKbOHEiMjIypNu1a9cquyUiIiIqJ1U6VCUmJiI1NRUtWrSAmZkZzMzM8Msvv2DhwoUwMzODg4MDsrOzkZ6ebvC4lJQUODo6AgAcHR0LfRqw4P7TalQqFSwtLWFnZwdTU9Miawr2URRzc3OoVCqDGxERET2fqnSo8vX1xcmTJ5GUlCTdWrZsicDAQOn/q1Wrhri4OOkx58+fx9WrV+Hj4wMA8PHxwcmTJw0+pRcbGwuVSgV3d3ep5tF9FNQU7EOpVMLLy8ugJj8/H3FxcVINERERvdiq9DVV1tbWaNasmcGYlZUVatasKY0HBwcjPDwctra2UKlU+Oijj+Dj44PWrVsDALp06QJ3d3cMGDAAUVFR0Ol0mDx5MkJCQmBubg4A+OCDD7B48WKMGzcOQ4YMwd69e/H9999j+/bt0nHDw8MRFBSEli1bolWrVpg/fz4yMzMxePDgCpoNIiIiqsqqdKgqiXnz5sHExAS9evVCVlYWtFotli5dKm03NTXFtm3b8OGHH8LHxwdWVlYICgrCZ599JtW4urpi+/btGDVqFBYsWIDatWtjxYoV0Gq1Uk2fPn1w69YtTJkyBTqdDp6enoiJiSl08ToRERG9mBRCCFHZTbwo9Ho91Go1MjIyZL++yhi/OoUqDr+mhorDvzvoeVJef9eV9PXb6M9UEdHzyRhf7BleiV5sVfpCdSIiIiJjwVBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIB16kiIpKJMa6tRUTy4ZkqIiIiIhkwVBERERHJgG//Eb0A+LYUEVH545kqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJoEqHqsjISLz66quwtraGvb09AgICcP78eYOaBw8eICQkBDVr1sRLL72EXr16ISUlxaDm6tWr8Pf3R/Xq1WFvb4+xY8ciNzfXoGb//v1o0aIFzM3N0aBBA6xevbpQP0uWLEHdunVhYWEBb29vHD58WPbnTERERMapSoeqX375BSEhIfj9998RGxuLnJwcdOnSBZmZmVLNqFGj8PPPP2Pjxo345ZdfcOPGDfTs2VPanpeXB39/f2RnZ+PQoUNYs2YNVq9ejSlTpkg1ycnJ8Pf3R8eOHZGUlISwsDC8//772LVrl1SzYcMGhIeHY+rUqTh27BiaN28OrVaL1NTUipkMIiIiqtIUQghR2U2U1K1bt2Bvb49ffvkFHTp0QEZGBmrVqoV169bhnXfeAQCcO3cOTZo0QXx8PFq3bo2dO3fijTfewI0bN+Dg4AAAiI6Oxvjx43Hr1i0olUqMHz8e27dvx6lTp6Rj9e3bF+np6YiJiQEAeHt749VXX8XixYsBAPn5+XB2dsZHH32ECRMmlKh/vV4PtVqNjIwMqFQqOacG82IvyLo/IiIiYzOqc8Ny2W9JX7+r9Jmqx2VkZAAAbG1tAQCJiYnIycmBn5+fVNO4cWPUqVMH8fHxAID4+Hh4eHhIgQoAtFot9Ho9Tp8+LdU8uo+CmoJ9ZGdnIzEx0aDGxMQEfn5+Uk1RsrKyoNfrDW5ERET0fDKaUJWfn4+wsDC0bdsWzZo1AwDodDoolUrY2NgY1Do4OECn00k1jwaqgu0F255Uo9frcf/+ffzzzz/Iy8srsqZgH0WJjIyEWq2Wbs7OzqV/4kRERGQUjCZUhYSE4NSpU1i/fn1lt1JiEydOREZGhnS7du1aZbdERERE5cSsshsoidDQUGzbtg0HDhxA7dq1pXFHR0dkZ2cjPT3d4GxVSkoKHB0dpZrHP6VX8OnAR2se/8RgSkoKVCoVLC0tYWpqClNT0yJrCvZRFHNzc5ibm5f+CRMREZHRqdJnqoQQCA0NxebNm7F37164uroabPfy8kK1atUQFxcnjZ0/fx5Xr16Fj48PAMDHxwcnT540+JRebGwsVCoV3N3dpZpH91FQU7APpVIJLy8vg5r8/HzExcVJNURERPRiq9JnqkJCQrBu3Tr89NNPsLa2lq5fUqvVsLS0hFqtRnBwMMLDw2FrawuVSoWPPvoIPj4+aN26NQCgS5cucHd3x4ABAxAVFQWdTofJkycjJCREOov0wQcfYPHixRg3bhyGDBmCvXv34vvvv8f27dulXsLDwxEUFISWLVuiVatWmD9/PjIzMzF48OCKnxgiIiKqcqp0qFq2bBkA4PXXXzcYX7VqFQYNGgQAmDdvHkxMTNCrVy9kZWVBq9Vi6dKlUq2pqSm2bduGDz/8ED4+PrCyskJQUBA+++wzqcbV1RXbt2/HqFGjsGDBAtSuXRsrVqyAVquVavr06YNbt25hypQp0Ol08PT0RExMTKGL14mIiOjFZFTrVBk7rlNFRERUfrhOFREREdFzgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBVSkuWLEHdunVhYWEBb29vHD58uLJbIiIioiqAoaoUNmzYgPDwcEydOhXHjh1D8+bNodVqkZqaWtmtERERUSVjqCqFuXPnYujQoRg8eDDc3d0RHR2N6tWrY+XKlZXdGhEREVUys8puwFhkZ2cjMTEREydOlMZMTEzg5+eH+Pj4Ih+TlZWFrKws6X5GRgYAQK/Xy97fg8y7su+TiIjImJTH6+uj+xVCPLGOoaqE/vnnH+Tl5cHBwcFg3MHBAefOnSvyMZGRkZg2bVqhcWdn53LpkYiI6EX2STnv/86dO1Cr1cVuZ6gqRxMnTkR4eLh0Pz8/H2lpaahZsyYUCoVsx9Hr9XB2dsa1a9egUqlk2y8VxrmuGJznisF5rhic54pRnvMshMCdO3eg0WieWMdQVUJ2dnYwNTVFSkqKwXhKSgocHR2LfIy5uTnMzc0NxmxsbMqrRahUKv6BrSCc64rBea4YnOeKwXmuGOU1z086Q1WAF6qXkFKphJeXF+Li4qSx/Px8xMXFwcfHpxI7IyIioqqAZ6pKITw8HEFBQWjZsiVatWqF+fPnIzMzE4MHD67s1oiIiKiSMVSVQp8+fXDr1i1MmTIFOp0Onp6eiImJKXTxekUzNzfH1KlTC73VSPLjXFcMznPF4DxXDM5zxagK86wQT/t8IBERERE9Fa+pIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBmJJUuWoG7durCwsIC3tzcOHz78xPqNGzeicePGsLCwgIeHB3bs2FFBnRq30szz119/jfbt26NGjRqoUaMG/Pz8nvpzof8p7e90gfXr10OhUCAgIKB8G3xOlHae09PTERISAicnJ5ibm6Nhw4b8+6MESjvP8+fPR6NGjWBpaQlnZ2eMGjUKDx48qKBujdOBAwfQo0cPaDQaKBQKbNmy5amP2b9/P1q0aAFzc3M0aNAAq1evLt8mBVV569evF0qlUqxcuVKcPn1aDB06VNjY2IiUlJQi6w8ePChMTU1FVFSUOHPmjJg8ebKoVq2aOHnyZAV3blxKO8/9+/cXS5YsEcePHxdnz54VgwYNEmq1Wly/fr2COzc+pZ3rAsnJyeI///mPaN++vXjrrbcqplkjVtp5zsrKEi1bthTdu3cXv/32m0hOThb79+8XSUlJFdy5cSntPK9du1aYm5uLtWvXiuTkZLFr1y7h5OQkRo0aVcGdG5cdO3aISZMmiU2bNgkAYvPmzU+sv3TpkqhevboIDw8XZ86cEYsWLRKmpqYiJiam3HpkqDICrVq1EiEhIdL9vLw8odFoRGRkZJH1vXv3Fv7+/gZj3t7eYvjw4eXap7Er7Tw/Ljc3V1hbW4s1a9aUV4vPjbLMdW5urmjTpo1YsWKFCAoKYqgqgdLO87Jly0S9evVEdnZ2RbX4XCjtPIeEhIhOnToZjIWHh4u2bduWa5/Pk5KEqnHjxommTZsajPXp00dotdpy64tv/1Vx2dnZSExMhJ+fnzRmYmICPz8/xMfHF/mY+Ph4g3oA0Gq1xdZT2eb5cffu3UNOTg5sbW3Lq83nQlnn+rPPPoO9vT2Cg4Mrok2jV5Z53rp1K3x8fBASEgIHBwc0a9YMM2bMQF5eXkW1bXTKMs9t2rRBYmKi9BbhpUuXsGPHDnTv3r1Cen5RVMZrIb+mpor7559/kJeXV+ircBwcHHDu3LkiH6PT6Yqs1+l05dansSvLPD9u/Pjx0Gg0hf4Qk6GyzPVvv/2Gb775BklJSRXQ4fOhLPN86dIl7N27F4GBgdixYwf+/PNPjBgxAjk5OZg6dWpFtG10yjLP/fv3xz///IN27dpBCIHc3Fx88MEH+OSTTyqi5RdGca+Fer0e9+/fh6WlpezH5JkqIhnMnDkT69evx+bNm2FhYVHZ7TxX7ty5gwEDBuDrr7+GnZ1dZbfzXMvPz4e9vT2WL18OLy8v9OnTB5MmTUJ0dHRlt/Zc2b9/P2bMmIGlS5fi2LFj2LRpE7Zv347PP/+8slujZ8QzVVWcnZ0dTE1NkZKSYjCekpICR0fHIh/j6OhYqnoq2zwX+PLLLzFz5kzs2bMHL7/8cnm2+Vwo7Vz/9ddfuHz5Mnr06CGN5efnAwDMzMxw/vx51K9fv3ybNkJl+Z12cnJCtWrVYGpqKo01adIEOp0O2dnZUCqV5dqzMSrLPH/66acYMGAA3n//fQCAh4cHMjMzMWzYMEyaNAkmJjzfIYfiXgtVKlW5nKUCeKaqylMqlfDy8kJcXJw0lp+fj7i4OPj4+BT5GB8fH4N6AIiNjS22nso2zwAQFRWFzz//HDExMWjZsmVFtGr0SjvXjRs3xsmTJ5GUlCTd3nzzTXTs2BFJSUlwdnauyPaNRll+p9u2bYs///xTCq0AcOHCBTg5OTFQFaMs83zv3r1CwakgyD68BpvkUCmvheV2CTzJZv369cLc3FysXr1anDlzRgwbNkzY2NgInU4nhBBiwIABYsKECVL9wYMHhZmZmfjyyy/F2bNnxdSpU7mkQgmUdp5nzpwplEql+OGHH8TNmzel2507dyrrKRiN0s714/jpv5Ip7TxfvXpVWFtbi9DQUHH+/Hmxbds2YW9vL6ZPn15ZT8EolHaep06dKqytrcX//d//iUuXLondu3eL+vXri969e1fWUzAKd+7cEcePHxfHjx8XAMTcuXPF8ePHxZUrV4QQQkyYMEEMGDBAqi9YUmHs2LHi7NmzYsmSJVxSgR5atGiRqFOnjlAqlaJVq1bi999/l7a99tprIigoyKD++++/Fw0bNhRKpVI0bdpUbN++vYI7Nk6lmWcXFxcBoNBt6tSpFd+4ESrt7/SjGKpKrrTzfOjQIeHt7S3Mzc1FvXr1xBdffCFyc3MruGvjU5p5zsnJEREREaJ+/frCwsJCODs7ixEjRojbt29XfONGZN++fUX+nVswt0FBQeK1114r9BhPT0+hVCpFvXr1xKpVq8q1R4UQPNdIRERE9Kx4TRURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCSD/wfDOkMSo5jcmQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pyspark.sql import Window\n", + "\n", + "window = Window.partitionBy(l2g[\"studyLocusId\"]).orderBy(l2g[\"score\"].desc())\n", + "l2g = l2g.withColumn(\"rn\", f.row_number().over(window))\n", + "l2g_max_scores = l2g.filter(l2g[\"rn\"] == 1).drop(\"rn\")\n", + "l2g_max_scores.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"l2g scores (top gene assignment)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 7. Analyze locus-to-gene (L2G) predictions:\n", + " - Consider only the top gene assignments from l2g:\n", + " - How does this look when separated by datasource?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary of finngen PICS l2g scores: mean: 0.5615633023783144 L.quart: 0.2371208220720291 Median: 0.6145811676979065 U.quart: 0.8626531362533569\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "l2g_finngen_pics=l2g_max_scores.join(finngen_pics.select(\"studyLocusId\", \"studyId\"), on=\"studyLocusId\", how=\"inner\")\n", + "l2g_finngen_pics.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"l2g scores (top gene assignment), finngen_pics\")\n", + "\n", + "sample_size_quartiles = l2g_finngen_pics.stat.approxQuantile(\"score\", [0.25, 0.5, 0.75], 0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary of l2g_finngen_susie: mean: 0.5593829938336615 L.quart: 0.2241434007883072 Median: 0.6244931817054749 U.quart: 0.8736903071403503\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "l2g_finngen_susie=l2g_max_scores.join(finngen_susie.select(\"studyLocusId\", \"studyId\"), on=\"studyLocusId\", how=\"inner\")\n", + "l2g_finngen_susie.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"(top gene assignment), l2g_finngen_susie\")\n", + "\n", + "sample_size_quartiles = l2g_finngen_susie.stat.approxQuantile(\"score\", [0.25, 0.5, 0.75], 0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary of l2g_gwas_curated PICS l2g scores: mean: 0.5761322645683256 L.quart: 0.22704362869262695 Median: 0.653652012348175 U.quart: 0.8937538862228394\n" + ] + }, + { + "data": { + "image/png": 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4lJQUVa9eXWPGjEl3+Z1v+Le/+d0u9Q04dQLUF1980ebsw+1Sry9Lz7Fjx9SsWTNVqlRJY8aMkb+/v5ycnLRkyRKNHTvW3L6dnZ2+//57/f777/r111+1fPlyvfzyyxo9erR+//13FS5cWF5eXtq1a5eWL1+upUuXaunSpZoxY4a6detm6V1t9xuT+/XLjjHNaE33khrAPD09M7xORsTExKhFixa6cuWKNmzYID8/P0u3n1W1atWSi4uL1q9fr1KlSsnLy0sVKlRQw4YNNXnyZMXHx2vDhg1q166duc6VK1fUuHFjubm56T//+Y/Kli0rFxcX7dixQwMGDLCZEHj06NHq0aOHfv75Z61YsUJvvvmmea1ketfqZdW9rhlKz+1npFJt2LBBbdq0UaNGjTR58mT5+vqqQIECmjFjhubMmXPfGh70OJBX5dQxJb8hVMESlSpVknTrLsDbDxB3O6iVLl1aknT48OE0yw4dOiRPT08VKlTIpv3o0aM2/5P8888/lZKSctezJrfv588//1STJk3M9qSkJJ08edKm1rJly2r37t1q1qyZJYGtRIkSKlKkiJKTk23uisyoX3/9VfHx8frll19szrbc+TFkqrp166pu3br6+OOPNWfOHHXt2lVz587VK6+8IunWbdOtW7dW69atlZKSor59++qLL77QBx98kO7ZisyMXU550DG9m/v9vk+cOCF7e3vzrKUVbt68qdatW+vIkSNauXJluh873f47uP21/88//2TqTFvqtlJSUnTixAmbsxd33p0p3XqtpN7pWapUKfMj04YNGyo+Pl6zZ89WdHS0GjVqZK6zdu1a/fPPP/rxxx9t2k+cOJFuPdWrV1f16tU1ePBgbdq0SfXr19fUqVP10Ucf3fe5PPbYY5J035nuixYtqitXrqRpTz0DmBE//PCDXFxctHz5cjk7O5vtM2bMSNM3vddRZl6zpUuX1r59+2QYhs220jtOZtTRo0dt/n6vXbumc+fOqWXLlnddp2zZstqyZYsSExNtPjq8U2aPKY8CPv6DJYKCgiTdug7ndgULFpSkNAc2X19f1ahRQ7NmzbJZtm/fPq1YsSLdP/hJkybZ/Dxx4kRJt27rvptatWqpePHi+vLLL5WUlGS2z549O82bUseOHfX333/ryy+/TLOdGzdupPk48n4cHBzUoUMH/fDDD+ke/C9cuHDf9SXbMyoxMTFpDuaXL19Oc9alRo0akmR+BPjPP//YLLe3tzdD0d1u187M2OWUBx3TuylUqNA9P8rZvn27qlatmuE70+4nOTlZnTp10ubNm7VgwQLz7+dOzZo1k6OjY5ppND7//PNM7zMkJESSNHnyZJv21L+jOzVs2FBbtmzRmjVrzFDl6empypUrm3d7pbZL6b9eExIS0uwvNjbW5vUk3QpY9vb2GZ46oESJEmrUqJGmT5+u06dP2yy7ff9ly5ZVTEyM9uzZY7adO3dOP/30U4b2I916XnZ2djZnt06ePJnuzOmFChVKc6zLzGu2ZcuWOnv2rM10DdevX9e0adMyXO+dpk2bZnOt5ZQpU5SUlHTP42aHDh108eLFdF9nqeOblWPKo4AzVbDEY489pmrVqmnlypU28yO5urqqSpUqmjdvnipUqKBixYqpWrVqqlatmkaNGqUWLVooKChIvXr1MqdUcHd3T/d70k6cOKE2bdro6aef1ubNm/Xdd9+pS5cueuKJJ+5al5OTk4YNG6Y33nhDTZs2VceOHXXy5EnNnDlTZcuWtfnf4EsvvaT58+frtdde05o1a1S/fn0lJyfr0KFDmj9/vpYvX65atWplalxGjhypNWvWqE6dOurdu7eqVKmiS5cuaceOHVq5cqUuXbp013WbN29u/k/w1Vdf1bVr1/Tll1/Ky8tL586dM/vNmjVLkydPVrt27VS2bFldvXpVX375pdzc3Mxw+sorr+jSpUtq2rSpSpYsqVOnTmnixImqUaOGKleu/MBjl5MeZEzvJjAwUPPmzVN4eLiefPJJFS5cWK1bt5YkJSYmat26dWku8F67dq2aNGmioUOHZvp7/d5++2398ssvat26tS5duqTvvvvOZnnqdUje3t7q16+fRo8ebb72d+/eraVLl8rT0zNTv4PAwEB16NBB48aN0z///GNOqZD61Th3bqthw4b6+OOPdebMGZvw1KhRI33xxRcqU6aMzUd19erVU9GiRdW9e3e9+eabsrOz07fffpsm8K9evVphYWF6/vnnVaFCBSUlJenbb781w0dGTZgwQQ0aNFDNmjXVp08fBQQE6OTJk1q8eLF27dolSercubMGDBigdu3a6c0339T169c1ZcoUVahQIUMXmUtSq1atNGbMGD399NPq0qWLzp8/r0mTJqlcuXI2YS11jFeuXKkxY8bIz89PAQEBqlOnToZfs71799bnn3+ubt26afv27fL19dW3335r/uc0KxISEtSsWTN17NhRhw8f1uTJk9WgQQNzuoz0dOvWTd98843Cw8P1xx9/qGHDhoqLi9PKlSvVt29fPfvss1k6pjwScvp2Qzy8xowZYxQuXDjN7cebNm0yAgMDDScnpzS3bq9cudKoX7++4erqari5uRmtW7c2Dhw4YLN+6u3PBw4cMJ577jmjSJEiRtGiRY2wsDDjxo0bGaptwoQJRunSpQ1nZ2ejdu3axsaNG43AwEDj6aeftumXkJBgfPrpp0bVqlUNZ2dno2jRokZgYKDx4YcfGjExMWY/SUZoaGia/ZQuXTrNLdXR0dFGaGio4e/vbxQoUMDw8fExmjVrZkybNu2+df/yyy/G448/bri4uBhlypQxPv30U/N25xMnThiGcevW9hdeeMEoVaqU4ezsbHh5eRnPPPOMsW3bNnM733//vdG8eXPDy8vLcHJyMkqVKmW8+uqrxrlz58w+d06pkJmxS133ztvkT5w4kWZKhMaNGxtVq1ZNd+xatWqVpj29sc7ImGampmvXrhldunQxPDw8DEk2t+EvXbrUkGQcPXrUZju//vqrIcmYOnVqmprTew63v+5Tp5W42+N2SUlJxgcffGD4+PgYrq6uRtOmTY2DBw8axYsXN1577bX77vt2cXFxRmhoqFGsWDGjcOHCRtu2bY3Dhw8bkoyRI0fa9I2NjTUcHByMIkWK2Nw2/9133xmSjJdeeinN9jdu3GjUrVvXcHV1Nfz8/Iz33nvPnBYj9XV1/Phx4+WXXzbKli1ruLi4GMWKFTOaNGlirFy5MlPPxTAMY9++fUa7du0MDw8Pw8XFxahYsaLxwQcf2PRZsWKFUa1aNcPJycmoWLGi8d133911SoX0/qYNwzC+/vpro3z58oazs7NRqVIlY8aMGelu49ChQ0ajRo0MV1fXNFNeZPQ4cOrUKaNNmzZGwYIFDU9PT6Nfv37GsmXLsjylwrp164w+ffoYRYsWNQoXLmx07drVZhobw0g7pYJh3JpGYtCgQUZAQIBZ73PPPWccO3bMMIyMHVMeRYQqWObKlStGsWLFjK+++srS7aYevC5cuGDZNpOTk41ixYoZr7zyimXbfFQ8amP37LPPppknyjAM49133zVKlixpzr+Uky5fvmxIMj766KMH3lbqvFHfffedBZUhr0gNVfeavw7W45oqWMbd3V3vvfeeRo0aZXOnT267efNmmo8gvvnmG126dIkveb6PR33sDh48qEWLFmn48OFplq1Zs0YffPCBzcXL2eHGjRtp2saNGycp819Sfrdt2dvb21xcDiBruKYKlhowYIAGDBiQ22XY+P3339W/f389//zzKl68uHbs2KGvv/5a1apV0/PPP5/b5eVpj/rYVa5cOc1F1am2bt2aIzXMmzdPM2fOVMuWLVW4cGH99ttv+u9//6vmzZurfv36km7NdXUvrq6ucnd3V0REhLZv364mTZrI0dHRvBW+T58+aaYMyS0xMTHphr/b+fj45FA1ec+NGzfuOz/W7XOHIWcRqvDQK1OmjPz9/TVhwgRdunRJxYoVU7du3TRy5Ei+mf0+GLvc9/jjj8vR0VERERGKjY01L16/feoBX1/fe26je/fumjlzpurVq6fIyEgNHz5c165dU6lSpTRs2DCbLyHObf369bvvPEd3nj19lMybN8+cNPRu7jbtCrKfnfEovzoB4CGwcuXKey738/NLdx6svOjAgQM6e/bsPftYOUdZfnPu3Dnt37//nn0CAwMzPeM+rEGoAgAAsAAXqgMAAFiAa6pyUEpKis6ePasiRYrk2sSJAAAgcwzD0NWrV+Xn5yd7+7ufjyJU5aCzZ8/mmTtsAABA5pw5c+aeX/pNqMpBRYoUkXTrl+Lm5pbL1QAAgIyIjY2Vv7+/+T5+N4SqHJT6kZ+bmxuhCgCAfOZ+l+5woToAAIAFCFUAAAAWIFQBAABYgGuq8hjDMJSUlKTk5OTcLuWh4ODgIEdHR6awAABkO0JVHpKQkKBz587p+vXruV3KQ6VgwYLy9fXlu+oAANmKUJVHpKSk6MSJE3JwcJCfn5+cnJw4u/KADMNQQkKCLly4oBMnTqh8+fL3nLQNAIAHQajKIxISEpSSkiJ/f38VLFgwt8t5aLi6uqpAgQI6deqUEhIS5OLiktslAQAeUvy3PY/hTIr1GFMAQE7g3QYAAMAChCoAAAALcE1VHjc28kiO7q//vyrk6P4AAHhYcKYKAADAAoQq5BsJCQm5XQIAAHdFqMID+/7771W9enW5urqqePHiCg4OVlxcnCRp+vTpqlq1qpydneXr66uwsDBzvdOnT+vZZ59V4cKF5ebmpo4dOyo6OtpcPmzYMNWoUUNfffWVAgICzOkQrly5oldeeUUlSpSQm5ubmjZtqt27d+fskwYA4A5cU4UHcu7cOb3wwguKiIhQu3btdPXqVW3YsEGGYWjKlCkKDw/XyJEj1aJFC8XExGjjxo2Sbk12mhqo1q1bp6SkJIWGhqpTp05au3atuf0///xTP/zwg3788Uc5ODhIkp5//nm5urpq6dKlcnd31xdffKFmzZrpyJEjKlasWG4MAwA8dHL6ml4r5PZ1wYQqPJBz584pKSlJ7du3V+nSpSVJ1atXlyR99NFHevvtt9WvXz+z/5NPPilJWrVqlfbu3asTJ07I399fkvTNN9+oatWq2rp1q9kvISFB33zzjUqUKCFJ+u233/THH3/o/PnzcnZ2liR99tlnWrhwob7//nv16dMnZ544AAB3IFThgTzxxBNq1qyZqlevrpCQEDVv3lzPPfecEhMTdfbsWTVr1izd9Q4ePCh/f38zUElSlSpV5OHhoYMHD5qhqnTp0magkqTdu3fr2rVrKl68uM32bty4oWPHjmXDMwQAIGMIVXggDg4OioyM1KZNm7RixQpNnDhRgwYN0qpVqyzZfqFChWx+vnbtmnx9fW0+Ikzl4eFhyT4BAMgKQhUemJ2dnerXr6/69etryJAhKl26tCIjI1WmTBmtWrVKTZo0SbNO5cqVdebMGZ05c8Y8W3XgwAFduXJFVapUueu+atasqaioKDk6OqpMmTLZ9ZQAAMg0QhUeyJYtW7Rq1So1b95cXl5e2rJliy5cuKDKlStr2LBheu211+Tl5aUWLVro6tWr2rhxo9544w0FBwerevXq6tq1q8aNG6ekpCT17dtXjRs3Vq1ate66v+DgYAUFBalt27aKiIhQhQoVdPbsWS1evFjt2rW757oAAGQnQlUel9t3MtyPm5ub1q9fr3Hjxik2NlalS5fW6NGj1aJFC0nSzZs3NXbsWL3zzjvy9PTUc889J+nW2a2ff/5Zb7zxhho1aiR7e3s9/fTTmjhx4j33Z2dnpyVLlmjQoEHq2bOnLly4IB8fHzVq1Eje3t7Z/nwBALgbO8MwjNwu4lERGxsrd3d3xcTEyM3NzWbZzZs3deLECZv5mGANxhYAMo8pFf7nXu/ft2PyTwAAAAsQqgAAACxAqAIAALAAoQoAAMAChKo8hvsGrMeYAgByAqEqjyhQoIAk6fr167lcycMndUxTxxgAgOzAPFV5hIODgzw8PHT+/HlJUsGCBWVnZ5fLVeVvhmHo+vXrOn/+vDw8POTg4JDbJQEAHmKEqjzEx8dHksxgBWt4eHiYYwsAQHYhVOUhdnZ28vX1lZeXlxITE3O7nIdCgQIFOEMFAMgRhKo8yMHBgSAAAEA+w4XqAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYIFcDVXr169X69at5efnJzs7Oy1cuNBmuWEYGjJkiHx9feXq6qrg4GAdPXrUps+lS5fUtWtXubm5ycPDQ7169dK1a9ds+uzZs0cNGzaUi4uL/P39FRERkaaWBQsWqFKlSnJxcVH16tW1ZMmSTNcCAAAeXbkaquLi4vTEE09o0qRJ6S6PiIjQhAkTNHXqVG3ZskWFChVSSEiIbt68afbp2rWr9u/fr8jISC1atEjr169Xnz59zOWxsbFq3ry5Spcure3bt2vUqFEaNmyYpk2bZvbZtGmTXnjhBfXq1Us7d+5U27Zt1bZtW+3bty9TtQAAgEeXnWEYRm4XIUl2dnb66aef1LZtW0m3zgz5+fnp7bff1jvvvCNJiomJkbe3t2bOnKnOnTvr4MGDqlKlirZu3apatWpJkpYtW6aWLVvqr7/+kp+fn6ZMmaJBgwYpKipKTk5OkqT3339fCxcu1KFDhyRJnTp1UlxcnBYtWmTWU7duXdWoUUNTp07NUC0ZERsbK3d3d8XExMjNzc2ScQMAIDuMjTyS2yVkWv9/VciW7Wb0/TvPXlN14sQJRUVFKTg42Gxzd3dXnTp1tHnzZknS5s2b5eHhYQYqSQoODpa9vb22bNli9mnUqJEZqCQpJCREhw8f1uXLl80+t+8ntU/qfjJSS3ri4+MVGxtr8wAAAA+nPBuqoqKiJEne3t427d7e3uayqKgoeXl52Sx3dHRUsWLFbPqkt43b93G3Prcvv18t6RkxYoTc3d3Nh7+//32eNQAAyK/ybKh6GAwcOFAxMTHm48yZM7ldEgAAyCZ5NlT5+PhIkqKjo23ao6OjzWU+Pj46f/68zfKkpCRdunTJpk9627h9H3frc/vy+9WSHmdnZ7m5udk8AADAwynPhqqAgAD5+Pho1apVZltsbKy2bNmioKAgSVJQUJCuXLmi7du3m31Wr16tlJQU1alTx+yzfv16JSYmmn0iIyNVsWJFFS1a1Oxz+35S+6TuJyO1AACAR1uuhqpr165p165d2rVrl6RbF4Tv2rVLp0+flp2dnd566y199NFH+uWXX7R3715169ZNfn5+5h2ClStX1tNPP63evXvrjz/+0MaNGxUWFqbOnTvLz89PktSlSxc5OTmpV69e2r9/v+bNm6fx48crPDzcrKNfv35atmyZRo8erUOHDmnYsGHatm2bwsLCJClDtQAAgEebY27ufNu2bWrSpIn5c2rQ6d69u2bOnKn33ntPcXFx6tOnj65cuaIGDRpo2bJlcnFxMdeZPXu2wsLC1KxZM9nb26tDhw6aMGGCudzd3V0rVqxQaGioAgMD5enpqSFDhtjMZVWvXj3NmTNHgwcP1r///W+VL19eCxcuVLVq1cw+GakFAAA8uvLMPFWPAuapAgDkF8xT9T/5fp4qAACA/IRQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABbI06EqOTlZH3zwgQICAuTq6qqyZctq+PDhMgzD7GMYhoYMGSJfX1+5uroqODhYR48etdnOpUuX1LVrV7m5ucnDw0O9evXStWvXbPrs2bNHDRs2lIuLi/z9/RUREZGmngULFqhSpUpycXFR9erVtWTJkux54gAAIN/J06Hq008/1ZQpU/T555/r4MGD+vTTTxUREaGJEyeafSIiIjRhwgRNnTpVW7ZsUaFChRQSEqKbN2+afbp27ar9+/crMjJSixYt0vr169WnTx9zeWxsrJo3b67SpUtr+/btGjVqlIYNG6Zp06aZfTZt2qQXXnhBvXr10s6dO9W2bVu1bdtW+/bty5nBAAAAeZqdcftpnzzmmWeekbe3t77++muzrUOHDnJ1ddV3330nwzDk5+ent99+W++8844kKSYmRt7e3po5c6Y6d+6sgwcPqkqVKtq6datq1aolSVq2bJlatmypv/76S35+fpoyZYoGDRqkqKgoOTk5SZLef/99LVy4UIcOHZIkderUSXFxcVq0aJFZS926dVWjRg1NnTo1Q88nNjZW7u7uiomJkZubmyVjBABAdhgbeSS3S8i0/v+qkC3bzej7d54+U1WvXj2tWrVKR47c+sXu3r1bv/32m1q0aCFJOnHihKKiohQcHGyu4+7urjp16mjz5s2SpM2bN8vDw8MMVJIUHBwse3t7bdmyxezTqFEjM1BJUkhIiA4fPqzLly+bfW7fT2qf1P2kJz4+XrGxsTYPAADwcHLM7QLu5f3331dsbKwqVaokBwcHJScn6+OPP1bXrl0lSVFRUZIkb29vm/W8vb3NZVFRUfLy8rJZ7ujoqGLFitn0CQgISLON1GVFixZVVFTUPfeTnhEjRujDDz/M7NMGAAD5UJ4+UzV//nzNnj1bc+bM0Y4dOzRr1ix99tlnmjVrVm6XliEDBw5UTEyM+Thz5kxulwQAALJJnj5T9e677+r9999X586dJUnVq1fXqVOnNGLECHXv3l0+Pj6SpOjoaPn6+prrRUdHq0aNGpIkHx8fnT9/3ma7SUlJunTpkrm+j4+PoqOjbfqk/ny/PqnL0+Ps7CxnZ+fMPm0AAJAP5ekzVdevX5e9vW2JDg4OSklJkSQFBATIx8dHq1atMpfHxsZqy5YtCgoKkiQFBQXpypUr2r59u9ln9erVSklJUZ06dcw+69evV2JiotknMjJSFStWVNGiRc0+t+8ntU/qfgAAwKMtT4eq1q1b6+OPP9bixYt18uRJ/fTTTxozZozatWsnSbKzs9Nbb72ljz76SL/88ov27t2rbt26yc/PT23btpUkVa5cWU8//bR69+6tP/74Qxs3blRYWJg6d+4sPz8/SVKXLl3k5OSkXr16af/+/Zo3b57Gjx+v8PBws5Z+/fpp2bJlGj16tA4dOqRhw4Zp27ZtCgsLy/FxAQAAeU+e/vhv4sSJ+uCDD9S3b1+dP39efn5+evXVVzVkyBCzz3vvvae4uDj16dNHV65cUYMGDbRs2TK5uLiYfWbPnq2wsDA1a9ZM9vb26tChgyZMmGAud3d314oVKxQaGqrAwEB5enpqyJAhNnNZ1atXT3PmzNHgwYP173//W+XLl9fChQtVrVq1nBkMAACQp+XpeaoeNsxTBQDIL5in6n8einmqAAAA8gtCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFggS6Hq+PHjVtcBAACQr2UpVJUrV05NmjTRd999p5s3b1pdEwAAQL6TpVC1Y8cOPf744woPD5ePj49effVV/fHHH1bXBgAAkG9kKVTVqFFD48eP19mzZzV9+nSdO3dODRo0ULVq1TRmzBhduHDB6joBAADytAe6UN3R0VHt27fXggUL9Omnn+rPP//UO++8I39/f3Xr1k3nzp2zqk4AAIA87YFC1bZt29S3b1/5+vpqzJgxeuedd3Ts2DFFRkbq7NmzevbZZ62qEwAAIE9zzMpKY8aM0YwZM3T48GG1bNlS33zzjVq2bCl7+1sZLSAgQDNnzlSZMmWsrBUAACDPylKomjJlil5++WX16NFDvr6+6fbx8vLS119//UDFAQAA5BdZClVHjx69bx8nJyd17949K5sHAADId7J0TdWMGTO0YMGCNO0LFizQrFmzHrgoAACA/CZLoWrEiBHy9PRM0+7l5aVPPvnkgYsCAADIb7IUqk6fPq2AgIA07aVLl9bp06cfuCgAAID8JkuhysvLS3v27EnTvnv3bhUvXvyBiwIAAMhvshSqXnjhBb355ptas2aNkpOTlZycrNWrV6tfv37q3Lmz1TUCAADkeVm6+2/48OE6efKkmjVrJkfHW5tISUlRt27duKYKAAA8krIUqpycnDRv3jwNHz5cu3fvlqurq6pXr67SpUtbXR8AAEC+kKVQlapChQqqUKGCVbUAAADkW1kKVcnJyZo5c6ZWrVql8+fPKyUlxWb56tWrLSkOAAAgv8hSqOrXr59mzpypVq1aqVq1arKzs7O6LgAAgHwlS6Fq7ty5mj9/vlq2bGl1PQAAAPlSlqZUcHJyUrly5ayuBQAAIN/KUqh6++23NX78eBmGYXU9AAAA+VKWPv777bfftGbNGi1dulRVq1ZVgQIFbJb/+OOPlhQHAACQX2QpVHl4eKhdu3ZW1wIAAJBvZenjvxkzZtzzYaW///5bL774oooXL25OMrpt2zZzuWEYGjJkiHx9feXq6qrg4GAdPXrUZhuXLl1S165d5ebmJg8PD/Xq1UvXrl2z6bNnzx41bNhQLi4u8vf3V0RERJpaFixYoEqVKsnFxUXVq1fXkiVLLH2uAAAg/8pSqJKkpKQkrVy5Ul988YWuXr0qSTp79myasPIgLl++rPr166tAgQJaunSpDhw4oNGjR6to0aJmn4iICE2YMEFTp07Vli1bVKhQIYWEhOjmzZtmn65du2r//v2KjIzUokWLtH79evXp08dcHhsbq+bNm6t06dLavn27Ro0apWHDhmnatGlmn02bNumFF15Qr169tHPnTrVt21Zt27bVvn37LHu+AAAg/7IzsnC1+alTp/T000/r9OnTio+P15EjR/TYY4+pX79+io+P19SpUy0p7v3339fGjRu1YcOGdJcbhiE/Pz+9/fbbeueddyRJMTEx8vb21syZM9W5c2cdPHhQVapU0datW1WrVi1J0rJly9SyZUv99ddf8vPz05QpUzRo0CBFRUXJycnJ3PfChQt16NAhSVKnTp0UFxenRYsWmfuvW7euatSokeHnGxsbK3d3d8XExMjNzS3L4wIAQHYbG3kkt0vItP7/yp5vecno+3eWzlT169dPtWrV0uXLl+Xq6mq2t2vXTqtWrcrKJtP1yy+/qFatWnr++efl5eWl//u//9OXX35pLj9x4oSioqIUHBxstrm7u6tOnTravHmzJGnz5s3y8PAwA5UkBQcHy97eXlu2bDH7NGrUyAxUkhQSEqLDhw/r8uXLZp/b95PaJ3U/6YmPj1dsbKzNAwAAPJyyFKo2bNigwYMH24QQSSpTpoz+/vtvSwqTpOPHj2vKlCkqX768li9frtdff11vvvmmZs2aJUmKioqSJHl7e9us5+3tbS6LioqSl5eXzXJHR0cVK1bMpk9627h9H3frk7o8PSNGjJC7u7v58Pf3z9TzBwAA+UeWQlVKSoqSk5PTtP/1118qUqTIAxd1+35q1qypTz75RP/3f/+nPn36qHfv3pZ9vJjdBg4cqJiYGPNx5syZ3C4JAABkkyyFqubNm2vcuHHmz3Z2drp27ZqGDh1q6VfX+Pr6qkqVKjZtlStX1unTpyVJPj4+kqTo6GibPtHR0eYyHx8fnT9/3mZ5UlKSLl26ZNMnvW3cvo+79Uldnh5nZ2e5ubnZPAAAwMMpS6Fq9OjR2rhxo6pUqaKbN2+qS5cu5kd/n376qWXF1a9fX4cPH7ZpO3LkiEqXLi1JCggIkI+Pj811XLGxsdqyZYuCgoIkSUFBQbpy5Yq2b99u9lm9erVSUlJUp04ds8/69euVmJho9omMjFTFihXNOw2DgoLSXC8WGRlp7gcAADzasjT5Z8mSJbV7927NnTtXe/bs0bVr19SrVy917drV5sL1B9W/f3/Vq1dPn3zyiTp27Kg//vhD06ZNM6c6sLOz01tvvaWPPvpI5cuXV0BAgD744AP5+fmpbdu2km6d2Xr66afNjw0TExMVFhamzp07y8/PT5LUpUsXffjhh+rVq5cGDBigffv2afz48Ro7dqxZS79+/dS4cWONHj1arVq10ty5c7Vt2zabaRcAAMCjK0tTKuSkRYsWaeDAgTp69KgCAgIUHh6u3r17m8sNw9DQoUM1bdo0XblyRQ0aNNDkyZNVocL/bqu8dOmSwsLC9Ouvv8re3l4dOnTQhAkTVLhwYbPPnj17FBoaqq1bt8rT01NvvPGGBgwYYFPLggULNHjwYJ08eVLly5dXREREpj7uZEoFAEB+wZQK/5PR9+8shapvvvnmnsu7deuW2U0+EghVAID8glD1Pxl9/87Sx3/9+vWz+TkxMVHXr1+Xk5OTChYsSKgCAACPnCxdqH758mWbx7Vr13T48GE1aNBA//3vf62uEQAAIM/L8nf/3al8+fIaOXJkmrNYAAAAjwLLQpV0a6bys2fPWrlJAACAfCFL11T98ssvNj8bhqFz587p888/V/369S0pDAAAID/JUqhKnQMqlZ2dnUqUKKGmTZtq9OjRVtQFAACQr2QpVKWkpFhdBwAAQL5m6TVVAAAAj6osnakKDw/PcN8xY8ZkZRcAAAD5SpZC1c6dO7Vz504lJiaqYsWKkm590bGDg4Nq1qxp9rOzs7OmSgAAgDwuS6GqdevWKlKkiGbNmqWiRYtKujUhaM+ePdWwYUO9/fbblhYJAACQ12XpmqrRo0drxIgRZqCSpKJFi+qjjz7i7j8AAPBIylKoio2N1YULF9K0X7hwQVevXn3gogAAAPKbLH38165dO/Xs2VOjR49W7dq1JUlbtmzRu+++q/bt21taIAAA+d3YyCO5XQJyQJZC1dSpU/XOO++oS5cuSkxMvLUhR0f16tVLo0aNsrRAAACA/CBLoapgwYKaPHmyRo0apWPHjkmSypYtq0KFCllaHDIuv/4vqP+/KuR2CQAAWOKBJv88d+6czp07p/Lly6tQoUIyDMOqugAAAPKVLIWqf/75R82aNVOFChXUsmVLnTt3TpLUq1cvplMAAACPpCyFqv79+6tAgQI6ffq0ChYsaLZ36tRJy5Yts6w4AACA/CJL11StWLFCy5cvV8mSJW3ay5cvr1OnTllSGAAAQH6SpTNVcXFxNmeoUl26dEnOzs4PXBQAAEB+k6VQ1bBhQ33zzTfmz3Z2dkpJSVFERISaNGliWXEAAAD5RZY+/ouIiFCzZs20bds2JSQk6L333tP+/ft16dIlbdy40eoaAQAA8rwsnamqVq2ajhw5ogYNGujZZ59VXFyc2rdvr507d6ps2bJW1wgAAJDnZfpMVWJiop5++mlNnTpVgwYNyo6aAAAA8p1Mn6kqUKCA9uzZkx21AAAA5FtZ+vjvxRdf1Ndff211LQAAAPlWli5UT0pK0vTp07Vy5UoFBgam+c6/MWPGWFIcAABAfpGpUHX8+HGVKVNG+/btU82aNSVJR47YfpGvnZ2dddUBAADkE5kKVeXLl9e5c+e0Zs0aSbe+lmbChAny9vbOluIAAADyi0xdU2UYhs3PS5cuVVxcnKUFAQAA5EdZuqYq1Z0hC8issZFH7t8pj+n/rwq5XQIAIA/K1JkqOzu7NNdMcQ0VAABAJs9UGYahHj16mF+afPPmTb322mtp7v778ccfrasQAAAgH8hUqOrevbvNzy+++KKlxQAAAORXmQpVM2bMyK46AAAA8rUszagOAAAAW4QqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsIBjbheQGSNHjtTAgQPVr18/jRs3TpJ08+ZNvf3225o7d67i4+MVEhKiyZMny9vb21zv9OnTev3117VmzRoVLlxY3bt314gRI+To+L+nv3btWoWHh2v//v3y9/fX4MGD1aNHD5v9T5o0SaNGjVJUVJSeeOIJTZw4UbVr186Jpw4A2WJs5JHcLiHT+v+rQm6XAKQr35yp2rp1q7744gs9/vjjNu39+/fXr7/+qgULFmjdunU6e/as2rdvby5PTk5Wq1atlJCQoE2bNmnWrFmaOXOmhgwZYvY5ceKEWrVqpSZNmmjXrl1666239Morr2j58uVmn3nz5ik8PFxDhw7Vjh079MQTTygkJETnz5/P/icPAADyvHwRqq5du6auXbvqyy+/VNGiRc32mJgYff311xozZoyaNm2qwMBAzZgxQ5s2bdLvv/8uSVqxYoUOHDig7777TjVq1FCLFi00fPhwTZo0SQkJCZKkqVOnKiAgQKNHj1blypUVFham5557TmPHjjX3NWbMGPXu3Vs9e/ZUlSpVNHXqVBUsWFDTp0/P2cEAAAB5Ur4IVaGhoWrVqpWCg4Nt2rdv367ExESb9kqVKqlUqVLavHmzJGnz5s2qXr26zceBISEhio2N1f79+80+d247JCTE3EZCQoK2b99u08fe3l7BwcFmn/TEx8crNjbW5gEAAB5Oef6aqrlz52rHjh3aunVrmmVRUVFycnKSh4eHTbu3t7eioqLMPrcHqtTlqcvu1Sc2NlY3btzQ5cuXlZycnG6fQ4cO3bX2ESNG6MMPP8zYEwUAAPlanj5TdebMGfXr10+zZ8+Wi4tLbpeTaQMHDlRMTIz5OHPmTG6XBAAAskmeDlXbt2/X+fPnVbNmTTk6OsrR0VHr1q3ThAkT5OjoKG9vbyUkJOjKlSs260VHR8vHx0eS5OPjo+jo6DTLU5fdq4+bm5tcXV3l6ekpBweHdPukbiM9zs7OcnNzs3kAAICHU54OVc2aNdPevXu1a9cu81GrVi117drV/HeBAgW0atUqc53Dhw/r9OnTCgoKkiQFBQVp7969NnfpRUZGys3NTVWqVDH73L6N1D6p23ByclJgYKBNn5SUFK1atcrsAwAAHm15+pqqIkWKqFq1ajZthQoVUvHixc32Xr16KTw8XMWKFZObm5veeOMNBQUFqW7dupKk5s2bq0qVKnrppZcUERGhqKgoDR48WKGhoXJ2dpYkvfbaa/r888/13nvv6eWXX9bq1as1f/58LV682NxveHi4unfvrlq1aql27doaN26c4uLi1LNnzxwaDQAAkJfl6VCVEWPHjpW9vb06dOhgM/lnKgcHBy1atEivv/66goKCVKhQIXXv3l3/+c9/zD4BAQFavHix+vfvr/Hjx6tkyZL66quvFBISYvbp1KmTLly4oCFDhigqKko1atTQsmXL0ly8DgAAHk12hmEYuV3EoyI2Nlbu7u6KiYmx/Pqq/Dgrcn7FbM54mOTHY0d+/BvMj+OcH2XXayOj7995+poqAACA/IJQBQAAYAFCFQAAgAXy/YXqAIBHC9cnIa/iTBUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAHH3C4AyG/GRh7J7RIyrf+/KuR2CQDw0ONMFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAG+UBlAnsQXVwPIbzhTBQAAYAFCFQAAgAUIVQAAABbgmioAsEh+vA4MgHU4UwUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAe7+Ax4B3JUGANmPM1UAAAAWIFQBAABYgFAFAABgAUIVAACABfJ0qBoxYoSefPJJFSlSRF5eXmrbtq0OHz5s0+fmzZsKDQ1V8eLFVbhwYXXo0EHR0dE2fU6fPq1WrVqpYMGC8vLy0rvvvqukpCSbPmvXrlXNmjXl7OyscuXKaebMmWnqmTRpksqUKSMXFxfVqVNHf/zxh+XPGQAA5E95OlStW7dOoaGh+v333xUZGanExEQ1b95ccXFxZp/+/fvr119/1YIFC7Ru3TqdPXtW7du3N5cnJyerVatWSkhI0KZNmzRr1izNnDlTQ4YMMfucOHFCrVq1UpMmTbRr1y699dZbeuWVV7R8+XKzz7x58xQeHq6hQ4dqx44deuKJJxQSEqLz58/nzGAAAIA8zc4wDCO3i8ioCxcuyMvLS+vWrVOjRo0UExOjEiVKaM6cOXruueckSYcOHVLlypW1efNm1a1bV0uXLtUzzzyjs2fPytvbW5I0depUDRgwQBcuXJCTk5MGDBigxYsXa9++fea+OnfurCtXrmjZsmWSpDp16ujJJ5/U559/LklKSUmRv7+/3njjDb3//vsZqj82Nlbu7u6KiYmRm5ublUPDLfMAgEde/39VyJbtZvT9O0+fqbpTTEyMJKlYsWKSpO3btysxMVHBwcFmn0qVKqlUqVLavHmzJGnz5s2qXr26GagkKSQkRLGxsdq/f7/Z5/ZtpPZJ3UZCQoK2b99u08fe3l7BwcFmn/TEx8crNjbW5gEAAB5O+SZUpaSk6K233lL9+vVVrVo1SVJUVJScnJzk4eFh09fb21tRUVFmn9sDVery1GX36hMbG6sbN27o4sWLSk5OTrdP6jbSM2LECLm7u5sPf3//zD9xAACQL+SbUBUaGqp9+/Zp7ty5uV1Khg0cOFAxMTHm48yZM7ldEgAAyCb54mtqwsLCtGjRIq1fv14lS5Y02318fJSQkKArV67YnK2Kjo6Wj4+P2efOu/RS7w68vc+ddwxGR0fLzc1Nrq6ucnBwkIODQ7p9UreRHmdnZzk7O2f+CQMAgHwnT5+pMgxDYWFh+umnn7R69WoFBATYLA8MDFSBAgW0atUqs+3w4cM6ffq0goKCJElBQUHau3evzV16kZGRcnNzU5UqVcw+t28jtU/qNpycnBQYGGjTJyUlRatWrTL7AACAR1uePlMVGhqqOXPm6Oeff1aRIkXM65fc3d3l6uoqd3d39erVS+Hh4SpWrJjc3Nz0xhtvKCgoSHXr1pUkNW/eXFWqVNFLL72kiIgIRUVFafDgwQoNDTXPIr322mv6/PPP9d577+nll1/W6tWrNX/+fC1evNisJTw8XN27d1etWrVUu3ZtjRs3TnFxcerZs2fODwwAAMhz8nSomjJliiTpqaeesmmfMWOGevToIUkaO3as7O3t1aFDB8XHxyskJESTJ082+zo4OGjRokV6/fXXFRQUpEKFCql79+76z3/+Y/YJCAjQ4sWL1b9/f40fP14lS5bUV199pZCQELNPp06ddOHCBQ0ZMkRRUVGqUaOGli1blubidQAA8GjKV/NU5XfMUwUAQPZhnioAAICHAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoyqRJkyapTJkycnFxUZ06dfTHH3/kdkkAACAPIFRlwrx58xQeHq6hQ4dqx44deuKJJxQSEqLz58/ndmkAACCXEaoyYcyYMerdu7d69uypKlWqaOrUqSpYsKCmT5+e26UBAIBc5pjbBeQXCQkJ2r59uwYOHGi22dvbKzg4WJs3b053nfj4eMXHx5s/x8TESJJiY2Mtr+9m3DXLtwkAQH6SHe+vt2/XMIx79iNUZdDFixeVnJwsb29vm3Zvb28dOnQo3XVGjBihDz/8ME27v79/ttQIAMCj7N/ZvP2rV6/K3d39rssJVdlo4MCBCg8PN39OSUnRpUuXVLx4cdnZ2Vm2n9jYWPn7++vMmTNyc3OzbLuwxTjnHMY6ZzDOOYNxzjnZNdaGYejq1avy8/O7Zz9CVQZ5enrKwcFB0dHRNu3R0dHy8fFJdx1nZ2c5OzvbtHl4eGRXiXJzc+MPNgcwzjmHsc4ZjHPOYJxzTnaM9b3OUKXiQvUMcnJyUmBgoFatWmW2paSkaNWqVQoKCsrFygAAQF7AmapMCA8PV/fu3VWrVi3Vrl1b48aNU1xcnHr27JnbpQEAgFxGqMqETp066cKFCxoyZIiioqJUo0YNLVu2LM3F6znN2dlZQ4cOTfNRI6zFOOccxjpnMM45g3HOObk91nbG/e4PBAAAwH1xTRUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhV+cSkSZNUpkwZubi4qE6dOvrjjz/u2X/BggWqVKmSXFxcVL16dS1ZsiSHKs3fMjPOX375pRo2bKiiRYuqaNGiCg4Ovu/vBf+T2dd0qrlz58rOzk5t27bN3gIfEpkd5ytXrig0NFS+vr5ydnZWhQoVOH5kQGbHedy4capYsaJcXV3l7++v/v376+bNmzlUbf60fv16tW7dWn5+frKzs9PChQvvu87atWtVs2ZNOTs7q1y5cpo5c2b2Fmkgz5s7d67h5ORkTJ8+3di/f7/Ru3dvw8PDw4iOjk63/8aNGw0HBwcjIiLCOHDggDF48GCjQIECxt69e3O48vwls+PcpUsXY9KkScbOnTuNgwcPGj169DDc3d2Nv/76K4crz38yO9apTpw4Yfy///f/jIYNGxrPPvtszhSbj2V2nOPj441atWoZLVu2NH777TfjxIkTxtq1a41du3blcOX5S2bHefbs2Yazs7Mxe/Zs48SJE8by5csNX19fo3///jlcef6yZMkSY9CgQcaPP/5oSDJ++umne/Y/fvy4UbBgQSM8PNw4cOCAMXHiRMPBwcFYtmxZttVIqMoHateubYSGhpo/JycnG35+fsaIESPS7d+xY0ejVatWNm116tQxXn311WytM7/L7DjfKSkpyShSpIgxa9as7CrxoZGVsU5KSjLq1atnfPXVV0b37t0JVRmQ2XGeMmWK8dhjjxkJCQk5VeJDIbPjHBoaajRt2tSmLTw83Khfv3621vkwyUioeu+994yqVavatHXq1MkICQnJtrr4+C+PS0hI0Pbt2xUcHGy22dvbKzg4WJs3b053nc2bN9v0l6SQkJC79kfWxvlO169fV2JioooVK5ZdZT4UsjrW//nPf+Tl5aVevXrlRJn5XlbG+ZdfflFQUJBCQ0Pl7e2tatWq6ZNPPlFycnJOlZ3vZGWc69Wrp+3bt5sfER4/flxLlixRy5Ytc6TmR0VuvBfyNTV53MWLF5WcnJzmq3C8vb116NChdNeJiopKt39UVFS21ZnfZWWc7zRgwAD5+fml+SOGrayM9W+//aavv/5au3btyoEKHw5ZGefjx49r9erV6tq1q5YsWaI///xTffv2VWJiooYOHZoTZec7WRnnLl266OLFi2rQoIEMw1BSUpJee+01/fvf/86Jkh8Zd3svjI2N1Y0bN+Tq6mr5PjlTBVhg5MiRmjt3rn766Se5uLjkdjkPlatXr+qll17Sl19+KU9Pz9wu56GWkpIiLy8vTZs2TYGBgerUqZMGDRqkqVOn5nZpD5W1a9fqk08+0eTJk7Vjxw79+OOPWrx4sYYPH57bpeEBcaYqj/P09JSDg4Oio6Nt2qOjo+Xj45PuOj4+Ppnqj6yNc6rPPvtMI0eO1MqVK/X4449nZ5kPhcyO9bFjx3Ty5Em1bt3abEtJSZEkOTo66vDhwypbtmz2Fp0PZeU17evrqwIFCsjBwcFsq1y5sqKiopSQkCAnJ6dsrTk/yso4f/DBB3rppZf0yiuvSJKqV6+uuLg49enTR4MGDZK9Pec7rHC390I3N7dsOUslcaYqz3NyclJgYKBWrVpltqWkpGjVqlUKCgpKd52goCCb/pIUGRl51/7I2jhLUkREhIYPH65ly5apVq1aOVFqvpfZsa5UqZL27t2rXbt2mY82bdqoSZMm2rVrl/z9/XOy/HwjK6/p+vXr688//zRDqyQdOXJEvr6+BKq7yMo4X79+PU1wSg2yt67BhhVy5b0w2y6Bh2Xmzp1rODs7GzNnzjQOHDhg9OnTx/Dw8DCioqIMwzCMl156yXj//ffN/hs3bjQcHR2Nzz77zDh48KAxdOhQplTIgMyO88iRIw0nJyfj+++/N86dO2c+rl69mltPId/I7Fjfibv/Miaz43z69GmjSJEiRlhYmHH48GFj0aJFhpeXl/HRRx/l1lPIFzI7zkOHDjWKFCli/Pe//zWOHz9urFixwihbtqzRsWPH3HoK+cLVq1eNnTt3Gjt37jQkGWPGjDF27txpnDp1yjAMw3j//feNl156yeyfOqXCu+++axw8eNCYNGkSUyrglokTJxqlSpUynJycjNq1axu///67uaxx48ZG9+7dbfrPnz/fqFChguHk5GRUrVrVWLx4cQ5XnD9lZpxLly5tSErzGDp0aM4Xng9l9jV9O0JVxmV2nDdt2mTUqVPHcHZ2Nh577DHj448/NpKSknK46vwnM+OcmJhoDBs2zChbtqzh4uJi+Pv7G3379jUuX76c84XnI2vWrEn3mJs6tt27dzcaN26cZp0aNWoYTk5OxmOPPWbMmDEjW2u0MwzONQIAADworqkCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAAL/H9j5VcqElnFuAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "l2g_gwas_curated=l2g_max_scores.join(gwascat_curated.select(\"studyLocusId\", \"studyId\"), on=\"studyLocusId\", how=\"inner\")\n", + "l2g_gwas_curated.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"(top gene assignment), l2g_gwas_curated_pics\")\n", + "\n", + "sample_size_quartiles = l2g_gwas_curated.stat.approxQuantile(\"score\", [0.25, 0.5, 0.75], 0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary of l2g_gwas_sumstats PICS l2g scores: mean: 0.5751686123272477 L.quart: 0.23706066608428955 Median: 0.647024929523468 U.quart: 0.8808161020278931\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "l2g_gwas_sumstats=l2g_max_scores.join(gwascat_sumstats.select(\"studyLocusId\", \"studyId\"), on=\"studyLocusId\", how=\"inner\")\n", + "l2g_gwas_sumstats.select(\"score\").toPandas().plot.hist(bins=10, alpha=0.5, title=\"(top gene assignment), l2g_gwas_sumstats\")\n", + "\n", + "sample_size_quartiles = l2g_gwas_sumstats.stat.approxQuantile(\"score\", [0.25, 0.5, 0.75], 0.01)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gentropy-NMtW8s8F-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/gwas_cat_benchmark.ipynb b/notebooks/gwas_cat_benchmark.ipynb new file mode 100644 index 000000000..a2ecf5455 --- /dev/null +++ b/notebooks/gwas_cat_benchmark.ipynb @@ -0,0 +1,1498 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + "
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For SparkR, use setLogLevel(newLevel).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/08/07 13:43:18 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n", + "24/08/07 13:43:20 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n", + "24/08/07 13:43:20 WARN Utils: Service 'SparkUI' could not bind on port 4041. Attempting port 4042.\n" + ] + } + ], + "source": [ + "session = Session(\n", + " extended_spark_conf={\n", + " \"spark.driver.memory\": \"10g\",\n", + " \"spark.executor.memory\": \"10g\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Context\n", + "\n", + "Gwas catalog studies with summary statistics were filtered using QC white-list \n", + "Locus breaker clumping was performed on the resulting studies\n", + "\n", + "Parameters: \n", + " lbc_baseline_pvalue: 1e-5, \n", + " lbc_distance_cutoff: 250_000, \n", + " lbc_pvalue_threshold: 1e-8, \n", + " lbc_flanking_distance: 100_000, \n", + " large_loci_size: 1_500_000, \n", + " wbc_clump_distance: 500_000, \n", + " wbc_pvalue_threshold: 1e-8, \n", + " collect_locus: bool = True, \n", + " remove_mhc: bool = True,\n", + "\n", + "Loci with less than 100 variants, or more than 15,000, were filtered out and fine-mapped with PICS.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/08/07 13:45:02 WARN SharedInMemoryCache: Evicting cached table partition metadata from memory due to size constraints (spark.sql.hive.filesourcePartitionFileCacheSize = 262144000 bytes). This may impact query planning performance.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "study_index = StudyIndex.from_parquet(\n", + " session, \"/Users/dc16/data/study_index/gwas_catalog/\"\n", + ")\n", + "ld_index = LDIndex.from_parquet(session, \"/Users/dc16/data/ld_index\")\n", + "susie_loci = StudyLocus(\n", + " session.spark.read.parquet(\"/Users/dc16/output/gwas_catalog/clean_loci.parquet\"),\n", + " StudyLocus.get_schema(),\n", + ")\n", + "pics_loci = StudyLocus(\n", + " session.spark.read.parquet(\"/Users/dc16/output/gwas_catalog/filtered_loci.parquet\"),\n", + " StudyLocus.get_schema(),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Susie fine mapping\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the total number of unique studyIds\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 7:==============> (1542 + 8) / 5556]\r" + ] + } + ], + "source": [ + "susie_loci.df.select(\"studyId\").distinct().count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the total number of loci for finemapping:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "176096" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "susie_loci.df.count()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = susie_loci.df.withColumns(\n", + " {\n", + " \"locusSize\": f.size(\"locus\"),\n", + " \"locusLength\": f.col(\"locusEnd\") - f.col(\"locusStart\"),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0------------------------------\n", + " meanLocusLength | 682633.2350024986 \n", + " q1LocusLength | 327752 \n", + " medianLocusLength | 517819 \n", + " q3LocusLength | 934946 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 21:> (0 + 1) / 1]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0-----------------------------\n", + " meanLocusSize | 3517.7594266763585 \n", + " minLocusSize | 100 \n", + " q1LocusSize | 1383 \n", + " medianLocusSize | 2565 \n", + " q3LocusSize | 4859 \n", + " maxLocusSize | 15000 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "length = df.select(\n", + " f.mean(\"locusLength\").alias(\"meanLocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.25).alias(\"q1LocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.5).alias(\"medianLocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.75).alias(\"q3LocusLength\"),\n", + ")\n", + "size = df.select(\n", + " f.mean(\"locusSize\").alias(\"meanLocusSize\"),\n", + " f.min(\"locusSize\").alias(\"minLocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.25).alias(\"q1LocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.5).alias(\"medianLocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.75).alias(\"q3LocusSize\"),\n", + " f.max(\"locusSize\").alias(\"maxLocusSize\"),\n", + ")\n", + "length.show(vertical=True)\n", + "size.show(vertical=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "panda_df = df.select(\"locusSize\", \"locusLength\").toPandas()\n", + "\n", + "# plt.figure(figsize=(12, 6))\n", + "\n", + "# # Histogram for locusLength\n", + "# plt.subplot(1, 2, 1) # 1 row, 2 columns, 1st subplot\n", + "# plt.hist(panda_df[\"locusLength\"], bins=30, alpha=0.7)\n", + "# plt.xlabel(\"Locus Length\")\n", + "# plt.ylabel(\"Frequency\")\n", + "# plt.title(\"Histogram of Locus Length\")\n", + "\n", + "# # Histogram for locusSize\n", + "# plt.subplot(1, 2, 2) # 1 row, 2 columns, 2nd subplot\n", + "# plt.hist(panda_df[\"locusSize\"], bins=30, alpha=0.7)\n", + "# plt.xlabel(\"Locus Size\")\n", + "# plt.ylabel(\"Frequency\")\n", + "# plt.title(\"Histogram of Locus Size\")\n", + "\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plt.figure(figsize=(10, 6))\n", + "# plt.scatter(panda_df[\"locusSize\"], panda_df[\"locusLength\"], alpha=0.5)\n", + "# plt.title(\"Scatter Plot of Locus Size vs Locus Length\")\n", + "# plt.xlabel(\"Locus Size\")\n", + "# plt.ylabel(\"Locus Length\")\n", + "# plt.grid(True)\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "susie_fm = StudyLocus.from_parquet(session, \"/Users/dc16/output/gwas_cat_fm\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Total credible sets:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "632123" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "susie_fm.df.count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems a very small fraction of loci have NaN assigned to values for their credible sets\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of credible sets with 'not a number' as the logBF: 39\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 14:====================================================> (175 + 6) / 181]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of credible sets with 'null' as the logBF: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "nan = susie_fm.df.filter(f.isnan(\"credibleSetlog10BF\"))\n", + "null = susie_fm.df.filter(f.isnull(\"credibleSetlog10BF\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0--------------------------\n", + " meanTopPP | 0.7341557442854078 \n", + " minTopPP | 5.30020287265377E-4 \n", + " q1TopPP | 0.43393218153901475 \n", + " medianTopPP | 0.9841312727728387 \n", + " q3TopPP | 0.9999999999927383 \n", + " maxTopPP | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0------------------------------\n", + " meanCredSetSize | 64.33602495870802 \n", + " minCredSetSize | 1 \n", + " q1CredSetSize | 1 \n", + " medianCredSetSize | 2 \n", + " q3CredSetSize | 13 \n", + " maxCredSetSize | 10710 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0---------------------------------\n", + " meanPurityMeanR2 | 0.6459716225861719 \n", + " minPurityMeanR2 | 0.00469889394266767 \n", + " q1PurityMeanR2 | 0.23746586177412243 \n", + " medianPurityMeanR2 | 0.8203905763487525 \n", + " q3PurityMeanR2 | 1.0 \n", + " maxPurityMeanR2 | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 28:> (0 + 1) / 1]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0---------------------------------\n", + " meanPurityMinR2 | 0.48250211427144807 \n", + " minPurityMinR2 | 0.0 \n", + " q1PurityMinR2 | 6.409217073028031... \n", + " medianPurityMinR2 | 0.46903358691552954 \n", + " q3PurityMinR2 | 1.0 \n", + " maxPurityMinR2 | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "susie_results = (\n", + " susie_fm.df.withColumn(\"credSetSize\", f.size(\"locus\"))\n", + " .withColumn(\n", + " \"locus\",\n", + " f.slice(order_array_of_structs_by_field(\"locus\", \"posteriorProbability\"), 1, 1)[\n", + " 0\n", + " ],\n", + " )\n", + " .withColumn(\"topPP\", f.col(\"locus\").getField(\"posteriorProbability\"))\n", + " .filter(~f.isnan(\"topPP\"))\n", + ")\n", + "(\n", + " susie_results.select(\n", + " f.mean(\"topPP\").alias(\"meanTopPP\"),\n", + " f.min(\"topPP\").alias(\"minTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.25).alias(\"q1TopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.5).alias(\"medianTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.75).alias(\"q3TopPP\"),\n", + " f.max(\"topPP\").alias(\"maxTopPP\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " susie_results.select(\n", + " f.mean(\"credSetSize\").alias(\"meanCredSetSize\"),\n", + " f.min(\"credSetSize\").alias(\"minCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.25).alias(\"q1CredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.5).alias(\"medianCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.75).alias(\"q3CredSetSize\"),\n", + " f.max(\"credSetSize\").alias(\"maxCredSetSize\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " susie_results.select(\n", + " f.mean(\"purityMeanR2\").alias(\"meanPurityMeanR2\"),\n", + " f.min(\"purityMeanR2\").alias(\"minPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.25).alias(\"q1PurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.5).alias(\"medianPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.75).alias(\"q3PurityMeanR2\"),\n", + " f.max(\"purityMeanR2\").alias(\"maxPurityMeanR2\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " susie_results.select(\n", + " f.mean(\"purityMinR2\").alias(\"meanPurityMinR2\"),\n", + " f.min(\"purityMinR2\").alias(\"minPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.25).alias(\"q1PurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.5).alias(\"medianPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.75).alias(\"q3PurityMinR2\"),\n", + " f.max(\"purityMinR2\").alias(\"maxPurityMinR2\"),\n", + " ).show(vertical=True)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pdf = susie_results.select(\"purityMinR2\", \"purityMeanR2\", \"topPP\", \"credSetSize\").toPandas()\n", + "# plt.figure(figsize=(12, 12))\n", + "\n", + "# # Histogram for purityMinR2\n", + "# plt.subplot(2, 2, 1)\n", + "# plt.hist(pdf[\"purityMinR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMinR2\")\n", + "# plt.xlabel(\"purityMinR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for purityMeanR2\n", + "# plt.subplot(2, 2, 2)\n", + "# plt.hist(pdf[\"purityMeanR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMeanR2\")\n", + "# plt.xlabel(\"purityMeanR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for topPP\n", + "# plt.subplot(2, 2, 3)\n", + "# plt.hist(pdf[\"topPP\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of topPP\")\n", + "# plt.xlabel(\"topPP\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for credSetSize\n", + "# plt.subplot(2, 2, 4)\n", + "# plt.hist(pdf[\"credSetSize\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of credSetSize\")\n", + "# plt.xlabel(\"credSetSize\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Adjust layout to prevent overlap\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Repeating the same steps, but filtering for only the first credible set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Total number of primary credible sets and number of unique studyIds:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of primary credible sets: 175991\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 46:====================================================> (176 + 5) / 181]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unique studyIds in primary credible sets: 5328\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "first_credset = susie_results.filter(f.col(\"credibleSetIndex\") == 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0--------------------------\n", + " meanTopPP | 0.6268846688460689 \n", + " minTopPP | 5.30020287265377E-4 \n", + " q1TopPP | 0.22323522754002711 \n", + " medianTopPP | 0.7547370350843897 \n", + " q3TopPP | 0.9999999999979536 \n", + " maxTopPP | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0------------------------------\n", + " meanCredSetSize | 50.48537709314681 \n", + " minCredSetSize | 1 \n", + " q1CredSetSize | 1 \n", + " medianCredSetSize | 3 \n", + " q3CredSetSize | 19 \n", + " maxCredSetSize | 10710 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0---------------------------------\n", + " meanPurityMeanR2 | 0.674055454136671 \n", + " minPurityMeanR2 | 0.00469889394266767 \n", + " q1PurityMeanR2 | 0.3980306136489271 \n", + " medianPurityMeanR2 | 0.8336824740390751 \n", + " q3PurityMeanR2 | 0.9776068324678869 \n", + " maxPurityMeanR2 | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 61:=====================================================>(178 + 3) / 181]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0---------------------------------\n", + " meanPurityMinR2 | 0.48629879351056204 \n", + " minPurityMinR2 | 0.0 \n", + " q1PurityMinR2 | 3.997840337231423E-6 \n", + " medianPurityMinR2 | 0.5018138600363721 \n", + " q3PurityMinR2 | 0.9490123559383069 \n", + " maxPurityMinR2 | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "(\n", + " first_credset.select(\n", + " f.mean(\"topPP\").alias(\"meanTopPP\"),\n", + " f.min(\"topPP\").alias(\"minTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.25).alias(\"q1TopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.5).alias(\"medianTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.75).alias(\"q3TopPP\"),\n", + " f.max(\"topPP\").alias(\"maxTopPP\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " first_credset.select(\n", + " f.mean(\"credSetSize\").alias(\"meanCredSetSize\"),\n", + " f.min(\"credSetSize\").alias(\"minCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.25).alias(\"q1CredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.5).alias(\"medianCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.75).alias(\"q3CredSetSize\"),\n", + " f.max(\"credSetSize\").alias(\"maxCredSetSize\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " first_credset.select(\n", + " f.mean(\"purityMeanR2\").alias(\"meanPurityMeanR2\"),\n", + " f.min(\"purityMeanR2\").alias(\"minPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.25).alias(\"q1PurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.5).alias(\"medianPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.75).alias(\"q3PurityMeanR2\"),\n", + " f.max(\"purityMeanR2\").alias(\"maxPurityMeanR2\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " first_credset.select(\n", + " f.mean(\"purityMinR2\").alias(\"meanPurityMinR2\"),\n", + " f.min(\"purityMinR2\").alias(\"minPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.25).alias(\"q1PurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.5).alias(\"medianPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.75).alias(\"q3PurityMinR2\"),\n", + " f.max(\"purityMinR2\").alias(\"maxPurityMinR2\"),\n", + " ).show(vertical=True)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pdf = first_credset.select(\"purityMinR2\", \"purityMeanR2\", \"topPP\", \"credSetSize\").toPandas()\n", + "# plt.figure(figsize=(12, 12))\n", + "\n", + "# # Histogram for purityMinR2\n", + "# plt.subplot(2, 2, 1)\n", + "# plt.hist(pdf[\"purityMinR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMinR2\")\n", + "# plt.xlabel(\"purityMinR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for purityMeanR2\n", + "# plt.subplot(2, 2, 2)\n", + "# plt.hist(pdf[\"purityMeanR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMeanR2\")\n", + "# plt.xlabel(\"purityMeanR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for topPP\n", + "# plt.subplot(2, 2, 3)\n", + "# plt.hist(pdf[\"topPP\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of topPP\")\n", + "# plt.xlabel(\"topPP\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for credSetSize\n", + "# plt.subplot(2, 2, 4)\n", + "# plt.hist(pdf[\"credSetSize\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of credSetSize\")\n", + "# plt.xlabel(\"credSetSize\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Adjust layout to prevent overlap\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Filtering credible sets with qc function" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/08/07 13:49:35 WARN package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of high quality credible sets: 220670\n", + "Number of unique studyIds in high quality credible sets: 4862\n" + ] + } + ], + "source": [ + "qc_credsets = SUSIE_inf.credible_set_qc(\n", + " susie_fm, study_index, ld_index, 1e-5, 0.25, 0.8\n", + ").persist()\n", + "\n", + "qc_credsets = (\n", + " qc_credsets.df.withColumn(\"credSetSize\", f.size(\"locus\"))\n", + " .withColumn(\n", + " \"locus\",\n", + " f.slice(order_array_of_structs_by_field(\"locus\", \"posteriorProbability\"), 1, 1)[\n", + " 0\n", + " ],\n", + " )\n", + " .withColumn(\"topPP\", f.col(\"locus\").getField(\"posteriorProbability\"))\n", + " .filter(~f.isnan(\"topPP\"))\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0---------------------------\n", + " meanTopPP | 0.7351960328222914 \n", + " minTopPP | 0.002487989359821... \n", + " q1TopPP | 0.4347870006604458 \n", + " medianTopPP | 0.9884991069860085 \n", + " q3TopPP | 0.9999999989221352 \n", + " maxTopPP | 1.0 \n", + "\n", + "-RECORD 0------------------------------\n", + " meanCredSetSize | 11.30863667991574 \n", + " minCredSetSize | 1 \n", + " q1CredSetSize | 1 \n", + " medianCredSetSize | 2 \n", + " q3CredSetSize | 8 \n", + " maxCredSetSize | 2681 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0--------------------------------\n", + " meanPurityMeanR2 | 0.9362013839231683 \n", + " minPurityMeanR2 | 0.50525100583065 \n", + " q1PurityMeanR2 | 0.8961545002734692 \n", + " medianPurityMeanR2 | 0.991509701456222 \n", + " q3PurityMeanR2 | 1.0 \n", + " maxPurityMeanR2 | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 192:==================================================> (191 + 8) / 200]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0--------------------------------\n", + " meanPurityMinR2 | 0.8346683610862959 \n", + " minPurityMinR2 | 0.25001241379266637 \n", + " q1PurityMinR2 | 0.6919444964125268 \n", + " medianPurityMinR2 | 0.9832675585945014 \n", + " q3PurityMinR2 | 1.0 \n", + " maxPurityMinR2 | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "(\n", + " qc_credsets.select(\n", + " f.mean(\"topPP\").alias(\"meanTopPP\"),\n", + " f.min(\"topPP\").alias(\"minTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.25).alias(\"q1TopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.5).alias(\"medianTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.75).alias(\"q3TopPP\"),\n", + " f.max(\"topPP\").alias(\"maxTopPP\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " qc_credsets.select(\n", + " f.mean(\"credSetSize\").alias(\"meanCredSetSize\"),\n", + " f.min(\"credSetSize\").alias(\"minCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.25).alias(\"q1CredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.5).alias(\"medianCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.75).alias(\"q3CredSetSize\"),\n", + " f.max(\"credSetSize\").alias(\"maxCredSetSize\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " qc_credsets.select(\n", + " f.mean(\"purityMeanR2\").alias(\"meanPurityMeanR2\"),\n", + " f.min(\"purityMeanR2\").alias(\"minPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.25).alias(\"q1PurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.5).alias(\"medianPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.75).alias(\"q3PurityMeanR2\"),\n", + " f.max(\"purityMeanR2\").alias(\"maxPurityMeanR2\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " qc_credsets.select(\n", + " f.mean(\"purityMinR2\").alias(\"meanPurityMinR2\"),\n", + " f.min(\"purityMinR2\").alias(\"minPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.25).alias(\"q1PurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.5).alias(\"medianPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.75).alias(\"q3PurityMinR2\"),\n", + " f.max(\"purityMinR2\").alias(\"maxPurityMinR2\"),\n", + " ).show(vertical=True)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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vXubz3GswjRgxQlOmTNFHH30kSWrXrp3Ddp9//rl69uwpV1dXrVixQlOmTFFmZqb8/f01btw4h2s52Ww2bdiwQREREQoMDFTdunUVHR2t8PBws+bWW2/V8uXLNWnSJD377LNq2rSp1qxZo9atW5s148ePV0ZGhsLDw3X27Fl17dpVcXFxcnNzK46hAQAAZYiTYRhGSTdRUdjtdtlsNqWnp3N9BAAA8hEWu6PQ2y4e2akIO7mM43fJKu7xL42fOQAAyrqCHr+5phQAAAAAAAAsRygFAAAAAAAAyxUqlPr++++Lug8AAIByjzkUAADAZYUKpZo0aaJevXrpvffe04ULF4q6JwAAgHKJORQAAMBlhQqldu3apTZt2igqKkre3t56+OGHtX379qLuDQAAoFxhDgUAAHBZoUKpdu3aad68eTp27JjeeecdHT9+XF27dlXr1q01Z84cnTp1qqj7BAAAKPOYQwEAAFz2ly50XrlyZd11111avXq1Xn75ZR0+fFhPPfWU/Pz89MADD+j48eNF1ScAAEC5wRwKAADgL4ZSO3fu1GOPPSYfHx/NmTNHTz31lI4cOaL4+HgdO3ZMd9xxR1H1CQAAUG4whwIAAJAqF2ajOXPmaMmSJUpJSVH//v317rvvqn///nJ2/iPj8vf3V2xsrBo1alSUvQIAAJRpzKEAAAAuK1QotXDhQj344IMaOXKkfHx88q3x9PTU4sWL/1JzAAAA5QlzKAAAgMsKFUodOnToujUuLi4aMWJEYXYPAABQLjGHAgAAuKxQ15RasmSJVq9enWf56tWrtXTp0r/cFAAAQHnEHAoAAOCyQoVSM2bMUN26dfMs9/T01EsvvfSXmwIAACiPmEMBAABcVqhQ6ujRo/L398+zvGHDhjp69OhfbgoAAKA8Yg4FAABwWaFCKU9PT+3ZsyfP8m+++UZ16tT5y00BAACUR8yhAAAALitUKDVs2DA98cQT+vzzz5Wdna3s7Gxt3LhRY8aM0dChQ4u6RwAAgHKBORQAAMBlhbr73vTp0/XDDz+od+/eqlz5j13k5OTogQce4HoIAAAA18AcCgAA4LJChVIuLi5auXKlpk+frm+++Ubu7u4KCAhQw4YNi7o/AACAcoM5FAAAwGWFCqVyNWvWTM2aNSuqXgAAACoE5lAAAACFDKWys7MVGxurhIQEnTx5Ujk5OQ7rN27cWCTNAQAAlCfMoQAAAC4rVCg1ZswYxcbGasCAAWrdurWcnJyKui8AAIByhzkUAADAZYUKpVasWKFVq1apf//+Rd0PAABAucUcCgAA4DLnwmzk4uKiJk2aFHUvAAAA5RpzKAAAgMsKFUo9+eSTmjdvngzDKOp+AAAAyi3mUAAAAJcVKpT68ssvtWzZMjVu3FgDBw7UXXfd5fAoSv/73/903333qU6dOuZtk3fu3GmuNwxD0dHR8vHxkbu7u0JCQnTo0CGHfZw+fVrDhw+Xh4eHatasqbCwMJ07d86hZs+ePerWrZvc3Nzk5+enmJiYPL2sXr1aLVq0kJubmwICArR+/foifa8AAKB8s3IOBQAAUNoV6ppSNWvW1J133lnUveRx5swZdenSRb169dInn3yievXq6dChQ6pVq5ZZExMTo1dffVVLly6Vv7+/nn/+eYWGhurbb7+Vm5ubJGn48OE6fvy44uPjlZWVpVGjRik8PFzLly+XJNntdvXp00chISFatGiR9u7dqwcffFA1a9ZUeHi4JGnr1q0aNmyYZsyYodtuu03Lly/XoEGDtGvXLrVu3brYxwIAAJR9Vs2hAAAAygInoxSfP/7MM8/oq6++0hdffJHvesMw5OvrqyeffFJPPfWUJCk9PV1eXl6KjY3V0KFDdeDAAbVq1Uo7duxQx44dJUlxcXHq37+/fv75Z/n6+mrhwoV67rnnlJaWJhcXF/O116xZo4MHD0qShgwZooyMDK1du9Z8/c6dO6tdu3ZatGhRvv1lZmYqMzPTfG632+Xn56f09HR5eHj89QECAKCcCYvdUehtF4/sVISdXGa322Wz2Th+l5DiHv/S+JkDAKCsK+jxu1Bf35OkS5cu6bPPPtMbb7yh3377TZJ07NixPF+L+ys++ugjdezYUf/4xz/k6emp9u3b66233jLXp6amKi0tTSEhIeYym82moKAgJSYmSpISExNVs2ZNM5CSpJCQEDk7O2vbtm1mTffu3c1ASpJCQ0OVkpKiM2fOmDVXvk5uTe7r5GfGjBmy2Wzmw8/P7y+MBgAAKA+smEMBAACUBYUKpX788UcFBATojjvuUEREhE6dOiVJevnll80zlorC999/r4ULF6pp06b69NNP9eijj+qJJ57Q0qVLJUlpaWmSJC8vL4ftvLy8zHVpaWny9PR0WF+5cmXVrl3boSa/fVz5GteqyV2fn4kTJyo9Pd18/PTTTzf0/gEAQPli1RwKAACgLChUKDVmzBh17NhRZ86ckbu7u7n8zjvvVEJCQpE1l5OTow4dOuill15S+/btFR4ertGjR1/z63Kljaurqzw8PBweAACg4rJqDgUAAFAWFOpC51988YW2bt3q8HU3SWrUqJH+97//FUljkuTj46NWrVo5LGvZsqX++9//SpK8vb0lSSdOnJCPj49Zc+LECbVr186sOXnypMM+Ll26pNOnT5vbe3t768SJEw41uc+vV5O7HgAA4HqsmkMBAACUBYU6UyonJ0fZ2dl5lv/888+qUaPGX24qV5cuXZSSkuKw7LvvvlPDhg0lSf7+/vL29nb4zaLdbte2bdsUHBwsSQoODtbZs2eVlJRk1mzcuFE5OTkKCgoya7Zs2aKsrCyzJj4+Xs2bNzfv9BccHJznN5jx8fHm6wAAAFyPVXMoAACAsqBQoVSfPn00d+5c87mTk5POnTunyZMnq3///kXVm8aNG6evv/5aL730kg4fPqzly5frzTffVEREhPm6Y8eO1QsvvKCPPvpIe/fu1QMPPCBfX18NGjRI0h9nVvXt21ejR4/W9u3b9dVXXykyMlJDhw6Vr6+vJOnee++Vi4uLwsLCtH//fq1cuVLz5s1TVFSU2cuYMWMUFxen2bNn6+DBg5oyZYp27typyMjIInu/AACgfLNqDgUAAFAWFOrre7Nnz1ZoaKhatWqlCxcu6N5779WhQ4dUt25d/fvf/y6y5jp16qQPPvhAEydO1LRp0+Tv76+5c+dq+PDhZs348eOVkZGh8PBwnT17Vl27dlVcXJzc3NzMmmXLlikyMlK9e/eWs7OzBg8erFdffdVcb7PZtGHDBkVERCgwMFB169ZVdHS0wsPDzZpbb71Vy5cv16RJk/Tss8+qadOmWrNmjVq3bl1k7xcAAJRvVs2hAAAAygInwzCMwmx46dIlrVixQnv27NG5c+fUoUMHDR8+3OGinXBkt9tls9mUnp7ORc8BAMhHWOyOQm+7eGSnIuzksqI+fjOHujHFPX8qjZ85AADKuoIevwt1ppQkVa5cWffdd19hNwcAAKiQmEMBAAD8oVCh1Lvvvvun6x944IFCNQMAAFCeMYcCAAC4rFCh1JgxYxyeZ2Vl6ffff5eLi4uqVq3KhAoAACAfzKEAAAAuK9Td986cOePwOHfunFJSUtS1a1cu0gkAAHANzKEAAAAuK1QolZ+mTZtq5syZeX4DCAAAgGtjDgUAACqqIgulpD8u3Hns2LGi3CUAAEC5xxwKAABURIW6ptRHH33k8NwwDB0/flzz589Xly5diqQxAACA8oY5FAAAwGWFCqUGDRrk8NzJyUn16tXT3//+d82ePbso+gIAACh3mEMBAABcVqhQKicnp6j7AAAAKPeYQwEAAFxWpNeUAgAAAAAAAAqiUGdKRUVFFbh2zpw5hXkJAACAcoc5FAAAwGWFCqV2796t3bt3KysrS82bN5ckfffdd6pUqZI6dOhg1jk5ORVNlwAAAOUAcygAAIDLChVKDRw4UDVq1NDSpUtVq1YtSdKZM2c0atQodevWTU8++WSRNgkAAFAeMIcCAAC4rFDXlJo9e7ZmzJhhTqYkqVatWnrhhRe4cwwAAMA1MIcCAAC4rFChlN1u16lTp/IsP3XqlH777be/3BQAAEB5VFRzqC1btmjgwIHy9fWVk5OT1qxZ47DeMAxFR0fLx8dH7u7uCgkJ0aFDhxxqTp8+reHDh8vDw0M1a9ZUWFiYzp0751CzZ88edevWTW5ubvLz81NMTEyeXlavXq0WLVrIzc1NAQEBWr9+/Q33AgAAKqZChVJ33nmnRo0apffff18///yzfv75Z/33v/9VWFiY7rrrrqLuEQAAoFwoqjlURkaG2rZtqwULFuS7PiYmRq+++qoWLVqkbdu2qVq1agoNDdWFCxfMmuHDh2v//v2Kj4/X2rVrtWXLFoWHh5vr7Xa7+vTpo4YNGyopKUmzZs3SlClT9Oabb5o1W7du1bBhwxQWFqbdu3dr0KBBGjRokPbt23dDvQAAgIrJyTAM40Y3+v333/XUU0/pnXfeUVZWliSpcuXKCgsL06xZs1StWrUib7Q8sNvtstlsSk9Pl4eHR0m3AwBAqRMWu6PQ2y4e2akIO7msKI/fxTGHcnJy0gcffKBBgwZJ+uPMJF9fXz355JN66qmnJEnp6eny8vJSbGyshg4dqgMHDqhVq1basWOHOnbsKEmKi4tT//799fPPP8vX11cLFy7Uc889p7S0NLm4uEiSnnnmGa1Zs0YHDx6UJA0ZMkQZGRlau3at2U/nzp3Vrl07LVq0qEC9XC0zM1OZmZnmc7vdLj8/v2KbP5XGzxwAAGVdQedPhTpTqmrVqnr99df166+/mneROX36tF5//XUCKQAAgGuwYg6VmpqqtLQ0hYSEmMtsNpuCgoKUmJgoSUpMTFTNmjXNQEqSQkJC5OzsrG3btpk13bt3NwMpSQoNDVVKSorOnDlj1lz5Ork1ua9TkF6uNmPGDNlsNvPh5+f3V4YDAACUYoUKpXIdP35cx48fV9OmTVWtWjUV4qQrAACACqc451BpaWmSJC8vL4flXl5e5rq0tDR5eno6rK9cubJq167tUJPfPq58jWvVXLn+er1cbeLEiUpPTzcfP/30UwHeNQAAKIsKFUr9+uuv6t27t5o1a6b+/fvr+PHjkqSwsDBuZQwAAHANzKGuz9XVVR4eHg4PAABQPhUqlBo3bpyqVKmio0ePqmrVqubyIUOGKC4ursiaAwAAKE+smEN5e3tLkk6cOOGw/MSJE+Y6b29vnTx50mH9pUuXdPr0aYea/PZx5Wtcq+bK9dfrBQAAVFyFCqU2bNigl19+WfXr13dY3rRpU/34449F0hgAAEB5Y8Ucyt/fX97e3kpISDCX2e12bdu2TcHBwZKk4OBgnT17VklJSWbNxo0blZOTo6CgILNmy5Yt5gXZJSk+Pl7NmzdXrVq1zJorXye3Jvd1CtILAACouAoVSmVkZDj8di/X6dOn5erq+pebAgAAKI+Kag517tw5JScnKzk5WdIfFxRPTk7W0aNH5eTkpLFjx+qFF17QRx99pL179+qBBx6Qr6+veYe+li1bqm/fvho9erS2b9+ur776SpGRkRo6dKh8fX0lSffee69cXFwUFham/fv3a+XKlZo3b56ioqLMPsaMGaO4uDjNnj1bBw8e1JQpU7Rz505FRkZKUoF6AQAAFVehQqlu3brp3XffNZ87OTkpJydHMTEx6tWrV5E1BwAAUJ4U1Rxq586dat++vdq3by9JioqKUvv27RUdHS1JGj9+vB5//HGFh4erU6dOOnfunOLi4uTm5mbuY9myZWrRooV69+6t/v37q2vXrnrzzTfN9TabTRs2bFBqaqoCAwP15JNPKjo6WuHh4WbNrbfequXLl+vNN99U27Zt9Z///Edr1qxR69atzZqC9AIAAComJ6MQt3vZt2+fevfurQ4dOmjjxo26/fbbtX//fp0+fVpfffWVGjduXBy9lnl2u102m03p6elctBMAgHyExe4o9LaLR3Yqwk4uK8rjN3OoG1fc86fS+JkDAKCsK+jxu1BnSrVu3VrfffedunbtqjvuuEMZGRm66667tHv3biZTAAAA18AcCgAA4LLKN7pBVlaW+vbtq0WLFum5554rjp4AAADKHeZQAAAAjm74TKkqVapoz549xdELAABAucUcCgAAwFGhvr533333afHixUXdCwAAQLnGHAoAAOCyG/76niRdunRJ77zzjj777DMFBgaqWrVqDuvnzJlTJM0BAACUJ8yhAAAALruhUOr7779Xo0aNtG/fPnXo0EGS9N133znUODk5FV13AAAA5QBzKAAAgLxuKJRq2rSpjh8/rs8//1ySNGTIEL366qvy8vIqluYAAADKA+ZQAAAAed3QNaUMw3B4/sknnygjI6NIGwIAAChvmEMBAADkVagLnee6eoIFAACA62MOBQAAcIOhlJOTU57rHXD9AwAAgD/HHAoAACCvG7qmlGEYGjlypFxdXSVJFy5c0COPPJLnzjHvv/9+0XUIAABQxjGHAgAAyOuGQqkRI0Y4PL/vvvuKtBkAAIDyiDkUAABAXjcUSi1ZsqS4+gAAACi3mEMBAADk9ZcudA4AAAAAAAAUBqEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAJQzjRo1kpOTU55HRESEJKlnz5551j3yyCMO+zh69KgGDBigqlWrytPTU08//bQuXbrkULNp0yZ16NBBrq6uatKkiWJjY/P0smDBAjVq1Ehubm4KCgrS9u3bi+19AwCAsoVQCgAAoJzZsWOHjh8/bj7i4+MlSf/4xz/MmtGjRzvUxMTEmOuys7M1YMAAXbx4UVu3btXSpUsVGxur6OhosyY1NVUDBgxQr169lJycrLFjx+qhhx7Sp59+atasXLlSUVFRmjx5snbt2qW2bdsqNDRUJ0+etGAUAABAaUcoBQAAUM7Uq1dP3t7e5mPt2rVq3LixevToYdZUrVrVocbDw8Nct2HDBn377bd677331K5dO/Xr10/Tp0/XggULdPHiRUnSokWL5O/vr9mzZ6tly5aKjIzU3XffrVdeecXcz5w5czR69GiNGjVKrVq10qJFi1S1alW988471g0GAAAotcpUKDVz5kw5OTlp7Nix5rILFy4oIiJCderUUfXq1TV48GCdOHHCYTtOPwcAABXVxYsX9d577+nBBx+Uk5OTuXzZsmWqW7euWrdurYkTJ+r333831yUmJiogIEBeXl7mstDQUNntdu3fv9+sCQkJcXit0NBQJSYmmq+blJTkUOPs7KyQkBCzJj+ZmZmy2+0ODwAAUD6VmVBqx44deuONN9SmTRuH5ePGjdPHH3+s1atXa/PmzTp27Jjuuusucz2nnwMAgIpszZo1Onv2rEaOHGkuu/fee/Xee+/p888/18SJE/Wvf/1L9913n7k+LS3NIZCSZD5PS0v70xq73a7z58/rl19+UXZ2dr41ufvIz4wZM2Sz2cyHn59fod43AAAo/cpEKHXu3DkNHz5cb731lmrVqmUuT09P1+LFizVnzhz9/e9/V2BgoJYsWaKtW7fq66+/lsTp5wAAoGJbvHix+vXrJ19fX3NZeHi4QkNDFRAQoOHDh+vdd9/VBx98oCNHjpRgp3+YOHGi0tPTzcdPP/1U0i0BAIBiUiZCqYiICA0YMCDPKeJJSUnKyspyWN6iRQs1aNDAPC2c088BAEBF9eOPP+qzzz7TQw899Kd1QUFBkqTDhw9Lkry9vfNcDiH3ube395/WeHh4yN3dXXXr1lWlSpXyrcndR35cXV3l4eHh8AAAAOVTqQ+lVqxYoV27dmnGjBl51qWlpcnFxUU1a9Z0WH7laeGcfg4AACqqJUuWyNPTUwMGDPjTuuTkZEmSj4+PJCk4OFh79+51uExBfHy8PDw81KpVK7MmISHBYT/x8fEKDg6WJLm4uCgwMNChJicnRwkJCWYNAACo2Ep1KPXTTz9pzJgxWrZsmdzc3Eq6nRvG6ecAAKCk5OTkaMmSJRoxYoQqV65sLj9y5IimT5+upKQk/fDDD/roo4/0wAMPqHv37ua1O/v06aNWrVrp/vvv1zfffKNPP/1UkyZNUkREhFxdXSVJjzzyiL7//nuNHz9eBw8e1Ouvv65Vq1Zp3Lhx5mtFRUXprbfe0tKlS3XgwAE9+uijysjI0KhRo6wdDAAAUCpVvn5JyUlKStLJkyfVoUMHc1l2dra2bNmi+fPn69NPP9XFixd19uxZh7Olrjwt3NvbO89d8m709PNKlSoV+vTz3IkbAACAlT777DMdPXpUDz74oMNyFxcXffbZZ5o7d64yMjLk5+enwYMHa9KkSWZNpUqVtHbtWj366KMKDg5WtWrVNGLECE2bNs2s8ff317p16zRu3DjNmzdP9evX19tvv63Q0FCzZsiQITp16pSio6OVlpamdu3aKS4uLs/Z5wAAoGIq1aFU7969tXfvXodlo0aNUosWLTRhwgT5+fmpSpUqSkhI0ODBgyVJKSkpOnr0qHlaeHBwsF588UWdPHlSnp6ekvI//Xz9+vUOr3Ot088HDRok6fLp55GRkcX2/gEAAAqrT58+Mgwjz3I/Pz9t3rz5uts3bNgwz/zoaj179tTu3bv/tCYyMpL5EgAAyFepDqVq1Kih1q1bOyyrVq2a6tSpYy4PCwtTVFSUateuLQ8PDz3++OMKDg5W586dJTmefh4TE6O0tLR8Tz+fP3++xo8frwcffFAbN27UqlWrtG7dOvN1o6KiNGLECHXs2FG33HKL+dtFTj8HAAAAAAC4caU6lCqIV155Rc7Ozho8eLAyMzMVGhqq119/3VzP6ecAAAAAAAClj5OR33ndKBZ2u102m03p6enc3hgAgHyExe4o9LaLR3Yqwk4u4/hdsop7/EvjZw4AgLKuoMfvUn33PQAAAAAAAJRPhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAADlzJQpU+Tk5OTwaNGihbn+woULioiIUJ06dVS9enUNHjxYJ06ccNjH0aNHNWDAAFWtWlWenp56+umndenSJYeaTZs2qUOHDnJ1dVWTJk0UGxu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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pdf = qc_credsets.select(\"purityMinR2\", \"purityMeanR2\", \"topPP\", \"credSetSize\").toPandas()\n", + "# plt.figure(figsize=(12, 12))\n", + "\n", + "# # Histogram for purityMinR2\n", + "# plt.subplot(2, 2, 1)\n", + "# plt.hist(pdf[\"purityMinR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMinR2\")\n", + "# plt.xlabel(\"purityMinR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for purityMeanR2\n", + "# plt.subplot(2, 2, 2)\n", + "# plt.hist(pdf[\"purityMeanR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMeanR2\")\n", + "# plt.xlabel(\"purityMeanR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for topPP\n", + "# plt.subplot(2, 2, 3)\n", + "# plt.hist(pdf[\"topPP\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of topPP\")\n", + "# plt.xlabel(\"topPP\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for credSetSize\n", + "# plt.subplot(2, 2, 4)\n", + "# plt.hist(pdf[\"credSetSize\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of credSetSize\")\n", + "# plt.xlabel(\"credSetSize\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Adjust layout to prevent overlap\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WIP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clumped loci filtered for usage with PICS\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Number of unique studyIds\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "231" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pics_loci.df.select(\"studyId\").distinct().count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Number of loci to fine map with PICS\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "9990" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pics_loci.df.count()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pics_loci.df.withColumns(\n", + " {\n", + " \"locusSize\": f.size(\"locus\"),\n", + " \"locusLength\": f.col(\"locusEnd\") - f.col(\"locusStart\"),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many loci with less than 100 variants from summary statistics?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "985" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.filter(f.col(\"locusSize\") < 100).count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many loci with more than 15,000 variants from summary statistics?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "9005" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.filter(f.col(\"locusSize\") > 15_000).count()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0------------------------------\n", + " meanLocusLength | 1294768.356956957 \n", + " q1LocusLength | 1231357 \n", + " medianLocusLength | 1500000 \n", + " q3LocusLength | 1500000 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 102:===================================================> (334 + 8) / 346]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0-----------------------------\n", + " meanLocusSize | 18537.992992992993 \n", + " minLocusSize | 1 \n", + " q1LocusSize | 16779 \n", + " medianLocusSize | 19456 \n", + " q3LocusSize | 22436 \n", + " maxLocusSize | 39972 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "length = df.select(\n", + " f.mean(\"locusLength\").alias(\"meanLocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.25).alias(\"q1LocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.5).alias(\"medianLocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.75).alias(\"q3LocusLength\"),\n", + ")\n", + "size = df.select(\n", + " f.mean(\"locusSize\").alias(\"meanLocusSize\"),\n", + " f.min(\"locusSize\").alias(\"minLocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.25).alias(\"q1LocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.5).alias(\"medianLocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.75).alias(\"q3LocusSize\"),\n", + " f.max(\"locusSize\").alias(\"maxLocusSize\"),\n", + ")\n", + "length.show(vertical=True)\n", + "size.show(vertical=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "panda_df = df.select(\"locusSize\", \"locusLength\").toPandas()\n", + "\n", + "# plt.figure(figsize=(12, 6))\n", + "\n", + "# # Histogram for locusLength\n", + "# plt.subplot(1, 2, 1) # 1 row, 2 columns, 1st subplot\n", + "# plt.hist(panda_df[\"locusLength\"], bins=30, alpha=0.7)\n", + "# plt.xlabel(\"Locus Length\")\n", + "# plt.ylabel(\"Frequency\")\n", + "# plt.title(\"Histogram of Locus Length\")\n", + "\n", + "# # Histogram for locusSize\n", + "# plt.subplot(1, 2, 2) # 1 row, 2 columns, 2nd subplot\n", + "# plt.hist(panda_df[\"locusSize\"], bins=30, alpha=0.7)\n", + "# plt.xlabel(\"Locus Size\")\n", + "# plt.ylabel(\"Frequency\")\n", + "# plt.title(\"Histogram of Locus Size\")\n", + "\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plt.figure(figsize=(10, 6))\n", + "# plt.scatter(panda_df[\"locusSize\"], panda_df[\"locusLength\"], alpha=0.5)\n", + "# plt.title(\"Scatter Plot of Locus Size vs Locus Length\")\n", + "# plt.xlabel(\"Locus Size\")\n", + "# plt.ylabel(\"Locus Length\")\n", + "# plt.grid(True)\n", + "# plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gentropy-iQynFIia-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/l2g_benchmark.ipynb b/notebooks/l2g_benchmark.ipynb deleted file mode 100644 index 857d25088..000000000 --- a/notebooks/l2g_benchmark.ipynb +++ /dev/null @@ -1,603 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Benchmarking L2G predictions\n", - "\n", - "The objective of this notebook is to compare the new implementation of L2G with the last results we display in production (22.09.1).\n", - "\n", - "The notebook is divided in 3 parts:\n", - "1. Data preparation\n", - "2. Describe the data\n", - "3. Compare the results\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Setting default log level to \"WARN\".\n", - "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "23/12/11 16:18:49 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n" - ] - } - ], - "source": [ - "from datetime import datetime\n", - "from gentropy.dataset.study_locus import StudyLocus\n", - "from gentropy.dataset.l2g_prediction import L2GPrediction\n", - "from gentropy.common.session import Session\n", - "\n", - "import pyspark.sql.functions as f\n", - "from pyspark.sql import DataFrame\n", - "\n", - "import wandb\n", - "\n", - "%matplotlib inline\n", - "\n", - "session = Session(spark_uri=\"local[*]\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data preparation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def prepare_predictions(credible_set: StudyLocus, predictions: L2GPrediction) -> DataFrame:\n", - " \"\"\"Prepares predictions dataframe for testing and comparison.\"\"\"\n", - " return (\n", - " credible_set.df\n", - " .select(\"studyLocusId\", \"variantId\", \"studyId\").distinct()\n", - " .join(\n", - " predictions.df, on=\"studyLocusId\"\n", - " )\n", - " .select(\"studyLocusId\", \"variantId\", \"studyId\", \"geneId\", \"score\")\n", - " .distinct()\n", - " )\n", - "\n", - "def prepare_production_predictions(old_predictions: DataFrame) -> DataFrame:\n", - " \"\"\"Prepares L2G predictions for testing and comparison.\"\"\"\n", - " return (\n", - " old_predictions\n", - " .select(\n", - " f.col(\"study_id\").alias(\"studyId\"),\n", - " f.concat_ws(\"_\", f.col(\"chrom\"), f.col(\"pos\"), f.col(\"ref\"), f.col(\"alt\")).alias(\"variantId\"),\n", - " f.col(\"gene_id\").alias(\"geneId\"),\n", - " f.col(\"y_proba_full_model\").alias(\"score\"),\n", - " )\n", - " .withColumn(\"studyLocusId\", StudyLocus.assign_study_locus_id(f.col(\"studyId\"), f.col(\"variantId\")))\n", - " .distinct()\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "# RAW DATA\n", - "credible_set_path = \"gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/credible_set\"\n", - "predictions_path = \"gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/l2g_predictions\"\n", - "old_predictions_path = \"gs://genetics-portal-dev-data/22.09.1/outputs/l2g\"\n", - "\n", - "predictions = L2GPrediction.from_parquet(session, predictions_path)\n", - "old_predictions = session.spark.read.parquet(old_predictions_path)\n", - "credible_set = StudyLocus.from_parquet(session, credible_set_path, recursiveFileLookup=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "predictions_prepared = prepare_predictions(credible_set, predictions).persist()\n", - "old_predictions_prepared = prepare_production_predictions(old_predictions).persist()\n", - "\n", - "joining_cols = [\"studyLocusId\", \"geneId\", \"variantId\", \"studyId\"]\n", - "comparison_df = (\n", - " predictions_prepared.selectExpr(*joining_cols, \"score as new_score\")\n", - " .join(\n", - " old_predictions_prepared.selectExpr(*joining_cols, \"score as old_score\"), on=joining_cols, how=\"inner\"\n", - " )\n", - " .distinct()\n", - " .persist()\n", - " )\n", - "\n", - "comparison_output_path = f\"gs://ot-team/irene/l2g_results_comparison-{datetime.today().strftime('%Y-%m-%d')}\"\n", - "# comparison_df.write.parquet(\"gs://ot-team/irene/l2g_results_comparison\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data description\n", - "\n", - "Note: Comparison_df is the result of intersecting the L2G datasets with both the production and the new implementation." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "COUNT PER DATASET\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "predictions_prepared: 9061746\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "old_predictions_prepared: 4083797\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 23:====================================================> (196 + 4) / 200]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "comparison_df: 2194371\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "## 1. L2G predicted credible sets count\n", - "\n", - "datasets = {\n", - " \"predictions_prepared\": predictions_prepared,\n", - " \"old_predictions_prepared\": old_predictions_prepared,\n", - " \"comparison_df\": comparison_df\n", - "}\n", - "\n", - "print(\"COUNT PER DATASET\")\n", - "for dataset in datasets.items():\n", - " print(f\"{dataset[0]}: {dataset[1].count()}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AVERAGE SCORES PER DATASET\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "predictions_prepared: 0.20072320074989783\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "old_predictions_prepared: 0.0720378814046517\n", - "MEDIAN SCORE PER DATASET\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "predictions_prepared: 0.023215007036924362\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 74:==============================================> (171 + 8) / 200]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "old_predictions_prepared: 0.012324056588113308\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "## 2. Descriptive stats for L2G scores\n", - "\n", - "print(\"AVERAGE SCORES PER DATASET\")\n", - "for dataset in datasets.items():\n", - " if dataset[0] != \"comparison_df\":\n", - " print(f\"{dataset[0]}: {dataset[1].agg(f.avg('score')).collect()[0][0]}\")\n", - "\n", - "print(\"MEDIAN SCORE PER DATASET\")\n", - "for dataset in datasets.items():\n", - " if dataset[0] != \"comparison_df\":\n", - " print(f\"{dataset[0]}: {dataset[1].approxQuantile('score',[0.5],0.1)[0]}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# OF ASSOCIATIONS WITH SCORE > 0.9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "predictions_prepared: 5275 (0.06 from total)\n", - "old_predictions_prepared: 3942 (0.1 from total)\n" - ] - } - ], - "source": [ - "## How many associations with a score > 0.9\n", - "\n", - "print(\"# OF ASSOCIATIONS WITH SCORE > 0.9\")\n", - "for dataset in datasets.items():\n", - " if dataset[0] != \"comparison_df\":\n", - " print(f\"{dataset[0]}: {dataset[1].filter(f.col('score') >= 0.9).count()} ({round(dataset[1].filter(f.col('score') >= 0.9).count()/dataset[1].count() * 100, 2)} from total)\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results comparison\n", - "\n", - "The comparisons dataset represents the intersection of the predictions of the production and the new implementation. The objective is to compare the L2G scores of both implementations for the same studyLoci." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "text/html": [ - "
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studyLocusIdgeneIdvariantIdstudyIdnew_scoreold_score
0-9221872607204368224ENSG0000006571719_3414090_G_AGCST0102410.0018870.008250
1-9219397145747036852ENSG0000016805611_65619907_T_AGCST0072340.0381770.014908
2-9219397145747036852ENSG0000017537611_65619907_T_AGCST0072340.0158460.011380
3-9217975156633736203ENSG000001874756_26325235_T_CGCST0094560.0396680.010985
4-9216978755013122322ENSG0000015761721_41816125_G_AGCST0059450.2710090.024520
\n", - "
" - ], - "text/plain": [ - " studyLocusId geneId variantId studyId \\\n", - "0 -9221872607204368224 ENSG00000065717 19_3414090_G_A GCST010241 \n", - "1 -9219397145747036852 ENSG00000168056 11_65619907_T_A GCST007234 \n", - "2 -9219397145747036852 ENSG00000175376 11_65619907_T_A GCST007234 \n", - "3 -9217975156633736203 ENSG00000187475 6_26325235_T_C GCST009456 \n", - "4 -9216978755013122322 ENSG00000157617 21_41816125_G_A GCST005945 \n", - "\n", - " new_score old_score \n", - "0 0.001887 0.008250 \n", - "1 0.038177 0.014908 \n", - "2 0.015846 0.011380 \n", - "3 0.039668 0.010985 \n", - "4 0.271009 0.024520 " - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "comparison_pdf = comparison_df.toPandas()\n", - "comparison_pdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SCORES CORRELATION 0.3968047070256464\n", - "Split by chromosome...\n", - "Chromosome 1: 0.4283002148537705\n", - "Chromosome 10: 0.4548083324805242\n", - "Chromosome 11: 0.3952137911268187\n", - "Chromosome 12: 0.4185634370780096\n", - "Chromosome 13: 0.42117917239984404\n", - "Chromosome 14: 0.39845735792304493\n", - "Chromosome 15: 0.40346861517903765\n", - "Chromosome 16: 0.39846240000598415\n", - "Chromosome 17: 0.395304979654295\n", - "Chromosome 18: 0.41437506238370103\n", - "Chromosome 19: 0.4425118135710343\n", - "Chromosome 2: 0.3889598461816933\n", - "Chromosome 20: 0.43686865328167446\n", - "Chromosome 21: 0.3849138600053731\n", - "Chromosome 22: 0.43046292359639526\n", - "Chromosome 3: 0.4111657046267522\n", - "Chromosome 4: 0.4291713768129795\n", - "Chromosome 5: 0.39581618467547613\n", - "Chromosome 6: 0.37446130946623823\n", - "Chromosome 7: 0.4059565003311735\n", - "Chromosome 8: 0.44203370064434944\n", - "Chromosome 9: 0.40815294157407295\n", - "Chromosome X: 0.12625008528673395\n" - ] - } - ], - "source": [ - "## Correlation between old and new scores\n", - "\n", - "overall_corr = comparison_pdf[\"old_score\"].corr(comparison_pdf[\"new_score\"])\n", - "print(\"SCORES CORRELATION\", overall_corr)\n", - "\n", - "print(\"Split by chromosome...\")\n", - "\n", - "comparison_pdf[\"chromosome\"] = comparison_pdf[\"variantId\"].str.split(\"_\", expand=True)[0]\n", - "\n", - "for group in comparison_pdf.sort_values(\"chromosome\").groupby(\"chromosome\"):\n", - " corr = group[1][\"old_score\"].corr(group[1][\"new_score\"])\n", - " print(f\"Chromosome {group[0]}: {corr}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Add correlation coefficient to finished run in W&B\n", - "# See dashboard here to find run ID: https://wandb.ai/open-targets/otg_l2g/table?workspace=user-opentargets\n", - "my_run = \"4cyi1qvz\"\n", - "\n", - "wandb.init(id=my_run, project=\"otg_l2g\", resume=True)\n", - "wandb.log({\"correlationProduction\": overall_corr})\n", - "wandb.finish()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "comparison_pdf.plot.scatter(\n", - " x=\"new_score\",\n", - " y=\"old_score\",\n", - " title=\"Distribution of L2G scores\",\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/pics_benchmark.ipynb b/notebooks/pics_benchmark.ipynb index d8a2f6e83..0dd05b18b 100644 --- a/notebooks/pics_benchmark.ipynb +++ b/notebooks/pics_benchmark.ipynb @@ -1,904 +1,878 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Benchmarking new PICS implementation\n", - "\n", - "The objective of this notebook is to compare the new implementation of PICS estimated on GWAS Catalog associations using gnomAD LD reference, against the previous implementation using 1000 genomes phase III LD reference. \n", - "\n", - "1. Describe the new dataset\n", - " - Number of signals covered.\n", - " - Number of signals dropped.\n", - "2. Copare with old PICS Dataset.\n", - " - Ξ” number of covered study (not particularly relevant given updates in GWAS Catalog)\n", - " - Ξ” number of covered peaks from studies found in the old release - might see increased coverage.\n", - " - Ξ” in the recovered credible set: number of variants, change in posterior probability.\n", - " - Ξ” in the average number of credible sets.\n", - "\n", - " " - ] - }, + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Benchmarking new PICS implementation\n", + "\n", + "The objective of this notebook is to compare the new implementation of PICS estimated on GWAS Catalog associations using gnomAD LD reference, against the previous implementation using 1000 genomes phase III LD reference. \n", + "\n", + "1. Describe the new dataset\n", + " - Number of signals covered.\n", + " - Number of signals dropped.\n", + "2. Copare with old PICS Dataset.\n", + " - Ξ” number of covered study (not particularly relevant given updates in GWAS Catalog)\n", + " - Ξ” number of covered peaks from studies found in the old release - might see increased coverage.\n", + " - Ξ” in the recovered credible set: number of variants, change in posterior probability.\n", + " - Ξ” in the average number of credible sets.\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pyspark.sql.functions as f\n", + "from pyspark.sql import SparkSession\n", + "from pyspark.sql.window import Window\n", + "\n", + "spark = SparkSession.builder.getOrCreate()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 Describing the new dataset\n", + "\n", + "1. Study count.\n", + "2. Association count.\n", + "3. Studies split.\n", + "4. Associations not resolved in LD set." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import pyspark.sql.functions as f\n", - "import pyspark.sql.types as t\n", - "from pyspark.sql import SparkSession, DataFrame\n", - "from pyspark.sql.window import Window\n", - "\n", - "spark = SparkSession.builder.getOrCreate()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "root\n", + " |-- chromosome: string (nullable = true)\n", + " |-- variantId: string (nullable = true)\n", + " |-- studyId: string (nullable = true)\n", + " |-- position: string (nullable = true)\n", + " |-- referenceAllele: string (nullable = true)\n", + " |-- alternateAllele: string (nullable = true)\n", + " |-- pValueMantissa: float (nullable = true)\n", + " |-- pValueExponent: integer (nullable = true)\n", + " |-- beta: string (nullable = true)\n", + " |-- beta_ci_lower: double (nullable = true)\n", + " |-- beta_ci_upper: double (nullable = true)\n", + " |-- odds_ratio: string (nullable = true)\n", + " |-- odds_ratio_ci_lower: double (nullable = true)\n", + " |-- odds_ratio_ci_upper: double (nullable = true)\n", + " |-- qualityControl: array (nullable = true)\n", + " | |-- element: string (containsNull = true)\n", + " |-- sampleSize: double (nullable = true)\n", + " |-- tagVariantId: string (nullable = true)\n", + " |-- R_overall: double (nullable = true)\n", + " |-- pics_mu: double (nullable = true)\n", + " |-- pics_std: double (nullable = true)\n", + " |-- pics_postprob: double (nullable = true)\n", + " |-- pics_95_perc_credset: boolean (nullable = true)\n", + " |-- pics_99_perc_credset: boolean (nullable = true)\n", + " |-- hasResolvedCredibleSet: boolean (nullable = false)\n", + "\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1 Describing the new dataset\n", - "\n", - "1. Study count.\n", - "2. Association count.\n", - "3. Studies split.\n", - "4. Associations not resolved in LD set." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 82:> (0 + 1) / 1]\r" + ] }, { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "root\n", - " |-- chromosome: string (nullable = true)\n", - " |-- variantId: string (nullable = true)\n", - " |-- studyId: string (nullable = true)\n", - " |-- position: string (nullable = true)\n", - " |-- referenceAllele: string (nullable = true)\n", - " |-- alternateAllele: string (nullable = true)\n", - " |-- pValueMantissa: float (nullable = true)\n", - " |-- pValueExponent: integer (nullable = true)\n", - " |-- beta: string (nullable = true)\n", - " |-- beta_ci_lower: double (nullable = true)\n", - " |-- beta_ci_upper: double (nullable = true)\n", - " |-- odds_ratio: string (nullable = true)\n", - " |-- odds_ratio_ci_lower: double (nullable = true)\n", - " |-- odds_ratio_ci_upper: double (nullable = true)\n", - " |-- qualityControl: array (nullable = true)\n", - " | |-- element: string (containsNull = true)\n", - " |-- sampleSize: double (nullable = true)\n", - " |-- tagVariantId: string (nullable = true)\n", - " |-- R_overall: double (nullable = true)\n", - " |-- pics_mu: double (nullable = true)\n", - " |-- pics_std: double (nullable = true)\n", - " |-- pics_postprob: double (nullable = true)\n", - " |-- pics_95_perc_credset: boolean (nullable = true)\n", - " |-- pics_99_perc_credset: boolean (nullable = true)\n", - " |-- hasResolvedCredibleSet: boolean (nullable = false)\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 82:> (0 + 1) / 1]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-RECORD 0------------------------------------------\n", - " chromosome | 6 \n", - " variantId | 6_13215826_A_G \n", - " studyId | GCST000101_1 \n", - " position | 13215826 \n", - " referenceAllele | A \n", - " alternateAllele | G \n", - " pValueMantissa | 3.0 \n", - " pValueExponent | -6 \n", - " beta | null \n", - " beta_ci_lower | null \n", - " beta_ci_upper | null \n", - " odds_ratio | null \n", - " odds_ratio_ci_lower | null \n", - " odds_ratio_ci_upper | null \n", - " qualityControl | [Subsignificant p-value] \n", - " sampleSize | 1094.0 \n", - " tagVariantId | 6_13215826_A_G \n", - " R_overall | 1.0 \n", - " pics_mu | 5.522878745280337 \n", - " pics_std | 0.0 \n", - " pics_postprob | 0.12718888994626093 \n", - " pics_95_perc_credset | true \n", - " pics_99_perc_credset | true \n", - " hasResolvedCredibleSet | true \n", - "only showing top 1 row\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "new_study_locus = (\n", - " spark.read.parquet(\"gs://genetics_etl_python_playground/XX.XX/output/python_etl/parquet/pics_credible_set/\")\n", - " .withColumn('pics_99_perc_credset', f.when(f.col('tagVariantId').isNull(), False).otherwise(f.col('pics_99_perc_credset')))\n", - " .withColumn(\n", - " 'hasResolvedCredibleSet', \n", - " f.when(\n", - " f.array_contains(\n", - " f.collect_set(f.col('pics_99_perc_credset')).over(Window.partitionBy('studyId', 'variantId')), \n", - " True\n", - " ),\n", - " True\n", - " ).otherwise(False)\n", - " )\n", - " .persist()\n", - ")\n", - "\n", - "\n", - "new_study_locus.printSchema()\n", - "new_study_locus.show(1, False, True)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0------------------------------------------\n", + " chromosome | 6 \n", + " variantId | 6_13215826_A_G \n", + " studyId | GCST000101_1 \n", + " position | 13215826 \n", + " referenceAllele | A \n", + " alternateAllele | G \n", + " pValueMantissa | 3.0 \n", + " pValueExponent | -6 \n", + " beta | null \n", + " beta_ci_lower | null \n", + " beta_ci_upper | null \n", + " odds_ratio | null \n", + " odds_ratio_ci_lower | null \n", + " odds_ratio_ci_upper | null \n", + " qualityControl | [Subsignificant p-value] \n", + " sampleSize | 1094.0 \n", + " tagVariantId | 6_13215826_A_G \n", + " R_overall | 1.0 \n", + " pics_mu | 5.522878745280337 \n", + " pics_std | 0.0 \n", + " pics_postprob | 0.12718888994626093 \n", + " pics_95_perc_credset | true \n", + " pics_99_perc_credset | true \n", + " hasResolvedCredibleSet | true \n", + "only showing top 1 row\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "22/12/19 11:45:49 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", - "[Stage 224:============================================> (173 + 16) / 200]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Study count: 35956\n", - "Association (unique study/variant pairs) count: 433108\n", - "Associations with resolved credible set: 381056 (88.0%)\n", - "Number of good (non-flagged) associations without resolved credible set: 39763 (9.2%)\n", - "Number of good (non-flagged) associations with resolved credible set: 260736 (60.2%)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "study_count = new_study_locus.select('studyId').distinct().count()\n", - "association_count = new_study_locus.select('studyId', 'variantId').distinct().count()\n", - "association_w_credible_set = new_study_locus.filter(f.col('hasResolvedCredibleSet')).persist()\n", - "credible_set_count = association_w_credible_set.select('studyId', 'variantId').distinct().count()\n", - "failed_w_ld = (\n", - " new_study_locus\n", - " # Selecting good associations without credible sets:\n", - " .filter(\n", - " (~f.col('hasResolvedCredibleSet')) & \n", - " (f.size(f.col('qualityControl'))>0)\n", - " )\n", - " # Get associations:\n", - " .select('studyId', 'variantId')\n", - " .distinct()\n", - " .count()\n", - ")\n", - "good_association_count = (\n", - " association_w_credible_set\n", - " # Drop failed associations:\n", - " .filter(f.size(f.col('qualityControl')) == 0)\n", - " .select('studyId', 'variantId')\n", - " .distinct()\n", - " .count()\n", - ")\n", - "\n", - "print(f'Study count: {study_count}')\n", - "print(f'Association (unique study/variant pairs) count: {association_count}')\n", - "print(f'Associations with resolved credible set: {credible_set_count} ({round(credible_set_count/association_count*100, 1)}%)')\n", - "print(f'Number of good (non-flagged) associations without resolved credible set: {failed_w_ld} ({round(failed_w_ld/association_count*100, 1)}%)')\n", - "print(f'Number of good (non-flagged) associations with resolved credible set: {good_association_count} ({round(good_association_count/association_count*100, 1)}%)')\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "new_study_locus = (\n", + " spark.read.parquet(\"gs://genetics_etl_python_playground/XX.XX/output/python_etl/parquet/pics_credible_set/\")\n", + " .withColumn(\"pics_99_perc_credset\", f.when(f.col(\"tagVariantId\").isNull(), False).otherwise(f.col(\"pics_99_perc_credset\")))\n", + " .withColumn(\n", + " \"hasResolvedCredibleSet\",\n", + " f.when(\n", + " f.array_contains(\n", + " f.collect_set(f.col(\"pics_99_perc_credset\")).over(Window.partitionBy(\"studyId\", \"variantId\")),\n", + " True\n", + " ),\n", + " True\n", + " ).otherwise(False)\n", + " )\n", + " .persist()\n", + ")\n", + "\n", + "\n", + "new_study_locus.printSchema()\n", + "new_study_locus.show(1, False, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22/12/19 11:45:49 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", + "[Stage 224:============================================> (173 + 16) / 200]\r" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Focusing only on the actual credible sets." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Study count: 35956\n", + "Association (unique study/variant pairs) count: 433108\n", + "Associations with resolved credible set: 381056 (88.0%)\n", + "Number of good (non-flagged) associations without resolved credible set: 39763 (9.2%)\n", + "Number of good (non-flagged) associations with resolved credible set: 260736 (60.2%)\n" + ] }, { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "22/12/19 11:57:56 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of resolved credible sets: 381056\n", - "Studies with resolved credible sets: 33723\n", - "Number of lead/tag pairs: 18722043\n" - ] - } - ], - "source": [ - "# Thu\n", - "credible_sets = new_study_locus.filter(f.col('pics_99_perc_credset')).persist()\n", - "resolved_assoc_count = credible_sets.select('studyId', 'variantId').distinct().count()\n", - "resolved_study_count = credible_sets.select('studyId').distinct().count()\n", - "lead_tag_pair_count = credible_sets.select('studyId', 'variantId', 'tagVariantId').distinct().count()\n", - "\n", - "grouped_credset_pdf = credible_sets.groupBy('studyId', 'variantId').count().toPandas()\n", - "\n", - "print(f'Number of resolved credible sets: {resolved_assoc_count}')\n", - "print(f'Studies with resolved credible sets: {resolved_study_count}')\n", - "print(f'Number of lead/tag pairs: {lead_tag_pair_count}')\n", - "\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "study_count = new_study_locus.select(\"studyId\").distinct().count()\n", + "association_count = new_study_locus.select(\"studyId\", \"variantId\").distinct().count()\n", + "association_w_credible_set = new_study_locus.filter(f.col(\"hasResolvedCredibleSet\")).persist()\n", + "credible_set_count = association_w_credible_set.select(\"studyId\", \"variantId\").distinct().count()\n", + "failed_w_ld = (\n", + " new_study_locus\n", + " # Selecting good associations without credible sets:\n", + " .filter(\n", + " (~f.col(\"hasResolvedCredibleSet\")) &\n", + " (f.size(f.col(\"qualityControl\"))>0)\n", + " )\n", + " # Get associations:\n", + " .select(\"studyId\", \"variantId\")\n", + " .distinct()\n", + " .count()\n", + ")\n", + "good_association_count = (\n", + " association_w_credible_set\n", + " # Drop failed associations:\n", + " .filter(f.size(f.col(\"qualityControl\")) == 0)\n", + " .select(\"studyId\", \"variantId\")\n", + " .distinct()\n", + " .count()\n", + ")\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Focusing only on the actual credible sets." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22/12/19 11:57:56 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", + " \r" + ] }, { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "grouped_credset_pdf.query('count < 500')['count'].hist(bins=100)" + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of resolved credible sets: 381056\n", + "Studies with resolved credible sets: 33723\n", + "Number of lead/tag pairs: 18722043\n" + ] + } + ], + "source": [ + "# Thu\n", + "credible_sets = new_study_locus.filter(f.col(\"pics_99_perc_credset\")).persist()\n", + "resolved_assoc_count = credible_sets.select(\"studyId\", \"variantId\").distinct().count()\n", + "resolved_study_count = credible_sets.select(\"studyId\").distinct().count()\n", + "lead_tag_pair_count = credible_sets.select(\"studyId\", \"variantId\", \"tagVariantId\").distinct().count()\n", + "\n", + "grouped_credset_pdf = credible_sets.groupBy(\"studyId\", \"variantId\").count().toPandas()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Credible sets with only one variant: 29536 (7.8)%\n", - "Median size of credible sets: 21.0\n" - ] - } - ], - "source": [ - "median_credset_size = grouped_credset_pdf['count'].median()\n", - "credsets_with_single = len(grouped_credset_pdf.query('count == 1'))\n", - "\n", - "print(f'Credible sets with only one variant: {credsets_with_single} ({round(credsets_with_single/len(grouped_credset_pdf)*100, 1)})%')\n", - "print(f'Median size of credible sets: {median_credset_size}')" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "grouped_credset_pdf.query(\"count < 500\")[\"count\"].hist(bins=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Credible sets with only one variant: 29536 (7.8)%\n", + "Median size of credible sets: 21.0\n" + ] + } + ], + "source": [ + "median_credset_size = grouped_credset_pdf[\"count\"].median()\n", + "credsets_with_single = len(grouped_credset_pdf.query(\"count == 1\"))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 285:====================================> (139 + 8) / 200]\r" + ] }, { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 285:====================================> (139 + 8) / 200]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+------------+---------------+-----+\n", - "| studyId| variantId|count|\n", - "+------------+---------------+-----+\n", - "|GCST90095125|17_46142465_T_A|11930|\n", - "|GCST90095124|17_46142465_T_A|11221|\n", - "|GCST006483_1|17_45608332_A_G| 9666|\n", - "| GCST011766|17_45846834_C_G| 9566|\n", - "|GCST006481_2|17_45608332_A_G| 9445|\n", - "|GCST006483_1|17_45605039_C_G| 8912|\n", - "|GCST006483_3|17_45605039_C_G| 8602|\n", - "|GCST006481_4|17_45605039_C_G| 8545|\n", - "|GCST006483_1|17_46770468_T_G| 7465|\n", - "|GCST006481_2|17_46770468_T_G| 7396|\n", - "|GCST006483_3|17_46770468_T_G| 7374|\n", - "|GCST006481_4|17_46770468_T_G| 7327|\n", - "|GCST001651_9|17_46257341_G_A| 6926|\n", - "|GCST90134596|17_45707983_T_C| 6748|\n", - "| GCST012099|17_45610951_A_G| 6603|\n", - "|GCST90104034|17_46152620_T_C| 6545|\n", - "| GCST012101|17_45610951_A_G| 6374|\n", - "|GCST90134597|17_45707983_T_C| 6372|\n", - "| GCST007692|17_45846834_C_G| 6331|\n", - "|GCST90013445|17_45996523_A_G| 5668|\n", - "|GCST008675_1|17_45733530_C_T| 5196|\n", - "|GCST004008_1|17_45749271_G_A| 5101|\n", - "|GCST006483_1|17_46785767_T_C| 4913|\n", - "|GCST006481_2|17_46785767_T_C| 4880|\n", - "| GCST007065|11_55736589_G_A| 4071|\n", - "|GCST90100220|10_73256607_T_A| 3897|\n", - "|GCST90095190|17_45913906_A_G| 3858|\n", - "|GCST90095190|17_46055092_G_A| 3855|\n", - "|GCST90095190|17_45609706_G_A| 3764|\n", - "|GCST000996_1|11_55368743_C_T| 3727|\n", - "+------------+---------------+-----+\n", - "only showing top 30 rows\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "credible_sets.groupBy('studyId', 'variantId').count().filter(f.col('count') > 1000).orderBy('count', ascending=False).show(30)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+---------------+-----+\n", + "| studyId| variantId|count|\n", + "+------------+---------------+-----+\n", + "|GCST90095125|17_46142465_T_A|11930|\n", + "|GCST90095124|17_46142465_T_A|11221|\n", + "|GCST006483_1|17_45608332_A_G| 9666|\n", + "| GCST011766|17_45846834_C_G| 9566|\n", + "|GCST006481_2|17_45608332_A_G| 9445|\n", + "|GCST006483_1|17_45605039_C_G| 8912|\n", + "|GCST006483_3|17_45605039_C_G| 8602|\n", + "|GCST006481_4|17_45605039_C_G| 8545|\n", + "|GCST006483_1|17_46770468_T_G| 7465|\n", + "|GCST006481_2|17_46770468_T_G| 7396|\n", + "|GCST006483_3|17_46770468_T_G| 7374|\n", + "|GCST006481_4|17_46770468_T_G| 7327|\n", + "|GCST001651_9|17_46257341_G_A| 6926|\n", + "|GCST90134596|17_45707983_T_C| 6748|\n", + "| GCST012099|17_45610951_A_G| 6603|\n", + "|GCST90104034|17_46152620_T_C| 6545|\n", + "| GCST012101|17_45610951_A_G| 6374|\n", + "|GCST90134597|17_45707983_T_C| 6372|\n", + "| GCST007692|17_45846834_C_G| 6331|\n", + "|GCST90013445|17_45996523_A_G| 5668|\n", + "|GCST008675_1|17_45733530_C_T| 5196|\n", + "|GCST004008_1|17_45749271_G_A| 5101|\n", + "|GCST006483_1|17_46785767_T_C| 4913|\n", + "|GCST006481_2|17_46785767_T_C| 4880|\n", + "| GCST007065|11_55736589_G_A| 4071|\n", + "|GCST90100220|10_73256607_T_A| 3897|\n", + "|GCST90095190|17_45913906_A_G| 3858|\n", + "|GCST90095190|17_46055092_G_A| 3855|\n", + "|GCST90095190|17_45609706_G_A| 3764|\n", + "|GCST000996_1|11_55368743_C_T| 3727|\n", + "+------------+---------------+-----+\n", + "only showing top 30 rows\n", + "\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Comparing with old dataset\n", - "\n", - "- Data: `gs://genetics-portal-dev-staging/v2d/220210/ld.parquet`" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "credible_sets.groupBy(\"studyId\", \"variantId\").count().filter(f.col(\"count\") > 1000).orderBy(\"count\", ascending=False).show(30)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing with old dataset\n", + "\n", + "- Data: `gs://genetics-portal-dev-staging/v2d/220210/ld.parquet`" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 336:===================================================> (16 + 1) / 17]\r" + ] }, { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 336:===================================================> (16 + 1) / 17]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of lead/tag count: 19406519\n", - "NUmber of studies covered: 18349\n", - "Number of associations covered: 265715\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "old_study_locus = (\n", - " spark.read.parquet(\"gs://genetics-portal-dev-staging/v2d/220210/ld.parquet\")\n", - " .select(\n", - " f.col('study_id').alias(\"studyId\"), \n", - " f.concat_ws(\"_\", f.col(\"lead_chrom\"), f.col(\"lead_pos\"), f.col(\"lead_ref\"), f.col(\"lead_alt\")).alias(\"variantId\"), \n", - " f.concat_ws(\"_\", f.col(\"tag_chrom\"), f.col(\"tag_pos\"), f.col(\"tag_ref\"), f.col(\"tag_alt\")).alias(\"tagVariantId\"),\n", - " 'pics_postprob',\n", - " 'pics_95perc_credset',\n", - " 'pics_99perc_credset'\n", - " )\n", - " .distinct()\n", - ")\n", - "lead_tag_pair_count = old_study_locus.count()\n", - "study_count = old_study_locus.select('studyId').distinct().count()\n", - "association_count = old_study_locus.select('studyId', 'variantId').distinct().count()\n", - "\n", - "print(f'Number of lead/tag count: {lead_tag_pair_count}')\n", - "print(f'NUmber of studies covered: {study_count}')\n", - "print(f'Number of associations covered: {association_count}')\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of lead/tag count: 19406519\n", + "NUmber of studies covered: 18349\n", + "Number of associations covered: 265715\n" + ] }, { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "22/12/19 13:53:16 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The median number of tag size: 21.0\n", - "Number of associations with single credible set: 9231\n", - "Number of associations with more than 1000 tags set: 441\n", - "+-------------+---------------+-----+\n", - "| studyId| variantId|count|\n", - "+-------------+---------------+-----+\n", - "| GCST001482|17_45900461_C_T| 3685|\n", - "| GCST90018953|17_45856424_G_T| 3684|\n", - "| GCST007692|17_45846834_C_G| 3649|\n", - "| GCST90018960|17_45761354_C_T| 3360|\n", - "| GCST90018996|17_46112544_A_G| 3348|\n", - "| GCST90091060|17_45873075_C_A| 3295|\n", - "| GCST002970|17_45846317_A_G| 3294|\n", - "| GCST001548|17_45846853_T_C| 3294|\n", - "| GCST007328|17_45887201_A_C| 3294|\n", - "| GCST007430|17_45887201_A_C| 3294|\n", - "| GCST010701|17_45855805_C_T| 3294|\n", - "| GCST001126|17_45846317_A_G| 3294|\n", - "| GCST012009|17_45862033_A_C| 3294|\n", - "| GCST006941|17_45841739_C_T| 3293|\n", - "| GCST004601|17_45841730_A_G| 3293|\n", - "| GCST010002|17_45895867_C_T| 3293|\n", - "| GCST90025948|17_45834077_T_C| 3293|\n", - "| GCST008733|17_45834077_T_C| 3293|\n", - "| GCST008734|17_45834077_T_C| 3293|\n", - "|GCST009518_66|17_45841730_A_G| 3293|\n", - "+-------------+---------------+-----+\n", - "only showing top 20 rows\n", - "\n" - ] - } - ], - "source": [ - "tag_count = old_study_locus.groupBy('studyId', 'variantId').count().persist()\n", - "median_tag_count = tag_count.toPandas()['count'].median()\n", - "single_count = tag_count.filter(f.col('count') == 1).count()\n", - "over_1000 = tag_count.filter(f.col('count') >= 1000).count()\n", - "\n", - "print(f'The median number of tag size: {median_credset_size}')\n", - "print(f'Number of associations with single credible set: {single_count}')\n", - "print(f'Number of associations with more than 1000 tags set: {over_1000}')\n", - "\n", - "tag_count.orderBy('count',ascending=False).show(20)" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "old_study_locus = (\n", + " spark.read.parquet(\"gs://genetics-portal-dev-staging/v2d/220210/ld.parquet\")\n", + " .select(\n", + " f.col(\"study_id\").alias(\"studyId\"),\n", + " f.concat_ws(\"_\", f.col(\"lead_chrom\"), f.col(\"lead_pos\"), f.col(\"lead_ref\"), f.col(\"lead_alt\")).alias(\"variantId\"),\n", + " f.concat_ws(\"_\", f.col(\"tag_chrom\"), f.col(\"tag_pos\"), f.col(\"tag_ref\"), f.col(\"tag_alt\")).alias(\"tagVariantId\"),\n", + " \"pics_postprob\",\n", + " \"pics_95perc_credset\",\n", + " \"pics_99perc_credset\"\n", + " )\n", + " .distinct()\n", + ")\n", + "lead_tag_pair_count = old_study_locus.count()\n", + "study_count = old_study_locus.select(\"studyId\").distinct().count()\n", + "association_count = old_study_locus.select(\"studyId\", \"variantId\").distinct().count()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22/12/19 13:53:16 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n" + ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compare credible sets\n", - "\n", - "To make datasets comparable, both datasets need to updated with `studyAccession`: getting the GWAS Catalog study identifier by removing the suffix." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "The median number of tag size: 21.0\n", + "Number of associations with single credible set: 9231\n", + "Number of associations with more than 1000 tags set: 441\n", + "+-------------+---------------+-----+\n", + "| studyId| variantId|count|\n", + "+-------------+---------------+-----+\n", + "| GCST001482|17_45900461_C_T| 3685|\n", + "| GCST90018953|17_45856424_G_T| 3684|\n", + "| GCST007692|17_45846834_C_G| 3649|\n", + "| GCST90018960|17_45761354_C_T| 3360|\n", + "| GCST90018996|17_46112544_A_G| 3348|\n", + "| GCST90091060|17_45873075_C_A| 3295|\n", + "| GCST002970|17_45846317_A_G| 3294|\n", + "| GCST001548|17_45846853_T_C| 3294|\n", + "| GCST007328|17_45887201_A_C| 3294|\n", + "| GCST007430|17_45887201_A_C| 3294|\n", + "| GCST010701|17_45855805_C_T| 3294|\n", + "| GCST001126|17_45846317_A_G| 3294|\n", + "| GCST012009|17_45862033_A_C| 3294|\n", + "| GCST006941|17_45841739_C_T| 3293|\n", + "| GCST004601|17_45841730_A_G| 3293|\n", + "| GCST010002|17_45895867_C_T| 3293|\n", + "| GCST90025948|17_45834077_T_C| 3293|\n", + "| GCST008733|17_45834077_T_C| 3293|\n", + "| GCST008734|17_45834077_T_C| 3293|\n", + "|GCST009518_66|17_45841730_A_G| 3293|\n", + "+-------------+---------------+-----+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "tag_count = old_study_locus.groupBy(\"studyId\", \"variantId\").count().persist()\n", + "median_tag_count = tag_count.toPandas()[\"count\"].median()\n", + "single_count = tag_count.filter(f.col(\"count\") == 1).count()\n", + "over_1000 = tag_count.filter(f.col(\"count\") >= 1000).count()\n", + "\n", + "\n", + "tag_count.orderBy(\"count\",ascending=False).show(20)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare credible sets\n", + "\n", + "To make datasets comparable, both datasets need to updated with `studyAccession`: getting the GWAS Catalog study identifier by removing the suffix." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 414:> (0 + 1) / 1][Stage 1072:========> (9 + 7) / 16]\r" + ] + } + ], + "source": [ + "processed_new = (\n", + " credible_sets\n", + " # Dropping leads with sub-significant p-values:\n", + " .filter(f.size(f.col(\"qualityControl\")) == 0)\n", + " .select(\n", + " f.split(f.col(\"studyId\"), \"_\").getItem(0).alias(\"studyAccession\"),\n", + " \"variantId\",\n", + " \"tagVariantId\",\n", + " \"pics_mu\",\n", + " \"pics_postprob\",\n", + " \"pics_95_perc_credset\",\n", + " \"pics_99_perc_credset\"\n", + " )\n", + " .persist()\n", + ")\n", + "\n", + "processed_old = (\n", + " old_study_locus\n", + " .select(\n", + " f.split(f.col(\"studyId\"), \"_\").getItem(0).alias(\"studyAccession\"),\n", + " \"variantId\",\n", + " \"tagVariantId\",\n", + " \"pics_postprob\",\n", + " \"pics_95perc_credset\",\n", + " \"pics_99perc_credset\"\n", + " )\n", + " .persist()\n", + ")\n", + "\n", + "processed_old.show(1, False, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22/12/19 14:19:35 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", + "22/12/19 14:19:35 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", + "[Stage 414:> (0 + 1) / 1][Stage 431:============>(187 + 7) / 200]\r" + ] }, { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 414:> (0 + 1) / 1][Stage 1072:========> (9 + 7) / 16]\r" - ] - } - ], - "source": [ - "processed_new = (\n", - " credible_sets\n", - " # Dropping leads with sub-significant p-values:\n", - " .filter(f.size(f.col('qualityControl')) == 0)\n", - " .select(\n", - " f.split(f.col('studyId'), '_').getItem(0).alias('studyAccession'),\n", - " 'variantId', \n", - " 'tagVariantId', \n", - " 'pics_mu', \n", - " 'pics_postprob', \n", - " 'pics_95_perc_credset', \n", - " 'pics_99_perc_credset'\n", - " )\n", - " .persist()\n", - ")\n", - "\n", - "processed_old = (\n", - " old_study_locus\n", - " .select(\n", - " f.split(f.col('studyId'), '_').getItem(0).alias('studyAccession'),\n", - " 'variantId', \n", - " 'tagVariantId',\n", - " 'pics_postprob',\n", - " 'pics_95perc_credset',\n", - " 'pics_99perc_credset' \n", - " )\n", - " .persist()\n", - ")\n", - "\n", - "processed_old.show(1, False, True)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+---------------+---------------------+---------------------+\n", + "|studyAccession| variantId|new_credible_set_size|old_credible_set_size|\n", + "+--------------+---------------+---------------------+---------------------+\n", + "| GCST000114|15_48099968_A_G| 13| 38|\n", + "| GCST000172|3_190632672_A_G| 12| null|\n", + "| GCST000184|18_60217517_G_A| 233| 214|\n", + "| GCST000189|16_81270154_T_C| 6| null|\n", + "| GCST000189|9_105892815_G_T| 26| null|\n", + "| GCST000282|19_11100236_C_T| 34| 69|\n", + "| GCST000425|16_23055939_T_G| 227| null|\n", + "| GCST000452|2_156696348_A_C| 19| null|\n", + "| GCST000679| 10_6056986_C_T| 12| null|\n", + "| GCST000817|9_136220024_G_T| 23| 27|\n", + "| GCST000876|11_18349351_G_C| 2| 6|\n", + "| GCST000943| 20_1960525_G_A| null| 2|\n", + "| GCST000957|22_49692725_G_A| 16| null|\n", + "| GCST000964|13_77957479_G_A| null| 47|\n", + "| GCST000998|10_44280376_C_T| 193| null|\n", + "| GCST000998|21_34226827_C_T| 29| 32|\n", + "| GCST001010| 6_32689801_T_C| 1| 112|\n", + "| GCST001040|17_37738049_G_A| null| 21|\n", + "| GCST001057|13_66393490_A_G| 7| null|\n", + "| GCST001059|2_198123211_C_A| 8| null|\n", + "+--------------+---------------+---------------------+---------------------+\n", + "only showing top 20 rows\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "22/12/19 14:19:35 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", - "22/12/19 14:19:35 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", - "[Stage 414:> (0 + 1) / 1][Stage 431:============>(187 + 7) / 200]\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------+---------------+---------------------+---------------------+\n", - "|studyAccession| variantId|new_credible_set_size|old_credible_set_size|\n", - "+--------------+---------------+---------------------+---------------------+\n", - "| GCST000114|15_48099968_A_G| 13| 38|\n", - "| GCST000172|3_190632672_A_G| 12| null|\n", - "| GCST000184|18_60217517_G_A| 233| 214|\n", - "| GCST000189|16_81270154_T_C| 6| null|\n", - "| GCST000189|9_105892815_G_T| 26| null|\n", - "| GCST000282|19_11100236_C_T| 34| 69|\n", - "| GCST000425|16_23055939_T_G| 227| null|\n", - "| GCST000452|2_156696348_A_C| 19| null|\n", - "| GCST000679| 10_6056986_C_T| 12| null|\n", - "| GCST000817|9_136220024_G_T| 23| 27|\n", - "| GCST000876|11_18349351_G_C| 2| 6|\n", - "| GCST000943| 20_1960525_G_A| null| 2|\n", - "| GCST000957|22_49692725_G_A| 16| null|\n", - "| GCST000964|13_77957479_G_A| null| 47|\n", - "| GCST000998|10_44280376_C_T| 193| null|\n", - "| GCST000998|21_34226827_C_T| 29| 32|\n", - "| GCST001010| 6_32689801_T_C| 1| 112|\n", - "| GCST001040|17_37738049_G_A| null| 21|\n", - "| GCST001057|13_66393490_A_G| 7| null|\n", - "| GCST001059|2_198123211_C_A| 8| null|\n", - "+--------------+---------------+---------------------+---------------------+\n", - "only showing top 20 rows\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 414:> (0 + 1) / 1]\r" - ] - } - ], - "source": [ - "aggregated_new = (\n", - " processed_new\n", - " .join(processed_old.select('studyAccession').distinct(), on='studyAccession', how='right')\n", - " .groupBy('studyAccession', 'variantId')\n", - " .agg(f.size(f.collect_list(f.col('tagVariantId'))).alias('new_credible_set_size'))\n", - " .persist()\n", - ")\n", - "\n", - "aggregated_old = (\n", - " processed_old\n", - " .groupBy('studyAccession', 'variantId')\n", - " .agg(f.size(f.collect_list(f.col('tagVariantId'))).alias('old_credible_set_size'))\n", - " .persist()\n", - ")\n", - "\n", - "credset_compare = (\n", - " aggregated_new\n", - " .join(aggregated_old.filter(f.col('studyAccession').startswith('GCST')), on=['studyAccession', 'variantId'], how='outer')\n", - " .persist()\n", - ")\n", - "\n", - "credset_compare.show()" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] }, { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The number of extra credible sets covered by the new dataset: 104508 (53.0%)\n", - "Number of lost credible sets in the new datasets: 49292 (25.0%)\n", - "The number of extra credible sets with more than 1 tags covered by the new dataset: 94745 (48.1%)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 414:> (0 + 1) / 1]\r" - ] - } - ], - "source": [ - "extra_coverage = credset_compare.filter(f.col('old_credible_set_size').isNull()).count()\n", - "lost_coverage = credset_compare.filter(f.col('new_credible_set_size').isNull()).count()\n", - "old_full_count = aggregated_old.filter(f.col('studyAccession').startswith('GCST')).count()\n", - "\n", - "print(f'The number of extra credible sets covered by the new dataset: {extra_coverage} ({round(extra_coverage/old_full_count * 100, 1)}%)')\n", - "print(f'Number of lost credible sets in the new datasets: {lost_coverage} ({round(lost_coverage/old_full_count*100, 1)}%)')\n", - "\n", - "extra_coverage_more = credset_compare.filter(f.col('old_credible_set_size').isNull() & (f.col('new_credible_set_size')>1)).count()\n", - "\n", - "print(f'The number of extra credible sets with more than 1 tags covered by the new dataset: {extra_coverage_more} ({round(extra_coverage_more/old_full_count * 100, 1)}%)')\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 414:> (0 + 1) / 1]\r" + ] + } + ], + "source": [ + "aggregated_new = (\n", + " processed_new\n", + " .join(processed_old.select(\"studyAccession\").distinct(), on=\"studyAccession\", how=\"right\")\n", + " .groupBy(\"studyAccession\", \"variantId\")\n", + " .agg(f.size(f.collect_list(f.col(\"tagVariantId\"))).alias(\"new_credible_set_size\"))\n", + " .persist()\n", + ")\n", + "\n", + "aggregated_old = (\n", + " processed_old\n", + " .groupBy(\"studyAccession\", \"variantId\")\n", + " .agg(f.size(f.collect_list(f.col(\"tagVariantId\"))).alias(\"old_credible_set_size\"))\n", + " .persist()\n", + ")\n", + "\n", + "credset_compare = (\n", + " aggregated_new\n", + " .join(aggregated_old.filter(f.col(\"studyAccession\").startswith(\"GCST\")), on=[\"studyAccession\", \"variantId\"], how=\"outer\")\n", + " .persist()\n", + ")\n", + "\n", + "credset_compare.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] }, { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------+--------------------+---------------------+---------------------+\n", - "|studyAccession| variantId|new_credible_set_size|old_credible_set_size|\n", - "+--------------+--------------------+---------------------+---------------------+\n", - "| GCST000943| 20_1960525_G_A| null| 2|\n", - "| GCST000964| 13_77957479_G_A| null| 47|\n", - "| GCST001040| 17_37738049_G_A| null| 21|\n", - "| GCST002216| 7_73450539_A_G| null| 94|\n", - "| GCST002221| 9_133372523_G_C| null| 20|\n", - "| GCST002223| 8_19973410_C_T| null| 106|\n", - "| GCST002223| 8_20009083_C_T| null| 98|\n", - "| GCST003043| 16_11271643_C_T| null| 14|\n", - "| GCST003191| 20_22824423_G_A| null| 30|\n", - "| GCST003879| 22_23030688_C_G| null| 104|\n", - "| GCST004132| 16_10871740_T_C| null| 74|\n", - "| GCST004365| 3_186755027_C_T| null| 12|\n", - "| GCST004600| 6_35756341_T_C| null| 17|\n", - "| GCST004601| 11_8721318_TC_T| null| 171|\n", - "| GCST004601| 6_27878966_G_C| null| 267|\n", - "| GCST004603|16_88730362_G_GGG...| null| 10|\n", - "| GCST004603| 4_17777672_A_T| null| 9|\n", - "| GCST004605| 6_28489735_CT_C| null| 1|\n", - "| GCST004607| 20_56413821_A_G| null| 15|\n", - "| GCST004607| 2_218258320_T_A| null| 235|\n", - "+--------------+--------------------+---------------------+---------------------+\n", - "only showing top 20 rows\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 414:> (0 + 1) / 1]\r" - ] - } - ], - "source": [ - "credset_compare.filter(f.col('new_credible_set_size').isNull()).show()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of extra credible sets covered by the new dataset: 104508 (53.0%)\n", + "Number of lost credible sets in the new datasets: 49292 (25.0%)\n", + "The number of extra credible sets with more than 1 tags covered by the new dataset: 94745 (48.1%)\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Conclusion:**\n", - "- The reason of the disagreement is the fact that the old dataset contains data from summary stats finemapping.\n", - "- To resolve this problem, we exclude those studies which have summary stats. These credible sets should be in a better agreement." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 414:> (0 + 1) / 1]\r" + ] + } + ], + "source": [ + "extra_coverage = credset_compare.filter(f.col(\"old_credible_set_size\").isNull()).count()\n", + "lost_coverage = credset_compare.filter(f.col(\"new_credible_set_size\").isNull()).count()\n", + "old_full_count = aggregated_old.filter(f.col(\"studyAccession\").startswith(\"GCST\")).count()\n", + "\n", + "\n", + "extra_coverage_more = credset_compare.filter(f.col(\"old_credible_set_size\").isNull() & (f.col(\"new_credible_set_size\")>1)).count()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+--------------------+---------------------+---------------------+\n", + "|studyAccession| variantId|new_credible_set_size|old_credible_set_size|\n", + "+--------------+--------------------+---------------------+---------------------+\n", + "| GCST000943| 20_1960525_G_A| null| 2|\n", + "| GCST000964| 13_77957479_G_A| null| 47|\n", + "| GCST001040| 17_37738049_G_A| null| 21|\n", + "| GCST002216| 7_73450539_A_G| null| 94|\n", + "| GCST002221| 9_133372523_G_C| null| 20|\n", + "| GCST002223| 8_19973410_C_T| null| 106|\n", + "| GCST002223| 8_20009083_C_T| null| 98|\n", + "| GCST003043| 16_11271643_C_T| null| 14|\n", + "| GCST003191| 20_22824423_G_A| null| 30|\n", + "| GCST003879| 22_23030688_C_G| null| 104|\n", + "| GCST004132| 16_10871740_T_C| null| 74|\n", + "| GCST004365| 3_186755027_C_T| null| 12|\n", + "| GCST004600| 6_35756341_T_C| null| 17|\n", + "| GCST004601| 11_8721318_TC_T| null| 171|\n", + "| GCST004601| 6_27878966_G_C| null| 267|\n", + "| GCST004603|16_88730362_G_GGG...| null| 10|\n", + "| GCST004603| 4_17777672_A_T| null| 9|\n", + "| GCST004605| 6_28489735_CT_C| null| 1|\n", + "| GCST004607| 20_56413821_A_G| null| 15|\n", + "| GCST004607| 2_218258320_T_A| null| 235|\n", + "+--------------+--------------------+---------------------+---------------------+\n", + "only showing top 20 rows\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "text/plain": [ - "141" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 414:> (0 + 1) / 1]\r" - ] - } - ], - "source": [ - "(\n", - " spark.read.parquet(\"gs://genetics-portal-dev-staging/v2d/220401/ld.parquet\")\n", - " .filter(f.col('study_id') == 'GCST002223')\n", - " .select('lead_chrom', 'lead_pos', 'lead_ref', 'lead_alt')\n", - " .distinct()\n", - " .count()\n", - ")" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] }, { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "22/12/19 15:57:00 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of credible sets in the selected studies (27054): 105658\n", - "The number of extra credible sets covered by the new dataset in the same studies: 73250 (69.3%)\n", - "Number of lost credible sets in the new datasets: 8454 (8.0%)\n", - "The number of extra credible sets with more than 1 tags covered by the new dataset: 67183 (63.6%)\n" - ] - } - ], - "source": [ - "studies_with_no_sumstats = (\n", - " spark.read.parquet('gs://genetics_etl_python_playground/XX.XX/output/python_etl/parquet/gwas_catalog_studies/')\n", - " .filter(~f.col('hasSumstats'))\n", - " .select(f.split(f.col('studyId'), '_').getItem(0).alias('studyAccession'))\n", - " .distinct()\n", - ")\n", - "\n", - "# Dropping studies with summary statistics:\n", - "credset_compare_update = credset_compare.join(studies_with_no_sumstats, on='studyAccession', how='inner').distinct().persist()\n", - "\n", - "old_full_count = credset_compare_update.filter(f.col('old_credible_set_size').isNotNull()).count()\n", - "extra_coverage = credset_compare_update.filter(f.col('old_credible_set_size').isNull()).count()\n", - "lost_coverage = credset_compare_update.filter(f.col('new_credible_set_size').isNull()).count()\n", - "\n", - "print(f'Number of credible sets in the selected studies ({studies_with_no_sumstats.count()}): {old_full_count}')\n", - "print(f'The number of extra credible sets covered by the new dataset in the same studies: {extra_coverage} ({round(extra_coverage/old_full_count * 100, 1)}%)')\n", - "print(f'Number of lost credible sets in the new datasets: {lost_coverage} ({round(lost_coverage/old_full_count*100, 1)}%)')\n", - "\n", - "extra_coverage_more = credset_compare_update.filter(f.col('old_credible_set_size').isNull() & (f.col('new_credible_set_size')>1)).count()\n", - "\n", - "print(f'The number of extra credible sets with more than 1 tags covered by the new dataset: {extra_coverage_more} ({round(extra_coverage_more/old_full_count * 100, 1)}%)')" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 414:> (0 + 1) / 1]\r" + ] + } + ], + "source": [ + "credset_compare.filter(f.col(\"new_credible_set_size\").isNull()).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Conclusion:**\n", + "- The reason of the disagreement is the fact that the old dataset contains data from summary stats finemapping.\n", + "- To resolve this problem, we exclude those studies which have summary stats. These credible sets should be in a better agreement." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] }, { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1+1" + "data": { + "text/plain": [ + "141" ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 414:> (0 + 1) / 1]\r" + ] } - ], - "metadata": { - "kernelspec": { - "display_name": "otgenetics-Z1loiStc-py3.8", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16 (default, Dec 7 2022, 01:39:17) \n[Clang 14.0.0 (clang-1400.0.29.202)]" + ], + "source": [ + "(\n", + " spark.read.parquet(\"gs://genetics-portal-dev-staging/v2d/220401/ld.parquet\")\n", + " .filter(f.col(\"study_id\") == \"GCST002223\")\n", + " .select(\"lead_chrom\", \"lead_pos\", \"lead_ref\", \"lead_alt\")\n", + " .distinct()\n", + " .count()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "22/12/19 15:57:00 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.\n", + " \r" + ] }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "5a448d06c31dd563cc2d2f896cd972f1626bb3e0fbcfc3d2f2ab4cc41131eab9" - } + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of credible sets in the selected studies (27054): 105658\n", + "The number of extra credible sets covered by the new dataset in the same studies: 73250 (69.3%)\n", + "Number of lost credible sets in the new datasets: 8454 (8.0%)\n", + "The number of extra credible sets with more than 1 tags covered by the new dataset: 67183 (63.6%)\n" + ] } + ], + "source": [ + "studies_with_no_sumstats = (\n", + " spark.read.parquet(\"gs://genetics_etl_python_playground/XX.XX/output/python_etl/parquet/gwas_catalog_studies/\")\n", + " .filter(~f.col(\"hasSumstats\"))\n", + " .select(f.split(f.col(\"studyId\"), \"_\").getItem(0).alias(\"studyAccession\"))\n", + " .distinct()\n", + ")\n", + "\n", + "# Dropping studies with summary statistics:\n", + "credset_compare_update = credset_compare.join(studies_with_no_sumstats, on=\"studyAccession\", how=\"inner\").distinct().persist()\n", + "\n", + "old_full_count = credset_compare_update.filter(f.col(\"old_credible_set_size\").isNotNull()).count()\n", + "extra_coverage = credset_compare_update.filter(f.col(\"old_credible_set_size\").isNull()).count()\n", + "lost_coverage = credset_compare_update.filter(f.col(\"new_credible_set_size\").isNull()).count()\n", + "\n", + "\n", + "extra_coverage_more = credset_compare_update.filter(f.col(\"old_credible_set_size\").isNull() & (f.col(\"new_credible_set_size\")>1)).count()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1+1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "otgenetics-Z1loiStc-py3.8", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16 (default, Dec 7 2022, 01:39:17) \n[Clang 14.0.0 (clang-1400.0.29.202)]" }, - "nbformat": 4, - "nbformat_minor": 2 + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "5a448d06c31dd563cc2d2f896cd972f1626bb3e0fbcfc3d2f2ab4cc41131eab9" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/notebooks/susie_inf_benchmark.ipynb b/notebooks/susie_inf_benchmark.ipynb index 21de23b47..d9039d0d1 100644 --- a/notebooks/susie_inf_benchmark.ipynb +++ b/notebooks/susie_inf_benchmark.ipynb @@ -28,8 +28,9 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", - "from gentropy.method.susie_inf import SUSIE_inf\n", - "from scipy.stats import pearsonr\n" + "from scipy.stats import pearsonr\n", + "\n", + "from gentropy.method.susie_inf import SUSIE_inf\n" ] }, { @@ -83,10 +84,10 @@ } ], "source": [ - "plt.scatter(x=first_lbf_susie_r, y=first_lbf_susie_inf)\n", - "plt.xlabel(\"susie_r\")\n", - "plt.ylabel(\"susie_inf\")\n", - "plt.show()\n", + "# plt.scatter(x=first_lbf_susie_r, y=first_lbf_susie_inf)\n", + "# plt.xlabel(\"susie_r\")\n", + "# plt.ylabel(\"susie_inf\")\n", + "# plt.show()\n", "\n", "corr = pearsonr(first_lbf_susie_r, first_lbf_susie_inf)\n", "print(\"Pearson's correlation coefficient: \" + str(corr[0])) # noqa: T201" @@ -116,10 +117,10 @@ } ], "source": [ - "plt.scatter(x=second_lbf_susie_r, y=second_lbf_susie_inf)\n", - "plt.xlabel(\"susie_r\")\n", - "plt.ylabel(\"susie_inf\")\n", - "plt.show()\n", + "# plt.scatter(x=second_lbf_susie_r, y=second_lbf_susie_inf)\n", + "# plt.xlabel(\"susie_r\")\n", + "# plt.ylabel(\"susie_inf\")\n", + "# plt.show()\n", "\n", "corr = pearsonr(second_lbf_susie_r, second_lbf_susie_inf)\n", "print(\"Pearson's correlation coefficient: \" + str(corr[0])) # noqa: T201" @@ -142,7 +143,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.14" }, "orig_nbformat": 4 }, diff --git a/notebooks/ukb_ppp_benchmark.ipynb b/notebooks/ukb_ppp_benchmark.ipynb new file mode 100644 index 000000000..3e2f4a23e --- /dev/null +++ b/notebooks/ukb_ppp_benchmark.ipynb @@ -0,0 +1,1459 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + "
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\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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error.toString();\n wrapper.append(content);\n el.append(wrapper);\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(() => display_loaded(error), 100);\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.4.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n try {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n\n } catch (error) {display_loaded(error);throw error;\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"d831fede-136f-40d0-a9cd-423fefca302f\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", + "application/vnd.bokehjs_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# import matplotlib.pyplot as plt\n", + "import pyspark.sql.functions as f\n", + "\n", + "from gentropy.common.session import Session\n", + "from gentropy.common.spark_helpers import order_array_of_structs_by_field\n", + "from gentropy.dataset.ld_index import LDIndex\n", + "from gentropy.dataset.study_index import StudyIndex\n", + "from gentropy.dataset.study_locus import StudyLocus\n", + "from gentropy.method.susie_inf import SUSIE_inf\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/08/07 09:34:15 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n" + ] + } + ], + "source": [ + "session = Session(\n", + " extended_spark_conf={\n", + " \"spark.driver.memory\": \"10g\",\n", + " \"spark.executor.memory\": \"10g\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Context\n", + "UKB-PPP summary statistics were ingested, a deduplication and sanity filter was run on harmonised summary statistics prior to clumping\n", + "Locus breaker clumping was performed on the resulting studies\n", + "\n", + "Parameters: \n", + " lbc_baseline_pvalue: 1e-5, \n", + " lbc_distance_cutoff: 250_000, \n", + " lbc_pvalue_threshold: 1e-8, \n", + " lbc_flanking_distance: 100_000, \n", + " large_loci_size: 1_500_000, \n", + " wbc_clump_distance: 500_000, \n", + " wbc_pvalue_threshold: 1e-8, \n", + " collect_locus: bool = True, \n", + " remove_mhc: bool = True,\n", + "\n", + "Loci with less than 100 variants, or more than 15,000, were filtered out and fine-mapped with PICS.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "study_index = StudyIndex.from_parquet(\n", + " session, \"/Users/dc16/data/study_index/ukb_ppp/\"\n", + ")\n", + "ld_index = LDIndex.from_parquet(session, \"/Users/dc16/data/ld_index\")\n", + "susie_loci = StudyLocus(\n", + " session.spark.read.parquet(\"/Users/dc16/output/ukb_ppp/clean_loci.parquet/\"),\n", + " StudyLocus.get_schema(),\n", + ")\n", + "pics_loci = StudyLocus(\n", + " session.spark.read.parquet(\"/Users/dc16/output/ukb_ppp/filtered_loci.parquet/\"),\n", + " StudyLocus.get_schema(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "susie_loci.df = susie_loci.df.filter(f.col(\"pValueExponent\") < -11)\n", + "pics_loci.df = pics_loci.df.filter(f.col(\"pValueExponent\") < -11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Susie fine mapping\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the total number of unique studyIds\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "2387" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "susie_loci.df.select(\"studyId\").distinct().count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the total number of loci for finemapping:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "17117" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "susie_loci.df.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "df = susie_loci.df.withColumns(\n", + " {\n", + " \"locusSize\": f.size(\"locus\"),\n", + " \"locusLength\": f.col(\"locusEnd\") - f.col(\"locusStart\"),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0------------------------------\n", + " meanLocusLength | 820240.6895484022 \n", + " q1LocusLength | 371912 \n", + " medianLocusLength | 651459 \n", + " q3LocusLength | 1500000 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 63:> (0 + 1) / 1]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0-----------------------------\n", + " meanLocusSize | 4029.3360986154116 \n", + " minLocusSize | 116 \n", + " q1LocusSize | 1997 \n", + " medianLocusSize | 3348 \n", + " q3LocusSize | 5727 \n", + " maxLocusSize | 13348 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "length = df.select(\n", + " f.mean(\"locusLength\").alias(\"meanLocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.25).alias(\"q1LocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.5).alias(\"medianLocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.75).alias(\"q3LocusLength\"),\n", + ")\n", + "size = df.select(\n", + " f.mean(\"locusSize\").alias(\"meanLocusSize\"),\n", + " f.min(\"locusSize\").alias(\"minLocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.25).alias(\"q1LocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.5).alias(\"medianLocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.75).alias(\"q3LocusSize\"),\n", + " f.max(\"locusSize\").alias(\"maxLocusSize\"),\n", + ")\n", + "length.show(vertical=True)\n", + "size.show(vertical=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "panda_df = df.select(\"locusSize\", \"locusLength\").toPandas()\n", + "\n", + "# plt.figure(figsize=(12, 6))\n", + "\n", + "# # Histogram for locusLength\n", + "# plt.subplot(1, 2, 1) # 1 row, 2 columns, 1st subplot\n", + "# plt.hist(panda_df[\"locusLength\"], bins=30, alpha=0.7)\n", + "# plt.xlabel(\"Locus Length\")\n", + "# plt.ylabel(\"Frequency\")\n", + "# plt.title(\"Histogram of Locus Length\")\n", + "\n", + "# # Histogram for locusSize\n", + "# plt.subplot(1, 2, 2) # 1 row, 2 columns, 2nd subplot\n", + "# plt.hist(panda_df[\"locusSize\"], bins=30, alpha=0.7)\n", + "# plt.xlabel(\"Locus Size\")\n", + "# plt.ylabel(\"Frequency\")\n", + "# plt.title(\"Histogram of Locus Size\")\n", + "\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plt.figure(figsize=(10, 6))\n", + "# plt.scatter(panda_df[\"locusSize\"], panda_df[\"locusLength\"], alpha=0.5)\n", + "# plt.title(\"Scatter Plot of Locus Size vs Locus Length\")\n", + "# plt.xlabel(\"Locus Size\")\n", + "# plt.ylabel(\"Locus Length\")\n", + "# plt.grid(True)\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/08/07 09:53:33 WARN SharedInMemoryCache: Evicting cached table partition metadata from memory due to size constraints (spark.sql.hive.filesourcePartitionFileCacheSize = 262144000 bytes). This may impact query planning performance.\n" + ] + } + ], + "source": [ + "susie_fm = StudyLocus.from_parquet(session, \"/Users/dc16/output/ukb_ppp_fm\")\n", + "susie_fm.df = (\n", + " susie_fm.df\n", + " .filter(f.col(\"pValueExponent\") < -11)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Total credible sets:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "57880" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "susie_fm.df.count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check for NaN and nulls in the credible set logBF\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of credible sets with 'not a number' as the logBF: 25\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 73:====================================================> (868 + 8) / 893]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of credible sets with 'null' as the logBF: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "nan = susie_fm.df.filter(f.isnan(\"credibleSetlog10BF\"))\n", + "null = susie_fm.df.filter(f.isnull(\"credibleSetlog10BF\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0---------------------------\n", + " meanTopPP | 0.9005192231704642 \n", + " minTopPP | 0.011231041748358246 \n", + " q1TopPP | 0.9884181401759444 \n", + " medianTopPP | 0.9999999997648601 \n", + " q3TopPP | 1.0 \n", + " maxTopPP | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0------------------------------\n", + " meanCredSetSize | 4.344101633393829 \n", + " minCredSetSize | 1 \n", + " q1CredSetSize | 1 \n", + " medianCredSetSize | 1 \n", + " q3CredSetSize | 2 \n", + " maxCredSetSize | 1767 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0----------------------------------\n", + " meanPurityMeanR2 | 0.8042287002688577 \n", + " minPurityMeanR2 | 0.011839476509578747 \n", + " q1PurityMeanR2 | 0.6278035788726534 \n", + " medianPurityMeanR2 | 1.0 \n", + " q3PurityMeanR2 | 1.0 \n", + " maxPurityMeanR2 | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 87:> (0 + 1) / 1]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0--------------------------------\n", + " meanPurityMinR2 | 0.7062586247097518 \n", + " minPurityMinR2 | 0.0 \n", + " q1PurityMinR2 | 0.06978249208480057 \n", + " medianPurityMinR2 | 1.0 \n", + " q3PurityMinR2 | 1.0 \n", + " maxPurityMinR2 | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "susie_results = (\n", + " susie_fm.df.withColumn(\"credSetSize\", f.size(\"locus\"))\n", + " .withColumn(\n", + " \"locus\",\n", + " f.slice(order_array_of_structs_by_field(\"locus\", \"posteriorProbability\"), 1, 1)[\n", + " 0\n", + " ],\n", + " )\n", + " .withColumn(\"topPP\", f.col(\"locus\").getField(\"posteriorProbability\"))\n", + " .filter(~f.isnan(\"topPP\"))\n", + ")\n", + "(\n", + " susie_results.select(\n", + " f.mean(\"topPP\").alias(\"meanTopPP\"),\n", + " f.min(\"topPP\").alias(\"minTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.25).alias(\"q1TopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.5).alias(\"medianTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.75).alias(\"q3TopPP\"),\n", + " f.max(\"topPP\").alias(\"maxTopPP\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " susie_results.select(\n", + " f.mean(\"credSetSize\").alias(\"meanCredSetSize\"),\n", + " f.min(\"credSetSize\").alias(\"minCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.25).alias(\"q1CredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.5).alias(\"medianCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.75).alias(\"q3CredSetSize\"),\n", + " f.max(\"credSetSize\").alias(\"maxCredSetSize\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " susie_results.select(\n", + " f.mean(\"purityMeanR2\").alias(\"meanPurityMeanR2\"),\n", + " f.min(\"purityMeanR2\").alias(\"minPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.25).alias(\"q1PurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.5).alias(\"medianPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.75).alias(\"q3PurityMeanR2\"),\n", + " f.max(\"purityMeanR2\").alias(\"maxPurityMeanR2\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " susie_results.select(\n", + " f.mean(\"purityMinR2\").alias(\"meanPurityMinR2\"),\n", + " f.min(\"purityMinR2\").alias(\"minPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.25).alias(\"q1PurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.5).alias(\"medianPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.75).alias(\"q3PurityMinR2\"),\n", + " f.max(\"purityMinR2\").alias(\"maxPurityMinR2\"),\n", + " ).show(vertical=True)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "pdf = susie_results.select(\"purityMinR2\", \"purityMeanR2\", \"topPP\", \"credSetSize\").toPandas()\n", + "# plt.figure(figsize=(12, 12))\n", + "\n", + "# Histogram for purityMinR2\n", + "# plt.subplot(2, 2, 1)\n", + "# plt.hist(pdf[\"purityMinR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMinR2\")\n", + "# plt.xlabel(\"purityMinR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for purityMeanR2\n", + "# plt.subplot(2, 2, 2)\n", + "# plt.hist(pdf[\"purityMeanR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMeanR2\")\n", + "# plt.xlabel(\"purityMeanR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for topPP\n", + "# plt.subplot(2, 2, 3)\n", + "# plt.hist(pdf[\"topPP\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of topPP\")\n", + "# plt.xlabel(\"topPP\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for credSetSize\n", + "# plt.subplot(2, 2, 4)\n", + "# plt.hist(pdf[\"credSetSize\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of credSetSize\")\n", + "# plt.xlabel(\"credSetSize\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Adjust layout to prevent overlap\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Repeating the same steps, but filtering for only the first credible set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Total number of primary credible sets and number of unique studyIds:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of primary credible sets: 9495\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 209:====================================================>(880 + 8) / 893]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of unique studyIds in primary credible sets: 2015\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "first_credset = susie_results.filter(f.col(\"credibleSetIndex\") == 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0---------------------------\n", + " meanTopPP | 0.8085156719419792 \n", + " minTopPP | 0.011231041748358246 \n", + " q1TopPP | 0.6270685668930966 \n", + " medianTopPP | 0.9999984922517341 \n", + " q3TopPP | 1.0 \n", + " maxTopPP | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0------------------------------\n", + " meanCredSetSize | 5.875934702474987 \n", + " minCredSetSize | 1 \n", + " q1CredSetSize | 1 \n", + " medianCredSetSize | 1 \n", + " q3CredSetSize | 4 \n", + " maxCredSetSize | 1022 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0----------------------------------\n", + " meanPurityMeanR2 | 0.7587573150386597 \n", + " minPurityMeanR2 | 0.011839476509578747 \n", + " q1PurityMeanR2 | 0.520396112272186 \n", + " medianPurityMeanR2 | 0.9839697266684606 \n", + " q3PurityMeanR2 | 1.0 \n", + " maxPurityMeanR2 | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 226:> (0 + 1) / 1]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0---------------------------------\n", + " meanPurityMinR2 | 0.6480281642261705 \n", + " minPurityMinR2 | 0.0 \n", + " q1PurityMinR2 | 0.002472980432442... \n", + " medianPurityMinR2 | 0.9659616302635498 \n", + " q3PurityMinR2 | 1.0 \n", + " maxPurityMinR2 | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "(\n", + " first_credset.select(\n", + " f.mean(\"topPP\").alias(\"meanTopPP\"),\n", + " f.min(\"topPP\").alias(\"minTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.25).alias(\"q1TopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.5).alias(\"medianTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.75).alias(\"q3TopPP\"),\n", + " f.max(\"topPP\").alias(\"maxTopPP\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " first_credset.select(\n", + " f.mean(\"credSetSize\").alias(\"meanCredSetSize\"),\n", + " f.min(\"credSetSize\").alias(\"minCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.25).alias(\"q1CredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.5).alias(\"medianCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.75).alias(\"q3CredSetSize\"),\n", + " f.max(\"credSetSize\").alias(\"maxCredSetSize\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " first_credset.select(\n", + " f.mean(\"purityMeanR2\").alias(\"meanPurityMeanR2\"),\n", + " f.min(\"purityMeanR2\").alias(\"minPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.25).alias(\"q1PurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.5).alias(\"medianPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.75).alias(\"q3PurityMeanR2\"),\n", + " f.max(\"purityMeanR2\").alias(\"maxPurityMeanR2\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " first_credset.select(\n", + " f.mean(\"purityMinR2\").alias(\"meanPurityMinR2\"),\n", + " f.min(\"purityMinR2\").alias(\"minPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.25).alias(\"q1PurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.5).alias(\"medianPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.75).alias(\"q3PurityMinR2\"),\n", + " f.max(\"purityMinR2\").alias(\"maxPurityMinR2\"),\n", + " ).show(vertical=True)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pdf = first_credset.select(\"purityMinR2\", \"purityMeanR2\", \"topPP\", \"credSetSize\").toPandas()\n", + "# plt.figure(figsize=(12, 12))\n", + "\n", + "# # Histogram for purityMinR2\n", + "# plt.subplot(2, 2, 1)\n", + "# plt.hist(pdf[\"purityMinR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMinR2\")\n", + "# plt.xlabel(\"purityMinR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for purityMeanR2\n", + "# plt.subplot(2, 2, 2)\n", + "# plt.hist(pdf[\"purityMeanR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMeanR2\")\n", + "# plt.xlabel(\"purityMeanR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for topPP\n", + "# plt.subplot(2, 2, 3)\n", + "# plt.hist(pdf[\"topPP\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of topPP\")\n", + "# plt.xlabel(\"topPP\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for credSetSize\n", + "# plt.subplot(2, 2, 4)\n", + "# plt.hist(pdf[\"credSetSize\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of credSetSize\")\n", + "# plt.xlabel(\"credSetSize\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Adjust layout to prevent overlap\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Filtering credible sets with qc function" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24/08/07 10:05:27 WARN package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of high quality credible sets: 33544\n", + "Number of unique studyIds in high quality credible sets: 2188\n" + ] + } + ], + "source": [ + "qc_credsets = SUSIE_inf.credible_set_qc(\n", + " susie_fm, study_index, ld_index, 1e-5, 0.25, 0.8\n", + ").persist()\n", + "\n", + "qc_credsets = (\n", + " qc_credsets.df.withColumn(\"credSetSize\", f.size(\"locus\"))\n", + " .withColumn(\n", + " \"locus\",\n", + " f.slice(order_array_of_structs_by_field(\"locus\", \"posteriorProbability\"), 1, 1)[\n", + " 0\n", + " ],\n", + " )\n", + " .withColumn(\"topPP\", f.col(\"locus\").getField(\"posteriorProbability\"))\n", + " .filter(~f.isnan(\"topPP\"))\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0---------------------------\n", + " meanTopPP | 0.9033882550691011 \n", + " minTopPP | 0.013988537311438214 \n", + " q1TopPP | 0.9937867852571437 \n", + " medianTopPP | 0.9999999999596412 \n", + " q3TopPP | 1.0 \n", + " maxTopPP | 1.0 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0-------------------------------\n", + " meanCredSetSize | 3.2039709038874316 \n", + " minCredSetSize | 1 \n", + " q1CredSetSize | 1 \n", + " medianCredSetSize | 1 \n", + " q3CredSetSize | 1 \n", + " maxCredSetSize | 1022 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0--------------------------------\n", + " meanPurityMeanR2 | 0.9789336464434549 \n", + " minPurityMeanR2 | 0.5417537849178955 \n", + " q1PurityMeanR2 | 1.0 \n", + " medianPurityMeanR2 | 1.0 \n", + " q3PurityMeanR2 | 1.0 \n", + " maxPurityMeanR2 | 1.0 \n", + "\n", + "-RECORD 0--------------------------------\n", + " meanPurityMinR2 | 0.9491503761431416 \n", + " minPurityMinR2 | 0.25018750677013313 \n", + " q1PurityMinR2 | 1.0 \n", + " medianPurityMinR2 | 1.0 \n", + " q3PurityMinR2 | 1.0 \n", + " maxPurityMinR2 | 1.0 \n", + "\n" + ] + } + ], + "source": [ + "(\n", + " qc_credsets.select(\n", + " f.mean(\"topPP\").alias(\"meanTopPP\"),\n", + " f.min(\"topPP\").alias(\"minTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.25).alias(\"q1TopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.5).alias(\"medianTopPP\"),\n", + " f.percentile_approx(\"topPP\", 0.75).alias(\"q3TopPP\"),\n", + " f.max(\"topPP\").alias(\"maxTopPP\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " qc_credsets.select(\n", + " f.mean(\"credSetSize\").alias(\"meanCredSetSize\"),\n", + " f.min(\"credSetSize\").alias(\"minCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.25).alias(\"q1CredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.5).alias(\"medianCredSetSize\"),\n", + " f.percentile_approx(\"credSetSize\", 0.75).alias(\"q3CredSetSize\"),\n", + " f.max(\"credSetSize\").alias(\"maxCredSetSize\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " qc_credsets.select(\n", + " f.mean(\"purityMeanR2\").alias(\"meanPurityMeanR2\"),\n", + " f.min(\"purityMeanR2\").alias(\"minPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.25).alias(\"q1PurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.5).alias(\"medianPurityMeanR2\"),\n", + " f.percentile_approx(\"purityMeanR2\", 0.75).alias(\"q3PurityMeanR2\"),\n", + " f.max(\"purityMeanR2\").alias(\"maxPurityMeanR2\"),\n", + " ).show(vertical=True)\n", + ")\n", + "(\n", + " qc_credsets.select(\n", + " f.mean(\"purityMinR2\").alias(\"meanPurityMinR2\"),\n", + " f.min(\"purityMinR2\").alias(\"minPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.25).alias(\"q1PurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.5).alias(\"medianPurityMinR2\"),\n", + " f.percentile_approx(\"purityMinR2\", 0.75).alias(\"q3PurityMinR2\"),\n", + " f.max(\"purityMinR2\").alias(\"maxPurityMinR2\"),\n", + " ).show(vertical=True)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pdf = qc_credsets.select(\"purityMinR2\", \"purityMeanR2\", \"topPP\", \"credSetSize\").toPandas()\n", + "# plt.figure(figsize=(12, 12))\n", + "\n", + "# # Histogram for purityMinR2\n", + "# plt.subplot(2, 2, 1)\n", + "# plt.hist(pdf[\"purityMinR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMinR2\")\n", + "# plt.xlabel(\"purityMinR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for purityMeanR2\n", + "# plt.subplot(2, 2, 2)\n", + "# plt.hist(pdf[\"purityMeanR2\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of purityMeanR2\")\n", + "# plt.xlabel(\"purityMeanR2\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for topPP\n", + "# plt.subplot(2, 2, 3)\n", + "# plt.hist(pdf[\"topPP\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of topPP\")\n", + "# plt.xlabel(\"topPP\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Histogram for credSetSize\n", + "# plt.subplot(2, 2, 4)\n", + "# plt.hist(pdf[\"credSetSize\"], bins=30, alpha=0.7)\n", + "# plt.title(\"Histogram of credSetSize\")\n", + "# plt.xlabel(\"credSetSize\")\n", + "# plt.ylabel(\"Frequency\")\n", + "\n", + "# # Adjust layout to prevent overlap\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WIP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Clumped loci filtered for usage with PICS\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Number of unique studyIds\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pics_loci.df.select(\"studyId\").distinct().count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Number of loci to fine map with PICS\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pics_loci.df.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "df = pics_loci.df.withColumns(\n", + " {\n", + " \"locusSize\": f.size(\"locus\"),\n", + " \"locusLength\": f.col(\"locusEnd\") - f.col(\"locusStart\"),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many loci with less than 100 variants from summary statistics?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.filter(f.col(\"locusSize\") < 100).count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many loci with more than 15,000 variants from summary statistics?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.filter(f.col(\"locusSize\") > 15_000).count()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0------------------------------\n", + " meanLocusLength | 273867.6666666667 \n", + " q1LocusLength | 200000 \n", + " medianLocusLength | 200000 \n", + " q3LocusLength | 302688 \n", + "\n", + "-RECORD 0-----------------------------\n", + " meanLocusSize | 30.055555555555557 \n", + " minLocusSize | 2 \n", + " q1LocusSize | 12 \n", + " medianLocusSize | 19 \n", + " q3LocusSize | 57 \n", + " maxLocusSize | 79 \n", + "\n" + ] + } + ], + "source": [ + "length = df.select(\n", + " f.mean(\"locusLength\").alias(\"meanLocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.25).alias(\"q1LocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.5).alias(\"medianLocusLength\"),\n", + " f.percentile_approx(\"locusLength\", 0.75).alias(\"q3LocusLength\"),\n", + ")\n", + "size = df.select(\n", + " f.mean(\"locusSize\").alias(\"meanLocusSize\"),\n", + " f.min(\"locusSize\").alias(\"minLocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.25).alias(\"q1LocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.5).alias(\"medianLocusSize\"),\n", + " f.percentile_approx(\"locusSize\", 0.75).alias(\"q3LocusSize\"),\n", + " f.max(\"locusSize\").alias(\"maxLocusSize\"),\n", + ")\n", + "length.show(vertical=True)\n", + "size.show(vertical=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "panda_df = df.select(\"locusSize\", \"locusLength\").toPandas()\n", + "\n", + "# plt.figure(figsize=(12, 6))\n", + "\n", + "# # Histogram for locusLength\n", + "# plt.subplot(1, 2, 1) # 1 row, 2 columns, 1st subplot\n", + "# plt.hist(panda_df[\"locusLength\"], bins=30, alpha=0.7)\n", + "# plt.xlabel(\"Locus Length\")\n", + "# plt.ylabel(\"Frequency\")\n", + "# plt.title(\"Histogram of Locus Length\")\n", + "\n", + "# # Histogram for locusSize\n", + "# plt.subplot(1, 2, 2) # 1 row, 2 columns, 2nd subplot\n", + "# plt.hist(panda_df[\"locusSize\"], bins=30, alpha=0.7)\n", + "# plt.xlabel(\"Locus Size\")\n", + "# plt.ylabel(\"Frequency\")\n", + "# plt.title(\"Histogram of Locus Size\")\n", + "\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rr76av/u7v5vyWe2e+9a3vkVHR8e0dXX90D/F73rXu/jMZz7DyMgI6XSan/zkJ9x4441T1n3nO9/JZZddxn333ce6dev4whe+wOc+9zl++MMfct11183p2E8EJ/p6zsSRvAc0TZvxe+II6kgPxYncl0QimV9e087Yddddd9AfKMdx+MQnPsF3v/tdJiYmWLVqFZ/73Oe44oorANi4cSNf/epXWb9+fX3G8HBn0CWSVzu11Lz+/n4Ali1bRhiGbNiwYVYj6Fe/+hWjo6P88Ic/5HWve119+UxqhLMZdrMtr6X+ZDKZV4zDtGTJEgA2b9487bNNmzbR0tJSl9M/nFSvydudHEGpLat9fqxpbW0lkUjMejyqqrJo0SIguh7r168/6PZqEYgDezLNFtlbvXo1q1ev5m//9m954oknWLt2LV/72temOUlz4bzzzuOee+6p378zUalUeOtb30o2m+W73/3utPTb2j3X1tZ2xPfcu971Lu68807+67/+i/b2dvL5PO9+97unfa+zs5MPfehDfOhDH2JoaIhzzjmHz3zmM0fljL2armeN4/EeWLJkCWEYsnPnTlasWFFfvm3btmnfPdyUTYlE8urlNZ2meCg+8pGP8Lvf/Y7//M//5MUXX+Qd73gHb3zjG+vpDT/96U/p6enhZz/7GUuXLqW7u5s/+ZM/kZExyWuSRx99dMZZ21q9U23C4oYbbkBVVT796U9Pi3DV1q/NCk/enuu6/Ou//uu07SeTyRnTlmrOy4EG37nnnsuyZcv44he/SLFYnLbe8PDwrMd4vOjs7OSss87innvumTLe9evXs27dOt70pjfVl812XDNx3nnn0dbWxte+9jUcx6kvv//++9m4ceM0lbdjhaZpXHPNNfz4xz+eItc9ODjId77zHS699FIymQwAb3vb23jhhRe47777pm2ndv1rhvOvf/3r+mdBEPDv//7vU76fz+fxfX/KstWrV6Oq6pTjP5Byuczvfve7GT+7//77gZlTPWt88IMfZMuWLdx33311R2My1157LZlMhr//+7/H87xpn8/lnjvttNNYvXo19957L/feey+dnZ1TJiqCIJj2HLS1tdHV1XXQY58LJ9v1nAvH4z1QU7U88D31la98Zdp3D+c5lkgkr25e05Gxg7Fnzx6+8Y1vsGfPHrq6ugD4H//jf/DAAw/wjW98g7//+79nx44d7N69m+9///t885vfJAgC/uqv/oq3v/3tPPLII/N8BBLJieWjH/0o5XKZt7zlLZx66qm4rssTTzzBvffeS3d3N7feeisQ1U984hOf4K677uKyyy7jrW99K5Zl8dRTT9HV1cVnP/tZLrnkEhobG7n55pv52Mc+hqIofOtb35rR2Tv33HO59957+fjHP875559PKpXizW9+M8uWLSObzfK1r32NdDpNMpnkwgsvZOnSpXz961/nuuuu44wzzuDWW29lwYIF9Pb28uijj5LJZPjpT396ok8fX/jCF7juuuu4+OKL+cAHPlCXtm9oaKj3J6odL0Sy/u9+97sxDIM3v/nNMzaiNgyDz33uc9x6661cfvnl3HjjjXVp++7ubv7qr/7qqMb8H//xHzP2PvqLv/gL/u7v/o6HHnqISy+9lA996EPous6//du/4TgOn//85+vf/eu//mt+8IMf8I53vIPbbruNc889l7GxMX7yk5/wta99jTVr1nDGGWdw0UUXcfvttzM2NkZTUxP/+Z//Oc1Qf+SRR/jIRz7CO97xDlauXInv+3zrW99C07QpNVwHUi6XueSSS7jooot44xvfyKJFi5iYmOBHP/oRv/nNb7jhhhs4++yzZ1z35z//Od/85jd529vexosvvsiLL75Y/yyVSnHDDTeQyWT46le/yvve9z7OOecc3v3ud9Pa2sqePXv4+c9/ztq1a/nnf/7nQ57vd73rXXzyk58kFovxgQ98YEoErlAosHDhQt7+9rezZs0aUqkUDz/8ME899RRf+tKXDrltePVczxqe580YPWtqauJDH/rQMX8PnHvuubztbW/jy1/+MqOjo3Vp+y1btgBTo2GH8xxLJJJXOfOm4/gKAxD33Xdf/f816edkMjnlj67r4p3vfKcQQog//dM/FYDYvHlzfb2atO2mTZtO9CFIJPPK/fffL2677TZx6qmnilQqJUzTFMuXLxcf/ehHxeDg4LTv/8d//Ic4++yzhWVZorGxUVx++eXioYceqn/++OOPi4suukjE43HR1dVVl8pnkvS1EEIUi0Vx0003iWw2K4ApMvc//vGPxemnny50XZ8mR/7cc8+Jt771raK5uVlYliWWLFki3vnOd4pf/vKX9e/UJK2Hh4fndA5q0tyHkuKeTR794YcfFmvXrhXxeFxkMhnx5je/WWzYsGHa+nfddZdYsGCBUFV1TjL39957b/1cNzU1ife85z1i3759U75zJNL2s/3Zu3evEEKIZ599Vlx77bUilUqJRCIhrrzySvHEE09M297o6Kj4yEc+IhYsWCBM0xQLFy4UN998sxgZGal/Z/v27eINb3iDsCxLtLe3i//1v/6XeOihh6bcDzt27BC33XabWLZsmYjFYqKpqUlceeWV4uGHHz7o8XieJ/6f/+f/ETfccINYsmSJsCxLJBIJcfbZZ4svfOELwnGc+ncPvHYHOxcHtlx49NFHxbXXXisaGhpELBYTy5YtE7fccot4+umnD3nOhYjaQtS2/dvf/nbKZ47jiL/+678Wa9asEel0WiSTSbFmzRrxr//6r4fc7qvtegoRSdvPdjzLli2rf+9o3gMzSfSXSiXx4Q9/WDQ1NYlUKiVuuOEGsXnzZgGI//2///eU9Wd7joEZWxQsWbJE3HzzzYc8dolEcnKhCCGrQSGasbrvvvu44YYbgEip6j3veQ8vv/zytELaVCpFR0cHn/rUp6alnVQqFRKJBOvWrePqq68+kYcgkUgkEonkFcbzzz/P2Wefzbe//W3e8573zPdwJBLJKwyZpjgLZ599NkEQMDQ0xGWXXTbjd9auXYvv+2zfvr2eA19LRzhehfESiUQikUhemVQqlWnqq1/+8pdRVXVKjZ9EIpHUeE07Y8VicYrK0c6dO3n++edpampi5cqVvOc97+H9738/X/rSlzj77LMZHh7ml7/8JWeeeSbXX389b3jDGzjnnHO47bbb+PKXv0wYhnz4wx/m6quvZuXKlfN4ZBKJRCKRSE40n//853nmmWe48sor0XWd+++/n/vvv58/+7M/qytOSiQSyWRe02mKv/rVr7jyyiunLb/55pu5++6768W/3/zmN+nt7aWlpYWLLrqIO++8k9WrVwPQ19fHRz/6UdatW0cymeS6667jS1/6Ek1NTSf6cCQSiUQikcwjDz30EHfeeScbNmygWCyyePFi3ve+9/GJT3xiTv3kJBLJa4/XtDMmkUgkEolEIpFIJPOF7DMmkUgkEolEIpFIJPOAdMYkEolEIpFIJBKJZB54zSUwh2FIX18f6XR6SgNGiUQikUgkEolE8tpCCEGhUKCrqwtVPfFxqtecM9bX1ycVjSQSiUQikUgkEkmdvXv3snDhwhO+39ecM5ZOp4HohGcymROyT8/zWLduHddccw2GYZyQfUqODHmtTg7kdTp5kNfq5EBep5MHea1ODuR1OnkYGxtj6dKldR/hRPOac8ZqqYmZTOaEOmOJRIJMJiMfyFc48lqdHMjrdPIgr9XJgbxOJw/yWp0cyOt08uB5HsC8lS9JAQ+JRCKRSCQSiUQimQekMyaRSCQSiUQikUgk84B0xiQSiUQikUgkEolkHpDOmEQikUgkEolEIpHMA9IZk0gkEolEIpFIJJJ5QDpjEolEIpFIJBKJRDIPSGdMIpFIJBKJRCKRSOYB6YxJJBKJRCKRSCQSyTwgnTGJRCKRSCQSiUQimQekMyaRSCQSiUQikUgk84B0xiQSiUQikUgkEolkHpDOmEQikUgkEolEIpHMA9IZk0gkEolEIpFIJJJ5QJ/vAbzW8f2QZ/eOM1pyaU6anLOoEV2fm48choLeiQol1ydp6izIxlFV5bitd7TrHq9th6Fgz1iJp3aNU/F8sgmTntYkDTFz1m3MtC+AfWNlAH67dZie9gYWNSbq609eJ2FoCKDiBbOOdaZ9hKGY9XrPtv3Z9jXb+Zp8T2XjBp4X8Ni2ERwvoKc1wTndTbieIGXpJAyNgYLNeNmjIa5DAJuHC2wayLN3pEzFC2lvsFi7opmelhSOFzJScBguOuwaLWHpKoubE5Rdj2d25dFQaErqtGZMFEUjrik8tXucTQMFXD+gMWlw9qIsS9pTFEouL+4rUHQD2pI6Zy1txFIMRooORdcjaWm0pExyZZ9do2WECGlOWsRMFQ1YA9zxkxcZLftsHyhQ9ATpmEZz3KA3Z1PxQzKmztLWBJ3ZGOOVAN8XDBUqVGyPCTtAiJB03OL0ziQx3WC04oEIKVVsntlTwhWHvm9VwFQgALw5fP9IOCUNOQy8IERVQBECLxTkbUFwfHZ5zLA0wecvgFV3PIgTHJt3heTY82q8ThpgqmBoUPQgnOE7CiCIjCFDAaGAE0bPtQao1fWzSZOUpREGAlVV8EJBoeIxUQ5wZnjuTSAZU0hZBjFDpTFu0t4YY6zgsHGgwFhl/2g0oCOt05Y2KQegKgqtaYtFjTHyFQ8hNBY2x3nLmQsYKjs8tnGQ1cCXHtxEOmFRtB0e3zLMhsEKoYCUATddvICWdIrxskcmZnL16W0sbkzSn7fJ2S47hktMFF3KXkBDwqA5adI7UaHi+QzlHBwvIGf7xHSFdFzH9QWqprCgIcGqrgx9eZuhvIOmQL7iUXJ8UjGdRc1JVAVyFZ+ErpGK6/SPl9k2XKYxaXLVaW1c2N08xc6p/YY/uXOU5/aM8cS2MfwgZHlriv/rTSsxFJ2i45MwNQoVn73jZdwgpKc1SaNl8O2n9jBScOnKmlx/Vhe+DxU3wAtCXurNsW0wT8UTLGlOcHpXhvO7m9BUlZLjU7A9Sm70Fo0bGrYXoCgKi5vi9Odsntk9DsC5S7K0J+M8s2+MLYNFMjGdC5Y2c/6SJnRdjY5htMRTu8fYN1HBcQNMLWQZ8P89uYtTOhrpyMZw/HCKzTGTHXI8bKyaXTBcdBBC0N2cpCE+1UY6nvbdgduPGxoKUD6IDfVaQhFCHCfz4ZVJPp+noaGBXC5HJpM5Ifv0PI9f/OIXvOlNb8IwjPryX24c5O7Hd7FrtIQXhBiaSndzklvWdnPVae0H3ea2oQIPrh9k+3AR2w+I6RrLWlNcu6qd5W3pY77e0a57KI7meP710W38ZusIedvDDwSqAgnL4PTONJcub522jZn2lU0YTJRdtg/muW3JBP+wOUnCsrhoaRM3XbQYoL7OSNFhpOgACi0pk5aUNW2sM+1DINgzWmao4Ey73kuaEzNuP2GqlN1g2r5O7Uyzqb8w7Xxl4jrrXh5k12iJgu1RcHyCAywQFcjEdRKmju0FBKEgFALHD/ECwcFeCCozGzQnmshwDPibP2ivGsPx1Yq8VicH8jqdPBzutVIB01BZ0BAjFTPYPVqm4HiEIQd93x8PVAWWNif4X9efzlWntdd/w3+5cYic7c+4jqFBc9Ki5PjYXoAgcqK9WX6MDBVQFLxg5qOzNIXGpImqKBQdnyAMCYUgDBV0DXRNxXaDWX8Pa8sMVWFFW4obL1zMs3vGeWzzMBMVj1DU9rP/OgVCIR0zWNaaZHFTkmzCAAETFW+KHXLgsmNhY9Vsza1DBYqOTxgK4qbOyvZU3UYCjpt9B1NtosjGcQFBS8qa0YY60YyOjtLS0nJCfYPJyMjYPPHLjYN89v5NFGyP5qRJ3NSouAFbhgp89v5NALM6ZNuGCnzj8V2MlVw6G2IkzDhl12d9X46+XIVb13bPeEMf6XpHu+6hOJrjuetnG3h61ziBECAEmhK9KEuOz0u9eVw/nLKNmfbVN1HmFy/1U7B9WpMaAC1Jk+GSz0MbB9k+UiQdMwhCQdxQGS05lB0fgYKiQEvKnDJWYNo+Ng/keWLHKGEo6GqI0ZaJ16/3nT/dwNKWBAlTn7J9NxD05wLihoqhafV9/X7nKPc930tnJsaK9lT9fD2yeZCtg0UMTYlmEG2fmX6LQmCi4pOr+KgK6NVZ3nAOv8qvBEdMIpFIJHMnBGwvZPtIuV6bMl/v8lDA9pEy/7//epGPXbWChzcO8uSOMWx/9hF5AQzkHWpup6YcPAshctJm/4ITCAbyDhpgGAp+AGHVhvBCCN2pY6lFTyf/X1HADwUbBwrc9fONCCHwDzKZ6YeQK3tsqf5GP7XLBuD87kZ6WlL0TZR5aMPglGXHwsaq2ZrjJZcgDNEVBcVQsb2Ajf153CBk40AegCAUx9y+g6k2XmTjuJQcHwXB6Aw21Hw5ZPOJrBmbB3w/5O7Hd1GwPRY3xknHDHRVJR0zWNwYp2B73PPELvwZXk5hKHhw/SBjJZcVbSnSMQNNjWZcVrSlGCu5rHt5kPAAy/pI1zvadQ/F0RzP/S/1s743eonoCiiKimloWIaGpoDjBwzlbUYKNuteHsT3w2n7UhXoz9k4XpSmoCrR694yNNozFqamsr43z+aBPMtaEgzkHBwvpC0Toz1j4XgBA3mH5a1JxkouD64f5IGXBqbsQwE2DxQQocDSNRxfoClK/XqPlRzW9+bpaY5P2b4CuH6Iqii0pU0cL6A/Z+N6PhNll7ztEYZRJDBp6gzlbBw/IKarTJTcGR2xyQhAEeAFc3PEJBKJRHJyE7LfEZvP+OdoyeXfH9vGC3vGD+qITUYQRdb8Y/R7FQC+LwhDgRCgqcz4W3jgoto41OrkrzuHrBKIznvZ9dnQW0BXBKauVp1MwUDOwdSU+jJV4ahtrLqtWfGI6QqKopCwdBKmRiam4wWCoZzDpv48WwYLLG9NHlP7DqbaeMtbk/RXU2DbMxZtmRiOF06xoY5mXycz0hmbB57dO86u0RLNSRNVnXoJVFWlOWmyc6TEs3vHp63bO1Fh+3CRzoYYijL1VaooCp0NMbYNFemdqByT9Y523UNxNMfz1K5xHD/A1FUCAZqqRDNWRGkGKjBe9tBUhW1DRZ7dOz5tXwXbZ6jggKJE0clJeQ+KomDpKo4f4PjRC2Os7JKK6ShK9GJLxXTGSi5FJ6CzIcaLvRO81Jubso/+fIWJikfC0rEMlYoX4FZ/fLwgcswcP2DHSLm+fdcPsf2wPiYvEKRiOvvGK2wZKuH5IduHi/x66whP7Rpn82CenO2TMHUKTkB5tvyNA1FktEsikUgkJ4bar3wooD/vUPbmt+q15thpCoecwDyQI3JmQyi6Pqqmka7aD/05m7GySzpu1JcVqimbR2Nj1WzNTFzH9iPnr2ZmqWpk84xXXCqOTxAKis7Ua3G09h1MtfGKTsB42SUVM2a1oY5mXycz0hmbB0ZLLl4QGdozETc1vCBktORO+6zk+th+QMKcOcM0bmo4fkDJ9Y/Jeke77qE4muMpOh6ilpooYLIvV/t3IKKoj+MHjJbcaftygxAvCAGBoSocWEKpqCCq2yh7AX4Y1XrVMDQVPwxxq9ez7AaUPH/KPipuVJdlaEp1rCJKq6yOT1GiWbmC69e3H417/5gCIfCDkLGSi+0GWLqKoUV/hgs2mwYKeEGIpSvVGrC5nf/X3vyTRCKRSGD+3/9hOHMk6mActzEfmIt4qHGIIxtLOGk3Nfthsm0x2aaocaQ2Vs3W1DWVUEQTv5Mx1Mhe8Ksjcg8sMD+KfdeYbOO5QYgfhBja/nEYmkowyYY6mn2dzEhnbB5oTpoYmkrFnXlGqOIGGFoUITuQpKkT0zXKs9ysFTfA0jWSBzg3R7re0a57KI7meFJWNLsSVB2xyX6UqM92KagKWLpGc9Kcti+z+vKDqG7qwOicCKPZIVWBhKGhq2rVeYvwghBdVTGr1zNhaiQNfco+4qaGpkbFxNFYlfpLUVMUhIhqz9KmXt9+NO79Y1KBkaKLQGAZKiFRSmXcUGlKmpEDFgpsL0RTo/HOBVmmL5FIJK9N5vv9r6rM+beqhnK8vLGaKshcx6Ec2flTJ+2mZj9Mti0m2xQ1jtTGqtmafhCVOwQHTDZ7oUBTFfTqiCbv82j3XWOyjWdqKrqmThFW8YIQbZINdTT7OpmRztg8cM6iRrqbk4yWXMJw6kxEGEYRsaUtSc5Z1Dht3QXZOMtaU/Tn7GlRHCEE/Tmb5W2pukTq0a53tOseiqM5nvO7G7F0DdcPoxSDMMrZFoAfhIRAYyIS3ljeluKcRY3T9pWO6bSlLRCCihuJZUzev+OHWLqGpat0ZCyaEiZF20cIgRCCou3TVJU87s/ZnLkgy+oFDVP20ZmJk40blB0fxwuJGxpmVdbX0KIXpKVr9LQk6ts3dZWYrtbHJISg5PhkYgYJQ6svj9IOFFpSFpqqUHZ90pZGwpjjoy3kS0AikUgkJ4bar7yqQGfGImHMnCE0K8fYg9TrWTRRquLhcER+oQopUycMAgpV+6GzIUZTwqRQ8erL0rHIITkaG6tma+YrUYsC1w/rE9VhGNk8jXGTuKWjqQopa+q1OFr7DqbaeClLozFhUrS9WW2oo9nXyYy0w+YBXVe5ZW036ZjBnvEKBdvDD0MKtsee8QqZmMHNl3TP2G9MVRWuXdVOU9Jk61Bxyrpbh4o0JU2uOaN9Wr+GI13vaNc9FEdzPNet7mTVgkiC1A8FQoS4XoDjBQQCYoZGWyZGSzrGNWe0o+vqtH0FIlIPsqo/CGH1TWV7AYN5BzcIWbUgwykdGbaPlOlosLAMlcG8zWDewTI0OjIW24ZLNCVNrl3VzhtXd0zZR4jglI40ihrVhll65IDVrndz0mLVggw7RitTti8AU4/SC0ZKHoqikIkb6LqKZaioioIbRJK8gigf3NA0bD8kmzQP+cOiEPXUMbS5R9IkEolEcvKist/wm880xeakyZ9dvpw1ixuJzbG3qkLkNB2rnysN0HUFVY2yU4JwZqP4wP0pROmVYXUs0W+vcshxqUDC1Dl9QRpfRM5RR8YiBDoaLNxA1JfVbISjsbHqtmbcwPYj56fs+JTdgLzto2sKbQ0Wp3ZmWNmeZttw6ZjadzDVxts2XKKzwcIyNAbzDoN5G8tQp9hQR7Ovk5nXXizwFUJNtr7WZ2ys5GJoKqe0p7n5koP3GVvelubWtd31ng2DeRtL11i9oIFrzpi9T8ORrne06x6Kozme//uPTp/SZyyoqgumLJ3TOtNctqJ1yjZm29ebVnfW+4xBmbGSS9KyuLiniRsvnNpnrDlpVWeXlGoqqTJtrAfuIx0zuPb0DnaPlhgqOPSOV6Zc78l9xiZvv6vaZ8wLQvwwJAgFCxsTtKRMRgouY+VIIjYU0QzUtas6eGpntWg3ppOfoc+YBqTjOklLr9ezmSdZnzGJRCKRzA0VsAyVrmyMlDX/fcZ6WhLc/qaoz9jFy5oPu89YGKnQz/pbpCugqHPvM6YpYdW5UtDVmfuMqew/V4Jo/4amsLItxbsvmLnP2JQxqZCJGfS0JulsSHBaZ0O9p9iukRKWrnH16e3Tlh2tjTXZ1qz3GfNCEqbOivZU3UYCjot9B9PtruakGdX5I2hOWsxkQ73WkE2fTwCzNX2G/V3RR0suzUmTcxY1zhgRm4kj7ZZ+NF3Wj2eH9qM5nj1jJZ7aNU7F88kmTHpakzTEzFm3MdO+AHYP53nx978is+J8etobWNSYmLE7fcLQEEDlIN3jZ9pHGIpZr/ds208YGqEQ3P34bnaOFjlzQQOqGqUuFmwfxw/onahwQXcz//2KZVP2kY0beF7AY9tGcLyAntYE53Q34XqClKWTMDQGCjbjZY+GuA4BbB4usGkgz96RMhUvpL3BYu2KZnpaUjheyEjBYbjosGu0hKWrLG5OUHY9ntmVR0OhKanTmjFRFI24pvDU7nE2DRRw/YDGpMHZi7IsaU9RKLm8uK9A0Q1oS+qctbQRSzEYKToUXY+kpdGSMsmVfXaNlhEipDlpETNVNGANu/iDv5DRss/2gQJFT5COaTTHDXpzNhU/JGPqLG1N0JmNMV4J8H3BUKFCxfaYsAOECEnHLU7vTBLTDUYrHoiQoYkSLw3Y817gXiMG6KaKqVVVQ0VIxRcU3fkvwj8UspnwycGr9TrVjmS256Sm26ADhhJlC9gHWPkqkLJUWlIGhhJFUrxQUKh4TJQDnFk2rgMxHTRNIWboxAyVouPjByG5SStpQEdapy1tUg6ieuDWtMWixhj5iocQGgub47zlzAUMlR0e2zjIarGDjVoP6YTFntECv9o8wmDRr2/v3O40Zy9uRtdUMjGTq09vY3Fjkv68Tc522TFcYqLoUvYCGhIGzUmT3okKFc9nqCo/nrN9XM9nuOgwUfEJhUI6pnNGZ5pFzQkgEqXKVzxKjk8qprOoOYmqQK7ik9A1UnGd/vEy24bLNCZNrjqtjQu7m6fYObXf8Cd3jvLcnjGe2DaGH4Qsb03xf71pJYaiU3T8qH9mxWfveJl9ExUGcjYjeZutI0V8X9CZtXj/Jd10VPt4ekHIS705tg3mqXiCJc0JTu/KcH53E5qqUnJ8CrZHqVq/Hzc0bC9AURQWN8Xpz9k8sztStT53SZb2ZJxn9o2xZbBIJqZzwdJmzl/ShK6r0TGMlnhq9xj7Jio4boCphSyztzPefDqndDTSkY3h+OEUm2MmO+R42Fg1W3O46CCEoLs5SUN8qo10PO27A7cfNzQUImG047Gvw2W+mz5LZ+wEcDBnTPLK4pV8rQ5sWF1rFN6fs2lKmq+pZonH8zpNbwweCbL0Tdj056I04rMXZwF4atcYmwcKBGGIUBTihkY2FhkOtVneRY0J/ujMTq5d1XHQ6zPX67ttqFCfYax4AWE11faq09pZu6xlTj9oe8fK/MNDW8gmDNKx6eevYHtMlD3+6uqVLMjG6Z2osLE/z89f6sfxArqy8fp56c/ZaKpCW8piouJh+wExXWNZa4prV7WzpDE25Vodzr4XNSXqyzcN5PmnX26lpyWFNukYR4oOj2wapFjxqPghuqqgqyqpmM6qBRlaUxbP7JlgUWMCy1DrYzu1M83j20b4/Y5Rdo+WKVcNMl0BVPCD6Qa8qkTpVZ3ZGCUnYPWCBhw/pClpcvPF3cRNbYohs3e8zH//9rOUHJ/2jIXthYyVHCpelF7s+CEpS+cLb1/N+t4C6/tyrGhLMV522T5UYqzs4gcBZTdkeVuKv/2j01jZPvPv1rahAv/x2130TpRpTJhkYgaaGjXLne394Psht979FFuGCixrsnhX5xiffSmGqun4QcBoyUNRIGlqhKKqtiaqKrVhpDZraiquH2LoKs0Jk4uXNWP7IVsHi3hBdH8O5GwMTcWu1qyYWtTvaGFjnJ6WJKBMu95HyuTnw/aCeir6XJ+P2Z7/ubxnw1Dw1V9tr1/HyYJQQgi2DhVZvaCBD16+7KgMz8nvv93j9hGPd/K4D2aEH28j/Uh5pY6rxivZnpBMZb6dMZmmKJGcJBzPVFFJxIFNyGvGVDpmsLJdp+T65CseWwYLpGMGg3mbIIzkPGO6Rns6cqIaEiYNtk/B8WlNW7x5TReLm5MH3fdcru9kQzFuqIyXXIaLDi/sneCxzcNceUobN120+JD3Qq2oen1fjpSlTzMa+3M2qxc0UPF8vvqr7WwdzPPCvhxFx2dhY5yWlEU6FjUGdf2Ax7aMEDc11i5rpsuKU3Z91vfl6MtVeP+FCwF4ZvcY43ZIyfapuD5d1Uhxf74SCdKYGp2ZOHFTYzBvT5M3nqzKNdmJG8zbjBRdgkCgaUq1ZYhCvuLxh53jpC0NgUJT0qA9M3VsN1/czTVntPPZX2yif6ICCNxAoKsKBcdHVaLWEn41XSlpanQ3J/BCgaGpNCaiQvvn9k5w18820JIycYKw7vA1Jg0mKi6tKQul2suwy4jj+iGBEHiBIF9xEUR1FX25CluHop48Zy3OMlxw6M9VaE5ZfPiK5axsP/h1jekqwwWXrUNFFKAhbnBRTzM3XTjzPdFfrdloTBiMl6tRFTVK3YJqbUwYPRe6rlVrVKHWo1cJwRORg+X5IQMFmye2DWP7AjcIycYNYoaGpqosbkqgqQojBZeGhMEF3Y00JEwCIdg1UjpmctbL29L0XJHiie0jPLxhiP5chcG8zX3P9vLi3hzXrpr9XXmw5z9l6WwdKrLu5UF6WlIHra/uy1XYMlgkHYuEEYIwymJoTh3bmpijHS8c4LweMJFSO0+qqhwTR/lY80odl0RyuEhnTCI5iagZGq/k2cCTmUM1IV/RlmLPWJnFTUm2DxfIVyIRmIaYQVPCRFWg7PpoikLC0nCCEIGYc2PTg13fyYZXc9LghX05Km5AOqbTmDAYLrj8dvsIth9w26VLD+qQTTYaa8b/gZG4UzrS3PPEbvaMlhkvu4wUHQC2DhXpy9lcuLSZ7uYEO4bLqEq1pkJAyfFxg5D2tMVA3uHrv9nJpTH4xH3rKXkChUhwpz9nM1ZymahEtZ6aqpCNG5zSkSYdM6bIG4dhVHyeiRlsHy6yuitDyQ1xvICXe3MEgUBUe/iVnQBVVdBVhZLjU/ECzl2cpbMhjqIoUwzVhzcOcsWprTQmjWp6EoyVXLxA1KWmVaVaqK9CSzpyqoq2R1smRjqmM152GcrbFGyfjoZmFjSm6g5fxQvw/egeEELUnTCtGkVVlCglSlQVVS9f2crTu8YZLti4QaTkesmylkNOtkx20i9c2kgQQt72GC+72Ae590pupNx6zuJG9o0WgWpfoTAkCKNjF4BQFFKmRqHi1ZvkKkTy3pqmEPpR2w4ElNxovZihUXKDegqYAOKmTluDgu2FqGqkBFtx/GMuZ71tuMD3n9nHaNGhsyFGWzpGxQvqTvhs0aJDPf+Tm9LO5gQsb0vz+lPbuPvxXbzcl8MLov5R3c1J3nHewmM6adafs49qvNOjgFMnK15L2RYSyXwinTGJ5CRDzgYeP/Y3qJxZWjduRm0Obji7i6Lj8+WHt7BvvBJFqcpePW1QVRQMTcHQVBKGfliG5mzXt2YodmRibB4oUHEDmpJm3QhrTBrYblQ/eKjZcIiMxpsv7ub7z+zl5b48XhDSmDBZszDL1ae389CGwbojlqt4oIBV7UNTtH2e3DFKxfXoz1XqDc+f2zuB40diM5EzEzBWsLn07EhUJ5vSKTs+O0bKjOzLYWiQiZsYWlRsP1J0GN3hcu3pHfX6idrM/bahAr0TFXaNlnhhb46UFdVSDuadqEVQtd+gpkYNgBw/iuIQRk7cZGN1sqF6XncjjXGTQsUnV/FoT1sMFd1qzcn+An6DKE1xrOQSN3WWtaYA2D5Uwg9CEqaGqUc9BWsO3wv7JkCB4byNpqr19MRaj8BQCOKmzhPbRvnpC/3YfoClqbSmLc5b2sRpHZlDTrb4fsj3ntrH7tESy1tTpKvH2pg0WdyUqEdHupuieqHJTn4t2hgztCj11h7m4mUtFJyQTQN5dnklVKL7uOAEUxwxreqoqcrUbrlBGCJUlbimIlQRqbapCrYXkLJ0DC2qm3KDcEoUdi5y1rOlpU2u11m/L8dvt42Qq7hkExYjRZfGRCSZvawlwVO7x/nKL7dx04WLOHdx05Tapbk8/zNFbSezbajAI5uGSFoaF/VEtUlBGFKwfR7ZNMSS5sQxc3COZrzHIqo2V17p6YQSyXwjnTGJRCKpMlsqXI1aU8p0zODUjgyXLGvh+0/vo2/CRlcVTENDU9SqNHBkhHZlY8ekb0rN8EqFOmNll1RsanqhoakUhU9jwjzk7D1ERuN/PrWH320fYazkEgLjJY8FDTEGCzbbhgqUXZ+87eH6AY4X4vphPXVvrOzy5I4x/GpUyw0Evh/S1hDD0HQ8P2TXSBGlqnmWiun4QiUdM9CUyMkJw2pvn2pUpdYcffdoiTAU7Bgp8o3Hd7FntEzZ9RktuRRtHycIqbg+MVOLGqADuhZFwwRRs3aIdKdr0ZsDqRmqKUtneVuakaKL44fkbA/PD9FUMFTwqil5Xgh7xm16WhKctShLU9IkX/EYK7tYRlRTNblpqqIo9LQk2TFcYrjoRBLYmlrvLThe9giFwNQ19o6Xq3V4UWRi73iFkjtMT0vyoEbrtqEC33tqL/evH4hSAIsujQmT5W0pmpJmdN4tnV9vHWbvWAnbC6ekUV59ens9XTXdGt2jqgLbh4uUHR9NiWrvOtIWAwUH1d4v5x0dJNXWGvsJRLQNN4jUX2OGStzQMDSVsZKLqVfbcvjBYUlnz5ZOd2pnmp+92Mdjm4fJVTxqAnoqkTpeWybGcMGmL1fB8QKKjs8Le3P8Ydcoy1vT3LJ2v3rxgc9/TSTJDcLqtRUHjeJNdnBWtqdnrBk7Vg7OTOM9kIM10T0WUcC5MJc0SInktY50xiQSiaTKXGupajO715zewU9f6MMPRd1JCUQUldHUqEZIVY7NDHDN8Kr1gTG0qa9vL4iiUemYzljJPeTs/Zcf3soLeyfQFGhviKGgMFH2eHjTENtHSlS8yPnJV6Wa9WrtCwq4gaguE5iqgl2NQrlBSBiCqis4VWPc1PafP4CC4+OHkaMTCCg5AX4ooohRNb1w61CRHz/fx0DerkfnKl6A7YWYenSMeTtqy6AQyUarqhKdc0OLnIQQcpXIyZxJSm+yY11L2RRCMFxwKLl+1EReQMxQaUtbFKs1gOMlr34sbhDiBQEIhfaGWL1Raw3bj/r1BEGIH4Jd9ew0NTqfqqIiBKxoS6GqkSM3OTLx4PpBjDXqjKqttRSz3aMlNBWaUyZBCMMFm4Lt0d5gMVJwGS06jJZctg8VWdyU4JSONDFDq6eivf7UNvpyFbYPl+hJw/ahIvmKh6FppCyBpalYhkZL0mSs6CJEdM5DomikEFE7EUWJ0lRrKau2F5CNG2QTBrYfsqI9xWjBZfdYmXRMxw/EnOtdZ0un+/3OUb795G7GSi5+tZ6tRgiMl6MU2GzCoC9ng4BMTEdXQxKGxpahAp+9fxMQSYBPfv5dP2DHcDkSUQlDNEVBCLh0RcuskysnysGp0dkQm/P76kCORRTwUMg0SIlkbkhnTCKRSKrMpZZq8ix+3NRY2BgJUQwXXYpOUK+XqSnFjZe9Y2J81QzFP+waRVMUvGpNEUSGV9H2acvE0FXlkLP3D6wfYMtAAVNTaK6KSwC0Z1RGSy57xspUXJ+xkhsZ2GpkTIYiSourBp0IQkGgKAQCMjGNQERpfF1GDLfafF2rxlEcL0A3NPwgrPfoCf0oJFZrmqoq4AeCkuvz3T/spiFhUHaj3kIpU2Oi7GEZGrqqkI5B0Y7OdyAiFUQ/FChK1CdIqKKeRtcQnxo1qBmqq7oaCIXADwVnLmxgc3+OfMWtC3YkDJXOhjhNKYtK3GffeIWJisfz+8Z53YpWXD+k4oakYzrtaYvRkoupqfVasid3jFLxAixdRQujKFLkQEapf6GInNCiE5CJT42qxQ2Vn7/Ux4v7JiJp9EkRhZ6WVD0Cs7w1FQmYhFHUJjBU9oyX2TpUiOrdqr2k0jGNXMXjpd4cZy3KsqItxdahIpsHCtx8yRIeXt8PxT56Jyromk57JsYZKZOdI6V6RCtmqJScAEEUiU1UPe2i40dOsaaytCXJkuYE24aKVSebalpmJGxzfkOM68/snFMKZu1+3X+sSYpOwGjJYaLs0j9RZihvE4jIua/5IjWBEQEUbJ+y40fy9ZqKoipoInLCm5Ime8Yr3PPELi5f0Yquq1y7qp2NA3ke2zKCqkBDwsBEJ1eNZA4VHHaMFGd0Ik6EgzOZw31fTeZoompz4USmQUokJzvSGZNIJCc1x7oe4XBUKzcO5Nk7XkFRoCGuE4SQjumc0p5icXPymCrF1Qyv3okK/RM24yWX1rSFH0aOWNzU6WlJMpB3DlqD0ztR4aXeHIEQpOPTa6kMVWG44FC0/XoKoIqCqiiESiRpDlWlPSFoTJpVp01B0xQqnk9frsJw3iEU4FTDFTuHS7Q0RAYxCPyAaqNViGkKrh/ihZGzEgrYO1GmP6+SNDVSMYMgjOqtNCVyWnRVRVMDdE1BhALXC1HVKAUw8MNIodHQiJsa/XmbhKVPMVQ1VWG06PCPD29lpOiwd6xMEEbNUI0wRKv28stVz23c1OnKxtg3XmG44LKxP082btLZEGOkFP3fDwW6FqkTlp2AiYqHqkQRu0xcJwgjBUXXjyJ8Zden5ES9AqOqtIixksPmwUI93W1hY2JKROGNqzrqEZiUpdOUMBkq2ASmYCDvYLtR7WIyplOwI2GS8VIkOlJ2A7YPlzhviVmP1Lx5TRcfuHQpDzywiSUtCRY3ZcgmonujIW5EMvslB12LjkVXFRQlevY0NWqi6wSCdEznrEVZmlMWTUmTbYPFKZGwMxcevvJrLdoUN1Se2T3BnrESoyW3mga5/3u1RryqqqAqoh4lC4nuMVON7tea6I2hKaiqSnMycjif3TvOBUujfoptKSuKagNlN0BXVRZUJ1dGS+6sTsTxdnBmYqb3lampLGqKc153E5auEYZi2lgPJwvgSDjRUUKJ5GRGOmMSieSkZcdwkYc3jR7zeoS5qFZuGyrw8xf6Kdo+2YRBQ8LECwRF22P7SJl0VZjiSIyv2RzM5W1pbru0m5ih8uimIfaNV0haOi0piwXZGKMl95A1OCXXp+z6QCTPPpmK6zNSdKi4Pqoa1V4JqDtItU3Won+GpnL2wgZe6s9juwGOH1JyIyEMpdoQtoYThuwZL9OeslAAt1aLFYTkKgJFiVI9g0BgmSqaolSVDXUaEiaIyCEMhECv/m1oCpqqoWsKthtg+yElJ8DQVJqSBm2ZGGsWZmlKmOwYKdUd686GGEMFh/68TUcmRt9ElKKoKFHtmwBShgYIKl5Yj/bpmkpbJkZT0uSd5y8mbel89w97GCo42F5AJmGgoLB3rMJY2aUhpuMoIYau4YeiLgoSCIHtByiATYDr7/cqhBBsHypF91XcoDFhThEF2TpU5JcbB6l4Pl1mpBC5rC1J3vboHa/g+gGiWsdVcqNUTl2DohNgj5VpiBv05yoU7DQJa3+kRk1HzkNrMoauKXUDuilp0dhtUrB99oyV2TxYoD1t4fghBccnCEI0LcQKYWFjHFNXq2m06hFFwma6X0eKDqMlh7GSG9U3hkT3wKT8Uz+MJg6o3qeTUxYhip6GoUAQYnu1yFHkdI2VXEZLLhA5ERMVj7XLmgGlXi+WrtZomro6qxNxvB2c2Zj8vtrYn48UOfM2P3qulwf0gRnfiUcTVZsLJzpKKJGczEhnTCKRnLR8+8k9jJT841KPcDDVyloKjuOHLGlKMFx0SFlg6SpmNVK0bahAQ9zgzIXZwzK+DlXwvrwtzd9efzqvP7Wt3kcp8qmUOdXgJE2dhKkD+1MdRbUB8WDeoVKVQo/pGgKB40fRKkQkHw/RzLapqWSTBk0pi85MnMGCTUNcYddogKZCU7VWyHY9ADTADkL25SowKaIRRcciy1kQ1R/Vol+CKAXO9QIsQyNuRE2VNSNqNGwZGnFdw9AUcorPgkad7uZkJLceQHPa4qYLF9PTst+xThgaP3m+j/6czYq2FAXbZ6LikU2YhEIwVnJxvABPVzANHVOParYcL5Jpb0gYdGZinNGZ4acv9BMKuHxlS72+KAiDqIZNiaKkth85W44X1cZNPu7o77DqHEcUbJ/RkgMoNKWsKXVotYhC70QFBPUITFPSYkV7it7xShR5q57foLoTRVHQNYUgjCI9Rcdn+0iR9nQMU1OnTBYsbUnyUn9xijMRtQSIzsU1p7fTlDDZPlxkouKiKirL21Kc193IloHilGjykUTCDiRhaIwUHUq2T8n2CcPoOROAFwbT6sREKKIIniLqYh5APZUxG9PRNJWSG+AGNmkrmlRorgqe1JyILis+pbl4jYM5EcfbwTkYqqrg+AGPbRme1gB6tnfi8exdOR9RQonkZEU+BRKJ5KQjrFpg4yWXFW2Zo6pHOJI0x1oKTlc2RmvapFitr0rFIuluU1fZPVrm/KVNh2V8zbXgXVUVLl3RyiXLWg577AuycVYvaGDncIlCxSOwBOMlj5LjU3A8hKj2hDJUTF2Loh+hwAsiw9dQIBACS4/qqTJxoxqZcembsAmFiGTno05UpC0dCBAo9bSy1pRBVzbB5oECjh/W4xuqAg0xHdPQKDkBmgIKCr05mwXZGNmEQcXzGS+5aGokmGKqKo0JnZihRTVzmoqlayzvSk0xKGuO9d6xMjtGSvX0KTeInKGCLbC9sBoJhPGyT0NcqUd6xsoeDXGDhKGzoj2NgHoaVs0hqinvOX7Ac7vHcfyQpKkzUnRxg0j4ota7CyVSkgwFvLA3R3smRsLS660EWlIWy1pT01K84qaGpkQqgf05u+401SJuuqZETp8CqogclEhghnpzZi8MeWHPOEnLYFlriornU0uTvOq0Nnrz7qzOxLvPX0zMUNkxUgIi521RYwJVVbjq1GMvYV51JyMFTS+I6r6qm9RmiIApQBBUj5+6UCcqkE2YUbqtEBhqlBo74PqcsyjLOYsagaN3InpaUly3qoOHNwzROx5NlMQM/Zg4OAfjSGu0jlfvyvmKEkokJyPSGZNIJCcd/TkbgI7M0dUjHKns8uQUHE1VOGtRNqqrKbuUHL8eSbh+deecja+5GlMz9Ys6HMNJVRXeuKqDTQMFnt41xmDBQQUUdX8URBECLxQ0WjpeKLA9nyCMjFhUED6UvICEqVbTBVUakxYFJ4q6hCIS4kiaOm1JHXBoy8Tw8w5BKDhvSVPk0AjBy335en8wtZoGpimRmIepqcQtnaSpMVxwMfWqyEcoqPgCCMlXokjSRT3N3HxJN61p66Dn5cD0qbLrk69ETmjM1DA1nVAIPD8kZ/uY1TqphoROY9xkcXOCa85oj9QdJ21HURQyVaEQIQT70hV2jpZojBsM5m2USYqDAjBUBava48rxA3rHKxi6ih8KmpIWK9v3y9NPpuIGxAyNN5zexv3rB9g6VKQjY7FvvFxX/TO0qGG1aWiEIsT1owinEBAoAkOPRFdiRiTAcc8Tu3n/hQsB6GlNzRotOaUjzUMbZn9ejkcPxIoX0JIyKdheXVa/loapKlPrw6Caokh0nhXA1BXa0hbjZS+q4YPqRqJJBVPX+KM1XfV+Y0fjREx+n1S8ABRoy8S46rR21i5rOa5CFUdTo3U8rtt8RgklkpMN6YxJJJKTjlqKUE3N7UDm2pz1SGWXD5w9n1xX4wZRPy4/CDmtMzPnY5qLMfXsnnE+/+BmRorOUdXILW9L87HXr+D2H77IlsFCZLwGAq3qRKqKQskNKLs+DXGdkuNHSoWArqhk0zpxXWP7UAnXF7SkLC7uaeaGs7r44oObiZsamZhBEAomKg4A+YqHF1TrwkJRPaY4G/sLkTNWVWv0QoEbBBhVCXvXF3zwdcvZPJjnyR2jDOQiYRBDhbihoaoqjh/y+2rPs0+86XQAtgwVZnTKJl+7lKUzkLOjtgRExx8IQdzQWZg1GCw4eIGgOWWyvCXFyo5MPbqxd6w8awRFUaL+coNVpb9avV0tmldL84ybOtmETtEOuOaMDlYtbKinUb7cn69/t8ZkZ+CSZS10NMR4cP0gL+wdZ9dICc8XuIToarRP2wswdRVdA6fasVmrqm1qqsLpXRl6WpJsHSryyKYhuibdHwdGSyqezz1P7D7hMuVJM6qJRMBQ3q63kVCUKF0xpipTGnT7VScspit0t6RImiorOjI8sW2EoYITRQ1DQInaCyRMjcbEfqf3SJ2IA98nXdXz05+zeWD9AJ0NseMq4/5KrNE6nmmQEsmrCemMSSSSk45ailDZDUjGp7/GDpVKdLSyyzPNntciI7XmrgdLwZkpNfJQxpTtBWwZLGB7ASvb00dtDMdNjcVNCZa2JKuGrGDvWJm87ZMwNfqqQgaOHxn3qhH1TGvPxLhgaRONCYMXe3MsbUly69qlLGpMEIaCHz3Xx5ahAglTY6jgooqoBs3UlUh4QYe+nE17Q5zmtEnC0nC8qCdZrRF0KqbTGNcpuyEJU+W0BWnefGYnN/2/v0dRFBKmSjpm1tPV4oZK3vZ5fm+OO3+ynrZMjIrnU3ZCGhMmV5zaypvPjKIfk69dezVi0paxGCu5lN1IAj1l6mhVQ10I+JPLerh8ZdsUx+5QEZSKF3LlqW1MlF32jJWqy0FTo/TPtKVHkS8l6rXWkrY4tSNy3t+4uoO+XIUX9k3QmDBJx3R0VWEg70xxBpa3pdnZUeK+Z/eRq/j1dM8w3N+Y2am2IoBIqr8tHYlWhAJaq20NOhti7Bgu0RXbf39MjpaEoeCrv9o+RV5+vBzJ+C9vTbJ1qMT3nt7LDWcvIG0ZxyTNrUbtPJccn9aUyWjZI2lq9Uiq7Ydk4wYFx6+ONeo3pigKI0WHEaJ+Y4amsqorQ9EJ8MIQQ43aKYwUXZ7ZPc4bTtvvYB2uE/FKkHF/pdZoHa80SInk1YR0xiQSySuGudZvdTbEeAEYyNv0xMzDrkc4Wtnl2WbPy47PjpESKUtn9cKGGfc9W2rkmYsasDSVwXwFU9emKLgJIdg8UMAPBMtbU3Vj62iMvZLr4wQhPY2pulBBQ9zg+b0TVNyA5pTJYN6p9pCK0ge7WxKsXpCtp88ta00xUY7k21U1+nPL2m7+/hcb2TlaRgUaq/2zKm6IZag0xHQqXsD24SIr26JUvFzZpeIJkqZGZ4OFqqqUHB9dV1nUGCdtGTzfO0HvRAVTU4iZOpMvm6oqGJpC0fHYNFgAhahZdMXDC0Ie3jTIt3+/mw9duZyrTmuvX7ttw0Uqnr8/8lKMImFFN2rwrKuRFP+2wRIX9QRTzu1cIig3XbgYTVV4cV+eouPRGDfQNAVdjeoKAQbzDg0xg5ihsmkgT9LUCcMohXC44LBtsAgKNMRNLu5p4sYLF9edgV9uHORz929mX64cjamaBlnTR1FFtfdWtX5KUxQmKh5jZY8FjXFSVhRZjpsaI/lg2j1Sex63Dxd5cd8ECVPl6d0TjJejJsu6FvUec/2Ql/tybBks0JSwjomi6UznuT0bp+AGVKrNv0W13UHZDVGIUlh1Tat+JvCCENsLGS97LGyMoapqvZ+bqIq1dGZjDOXtac/64TgRk98nEEWBJ6swnggZ91dyjdbxSIOUSF5NSGdMIpG8Ijic+q2aQdSYNI+oHuFYpPQcOHu+bajISDFSwRMC7nu2lxf35qaM/2CpkRv68/Tnyuwbr2BVnbG2jMXytjSaEtXJdWZj9bqkGkfas2emmfSmpFWvfxssRP2KDFWhK5tgWWuSRU2JKUbeTOfpqtPaGSk6fPHBzVS8gKIdGflNKZNFzWlGig75isdg3qanJUlTwiRf8cnEFBKWRqUaiWtNW+iqyjmLG1mQjbO+L4frhyhKlE44GSGoN5MOQsFLvXmCUBA3NVJWJAayebDAZ3+xqT7GW9d2872n9rJ7tMxo0SVmRJHCibJHEEYqk6oCtheyoX+C//OQzU0XLOaSSbU/c4mghKHgdStaeGjjIG4oSJuRep8bhBTsyCFuMU3u/cNenGqK63DBIRM3uHBpM0EoKNgeY2WXirdfhtL3Q+5+fBcjJYcw3C8MUmsn4Fdr0xDUxUK8MMT2owjkvrEyv9k2wuoF2XoLhslMfh6HizabBwqAQkxXaUpZGDGdgu2xYziK+qUsnY5MnISpHfPUxcnn2dRUtgwWq+0XooNOWTqKgIofkonpqGr0DFaqdY0TFZ/BvEMmpmMZOl4Q1nvzrWxPk694s6ojzuV5qr1PbE9jY//4FGe1MWHS3ZLA8YPjmiIoa7QkkpMX6YxJJJJ550jrt9574eJ6n7HDqUc4Vik9tdnzJ7aP8J0/7EFRoKclSdIypo2/pyU1ayqT6wc8vHGIihcShgFF28fQVMbLLgM5m8akga4pnNKemRbJgyOrB5ltJr0paZFdEqUgti7Kkq9EUZRMfGYxiZnO05pFWc5b0kjc1PB9H+jjTas6UTSdsZLD1sEie8fL7B4r0Zg08UNRjyBoqlJ1QHyaU1bdgGxOmlGjZEfUe43VCEIRqRUqkSCHpqpk40bd8ExYCp4fMFFxueeJXVy+opXlbWn+5tpTAYUN/TmWtSTZMlhkLHQRAsZKLhUvaphccny2D5fYPVLiutWdvHFVR/3+OlQERVUVbrpoMUNFhy2DBQr2/mvkhwJLU0mYOo1Jk7ih8fsdowzkbYIwxG9ORPWISZNFTYkpEdCn94yxdTBPGIqqOiD4Iqp9U9mvpAj7Uxa9QBAzVBKGhhsI9oyV8QNBY8Lkkp4sFKLvHfg8Ji2N9b15bC9AWFETYUVTKNpBXTDDDUJiunrcUvNq5/mPz+oiV3HZNVpirOTy8MtDJE2Vx3eMRY2a60I0YOoqrhcS0yOFyfGyj2WE6GrUL25ZawpDU3C88KjS95KmjuuHPLN7jCAUpGIGRkzHCwTDBZuxksOipsRxTxE8njVaR6I6K5FI5oZ0xiQSybxyNPUWPa0p/ntH9ojk3Y9lSs8Le3MIAWsWZmcd//VnqjOmRgohWN+Xp+j4ICL1NccPKbsBthdgeyHJmM6KthQxQ51x/0dSD3KomfSFjQluvribhzYMsr4vRzpmzPk8JU29Kk5hkLFiUIn2J4icvdM6FRoSBjdesDiqB3I9fvhMH9uHi4QiJBs3OXNhljec1o6la2wayNOWsuhpSfLCvhyO66NZRj1VMQijmjNNVRChIBHTptwDmqLgoZCJ6ewcKfHs3nEuWNqMrqu88/yFfONxn11jZfZNlCk5PkEY9T4TIlrXF1FkKW/7PLVrjP6cPWWC4FARlJpgyvef3sPLfXm8UJCNG1TcAEVROHtxdN9EEZqAzkws6gU2XKIxYdZrEmsR0Ce2j/C9p/cyVvYIhSAMIVAVdA3CmuplVTFQrQpdJC2dIAwRKKiaiqWC6wcM5G00VeGKU1rZ8vSmGZ/HXCVqJaApUT+6oYJNW9qi4gVYuoo9KWIHRx6tPRS187yIBKsWZNk0kOf3O8YIw6gXnnHAc68pCiiQtHTyts/yliRdTYl6+iBwRPWdB75fOjMxHC9kouKxuDGOqkbPqaUrGAmDPeMV2v2Qzkxspl0cU45HjdaRqs5KJJK5IZ0xiUQyrxyL+q3DNfaOZUrPXMe/c6Q0Y2pk3vbYN16JanqqRnNLSosUGUPBeMkhDAQ9LSn2TVSmOI9CCPIVj23DRU7vyhy2sTd5Jn3bUIGdIx6qAsvaUrz9nEWs7Eijqhz0PL3htPZpht9kZzfdOvV4hRAM5B3WLMzyuhWt7Bgp8ssNwwwXbAIRpSC2piNp9wNl1NsyMdKxMhNll8D2MLRIot32IqcmaemRgqA21WkNqqqECVNnpOgwWnKnnYNv/m43z+4eJxQQ01W8IBIdSVk6ICh7UT+yroYYo0X3sAQrtg0VeGjDICNFF9NQiSsqLSmToYLDkuZk/Xq6QaTCacR0UorOWMmlYPv11NS4qbFtqMh3/rCHsWK111oQ9RALhEAECqYGgVAIquqJgYi2m1EVGpMxyk5AxQsIwxAvEDQldVKxKFIM0f384r4J4qZGwfZJx6I+aZ4ffT/wfCpegBeEOH5IWK1/i5sa3iSN+ROh3leLcDte1JPO9kMMTUEhakcQiEh5MWGolN2Qsh85j3FTo+j4h3zW5+qE9OdtLEOlMWEwXvbq/QZr6ZDZuIGpq/Tn7RNSO3Usa7SORnVWIpHMDemMSSSSeWW+JJmPVUrPXMcPzJgaOV72cKoS5KCgVaMglqFhAaoKo0WXntYkZS+oO0W2F7B5oEB/zkbXFGKGxr/9escRydyHZwhyFY9cxaPoBmwbLPCDZ/by9nMjh2yqw+aiKirLWlN0tyb4zh9201+Vh48b+43VmrO7fbhETzqKXpW8cIoBvGOkOMXQW9CYoOz6bBwo8MjmYTozMVa0p+oGYH/OZmV7mn1jJfaNVyi5UWGUoSkkLY2YruJ4kcy5pdYcVnD9kKSlE4ZRT7TmA/p3LW9L86bVHTz08gBxU8PSo2tm6LUGwwp6tUnwaMlltOjOKFjR0zI9IjHbMW4ZLLBnrEx7Jk666kObmoquqXhBNM6SE7VKqFF2fEaKDooC5y1pZM9YORpn1fAPQkFlv3gi0cgjCXfbCwiFoD0TIxVojBRclCDAD0J2jZT41u/3sAb4Pw9u4nc7xjF1jZSp0ZAwyFV8AiFIWBqeH+L4IuonF0IsodGUNPGDaFkt6nQi1PtqTv/vdgyjEAln6CqoqoquRs9R2tKwfcGpnWkuXdbCztHynJ71w3FCSq6PqUf1jbtGyvV+g1o1HbK7OUFulrq0VzK1KOlo0aGjGrEXInLQV7SlTohKpETyWkA6YxLJa4xXWu7/fEoyzzWl52DnbK7j72lJzpoaGVabLGcsva6yt5/oe53ZOOcvbeLB9YM8t3ecLYORumJnNsYp7RlihnpEs9Xbhgrc88Ru9oyVKTs+Rcdn0A/YNFDkyZ1jfOyqFVx1Wjvh6ZCruORtj4Lt8cuNA4w+HRm/DQmT1pRFPBubMoZb13az7qU+KPaxe7SMrht1A7inJVWXS5+cnpqydHw/pGB7tKbM+rmqpX0+t3eCpKVz5sIGNFUlV3bpz1UoOT6Fik8I2L5LJqZhGVEtj6EpZOM6w0WXlW1pWtNWXbmwdi0zMYN0zCAIwyjiBHWhkJpAiKLAjuESQkQpkZMFKzZWUyknKl49itLTmmSs6M6Ygru8NcWO4RKbB/O0pFrqjcIbEybDBZukpUUy+Np+9b8dIyVAoacliaapnLukkYc2DuL5IZoKwQGCiGqUpUdYTbf0AsFQwSEMBRUvIBPXySYsSq7P73aMsKYHfrl5mEqgAAHjCvTmKphaJITiBoKkpWNogtZ0FNlzvIDhgoOqKqzvncDQNBrjBrqucnFP83FV71NVhVM709z3fC+oKpoaydoTCip+iKpGjaFb0xYfumI5V57SNqd33+GmTtfeATFD47zuxnq/wZpjWnT8KN34BMvKHy29ExWe2zvOeMll12h5iijJ8rbUCVGJlEheC5xcbwaJRHJUvBJz/+dbkvlQKT2HOmdzHf/CxsS01MiYoeH6AaAQBIJsYvr6ubJHQ9xkaUuSJc1Jul+X5PMPbsb2Apa3psjEjSmOzOHMVteMzj1jZcZLDrYXkorpZOKRqMhAzuaLD25mz1iJ5/fmcLyAhrhBf85mtOTi+ALD0qr9mhxKrs+ahQ2MllzWvTzIBy9fxgcuXcoDD2zizy9fRjpuIYhU7p7ePca2ocK09M6C7TNe8WhOmoyXvSlpelCLDrlcsLSRTQMFto+UCENRlTWP1PXcUDBRCYj7gsaEQdzUGC66xHSN1pTJV365bdq1TMcMFjcn2DdepmB7QCQMghJF1upNqYOQbMLE8SPBClGty3p29zhJS+PS5S10WVEU5aldY+wZLddrwiaTiRt0NsTon7DJVzwaqo2HOzIWwwWb3gmbJU2JarqgR99ENfpoKgRhdG/0tKa42PH5w84xyu5+T0wlksaPmSphKCi5AUXXJ21p5MoeihLdKy1Ji7LrY6gqe8fy0LNf9AOiFMcggCAIiBkqmgJFJyAdj+4R2wvonbAxNMGCbIxswqTiBuwYLZGJGZzSkT6uEz1hKNjUX6AzE6M1ZbJnrMxoya06zgoKkE0Y/M83nspVp7UDzMlpONzU6cnvgBVtqSn36/F+hx3PybWNA3m2DBYwVJV0fKooSdHxWbUgg11tU/FKmdyTSE5G5tUZ+/Wvf80XvvAFnnnmGfr7+7nvvvu44YYb5rTu448/zuWXX86qVat4/vnnj+s4JZJXA6/U3P9XiiSz74c8u3ec4YKDQNDdkmQwX+GLP1/P5mGXkMjIPavDomi7U87ZXMc/OTXyub3j7BkrU3EDDE3F9nx6J2zaM4J0zMCrSp+HQnBxTxOLGiMjsj9vM1J0WNmenhaJO1zhhN6JCtuGCpSrM/dNSXNSPVqkSLdrtMT/WbcVS4+c1jCkmoKlkI5FKXVFO6CzIWqgvGOkzCntKbYNFdkzWmIgF0mfbxkosG9ihJ2jZWw/wPEC9o5XOG9JE+lYZLQWbJ+hgh31/kpa5GxvSppewY76fykKrO/N0ztRQQhB3FARKPhB1O24LW0yVnIJQkEowPUFCxvjJE2d0bJLU8KkJWmhqUr9/r/54m7OXtSI44d4fkDJLVF0/Eh9sNr82Q1CGuI6JccnFdPZPFBgvOwyVHAoez5lV2ffeIUFWfBCQUM8UtXsm4gEUSYb9oqicEpHmuGiw7bhIm1pi74Jm+GiQ67s4ocwUfZY35vD1FUcL+qp1Z9zmCiP0J6OsawtyZkLszTEdB7dMoztBXh+pEyZjhn1fnB9ExVyFY+CE+AHkXhIU9Ikb/sYmsJQwWayBoemRNG0ehNpIF/2aEmbaKpO3NCYKHuU3RBLV1nYFEdVVHIVD11V6WlJoqsqmwcKXHlK25wmBY7Eoag5TSvaU6QsnVM7Mtiez3jJw/YC/FAQN1VWtqcOua3JHG7q9Hy9w3YMF+tqssd6ci0MBU/vHKuqbWpY+n5REjMZPV8v7J1ACMF3n9yDpimviMk9ieRkZF6dsVKpxJo1a7jtttt461vfOuf1JiYmeP/7389VV13F4ODgcRyhRPLq4GgUC08Ex1OSeS78cuMgdz++i61DBYpVByiEaSpxIfDsgAMDw5zeGUXFelpShzX+Wo3WlqECbWmLzoYYuqrwux1jjBQd+iZsGuJRDZmmKqxZlOXGCxfXr8uxrLEruVEUqlh1Lmr3RcX1GcjbVNzIeHd9n1BE/Z2CUNCUNAkFWKqKooiqoIMgFdOrThDsGSvz8e+9QK5s87GVcNfPNqBoOmcvbuDUjgaG8jYb+ws8s3uMUzrSjBTcai+tgPGSR8UJSMb0KWIcbhDieAGOFwldBNUasFoURNcUXF+goLCsJcFY2ec9Fy7mgqVN/OLFPn69dQwhQnYpJeK6RlPSoqc1wWjJ5eGNg1x9Rht9uQqjRZdzFhtsGshjuwEIQczQAEHJCVFVhZLjU7T9KNLnRoZ/zvN4dMswSVOjIW5gqCpuEDkaB0b4IIperWxP05gweHrXOBUvIGnpLG9L09lgMVb2qr3Tov5qLUmTkuMzVnIZzFfI2x4r2lOR4xfTSZo6ZTego8EiM0n9srslQd+EjaLASNHBNNS6iImqwETFoybUqRA54ZoCYbg/UuaJqPH0ed1NGJrKWMnlub3jZOI6l/REaZYHpuZtHSzw9O4xMnFjVierFnneNlRgvOKiVesR337eAla2Zw55/9aeBUVRyMQN/DAkb/uMlV28IFIj/Y/f7uK9Fy+e83vkSFKn5+Md9u0n9zBS8o/L5FrvRIXhgkNnQ4xcxcPStfr9pCiRQMqukRLtDTG6srEZ23lIh0wimRvz6oxdd911XHfddYe93gc/+EFuuukmNE3jRz/60bEfmETyKuNoFQtPBMdDknku/HLjIJ+9fxPjJZcgjBoO277A9sVB19vQX8DQBnjzmi4WNSUOq/7soZeHcP2QcxY31q/HZSta2DpYYMdIGVWFZS1J1izKcu2knlZwbGvskqaOpoDjB3VHQQjBWMnD9kK8IERU4yNxQ8MLQipeJGJh6QqBiFLXPBH1/orpGiXHZ8tgnk0DeWKGxoJ0FJ1RlSg98dndORKmQXdzgiVNCbYMFZnYMUrK0knHDdKWjlN1EhVFwfUD8pXIEbM9P+rFJgRJQ0cID+2A86uqCo4fYhgmmhqwvD3NrtEyP35hADeIopC6quBqUfuAguOxoi2K5L15TRe3ru3mO7/fw+93jmF70fHafqTGGISCmKniBwLbC0nHdPaMVXCqDacFREIWto8IBU0pk1AIRooOQwV7xvS1sxc1EoYhQwWHBdk4lq6RrjrGCxtDfvJCP0XHpyVp0DcRKU46QYgbwEgxqpeL6SpjZY+kpdORsfCDqfeuHwoyMZ1MXEdTFVTA1KIUvpITMFJwp3w/csgUFA3C6rYUYGFjguaUBUT3jOuHdDUnp6TK1rC9gJf783ztV9sJEdOcrDAUUX++J3ezZ7TEWDm651QFNvTleHLnaL1e8WD37+RnYazk8PzeCSpuQCpWq7/02TkaiajM1UE40tTpE/UOC6uqleMllxVtmeMyuVZyo4bkp3Skeak3x1jJ3a8S6YcM5W0EcHpnpt6D8JUyuSeRnGycdDVj3/jGN9ixYwff/va3+bu/+7tDft9xHBzHqf8/n88D4Hkenucdt3FOprafE7U/yZHzar1W+bKN53ukDBNFBNM+Txow4nvR99LTDfwTSUfaAKIxBIE/TZQAjt118v2Qbz+xA8dxyZhQ8cDSNSoFH0s79Pqb+icYLZaqY57b+HvHK+wazrMgY6KyX/quOaHT1J2luynOeMnh1rWLOXtRI6qqTDnOlrhGW1Jn02COnpZU3XCHyFAcypU5oytDW1I/5PlpS+qsaImzfTCPCHz0ag1bEHjoIiBUBHEjingEftQHzVQFiAARQOAJTFPH0iCmCgh9TEWwc6iAqQpWNFsoIjrAhrhGMlQo2D4besfobjRZ0ZZg90iBouPTnjJJ6OAFPilLBaERhgGPbx0kYep4YYjnCzQlRFFC0qZCTBNohOiqghACH4gbCroa4ns+SUOhXHb55hM7CQOPppiBrqkEQuD5AQEhFVswlC/TGDfJl210VcHzfTozOqe3N5GwomjfntEyQwWH0XwFLwgxVJVC2SEIAswDNFcE4Po+o4UAS1MIEGzYN0ZHyiBhaVTckIG8TUvSZHVXkp++0M+KlgSpWO0nObov+ifKlCt2FIXUTVKWFolw+D65igcIRKCSSugYqo7jh6iEmCoUyg6pmIauKpQqHqausrIlgSpCenM2i7IxFFVFhCFxQ6BVb0RTFQTVOjgAXYlSPRXAUALCwKPihgzmSjRYGouyU+9jgPGyy4t7xilVXMb1qNm0G4RsH8zx7K5h/ttZC8jbHj97oZ+94yUOCECjK9Gz/dVHNtOVNlk+S5phW1JneUucDf15UkaC3cMFfM+nLRU9f+MljwUNFmu6UuwYKfPQ+j4WrV06JwfhDac2M5ArsWMoT0cmRtxUp1y3q05pnvX9NJd32JHg+yEv9E6wtT9HBuhI69POvQIsyJjsHMqzZ6TAgsYjq1OLqZDUFRK6wjmLMuwcLjFe9nDdKFpradCUMOlITf1NOVb7fzXwarUnXo3M9zVShBAHn/49QSiKcsiasa1bt3LppZfym9/8hpUrV3LHHXfwox/96KA1Y3fccQd33nnntOXf+c53SCSk+o9EIpFIJBKJRPJapVwuc9NNN5HL5chkDp4efTw4aSJjQRBw0003ceedd7Jy5co5r3f77bfz8Y9/vP7/fD7PokWLuOaaa07YCfc8j4ceeoirr74aw5jfqIPk4Lxar1UYCv7f3+5kQ3+eZa3JaWk324dLnNGV4bY5zhrPN8fqOj20cZD//YuNZBMmQ3mbmKGRr3iUvLlPZf+Pa1ZyyyVL5/z93vEK//LoNhrixqRIyH6KdhT1+PCVy6fMKu8YLvLtJ/cwXnKJGyo7hkv0TlSoeAGqotCRsVi7vJV3nLeQvWNl/r8n97BnrBxFcTSVxU0J3nPhYi4/pW3aPh/bPMTXHtvOSNGNaojKbtSrS9cIRa15sCAMBQoCL9LJQFMVDE0lmzAisQRDY0lzgvW9ORY3J9BUFeH7vGvBBP+0OU5IpO5XsH3WrmghmzD5/Y4RTE1jVVcG09AwVZWkpfL83hxDBZsgFJzamaEtFSMV0xBCcP/6QYqOj6UrjBRdQiGiZr5G1Cw7BNrTMW5e282jGwfZMVLED6MUzpih1u9/P4wiZKqq8sYzOnjvRUv46q+20xA38MKQl/blqLgBZdfH8SKp9LIbEISRtH3FD+v1VX71nAiqaX7Vf6ctnQWNcUZLLpcsa+a9Fy8hbUZKiqqq1O8HiNLfRoouXrX583jZxa8qRSZNg6QVpZ+OlFwUJXquswmTtctaSMd1xssum/vzDBddlrYksHSN9kyMK05p5cKlzWwbLvK1x7aTjZvsGi0xUfbwwxDbC5goVrjj3JD/+2kVJ4xSGQVR/VgmZvDm1V3cdNHiqK7N1OlsiPGbrcP1+8bQFUxNw9RUhoo2QkDC0GjLWFMit3vHyuTKHjFTJWcf/DnTgLXLm/nE9WccNMKyY7jIvU/t5ZHNQ8R1LZJfTxosbUnRmKhGqMKQ3aNl/vzyZaxsn3stUxhG16WWdli7bieSxzYP8aWHtlJ0PJoSJpYK71k0wd8/b6BoOhcva2ZJ8/7J5dneIYfL5HfO5OjgztEi+8ZszlyYYUHj9EntY7X/k51Xqz3xamR0dHRe93/SOGOFQoGnn36a5557jo985CMAhGGIEAJd11m3bh2vf/3rp61nWRaWZU1bbhjGCX845mOfkiPj1XitrlndRW/eZctwZQa1rxhXr+rCssxDb+gVxEzX6XCU2VrScYSiUfEFnlARAfioOEE44/dnorMxeVj3yuIWne7WTCSDHTOnOca9eZfVCxpY3LJfFjwMBQ9vGmWk5NOctHhhX46KG9KRTRKEgtGSSyVQKHkhf9id457f7aZgR/Lw2ep13jBY4n+v24ai6dPqcN6wagGLW9P84Oletg7mKQ/kKZZcDEXFDSLpfS8UeD4IFBQlUpZMGjqarrCwKc2ythRXndaGrin89fdfJO8I0jEVTYvyPUuuQNUU3EDgo2IZBpqqUXIhkzVozyaBSDFx94RLb87F0nVQoSWdIFWtt1KA1YsaeXbPBO0Zi45sit0jRfKOT7kSoKsqp3Wm+e9XRIbgAxuGUTUDS4eSBzknxNSitEYvFJTdkIa4xqpFTdghlHxBm2mwYc8EeSckaRkMlXx0TQMFFE1BUQReGOKHCrqm4Ie1XnHUxwiRI5OMm6BqmIbBRCUkm4jX6zLDUKBqGl6o8Nvto3VJ9iAMq6Ig0dZ0DRKKyrgdMmFHDlrCVCm4Ie1Zg2TcRCgK2WScc5ZabOzP87bzF7Gqq2HK/Z9JxDB0A8PQOXtJ85R+WHtH88AQTqjgBkq9VUBT0uKcJY28++JueibVW20bKvDo1jHilkWzolF0fEpeyFDRqdbTGaQSMQLUehpdruIyUg5wfch74aQzNZWaIwvQl/ewQw76jJ3S1cjNl5r05lzipkZjwqyn79a2U/JCdN2IzsFhvtu72+bnvRiGgj2jJf71sV0Mlzy6m+JomoZG1YlVFApuyDN7cyxsSqKq6qzvkCPhlK5Gbl6r10VJnIKLpWucs6SVJc0O/XmbEHVO77DXMq9Ge+LVxnxfn5PGGctkMrz00ktTlv3rv/4rjzzyCD/4wQ9YunTuM9MSyWuR+VYsPBEcbh+1cxY10t2cZMtggZiuUPaiBsFzpcmAa07tOKwxHokMdk2ApSMTY/NAgYobTJGh1zQF2w3oHbd5YvsYFc9nSVMCVY2KmdIxlaSpsWe8wj1P7OLyFa3oBzSXXtme4e3nKvzgmb2UvZCSk6Nge/hhFPnRVAWt2scLRZA0DdYuayLvBNx26VJet6IVVVXw/TA6p0MFkqYW9f4iMuyLjocTQHMyqn8ayDu0pi0Shs5YyWHHcJmxskvZ9RktOli6xuLmBOkDIoid2TiLiy49LSnytkdjVWWwMWly+cpW/vjMLnRdZe9Ymca4QaHikat4tKcthooO+YqHF4R1tcCKG/LzF/vY1J/C9UOGCw5j5UiwIJLHjwQoAiFQFYXGlBE1Oq5Gp1RVQVUUlGoUS1NBhKDrKpmYznjJoyGu4/g+BSeqTZisIvjUrjHGSy6qopC09LpAA0SOiVpTKtRVbC/EDUJCO8TQNHpaUlOMYdsLaEyYrOpqmCbGM1mYYnlrcspnp3dmwBliVWcGr7r79nSMsxY3ThORmazOevbiLEDdsbPdgMe2DlN2AybfYhXXZzDv4AWRSMdk6fyDoSjKnMRoFjUmOHNhlvV9uSl1lHBi+hUea2r3x5M7R9k4kMfQVAbyDk1Ji7QVHZthaKhByFjJZd94hcakecyl9GcTJdkxEomizGc7Eonk1cK8OmPFYpFt27bV/79z506ef/55mpqaWLx4Mbfffju9vb1885vfRFVVVq1aNWX9trY2YrHYtOUSiWRm5kux8ERwJH3UdF3llrXddTVFISKZ9rnyV286A9Ocg9LHARyuY1yT8E6Fet1JmGxsGppKUfiESshwwaYrG687YjVUVaU5abJzpMSze8e5YGnzlM+3DRW453fR+Tu9I0PF83lpXz5SCBRMUejTFLD9gOf2TnBaZ4ZlrftV0yaf0z3jFTqqYgppS2fCdtFVhY4Gi7wdcObCBt5x3kLue66Xx7aMoCrQkDBA6Izi4gaCkhMwXvZoTBh1g9/1Q5qTJrdd2o2iKLPeywuycZa3pRkpuTh+QM728PwgUoCsfsfSFRpiUX8wxxcUbI+xkoMXBJETKEJURcEPQ7xAkDR1mpImfigoOwHjZZcwEHUnvtYoWSGKHu4aLaMqKl4QMl72+NGzvfT32DyyaShSqLM0bD/AMlTCEMpegAhDNFUFwshpEaApkWw/1XTRUFPobk6wqGm/c3Eop6M2EbBxIM+DGwajpta186DCsh74k8t7aE7FI3XLmDHj+2EmddZ0TKdg+whD0JQw2TNWouQEmHr0TI0VXTw/qKe3hiISCpmJ2mIF6GlNzMmBeqX0KzwWTH6X6VrUtiFmqJTcADewMRqiSF17OkaIy1jJY/dYCUVRjsvkmqoq0xz718LknkRyophXZ+zpp5/myiuvrP+/Vtt18803c/fdd9Pf38+ePXvma3gSyauSmX5YT3aOpo9aLWWv1mfM8UPmMmf/gUu7ed/F3Uc85sNxjGsS3lGkKsTQpr66vSBEV1VUouiNeUDUq0bc1BgrufUeUzUmnz9dhQde7me46Mx6FhRAhIKRoktfLupHNpnJ57RvvAhETsaKthTXnNHB5ae0kraMupH9+NYR4qaGSlSTpSsKzUkTIaKo1Eu9E8R1jfGKhxcEVNyQZa0pbD84aC+qyQY6AsZKLo4f4gcCIaLzsSAbJ5swqv3RIrn60aJL2QmwdJ+EqWFokQpkwtRIWRoFO+rhtmpBmhd787heSCjCKemJqgKBH+IIla4GA1WBbNJk92iZ32wdIW6qnLkgy1g5GlPair5TcHw8oZCuRuVyFQ83iBwaU9cwdBUUhYaEQTpmUHT8Qzodk1N3RwoOQuwPSykIHD+k7EfX8PtP7SWbjNcjyjPdjwf2uhsruWwbKkY1bkGIX222vW24iEpUb1jtx11PcU2YGgXn4BMfSUvjg69bPmcHquYgPPDSAC/15ih7PglD58yFDdOie69UDnyX9eUq6JpKKCBuqFS8yKkHiJka2biJAG68YAkX9TSf0Mm1V/PknkRyIplXZ+yKK67gYGKOd99990HXv+OOO7jjjjuO7aAkEslJx9H2UbvqtHYuX9HKs3vHGczZ/Muj2xjIV1iYTTBWchjIu/Xmt2lL48yFWW5/42lHPe65Osa19LI/7BpFUxS8IMTSo4icEFFfq7ZMjKSlViMoM9e8Vdyoz1ZzcmoNTO38+UHI73eMU3RmbxitKZFBHVRT8uKGxsMbB1neNtXRrZ3Tnz6/F3qfZ1lLCtPU2TpYJAypG/p7x8pMVDzWLmsGonQ8Q1WYKLu83J8nV/EYzFVoSpqkYgYIhXS1fuyeJ3YfsndUzUD/3lN72TVaIh2LImyZhEZHOkbCin4GUzGd8bLH6gUZYrpGytLpnajg+iFxU6vLs9dFU4DRkktM14gZKl6gkI6plN1IUKVW+6UokKt4dGVjLMjG2TtWZtdoCUvXKDkBiqIQhgKBQFVVYnokQuIFIW4gIudFRKmKCIHtRc72O85diKaoh4xKTE7drXgBu0dL+KHg/O5GTF1juOiwdbCAYkTXzvVDGuL6QSPKk/t7eYGo9vbyScUMjFiUciqAIBAEgKruFzYRQBCCbirEDYWKN7MNYKoKt65dyukLGma9trNSU1Cp/vsVIRk9Rw58l3Vm4mTjBqMlFzMe9U6rRe9FGDJadjmlPc1bzlowLfX4RPBqnNyTSE40J03NmEQikczGgTP1BxI3NQbzkSLabOi6Wk/dS1g6n71/E8Mll+akRWdjgoobMFpyycQMbrt06Qk1fGoRnt6JCv0TNuMll9a0hR9Gjljc1OlpSTJScGhNx6I6qoQxJVUxDKNmzae0pzlnUeOU7Zdcn4rrs6k/j+1FaXwoKn4YThFTgP21PpauRiqODdasju6usRLP7p3gPBV6WlPELHNa6qgfCmw/oMuKo6kKYyWXzYNRlMXxQoq2j+NHjkkooL0hxrLWSCVvrs1ll7elueGcBWwZKhDTNTYO5GlNWdVUwAhDUyk6PpqqYBkqf3L+Un7x0gCjRYekqbFtqMBQIagbwroWKUNWXB8/FJiaSjZhsmpBnLGSy/bhEqqqoKCgqgodDTF2jkQKhroaCWSoqkLZ8QkFlJ2ATFzF0KL6s7JXTelTFAxDYVFjHE1TyVdcYoaOpqj8+et66M/bs0YlDkzdTQU6WwYL+IHgxd48axY2MFZ0CUJBWypy0HMVH1BY0Zaa9fzWJgde6p0gV/GouH69hlEIwXjJQ1OiZyqqMxTYfrUxtgBB1DTb1DUEAfYkh0xXoCll8uErl3PzYaiUHni8C7JxEqZO2fV5uS9Pf86uO5aHI/JzojnwXaaqCmctzvKbrSPkKj4xQ0WvTg3tnbDJxExuvqR7XhwxiURybJDOmEQiOemZPFOfjk1XRaq4AZauzUkIAKam2e0aLTFWcjE0lVPa09x8Sfc0NcITwfK2NLdd2o2lKzy0YZCdIyWSlk5HJsbCqnR6c9riT1a0cM/vdrNnvEJz0qynsNUcyZkMt6SpU3IDxiseMT1KhVIVpjliAIYGCgq6qmLqSpQqZ/vTHN1autV4yYV0FHkSijItdfT6MztnjbKYmkKu4iJQiRkap3WkWdSUqEc/DxXxnEzaMmhKWOiqQtzQ8UOBNuk01FI9gzCS9D+tI0PS1Fn38gCPbRkmb3toikLC0MgmTUpOgB8EhIKqsAcMFx0MTWVJc4KxskfK0tGUKPVyuODWhVdKjk8gBLqq0Ja2mKh42G5A2fHQ9Sgt0vbCqPmyqpCNGZiGSskJaEiYrGhLsX24SH/envW4Z0rdHSk6KAq0pk0myh4b+vOUHH9KDaIfRgIhkyPKe0ZLDBWd6B5LmpyzqJFrV7WzZajA7n05sgmj2ug6YLzk4oUCU9cw9UjdryFu0D9hR20W9Ejh0NBUUqZOa8oipqs0pQxWLchyameaN57Wedi1mHNNVQ6F4KGXh+Ys8nMs8f2QZ/eOTzmPMz2LB77LlrZETa+f3zPBWNnFF5EztqItxXsu7pmX95FEIjl2SGdMIpGc9CzIxulpSfLU7jEWZONYurZf2voIldQmpy4ezHg60TSnLJa0JNk3VsH1AyYqLpm4wTmLG+spal3Z+GE5krW6KS8Iiek6SjW1S1WYJrIgqn80VSETNwhDZnR0JytAHujRTTb0FZg1ylJyQvwQGhMGmqowWHCmOB9ziXhOPsaeliR/2DWGqSmMl1za0lZdDrxo+7SmLQq2T1c2zk+e7+P5fRNsHyoymHfQ1OheysQNHC8kFIKYoeH4IaEHmhHl4hVsn/6cTUxX66Ieoro8E9cxNRVTj6JwKpGwSmcmRu9EhVBEEwe6GqX26YqKrqnEDBXHF7RlYixrTZKJG+waKR30uGdK3TU1FV1V8UMRpWWWXEKiYxJVAz8MBY4XIIQgbmq8uC/Hx7/3AsNFh4oXoKtRlO7PL1/O9as72diXJwgFE2UXTY2ig04Q4nhRuqntRyIshq5G6a1C1MsTkjGNXMUnacX42z8646D1f4diLqnKz+4ZZ/NgAdcP5yzyc6z45cbB+jNZ6/3XkbH4ozVdvP7U9np0brLiZcra7yQvbUmxuDHB07vH6WowgX38y43nEI9Pb90jkUhOLqQzJpFITnp2jBQZK7vsGS2zaaBAytJpSZksyMapeOERK6lNTl2cbyanYJ3SnuashVmGCw59uQoJU+OMBRlcP+QPO0fpzMa467+dwUDBZrzsHdSRrKVsndKe5uENgzh+VT1wFglyEULCipzdjkyMgu1z5sLpju7+dCsTnOnHU3Okyl4wa5SlYHuoqoKla1iayljRqTo1UcTgYBHPA1PRKp7PWNll71iZvO3j+gF526cpYQAKuh45KrqmMlRw2DpUZLiwP5qDACeIGlZ7QYimKthetJ9ACEquR8zQiRkqZSfaZ67igqKQiukUHQ+jGoozdZUkOkXHR1EVkjGdTNyoqgwS1QgVHTobYixuSiKqIcrGhEEmHol2mJpKvuKxaSA/Y6rdTKm76ZhOU8JkqGDXz7OCQsH2cBwPOqL6tpd6c/RO2IBg00A+ioJqCmEYUvIFL/Xm+Z//9SI3X7KEM7oyGJqKrin1WrfxkostArzqzeNU6+6EiKJ7rh8QhCF+AAsb4zQmTOLG0Zkjh0pVjhkae8bKtKUtzlnceFgiP0fLLzcO8tn7N9V7/wGMFBxe6s3zcn+BHz7Ty4U9zbz9vAWsbM8cVBVyWVuK91+4kE1P7Zv3iSGJRHJskM6YRCI5qZnspJy9OEvfhM1w0WH3aJmhvMPrT23jxgsXnxRKarMxWwpWZzaOqSv8Yec4n/7pBlTArhq+rWmLFW0prjqtnfOWNM1oYE4Rd3B9LCNq3mvp6qQIWOSA1SRBYtXeWQlTQ9dUmlP7Hd3JDlC+4mFpkaDFTEx2pBY1JaZFWfwwUjzUFRgqOFh6pMowXHSqkZzZI54H9pur9Q7LxIz6PdI7UWa87NV7nS1tjHP2okZGiw59ORu/qrrYnLKoeJHCpu2H+FVhDS0UWNWaKIHAD6Ho+OQrLnFTZ0HSYLjoAIKO1hg7RwNKjl8VyDBZ2pJgpOAyVnaxPR9VUXjjGZ1ctrKF5qTJj57rY0NfjoF8heGCW4+mtKZNvFCQMHS+++QenCCcMdVupnQ3RVFY1pak4HgMFxx0VcHQVPaNVzCVyOXubIgjVI2hvM2+iQogsDQFLwRT14gpCr4etQn4zpN7eOPp7WwfKeOHkcqfH4SUvQDbD1D8kExMx/FDNCVSfjG0EEMzWNKc4OxFWRKWzu7Rg0f55sKhUpWHCw4VNzhikZ8jxfdD7n58FwXbY3FjHMcPGcjb2H6IqggcL2TTYIHBfIUnd47ysatWcNVp7QeVjV/SGGPTMRuhRCKZb6QzJpFITlpmclIWNiYo2D6OH9A7UaEpadFTrbk4GQhDwb7xMjtGSgAsbYma8x6YgiWEYO94mZd6cxQqPmXXpzFhYOoqIwWHgZzN5v48j20e5spT2rjposXTVPYmizt0ZeP4oeDx7SN4QUhj0kAIKDkethfVRCUMnaSlkbQiB2pyamTNAdrcP8HWkRKeH8nSL0jrrD6gL/ZMjtRpnZl6lKXsBWwdLOArgvaGGKNFF9sLCIRg80ABU1MouyGWobK8PZLWrkWGJh9XRzoGFcGmvjzjFY+uhhjdLUkuWJqgYGewPZ+dI2VO6Uxz29pILOIfH95KJqaza7REKhalFcYNjZLrY2oKFXeyUqXACyJ1wFrLrv68i6l5NMQNrj69HQSMl6NWAhNljyXNCZa3pWlKmnQ3C/IVj23DRU7vyvA315xSj3b052weeHmAsZKDpigoikAIhb6cjaYqnNKeojGZqotUHJhqN1u6W1PSYs3CBv6wcxxdjXq0haHAtKL9xk2Vsi+oeD5+tdF0ICIp+poPY+gaaUshV/HYMVKmL1eh6Pg0J00aEgYCUW3DIKI2AoDjB/V/t2cszlyYpSFhUrC9w6rnnI3Zjnf//RZFkNvSsRnXP5yU18Ph2b3j7Bot0VxNvR0redhe5NSHQkHXovElLYOBvM0//XIrixoTrOyYXTbe87xD71gikZw0SGdMIpGctPTn7GlOiqIo1TQ2g5ihsX342M92Hy+2DRX4zpN7+P2OUXJlD6FANm6ysj3FWMmlK1vr6+SwbajI1qEiFTdK+QpCQIn6YYXVuhxVVXH9gP8/e38ebdl13/eBn73PeOc3v1dzoQagABLEQJCUSEqcNbSs2JbtOFa6I0dtx7HV7ay43SvJysrqeKUjO4mdOF7RSuQ4LclOKDkeNJsSKZIiKc4giBmFQs2v6s3vzvfMZ+/+Y597672qVwOAQgEFnu9atYB679577j3n3FP7e77f3/f7tbObRFnOz3/0gUma3F5K2yP7W/iO5Bvn2kSJwnckzYrLg4s+P/aeJX7kwTmCOL+hEPjMep9f+vI5vnexzXo/It3BV1Y6mj+/BMMow/PErj6sTz+8OFlsVh2rWEz36QUm4W+8gHWk4GovwkPQDxOeu9JjX9MnUxa/9f2r/IG9xrH5Gu872OJLr2xypRPQ8Cy+eHqddmCCM4SA82lOruHHHlmkWTGWv4pr0w1SZFEeHWU5Td+Z9LkJIZipOSR5TpJds2wqDUlmios1JvLflpApikV3woePz/HJUwtc7Ya8strn919YJU5zHMuUSIdJzvog5shsjX/7qUPYtiTLFE9fbvO/f+sSwzjF1IoZVVAAWucFAdQT0nEzq93Y7nZmfUjDt7GkIFfGavmBozO8/+g0v//8KqeWGgRRAkR0gxQlLBq+w9bQdK/Zls11YhJOUUZ8fmvIvqkK83WXzUHCMM5wpOTUYoPNoekdi5OcODUhLgenKzx6YIqZokfujcxz7oXrS5+Xmh65gn6U0gkSZmoevmMRpjkN60Z73+sN+blTbI+MqllxTWVBmBryqzTYlkAX55GUsNT02BzE/MtnlvlPf+LhMja+RIkfEJRkrESJEvct7kak/d3G64nNvr6M99e/c5nnr/SwBMw2XASCbpDy7QttJDBXd6l5Ns8ud+mHKbnSVBxJPzI2us1BjBQmNj3JFVFgLGxxrnlt/dpC/VZhB8fmG8zWPM5vjfix9yzyYBGFf7P5lDNrA/6r33uF71/uMNxhSbS4FvYB8K1zWxxZaE7sVg8tNfjCy+u7Uu2mqg5JlnOpHeyaHQtTxf5WhZMLdfpRxqvrA6QUHJ6pUnVtVroBv/PsCr/x3WWSNEdKQTdMERhbZSpN71eSKc5tDHmx5XFktk6SG/tclOaT4+XbFkppbCknfW4V18zHrXQjojRBApk2f8BYKW3bBHjYFhya8hkkOf/0mxf5+IPzHJqpcmimyrH52k2tZycWGpOQBxOjbwbtaq7FbN2h7ttkuTZkN1dc6Yb0w5RmxaEfpnSK8+HZyx2WOwFHZmucWGjwyVML/OrXL/LSSm9idTw6W+MvPHWQA9MV/ugVc05Y5BBt8f6jM9iWTT9KWW4HxLlmr7M3VRpZ7Lumbxs76o5ur6pn86H5OqMk57GDLZ6+1CFXmuPzNaqezSBKb1pS/UYx7pSb3NAIUzTmhsaxuRpzNZfVXnQT5ezukMLrMVtzcSxJmORYUpAVM4aWNGQ216a3z6STWri24NxbYJcsUaLEOxclGStRosR9i7sdaf9mcf2s0q1is/cq413vx1QdyUKrMlksLjYl28OEbpjy/JUeszWz+HYsidIaiVFpbCmIU1Us7IyaIjCpfGGSsTmIeOZyZ0L+bkVia75Nw7d56ugMp5ZunnB3dmPAL335LGc3Box2EDEBaGHUonFA+Uo/4v/7Fx5jtuoTphm/9o1LE4vkONVutReRaY1jm4j5TpBgS1mkCJpuse9caJPlioPTFRq+Q3tkwjZypVBKkylNHKekmcZzJIoiwa+YQUu15muvbXFuYwhSoHKN79hs9CM+emK+SHbsMV0xM19uTSKEwHcsap5Frhx823SHrfVipBSThbW0TOR/3XdwHYsLWyOeWe5MQmBOLNzcerYz5MF0uJmi5zBVrPYiDtvVySzfWOG53A5ojxKudEOiov9MAP/DF87wNz5+go1BxL965gpCwIcemMaWkkGU0Q0TfufZFX7m/Qcm359mYVOcrbloYRXqp000SFBKATs76zRhklN1LbRWXG4HaG0CQhzLkNjNQUw/SpmteXzqkUV+/L1Lk/PdzADuXVJ9NxClOfMNjwcXGzR9B0vCWpGIaUmxZzDG3SSFO/HkoWmOztY4szFgseEhAFWQMaXN+VqxLeqeRZqb7jWluac3kEqUKPH2oiRjJUqUuGO808pS9xUFwDefE3lr7nbvhetnsG4Vm71XGe/LK73JgjpKja0JDKlqVGySXNENjZpgFzHrQZIb9UlDRhGyoUEoU6yrtSFilpTEqWK5HTCIUxqe86ZJ7NjquD2KSfJ8ooBJwaTcNwfGdVFhkvPc5S7/tx96gP/5j8/dtA/quStdao7FI/sbuLaxdrm2xJaCfpSyOYypeTZeoWC9tNKnG6TM1FxcW9ENE5Jc41iCPNekmSlVFhgFAg2pgtV+TMWRpDlYMuXv/+GrpLkuyrUDtkcxWa5Y64Wmhy3NkUJQdW2khAdmq3SClJp3zcKXZMrMmRXBHu1RwvYoue25c33IQz/KQAgsS+AgTOhDL+KB2SpSmEW80ibpcBgbC2bNtY31Mc35+rktXlntk+WKYZIzVXHYGsTkWpPmGlBc2Apoj2JOzNeNWjRnZqm2Rwm2ZVP3LA5OV+lHGYM4QwgT1Z8WRMy1JQuFrS5JcxZ33EDwbAu3JlnvR6Ch6lgcnq3dlIi+WYyvS4Mo5be+v0J7lPDYwald14OG70xI2EzV5fzWaE918m7DtiV/+SNH+bufO8163yjXeXEMlDYVEUuFQj2MUlpVh6mKs+u7d/11d6FWLt1KlHg3ofxGlyhR4o7welSfe4Xr50Tu1d3u63GnhbPjIJG9ynhNEa5RcdqjhP3ONQvhOBLdDP1rhJBozNzJWLPQO+a0Mm1+Nh6N0UCY5ejQzG6dWmxOur2WmtqU9FqShm/+SbgTErvT6vjClR5QuNQK29WYkI1ZmgaudqLb9kEdm6uxOYi51A6xhaATpmRKTeLVe0HKiYU6aZ7z9XNd01VWFCv7tpnBUQos29gScwWuLUhzvaszLcs1kdA0PItmxeFqN+Tvf/5V/uPPnMR3LEZxRpDmjOKc9b5JHqz6Nk3fwS5skFKa7UkpJqRxpuoghCCMMxxLTqLM4ebfoX1T/iTkQUqJX5DPXJlZItsydsAk1/i2pB0kCMyxtCxJxZGAIMlNufUoTlmOcjxHMl932B4lZgYRE8TR8G0qjuTc5miiQn7xlU2OH4OnL2yTaIklBA8uNfjwiTn+xdPL9ArbpyUFrYrNvlaFA9NVokSRKc3eEGjENaL+JmagbnYjaOc+bQcJ5zaGLDQ85hs+Mzv2/TgxsRuk/OUPHzU9dvfoptK42+9Xvn6BV9cGhf1WU/Uk+5oVqp5Fe5TgO5Kqa3NysTH57u11zpyYq3DgLXu3JUqUuNcoyViJEiVui9ej+txrjOdEbjWL81bjTgpnx7HZcGMyomtJHEsihFkEj+KMXphScSxcW5Jkin6UgobZmkPDc1gfxNgyJ1WG6Kjr3pMGEOBYFhVHEqYKW2qqroWUglP7Gnz+5TWev9LDtU0RccOzqbo2h2ertyWxoyRjaxiblDylJtvUgNixNh+v06WAA9P+bS2S1eI9rPUjkkyZhL6KQ5jkrPQi4kwhhOa5Kz26QYoQhmAoDcP4mlUvyXURakJh1zSWxPFbu5bhIOiFpjvs/OaQ//xfv8jhmQoPLtY5MFXhtfU+vSinVXF438EpZmsOZzdH9AJjFR3EKTXXouY5zFRNIIhSiu1xH9yBKZbbwa4Aj/1TlV3foW9d2CZMMhaapsDXcyyavlMQUbPvlNIESTZRPBUKqSUVy6QdJpkpZDafTSK0JkpztoYQpDkCbfaB1kXEvkmJ7AYpozhHFmw+zjWZVshiRvBPP76fn3rfEr/29Uuc3xwhpGap4fPgUpMTi3W6gVH/2qOE+g6b4jDKqPs2szWXMN273uBOcTMSe2pfgy+d3phcl1xbcnFrSDdIeXa5y+OHpnYRsp3ddrey374VODJb5UNHZ5AIrnQD1vsxKEWQZEgpaFUd892bufbdu9l19+XVPgcacH5zyEP7p+/p5yhRosTdR0nGSpQocUu8HtXn7bIs3moW517g9QaJ7FXGu9Dw2BrEBGmKQpD3NI4lsaSx+IVpXpQXK7phSJyZoArfEqSZmoRJ7IRrSXzHLNYdS2BbgiDJObsx4EunN2hWjMozjE0VwGqUMd/w+OSphduS2I1+xNn1PnGmqLkWox2R77tKosef0XP4i08eZjNIbmmRDGIT07/U8LEtQSdI6YUptpScnK+z0os4sz6i5VvM1FyCJDd2L2EIV0VaWALCVE2IoQJcS6CVUccsCRVHEqSKKM2peTauZRMmMdsjE6O/OUgI0tzE80/5RKliaxhztOjHOrM+ZN+Uz8srPcJU0fRsHFsyiFK2RwlN30Tb//LXzvOdi9ucWx8R5zknF+rMN3wsKah7NgsNl/NbQ4I0pzOMmWsYkr7Y9EiLzq5UaRTQLiyPvmsRp4ZwiiTDtU3dQN212BwmeI4kSnPSXJkMRm3i6EGTK0PI40yhNcRpRnuUcHTaEMGxullxLNZ6IX/44hp//eMn+K//7KM3fL+udkPm6h5zdZfVXkwnMGmK4zm/paYHiDc1s3kzQvLC1R6ff3lt0h0nhFEpfcfGs80NjXObQ6ar1wqe7/UM6V6f4cRinfcdmuJqJ+DZ5S5JrpiqmGTSk4uNyQ2kW113G24NIvjS6Q1OLk3ds+vcO82mXqLEuwUlGStRosQt8XpUn7cz/WtsgRovGM5sDO7ZguH1BonsVcY73/DQjKPRNbaEXCm6QYbGkIkDMxWU0pzbHJHl4NnGNiZsic4UuTYqimQct24i0WuuLBQkm4pt8RvfvsxzVzrM1TyOzlRNUp8GRwrW+jGvrg34xEMLN91vZ9YG/N1/8wprg4SbOtQKjH/9Fz94CN+3OeBakzm/qiNZ68cEaU7VsVhqekW/muC9B5o0fIdBlJHkamKjfHV9wJ+8toVvSxqYsJJRbEqTfUcyU3MZRhkECaNE4ViChmeTKU2UKgQU/VpmZict1IlcaVJl9l2uFHFuuqC0ho1BwkzVpT1KGEQZzYrD/iljeft/fPIkv/fcKhe3R7SDBMeSPLTY4DOPLPJHr2zw4tUeYZoRJQop4bkrPVb7MY8dbHG5HXClExIkGWGiuLht+rqWWlUqromB3xpEbA4TpDDve3/Rp/XscofNYYIlBfN1l2bFISzIo4Us7KLCsFGtzf8X1tEsV+TK6KmdUUKY5rQD012llKYf5WyPzGt3RinvOzTFj5ycv+H7vbPb66kjUwzjfHKs6p7F2c3Rm5rZvBUhWWpqnr/SxbZ2zoXZzFRdNgYRNc/edbzu9Qzp7T7D4dkaB6crPH+1xwNzNf79jzzAoenq5Dt3u+suwPnN0T277r4TbeolSrxbUJKxEiVK3BLvxPj4m+HtWjBcXzgLTEiEIwVrvYjDc1UGcUrdtTk2V+Ol1f4kdERrzdYgoeXbIMxiOc4UaW7Kln3HwrIkjhSIYqZopHLSXKO1KuyIEl9CpjRKaXzbYrFVwbFMAIQtBTM1l//uD1/l2xe2izmfAVbx8w8+MMsDc7XCHnVzcn12Y8B/8dsv8NrG6I72jVssLv+fnzwJXJvz+87FbX79u1eIsxxdzJl5tsUDc9Uiwt/Z0Rl3DdNVl4pjUfNtrnYNkUkyhRQCt0g5tC3J4ZkalzshaW5URYHAsU2Qybh82JYm6GUQX7PRKYziNAhTbEtS8yyiVDGIjW00yY0COD7vHzs0xZ974iDPLHfYHiXM1lwePzDFf/Kbz/P0pTZaaTxbktsmCj7NFFc7Ie2hia6XQlBzLdAQZ4qtUUqUjTg4UzE/yzWWlHi2pFGxyZVmcxgzU/MYxBlJphnGhnBYRa1BlBgC71kmPTDIdFEabQI8euE4dVMQJKYXrGKbpJUwzfEcY20dpTnrg4jPfvsS+1r+Dd+hnTObZzdH7Gv5TFWNpfTs5uhNz2zeipCkSuPYgmGUTQiXEILjCzUGccowSsk1hGmGENzTGdI7/QxSSo7P1yc9dzvf1+2uu2CKtO/FdfedbFMvUeLdgJKMlXhXoLRPvHV4p8XH3wxv54Jh56L0+8tdgtgk0MVZTpgYotCLUq50wkmf1s6I7SzXrA8ipJQcnq5yYr7OKM14ZaVPw7dxbYur3ZBukNLwHWxLUnE0SaaouhY5JrFOqWIeiHGhrEIpSdO3GUQZ57dGtIcxmQLPFkhheo+2hglfeXUDWODQTOUGcr0zre5fP3OFV1b6plS6+IrtpY4JYF/L431LVWATteNBl7YDLmwFxFmOJQRCarQWxJmZCzsyU73p+ZYrjW0J0iw3HVw1D60VvdDYGy9vB9Q9mx9/zxLvPzLNr3z9Ahe3AgSKfpgSw7UgDwHJHuNMmQKlFT6gtJnbG8WG3LvFsNnO89625SS+HuDi5pA/eW0LpTTTVYck11DMTfmOZJQoBnFOwxXUfI9cayquYKFpsd6PCZKMS1sj6p6NFIKKa7Gv5VPz7Els/FjpjFIzs+XaEkuaoI84MwRQa5CFWhoVtk0owl2EOW5pbv5/e2TIoWMJcgRCGJtrrHLao+SmVuS3cmbzVoTEtSSebeyWY4IMMFPzePzQFC+v9NkYxKz3I6ar3j2dId2JN3oz63bXXeCeXHfvB5t6iRL3O0oyVuK+R2mfeGtxverzdsbH3wzvhAXDuGD3H33xNTYHMa4tEZjAhFwp2qOEB+bq+I5ktRdhScG+prG6bQ5jwjTn8HSVk4sNZmouW8OYC+6IerEQq7qGFAwiYydzbUmSa4LUEJoQQ1SiTCEFHJ+vs9D0Obs+4OzGkChVpAUhksJYnSxp7sanmZlN+u7FbdDTZEpTdYxScnZjwOdeWOO7F9tsj2KudkJGST7pNxtH2Y+hMUSs6lpUHJupuolMX+1FHF1wJzHucZZzarHBKM3JcmVUKMdiuROy0o1o+jYPLTVvON8GYYpnW4ySjCMzVaQ05Giq6hKleaHoVfh/ffpBLncDZuse26OEOM1B5lQcs49UEXF/Mxg7nyZKMiquRapMbH3Dt2973j99qcMwzvBswSAyASdGjdNY4lq6oJBmjivJlIksb/i0Ki6bw4j5msfh2Rrbw4iNQUKtIGaebZG7mqudEF0cyyBRXNoOkEKg0UgEVVcipSSIM4qxMQAcaVTUcfkwGEWuExqy2A8zlFBUHIssNwmRS81bW5HfqpnNWxGShm/T8GxWowjnuu1MV13mGz7vPzrNn3niAA3Pedtu0L3Rm1m3u+4CHJuvveXX3fvFpl6ixP2MkoyVuK9R2ifuLm6mML4T4uNvhXfCgkEpzSsrfVoVh6MzVbSA5e0AS8BMzaUTpFzcHvHUkWlOLtR5bWPIbN3j5z5ylAtbI37925fZP+XT8E2p8zDO0ArSzNgQq67Nw0sN1voRo40ho/haet4YriUQroWUktVeyKX2iCDOsaXEsQz5EBiiEWcKUagpUgrSVLHRj/na2W32tSr8zrMrPLy/yT/95iVevNozqYm5Is6MxU8XJGw8sjPu8BonKnq2UVes4j2O7/w/s9zh4vaIumex1o8nc05SCCqOKb/tRxndKOW5K12OzdWoevbkfPNci/1TPp0goROkuxL8giRnru7RqrisDSL+8MV1cqX58UcWudwJeeZyh+mKQ5YrTq8PbznvZiaqTH1ALzQx9Udmqwzj7LbnfZTlhhinhppaUuDZkiTfHQOvlCZMFY5lZt2EELi2pOLYuI5FkOQcmK7SDjLSXOPZgjDJWO9H5GpstZT4jlFGM63xLIFrW6ZPTJvus2RHpr/SkORGrbWE6YLb0UBAphVJbs6PimNi/5sVh/YouaUl7s3E1t8MtyIkYJI35xsea/3IhLfsuC7N1l3+7acOve3X/zd6M+tW192NXsCxBnzy1M3nOu8W7iebeokS9ytKMlbivsU7QQ15N+F2CuPbHR9/K9ytBcObsbt+/dwW/+bFNWO164ZoDb0oZa5uuqPq/u5AgX0tn3ObQ6QQ/OjJeV662udbF7bJMmUizXNFL0ppBzF1z+HQTHXyZ7Hp8a3zbaJU0fAkUzWPJFPm777N+w60+Ob5bUZJzmzdpenbXNwOkeRYEnJ1jZA5liDLTVqf0FD3bE4t1Xlxpc+/fOYKq/0QoaHi2vi2JFNpYeMzn3vsENupjgmg5hk7ZV48cHznf3uUmHTIxCQburbEEpJca/pRSprl5BoGQcZAZGwOEubqLnN1YzU7sVjnt75/lQfmalzcCmgHCaPYFFsvNH2OzlbphSnnt0YTgi6lZKbqUnNNQbNGYElQhUXRFkzSKMdHW2M+Y8WxyDVMFSSuG6S3Pe+Pz9cQQJprfMfMcSEErpAk6bXkS6UVNddlpuZOSr7TYofaUjBMzPlTdS16YYpTdWiPUtJcU/UsOsMEIQWzNZeZqsOF7QBZWBrX+jFhktPwbXphOiHiY0XUEhRkcTcyVVhBTXUZS00fW4q3xYp8uxtBh2eqfPLUAqdXB+/I69KdfIZbkfqbXXffs78JgxWOzdff8vd/v9jUS5S4n1F+e0rct3gnqCHvFtypwvh2xsffCndjwfBm7K5nNwb8+ncu0x7FLDb9iZ0wTHO2hwmuZeE5kmGc3RAAMSp6hk7ta/Cbz15lEKWmW6vqoNGsdCOSLOGhpTq51oSJmQl6ZH+LS1sjECZ0wZaSA9PmLrwtBUqZGPO67xQR+WJiVxsTMjCEQWmzULctwcNLTQ7N1Li4OWC5HZBr8CyIUqPEuZac9IrBNVvizmW97whsAVXHKHTMw2LDRKfPVB2SXKG1puE7jL+6Wmmy3Kg4lhQ8tNRgqmpi32uezZ998gAfOT7H1W7IH9hr+I7FU0enb0hbHMYZUeE/3EnQG74hk5fbAbnWjD+CgMnnByazUplSWFKyf8rnkX0tfvJ9+5hveJPzHmC5Hez5XVhqVah6Nv0oIyuIjSi2ZVmCvGBjTc9iX8ubWC211gyK56RZzlo/Zr1n5sOCOCdMcoIkx7WFKVvWUHcsZmqesScWASFJpkhzRZTl7G/5RglLFQqouxZhMVdmEjfZVYY9jrYfX1MXG4bY3cqK/FbO7O4kJGc3BlzYSpBCcmKhzp97/wEeXGzyiYcW3pHXpTHezM2sva67CzWbP/iD0/fkvd8PNvUSJe53lGSsxH2L0j5xd/B6FcZ3IrF9swuGN2N3He+/UZzRqhhyIYXAty08y1jT2qOE2bqDLeWeARBKaU6vDtjX9Jmvu5NuLceyeGixQSdIWenGuNYQ37EnCtFvPnOFuZpHPzJzQdOFpWx7lJAX5cCysL41fZsgyVDqWuGxLLyFZncJlhoeDy3VaY9inr7cNVH5FPH5gsnMmSOvzVuZqadrZExgiN76MKYbZcxWLZiHX/nGRX7s0f0sNH082zI2zCJyXWsIE0WmFEKYPrSmb9OsODx2cIrXNoa8cKXHR47P7TrWJxfqu9IWdx7rB+Zquwh6J0gZJTmZ0pPwkcLJhxAmuKJShKDEmekua3kWn354kZ94dN+u43874h5niuPzNc6sD00PWvG5zHs083SOLdDSYmMQ06o6gKAXpMS5wrMkvuuw2BR0g4SG75DnmmGSk+Q5AjlRNQ9MGaUlTnMsaQJZooKQGQJuglqEMIRXFvv7egJqT0qwr8GzJVujmCOztZuqN/diZvfEQgP1CPTChH6UkmvNRj/iCy9tIIXgxELjbau1uFO8mZtZ11930zR9K9/qDdt+p9vUS5S431GSsRL3LUr7xN3BW6kw3quUyzezYHizdtfx/js2VyfNNBuDCLcmcW1J1bWNQpZkdAM4MF3dMwBi/BonF+vUPfsGtWcQpaz0Iv7tDxzm+Hx98pz/M9e8vNo3JKMIwZiuuiw1PawiEU8VhGem5jIoYsCzsbUQoxBJaeLVnzgygxCCcxsj4myHdIRACIHUmrSYVRpjZxihFOBY18Ivqq7gkX0tYMjLq32u9hM+9tA8R2YrnNsc0Y9MOIZgTIAMEWtVnGu2wT3OwTs51oemqxPSVnMtzm4M0VpzZKZCO0iJM4UljFVSF0pQxZZkytg3G77F3/7xh/iLTx3eddzvhLjXXJupqst83WWlFxJngNJYEmbqLo8eaAGC6arDSyt9toemzLnlO8y5LlXX5onDU3SChGeXuwzjjOmaUUrzXOI5FtM1D6XNvBkYu6dry4LkGqshAvpBUiQOagQwiLOJdXRMom0hTK0CORVbGtJdnDfv2d/iLzx1cE9ida9mds9uDPi1b5rtHJmtUXXtG7YDvOODnN6pN7Nuh3e6Tb1Eifsd5Sq1xH2L0j5xd/BWKYz3OuXyjS4YxkRoqendQILuhIyO999+rzLpOGqPEuq+zVTVIUhShklGo+LcNABi5zHYq1ur6tnYUrDU8ifvYRRnXO2ErA8iFhum3ylTsDmITOJikbwXpznas6m4Nkdmq1zthrRH44JfQ6Bmag4/dGyOB+Zq9MOUdpAwVXFoD5NCRdFkmbE9ji1tAqOwzVRtgkTTqto8ur/FcjekF6TMVB16UcZKN4A6HJur8tpWxNMX2xyarjJVdXh1bWjmmXJFrjU1x2K24eHtiJCHG8/BOz3WY9L2/NUeG4OouE6Y3rb9UxVUYfscxSlJruiG6YS4/o2Pn+CjJ+Y5vdZnGGfUfZu6a/MHL6zdlrh/8tQCm4OYYZJzcr5OkpvC6TTPaVUc4lTzw8dn+A9+5BgrvbAoujax9//8O8tMF2Ee45j2cxumUFoWSYwV1+KHj81wYSuYkH8whKyGTZikEzusVsb26VrXqgx2HkMB1H0bzzGvUfcdcnJsS/LJU/P8v3/sIew9ZLN7NbN7J9v5P751me1hTDtI2N+q8MBsjTDNyyCnu4h3sk29RIn7HSUZK3HforRP3B28FQrj25VyeScLhuvVukGcsjWMWemGdIvgjLHCdGKhTrNi35KM7tx/1y+ec6WoeTaWlByYqpieq1TdQBpe7zE4szbgv/79V9gaxWS5YrkTUPdtZmsujhRc6QTUPJtjcxVWezHr/cIKJ0xAh2uZsIsffXCOzX5MJ0g4OmtInkn8U7QqDo4tSDJNnBlb3870QZPkJ6i4NvumXEZxzsYwIcu1CSsJMoZxxjCM4SA8u9xjrllhsx8z3/CJMsWffmwfa/2Y7WHC2c0hUxWLMNXM1Fwavn3Tz3+nx3pM2v73b17m7PoQgca2LBabPsfnawCc2xixOYzYHiYcmKrw6MEWH3tonrPrI/7O777E5e2AMM2puBbzDY/OKOXUUv2mKvJr6wO6QUqz4pArRZCacwABOtGs9WMcy+KTpxZY7UcEaT5RO89sDIhzRXXH55ypeUwfNapmmOac3xwyV3dpBylLLY9elLDejwBBw3d4sOVxaTtAD1Nyrag4NlXPoepaBLEhnnlk1DPPNmmMudKT4JAoyVAYBeff/8gDexIxuHczu7fbju9Ifu/5FYQwc4Fbw4SZqsvxhdoktfROSGHZVXl73K/KXokS73SUZKzEfY3SPvHmcbcVxrc75fJWC4a91DpLCs5tDA0Bq7k4vk2a64nCdGDKJ1PQD1OU0je85+v3387Fc5yZ3qunjszwbz2+nzDN91zo3eoYKKU4tznkgbkaSmvOrPf5pS+f49zmkPm6hxSwNUyNojVMig4iQVj0jzUrDnGq2B6aUt+pisOHHp7lZz90mBMLjQlxHt/QkALSVLEaR8zVPTb6EVmRvrjTnuhYhtgN4pz5hqTuCzqjhDjLSXNNpjSuLZDaLOY3BzHbYcZszeMnH93HKMk4txWwr+Wz0PTpRylXuiHzdY/j89fOm9vFf99ucXhiocHPf/Qoq/2QqmMxVXUnqifA9FGX1V5EexTz1z52nKmqw6994xKX2wGbg4hcmYTKOFVc7YQMoxQhTFrkTM3dta2Ka3FhK6UfZZxcqJPmVb57sc1r64MitMQEZlzYHvHff+FV6p6zSzV+36HWnqR8rJYKAQenq/zMkwd4brnHuc1hUXoNYFIVm77LJx6q891LbTqjFK1hquoUkfnmfHdsyTDKyJUiVaCVol8kmmTKbONvfuokDy42b7pf79XM7q220x7FvHi1xyDK2D/lM1PzSHPFxiBiEKc8fmjqjkhh2VVZokSJtxMlGStx36O0T7w53G2F8c3eMc8yxTPLHbZHCbM1lycPTd/07vzrwV5q3SjO+JOzWwzjjKZvm54uIfBsQe5YXOmGXNoesa9V4de/fZnvXujcsEC72f4TAjpBysHpKj/56BJHZms3fW/Xv8ZS0yNTmpVuyPnNkbEKKvjF33+FjUFMkuf4jpgUAQshCJKUODWWtKZvkeaQKZPOd2KhxmceWWL/VIVjczUOTlcnx3PnDY3vL3e43A7ohCYkYbrimPJfZWaOVBGP7kjBkZkqjiW50g0ZxTmOJYiznGGcI9DUPIdca0QREzFdc7jaS0DDqaUGx+ZrkwVwnEVM11wypWn6jonbV+quqdwHp6u878BUQXatXXbUumfCRN5/ZIYnD03zy189z/YwIctMUfNs3UMIQd3TrPdjQDAIU85tDpmuTu86x8MkL4JBjLp1uT1ivR+jMcTVsSRRaub2vnpmi4+emOPUvuZENb7aDZmqOKz2olveGPmhB2Y5PFOd2BuPzlYRQkzI/iA2xPbhpRYXtkZ0AtMRZkvJdM3Bicz51PRtekXq5xgfPjHLf/SZh3lw6dYk5F7N7N5sO1przm2MGMYZtiWo7yjFdmuS9ijh3OaIxw9NEWc3J4VlV2WJEiXebpRkrMS7AqV94s3hbiqMb+aO+RdfWedXv36Ri9sj0tyEExydrfGXP3KUTz28+IY/30617sR8jWGc0wkS4nRMHGwSpdkexjQqDlluFr5RakjGw/saTNfcmy7Q7sb+G7/GZ791mW9daLM1jBhGGZYULDQ9+mE6WdzblsC2JK6d0vSdQpFSRaGzJsmNAjNfdxkmOVe7EZ1Rwr/zgcN7EpoTCw3UezRnNgYsNDwemK2y3AnoBBmpMsRqyhK0RymuLTk8UzFkSymEMARcCmNxTDOFQCBFhgJmK9aOLQk0Zu5pr5soYZrxhZc27rrKPSa7r6z1+cOX1sn1OLpCYAnBg0sNfuw9i6z2I85tDotethH1wioZpzm51lRdi1FiFKX2MJ50xsE1snR8oc5mP2YUZTx7uUuSKaarDpnSRTS9UaDiTPHdi20WWx5zdZ+aa/H81R7TVRfHEpxZH7B/qnLDjZGHlhr88lfP76ninFoyStZyO8C3LXxH8oEd8f+OFLy6NqCTm+62Hzo2C8VNA6lz4Crv2d/ixMLt+6vu1czuzbYziDK2RzGWEPiOtWv7QohJr9/mIL4pKXy7VfwSJUqUgJKMlShRosDdUhjf6B3zL76yzt/93OlJz9Z4EXpmY8Df/Zzp1LkTQrbX7MdYras4kqcvdekECVmuyJUuipmNvalVcRklGVvDeLKIti1J3Xduu0C7W/svyhTzDRetNb4tqboWK72YTYxS6DsCpQVxmnO1E6KnzGI6KXqj0JBGGZ4lCIrS30GU8fyV3i41cud+qjoWn39xnSRTPHnYqD0LTZ+XVvoMo5QgzsG1mK+bYInxTNMwylBKkymF1iYkIlaQo0lDo1g0XPP5O6N0Mtc2VmL2uolyYr7x1qrck1I0ce3vBcY3Epq+Q6YUWS7YGkSEaW5SKYFca3zHoRumdIKEqrebLP35Jw/xhZfX+dprm3SChIprkSnNMMpIMoXSIKWpBBglGd88t817D7TYGiSsDyJe2xjywGwNKQSX2wGeLSek9KGlBl86vXFbFedm8f+90MyXpblmtm7T8G2klLQqLkLnEMKFrdEdzXndq5ndm22nEyT0wpTZusesFIziDM+WE0LlWJJhlLLaC/lwUYlwPcquyhIlSrwTUJKxEiVKTHA3FMadC8GaazGM812WsL3umGeZ4le/fpFBlHJ4ujIpwW34kpprcbkT8mvfuMjHTs7f0rJ4s9mPk0t1toYx2yOjhtV9B8e3GUWGeG0NY+qezal9DZJM8b1LHSqOhWtLokxNkv1ut0B7M/tvfJe+EyQ8MFtjvR8zU/PYGiaYuihjQ5NCYlsCKUx8/HInmKgtO5FrWO6GHCz2c5BmEzXy+v2UK81yO9wVTDFT8/jI8TkAVnshFcfifQdbnN0YFaqVZLUXkWQKKQRKa0aJKRa2ZREXjzZEDqj7Fodm64C4pXVtr334ZsMVxvs2V5off2TxhnPy7OaIz7+0zk+9bx++bU0Ks1d7IUqblEJLmM64JDWF1TXXJkxyLm6NblDwpIRvXdgmyRUVx2IYZUSZmszdaYWJsdSafpjx7fNt6r5NzTMq4nzDI0oVni35qcf28dBiA600v/L1i5zdGHBsrjbZv1rDdNXhSifgcy+s8W89LgnTnMcOtbjaDSYEJkpzvn+5y0o/wrUknSDh6UtdTizUd82+xVl+x3Ne92pmd6/tZEozU/N4aLFOzXN4drk7STF1LMkozggSxWzduykpLLsqS5Qo8U5AScZKlChxV7HLEvby+qTTCEx58IOLjRsWR88sd7i4PWK25k6I2LXXk8zWXC5sjXhmucMHH5jdc7u3mv14db3P1U5IpjSLTW9COOq+TdN3TGw4Atcyd9YtKah6Ft0gZaHp70r2e6sWaDvv0seZSTRU2iysPccs0tOCQERZjhCCubrDcie84bUsAZbQZDms9SPm6y5Vx6bm2nvup6udgPYo5tX13cEUUgres79JnOVsDxMGUYaU0I9MUmKWaywBjaqNAKJUEecKDTQ9C41gX9MBEnzbYhTnHJmrMohTltvBHZGqM+t9/uXTVzm3OSTXiinfYaFZ4amj0zy8r3lHr7Fz30opaVZ2n2Njgi2A4/N1XrjSJcsUUapoVeyimNoQtJZvk+aKY3N1/tP/yyniTN1AEE8sNPizTxzg+eUuwyQjTNWkCmBcJ5CZ1A3SXBFlmqWWX5RPS6YqDlTg7OaQL7y8zqurfb51oc3zyz00mtfWh1RcC0sILMtYLUeJsUX+mxdXWWx4TFddpmsu+5oWl9sBZ9YHRGlO1bGYa7hUXYfNQcQwznj80JQp54bXPed1r2Z2r99O1bH4nWdXeGm1z8Fph8cPTXF2Y0gnSBhGGUGSc2Khzi98/MRNSWHZVVmiRIl3AsorTIkSJd46TPqMNBqxO45vB7ZHCWmuqLjWnr+vuBbtUcL2KNnz97eb/XjuSpdBnOJbxt6nAEsIXFsyU3fpRWkRPmFKiDWwNTDzY8fnazeENNytBdpOxWetFxGmGfvdClqbsIUkM/Y4S0hAk2poVGzSkSLOVFGUvPs1BSaCPskBNKM4p+bmvO9gi31Nn//lK+e40jFESGtDDKaqLq2KwzDKbgimMOpDg+fSHq+uDYwiUXWxhKAfJfiOhdaQaqj6Nr5S9MOMIM1xbYlTKJnnt0c0K2b26n/60tk7Sqz74ivr/KMvvsbmIMYtXicp0hr/4MVVHlxq8MSh6dum3t2pAjKMMx471OLpi22CVGFLCBLTuZUpbciPlNQrppfLlpIHlvaer/pT793H//HNizy/0kMCjg2WlKS5IXWqSFaM8xzXkuTKkL+6b5u5rjClHyZ8/3KXqYqDFpArEwyS5DlbQxMW4tkSDQxCM6M3XBuw0g050Kow2/A4OFVhuupyeKbK8bkaZ9aHbA5jXMv0qbVHCWc3BtgLVRAwW3fY1/Rvui/3wr2a2b1+Oz/x6BKr/Wii/j1xuMXmIGa1FzFb9/iFTxznwcWbnxdlV2WJEiXeCSjJWIkSJe4qdlnC3nNzS9jOmavZmotjScIkp+HfaEMMkxzHMgrZXrjd7Md01SXLNSOlaW+OEMIs7FxL4tuSxaZPrjSdICVIMjzbIhOCxw62mKl5k9e6mwu0m1kFK47FwekqM1WXq92gSOfTk89SdSxiz6bqwvYwMn1fBc/Nd8TPT0ajMHHlp/Y1+eaFbf7Ni2smcr8TTvrUjs/XmK15XO0GewZTBIlif6HcHJiq0A1Snr/aw89tPFsQZ5o4y3Ets6Adzw4pBUGhIKapotK0ODxTperauxIEf/LRJeYb3i5V5czagH/0xddY60fsa/pk6lqoiiGAmk6Q8MLV26fe3YkCEmeK3/r+ClvDmH6cEqUpaQa50ggBFRumW1X2tSocma3SD1MGkVH4rleExsdWC4FSkAM6B6c4Iqogwb4jiTOFI2EQGfI6ijOGUUbds+ljCrcHcYpS4NgSSwpUan5uScEwzsiVOdYSQ/CiJGe5E5DkiijNGcU5H3pgmmbF5cQiDJNsYumTRajH8vaAxx+Bi1sBv/zV87ckuO+UTq7r7YtxZm6UfPj43B3ZJMuuyhIlSrwTUJKxEiVK3FXcqSVs58zVk4emOTpb48zGgJpr7bIqKqXYHiU8tNjgyUPTe27zdsqH0poozfFsiUKTJGZ+BwEVx+LRgxWWmhX+0ocO06w4bA5iPvfCGtujBNe27voCbadVcKnpU1c2/SghTHK+e7FD1bU5vlCjH6Wm7DfOkFLiu5JhnNGquBybq/LV17ZI8pS6a5MoRZyagIhxViAYonZgyufrZ7dY7YW0RzGLTR/XlpM+tWGc8cBclV6UsD1MbgimcG1JPzSv+P3LPQZRQidI0RgFzpGCNNf0wgxZkGGlYaHp8cEHpoEhVc/MnI3JUMN3SLKc71xo8+xylyOzFSqOzfH5Op86tcCvffMCK92QubpJF9waJiitafo2UaZItQnFeHS/x/ogvmXq3e0UkNc2hvTDFFsK9k9V+N6lmFGyu+F6kMKs0jx1ZJphnLE5iCfkbTyfeGy+xnzD4ytnNhnFGYenK1zeHrE9SlEa4sy8pmMJaq4hlKEyJHt/yydMjTo7U3NJMkWQmERRKUAUc3hhkexoS0GSqV1EbDxLOA6LbI8SHEvQC1OKTuddxeSr/ZCtQUyaaw5NVYGQg9PVW8a6v9M6ud6sTbLsqixRosTbjZKMlShR4q7ijQzF27bkL3/kKH/3c6e53Al3pSlujxKavsPPffjoTcM7bqV8pGnOMxfbRKkiSM2K1BbFwlUKcq15eWXAw0tNnjoyg5SCU0uGNL4VC7SdlsrZmsOrawPaQWISCVH0opSvvbbFJ0/N876DLZTWXNwK0MokI841fA5M+WwMTOiIZwl6UWbSF4vZMqU1qij0nW14PHGoxbfOb9PwbVpFebAs+tTcwqq2NUx4cKHOq3q4K5hiX8vn0vaI5650iYsQCrtY6I4DTpLUBHdorZHSBIuAQGkI4xyqsNTyJ2obmMLe5670iDNDLuZqHnGm+OLpdX7z+1fojJJJimEnSAlThe/IiaIZJTkS6IQpS01vQvDH6ZnXL8xvpoCsdCP6oakIeHCxwedeXONS+8Y5PICL7ZB/88IK+6aqu8hb1a2w0g347WcNOZMC5useG/2YIMnxHUmamVk6Wwp8xyhclpQobXrCHlys8+2LHepFIXWmFGmuqHkWSabwHElSvEaWayzJhIgBCGH2f1oUdCsUqTLqodaCfpQyXSjLMzWPqSMuXz+XE6U5Fcfi/UemgS513+ak7+6ZGvpO7eR6szbJsquyRIkSbydKMlaiRIm7ijc6FD+OrR/3jJm7+pKHFhv83Idv3TN2M+Xjm+e2ePpS54a5qkyDFGZuRyuIdc4g3h3I8VYt0HbG7D93pUeY5EUCnAmGSDJFL0w5sz5kqurw0GKD9x5okaSKMM0xwY6CR/Y3Tb8S8O0LbUZxhhBmrgnMQt2WgkcPtNBIemHKyYUGlpBsDCLc2ngGS2FJwVovxJaCn3p0P3/qsX2Eac7WIObXv3OZl1cHJLmJdndtSa40mdKoTGEJyARIbUhAmmuklNRdiyTNeeZymx89BScXmpPjMi7sDZOc+YbHxiDm+as9+lHK1iCedKZ59rjQOi9SLQWp0gxj05sVpzkvXOmy0DBK3yurfX7n2ZWbqjZ7KSCHZytkSnF4pkqaKc5uDG95/M5uBjhCM9es8eBiAyEE7VHMaxtD4tTE19d9G43m0nZAnCljH7QEqpgV01CQIJtPP7JIxbE4tzUiTDNqnkec5QwiY81teDZdlRXdbYJmxSHJIrIiKGWM8TkuAKQJC8lzRS/MmKt5dIKEwzPVyTEYxhmjOMO1LJZaFRNSE117vbpn871LbZ6+NM1TR2YA3tWdXNcTOqX0nhbUEiVKlLjbKMlYiRIl7irezFD8px5e5GMn53lmucP2yPRqPXlo+pZx9rD37MfzV7o8fal70+ckGThSY1kSVwq2h8kNcfVvRTDBKMkI05zOKCFIcuqebRIntcK1JfunKix3QvZN+fz8Rx+g4TmTfbWTGO5r+vzyV8/z4kqPp45O861zbVKlipQ+jWNLjszWeGRfk8vtwCQc+rYpae4FXNgaARTESpFkGtuSnNrX4MhsDaU0f/TSOq9tDHEtgSVAY9REKQUqNXNuWRFEYdmSuZpDP8qLhMDcKKRFJkt7GKGExLUkWmvagZlZGkYZ/TAFIM0UthTYrs0gTk0IiVD4tkmV7Ecpea4ns3FWEbBxfnNAruGXvvQarWIGbr+3t2ozJtiDKDV2w2HMmfUBFcfi6+c2b5Yxswv9WPHelsf2KMGRgrPrQ4I4L5QuY5+MUrMfrMI3qJWeDPLVPQcpBI4l+JknD7Kv5fN/fneZS9sB28MEz5bUPRuEsSW6EoI0x7YErYpDmuVsBykCRaZ221I1JpxGIlDCkIpW1WF/y9+lCrZHpuqhVXFYbFybi+wECa9thGyNYvphyi9/5TzfPdzhfYdaN53LhBvJ2/1MXN5pVswSJUq8u1GSsRIlStxVjInR1W7Ac1e6TFddGr6NLQVr/fi2M1e2LW8aX38r7FQ+zqz1eXa5e9vnZAoaniRVelcP11uJmmujtOZqNyDLNd0gRRUFVK5tQk7qrsUgzGh4zi4yeD0xHBPQ7WHCqaU6a/0Ip0j+m666PHHYzNh1goSKLfnG2S22A0MCx0qXYwm8oly65Tt86fQGR2areLbFC1d75EpTcS1c2yLLVWGRE7i2RZzmZFDYHs3/VF0LKS2avoPWMIpiAL5+dotmrULVs/FsSZBk1F2PjWGMFIKpisPVboTrWEggTCWagijm5vXHM1cC02Xm25LOKCEtFKfOKGW65tCPUh49MMVMzd1TtYmznC+f3uTc5tBE+q8NWOtFbA3iOzqGnSDl+Stm32gNmwPTe5XmmrSQqCSGLDqWIFcCWwqz/9CEac7JBZPCOIxNYMzf/sxDaOBLp9fZHiWEsUlHzHJd9MyBFBZpppBS4khJWKhjYyKWa7NNY2E0JeDNisNi0+On3ref55Z7nNsccnZjyGo3Is0UYZLxytqA7UHIsQX43sUOvdhYQpsVh5maw4srPV5a7TGMMvZfdxOlPYo5tzG6gbzdr8TlnWrFLFGixLsXJRkrUaLEWwLfsdgcxJxdH4KAVsXlh4/N8Jc+dPgtW8yMlY9/9KVXyW7sQb4BGohzE5Iw7uF6q3FgqkLFsdgaJniWxLLMIj3JTWpeeySYrbsEye3J4U4C+n2l2BolpLlm35TPg4sNHEvwWmG72x6l9KMUWXxuWRAopTW5guPzVX7o2Mwk7fLjp+YZpWb7riVxLIljCZLMECRdJDzaEubqHp5jUfMsolRRdS06IxPNnivzGrmGbphQ92w6QUI3SE3giIKllofGvBetBYkylkQhJKlS2JZE7bCRWtKQvijNiQtSOSY/Sao4vzliFOf80LFZZmrurtCYOMsni+2KYxS3IMnYGMQ32Fl3Ymc6pcYQ0mbFYaUbMiiKrV1bYGtjg1WYTjGRK6SQVBxJagkcKfEdk5rYjzL++Xcv84Wqx1TV4ZWVHpe3QzKtcYRJT/QcQZgohBDYlmQQm/lA2xJ4GCtoMQppag0UxEojJLR8hw8+MIMA5hoef/3jx/nGuS0++53L7JvyafgWvTDDswVXuyEswNVOgJYW3UAzVXWLucEKz13psjVMGMUpzYqZPWuPYp5d7hImJgmytYO83Y/E5XYVGfe7FbNEiRLvTJRkrESJEncVO+8sf+iBWXKlGUQp7SAhTO+AIb1JSCkYBOnreIbAsSQnFxv3rE/IscwsVKY1UZKjlXnfSIHWRi1ZbgdGqVm69WvttN69strn6YsdNgcmlCJOFUtNn2+c2yIu5rugIAnjtaQ20fkak+o3Ji5PHplCakGcqUkFQJCaEJGxtTEt5s1SpdlXdRglOY4lWO/HJJkhSl6xIVsKRpmmH6Z4jrEqRllOw7No+A6DKCVKc4JCEbKloO5JyCS+bTGQAqk0nmusjnFqAiosCbYwaY5CCHzXIs0V26OYsxsDPnB0ZhIaM4hSvnx6cxKe8tyVXpGGqMjvkIgBPLJQo5coai70wrSwBrLLiigxhCzJwbM1SW4CVpq+xcYgJkhzHpir8Z59LVZ7IV94aZ2NYYwQULUluYIkM4EoszWHVBnS++j+Bl8/3ybOcoTWuLZEpaoI7biGqmNxcLqCb0tATG40PLfcQ2t4/NAUnSDl2eUunSAhis13RqERmA4+ATx3pcfjh6Y4NldjcxBzfmvEYwfNLOh47m+66tApCtL3tcx36H4kLreryNgrCbZEiRIl3ixKMlaiRIm7hpvdWZ6uuRyaqd6TBdrZjQHPXO7e8eOlMIl4f+79B+7Ke7pdB9PVbkiWa47MVrmwNSLPNVYR5e8VClScK4QUPLfc5cPH5254X3tt49BMlUMzVT798OLkd1XH4p998yKdIMWzBVXXJUpzwjRHT+auBI4UDKOMQZRR9SzObgz53AtrbA1jukFCN0iouSYsJExzXEuQF0EdzYqNY0kQTOak4jQ39jmtkdY4UEST55ooVzx2eIrjC5rnr3QJUs1GL2SU5Chl5sFcW1BxLJLMkJuDMz7DJKPqwqEpn1TBlU6IKhQiNCS5nih+nmMRp4qNvulMEwI822IYm1LrpabPq2sDemFKLzAdXhVHkuWKve4X7CRiR2crPHJohmeXu6z0QuI0xxYmQCPT4NhiQnR18Z6M1VMyV/fY7EfEmWap6fHIvlYRnhKTa0WeK1xH0vQdlDZKYZIpXNui4Ugz+6cNoXWkKdTOcjWxSNrF53Qswb5WhVFRlfBjjyxOUiZ3ko2ZmstjB1t85cwmaSElKwU1T7LQ8PEdSXuUcG5zyOOHWszVPWqFQlT3bLZGpoy7E6RUXFNLMP7O34/E5Y0kwZYoUaLEm0VJxkqUKHHX8HbfWT67MeAf/tFrvLp+60S8nVhqefzNT53kwcXmXdn+7Qb/R0lGnCseXKiz0glQwsJx5CQuPskUUgiOzdU4tzm6YV/dbhs7Q0eW2wHnt0ZYlkDkZjbMlpAVRcYCYwsUAlKlSHJFr5uw3A4QAk7ta5DkirV+RD/KcW2BI0RhlYOZmsunH17kwyfm+Pb5bX73uRX6UYZrS6QQhEmG0uZzWZbAKkqgn13uUnXN/FOmNFd7CQIT+W7KjE30u4lu11zZDqk4kpprM0oVFceaxPNLIVC6+DzFz8y5p0mVIs5yOkHKowda1H2bKMupK5v2KCZMclKli/cLIMiKWau9MFdz+FPvO4AlBY8fmuLbF9oobQityg1x9G2LvNiXjmVeb6nhU/NthlHKIM6YrRn74EzNpR8a1di1i1oCRUEyjaIlpSBMc5LMRN13w5RMaVq+ZcJnirJ0dBFvXyhbxvKpyZVme5Sw3An2JBuOZeYF604FSPFsyVzdVEsA1H2b9ihhcxAzV/f4mScP8Nxyj2cut+mHKa2Kw0LT5/h8nZkdpez3I3F5o0mwJUqUKPFmUF5RSpQocdfwdt5ZVkrz2W9d5itnNid9YrdD07N4cLHBkdk3TwzPbgz4J189x+m1AY5tMVNxadTkDfMz4wWfUoqaZ5MpTZJpEqWwpMR3LSqOxf6pCu1Rsmtf3Um4wLG5a3H8az0TLFGxJKE2cfAm/MLMqVmWKNIXTSCELeDFq30cW/K+Ay2klNQ8mxeu9IrFfI6SpqD4vQen+HNPHuQjhXL3sZPzbA8Tvnxmw/Sg9WOEEBNrpAmiEGS5Zr0XgQDPliw2fdZ6Obk2vVsLNZtRohjFWaF0CfpxRsO36UcZaE2QZIUbUJMV6YqebayUUWGdHBOqq11TZPxj71nEsy1jeYxSwkwVxHdMYiDNCrsm16yJUoBnCaqOoFV1JzNTMzWPRw+0WOmERp3DBIMYxdH8XWuNLeGBuRq+a3G1GzKVKT724AKzdZNgmOSKTCl82xBMpXeTQUsIRplRG5WGdpAYK2uSU3VBIIp5MnNc06IWYJSYAm5LwncutvnF33+FI7PVokw6o+7ZDKKM9UFElObsa5jlgFFLr6WXOpZkGGWs9iI+fHxu8ufpS9P88lfOM1Nz2Neq3HDz5X4kLm8mCbZEiRIl3ijun6tkiRIl3vF4O+8sL3cCvn52g0F0Z0TPlYI//dg+toLsTVsnldL8vX/zCl8/t02am/kdIcz+eOLwFG2YbOPAVIWpisOXXjXvVQpDHGxL0PRtpBAstnxsKXbtqzsJF/jsty8zU3U5vzUiykz0/GY/NhbCTJHH2UQ1ylVRzizAtTStis2F7RFprnnisCFiYAqCf/TBefpRyko3Yhhn/EefOsEHjs7u2l+2LfmzTx7gmcsd2qN00l8W5ybcIlOaOC8W+xiSVfFsQ4ikpOZIolTRCTPmay4zNZdhbMiXlIJH9plo9a1hgso0Aj1JM/QdiwNTlYmtrhMkCCGouzYfPDrDj793iRMLDZTSHJ+v852L2witUdoQRK3NYrvYHUUypPkHsurZHJmp0g1T+mHKuc0Rjx9yEEKwv1VhpuayNYyxLWP3FJMBM1PALICnL3ewpWS65lD1bOIsv3YeWhJbSjxb4DmmiFrvYGNxlhMlObkG35E4QpNjZvmU1vi2BZjPUXEtSATzDc9YSTXUPItRnFNxLS63QzYHMZ1RMrEXRolRDrXKYT9UXZthbKydjmWCRoIkZ7bu7UpBferIDN893OHFld4N34f7lbjcqhx8tRfdNgm2RIkSJd4ISjJWokSJu4a3887yha0Ry93o9g8EbAGzDRcl5Ju2TiZJzn/8L77Pl05vogBXmhS8XJty4m+eb/Pk4dauNL+NYWy6xYAwMQvfUQz9MKPu2zy8r8FaP961r25nAa04ki+f3uDwbJXj83WqboVRnHF6dcDGMC4sa0ZNgh1zUNqoM70w5fBcDRDsn6re8PqtikvNs7m4NaJVdfdckH74+ByfOLXAF1/ZIMpyE4Mvxq9x7XE5hvBMV1yqjimktqTk4JRHlCnee2CKlW5Ikhn1MM4Ui02f2brH2Y0Bl7YDfFfi2CZRse7Z+K4hJZY07/XEYp2/+iPH+MjxOYBJge9jh1pc6QRc3g7ItSF1Wa5RxT4Za0LjAA7fsfAci1bR+WVJwZn1AQ3fxpKCwzNVNgYRw9gkQLq2RZrlEyLm2pJm8VjftogzxXcvdqi6NjM1t6g10Cy3QwRGCRuECfWKiy2gH6aTQJPZmkuYKhxA6Yw016DNrJ0QFPNlZt4uzRWzNbewS8pJxcTmMOLCVjBJwZypOwRJRjdIADi1VGeUCtpBwjBKCRLFiYU6v/CJ47uSEd+txOVm5eCPHmjxY++5P+P6S5Qo8c5GScZKlChx1/B2LtDM4v1OKnsxnVoVF0ca29zmMObc5vCGsI3b4Z998yL/61fPc7kTTn6WKEAqPMvCEpooU7x0dUDLtydpfrnSPH6oxTfObROkOWoslGiTPPmV1zZ5/+GZXfvqVhZQrTUr3YgwzTkwVZmokg3fZqpqs9YPUUXyYK7ZFeHuWoLHD7bwXRuVm/mpN6psSin42Q8dZnMQ86XTMY6taboCMBa7sWo0nu2qOrKIxDdqzHzDxdJmzqoTpoWqk7HQ9Gn4htx/4OgMB6crtEcpf+p9+3h+ucu3L3bYHpqOsKmKw4eOzfKzRYXCXjN2U1WHxw9N8eVXNxkVlsexHDWePdOYMIz5uleQX5O6+dihFt+72OWllT5pbuywlhTI3IRoxFlW/KyImteaXpRRLZS/+YZHkOR87ewWVUdypRMyjPMJOR53qPXDhEyZY+VaRjUbJSY8Jck1Dc9mlBhCZvatxrEkiy0fpTR13xyjQZTRqjjGQhmZOTJLwoFWhTBTDKKMmm/jSg1kDOKMJ47MsTVMWO2FzNY9fuHjJ3hw8UYS8m4lLjsTSm8WxFOiRIkSdwslGStRosRdxduxQFNKc2FrhCXgTkyKrm26nl5dM6pRmOb8+rcv89LV/h2X1f6zb17kv/vDVwmS/IbfmTGvHM+ysC3BKM1oj7Ib0vyqrk2Wqx2R/8Z2lynNas+kBY5xKwvoIMrYHMbUPBuvCIIY/zxOFVXXZhRnVBxroli5tgl/yLWx033wQIvX1of0gpRXkz4nFxo0K85EhbtTZfPEQoM//9RBvnW+Xcy7mecbQmGbJEcKKyBGdZuruwRJztYgoerZ5FoTphlpJql6u1P6hBAsNH2CJOfh/U3+9OMHWO4EXNgaAXBsrsbB6SpSipvO2K32IqarLj//0aP83vOrbA1i0lyRxYY0yuIcWWh4VFwLrTW9IMV3LM5vjqh5Nj98bJZhnPHi1S6Z0oVd1KIdJCaWPjdVAuNgFiklaaZY70c8MF/j7PqQlR0dYWNoIFXmPdQ8G9cWHJ6u0I8y1gYxcapxLEGqzYyc0opWxUFjSrD3t3wubo9Q2mKtH5kuN635zoW2UUCDFN+RnNrXwHdsktxUF+R5Blxkc5Bweq3PdNXjw8fndn1n90rxfLcSl51BOCVKlCjxVqIkYyVKlLjruNcLtKvdkK1BzAPzNU6v3TpJUQCtiiEngzAlU3B4usr+Kf+Oy2qTJOeffO0CcZbj24JhcqMil2RgC7MgT4oesZdWury00qXlO6z0QtJiXmqubpMrUFqRZJqFpscgyvhX37vKf/ITJiHxVhbQOMsZxRlHZ2s0/GuX9SRXhJlCoKk4FjN1l84owfYNEQPNKMlpjxKWOyFXuiFrvQgp4PzmiP1TFR5calBxrNelbC42fU4t1Vnrx6jc0OOqY5EhSXJFroyKM+YhtiWZLZL4PNtiGJm4+ammwyP7WrtS+gCCOCNTmrVeRM21OTRd5chsbddj7mTG7uB0hX/4Fx/nn33zImc3h5xdH9ILM0MUtWZ7FDOI0sl7nfdd4jTnoSWTvPn0xQ4aYxHVApLx59J5MTcokGhyLZACqp5FP8xY3g5MOuJ1RGxH9RsK8GxBzbNY68ckuUZiCp1zwLPM820peHCxwUNLDdBwtRcSpYokSwiTHNsyiZNhmjMIU4ZxTpDAs8s9njo6w1wRJCK0hNCQ2Z/5wGHeu7+16zt7uxTPkriUKFGixBtDScZKlPgBwe36r+427uWd5XFc/JOHp1nvx3RuUfq82HCpey7dMMG2TE/WycUGzYpLw3fuqAvt86fX2BxEVMd2vUTdUAwMRuEyc1rw8mqP711q71qAW8JEh4/T8DIlsS1dzPzoXbNst7KAXu2GVByb/VO758lcSyKBtOgy00VSn2tJhICsWMyPkoyvntmcpAFOVV1Ac7kdsDmMeXCxwZOHp+9Y2ay5NvMNn/mGx2Y/BIYIIYjSnGbFIUpNuEiY5ORK0Q8zKq7FB47M8JPv28dszeW3vr/C5faI6epuFXB7GPGdCx1sS/LPv3uZimPfUB8AN87Yaa0ZRNlECVpqejxzuc3l9ohL2wG9IAUBjg15bvZNEucMdI7AHKetoYmgr7YDFLDej2h4NkGSm5mtVKE05MW8WK41Ftd6x6IkJ8kVQZLfcK7sdaZ1g5Qs16RKU/dsPN/ByxRBmjNddZmquDx+eIqf/+gDHJo237UrnYB/8PlX+cqZLZIsQwoTaiIEVGzL1BwAa72Q71/u8MTh6V1kd6rq8t79rRvqFP5/f3KRq92AmarLXM3DkuKOb16UKFGiRImboyRjJUr8AOBO+q/uZ4wtfL5j8afet48vnd5gpRvtWvBKAacWGxycqfDSSh/fsVi8rh/pTrvQTEmvxrMFAjEJe7geWW5+LoBBlCMFux6baxhGGZYwHVtJpqi5NkIYm5zSale0/c0soB88OsvxuZjVfoTWeocKZOM7kiDJsS2FGimiVJHnmoprkSmNJQS9IMWSgppro9G0qg5JpqhLQd2zODZf4z/4kWPYtrz+I+6JnSreE4daEG/ywWMzvLIWECYpqRAorVjpBuQabGlmnT71yCI/cnIeMJ//V75+cRfxXO2GfPdiB4APHGiyf6p6Q7T/zj638YxdexRzbmNEO0jIlMKWZt9eaYfFcTfhF+O5NiEFlhDFgdIFUdIopbm4NeL85oiKKyfzWLY0sfZCCOyC+ElpSNlYBRyEKXGm9zxPdmLnOZsV76fqWoZEColjCZzcdL1NVV3+3R86vEsVTHLFIM4I08yUaKt8ss0sz7AtiSONWjaIUs5tDpmuTk9m5o7N13bZUJXSfPbbl3n6YhshDMm1pWSm6nJsvsr2KHnLi9xLlChR4t2MkoyVuGu418pLiTvDnXRT3e+EbOfi/+RCnT/35EE6QcIrq32CJEMj+IlHlviFT5zgT85t8U/+5DzHZutMVR2uTya8ky60pZaHJQRBoqAIRFB7rLIVhgTKQjbzXQulNUmqGGeN5Br6UUquLDzHZrrqMIpzWlWHqYp7Q1jGzSyg57eGe5KXTkG0wKT55bkmSo1CM04qBKi7kkxraq5Nw7PBo1BUBJt9Q/TuVOncqeKd3wo43oCm72BbgnaQTYhL1bE40PTY16qQa83vPLvCoekqDy41OLHQ4Oc+fIR/+fRVzm0OyZWZt6q4Fh88OjPp6dppO9xJCsYEfaUb8NrGkDDJqfs2jrTZHsVc2jZEsGJDxXXIcxN1nyuwpYnTdxw5KZjuRRlRmuLbAiElShtVcRhlptdMQ6YUMzWXUWGjBBPAkWQ5xW7GEuaYX4+bRc/UfZvpqkt7lBKmOVmu0MD+lk/dt1npRlQcm31Nn5VeyP/+zctc3h4VROwa8ReMg1sMOYwyRUsKtoYxq72QME451oBPnlrYdd3+xrktvnx6A601UzUXx5KkuWJjEDGIU04u1N/SIvcSJUqUeLejJGMl7gre7crL/Yo7mZt5N9zV3svC16w4vO/g1GTW6ace24eUgqpr4VkWYZoxxRvrQvuxU0v8YvU0a/0IxxK4tkTkiuuzPDwLE1qRZNi2sQmCib7XmZosyjNlOp1mag5hmuM7FlXH2Cf3CsvYywJ6vWq21ou4uD2iWXE4tdTglbUBozhDSIGUglwZUjZO70u1wLUkMzV3cp7UfVMM3A2TOy7qHt+UyZTmJ9+7xHOXtmEE3zi3zSBRVF2Lqmu2M4hy+mFGpgKkgAubI/6r33uZ/+KnH0YKwedfWufS9qggqyat8NEDrQkRG2MvRfPAVIVj8zV+59kVcqWYrXuESc5ad8T2KNm175XW6OL0lwVZylLFbM3BtiSZUihlyBrCFEdnucK2pCljzjVCCqZ9l4ot8RxJFucTdVMpo3DZ0lgEKfrRbqeSCaDl21Rcm/2ORZzmtIOUhm9TdS0ubo34J39y3kTmpwqF5szagF4xC2lL0GpnWIpR6hCCiivRmOj89ijlqcNNGKxwbL6+61j+0csbBGnOoenKpAzasy3cmrE/rvQiZqruW1LkXqJEiRI/CCjJWIk3jR8E5eV+xe26qd5sx9Y7CbdLcQT4n//4HGc3Biy3A15ZzTgyW+XEQmNiU7zTxEDbljyyr8nGICbNNMI2sedSaKJMI4Wxe83VbC5sRROlRCk9UUCkAKmNIjL+mdLQqjpUHZvDs9XXXQOwUzU7tznk1799mf1TPs2Ky8HpKi+t9NkYRPSDlFDlxJlCFu+l6sFS0yhqYziWJMlSpJB3VNS9102ZY7P+5PN6lqQdmLmrbpiRpDlJrhESDk5V8Oycc5tD/t7nTpMrzWo3ItdmDyWpohOmgKDuOzeEelyvaEopeOzQFP/qe1dQGjYHMeuDiCRTE5UKDBkLEzVJrhTCEBgNRGlOkCiyPCdRRtVKMmWSF5XGlqY4W2mwLMmTh2rMN3xqm0O2RwlNz6IdpAgBnVGCFMIkIeaaJFfEt6licKxxFYEmzRWjxBD1NFes9zN8x2Km6nJmfUAnSKnsKI02M2vXagTAvE+NmV17eH+DY3N1OkHCX/vYMR7b3+AP/uD0ru1f7Yas9kLqnk2mTFz/GEII6r7N5iCmVXHekiL3EiVKlPhBQHn1LPGm8IOivNyvuFU3FdyZJe9+wu0sfOMbBk8dneaZyx3Ob41ojxKePDL9uhIDr3ZDmhWHDxyd5uWVftH3ZBa+rYrNI/uaNCsOYZyjCVEaVK53WdHGXV+ONPNRh2dqtKo2UxWXk4uNN1wDMFbNRkmGZQlqnlH/Zuse79nfoHM2QUgzoxZnysxIIQgTNVHuxkgyRZKZ0t/bFXXvdVNmFGd8+dUN/r2Dpty64rlYUiLQdEYJWmuaFYc0M8XLNc8myRSvrAwIkozZmkOzaqxxozijHSSs9UNeuNrlR0/O77rBsJeiOVt3mW94dIKES9sBWa7ZkfwPGHKS5QohBEprLK6R42FiyKrGkBspjBVxTOa0AssSOEKQac3p9QG2Jfn0w4s8tNTga2e2+MLLa1gSLGli+ufrLp1RSj9KyfJsT8uiFNDwbGqeRTdISXONbUnmG0bdG0QptmWi9zf7MVmuOTxdYaUXFbZTjVccX62LIJGCSGkNUsK+VoVRkvP+IzM8dWSmiLbfjVGSISXM1z22hjHuDtUUivCXOGNfy39LitxLlChR4gcBJRkr8abwg6S83I+4VTcV3Jkl737D9Ra+vW4YNHyHDxyd4ez6kEvtgGcudXhkX/OOu9DGJPf9R2Z4/+Fpzm0NGcY5dc/i+FwdXVjujs5VeWmlB9x8JihVcHy6wj/4i48RFwEeb2becmwTXOtF5LlmFKc0Ky5aa15c6bM5iMmV4hrvMiXLuVZcbgccnavi2hZJlrPWj1lq+vy59x+45fvZax+3RzFnN4YsbwdwEPpRRicy5E4Is01BcQ46llHAcqNQjZKMXCkavjPpTat7ZnZqe5RwpRPSD1Na1Zsrmmc3BvzWM1dZ7UV0g4RcaVxH4khJrnaToFyDLbTpqdvjQAmMpTBT146jI6HuWeRaYBf216WWvyvs5ORCndV+SMWWXO6E9EKjXokahGmGEGBRqHGFiuXZFifmaxxfqNOPUkOU05x9LR/ftvjG+W0yBc2KzVLT55W1AXXfRkpJzbOByHSlCfBsEwqjYZLqKYXZl8M44+D0NfU1v7Euj5prU3FsKlPWpAKh7tuTubHOKKXi2Hzq4bemyL1EiRIlfhBwZ9FYbxG++tWv8tM//dPs378fIQS/9Vu/dcvH/+t//a/5zGc+w/z8PM1mkx/+4R/mD//wD+/Nmy2xJ64pL3sv5iuuZTqQ3iXKy/2GcbDFai+6QfXQWrPSjZhruAzilOV2gFK3tk2BWXgvtwNOr/VveM6tfvd24WY3DGZqHh94YIaPnJjj4HSFv/Shw/yHHzt+x9HtY5JrWZIHF5s8eXiaBxebWJYsSK7k0EwVjb4pERsjU4JDU1VOLTUnMfZvBGc3BvzPf3yO/+ELZ/jn311muRPw9XPbbA9j+lHKpe2AJC8UMAG+Y1H3LCwpUEoTZTm9MGVzELE5SNjX9PmbnzrJg4vNyTb2OsbX7+P2KObZ5S4r3XDyvDTXRqnBbGu8U6LMpDtKTLKkZ1vkeW4SD3d8NiEEMzWXiiMZRhkrvZBMKQZRymsbw12K5lilu9wOaVUcsqKAWSltSqevOyAaQ8hc+7obShiCZFli8jgoLKZS4NoWNdcydk8peO/+JluDhNV+BMD+VoUjMzW2RgmHZ6pUHIv2KEFKwVQR2W9JQdW12d+q8J79LX7skUU+9tAC+6YqeLbFX3jqED98bI5cwcV2MCFmjx+aourZZErhFLKXb0scS2JbgiQzZNexBDXXlI/b0myv6Tt88OjsbS3k47m77VHC4ZmKqSVIcjpBQpTkeI7kk6fm+cjxuTs5PUuUKFGixB54W2+Hj0YjHnvsMX7+53+en/mZn7nt47/61a/ymc98hl/8xV9kamqKX/mVX+Gnf/qn+fa3v80TTzxxD95xievxg6i83E+4VTfVaxtD+mFKphT/05fO3lHoyq2CWoAb54Xmazx2aIr5hve2JWzeyqophGCh6REkGc2Kc8fv7VYFzFprXtsYEiQZzy536Ed7SA4FbGHCPAZxyjPLHT74wOzr/4AFrrcJ7ncrVBzJdy92+MqZTQ5NVxhGpn9NSIklBRXXKhbvku7IqEcN32Gh4XF8oc6ff/IQDy6Zc0EpzTfObfFHL2+w2guRkknH18ml+mQfa605tzEiTHIavkOcJGa/AI4UpoeLIkm94GRjUlXzXeYbLpfaI4Qo4uV3oOLazDV8Vrshwyjn4tZo11zgiYXGLpXuwcU6tiW4uD0iVTm6mNkTAoTerVZqDXFm1DHXlmg0tpTk47ANcW3mypbG2pkqjdYa3zH7M9dMbj6Nvyvntwac3xzx6tqAVsXGErA1TBjFGa4lOTBd4aGlJvN1j4Z/7VwKY0NMH97X5NMPL94wB9jwHVa6IVmuGcYZjeI8rHo2AhjFplNNIPAdwXTVIc40i02P//ynHuajJ+Zve76f3xrSHiZc3jYksOpaNH2H2bpLpjQHpsxNjFIVK1GiRIk3jrd1hfyTP/mT/ORP/uQdP/4f/sN/uOvvv/iLv8hv//Zv87u/+7slGbsF3srI+dstSu8kDKHEW4u9gi3iTNEPU5q+w+GZKlXXvm3oyq2CWl5Z6wMmqW38u5VuwO88u8K/+t4VDs1Umat7ryth826dt2/FDYPbkdzL2wFxlmNJgWtds+SNF/8WZm7HtSVWseDfHiV3vP0sUzyz3GF7lDBbc3n8wNSes5uHZmpUXYvvXOhwcXtUpDaa9+QXRAxMUEej4jCMcz7zyCJ/9omD7Gv6rPYjTq/12RrE/NHLa/zR6U3CJKPm2cWckMWLKz3OrA9IMmVqBDS0A2NnM+kk5r2IIqXQkkV/l7xWiK20SZ08MV+j4VtYwiT9KaUIEtOF5hYdZ3GqODRd5W9++gRTVfeGc+N6lW6+7jHf8FjvR4Tjcm4BvmNeLylSLUVh65uuOrz/6AwvXe1jWwLfsQiTjJVeBFozSkw6IlKjlEmcnKrYRJliUCh7W4OYz724xuV2QBBnuLZgEOV0ghSB6Q2brXtUHYupmsOxudotr51j6+2BqQovXe3zrfPbZErRGSX0w5StYUzLt7Gk5OB0hSzX9MKUXpAiBdRcC61hruHwyVOL+I6FUvqW36ed3/cnDk+x0o3YHMas9SN6YconTi3wsx86XIYzlShRosSbxH0tVyilGAwGzMzM3PQxcRwTx/Hk7/2+WTSmaUqapm/5exxva+d/7yXObw754isbXNgaTdSKB+ZqfOrhhV0Rxm8Gnz41y1pvxPmNfpHGJgkTxVo/Yq7m8qmHZsnzbM+ZhHca3s5j9VbiyLTPX/nIYVZ7phvo959bxZea45OFu6LpSRrzFc5tjvjCiysc+sgDk8WaUprPv7BCbxTx4Hxt13Pqcz5ffGUTBHzq1DxSSjpBxMXNARY5WoDKM6b9Cq+sdFjrjfi/fujwLc+/m523n3hogYprMQiNDSxJbk9gFmo2J+YqvLzap+HeuOjd6AW8Z3+ThZq953HPMsVzV7u0g5SZqsNjB6awbcmRaZ9/70MHJ+9zq5/jWhKbHFdqqr5kuuaCylEqnxCxcZS8LQSZVqAUFcth2pd3dN595dUN/o9vX+ZyOyDNjUVtseFiWZKHFptI1C7JZ7Zq8+ShBi+vDBgEJoq/4UukNOXEudIorRBK0XDgPUs1gijmH39/mQtbI7ZHMReKoBOlFZ5lEQQ556KYzjDiQw/M0Bml5FnGRnfEdM1F6JxqQZ4KNx4tV5AqQaYUuYSGLQkzYxm0hWIYRbyykiGFZK5q0Q9TrnZGWEIUlsDCgifgI8fmeOJAc3J+7ry+9IOINEupO+Z9ND3BoZZHnmW0dYIpcRY0KhaWgEGk8WzJicU6URG9P1e1OdBy2RzEWAiiJEWoHND49rXI+QNTPq5j5qeEgkEY8cShab52eo2za12GUYbSmoYrSTyJK0z4R9URPHGgTjtMWetFvLDc5vh8/Y6unQ8tVPi950KGccpM1eXQlMdaL2QQxviOzaGpBr4teXl1QNU2KlaSKbaHMUmS8vvPLfP5F69yeKbKv/uhw3zsoQVg97Vvr+/7oSmPYZST5DkrvZC5qsWhlveuu1beD3i3/jv1bkN5nO4fvN3HSOjrB0neJggh+M3f/E3+zJ/5M3f8nP/2v/1v+Xt/7+9x+vRpFhYW9nzMf/lf/pf8nb/zd274+Wc/+1mq1TJQokSJEiVKlChRokSJH1QEQcDP/uzP0uv1aDabt3/CXcZ9S8Y++9nP8lf/6l/lt3/7t/n0pz9908ftpYwdOnSIra2te7bD0zTlC1/4Ap/5zGdwnBttUm8FlNL8b39ygZdX+xyfv1ENOLc54j37m/z8DvXjbmxztRdNbGX7Wv59N0vwdhyre40z6wP+l6+c44HZ2p7HJ1eKS9sBf+1jx3lwsXHb52yPEp6+sA0InnpgBldKvnOxje+YgAOtNZ0g5cHFOp1RyuYgJsxy3rPU5D0HWrtU2pudt50g4fnlLlujhANTPh88PMXx9DxfGizRrPm3Vdpgt9oWZ8aaeGy+xidP7a0Sf+XVDf7BF16bKBAVV9ILUq52QpJi9kjCJPr7//7RYzx5ZJq///nTrHUjoxAJQZLmnNscEaT5rhklVwqEANeyOLZQ5b/5mcc4OFO9qSr4sQfn+fuff5Wzm0MOTfkIeS1/KUpyzm4Mma45/FuP7S/UyYQXrvQIE1M+PO45u9oN0JpJsIhETOaz5hoeTd8uCrNbDKKM331uhWGc3VBQLAvfpRBwYMrn4HSVf+eDhzlb1Fl0goSW7zBXt/gz823+wUseiYIk1zhSEKU5Sms828J3LaYqDnGmCNOcXGkOTlVwLMH6ICHJFa5lSqk/emKev/3jD9302nKzc6gdJHz9zCZbwwTPkUxVHCquzSjJGEYpSaZxbUnVtRhGGb5rwjk6owQNxJkiV5qKazFVdeiHJpRobD+teTZZ0T+WpLkpeS7KoaNUFfN5ArSxRk7VXH7kxDxCQDdI+PNPHaThO7e8dl7thPzSl8+aXi/PMkqVUrhSUvMsNgYxnVHMv//RB3ji0DRKaf7GZ5/Z85zRSrHcjTi5UOeX/tKTaJ1Prn0X2tHrvkaUuHf4Qfh36t2A8jjdP9je3mbfvn1vGxm7L22Kv/Ebv8Ff+St/hX/xL/7FLYkYgOd5eJ53w88dx7nnX457uc3ldsDZrZCFVhWkvTvNTcBCq8prmyEbo+yuRs4fXXBv/6D7AG/H+XGv0Kz6OLbDMNU0/BsvAaNUYduOeVyxD271HNuySbSc/H+UK8Jc43sOmRbEWU6Uw+n1gFxpqp5NogWO6/DC6pCr/WQyo7bXeWsCMUIGiWamXqEbKUaFo+DofIMzmyFffHWbk0tTyCIVcOes2Xj2SUuLn3r8IAII0vyWc2hZpvi1b12hHWYcnq4ipSRIMpZ7CcMUTHwDuBKyHC53E/6bz5/lP/jRB3Adl0TFdCOFJU2qnZYWriMZRNnkuxjnpkTYtgVXeym/9q0r/MiDc3zp9MZkLm+xmOV7YXXI96/0eWV9xEzNIxf2xIqotSZDIi2b1UHK1W7CgZkqr22E9GPFdNWlE6QsNH2OzVVJzwsutQOUhqotTZiHgLmGz3v2NfnOxTZaWvQixcurQ7aCHDPxdnNc6sT0Ik3Fc/nrn3iIxw7P8tnvXGYUZxybrQBtGlWf9WGK70rCNGeY5oasarAcibQcqpZmbTCi4lpsBhnTVReEZQiVkFR8mwud6LbXrR97dD9X+wlnNsPJPJ9t2eyfaZCLgJmaw/H5Oq+uDVgbpGgNvmMz1/SxLUmqYzpBSjvIma272FKy5NvMNVyC2Mx+ea4gSHIavs1MzTVzf8MY15W0Q0MoW1KQKcEw1QRZZgJJihJmO1FECqaqDuEgYape4dTSrRcBkQoZZZpFzwUpqFd3fxdnm5JBopiqV/A8l+9c2ObsVkijsvucAUBIGhWP1zZDXlgb8sRBs23HcWhWed3XiBL3Hu/mf6feTSiP0zsfb/fxue/I2K//+q/z8z//8/zGb/wGP/VTP/V2v513LH7Qyn5L3DneSOjKrZ5T96zJIrPuWQxjsKWZo3EtQ0DyXJOhmK17JMWc03TVpeHvLga//rzVWrPSC1nthRNlYZQoEqVA3thlF2f5rkTHJFPEqcJzJK4td6U/3mox/8yyCbyYrblIKdHaBGyM4mvDOxqQlsCRkiTLCZOMf/W9K3zs5CxPX8yI+lEROiEIkxylFbIIsRgj19CLMqQQnN8a8v3lDk3f4YnDUzcUqH/ttS2Gccb+KX/y/DDJaI9SwjQnUzlprvnG+W3el+RsjWJcW9IJUiquST2cqbk8erDF9jAhznPqFYeKY7PY9EwSodZorbiwOWKjH7FdqEJ3gjBV/MvvXWH/VIWPnpxnqeXzhy+uc3GzDz4cmq7guw5XuiFBnCEwUeyeY4qe1/oRrYqNFIIoVQRxTq5MWbVjCdJc0wtStoYJr6z1b3n8xqE1f/DiGi9c7REkJgnwh47N8pf3HeX06oDnrnQ4tzlCaU2r4jBf96h65p/EfS2fXCl6Ycp7D7RYaPiTpEOtNYMoI0gyvnOxzVLTp+pKvn2hM+n3ynNFqih6wkxSowIcy4SVKKUZxRlBnOHZ8o4DZF5vGM32KCHNjSq3Fyquidq/PjymDGYqUaJEiXuHt5WMDYdDzp49O/n7hQsXePbZZ5mZmeHw4cP8Z//Zf8bVq1f5p//0nwLGmvhzP/dz/I//4//Ihz70IdbW1gCoVCq0Wq235TO8U1FGzpe4GW6VBLjai3b1Nd3pc8bx52c3Ryw1PaYqDqu9ENuSpi/K0jQq5jwcRhkLzWuL251kaud5m+aKcxsjVnshG4MY3zEBDhXXwt1htxrfWHhltc9XzmxOVKUolTxzuUMnSJmqOLz/yAy+I2+ZGDnG9YvYJDMLcw0TjciodhRdThbo3IQxrAxwLEmcCVQOtmWev6szC6OKjct4u2GGJWBzEGNbN6pQQgj2TXm8utanH2bM1k3C32ovIskUtiWwpIVvm7TCF1d6xGnObN1joelPiBhA1bWZqTmkyub4XJ1GxcaRglwpLm6P2B4mJqI9N2T2TuBYkqmqTXuUTIj1iYUGxz5e5/LWgOe+ucx/8hOn+L0X1/nKmU0EEKU5thRICZYWpLlJI5TCJBZaUjBTdfCKIBDPFmjfYnOQ8/TFNp8+dQdFw5P4fI1Wmm4Qk+s6jx5s8vxyh0wpXClIMmWSDoWJzxdC0PSNophkimbFQWtNP0wLy6RJwAyTnCudgLV+TJIb/dCSYAmBRpuofIydVZlO66Lvy0Thr/ZCwlTxvoN3RmxeL0marbk4Re9dw7+xVjRMchxLMlvb7Wh4I9eIEiVKlCjxxvC2rsSffvppPvGJT0z+/rf+1t8C4Od+7uf41V/9VVZXV7l8+fLk9//4H/9jsizjF37hF/iFX/iFyc/Hjy9xDeWdzRK3wl5x99f3Nb3e58C1njHXlkghsKTg4LTPxe0RSkN7lExUmvE5uVOlfXChwfH5Ot+6sE1nFE9mbcYx5GObX1ooY2AWlK4lefpiZxLtDnB6dUCWaw5PV9gYJLy00uOJQ1OcmK9xdnM0IQ17LShnay62FPSClIprkWSKLDfEZGdE/fhrNf5vmms6o4QfOjbDha0hV7sRYZLvfhJmwS4wKosAlNa8sNKn4giGUcYgMr1nO3F0pk7V7dAOEqarDuv9mEGhMIWpIldQ92x+5MQsF7ZDNgcxjx5osn+quuv771qSJFeM4pzXNgcEcT6J3g/TnFQZdce6TsW7FVxbFrNOlQmxHpdXH5iu8ByGUJ7fHLHY8FntRiS5YhgrbCkRhXVPa40UkizPaVYcPOeaoqO1ZhTn7Jvy2ezHk23shZ2x7AemK0Rpzum1Ac9c7vIvv3cVSxpSJIUw5EsKRklGkudFIqyNawtsKWgHCdvDmHObIzpBQpYrc25EKd0wIctNxL3A7DelYGzs1JhONXHtQyCFxHMkTc/mcjtksVW5LbHZab197FCLq93gjkjSk4emOTpb48zGgJprIXfcxFBKsT1KeGixwZOHptF6d9ztG7lGlChRokSJ14+3lYx9/OMf51b5IdcTrD/+4z9+a9/Quwjlnc0St8NYuXg9XV63e87O320NYp693OWFlR5RqkCntKou+6cq2EWsuhBil0orpeAzjyzy+ZfX2BwmLDV9HEvgFHbHqmvhWZKL2yPeP1/YGLsR0zWHsxt95urGwjeIMtpBgm0JVnsxwzhjexQziDIWmz77Wt4u0nA9mp6DJSXLnZCqY6yGqrhUjbUiKSZ8kFxp0iJRfhhnnF4bMF11+OjxObpRwvcvd+iH1wI8lGZy7RPC/L0bJFg1F0sqkvxGRSrKck4u1lnpRby2OWJUFDhLKdAIbEtQdS1eXB1yYr5GP0rZHCbsn9r9+ZIspxdlpIXqpTAErR+lJLlmfPgH8Z2pYp5tlKWqZzPf8Li0Pdplf1bFjvvyqxs8e6WLUpqtYUxaEMBcKywkSZ4TZ+a92FLgW2JiaU1zxTDKqLgWDy026IXpTS3WO0ufTy7U6QQJXz+7xcYgJi06xcBYBjVm7qtZcag4FmGa0w5S9jsWvdDM2TV9m6+c2UQKwVTVwbUFW4OE7WFCUryYOUOYnCPX/6smhFHLKq7EsS0qtmVKsG3Jhx6Y4djczcNn9ipan6o67GtadIP0liTJtiV/+SNH+bufO83lTshszZ38O7A9Smj6Dj/34aPYtiRNb+weeSPXiBIlSpQo8fpQetTexSjvbL71eCsLte8FxmWyd+s5u363BB8+PseVTsB///kzPHe1yyhKOb2acraYGTs+X2N7lOxSaSuuxXzDw5aCUZIzSow6pgHPktR9m+1hAvPw/NUe7SCnGyRc3B7RqkRc6XjM1F2CJCNMjOrj2gKRG5vj5iCiH6XM1tw9F/RnNwb8s29fYq7h0g0SoizHtXbP3AjAtQSyKGyOUoXGqEkHp30QFpuDmK2hIYRJpndZHClmi8ypIkBrlIYgyVEabAG9IKETpqBhqmKzPoj5kZPznFio8//5nRfpB0Zh0xoqtsVSy2eq6tAeJaz0ImZrLjXP3nUzJogzvnuxQ9WxSIUgyhQ177p5Is0NyYk3PReEIU9CCPZPGQXKtST9MJ2URT93aZuDwK994yLrQ1NCbBVJkmluip8zrSYExrE0D8xVqfsOozijm5m5q+mayyP7mri2JErVTS3WO0ufAb57sc1K1/TS2ZZEFSQ0y419UakcKTQVzzEziXHGWi9EI/jRk/OA5monQggIkoxMaYIkI1fXKNet9pfAzFJWXZv9LZ9D0xUutkM6gbHCfuXVTTqjdM8y9JsVra/2IqarLn/2yQPMN7xbXns+9bBRrX/16xe5uG364hxL8tBig5/78NHJ7296jN/ANaJEiRIlStw5SjL2Lkd5Z/Otw153rMfhECXRNZDSqBuZ1mS5mdtpVh0EgpVuyJVOwGOHpnaptKMkw7UlP3RsliDJJzM6aZ5zfjNgaxQTxUYVGoQpTd9jqeXRCRIsKdgYRGyNYgahKdyteTa5NopPxZG4ts16PzYkxtlNRHaqKh85PkfTd3jmUodRkrFTrCpGkUxseUG0HCmYrjkgJJ4tyR3JpXaILQUVRxKmyqgnxXNtAQiT/igFxecXaA1fOr1BP8omCpktDdn5C08d4sHFBu/d30ToHrKw+Pm2nMy31X2brWHMifk6P/vBwzy33JvcjMmUxrYkjx1scWZjiJMo0tz80bqYbXodx9e1TKS7Y0mkgBev9vBsya9/+zLbo4TldkDFFvy1Y6bo2rONwunaEs+xyPIMpa9Z/IzdsbDTCRMjH2cKhSaIM85tDLFtyQ8fm72pxXqUZIRpRj23ubg14uKWCemoOBaqiPPfeRxzDWGm0ORYEpLcFD8fX6jx6MEWXzuzyUdOzAEQZznPLnfZ2EO5vBk0MFvzaFUdOmHKKMnJcoVAcGSmxv4pf885xusVvusDXV7bGPLClR7/4ceO3/Z6/qmHF/nYyXmeWe6wPUqYrbk8eWga275xjqxEiRIlStxblGTsBwDlnc27j5vdsb6TcIgfJIwXlLnSfOzBOc5vBrSDhFzl+I5Ea1hoeLtsWtdCPHbbpmZqHjM1j9VeRG8UAgFLTZ9jiya850onYnMQMV112BjExHmOU9ghk8woKe5k8akROwLbs0zxzHKHV9cGfPv8Nsfmq3SChK1hzFzdY156oE164dYwMV1Smd7xniUfODpDkmk2BhFuTdIJzDyXEGYx3guzXTNYmeLaeyjUopmqTTfM6Ecpubr2O60FG4OYX/vGRX7ivUuEqVHQtgaxsTsKgS0jmr7DXMNlFGfsn6rw4eNzfPj4HFe7IYMo5aWVPr/17BUc20JiVLws14Rpzno/YhhlqOs9dreAb1sMoxSN4I9f3UApmG14zNU9kkyhtVGRAHphRqYM+YkzQwClAMsS5EpT9xwOTPvERUfXha0QW8Jc3ZtY685vj2j6Dg8tNW5KQDYHMZe2Q86sDxlEKVGmsaWZy8u1vsFCCNeUOimKOTLjYeT3n19huRPy1JEZ5uou/W7KlU6I0uY5Ote3Ja8SMytZ9y16QYpnS6qeTbNic3KxQbPi0vCdXamiUopdCt/OmT+4MUX0Tq7vti354AOzt31ciRIlSpS4tyjJWIkSrxN3csf6VuEQP0jYuaBs+A4zNY9Bofi4lgQ03SDdtaA8MFVhqurwJ69tIQTkWmNLyUzV5dh8lWGccWy+BhkstSqT/X9ioc4wzugEqVkoa1Np3I8yfMfM2STF7FHdt5mteQRpzhdfWZ9YuIIkI0hyzm8NqXs2mdLM1t3JNpR2qbg2rYpD3bdZanqcXu3z3v0tkJIgzuhHySSQxLUFSa4JU8Vi02dzGLFzNGessFkwmY8b9SIcKWlUTcx7mmuiNKMfpjx9scOl7RHtYTwpnhbSFDbnStMOEvpRykzN5VMPL1xTG6OMf/rNS7yy2udqJ6TqBgSpwrEtWhUH15b0wpRB9PqqLrqR+TASDKEUEKc5L6/2SXKF71p0BiY2PVeKiuOQZIo012TaqGU2htg0K7YhOUKYImqtmal5RKkiznIcy+LYXA1bSl5dG/CJhxZu+H6d3RjwuRfWyHJFniv8gnznCiKlbkqcbCHxbEGQKubrLh89MUvdd9noR7yyOuCb57bwbMlKL6IfZbsSNW8FS4BrGzvoej8mTHPqvs3idQmXe5Grsp6kxL3A/W61L1Hi3YCSjJUo8Tpxt+9Yv5tx/YJSCLErJTBTio1BvGtBeX5ryMbALFylELSqDqC52g1YLmyNTxyeJjoP1R39STM1l8cPTXF2Y8h6P0RrYWLwLYllmfLpTEkWmj5LTQ8QPLfc5X/92gUGxQxZq+Kw3A7YGiWs9eNJ4e101UEW3Wm2JXlgrsYgzjg8U+N7l3p880LbFPlaEt+R+I7F9ihBKYEG5hse79nf4rX1Pt+92JkoZPbYmudZ2JYwqosC37PwbEmmIMlN8IeUgkzltEcJYaYAQcOzyJQmUxopBJlSZAqWmh4/VKgg/+ybF/mlL5+jFxpSlOWKYWze60o3xJFiklg5SYnk9kRjDMcS1F3JMDYZgt0gJcsVSjNRP82xNp/Xsy20zorURqNIKQXdIKU9SnBtSZLm5NrUIHi2Rd1zODZX49CMIeN7fb/GN0k6QcIHH5jmuSs9rnZC4BrpvRk00A8zpIDHD03TqnoALLV85hsup9cGaG1m9CQUNwluvV9sKWj6Np4j6QUpIFhoePzoyXmma+4N144xuRpEKcvtgLVeRJ5rRnFKs+Le8Po/aPUkJWm4+yit9iVKvDPwg3EVL1HiLqK8Y33neL19d7ttjfPX4sSVKmZ+YLHhcXKhwQvnTeBFrXLtMjZTc/nA0WlWuj7QYb7h8YEj06YoulDj6p7F2c0Rj+xr8rvPrjCIUg5PVyblzkJg0h+BTpDSCVI8S7DU8nEsWaifA5bbId+9sE2UmqjzgzMV3MK2J6VgyndQmAX9R47PIoQo+qts4ixHSsliw2em5hClORe3jVoFMIxTwsykc+TKpE4qpck1pHmOZcbLCDNFsygqzrUmyQQIge/YrPYjTq/1+R/+6DVGcUrds4vwi5xBlJHmpmPqcjvAtQTDJIdifmv8Zy8laedcmWcZkiilxJIaS0CijO1Ra02mLFpFQIgplIaKK0lyEyGZ5BpLCzxboJQiK7q78iLEJM3VJMjj7OaIuu/QrNh7fr+uV2Hn6hFn1ga3PUctYeyiSmsavrOL4MO1CoJcQdWxSbLstkRMAE3PENwkN8fP1DxUcWx5AxED812IM8VvfX+FraG5GbHcCbiwPeKDR2eYrXuTx/6g1ZOUpOHuo7TalyjxzkFJxkqUeJ0oC7XvHK+37+5GW6N7g62xE6QTm9haP+KYf6PKMEpyPv7QAlGac24rYF+RNBgmOWc3R8zUXA5MV7jUDpgtVIo4zdkYxHTDG0l0nGsut0Omqw6dUcIozUnScbgGxBmc2wzYP+UzW3PZHMSEmaLiSB7Z10RKkzDYCVIWmh6rvRhbCuq+RZgqVnsR0Q7/YqqKLrUCcsySuJbIaAmzD8dx9GPVUQpTpNwPUv7xV84zijOmKg62ZSx7VdfGlpJumKKUYhQroqKI2LaECVqh6BrTpifr2vsA1xGkmdmmbUlyVZRfFwNytjQpiY4UpJlCVsx2pRDEmcJ3JK4tSbMcVXw2S8hJ3P+YCGpMeqHSxsIZJhnnNoc8tFjf8/u18yaJUpqzG0NUEdxyqzm4mmeZQuYizfKV1QGWlJNzb1Coc0mu0MVsX14UPEuuqW6TfSbBFv9/9v48yLLsvu8DP+fc9a2ZL/faq7qqutEAekFjEwCS4AJwMaUZmWNLNkMyJQZHosMjD22HLDNiHGMqJoITM2PZtGVR0sgUQEscWaZMBm2CFHZSALE3Guiturv2Lfd8+da7nnPmj3Pfy8yqrKrMquzuqsb9RvRSmfnuve/edyvP936/v+9XoBFEmZ2Na1V9Ts1UOT3bYLET73ovvLHSpxtl47CWw36Fiif51uU2f/L6Kh882eLQZOUHrp6kJA0Hj9JqX6LEw4VytViixD5RFmrvHfvtu9urrXFYEJdWzb/jdn/+w8cB7ljt8MZK36YIApfWBkSZop/c3rU0ggE2hhmeAFNccs+xSpRjNErbzjOl7CzWZFVyYrrK+iDFcwSr/ZROlOJJyVwjoB64xJlipZ/sIGK7QY8OAMDuEoNBIplr+ASegyMEBkM/UVQ9l0sbfa62rerl3LKg8l3JROjRiVICz6FV9dgcprYPS9pd6V0i7h0HPCnJUON+NCFsgbKrrI3TEZAZQ+C7ZLlmWMyhTVQ8Uk0RzCLwXYHW0KzYeyjJFZnaIjajoI9c56z0ExaaARv9hIuO4MOnbk9T3P6QpBtnbAyScZ3AnWyXovi3J+37qPgO3cj2wp2dq5MoRTfJUEUa46FmyCBT3CxCPIpWgvH2JdAIXIyByZpHruzrTk5XUcbw9LEJvn1Z3/aZvbkZ040ymqHH4/ON8d8px6ZqVH2Hb15qc26pT5wpQs/9gaknKUnDm4PSal+ixMOFkoyVKLFPvJMKtUcpgm9m3PV++u72qzr+lQ8f5/Pn1nfd7smpGs9fa3NypsqJmQonZ2pMhP541mRjkBLnmjeWe1bduct72L6Y18Z+BlzH4BXnKlfSlggbqAUeHzjRYn2Q8hffd4SvX1jnaxfXWe0l9JKM0HU4OVPlvUcmiHPFty+3MRqi9N5x6bIIydAGhAGkwZGSqu9ijGF9YOP9nz46gShCPTxHFsEYW9sxBjTGJjoqxebQWummalY97BWpitstiaELVd+jFlg1T2mDNtY+6kpbaKwSTZbbmPz5RgjGsNIdAhB4kprjcqTlUvcdXlnqcXgixJGCC6sDcmXICpXPd+yMmTGglGZzkFL1JVGqeWyuvuv9dagZMlMPePlmh16cEqV3DuwYnwfseUmVIXQdqp61Fl5ZH3BprY/WmpFQmjmabpwReA4V3yFXmlwZVPH5cIXd3iBRNp0RwXTdw2h46WaXRujyb15fpVX1OTQR7ihsPjZVsXOLdavGNcKthzzT9ZCPnZnmZifmL33wOKdn6xxqhmMbas13x39+p81TlaThzUFptS9R4uFCScZKlLgPvBMKtbenCGZK4zmSk9M1/trH7l0Eu1/ste/ubqqj1poLq31OzdTQRSrEY7N1/sOFydu2+6XXVvi1P3hl1/c2WrStDxJ6cYbSW3azO2HH94Tt1koyZbu5RtY1rXEKO+L6ILUR5r7DchFGUvElmXbIlObqRsQw1Tyx0Cji321vmiNGRGl31DyHSuCw0U/t3JIxZFoRpZKNgZ2tOzVT4+R0jTeWuyil0EYQpTm14nxmShOlmmGaY4BUUZwjAQUJc6VAFkXUgQNGg+c4KGPQ2pKlKCtSFLFkRmC71pLi9QKN77kcnqgAKc8cnaQa+rhScHFtQOjZcu9a4LI+SBkkW3ZUIUAIm6KptE1fXO9nHG1V+PkPHd+1HPlfv7TMizc2efFGh2GidpzD7emHt6pkca5oVnzefajBuaUe55b6u86EKQUrvWQ8W2ij8u01C1zbsxZlmlQZfGC65tKJcjpxzmTV5/3HW4SeLAqbPX7uuSNM1Ww4yJfOLXN+pcdSJ8ZzbSH6mbmttMVq4OJKO7eY5Ip/9KcXx/NTaa5JMk1Q2D/fSfNUJWl4c1Ba7UuUeLhQ3mklStwnHuVC7S+8usyv/9G5cYrgSNl7faXHr//ROYADJ2R76bu7k+q4uBnx0o0umbIhEL/55Qv8UAgXV/s8cbi1Y7vj9xZlNCsu9dAlV5rXl7fe2w+fnuE3v3QBYe4cVDGCKwuFpligOwKqnoNShkxphDPqMjNIqdgYar51eYN3LTT4V9++xvevb+K7kslqyERFFfNhdk7MlZYMZNra5AJXoowhyXenhqnW+NoZlyG7UtAeZCznhSVPCF660eE/+1++h8EqTdqAJ63SFHqSYZpbRWf0frC2S20gLvrLfNd6FfPcUPGs7S707D67sbXTKWO70DKtiYdqbGt0BFQCh9VBhiRnqpgZGyQ5sbLWQ60NgSN5+WaXqu/Qj3NypQldS1aFEASOpBa4ZMoU0faSp49OMFX3ubYxHN9r51d6/NZXLvPGSo/VXgzG9optFxpH9QHcMj8msAres8da5FqzMUjvGM6hsamPrrHEdVSDp40tipbFgwMp7Mzc9673mKg4nJlrcGauMSZWI3vdF8+tYLThy6+v0kty4kwhgEk3YLUX009ynj02yVTNHy+O13oJf/TS0nh+Ks4kz19t0x5mTFY83n9iCt+BP31jha9dWuP/9L6j/IWnDz+y5c4HQRrKFMbbUVrtS5R4uFCSsRIlHgCPYqF2nms+9dXLO1IEARqhpOY7XG1HfPrPLvPxs7MHtojT2nC9PeTi2gCAUzM1jrWquy6KblUdz6/0ubYxxHMl7zs+weHJKnGSgoF/9o2r/MLH3LECMHpv7UFK6Ao2BlkR4iAIXUF7kPIPvnyeP37pJueWepY8SO5pU9z+TSns7NrIlpfmehx6MerIaoQeUab48htrVDxnHBISuJIjk4L1fspmZAuE675jwyAKm1t2ByJm359hKHJaVY8g1zx3vEWqNNfaEevF7FlcvF4CnmvDNjINOlOkSqF1YXPEEicpR6EdZjwHlheEaDRv5TiShWaAATajjPcda9EeJHzj8kYR+S5xhb3OGkOuYL7hE3iSOLaR+toYPnJqiuevbuJKwTPHJji31KMzzOxxZ5q6L4ufBYQgymwEvoMgU4rLa0P+/hfPj9WfT757nn/xrat86/IGG4OEYaqsNdOVSG3G5wIsmRJmK2hDGPA8gTaGV5e63GgPLaFmi5y7xcdz+yVRBnRuxrZEZeznRzq2asF3JFGmiHNNzQ/4wIkWQgi6UTZW/kJX8MVzK+PzfXKqyo3NiG6co0yyI7BksjLJYifmvYcneOHq5nh+CuDcYo9cGY63KrSHGc9f3WCQ5HSGGYnSvHitw796/jp//WOnDvzhyn5xP6ToQUlDmcK4O95JVvsSJd4JKMlYiRKPEA7iKe/z19pcXh8wXfPHRGwEKSXTNZ9LawOev9bmQ0VX1YPg/EqP3/nGVb5+cZ3OMMMImKz4/LlTU/z8n7vdcgZbquP19pDf+splhICnj0yMj7ceuhBBe5Dy2ZeXOTlVY7Eb842L67y61CVXioG2XVqusF1fg2Kh/tKNDpdWB1Zlg3EP1p1CHrTeWpw3Q5fQlQwSRT1wqfkOm8PMpgIW4RfTjYCPnp6mG2e8dLNLcAuhrfguR1oOjdBjfZjw5589zP/y7esMEkWS3X3OyXMFbsHalJq3RwABAABJREFUGqHHRNVnsRPRjzNbpKzNmNhZUmUIPIlSmlRbq50v7XZmqj79Ik5dF/bD0b5ttLzAkXabE4FD4NlEwarv0qp6XG8Pmar6PHN0Einh8vqQm5tDwEb4DxJly7EbAdDnRjvm868sUwtcHp9v0B5m+K4kKUJUNNBPNRVPkOaabqzH10UW1tDpmk8j9NDa8OL1Tb56fo3zKz3b+6UNlUJlG1UTbMcoGEQUyqY2Bt+VzDYCVvspg1Tf1kdmivnA0Ydk1AsXeg6OhF6UI7VBSkEjtNUBINBYy+LGIOPqxpCVXmorGpRVMwdJTi9VTIYurVqA49jjyLVhmCqWuvH4HLvFA5+nj03we8/fGM9PdaOMjWFKPXSR0iqqV1cjHCmoBQ6BJ0lzw7mlN0/t3ivulxQ9CGkoUxjvjneC1b5EiXcKSjJWosQjgoN6yrs+SMmUpnJLn9IIFd9hY5CyPkgP5Jj/28+/wfeubeIImG74iKIY+HOvLrPST/iVT5zd9fi1Nnz/xiavLnaZrt9uUQIbef781Tb/r3/9Gmv9hEtrfdqDFKeIXR9Z4hB2jinJNLmBiqeRwpKOe5Ubj4jWTN3ngyenWOrGXFkbMkjyMYkIHLsYn66HfPBki+l6QC/JcYS1/qW5JvC2zrcQAt8T+I7ko6dnWO+n/PHLS2Tp7kcjGCl4Bimt8vOBUy2yTLE5TBmkaofCp82WaqO1oeK7NB1BJ854z+EJOlHKdC1ksROjTWbnnVI9VsxsVL1BCEskW1V7/vtxzlwzHKsSh1sVHl9o0ItzXlvqIxA4UmIwDJKcJFN4gd1GqhTnV/r80NkZ2sOMF65tEqU5RycrKGO4sj6glyiGmT0HniNsPL7SZNrG3H/3Wptq4KKUIcpy2oOMTBtqviTTdmYtL978bmey4tpkycCVNCqefR+bMZ043/XnFWBu8TU6UlD1XXJV9KG5AmNEUcpte9LS3BZ195OcP3l9lWboMVUP8EKXQZyzMcyKc1LM6mFJeqvmE2UR7WFKL84QQjBTD/jxd80x2wh2zE+lSpNrjee4aK1Z7yUorWmGHoFry7aVVsw3AtYG6YGr3XvFg5Ki+yENZQrj3vAoW+1LlHgnoSRjJUo8AjjIp7zTNR/PkUSpohHevjCLUjufM13MuNwvtDb88UtLvL7Uw3cE0/VgvCiab0rWBymvL/f44xeX+AvPWmvX9gCOT33VzgG1hym+I3nxRpf3HW9xaqY23keSK15f7hFnisfnG8w1AoyBDGtZHPdAGUiFGatgrpQoc/c4eYDQgYrvcWyqws//uRNMVX2+fbnNROhyaX1IN8owGGbrAUdaVU7PboUutKoeoWef4ufaEGzbrjGGzjBjouJR8R1++r2HuNYe8sK1TW5NuZfCxqX7rmCQKkJX8syxST757gV+5xtX6RfR8VGqtqLcC5nPYDvScp1T8SRa2337rkumNVM1n1RpshycQBDnCq0gx849VT2J50jWBwle7FAPXRqBy/dvdAF4fM7GsKfKBoJEmQa0DbgAlroJRyfs+dDGkCqNIwTnV/pEac5Ubasj7lirxquLXQwQeoJ64KK1DQeR2NLlbmzfx1I3Ic31eL4ryTSZgeQe5DrJDUIYQk8isIRmY5iRqTvrkdu/Y7S1qM427BzXaj/FIBDCWjuHmbLk39gZNcXW/aS1QboSKe2Moeda5SrNdfE5yWkPUqSAwHWYrLg4Rcn4F8+t8NPvXdgxP+U7ElfaOoEk00S5xpGWDIMtARdCjO/lg1S794qDIkX7JQ1lCuPe8Sha7UuUeKehJGMlSjzkuJ8Fzd0i65871uLkdI3XV3rUfGeHVVFrzfog5Yn5Bs8daz3Qcd/YjHjxRgdlDI2Kt2NRJIRVktb7Kb//wg1evNHBcQSh62AwfOfKJkmuaIZuoTLYKPqvvLEGwGPTIQBvLNuZmTOzdRqhR6vuI4uwipHlzBEgiq+NsDbI7mhL3HHuheTUbJVnjkzyzYsbxLkicCRn5hr8pQ8eJ9ea//U7N5iu+xyaqOx4j83QY74ZcnltQDdKcR27MM6UphdlJLlmpu7wP3/zGonS+K7DZMVnmCoypXEdq5z5jkAZYc+DsQvT/8uPn6HiubbTS9mY+e0wu5ASUZCGG5sxhycq9GNLhhaaIev9hHaU2Z+RhqnQY7buk2lDN7Iky5GSfpLzymIX17GzVq8v93AdGwrST3JybfAcqxKhIcoUy70EsKXPviPpxhntYUotcAtCZee02kMbQuIWlsM41aR6y3opgGGiuJZHGCPwHIHOrdV0D60ASGwf2ihsZZgq4sye6zuFdtwKVZzJo60qTpEKmeUG3xX04pwxpxMgJQhtA2BSpVkfJMwIn16c4bsO0zWPTpyzOUyZawQs9xJ6cY7BoLRhM7IPTo61QjYGKd+/1uGxmRovL3apBy6N0GWq6rPSiy351YaKJ3GksA8fck0tsNZJxxEHpnbvBwdJivZDGsoUxhIlSjxKKMlYiXcc3mnpWftd0Nwrst51JX/tYyf59T86x9V2tCNNcX2Q0gw9fuGjJx/YzjRIc4ZpDtiuq1uRK21LkB1JxXc42qoyiHP+4Ps3GaY5p2drVH2XKNUM0pxm6NCNFS9ca3OyZWdflroJhyYr42LoXNl95dquikeWPb3LYlsIu0C/00K85glSbXj5RhcMPHu8NVYkr7UjBqniFz5ykvefiHjpZmfXbUzXA5S2JLcX54zC1XNt1Y9qYU2r+i6DJOf6xpBBqnCl/RwrYcCVVF3JwGhOTlb5u//H9/L4fBOtDadn63z/emccUmJrn2+HATKtmakHOALWBgmugKVORC1wCTyHIFNoRzDfCPnQqSmmarbzKskVbyz36CWK6ZrH4ckKs/WAb1za4PpmRFIoMlIIG26itO1hk5ZIpoXU5zkOZ+ZCOlHGMMnpxYY40yS5IsrUmMhkBlCglbVejiyao0TLVEHgWnK5nXLejVyP+tnA+jDjXIEQRXn23iCw82L1wGWll/DkQp2ZesBKz6p0I4unLI5Zm8LSGLhkCrpRRuA6HJms0qrm9OLMBn64Djc2Y9qDpLCHWott6Nl75vs3upydq3Nhtc/PPXeExW48np86OVNlfZCw0kvHr1PGqm2eI5iq2ocgUZIfiNq9X7xdpKiMbi9RosSjhPJvohLvKLwT07P2s6DZa2T9aJB/RNpWewmOFByfqvA3f+TMgQz613yXqu8Cdu4ncLdmpowxrPVTcm2YqXu0qj6OFHSTjDjNkUKw1k851nKYqnmkyqYE+q6kPUi5tDbgdMMqHU/MN8ck1XckgSt3WNjuJJqIbf+9XUUqEgLRJJnh+mZUdIJBI3Q5O1fnjZU+n391mU++Z27XgIGb7YjQk/z4k7O0C1UizjRVz7Ex77ni2FRl3FVm539sh5UoiE2cKQapjTxv1Xx+/sPHedehJmCVgn/n/cf48uurbEZpcbzWXnjrezEGfM/hyUMNlrsJ19uRjWfPNZ0opx66NumxVeHdhyaYqvkYs7WdKNN0o5Q/d2qKZsWjF+ccmgzZGCRcWR9YQiYMShXqkTZoqciUwhV2O7VA8ol3z/PHLy2x2k9wpSjmqna3i+ZsqZnGbAWtAKSF3XD0pVE8v7kDs3KEVeZybXAdyJX9WpzrPZExT8LCZIWKK0kyzbWNAYEr+dCpKb59eYPVXkymLPkSxb4qriXdjpQsNH06cc5TR5q0aj5r/ZRXbnbIleFd81W+d6NDrikIrWCi4jHfCAk92x93sxMzVfWZaQQ75qeSXHFsqspMzeeVpR7DNC/CTjymqh4V3z1QtXu/eLtIURndXqJEiUcJJRkr8Y7BOzU9a68LmtCR+4qs/4kn5znSCvn0V69wcXUA0nCoEfLaUo+TM9UHPldHJis8dWSCS6sDelGGX5fjRVGSKfpJjuc4zDdDGqHLxiDhxRsd4lzjSNgYaDCGuWaFhWbIxiBjkNp48I1BBg04M1sfKwhg1aZa4NJP8nGMOWyVE++Y/+HOaooButtIwlov5U/fWKUR+kxVfU7P1caK5F945vBtAQMbg5SbmzFJrnjxegdXCg5NVPjzzxyiEXr8f//0IqnSfPPSBq4jaVU9oqK4aqEZcrPoIAuL2HuD/Ry8utjj/EpvfG0eX2jwiz90iv/nZ84R52qsAG3nYxIIfMm7DzVY7aX044x64PLkQpN6KFnuWgtlkis+dHIa15FsDBIurAzYGKZEmWKjb8n6pbU+vVix3IvpF6pZUlgFt8+qAWSFRc8pVOnFzZjPv7LCSjcGIM0Vd+Bht+FW9XJ7CiZYAih2Uz8ZEWtBpm3nGsYqbYNMIYrZru26TJGngStt2IfnCgQ2TEQbO4s1TBXdKEMUhM4t7KfaGCT2NQvNCt04o5/k44cN1zYiXlvukylFP1FUPId+mmO0nWOr+A6ztZBmZYtA1EOX1V7CRMWj5rscm6reNj91qBnyu9+9zj/40nmGqaIZuHiupBdnB6p27xdvFykqo9tLlCjxKKEkYyXeEXgnp2ftdUGz1Iv3FVl/fqXH//S1q2xGGe850qTquwdKXqUU/PR7Fzi31ON71zZZ7sZMVD3Aql5KGxaaPmfmGrSHKS9c22SQ5Egpxp1bw0yz1I1ZaIYcngxpDyW9OOff+/Ax9JU1nj46yYuL/fF5aYQu882Q5Y5d8DtFxPvIOuZg1ZGRfXGvnwSbqCiRAq61hyx1Ix6fb5Dkml6c8e7DE+MF8udfXeKf/JtLRUBFQLPiEmeaq+0h/+NXLjPX8GkPU+abIb4ryZTh5mbExjCjGbhEmUJgODxRIfRdnOJ6R5nixubwts/xX3r/Mc7d6PC5c8ukuSZThmGSE3oOzYqLQXCsVSFKFDc7EbqYw7q0PmC+GXJmrs619pBBYkM4cm2KpENFPXRxpGCjnxBnmq9f2ihi5AFjxqmNsEV8PGnts9pAxZOEcpSOKOknGQZoVX3W+gl7NwneHXdUN8WWVdUVgkwYcg0TFa9Iw7QqpDBm3D82+utBG0PoOLYewLWfLaVhrZ9YpbAov44zBUbgu/bzUfVdlNZsFqXjwzTnZifCkxJhDKHvghG0aj6zdR/XERxtVdAGenG2g4gB4yj8QxPhmLTsNj/1733wOLP1YKx2bwytBfiJ+Qa/8NGTBxprv1cr+NtJisro9hIlSjwqKMlYiXcE3snpWXtd0Lyx0t9zZP1bRV7PzDX4lU+cHfeMrfdtgEAtcKh4FZ4+OkGr6vHty22iVHF4okIvzhkmOZ5r52YypdkYpCw0fbpxzhPzDT7xxDyfvQI/8eQcN7rpjvMyUw8QUiCNoea5xHleFBjbYAnPWDKiRkrJHrHeT+hG1mqYa8P1dsRUzeP3v3sT37WhHoM451NfvcJaP6XqSdpFofFULeB4q8KrSz26UcZs3Rvb2ew8WUY/zukXEesSaA8yDvseQdGJNUitwjL6HB+ZrHBjM+LVxS6L3Zh+bGevpBBIKdHYaPpW1acWurbkWhuElExUbADEai+mn+Scma2x2ku4sDogyzVRqsZJh0bbYIpM6aLguZiNcgR5tvME6qKXy3NskXSUKXDsz9RDl/YgxQgbTLHeT/b9eRqnYxZ/HhGukXI2+v4o5l8VFlDfESAEpijoznI1LoGWEnxhlTOMtTs6QqCM/Yw4UnBkskLgOtxoR2TKMFOzCvX51R7GGGqBS5xrlLZx8yOr7GhOzH7eNMo4KG2Ynwg5PVunVfX43vVN4tzwxHyN86tDNga2O2wU9tIeZFQ8l5948t6k5SeenOfjZ2fvGN5zENivFfztJEVldHuJEiUeBZRkrMQ7Au/09Ky9LGg2BumeI+vfSvJ6Zq7B/+1n38319pCLawMATk5X+d+/t8hLNzvcaEfc7ERUfAchYL4ZcHldkSu7GPYcQTfOGKQ5jdDl33r60HgxFbgOH398lm9fbrPSjejENjRkquaNVSIhBFqD71q7mu9IfNdhquZzYbVfxLHfG8NM4+RbFjmlDJvDnG9fWWeQ5vz4u+b4F9+8ykovpuo7Nkre2LLpVMVMVT0cIciL5MR+nKN8w9WNYZGitwUDdOIc3R5ytFVFSjt71Aw91gcJry52+YMXbvLda21evtGhE9tOs2bokWtNnCmiFCDj8fk6K92EJLdze74rma4FhJ5D4G7NJE3XfBs7vzFksurZlMLc2ukoagE8x9oPR0XWo6ktgVUPBVY1MUVqYa5BFc8G1voJUS6o+g6Za9Mik73GGBa49UqJ4l+mUDkboQ1CAQg9WaRp2lgTTwomGwGLnZgo0+PACwN2lkzaQm3fdQhcQbdINpytB4RFmXQ3zgg9h+l6SK40a72Equ+SaUPFc0hyCF2HTBuU0QwjzWTVwxcCz3GQQlALXB6bqY0rEB6bsUR4Y5jxzNEJLqwOaA9T+kmOKwSBJ/nhMzN87PTMns6R68o3Lb7+fq3gbycpKqPbS5Qo8bCjJGMl3hH4QUjPuteCZj+R9efX+m8peZVScHy6xvHprY6wJw83+dyry9zcjOgnORXPoe1JfCk5PFkhLcIrBkVkejP0mGsEfPPiBucXOzwF/A9fOs8gN6S5ojPM7XySEHhSMt30MQZW+wm9JC+SFu3CO44yenF+136p3aBMEZUvRqTMcH0jouq543k9gSUCQggcAaErGWaK1b5GaYVBMl332RhkW0RsF06ijC1OXu/HhJ7D/EQFR0KSa/7wxUWSTFv7YJGcN5rZWmiGuI5kvZ+QKs2VDduH5khB6Evm6uFYPRVCUA9d1voJZ2brfPxds9zoRChtaA9TXCmZqHr0koxMZShtijANs+OYi9NubYsGstww4rhVxzZuiSLIZZhCrpQN1eDOASu7wRVbZNh1rPolhcCTZpwYOULgWvLquRJXWlV0aTOyxwrkBnRx7nxXoo2hEXr8ucemWB+kVH2Xi6s9HClpD1OUst1gIwW2n9iQmImKR7eYnzPAVN12t7UHhlQZWhWPONfjZM1ulPG96x2ePTbJVM2nGrjM1ANqgcv6IOVdC3VybejFOe1hypHJCv/+h4+/7WrOg6rpJSkqUaJEid3x6K5MS5TYhh+U9Ky7LWj2E1n/VpPXW2dMoizni+dWaFY82ym1PiDTmizWmMDlw8enOTFV5fXlHt+/sUmaa3708VkOTVa5uTnkS6+t8NTjAIbJisfzV/u0hxmTFY/njrfIc20td6qwjilrSUyi3EaUO0VR7/64GBJLOmyEuaARuAwzxUo/IUoVp+dqRZiDnTPKlCZKNamyZcBKg5QKo+H0bI0LK/1xnPmopHl7EEamDe1hxrEpj8dmqix2EpJM40rFQjPk9eUeABXPqi5RpuinisMTHu5ESC+yc02TFY9cGXpxtiPwBLZmkg5PVvjY6Rm+dXHDdpy5Dr4jSXLFUidGYmPnDbY8eURqRnCkwC0IlxRboSlesb9Bau2Oudb04p3zZnuFkNAMXHqJGsfI+47AcRzqvkvoSRY7McoYPnBykvlmlWGa88Zyn26UkirDVD1gsuJyczMeq6KeIwg9B88RbEYZJ6Zr/PR7F/hfv3N9fC6STPHijQ5ukfBhNEXAh2ShGbLSi+nFOTfbEdoYkoLoSykRwqq8gevg13w2BikXVvu0qi2iVDFTD/i5547wvWudcUpi4Dp8+NT0QzPf9E62gpcoUaLE24mSjJV4R6BMz7K4NbJ+ZF28dYj/rSSvt86YBI5krZgde9/xSYyBr15YY7WX0Kp6DFLFWj/hxFSFbpShDTw+3+BIyy7wljqJnQEClroxuU7JleF4q0J7mHFlY1jY1TJybYmNw9as0UjVyvdpkbM9UzZcRBkIHGnnerShH2dkyjBfD5mseKwPUgSWgGi91TulsYv4711vM90IUcaMo+ehUN2wP6dhTNCm6z5r/YzAk+TaKodJrsdK0EiZ8l1rU01zjefa4S5HCpqhx0TFqhe3zySlVDyHH3tiDgFMVHwurfd5+sgEUkqubqQM0hxjjLVLFsdri51NkTAIQtkzbAwkypK1wJUkRc+Y1obQc23gBfsnYvbc2Lh4V0KUGXwJruMWRd+WuBpAa3j+SocfOeux1E3oxRkGm444W6hQzdDjxmaMNtYKa22ZhpPTNf7qR07w2Eyd71/r8NLNDmcn7H1wczNmpRfjVQWJ0kxWPNJMUa85ti/Pcwg9W6+w1I3tLFmmGCYKpQwLBZGphy4bg5RulLHcS3jqyAQfPT3DR0/PPLTzTe90K3iJEiVKvF0oyViJdwzK9CyLvQzxv1XkdbcZk5VuzIXVPo3QpT3MmKr5vOdwkxeubTJMFYErWevbMIkrG0MmqzZxUQhBN8rYGKY0QvtX11ovJdGCZsVFSkk9dFnvJ/SiDFdaE1yuraIChc3NWCK2H1FMYAmAEAKNVZNC30FjyY4qCn41hmePT/Jv3lhjM8rGsepJZvfnSQg9G+iw1IlJC0IoizAPzxEYY5P8lDIobInypdUBZ+YkHzk9xzcubVDxHLpRhtGgijAS1xE4QpAajTKmiDwUTFV9TkzXuNYe8szRCS6uDtkYJGzmKRir3Dx7bILvX9/k91+4wVo/4drGkMVOzHsONbnZiQBwHIkcvU9j0Lekn+TGvj/fEaTKFKR3SyGq+Q6D3L7ufjIUhbDn3e5764tCGOJUMSjmxkLPoVWRdOOcP3ljDaUN9cBhvhHgOmKsbEkpaYYua337wEJpXYR9yOL7t98jo5Llq+2IyarPuxYanF/pc3UjQhlDxZNM1QM2Brb3ba4RUvUdch2TZNYq3CgSKuNMcX61z4np2o577WFVlX4QrOAlSpQo8Xag/FuzxDsKZXqWxb2G+HVhmRqFX6z2Ypa7+kDJ651mTDxXUvEluTJjq9ZULeDZY5NcWBmwNkjoRhnr/YRG6PL+461x2EGqdFGObP/qyrQm0wKvWEB7jmQ1SejEOaEvGcS2s8z3HMgUnivJck26DzbgFqqTlDYWP3RtH5QrJVGmCFxb1Htk0iZBPj7fYJAqvnZ+nUxp4swWCweO4PhUlYrvsNJLdqYJGqu6OVKgtCHJ7QG6Ek7N1Hn6aJN+onj+yibtQcpyJ2aQ2LCSOFOkStMI7JzgqDi4G1mVaGEy5IfOTvPHL2WsDzLmmwHDLGeQ5ES5JnANr9zsstZLOTtf5/BkhZm6z0s3unzrSps0VzRDj7SwHxoDUZoTm63wDs8VdrHuWbWwG2UYY+x7L269KNfERfriflIsR6qfwCY6+kIy3wzt+ROC9nCnEpPkOXFm0xzjTKOxhNUpwjP6cY5fk8SZYn1gY+qn6h5KSSZrHtfaQ/7pVy+Pwyh2K1meyzRBYb88NlWlGaZca0c4jiTJNbNj4mdnB6dqPp0oY6LiMUwVcWYtm+85PMG/+4Gje77X9hop/2bgB8UKXqJEiRJvNUoyVuIdh3JQ/O7YzTY4XfdpVX0mqh5n5uqcnKrde0PcfXF4pxkT35FFshxsDFJ6cU6z4jFVC2id9FnsxGwMEv7t547whVdWdsw4+Y7ElXIcvOFJiS7mlALXGX891xqTCbLCIpjmdlE+stTtB/+H9x3i1Zt9bnaicZ+VNjBMc6QQZAqOtQL+5scf40uvrfLGSp9G4LIw4aMULPcSfFdyfKpC1XfpxTmBYwMlKtKQKnuMqmAo6bZBtqrv8sGTLWYaIcYYvnt1k0trA7SxPWSHJ0Oub0T0U8VmlBNISb3isNKLGaSKRuByaXXA70c3max6VDzFd69uEmWKWuByuFUdFwN7xaybIwXHpmocmazwtYvrrPYTfuTMLAa4sDpgY5BgAFdqHEeQZIoPnmxxtFUj04alTsTXLqyT5DaxcASJTTXcz5yeFDYMpea7SAHDVNnZNAdSZXYEdoxggLggs54EacCTgo1hRjfOmaxaG+kwyUkzjedIkkzTrPi8+5CtWtgeRrHbA55DzZDFwpJX8106ccr/51+/xkKzQsVzqAcO376yyWovxq/5eI4NEXnXQgPfkZxf7fPuw03+9k8+sefY+f1Gyh80Sit4iRIlSrw5KMlYiRI/QNjNNvjaUpc/fWONOFNUPEnFdzk5XeOvfezuRbH3WhzeacakEbpMVX2WuxFCCNJtiYbGGFZ6Madmarz38ASXV4e8vNjdUeo8VfVp94cAzDR8ci1Z7Sd4VUE/zqn4jrX4CWuVc0TRO5WLsUq1V3gOtKoBv/LJQ/yDL13g5mZEpjRJZkmB4wgOT1T49z58nKNTVX7mvQu8cHWTF292ipALQTVwODxRIVWaq0s94kxZa6GB0LPEwpOSXBuSXO8gi6Enubg2REpJq+oxTHOUsQl9/SSnHrocnaqy2InYHGYkRuFlAoOgVfF5/4lJDk/aEIubmzGLnYjpus/j8w0C18EYw9cvbbDQDBgkaqxUiqKr7NRMneVuQpwrDk9W6UQp51d6tIcpWm/NfV1cG3JosoYQ8NKNLgYbiqG0GYd85Fqzn/BK37HWRlP0wzlC4Dq20Hopt11v98KI+CljaIYO3ViRKE2r6rHSjcm1IZAUVtmJsQJ7axjFbg94dvx5A6aqAVXfGVv4zszV6Sc5G4MU37UKWaYM7WHCiakaP3x2lvNr/T0pXPcbKX/QKK3gJUqUKHHwKMlYiRJvMd4uq9FutsFLa32+faU9Jhe+K6kHDq+v9Pj1PzoHsCshu9fi8K9++ATn13qs91IwPU7P1sdR+0IITs/VWB8k9OKcNFfkWnOzHfHCtU1SpRmmit/4/Bu0aj6OFDuexM9PBNxs9wGbrNeshWwMUzvHU/EIXIlTlA47wqYmOlIihdq3KtYIPD778jKOkPytHz/LNy+tc36lxzDTVD2H6bpP3Xf55sUN/uS1VbQxzDdDfvLd8yw0Qs4td2kPbADG4mZclAgDUmC0Ic0MjiOYrHp0ooxkm+POk9Z2eWNzyHo/5mirysYwpeo5HJ2qst5P6UU5QsBsPeDQhI20P9SsMEhyTs1UyQ0MEtvPttAM+P71TQ5NhrYYWwjW+gm51jRcW/C8XakEmG0EVHyHxU5MnCn+5PW1sSLouZbs5NpwZX2I0qtUA4dBmtMMPQw2ml/rrcCO/Zx+RwjmmxX6iS2zTnXxX2Uwe7yQo5/KlCbOJb4rGcQ5ohmijJ1Bq/qSYaq4sDoYWwr3G0axm4Vvqubz7LFJzq/0uLJuQ2VypTk0EYKB33v+xp4UrreqoH2vKK3gJUqUKHGwKMlYiRJvId5Oq9GttkGtDS9ctbHxk1UPZWxkeeA6HG+5XG1HfPrPLvPxs7M7rFT3Whx+9cIaf/OffQelNZvDDGUML97o8L7jU5yasfbHyYrHRMWzFq9hxsXVARdWByitaYQu3SjjqjasDVImKh6HmiGbw4zzK33W+gkVz/ZknVvu47rWIjXXCNEYLq8NaFV9Nou5JTvjtH8iNlPzODFTY72f8spih0Ga8wsfOUnFt4RjrZfwmRcXWeolVDxJe5Cy2k/43rVN/s3rq7zv+CTTtYDlbsyNzQit7FyYEaIoGLZKmNaG9UGKNAbfsQRHY/+73I1xpA31uLkZk2uNEIIkt/Y6Iey5PD1X59BEhVcXu7SHKZnSfONym1xrXCmZqvpM1X08R9AepFzZGDJV9fGkGNs+PUfST3JSpTFmq+dqthEwWfH46vk1BklezM9Zq6bnSjyjSXLDUjcidCWySHAE8F2HiusAEc4or3+PUEXQx8JEwDCx9QF5ko/j//dzOZPcIITCc+y5u7I+xBEw1wiYqgdkyrDai+knOc8em8RzxL7CKO5k4fMcwUTF44OnpvjZpw5R9R0+8+Ii7WG2Z4XrYYyUL63gB4e3cw6wRIkSDwdKMlaixFuE/VqN9vpLOs/1XZMTR7jVNrjYjdiMsqIAWGC0tr1YmcJ3PaZrPpfWBjx/rb0jDOTKxoBvXFzHcQQ3NyMOTYRj1evy+oDzy33iXHOsVeH4tMfNzYiVXsKfvL5CrmcIXclLN7pkynC0VWGQKi6v2zmohYmAiuciBHSijDhTxJliuubzsTPTfP7VZYQIODNdAbp86OQUVzcTqr7Dzz13hH6i+P0XrnN6pkF7mNhwkn6ybyJW9QSHJ0JyZQg9hzOzdZZ7CZ9/dZlf/vhpAL7wygrtYcZ0zeN71ztEqaIRurSqHqu9lO9c3SRwBINEUaS5kxb9YxVPjmeqcs2472o7yZDY742sfplSxfcMSZZQ8W0PWD/OiTLbuxWliqvrAyq+Q6Pi4TkumdKs9GJudiI6w4xMG7IrbWqBPdbQtduoBS6ulAyTnEurA9YHCZ0oY6oWIIFenNvgjSLt0ZOC0JW4joOKMrLcYIyi4jmkSqO0wXMkh5o+EJFqzc5msjvDEdCoeGTKsBllOEKQFScxKFRPtY/rabvRNA6WCB9r2dmutaKCwHcENd9hY5jy8o1NZhshzxyb3FcYxZ0sfE8fneQn3zPPYzN1fvPLF2gPs30pXGWk/DsXb/ccYIkSJR4OlGSsRIm3APu1Gu31l/QXXl0ed4qN1I07zXvdGk0dpWocGtGLM1Kl0QaWuwmDJKdZ9ciUjePevr+//6U3OLfYQwqb/jdR8Xjf8RYnpqq8cHWTTGmqnk0crPoux6cE6/2E9UHGn51fZyJ0CHyX9x2f4NBEhc++ssTmMMORkuVuiufkxeyatccZ4EZ7yFfPryGl4EMnp2hUXIjA9yRaG16+2eXqxpAT01Xag5x+I+PUTJ21XooQgtVeTLaPeSUhJNc3YxBwfKpKs+IV16XP9faQpW7M81fbtKoeF1YGRKliquaPr2vFkyx2YzxH2gj/fooo1KRMaWQGBqtK5dtCKLZzRr3ta7dySY0NszCewXUky92ENN9ktuGTaU3D8awNUqtx/P5yNyZVGkcIJqsujnRY7SXFMRuWujHTNZ8Xb2wySBWOEEzXAw5NBHz3asfG9ws7C0aRqhjnmrrj0AhcNnVWvD9DpnNaFY/pesioRGA/CYrN0GGq5nNmtoZB0I5SLq/1qfqWqA+TfF/zZ6P9R7kh9GyUv0YwuLbJUjcmyzWJ0mS5Zq1vExb/0geP7VuhuFvYx5++scr3r29yeHJ/Ctd+IuVLleXRwcMyB1iiRIm3HyUZK1HiLcB+rEZJrvb0S/oLry7z6390jl6cMV3MuUSpsvNenznHaj/h2WOT40XZrXMtVhGDfmwLfREQupLAFQxSxaDo/JouQg1G+9scpviOtJY77JzRV95YY+1Qnc0oI/AcS9RGxMR3OdJyqAYpi5sxrVqFHz47WxQKD1jqxPY8YDDG4EroxjlrgxRX2JLlXAoGSY7vSr53vcNzx5qcEvDi9Q7dpLBZakMz9MjVkG9dbtOLM661hyS52leCX8N3qAYuca4x2jBIFO1hRrPicn6lz2995TLX2gNevtml6jt045yZ+hYRG1n8cmVoVSzhUTBmVxqgsBnuv/p4C7akWKONNe6tDxK0siEZVzeG458blTtrbRBCEHgO7UFGswITFc8WcAuYCD0WOzFRqnAdQeg5TEvBai+laA6whKqYwdNao5RmkJhxIbPriLFFcZhp6rmiHSe3HfvdUPMdHl+YIFeal272SHM1DjaZqLikuWaYiCKbcf/wXIFBMlXzOTVT4xsXNxikOY60PW+OFFQ8hy+eW+HEdPW2BfG9CM92C9/5lR7/6E8vcmG1z2o/5sLqgE6UcXa+MQ4LGeFOCtdeI+WjLOc3v3yhVFkeATxsc4AlSpR4e1GSsRIl3gLs1WrUizO+dG71nr+kj09W+dRXL9OLM463KmObYCO0MzuX14f8vc++znPHJ6j63nhRtn2uZa4e4Ajb/+S5VqWp+A6uI5FCszHMmKhUePbIJHmux/s7NV1lsZMwSK2C5YWCbpzz+lKfNMsJfY+KLzHGMExzHGGDQQLXlh23agFS2u9fXB2Qa4PnCISwPV4Gu1jJlUELW2icKoOUdj6qE6VcXO3x3ByFIhVggPYwJfAcnlio86XXVvniudU72hNHfVmwkw452ILoXBsahY0vymzK4HzD59rGECFgvhEyUfHIC1vnej/FdxwqvkOaawZpjgBW+wnxNknOkbaLOTOgtB6b9kTxzz7FHnIDJje4LiSZZsNYQtqPM5QGxxEYY0mTxpYwV3EYZopunOMW9jzfcZiqeWxGKY1mQOBK3EJR3Bhk9voACogzgyOtSVBrq4SNCq1PTtUQEjaHOcNUcbMTk2XZvt5T1ZcErqRV9ZmtB9zYjLjZjVAaFjsxNd8pCKHYU6LidjQCl8CRdOOMyarHWi+lHrosTIRoYz9zSa44OlXhenvIv35pmcd+dGtBvB9b2a3KRz1wi0RLWzvw7LHJHYTsTqXJt86jLTQDcr0103dkssITCw0+/WdXSpXlEcHDOAdYokSJtw8lGXsEUVpRHj3s1WrUT/I9/ZL+7LklLq8PmK75YyJmt5Oz0ksQ2B6squcxWfV2LMpGcy3fv7GJ60gcqdAaPF/iSBtwEKV2oXloImS5n7DYiXbsb6rmkSpVzJdJQs+GWtguL0OmNNc3Y7Sx8e4Vb5SkCK2KXYD24px+khO6tt8qUzacIi/6o4wxZNoSmHrgkmvNIFE4UnN1XcGcDZAAa/0bzTudW+qT3hIRfyt2W8NP1zzmGiH9JGeq6hF4DkLY+Pr1fsLiZoTnSp4+MoEQguvtgBubQwLHxvNvDFIOe/b1g1RhjO3UGpUi57lhey3WduI1Vp72y8awxHJUEq1STZxZJcoSMUOidipIw0wxVfHQjiWEvmMLkJe7CXGmyJWmh42lj9OczICUkkbgsBnbubVc2/1ufx+B53J2oUmz4vLKzS7r/YSVXrIv9cp3BVGqeX25xwdPTnFuqW8VP23GM3T9xBaz7dem6Aio+IKK77IxsGrfYiei4jsEriRKc5Z7MVIIXr7ZAQSrvZs8c2yCHzo7uy9b2W7KhzGG+UbIcjdimOY7agTuVZo8mkf7na9f5euXNuhEKRiYrHo8NlPnq2+slSrLI4RyDrBEiRLbUZKxRwzlwO+jib1ajeqhu6df0kudhEzpsdVwtJ2NQUamDLXAoRcrYqVohNUdi7Jf/vhp/sMfrfOnb6wySHN8KXhjuU8nzulG1q41Xfd56sgEYBcO64N0x/4qvstCM2RjkBFlCqUVxljSOcwUUghCTyKFQBu7gI4zRdV3aFbsNlJllaFqYIuQhbZ9UKpQKEaEyZOShYmAzWFON0oZpIoks99c7cU4jo1aP9KqsNiJWO8n5PtM7PAdwSefnMdzJS9cszNTQgo8R6KNTTsMHIf3HW+Oye/puRrdOLPBFlozTHPWRgTEWOVNUJAWbfBca+3bbpmUwu7bGBvucT/Itr3MdSBTNu3QGHsOb00ejDPNzYKwSWCtn9kQC1eQFSqlROzoPIszjR+4+I7tyhoVX4/Q8CWtmsfVjQEV12FYZPSnuWaPncYAVFyJMhBlim9eXmeY2MqFwHVQgaGfFKmY+zxVjoDAleRacHauwWIn4tWlru2l8yRSSqLMzskdngxohB5JrljpJvzON68y1wj53Ct7t5XtpnyMKh16SUY3yljuxrSHtmx7r6XJca6Zbfg8MV+nEXo4UnBxrc/V9SHvOz5ZqiyPCPYzB1iiRIl3Pso7/RFCOfD78GIvcyS7RV9HqdqxEAtcZ0+/pBcmAjxHFgl+drWb5nqsVOXa4EhBtYiA321Rdnq2zlw9ZLLq8Z7Dkyx2I6JUUfEd21WV5mwOM2q+y3TNx5WCztCmLzpC2Hmyuk+cKfs6T3N6tsZ3r3dIczt35DnWSpbmCscRLDRDlroJzYqP70hcR9JwBGmuyJQgdGzkvjZ2ve0IWJgIqQUeqTKs9S1Rc90iV9BAe5gRuA5V3+Hy+tBGs4/OO3fut5LFjFyr5nOsVaEauDRCj2ePTXJhZcDGMLU9WQaaoctMPeDwZHU8E6YNnJ2v40rb1zZI88JyKcFg1bHiGBWAMgSuJHCttS9VhoorCTyHXhHZ/qAYz4cZxufwbtgREmIMviOIM4MQ9sWusFZIbaCXWFvjbN0n04YkU2Nl8uysLTi+vDZgouLRqvpkypK5/fySGST28yuA9V6K44DQgm6U77unbARHQtVzSJWhEXq4jmCi4qMNxOmATBmSxBLSw1NVJgrlVgrBZMVjkOT87vPXWO0me7aV3Un5mKoFPHtskjeW+1xrD7m8PmC2Ht6zNHmktLWHKc8c3Um6jkxWeG2px81N20V36/GVKsvDh70+nNtPmmeJEiUeXZRk7BFBOfD78GKvauWdoq+3L8S0Nnv6Jf2T71rgf/7mdV5f6VHzHaSUKGPQxowjzqfrvi2YLXDromz7guDsXJ0jk9Vd93VkssIgznGk5NrGEMcRjFLKfWmJZpwZpmseviv56GPTvLbUYzPKiFJLCmcaAU/MNzBA4Dnj2ZfJisdiJyL0XKSQVH2HOFcMU4UQgumax2wjsPNnSY4jQZstrSfJFZMVH8eRrPVT0lyhtR53UcliGGtETrbDEfDeIxP8jR85zWtLvfE5n6oFtE769OKcOMu5tDbkSKtCL8q40R6w3E1ZHyT0C/VnsurxniMTXN+IiDLbZZVuk7+ELObBtCHODI2Kx0zDRv6nSiMLm9pBIMk1ntzqKtsPMgVB8XEbH44AaSjsp2D0SM2TpErTqvpFPL8lqJky+K69Fp0ovdOu7ojcgFEakxVdazk8SMiJBAJpyW+r6vHMkSaxMrzv+CTGGL56XrLYiZDCkr0o0+Nr0Y9z5pohj83UuLDSRxnDkdbuytKt99bdlI+pWsCThwQTVY9//0PHOT1bv+3hza0Pd4wxd7QvB65DLXBZ7Sc7CrtHKFWWhw97fThX/i4vUeIHA+Xfzo8IyoHfhxP7VSt3i77evhDb6y9p33f4ax87ya//0TmutiM7y1UUOXciayd89ljrlnmynYuyve7r4lqf/+kbV9DF/NH2YawYcCXUAo9m1eP86oAfOjPLuxaatylto0Lmn33qEG8s97mw2sd3rZUx8CQfONGiEXosdWO+nW+glC2gTpW1AK72E5S2M2ljc6axC+iW79CLM6uqYZP2wM6oCSFsUXGhuAlh1ZKzcw3+73/+Pbzn6AQnZ6q3nYdOlI770DxHcrMTsdZL8BxBnOtxkuFSJ8Z1bFx64Eo2hxmuI6mlOevDDFMQVyFGap/BEYKZuk8nykiKuPmD0MZybUnmfojYiNpqIM2tTVEVPV5KF+cLqPoOUWZTNl1HUvEcnj06yUov4cbmsEgkhI1BijY2YON+lpL7nQW7GyouVEKP2UbIf/CRE3zrUptDda/4TAjec6RJN8noxjmh5xBlOb0kJ8s1Fd/l9GydauDa9yMkwzSnXthqU2Xn7Rqhe9u9dS/lY6mb8MzRSX7k7OxtC+7dHu40Q4+1fsLhXZSSRugyWw+4vD4gyRWwRcZKleXhxV4ezpUoUeIHAyUZe0RQDvw+fLhftXJ79PVu2Osv6VGP2PaeMYNdNH/09DSnZmrjbd5pUXavfY2Kar97rc1SJ0Jy+0I/13BsMuQDJ6f47CvLvLbc5WOnZ3YobQCDOCdwHZ481OQTT86PCelaL+GFq5tcXLNFwzXf4T2HJhimNsp+qRuz1InJ8i2CFXoSsJa2WJmCJDk0Ky4136BUSq4ZK2QYO+ckClI0Uw84O9+gWfV2PQ/nV/pc2xjiuXLch7beT4gyRS8241AO19kKH3nlZo/Tc1UGReeYrvhkekg/zovrbhWnKNMEmeLJhSYbw3QcdPEg2D4Xtt/Rs+0/rgC1bQMGS8RqoctCM+DmZozvCmq+y+FJa3HNjeH6pu1ac12JUJrQc8el1m8XBHB8ps6zx1t8/IlZQtchylY5vO3v0KlawFNHJtgcZuRKkylDlCoOF2RqqubTizMmK5bQvbrYJdea9iAlzuz7m6x61AKPj56eHt9b96t83OnhzoVV+3mcqfscm6rteI0QgsOTIcvdmBubEaHnlCrLI4J7PZwrUaLEDwZKMvaIoBz4ffjwZqqVe/0l/RNPzvPxs7M8f63N+iAlThXfvLTOZpTTi7M9Lcrutq9rG0NeX+5xdW2A0kXIQ75FbExhYbuyMeTjj89waCJkcTOmG2VMVLdiu28lgzsI6QJ89PTMjv1HWc6n/+wKV9YGxdyWHpNAT9hAB8jItSkSISX1wOU/+rEzfPHcCl+/sM76MCvSDC15wnYVU/UcDk1WeHy+cRsxfexH61xvD/mtr1xGCHj6yARSSrpRhjK2Tytji1gZY0MnPEfSTxU3N2Mw1hLnuJKjkxVubEb04oxM2RksVwjefahBI/B46UaHeMQa94jQFQSOYJBthYEcjMlxd+TGliwvdgy5NmSJIclt3P3GcJ0kV0XCoSBXttOtE6s38YjuDgHUfIlGMF3ziZKcP3jhJkobrm1EVDxnB6E51qpydq7O1fUBnuvwgRMtDk9Wbks5PDtf54vnllkbpAgD2mhU8f1a4PJTt9xb+1U+7vZw5+kjEyx2Yl660S3uny3F2xhDlGl+7F1zTFV9Lq4N9rSvW+/3Em8P7vVwrkSJEu98lCv3RwTlwO/DhzdbrdzrL2nXlXzo1PT4z+850uR3v32DC6t9tNFMVvx7Wl/utK9ekvHSjU36qU0+TAoiJsXWPJbRMEwVF9eGPLHQYLWfcH61z+PzjT0/od9t/z/+rjn+3//6NXpxjisdMkYLfEOvmNcapIpY2cS+XBlmGgE/99xRljoxg5tdW9yMJSuugamGz7GpKu9aaO56LFJa+1onSplvhGwMM3xHkihFP8lRxhJSsHH7rtwqOU6VJss1gStZ7aVICWu9hChVKOz8Uui7HJ4IqfoONztDe3zGUHGFtYDeBaMjjXNDpswdFbBb0xMPApkGkygmqz4TFZelXsJiN2Gy4tEIXaanAl5b7jHY/5jYgWAU1CIEuFJYS22ueXWpx8lpzRMLTQJXcml1wLcut6n6LtP1AChSDmdrXG9H+K61HSpjiJJ8/Ln9xJPz/ItvXWWYKoZJjtK2ssF1BLXAmmZ/9zvX+fBjUzw+3xwf136Uj7s93JFS8t7DTb57dZPv3+hwerZ+27318x8+zmMzd9+X1oY/u7DG519ZsbNyEiqetWN+4l3Ttx5SiRIlSpR4C1CSsUcE5cDvw4eHUa08v9Ljcy+vsNqLCyVHMNsI+MST+59BOL/S459+5TLX2hGwzfLHVkKgZKtAuZfmY8XpsZk6a/3kvucgtDacW+wxVXXpRi5SOhiTWCuZBikKy+K2YxqkOf/9F86zMBEy2wj580/XuNYecml1QJTZ2bWnjkzw/hNTdz2WVxe7vHyzixA2at+VNljEphQaAkfaQmVpbYrAOEVRSsGThyZ4bbnLyqadcRsFiUhgkORcXBvQiaxqGWdqHBV/L7jSKnJJfm8r4m520gdFbuCpw3XWhzlTRfDIZNXn1HSVF67bOoB74c0gimBnAIWws4K5Nihti8aPTIR0oowXb3R49tgkHzrV4k9eX+Oblzf42OlpqoGd91ofZDxzbJK5esBmlLHSS3Z8bpc6MV88t8Iwyal4DoEnUUUxuRSCqZrPai/hX33nBn/npxv7siWPcK+HO4cmK6z1U07N1NgcZne8t+60r/MrPX7nG1f50rkVhpmiHthZs8qkw0s3Oyx1BnxgHzUEJUqUKFHiYFCSsUcI5cDvw4W7qZVaay6s9jk1U0MbY0tr32SifOu8yZFWlWGac60d8emvXd5X9cH5lR6/9ZXLvHBtE1eK2zqwTPEvRUGIBNQ9l8VOzHPHW/yNH36MxUIVvJ85iBubEc9f3WCtn9GJFJCPZ+JuXcw7Euq+S6Y151f6rPVjTk1XuVIEGjQqLvXARRmYqgb8zFMLJLnm2sbwtuM6v9LjD19cpJ/kTFY9moFHpjSdyPa3GWOJlxRiGzE1pLnGcwS+I/m3njrE929sWsImwHVsgIhNI7SR9uuDlBNBdRzqcS/UfVtXkClrlbybKiaEtU5Gud613PpB8L0i0MR1JIErWe8nDJOcqxvRnl5vgIpn0ze3BTaOv3e/cKQ9x3nBB2VRiVAJPELfsDFIubDa5wMnWnzwZItzRRS864jx36GfePccoetwcW0AwKmZGseK9MR/9rWr9JMcKcFzHVwp8Bwwru1E6yc5viseKERpLw93ZuoBf/1jp5BC7OveGt3P3768gTGGY60KuYa1fsIgVTxzdIL2IIaGfRByr6qOEiVKlChxcCjJ2COGM3MNTv5IbTwjNF3zee5YC3c/zaolDgR3UisXN6Mihc+mAP7d/+0VTs/W+Xc+cGSHhekgcZDVB6Nt3dgcIoV90v7GSn/cWzVSN7Yvnmu+gzaa2VqFn3zPPK4rH2gO4ovnlvn25TZKm6Kvyu5cjYqUi59zJLjSAWEjvrtRRj/OuFaQA8+Rtveq5tGNMj736jI3OxETVW9cQfDJd89TKdIYf/+7N4nTnBPTVVZ7CfXAbtevSdJc04sz0txQ9S0ZSzJdxNNbC+eJ6SoLkz5xZsmZI8U4Jt53BLGxbCFThjS327oXfGl7zoaZphbY7W1G2a5Ea0SSrf3xztuUgOdYa6QL7NVImyuDW5DOUb9c6Dn7In02aZEd8277XeYLoOJJHCnICpUuKTZY8R3qgcPMNhtiPXTZGKT0YqvexpnmL33wmO2w86069rmXd6+nCFyHxY6dNetGmnDbQxchwHclg0TRqnloo+/blrxXK/qxVnVfxGj7/SwETNZ8HClxJPg1n41BysW1AU/O2fv1G5fWefHm4J5VHSVKlChR4mBQkrFHDLvFHn/rUrv8Rfk24U4pfNpALXDoRjmrvZRXF7t849I6//FPnB2nIB4kDjJMZLStqarPjc2IVtVnoRmy2Il3VaakgMdm6zx7/O72vxHu9dT99aUe/+Kb14gzVQRlGPS2jItRDPv4/7VVixKlGXEbV9pFstLQHqbEmUIIG3TQHqY8e2ySKFN8/dI6n31lidlGQK4NF1b6zDUCjrSq9BPFxiClHrp4RYR5J3aJUxtrv6GyQvW0x2H7xQyf/upVMqVphg6OlGhjiDPNMFVjRcsA1zfTO6pBbqF+jc53NfCI8tRG/d8j8MOwUzlzBFQ9SVwoZSPCNlI790MdHEdijEFpewVybUjV/sI6ktzssJfO1D1qgcvl9Wj89TsVPDtAq+YV11PQCD2OtSp4juTFmx2mqj5PH5ngtZU+mdJF0Isl5f3ExtGLFELPEoxjU1XOr/T49NfuXE/x8SdmkRKm6z6bw5Rca1vsXUACmdaErsNkxafqOVzbGO5bVXqzrOi33s/bj307Uc1NBQT8L9+5Tm7knqo6SpQoUaLEg6MkY48Q9ttpVeKtwa0pfFGqGKY5Sa6phx7NiiDLNYvdmP/uC29wrFXl8YWDvU4HGSYy2tZMLcCVkkxpZuo+gzSnG+1swwpcwV/+wDH+zz9yes92qbsVZGtt+N3vXGN9kGIwJPmW9W43pSfXdiGcqa3iZIFdfDtCIB1bZjxIcxwBFd8dEyMbUZ6w2k9xHcGZ2TqX1/psDjNSNeDUTI3VXkJ7mFqLmhAcaoa87/gkn391hU6UUnASqr6kEViScGVjgDKGXAukhF6iiLPbJ7juNvelt71nKYSdRxMCZYwt977rWd6CxAZauI5EZQ9uW8yyjEwXttWCNSXZ/jZ6qy3RztWJHSRsZO8EyPKt9zvfcKlXA1wpGCSq6KizUe5HJqu8a6HO0VaV1X7KSi/Gr0mEsOqZKyWeFDvCjkaq0Xo/ZaEZkOQaY3IaocvZuTpvrPT59uUNAkcy3wi4thHZYuXQw5H2GsaZwpM2yKNV8/mDF25ycW1wX6rSm2FFv/V+TnOFQIxnSl1HkGtNL8qgCsM05z1Hph5IXS9RokSJEntHScYeERykDa3EwWN7Ct8oyW2q5o+vU+A5LDQDVnsJv/v8Nf6Ln37yQK/TQYaJjLblSMFU1ef65pAotQvfuUZAnOYkypYDn5qp8Vc+cuI2tW039eviWv+eDxMC1+H8ap8oy8fEwYz/dTscAUYyDsEYL/S1Rgk5frEq2E3gOQhhSHLF5bUhcaZZaIYMEoXShtBzCVzBIMlZ6yd84MQk/USRKk2aK7Jc0wx9njs2yfV2xPowZariUQ+trWxjkNIIXSTQizN8R5DswYp4K7aTJlcKar4tWR6k+e6s9A6wM1GSXpI/MBGT2FQ/o7b8hQZrOdT7SAu59TA2hhlJpnZYF5UBVShoBnsOHp+v0yjO80Iz5Oxcg6ePTTDbCKh6Dn/wwk1eXuwCcHquRi/J2Bik1AKHfqyYrNlC8el6MFaYrm0M+e61NhuDlDeWe6Ra40vJXDPgzFydQxMhF1f7dIZ5oSrZ0vH2MMV3Jb4jAcFE1WO+GbLSS1jsxA/0sOygu6e238+hK7m6MRzPMEoh8FxBxXVY7sVQhZNTtQOv6ihRokSJEndGScYeEbyZnVYlDgaDNKcdWRWlHnq3XSffdfBdwYU34TodZPXB9m2dmqlyYbXPMLXpa7KIBqw61sY0UfH5/CsrnJndSpA7v9Ljj19a4sUbHYZpTtV3ee/hCdqDdPwwAaAXW9vYfCNgqZvw2ZeX+dF3zTJMcuKC/LlSkOb6jkqQNlZZ2W5hBEgUW6Rh9LNYJcBzrNVvY7hlQRwUBdNTVZ+Vnu2N2hik9BNFs+JhjOGNlT7HpiqsdKzVa5jZcufGtvNdD+380aGJkGvtmDjfGVQx/n9hLXd74WmBK9iMcuqBQ5LnttNrHxBwIEEeQkAvsefUJjtaopkfQGxjkmvmmgEbg5R420kZqWSN0GWy4tmibekw1wj55Hvmdsxg/vRTCyx247HF76kjE7y21GOxY4M6Jisex6erfODkFIHroLXh1cUuL93okOSjKBqLdpSy1k95fL7O68t9ZmoBjdAlV4aq77DWS1HaYCS0Kh4//sQcAIvd+EAelh1k99Tofv76xXUGaU6uLckNPGtX7AxzIldxasrO2NWC3ZcFD1rVUaJEiRIldkdJxh4RvNmdViUeHDXfxRGSJNc0K7cvuDKl8V0bdnDQ12mv8ybAPedZtm/renuI7woaOMS5JtMaT0qOT1d56sgknrMzQe78So//9vNv8PpSz4ZuFHrV60s9UqX58Kkp2sOUCysDNor5G1dKar7D81c1z52YLKxiQBFTLgRIs3tU+63zUQCetL1Yu/1wN8qYawb4rixmf9yxhS1wnbGi0o8zlIEoyxGC8Tk8Mlnhy6+tkuSatV5C6El6nsNULaDiO3Y2yeQ8NltnqZugtNkxJya2/XOveHqBJT0KyXTNoxMrKr5LN9r7ZydVdqZL8mCx8g5FyXXx50yzg+FVPYHShr3yxFuPRRnoDDMqnkOr6tBLMuLMdraZIgjn3FKPs3N1jrQCrrWHfPrPruxQm261+CW54vhUlfefbHFkssL1dsTltQGvLnbHDwheur5JN8rwHEnoW2urKtIxl7sxnaG1sD59dAJl4PxKn/YwZa4p6cYZR1sV/q+feJyT0zV+4/NvPJQPy6QUfPI9c3z2lSW6cc6hiYB+ohimijTX+I6gVfGZm7C/W4apola5fWnwdlR1lChRosQPAsq/VR8RPIydViV2YvQE+tXFri3/9Zzx94wx9OOciarHZMV7U67TveZNAH7zyxf2lJI22tY/+/oV3ljpUws9asYqP4/N1Dg2VUUIO2syegigteGff/0K37m8gSMFjYpHzXfItWGtl7AxSHn5ZhfPEcSZLlQpS4Y2hyk3OxH/v29cZb2f2vCKYrV+NxLhS4HvOjvCMUZr4Vtnk7bPniWZIleGfpyR5pr5icrYAvfssUleudllpWd70lrVgKeOTPDEQoM/eOEm/TinFjgErp1HGqSKVMUsNEOkxM4mOZLJqkcjcLm0Phx3jTlFU3Z8DzlJAkdbIZ4rWeokGARPHWmy2ktY7yfk2myFcewB+xWvXGHPldL2tffiWNm9mOUt2O2n41xTCVykFFQ8hySzdt+Ka9MRM2VY6sYMM80zRydYH6S3qU27WfyiLOe/+8L5XR8QrA1SpLD9cE7xnl0hcDybzLkxzHjXQp1mxSrdHzzZGiu6aW4/Q4/N1B/6h2UVz2W2UczapYqKZ0NM6oHLY7M1JisevWECHlxa7zM/YQhcZ3xP7FddvxVlVH6JEiVK3Bnlyv0RwUHa0Eq8OZBS8O984AjfuLTOYjdmoRngu7YsuB/nhJ6k6rucnW+8adfpTvMme5nX2p2QnWJxM6biO7Sq/nhxNsL2hwBfOb/K//a9mwzSHM9x6CeKmi+ZrodM1wM2hilXN4bM1n0WJipb83SuQ+QoOp2Y79/oMFXzuNre6q3abeE+OgIpBI4jEULhYBWWVFkysz2RTwKNiovrCC6vD1jrxXRjG7LSqvq8px6Mj6dV9ZlthDx3osVHTk8jheDkVJX//fuLJLnmxFSVlX5C1XcYpIrQtUmFG4ME35VMVjzWBwnN0Ofdh2qs9JJxIbJNMLwzcZECAlcSOIKpWoDjCDrDnEPNCrkyXFobkmuYqLi0h2/ewj4vDnOvhRm7KpHcObJ+eyn1+GdGmSAGhqlmZPD0PIkrBbky1AOXblHifHauzhvLvdvUpu0WP60N/48/fIXvXdvEd+wDAs+xoTRLnYhhYh9uuVIQZRrflVs9bsL+3Tq97bMhhKBZsQ/Dcq25vDYY32cP88OykQ33zz02bRUxpfGLhNDRQ5Uraz2owPWNiJcWB9QDl5m6VYOjTN93muO9QntKlChR4gcdb2s51Z/+6Z/yF/7CX+Dw4cMIIfj93//9e77my1/+Ms899xxBEHDmzBk+9alPvenH+TBgZB2bqvm8sdKnF2c2ASvOeGOlf9+/KEscLB6fb/If/8RZFpohq72U1V7MMFVMVD1atYDjU9U3/TqNFqPvWmiOF6Xbw18aoWeVq9Dj7FydjUJh0LsMFh1rVXn6qLUO3krERg8BzszVibKc//6L52kPM5SGNFfEmWJ9mHG9PURrQ9VzSHJdFPTuLMhe7Sd4rmSYZJxfGdzzL6YiaA9DEbNurOq09fXiXGAj50Pf4UirwjBR9OKc9YENWnGlIM4VL93Y5PXlLjc3h7y+3MN1BEoZ/vfvLfIvv32NX//jc3zmpSWqvuTMfJ2q7yClQAqbpgewMUhpDzOutSPWeilJrviT19cJXJvil2t7YLvNb40j3Y21s2oDyhiiROG5gh86O81PvHuOwxMBU1VbRL0XPOjS/0HHwe5EO2/drixUKQPkyna3OcU94kqB0vbry92EdlHg/J2rbV5e7PJqEdqxG663h3z94jqOgOl6QOA6NoHRdWhWfIyxpc1TVd+quMrWEOTKEHrOOABlN2wnWKOHZYudeJzqOT4H2+6Tt+th2YgsRpmdgZypB2O1D2BxM+JG8QDk6aMTnJquAXBlfch3r25yeCK8r7TeUQLwSzc7TFY9HpupM1n1eOlmh3/61cucX+kd7BstUaJEiUcQb6syNhgMeOaZZ/jFX/xFfu7nfu6eP3/p0iV+9md/ll/+5V/mn//zf84XvvAFfumXfolDhw7xUz/1U2/BEb+9eDNij0scPH7iyXmOtar87neucWHVliVPVjzOzjfeluv0IOEve5lF+8ST8/yLb1zl8toA2CJFBlDa0E8VKz2rJG0MM6JUk+RqrFC0BxmZMvhSsNrLbGdXkax3JzIwynjItSHNFNpYe9lEzSXNNULYwmVX2l6sfqK4tDpAG6uuzDcDPClpD1O6sU3KW+klTFZsKl49dFHajFXEG+0hG4OE15bhfccmOTNX5+LqAGNgkOTEmVUbAldyfLrG43MNvndtk7V+gkHSqnmsdtPb7H6esBZAXahCUthkwkRp4jRnsZtQ9V2+cG6ZtKhHSDIblT8Kt7hXRL4UBxPgcdDYHmUvsYEScZZjzIiESaQwZLliWBCkYarGZD7LNUmm+cMXF3lstjauRtiuCp9f7dMZZkw3/Ns++xXPwXMEubIq3OHJCmluC8Yltp9O4BGnis4wJdNmrCYBO9wIb1ZH2EHhbs4KrTUv3eiO+8eOtKocatXpxTlJrrixGTFVC3hspr6vfZYJwCVKlCixN7ytZOxnfuZn+Jmf+Zk9//w//If/kFOnTvFf/9f/NQBPPvkkX/nKV/hv/pv/5geCjMHBxx6XeHPw+EKD/+JnnnwortODzrPc6yGA50i+fmnDKkCOsCXMrrShERIybejGGc3Apeo7TNftAnczzxDYaPRcKeLU9kkJCrIlCjvbLUTClVvK2GhuKvDsnNaRySpr/ZRBmo8Lf6NMgzAkmabiOzRDO7fXS3KiTI0JWi1wee/hJm+sDFjpJXz88VkaoYfWhihXaK250R6y3k+ouJLcWDXnaKtCJ8oYpoofOTvLkVaFXpyjgeNTFVb7KcNE7Tr8lhl2qHmj96q04crGENdxeGymiu+6XNvocXMzHsf4w71DQDRF11hByA4g+HDPEIDv2H3vBlcU82jGXm+pjU0oLOYFtTS4UtJLFMaYwsLpFB1uhs1hxum5OkmmrLJrDJ97eWWHHU4ISLVGYOeeRmTLEVbxqocu7WFGN8qoBi6eK0HZzi2D4Jljk6x0Yz77yrKNsncljcCl6rscn96pcj/MD8vuRhYvrPbJlOGZIw2gA2y3Y3qEnsOF1f2Hj5QJwCVKlCixNzxSM2Nf+9rX+MQnPrHjaz/1Uz/Fr/zKr9zxNUmSkCTJ+M/drrW0ZFlGlmVvynHeitF+DnJ/Cw37ixJAqRy1v7TrEnfAQV+rh+E6hRJqriBObJT7rUiSnKorCOWd3/eJVsgvfew4i514TC4PTYRIKfjKG6sMk4SFuo8wNoxDGBtNLwRIaci1xpEO712o4buCTqRIUgVYoma0tott5/Z9234roFi0h45gInQBxZOzNSYaAev9jMXNIVEc06q4GL31Xnwh0EZjpKHmwkQgWO9FbAxScm2sOpUbpC8IHUEgDThwdb1HP4r57tVNNgZWGTFAlGb0BFR9F0cKFuOENDecnKlxdNJHoMlVhlIZNd9jruay1Inx5Z3Lmm+9KpbICKZrLmu9iEsrPYwx+EIjnJ0zV/eCFJb4BI4kyvWe4vQPCgLwhd1hRZrbQlUcRncHYAwNzxZUp8qgtAKtCaU977b4Wttr5oDvGHypOdL0+f7Vdc4vd0hz2xtX9X2GqeLSeh/HaNa6Q3zHzvYZYxDCdm5VXJChpBlIkiQjKcI9QkdwarpCzbPXoOpS9M3lrPcyRN3nx84e5UQr3HHP3O0+eat+39wJJ1oh/8GHj/KFV1e4tDZgrWttlqenKzhoDjd9SEGYnX9J1TxYyzO6w5iscfs83J3QHcZkeUbd82/b5oNst8Sbs6YocfAor9Ojg7f7Gglzq8H9bYIQgt/7vd/jL/7Fv3jHn3n88cf563/9r/Orv/qr46995jOf4Wd/9mcZDodUKrc/+f+v/qv/il/7tV+77eu/8zu/Q7VaPo0rUaJEiRIlSpQoUeIHFcPhkJ//+Z+n0+nQbDbv/YIDxiOljN0PfvVXf5X/9D/9T8d/7na7HDt2jJ/8yZ98y054lmV87nOf45Of/CSeVz4BfJhxUNfq4mp//AR6ZJk6NVPjJ56c47HZ/c1eHAQurvb5Z9+4SnuQstAMqfiSKNUsdWNaNZ+/8uHj931c1zeG/Mr//AJRasuub25GRKm1lhlsz5YjBE8dbdKNFKu9hEwpMmXtZntReEaKymzd599+3xF6ccqz4ir/48UGRkp6ccZaPwUMoecyVfOJM2W7xKSw3VwCUqWRCGqBLWd2HYE2hkzZgJGTM1Wi1KbqXVjtk2n78waza2KgYGu+LXAlHz7VYrWfEaWKYZrTizOi7M6l1dvhCJhrhMVrFHXfQQrJMMvtMd6nx1AWqthUPUAAZ+fqrPYSrrcjNuM372mgAHwJUhh+7f2a/+FcQDs2RLlGG7PDYjlKvxzVD4xso+v9BEcIEHaGLPQdJkOPRtGD1R5mnJqu8dpyjw+ebLEwsfOBnDGGL7+2yuW1gU1KvMUm7LuSn3z3Av/ZJx9nuZeM1SxtDL/55QtMVLxd1eR+nNOJMv6jHzvDkdajnWCrteF//MolXlvc5McbS1wOT2OElaiNMVxYHfCew01+8WOn9mWzHm33lcUup2drt4X/3O92S5RrikcF5XV6dLC+vv627v+RImMLCwssLy/v+Nry8jLNZnNXVQwgCAKCILjt657nveU3x9uxzxL3hwe5VudXevz2N66PY+TnfZdhmvPiYp8b3fS+UskeFE8cbvELH3O3CnF7KYHr8O4jrQeeZzkx2+T9p2b47MtLLK0O0QYqgYdWpiA/8NhMnZudjJV+TOBIkC6BK0jjjGQfvrlUCzYizdW1iGfnwPVcrncSkkwjHQdHCLqJYn0YjYMhRqmHoWe7qjQClWiUBlGUMvuOQ6sR0k0M1cDj+mZELzUILBHQRuwaguEIaFR8+klGNzF8/XKXiYrHwkSIEpKb3ZRM722hKYEbnQQEBI4LjkOqDL0URga/vRQ3O8XPbOduShsmEbz38CRzjYBBbjiEoL/SL2LkGW9fcHCzZdKRhNIezdJAMcxtwEua73wfo/clsJbKbqJpVl20yMkNzNYDMIa5RoCUEgUkucIgWRlkuK7LdKOKETuTD7txRqSgXg2Yrvn0EzUuGp9vBhyaCNmIFGuR4uTc1sO5c0tdBrlhPvAx4vbrFwSCYS8l1rwj/k7/yacOs9iNAeglhiAQ28JHQj753sMEgX9f273RTXl9Ndol1OT+t1vColxTPBoor9PDj7f7+jxSZOwjH/kIn/nMZ3Z87XOf+xwf+chH3qYjKlFiJx7mBLE3K/xFSsHHzszw5ddWGBYx70mukFISepK5Rsj8RMBSNybNNY4QNIq0QmGjFXaQAbidcFQ9gRSSTBteudkhzWzYyObQljZLKaj5Ho6ETpRvbU+C59jwiihTCCGKmHkbh59rimMMcB2HQZJzei7gyvpwHKgh7pZWaOzRVjyHNM9tcIgnUVrTjTIcKVFG3zHOfvuXdfHGPSGo+JJMmXEq5Qh7oa0KSxLFtlLoTEPVd/jY2Wm+8OoK1zYiurE9PldahXKUbLj9Wrjyzh1i94IAlNakhRM+Uwal7dUeEa/Rvrbv03EEytgOudCVdOMcT9rwjvYwox66uFLQHmQEnuTwZIVhqogyRcPZScZSZZM764HLc8dbCCF2dGwpY8ZdYdvxsPeGHTTOzDX4Kx8+zrlvXbZhNMXDmgcNH3mYQ01KlChR4mHB2/qbpN/vc/78+fGfL126xAsvvMDU1BTHjx/nV3/1V7lx4wa//du/DcAv//Iv8/f//t/nP//P/3N+8Rd/kS9+8Yv8y3/5L/nDP/zDt+stlCixAw97gtj2QtyDwvmVHl88t8Jk1WdjkGIKC5ox1lrmCHhjuc/mMCXXlqh5mY0nN8bYNMViwT9WR2yoHQZohi4np6v0khwBXGtH1IuH6YPEEi/fkbSqHlc2hlBsw3PsOR+V+q4PMrQ2+BIybJJemhu00bSHKcvdmExpNocZ03WfXpxSdDXfsbwYLCFT2gZ6GKOJUsVSNykKhS3p7Cf5jhTE0XsdYRTYEbqSQRFd7zmS+D6Z0K0pi46AK+sRv/nlC/TjnDRXeK5DxXcIXMkgyYmyLYXMLSLz75eIgX1/SY6V6oCqJ0m0Qd+lIFpKe82M1qwPEpuEaQzX2zFHWxVqvksvzhkkORXP5YfPzPCXP3icz72yvGtsuycFWW6Yqbs7erVGiJJ8V1J1tyj4UW/YKNb+nYLHZuucA/6jHztDrDmwhzVlAnCJEiVK3B33Rca+8IUv8IUvfIGVlRX0Lb9Zf+u3fmvP2/n2t7/Nj/3Yj43/PJrt+oVf+AU+9alPsbi4yNWrV8ffP3XqFH/4h3/If/Kf/Cf8xm/8BkePHuWf/JN/8gMTa1/i4ceDxsg/atiuBD51uMnmMAVs8p3RmqVewkovYb4Z4BSlWKmCzWGOI+28liN3pgMaLJGTwlraDjVDhqni0ESFE1MVvn5pgzy35y/XmnrgM10LyLQmzhSeI8i0QYqR6a4gZYFLN87wXIdW6PH4XJ2La32Wugn9IooeYDPK2IyyHSrdnZaNAhgmOY4jOdaqkitFlGnmmyH9JEdpvYNo3glWkTJ4rsTJbadWNZAIYJCqPSlid0NuIE8VUTsqOtmg4luFCSkAlyRPxyTOdUAaQbqN1Y3OphTgu4IkN/eM1t+Oqu8Qa0OUKRtfv227I0VOsFV63U9yQs9hth7SieyM1iBVzDcCzs7X+aEzM/zw2VmOtapIya6x7UvdhNlGQNW7/Vfd3UjVw94b9mbiSKty4JadN+MhUIkSJUq8U7BvMvZrv/Zr/N2/+3f5wAc+wKFDh2570rgf/OiP/ih3C3P81Kc+tetrvvvd7973PkuUeDPxg2ZvGimBFU/y+kqfbpwTpYrQk2S5sTY/x84s5Wor2lxjiRwwjvsflROPFv2uI6iHHrnWVHyXuUbA+iBjoRkyV3OBIbONgMC3XUjDQY7SZlw6bf/f7idDIyRjS6TnSNb6Cf0kJ0rUrjNSt1kId4HG9qi5rsB3LXnajHM2BglKGzzPoe5JXAkbw7sT8FRBe5DZGH8BvSjj0ESFfpqT5eY2S9/9YGSXzIuyaiFcQDBI8h1WylyBJ81thdGuA9XAw5MCIXIG6d6ls0Gq8BwHrQVx8X4ktmcu0/b626LwrdcYA51IgRA0i06wy+tDVnsxF1cHfPm1Vd53rMVPvXd+Vzvc00cn+Hc/cJQvnlvZN6kqLXYlSpQoUeKtwL5XhP/wH/5DPvWpT/FX/+pffTOOp0SJRxqPor1Ja3PfFqJeknG9PWR9kJIrzXTNYwNDnGniYj6MzCpkniMBRXaHrjVjrJUtV9omMEo719OoeMSZ4t+8scogUThSMF9z+PF3gVK2/LceGjb6qS2B1nbWShn7/W6cI4s/GwPHpqqcnavz9YsbtIc5D1L9JoBW1WO6HtCPrXJjNAwSO6cUZzZVspfsbS8G8F0HgU14jDNF4EoypRDGlijnZsvWud9j3U7kklwjEgXG7LAoQrGPWw55pFjGqWKo9Z4tjKPS5yTXZEVRtucw/hxoYwmZI7ZKvEcfP601OQKlNEu57a4zBiq+j1KGaxtDkkxzYzPiZ55a4MeenOWDp1rUA5dG6I0/yyemq/dFqrZb7HpJRj/OqQcugeugtUFK8UD3T4kSJUqUKLFvMpamKR/96EffjGMpUeKRx6Nmbzq/0hsvUkcR/Kdn6/zUe+/95P/8So/fe/4G55Z6ZEpT9R2UdqiHHtpkDFOFwaC1oOo4HG5VWO0nbA6zsdribJsNk8IqVlM1n06UEbiSwxMhi52YlV5Cro0tkRY20hygm+RkWrDSS3bMmlmZxe5kNMNmwzocDk9U+Il3z/PGSp+VbvTA5zDNLAFD2Dm4ii+Zrfu0o4xenNNPcvba5jj6sdBzAE0tsIrg6yt9kkyNCeX9YLeXJbnaF7HLNGiji0LvLa/h9lj60RfGmy2+Vg9chpn9WUcKGqHAlQKloeJJ1gYpukjfDF2BQZAoY8uuHZtmmWtNxbOzbhXPYWOQ0oszvnVpnReubXJiukLFc8ef4dF99iBzS1IKklzxpVdXx/dJ4EhmGwFHWhVutGNWezGJ0vu6f0qUKFGiRAm4DzL2S7/0S/zO7/wO/+V/+V++GcdTosQjj0fF3nR+pcc//erlcQR/1a8wTHNeutnhZie6awT/6LXX20Pb3WTsonZ9YNUpr4iTB9vl1AgcfFda9ct3SHJdpClaRcR1JDN1H8+RvP9Ei8AVfPPyJm+s9OlH2Xhh70hpF+KOA2RkuUYLyWTFpVJUCKwPMrttYw8rL8ieK+F4K6SX5Dx/pY3viNtS/fYLg41hzzaHnJ1rcHK6yqtLPd5zeAJHCr52cd2mSGaKaA8R/p5jtzlMFb4rGaY5Z+br3OhEeI5VhbSxitmDhGuAVQrvlhR5JxgDzYpLnCuUMqTaYIztW3NdaesAMGMLoyetIjrXCGnHmmGa0U8UnhYcm6zQqHisDzKaFY9hkhNnukhTtMEsSa4AgdLavn9t7Y1CWDJ3eX1AzXcIgdl6iOuIXT/D9zu3dOt9EmeS15Z6fPXCOsMkp+I7HJ+q8sRCk9CTe7p/SpQoUaJEiRH2RMa2lyZrrfnH//gf8/nPf56nn376tkHfv/f3/t7BHmGJEo8gHvYEsQeJ4N/+2iOTFS6tDejFOb04B2NsGh5WJcqLYudUwVo/oRNnNrzBbPVZhZ4k9Bymaj7DTBF4DtM1nzNzNb7yRoQyNoHPik+GOFX4oY0wd6RgkCqOtCp88MQUqdK8ttTje9c2yWHMshwBjdAlyjQrXdt1NtesIOhg7pOKjYqKK54lIFGmqAUeAuglOa2qjxCCk9M1ljoxUS+55zbrviRRNlzjAydbxJlGCkOmDVNVHykFgSu5tD4g28e81q24U4XAXlHxHJQxKK3HUl2uwUMghI2xB3uOxu8tdJCui+4ZUmXwHcl7Dk/STzOubUS2bgCbnplpq4YpbUhzYcn76GDznKVuXBRk25TKiapPqjTKGFqhf2A1ErfeJ+1hyos3OgxThSw+OY4UbBZff/bYJGfn6m9JhUVpjyxRokSJdwb2RMZuDcx49tlnAXjppZcO/IBKlHin4K1MENvvwmwvEfxvLPf49pUNmhVvxza3v9bOeTkME4WUtjVMa0OaK1xXFv+v2RgkYyVHYlUPUahWUaYJPRchbBT+IMn57tU2l9cGDLfJP3b2yJAWM1kWdoU+iG3a3nI3Yakbjxfuo33VQhetKZL8DFfbQ/7Kh4/ztQtrJIPsvs65KP5V8R2UNkSp4np7QCNwubzWZy106cUpbsUSBQfuOZ+mcWhUJBXPoR64XNvo8q3LQ4apojPIcB2B60iSB5XFsOd/v5bHEQFd7Vvb6PY0RYOdJxPFfJ4U0Kq4VD0JZGxGOQaJKwWtikduDF+9uEYvzokzNVYotWeVtjgzaGNus1EaDRvDzM4CAr7nFMqnxC96xg6qRmL7Zx3gwsqAKLXzgJvDjIpvi8Trgcsgzbmw2ucDJ1pveoXFg9iLS5QoUaLEw4U9kbEvfelLb/ZxlCjx0CLPNc9fa7M+SJmu+Tx3rIXrynu/8C3C/SzM7hXBH2eKlxe7/KM/uUjgyR3bzLUZv1YKqAYu8WbERGiT+dJc0U8VLuD6DtpY290IGpDbZp/s93N6UYbvufzp6yt04/y2RXhuwOSGwN1K+LNdZlaVeulmB23sscNWXL42Rfy8lCRKk+aCTpzx+VdWWJgI2YyyHQl+e4UG/JEdU0rqgcvltSGuI+jHeUFWDBuDjEzpexIxV8BkxWWmEbDSS/jiuRU7X1d834BVGtXu6Y/7gSysoaOwlL1itN9EGVxxeyXBqLjalYKq73BiusZ83QOGnJyqUgk8Xl7sEaU5a70UVwqrsmkNCOJcM0g1vgOe4xQWxS0IIPCs3TXJbQR+NXBIM8X8RIVGuPUr7SBqJLbfJ704Z2OYUi+647QxhI60xwHUQ7eYYcupBm9ehcWD2IsPEqUyV6JEiRIHg33PjP3iL/4iv/Ebv0GjsfMv+8FgwN/6W39rXz1jJUo87PjCq8t86quXubw+IFO2iPfkdI2/9rGT/MST82/34d33wuxuEfwbg4TvXGnTj3Omah7zzZ3b/On3LhC6DoMkAwT1wAUDSaYJPIdUWeuakA6h51D1Ha5tDPEcKML70FjroCz+P8o0g1TRifJx8uBus1zKQJwb/KL0WQpBM3ToxTm+awlRlKlxb9UIuQZH2nj9JLM9WYEneerIJJMVj29ebo+tdXuFjcgXxJkh9AUbg5hunFELXA5NhkSpYr2fEt+SVLgbRkvYfqrINyNW+um442u7pfBBSdioWBkDjhDUKu49I/fvBCkFrmB8nKZIRZyoeBxrVVnrJ1xYHXBtTfPTz8K3r7RxXBeMoRPnYCz5FEKSKU2S6zG5yxXUfchuOTQpbA+ZI+U4IXOQauabNrRju8p7EDUS2++TVGlyrfEcF4oQk0xbW64jBJ4j6Sf250TKm1Jh8SD24oNEqcyVKFGixMFh34/3P/3pTxNFtyeQRVHEb//2bx/IQZUo8TDgC68u8+t/dI7XV3o0QpcjLfvk/fWVHr/+R+f4wqvLb+vx5bnmX37rOlfWB8w3AuqBW6TUeZydq7MxSPnsy8vjPq/tGEXwL3biHV1/xhjOL/fZjDJOTFc5NFG5bZvfv9ZhInT56oV1vnZhjavrQ6vaGDMOYHAcSSNwWWiGjGiRlBJPgu8IQlfiOgJHbiMiiWKQ5hhtxhHnu0EbSIowjEbFY6oeYLAdXZfXBwx3maUyQJpvbdcYw/mVPt++ssH6IOOxmeodi53vBIklkVGWM0wU7UGOAE5MVZiphxxtVTk5U9vTdg2WWGz0U252kl2J2P3AkxAU2fIONqXQc+w/Susdpc57hcRG7Nd8F4S1TU5WPCYqHoFnCchUzYZyDNIcXXy+PEfSiTJW+ylJpqmFLlJK8uJ659qMyaYGBpkiN3Z/gSuoeJaACewsmRQCVwLGcGauzlTNHx/jqEbizFz9gWoktt8nnhS40hJH35WEriRKFRVP4rv2666UeFIcyL53w17sxSN75JuF0QOgl252mKx6PDZTZ7Lq8dLNDv/0q5c5v9J70/ZdokSJEu9E7PmxXbfbxRgbM9zr9QjDcPw9pRSf+cxnmJube1MOskSJtxp5rvnUVy/TizOOtypIaZ9bNEJJzXe42o749J9d5uNnZ98Wy+L5lR7/8lvX+KOXlnCkYK2f0qr640Xpvea+7hTBv9JNuLIxZLLqc2ausWPBN9rmd6+10QXxypQm9GxSYpTmSGHLj4+1KkxW7eJ4fWCLmPNc4zoSbWyYhhS2gjnJFMNUEadqz51fo0V7s+LSjhSZMraT6i7cYtSTRfHf0JPUQ49MadqDrS6yPUPYsInpekCqFNfbcZEYKcbny55rmwB4LzjSvkbfwkTvl4jZqH9JLZAkue1TS5WdxxJFp5c2eofV8G6Q2LTHXFtibYxNw6x4kkwZMq3Jc82aTtgYpGhjo/lrvp0Z08ZQcSVpYevzpe1S6yc5WhtcaYuvt7+D0JUgDBOFejtMFbONAM+RqOIzqLRhrZ8wVfMPvEZi+32y1E2o+Q6bw5R66OE4Vl2Vwkbf92JF1Xd4Y6XHTCPkE08efIXFvezFB2HNvBseFmWuRIkSJd5J2DMZm5ycRBQ9Oo8//vht3xdC8Gu/9msHenAlSrxd+N6NTS6vD5iu+WMiNoKUkumaz6W1Ac9fa/OhU9Nv6bGNnkxfWR/gSJiu+ygNq72YfpLz7LFJpmr+bXNfo26kD5ya4smFJo/N1PnrHzvJH7+0NE6IU9pQDx3ef7y1Q2kYIfQcrm4McaUg14b2MEPpFBjFxxsqnkvgSVKl6cc2VTDJFO1hhoO1ddmZJYHWdnFfDVw60f4XkBJBlpux9e5OzGUU5JEXKY6+IwurmyBwHZqhhzH7UxMqnsPRqQqOlCx1LJnzpKRdBDvYNElLc/YSn6+0PX+OtPbNTN8/EaPYX5Rrom3Dd7ogYqNTlRcF2buIp7dhtuGjjQ1BmakHBK7k5mZEltsUQ4ElnqNofs+xO5quB8CQw5MVNJKVXszNTkIvUUhhUErjOAKjBI40Y7vGZMVDSkE3skRupMLVAxfflWwMUuYnQiYrHqdnG6z1k3vWSNzPnNP2qorvXtOs9hNWewmHJkOeOjLBUifmWjsiyRXD1CFTmorv8rlXlpGSAzmGEe5mL4aDsWbeDftR5t6q8KISJUqUeNSx57+xv/SlL2GM4cd//Mf5V//qXzE1NTX+nu/7nDhxgsOHD78pB1mixFuNjWFWLKqcXb9f8W3h7PogfUuPa/uT6TOzddb6KUobAtfBr/lsDFIurPYxprpj7stzbDfSNy5t8EcvLXF8qsrZuQZPHm5gCiXGYHClwBGSOMuB4Lb9r/as6pFkCm2gGXoIYUhyMw7OyJRmsZMwU/OZa4Y8NlPjYsXjGxfXiXND6NomqiTXREWfltZ2ER54gqSwE94J25eAzYpDL9V0YkmsFI7Ymknbcd7YSThSZVjqxkxXrbXuZmd/RMwWVAtLOqShWXHpRBlSQJTlpLmdn3MdWUT935vtjJQ7Twqy+212vgeEYEy+BFsEdS8YJDnPHJ3keifGKSL2c23n8ITAzkoViZf2fBubIllcsIrvkBvJwkSFlV6C0gaFKmyH4DgCoW0sfs13mG8W0fWeoRfnuK61vhoMG4PUziN6Lu8/McXf+OHHWCwUoTsRnAeZc9peVfHqYpdvX26z2otJlaZV8ximitCTHJ6oUA9djIEXb9w+t/mgs1Yj2+RLNzvUA3cHIRpZM586MnHg9sgR3m5lrkSJEiXeidgzGfv4xz8OwKVLlzh+/PhtT8VKlHgnYapqCUyUKhrh7TbEKFV4jlXI3kpsfzJdD1ymqj4rvRi/Zhf99dBlo58QJTmbUcZjMzUCV/K9653ivTis9lJeXezy6mKX331eM10LeP+JSc7ONxgkOUudmG9dblP13ULVsDDGcHNzyCCxs1H1wCnSCXUxF2RQ2i7yj02GNKs+U1WP8yt9VnsxnmPVslxpOnFuz1/d58hkyAvXOoS+xBGCjDunBQrA9wQVKQBFxXVJVIpTzPOkSt/RFuhIxqmJ2hjag5T2MMURYs+zU560Sp4rBe861KDqu7QHKYPELj5tkIjtPgs8h0bgEjhiRzjI3VSy3IDQ5oELne8EPZ7BkkX/295JX6YNZ+cbBL7D60s94swqqXnBckVR0mznp3Ibb78LqYwzxWTVR2CVVUda+3uWbUX/O0XZc641x1oVzq/1yQuLZZRpJqoeVc/l+HSVT7x7bk9E7EETCEdVFcemqnziyXlubEb04ozf/+5NW1StDBfWBuTazo61Kh6DNB/b9i6u9Q/kGHazFx+kNfNueLuVuRIlSpR4J2Lff2N2Oh1efPHF274uhCAMQ44fP04Q3P5EvUSJhxG3WobmavaWeObIJCena7y+0qPmOzusilpr1gcpT8w3eO5Y6y093u1PpoUQnJ6r0UsyNgY2ctuRgn6Ssz5ImaoHnJ6tc3HVdiPZp9bJOPzAdwVRZuglGa8v96kFHlM1nw+davEnr6/xzcsbfOz0NNXApR/lvHB9k+VeTFQwhe0dYCOVRQq7aEcIpms+3726WZQhuzyx0GBzmLIZZfiO5OxcnbNzNb5/ww78T9d8NgbZHQuwHGFnxNLc0Kg4QIbvOWNbWyO06lSmzA7CI4p/XLGlmAlhCZtV0bbCMrb3n+2GkbomBFxeGxJnCikEc82Ao60KV9YGpLlhuRvjudam5zoOrtwiLfeiP3uZL3sQ5AaE0mPCtxcLJdgAlH/9yhLvPtRkvhmy3I1R2mp+I4JXD+ws4CDJ7bkyoLZdy9G98+5DTZ47PsFv/smlsZXTsHUNulFGL8qQ0haHn52rM1sPi7APzWTF5+x8gycWGnzu5ZW7Kk1vxpzTiJhd2xhyca3PWj8hV4Z66OI5LpmydkZHCp6/2uZ6e3hgx7DdNnlhtX9Pa+ZB4u1W5kqUKFHinYh9k7Fnn332rqqY53n85b/8l/lH/+gf7Qj5KFHiYcNulqEzMxWOAK4r+WsfO8mv/9E5rrYjpreFA6wPUpqhxy989ORbHt5x65PpqVrAs8cmubAyYGOYEme236oRurz/eAvPkbYbKXBZ66dkSlP1HRuYkRtqgYvW1gZ2YbVPq9piuh7ywZMtzi31uLkZc30z4vxyj+QuKsooDVAV9rfOMOXSmp0zOj1bJ/RcMqV46WaXfpLTi3NeutGhPcw4NVvj2saAfqJwpCD0JHGmbiNEykB7aIt+TfG8J8kU1cBhpadxpcB1xDid79YkQoPAd7BhItqghUGYnaSt4tnrGWW3929Zx6pAFOQizRXKyj92fqkRMF0PGCY5Sa64uj5ksuLx9NEJnjk6wb/45lVWbymY3jW+/y7X/yAggVbNZ62XonfZ/x1fV5RyT9V8PMch9B36qaLiCoyx1s9cG6SAyapPZ5iOAzoA+nHOUj+jGXr8lQ+f4FuXN5hr+AxTRZopDDZdM841qhjuq3gOTx1pIqXDVM3nZ55aYLYRUPNdolTx6a/dW2l6M+ecenHG1fUhSmum68F4+9Y2LFnvJ1zbGHJ+tX+gx7DdNvlW9ny93cpciRIlSrwTsW8y9nu/93v8nb/zd/jbf/tv86EPfQj+/+z9eZhk51nfjX+e52y19949PT37on2zJCwkGWQsbxhMCEsITrBNCAQMhmC/xHZA2FzghQCOIXZsjLEDIX7jBK4f8Hq35QUseZdlaxvNrtl6X6prO+vz/P44VTW9Tlf3dI9mRs/nuqSZqa4651Sdqurne+77/n6Bb3zjG/zJn/wJb33rW4njmDe/+c38zu/8Dn/8x3+86QdsMGwGq7UtPTk6z0gRjk9W2zlirZyxmVqIY0muHSrymnuenZyxla5M9+Y9eva4zDcijk5W2dmboxHEZJw0kDZWCqUljag5n8V5Nz3HEvhKk3FkO7C2lHUY7s7iR4q+gssXn55YVzubBs6UG5yd8xnqymBJi4FianjSCBN29uTwI0UtjCllbUqujWtbTNcCenMOQqTzSLO1aMV2RQVMNrOx/vnoJAOlPErDXD3CsgROU2y1CjICUjdJT7ZF6XwjxrUkAk2s0mpea67MtWSaI0X6+iidCpFCxiaIEvw4nWuarcc4lsAVktCPKNcjHFuyreSxrTtDGGteffduXn7DMF8+Msk/H51iul5eNLsm2XrxtRRNWqFa71SaZ6WPDmPNwcE8j5yawxJpaHN/MUPYNPKwmo6ap2ag3Ijbs4TVIObaoSIvuWGIbz0zw6efGCNRaaRBolQ6g+eft7dHQyNOODRW5QcO9jFTD3nsTJlfvm8/AO//0rGOKk1bOedUDWIaUUIxY68osjwnzcCbroabfgyt6tyl5tmszBkMBsPVyLrF2Nvf/nb+9E//lJe97GXt226++WZ27NjBAw88wDe+8Q3y+TxvfOMbjRgzXJZcqG2p6ObBhy8cmuDgtm7uv36I+w4O8MjpWaZrIX15l9t39jwrdvZw4SvT45WA3X15XnP3Hj735DiPnyszVPSwZTp/o7RGIvDj1I4+jBVRopuOgpJ6lKSBzZyfifv0E2MbyqISOhU2ni2ZmPd54lyZME7nSYIoZrCUIdaSff15TkzVSbTGEYJyIybn2mkAcAeW8JGCc+UGlkhFk45TMwnLEuRdm0QpGpEi50pcS1DKpo6A1aYlerIg2wrS1suF7Zdh05wi59mEcUKQaAQaz5YkSVpXqgbn5VSQJBydrHN0sk5Xxsa1LL58ZJJ3fvIQc42Q3rzLVPW86culFmKQirF5P163GNNCMFuPeOxsmclKQDFrp46JQrTbZDN26iY4UwvpyrrcuL2LH79tG+qZ7/D2f3kT3YUM//Orp9pOoF1Zl3IjJErAj/SiKiWkrW9TtYBvPTPHjdtLizK0Oq00beWcUyFjk3UtgkhR8PSytr0gSivRfQX3qpq1erYqcwaDwXA1su5v/scee4zdu3cvu3337t3tWbLbbruN0dHRiz86g2ELWKttCeD4ZK3dMmTbsmP7+ouxre6UPb157tzdw5cOTXJ0vErek2RdZ9GVaaU1j56e5dHTcyilqYcRWmvqkcKzJQMFj9l6yGw9oqdZjbKlxLVke/bDljBdWb9bpCXAs23qUcxsLWS6FrUX2X4cU/ZjTs8FdGVtvne2zFQlNdLozjkEkaIexqkYE51NM6UB0Lq9gG/NiFWDCBAkiWa0HODYqVNjPVLt0Oi10KRhyWGsiGKFlIKCZ6ctmVJQD1aXU2U/5u0ff4J8xmFsvoFriWar5MqGIc0RswsGXl+IZrZzR+6InVjZL0QAlpR0ZR2KGYuJis9cQ5JxLLpzLp4lmW1E1IIYS6YRCrYluWN3Ly+9fhuffgaet7OHDz10apETaJSo1NJfQEw6w2fRHBsUTVGv0hmyIxNV+vIeFT+dJeu00nTNYHHL5pyKnsOu5uxYS5A6VhoAXW06QO7syXJgoHDVzVo9W5U5g8FguNpYtxi77rrreNe73sUHP/hBXDd1kouiiHe9611cd911AJw9e5ahoUvfwmUwdMLStiWt05mpMFG0jBODOFl329LF2lZ3woNPjbfbJsNEIQVs68rwM983xL+6YydSivZ9jkxUqAZx04VOI2SaqTVQdLGsNJDYsyWCtMVvW1cG0ByZqNKbd1G6E0P2xQjS/LCWGcPUkhmphZQbMTW/QsaRJEoTxSrN+yK1yXdtybly0PG+Nc1Q4gTitiI5/wzCWK9rey26shZ+3AyEVppa07RDKbVmWPIzsz7gU3Aljm1jNRfhUT1a9touP+LOkXQWWC1JXSUdS6L18rm41dCAJQS9OZeMY+PZFmdn60gpydoSDewbyFHMOCiVukr2FRbPD42W/WVOoM/M1NKKrZSLXFNa+wPdzqsr+xFTlYC//845fuCa/o4rTVs55zTSneV5O3sIorQdeLYeUQ1i7JYglZLbd/WwoydnZq0MBoPBsCLrFmPve9/7+LEf+zF27NjBLbfcAqTVsiRJ+PjHPw7A8ePHed3rXre5R2owbBIL25aiRLXNL2KlyFqCW/aklZD1tAxthnX2Wjz41Djv/NQhKn60yFDk3JzPh/75BANNG/qF9xnpzjLvR0xWQhKtcSVMV0Pyns327ixFz+aZ6TpRkrbelRtxu8L2uSfG1i0MWmtJ2xIkHeie1KdBY0lBI0qwLYljSRBiJUPFNQm3oO9vpp5gN+PmlAad6HUL1YxrYQlBotIst9SkZPHc1ga6Qdt0Z20QqQjx4wsfm2unLZuN1XXyikRJwmQ1oBgn1IOEmXpEojT1MAYEjiUoZdNK0e27enjx9UN4tsXh8dQtsxJGy5xAJ6p+av7SdGUUpDOBlhAIoYkTQKcCUilBd87l1EyNTz0W0511GC37HVWatmrOaaHQm64G7OjJYsn0PKeC1GuLLDNrZTAYDIaVWLcYu+eeezhx4gT/63/9Lw4fPgzAT//0T/OqV72KYjH9ZfJzP/dzm3uUBsMm0jLB+NqJaWZrAX6k2pbUOkmrYVPVgEbUWWVMKc2nvjfK0YkKffnUSCPvWhdlnb2UOFb8j4dOUvEjdvVk21b7xYwk71qcmm3wkYdOoJVedp/evEd31uHUTIOBksftO7sYr6T5WhnH4l/cNsItO7vaLnUj3Vm++PQE/9/31t9qLKWgO+ukboUdPqYS6rSFT2vKjZC856CVJr5MigQaaHpQpCJnA6IpiBRBpImVSu30NzHUWZC+7p5jkXFsZmpBuw0zrTClf1HNOyudOhau9wjCWDNVDZisBs0YA9Gc+3IQpMHWBc8i79kcHCrwuSdT0RHFES8uwCcePUcYq0VOoDePdDFdDak2Z9ja8QKkQkxrsKxUqDqW5LptRXb25trVpZ6c03GlaavmnJaKrHoY49kWt+zoXiayzKyVwWAwGJayoWnhYrHIL//yL2/2sRgMlwQpBS+5YYjPPjnGZDVkWynTnvMImjNAxYzD55+c4MBAcc2F0v/59mn++munaERxuniUgq6sw/N29bC3P39R1tktHjk9y8npGn15d1HmWfp80vDpw+MVtIaBorfyfQou5XrEj9yyne3duVUXgw8+Nc47P3mI0fkGEjoSVQXXwnMkloC8ZzPvr6/FU2mNUum+onrUbqXrdP+XO5UgwRbpe0ND6va4CdsVpGKr4qfOk60pO3tB158U6fCVa6XviTg5n3nWKQvz13RrHzbkXJtSxsG1UzdOz7aYrga89wtHGe7KsL07S8FxIYAzsz6TlYAgUjxvVzdCCHb25LhuW5ETU1Vm6xFK6WYgNSSJQjTt9B1LsqsvDVxuGXTM1SN+4vYRvnu63HGlaavmnNYjssyslcFgMBgWsiExduTIEb74xS8yMTGBWmJ39ru/+7ubcmAGw1aSda3mTIegFqbzYbaUDJbSVr/9A4WOBNSDT43z3794lHk/pOg5OJYgUmnu1FeOTAGwszfbsW31agYg07XU7CDrWqs+n7BZ7bjQfWZqIVPzIUKItjvkcCnTXjSGYcIHvnSMqaqPLaA7lwqr1UKQ9/fn2DdQQACNKOHMbANLpiYg60Hr86G/uvlvS6YW6f5qO7/CiHUqhDYTKdI5uUQL5mpRu/K1kLQ1Mp0XK2UdZutRGsy9DhaZozT3kei05TJWCuJUhM/WQmxLMFOLeN7ObgqeTa2RmsAMljzKYcJ8I+LweIXt3VmyrsVwV4Yzsw16cgJbtuxXFJOVAM+xKXhWs4rW3W5HbBl09Bc9fuWF+y+LSpMRWQaDwWDYCOsWY3/xF3/Br/zKr9Df38+2bdsW9eoLIYwYM1wR1MIY15Z8/76+NHQ2UbiWpOQJ8CfJupKgEl5QQLVaB+thQt61zptiyDTrat6PefT0LL05uyPb6gsZgPTlXRxL0ggTipnltvqNMDW80JoL3idWmj//52PpgjxROJZkT1+e1967h529Wf7swaM8dnYOIdIsLduS7daxlmACcAV05V1+6+XX8dIbtrUXw6nBwln+8bvn1nM6ljkIJhr8MEGY9q21EZLurMVsPSZaQexp0giAWCvierghQZg057Za24O0dbMeKs7O+UghyDSNPBwrFdFTtYDD41UqjYBb9sA3T87iOjY512Jnb45TM3XqYULOtXjxDYMILTg1W+f0TJ15X+HaNl1Zh529OfYPFOjNu+3jWWrQcalEUKduqZfCVdVgMBgMVwfrFmN/8Ad/wNvf/nbe9KY3bcXxGAyXhJaJRyNKKGXPu7EJnbYpNkK1poBqtQ4OFjzm/ZhaGGM5FkKkV8mzrsVcLeSpsQr3XTN4Qdvqx8/N8ta/f4Lx+YCenMPN20t4rt02APm5u3azpy/P4YkKedda1IaolGK6FnLNUBGtNEenaive59ycTxAnjJYbDBTPz9gcnqjwO3//eNPAIyJMNJaARJFWPWhZm6eiTGkY7s4SJZpHTs3y0hu2tRfDR2WlKWDXWRlb4bZYc3GuFlcprdpR2wZfa2ar0ZqW9kqnc1/rfUXt5g5bp6JV9NSAbUHWsUi0TrPbtCbnWuRcmyPjVRKl6cmm78OMI5iup8HiGdtqmnYo0BZ9OY+X3riNrGtRCSIqjYhPfm+MkzM1bhnpWvRefras4Dt1S70UrqoGg8FguHpYtxibnZ3lp3/6p7fiWAyGS0bLxGOl3B+AsXmfG0Z6LrjYa7cOeha2JQiThEbTkt0SqaVBkCgcW17QtvpPPvM0f/5Px9rZU2fmfJ44V2FbV4YXXTfIdC3kC09P8Op7dvOHn36aU7ONRW6K07WQUsbh5+/dC6RuiovuEyRMVAL8KMGxJbt7c1hW2spYzKRzWYfGqwgBO7pc5uupEFq4aE/nnNK/2xLiRDHcnWVyPmi3crbCtGtBsiEnxE55rtcXWjNbLfw1VNjS87iR/blSogApF5t/SCHSkG3SCxBoUjMOpVGuxVApQ6trtpUXNluP+NrJaYZLGfrzLmEc8vHvjfK149P851dczw0jXQD0FTw+/JUTfO9smZ6cSzFjY0vB2Hxwya3gO3VLvRSuqgaDwWC4uljey7QGP/3TP81nP/vZrTgWg+GS0bKk7s27HJmoUvEjYpXmGQH0dLDYW9g6mHVttpUy5F2bOElzssJYk7EtfvJ5O1ZdgP3JZ5/m/V8+tiwEWAHnyj6feWKMrCM5OlHlum0l3vLD13HNYJGKH3N2tkHFj7l2qMibf/g67r9+iPuvH1p0n2em6pyZaxArTaIVAhibD2g0PeC11kzVQrROs8gqgVq22F9KotLXb7CYYa4RUfFTj/RWmHbBszfFnOJCPNcF2aUk0RAlCrRGNI08Wq2rQZQQxIp6pNBakySaKFHMNSLm/ZjRss98vfX+qFP2YywpyFiSWhDzyOk5vnlylsPjFR4+Ps3P/sXX+euHT7T3nXEsJisB3zwxwxcOTfD1E7Ns78pcUlHTusgwUws5OFigmHGwpKCYcTg4WGCmFvLZJ8aJY9XR/dR6E7cNBoPBcFWz7srYgQMHeOCBB/ja177GzTffjOMsDtz89V//9U07OINhK1kp9ydnC8jAv71r15qLvdt39ixqHcy6Ntud1EgjShTjlYDrt5V45S3bV3y878f89cMn2852C3Vfa702VQ15ZqrKUFeWWhhz//VD3HdwgEdOz7YNOG4b6Wa8GnBobJ68a/ND1w5y38EB/r/vnePvvnOGKFZkHck3TsyScSS1MCFMfLaVMkgBfpQgSEVWLUyamU4Xfu3mGzFPnJtDa8Hff+ccri2JlcaPE7TWqYPfFkmytcSiYXNY6GQZaxAacrZFos8LMltKwjh1PVRK49gC13bwo3Q+caISMFfTsBfm/RilBQXXwo8V5UbcPo/NaDnmg4j/8pnDTFZDZmrpf3ft7WvmdkXM1EMa0aU1dGldZBjuyiyroLecHY9OVHnk9GxH91uPq6qZPTMYDIarn3WLsQ9+8IMUCgW+/OUv8+Uvf3nRz4QQRowZriiWWlJnJHz3q6fZN1BY87G2LXntvXuWtQWGSTrD1ZNzee29e7DtlQvQH3vkFJUgPl/ladkJcn42SwMnZxr0F7Pt+TXbljx/bx+Qtk/9xVdOLJtPeckNQ5yb8yl6Dgd3FjhXbmBbEqUh60gakWKmFtKVs0mUJmnuy5GgtEBqvcyVT5Iumts5WVowWPI4NVPjIw+d5OU3bSNjW/hxgmtJ4iTZElt6wQZK+oYNYYumINOQcSQ9eQfZAM+22NaV4dqhAmGiOTJeodyI6M65VP2YciNirhESqzQzDJqzZs2Ms9bsmi3Pz785lkSgCOKEv/7qM9y1p5vrhrvawqYn77Yzxi42t2891MK4HVa9Ei1nx+la2NH9OnFVBTN7ZjAYDM8V1i3GTpw4sfadDIYriIVubFEU8d11PPb+64cA+B8PneTkdI2ZWohjSa4dKvKae/a0f74SZ2f99myVaLoVrrS0rIcJ27uzy+bXLjSfcng8NdLY1Xxe+aaLXbkR4WRtXFtSDSL8OKEeJu0KRT1UbRHmyLRa1sqvsmV6oEKkOVVSCIa7svTnXcbmA753usy+/jyPny3TnXPSKllTjW1mJUtzdWSPXe4o0gsCAsi4kjt29zBQ9Hj0dBk/SrhhuERfwWO+ERHGilwzOqHg2dSCKBX+tsQRaUtsy5EzTPSCgGfRbJFN9yWlxJWCqp8ayWxWheliaJn9tMKql9JyduzLux3dby1XVeh8Rs1gMBgMVz4byhkDCMOQEydOsH//fmx7w5sxGK54VmodvH1nz6oVsRYjPZm2CJNNt7qWIFsoXjK25P7rBxdVAZbOsbQWrcWMQ8GzeeTULJOVgIGCx1OjFWbrYTO0VzNVjcg4giDWSJEsqoAt9IJQ6nyosGxXSHTTnCS1Sj80No8tJXnX4junFa+9Zw+HxitNl7+tbyc0FbKtRwB9OYdYaeb9mBcc7GOyEjJdC3Ftydi8z+i837bMn62HRErjWGlNrPXebEeINd8UC4tarTZHz7ZwrFSYRcnKknu9FaaL5UJmPwudHW/f2cM3T8yueb+1HCDX+mxf6sqgwWAwGLaWda9l6vU6v/ALv0Aul+PGG2/k1KlTALz+9a/nXe9616YfoMFwJdBqHfzhm4Z5/t6+NYUYwM/cvoti0+wiUc25GVLRs1DE7OrN0F/wOD1T57HTc/zVw8f5488e4stPTzBYcFapHmQpNyK+fmKayYpPxrHY0ZNjR08W2xLUAkWYaOJEt/OjVkIDni3JOBaWFGSbf2actBLQk3PJOJK5esjTYxW+cGiC0bkGUkJf3mmG+G4drdZKxxK4C/ZlQzr/t8U8F5bCtgVDpWxbSPXlPP7l80a4aXsXp2bqfOvkDLUgJk7Sd64fJs22V0HTYBFI39eeLdsirGXL32rHtZtxEIlqhVmv/MZcT4VpM1jN7KfiRxyZqLadHW1bdnS/tQRUpzNqZ+caW/m0DQaDwXCJWPdvs7e85S1897vf5Utf+hIvf/nL27e/+MUv5m1vextvfvObN/UADYbN4nIZhl94HD/+vBH+5mvPkOjz1vEL6ck52LbNm/7ue/hRzDPTdYJm+UoDh8bmuWNPL7fs6F70uIGCR5xo5hoR+/pyqblGpOjKOOQdydMTNVwh6M5YVIKYZEF5bGk1qxEpcq4kY6X25rYlGenOknHSrw/PtijrkPF5n7/56kkQ6W2FjE3GElSVXlbt2yw0YElBzrVpRAkOmkhBAgSJboulrarQXU5GIkuDuVtc7GsfJanYvXWkm8lKwNdOTHNkosp/+MF9nJyuIqUg51gonYaEh7FCqzT+wLYlXvPCRNGzacQgSGcWW3EJUkDGtsh7NpZInUgLGQfXSlsYN1ph2kxWMvvxbIubR7p46Y3nZ7g6vd+F6HRG7VJVBg0Gg8GwtaxbjP393/89H/vYx/j+7//+Rb8kb7zxRo4dO7apB2cwbBaXyzD8SsfxfXt7+d7pOeoLXOIkMNyd4Y7dPRyfrHJiqk6taUe/kPkg4aGjUwCLBNlkNcC2BLYlOT5VTx0Im8YbSmuE0Egp8DybmUaMZwtipdszbAud9jUQRAplSzSwuyeDJQX1MMYSgloQcWbORzVFVyljI6Vkth7hRwoLsCxY4fA3hVhpGmFMkGgsKXBkOpOUbwo0DSSJvmLmzDYiniyabX8yFU8Arp2qs2gDr/tCJ0WAQ2NVGmFCkGiiJOH4ZI1zc3WmqwGWEGRdm7lGhB/HKNV8fNOsoyuT/poZ6c5xdj4kbET05h3m6hGaVFzkm8Y35TAm41i8+u7dzNRCjkykFaJWpt5o2b/kGWMtlpr9rHZBp9P7rUanM2qXqjJoMBgMhq1l3d/mk5OTDA4OLru9Vqsta6kwGC4Hjk5U+PBXTnB2rkFPzqU/72FJVhyGb2UAHR6vUMplNrV69vi5Wd72908yVQ0Y6srwvB1dhAoaUcIPXT/IkdF5tBR0eQ7XbyvQnff49jNzlBvxIiG2dLEeJppvnpzhxuEilmU1qweNtLVQQFUnRElClKhm5USgNCSxwg9jtE7bLGUzzDlaoMSanh30FVzqoaIexpyd87Et2T6KapCg0RQzNrUgwZIS15aEEfikVSq1RUIMUpEZxKnYsrWmkLGbx5RWzYJItYVFq/HtchRmtkwrResVYhLIZ2z6cg7DPVkeP1smiJM0701DuRGRrGOjYsnfXQsqfsTxKcVwV5ql51qKY5M1qmFM1pZIKSl4NmHTEKa1P60h66Spz635yD19eX7jJQc5PFbh7751hslqwFwjwmq21/7CC/byc3fvWXThYiMVpq1godnPZtxvJTqdUbuUlUGDwWAwbB3rFmN33nknn/jEJ3j9618PnB/O/tCHPsTdd9+9uUdnMFwkSmk++rVTfOuZWSRwdja1eO/JuewfyDPdDGLd11/g+FSVzz52jhHgA18+hmM7m1Y9+5PPPM0H/+kYQXOVenKmwaOn5rh1Zzffv6+PR07NIqTkJdcPNYUOzDciZmoB881QZUgXx62Mp4XGG9Ug4enxCjt7883qgccz03Xm/Bit0/kw1WwJk6RW9gqYqUVoDYlK28G01svEgBTQm/foyiqOTsY0ooSCTJ3vokSRtMwaNGh0Wo3SelHrY6dYpOJtXSxozQtVmmdlSYklwFfLn8/lKMSA9vnZSCZwI4zxXclAwWOkO8do2acRJhQyqXNmlKgV22BXYmGOm2OL5ntNUw8Tzsw2cG1J1knfo3GsaShFb97CstKWRNdOA53DZjutaobWTVVTZ8DX33+Q+68f4uU3DvPLP7Cfzx4aY6wcsK3L46XXbcN1U/F2sRWmK5XWjNq5cuOyqgwaDAaDYWtYtxh7xzvewQ//8A/z5JNPEscxf/qnf8qTTz7Jww8/vCx3zGB4tnno2BRffHoCpdOcIscSRIlmsuJTDWIODuY5OlHl4WNTfOrxMco1n5Ei7O3LU430plhJ/8lnn+a/f+nosupEkGi+9cwsALv78jwzXWei4rO9O72iHiaKRqwIVukzW1ohOz5Vo5hxuXmki568wz8dniSIE6Q4X+1IVCrmLJE6J0ZNx8QkUsuEQMtMxEIghaYSJthN18eSZxFqSJL0GOJEM5+kIb7VIKEeLM4YW3icS4+76ArqUdpiaEsIIr3MxORCJJyfl2o9R6UUFaUWOVVC+twvq0GvBbTyvNaNSM/jTC3iyESFm3d0019wm1XVqK2u1vPUJWlrqSNluyqb6HR+0I8V8/7590eoNJUgJu/ZOFZq9iJFGgyutKa/mAEavPymIX7ijj3s68/zjRPTbefRl98wvKrhzcVUmK5kNmP2zGAwGAxXBusWYy94wQt49NFHede73sXNN9/MZz/7WW6//Xa++tWvcvPNN2/FMRoMG0IpzYNPjdOIEnb0ZLFkuuDzbIGbd5mphZyd88nYkv/7zTPM+SG3bi9AkC4Cixn7oq2kfT/mL//5+KptYkrDo6fnuG1HiaxrMVr2Ge7KIoTAtWRz9mb1JXRrgS2AF147yGvv2ctwKcMffOKppuuhpBYk7W1IuSA7yhYkKjW8gHSxLRZsF9JFuW0Jzsz5hFGSCjcNp8vh4ufR/HNhNtlqLBVmiRZYMnXTS7RGLB1YWgNBOh8VRLpd1dGklabWz88HCwt0fOXMj3VC6+0RJJqTU3Ucy8KSkPMk5XrSbn2UIg1xbglwSMVp0bMRTfHUijAYKroEsWKmvtgkYmFFdqF8CmKF0jEFz0JKSazAdSxKGZvXvmAvyclZfvPF1/KV47P8/sef5OR0jShROJZkW8njR2/dzouuG3pOVL465blaGTQYDIbnGhuaAN6/fz9/8Rd/sei2iYkJ3vGOd/Cf//N/3pQDMxgulrNzDUbLPnnPJlaLLdyFENhScGS8gmUJJFDIOKgkYX9zJFIpzeh8gyBK+NrxaV5x0zb2DBTWdQx/9qUji4w5ViJMNN89U2ZXb458U/y1WpPyno1YUNNouwMu0WcZR/Bv7trFzt4cp2fqjJYbdOccEqWph+fNNVo5ZokGoVITD6lScSKbO2g58tnNsbBGpGis8RwWYkmBXqE9cCmS1Oo8SlJTESnBlZJEasK487Y6SA06Vttf67m0Zstg4+2AlyPtlkKZmmE8M10nShKklPTmHZROK5eeLenK2hyfqmNLQd5z6Mu7dOUcGlFqytFoVsGqfoy/5ARIFgtpLUA030+iuY9KkJCx00DwvrzLgcECNw138d2T8M9HJnnXZ49S8SP68i4AU5WAx87O89RYhU8/PsYLDgxcclOdy5nnamXQYDAYnktsWmbq6OgoDzzwwGZtzmC4aGphjBSCgYJH1Y/RCxRMI4yZqgbUwpiia5P3LAqezVQlAOCJs3P8w6Nn+ewT43zt+DTfPDnDG/7Pd3nwqfGO96+U5itHpjq679k5n9t39fCrP7Sfm7Z3MVePeGa6xrZSJm2vbH5SFedFRKsCJIB79vexp69w/nnL1N5eKY3TzC/TOhVhSVNsReq88QWk80HXDhXoyzlpG6NaHAK9Ekuv0UcKIqVZ6OUjSUODPTvNnbIl9GYdhrsz7B3Is63kMlD0yNg2sVKpeFhn6UqtdX9NW4yL5r+vpvqCIwU5z0ZpqAYRkmYlzLLobQqfeT+hGiSpwJeCoVIqxMLmi92VtdvvqVjpZWK19TPZbHNdaKNvWaKdH1bMOFwzVGBnb47bd/Uw3JUB4H99/RQVP2JXTxZbCqaqAZHSlDIWWsMz0zW+d2aWjzx0kqMTlS1+xQwGg8FguDww3riGq5a8a5N1LLLdGWphzEwtpJCxsaVkshJQCxMyjsWBwQKnZhsIAT351Er6GydniVS6wM04Ej9SnJqt885PHQLg/uuH1tz/2bkG5UZnWUDFjNOeBTkwUFzUmnRobJ4//szTHJ+sETVXyO1FsITrthV5yytuaLcvpc/bJsoogjital3ITc9qzlSFsebkdJ3+vMN0vaPDXrUatXAhr0jn31zLIu8J4kSRz9gMFD1qfkQx61IPY6p+3BSH6ytZadY2/Yg1iKRpYNKcl7tcZ8fWS1rp1ESxImmq0oxrI6WgFsT4zZlDS6bi+8bhEo+eLXN0sk7etdCk4d/1MMGS0JtzEVIwWwtIkvT9oUirX5CK64Xvp6xrpS2mzWrozp4spaxLX2Gx0cSpmTp9eRchBDO1iCjRZB0LISCLoBokFDyHmQWmOqYlz2AwGAxXO0aMGa5aFlpE37qji+OTdWbqIX4UUQ1iPNtiT3+Oa7cVqQYJExWfXD79SESJopD1sKWgESlKGYdtJZfTcz5/9fBJ7js4sKrpQItKECFEZyv+N77sYLs1a2lr0s7eHLv7cvzFPx3nWydnmK5FaK3pyjq8+Poh/u3duxe1dY10Z+nOOnzz5Ayig3Y8pcGVqZOiHylOzwUdHfNaLBz9Si3vBVKmdus516aYsTk5lc4ONcLU8GMrZ7k0zapg0249ihOiK1iQuZYgbg76aQ1BkoYtO80KpIQ0500Kcq6FlJpGFNNfzPB9uyVfPT7NvB/hSNG26LSkwI8Vec9KQ711AgIcAVGi29VVveAYdnRnqIWKapDmyk1UQu490M/LbtrGgcEiUZS6gUaJotu1CGNFI0pwbdmuoDpS0FCaRpww3JXh6ESVs3MN06JnMBgMhqseI8YMVy0LLaKnayHXbiuQKDgzW6caxAyVMtw80o2Ukv2DeSpBxOi8D8MgRRqCHCXp/EvWTU0J+nIuJ6ZqPHJ6lufv7bvg/qt+TKLO50etxkDB5YUHL1xpu2aoxB/+5K2cma1zfKoGwN7+PDt7citXD5o3lRthRwWgjCuJYkW0Vl/iOlBL/q6VpupHbO/OcM1QnsfOzlMNYhKl15WDtRnH1QqDvpIJmy+a0E3h2zTqsIRohnInhInC1oKKr9Gk8Qbn5uokGoaKHiDY1pXh7Fw9rRx7NpFKxZ0l0rkwNCg0dtOWs2VZbwnoybl05VxKWc1ERTDcZbGzN8srb93Orr78ouN1LEkjTEW50hpLnL+YEanUTTPnWGRdi/F5n1rYWVXZYDAYDIYrmY7F2Bve8IYL/nxycvKiD8Zg2GyWWkQHcYJjSwaLGa4dKrTnaXrzHrft7OaRk9PNR6ZtXwKBkmlG0oyI8GxBGGuma+HqO22Sc9MAZksKlFrZwU8K2NefZ3TeX7MKIKVgV19+2SJ3KWfnGszVI3b2Zjk721jzOFtzZ8kFXBs3A026yB/pznJ4vNqcz7u0QmzhsVzpyLZQWtymGcVppbFlSZ/mc2vCOG0jfGJ0HoGg6Fm4jkUQK2wpybk2Uko8mVbBBksZwtkGYaLagqxl4iGAnGfTX3Sp+jGVICbn2ty6s5uKH1FfIY5hV2+OJ8drDBU9pEidM22RfjYaYUJfwWW4K0MtTPBsi7xrrhUaDAaD4eqn49923/nOd9a8zw/+4A9e1MEYLj+U0le8tfJSi+icY/GPj57jidF5tNbt4PLevMcNw0WgTNaxiLUgShQWEikktoSKH6M0+OGFp5SOTlT4x0fP4ceKOEmdAi3SikBrMevYgr68R9a1ODZZ3bTXuBamwcwz1fOCse3CuOpjkkviLqiAb56cTY9HpFWcSy2NVtrjZh+FDWxVXUcArpVWW1cS+AlQ9uM0K6zZpho13SY9W2AJQRAlzCYJIKgHEdu7siQqfe9kbInWGteW7OzNMjbnE6m0smYLgZSavGvhOVb7Z7YlsK3UnbQn764opP7NXbt412ePMlb2QWvqfoxtWQRxQsa1uW1nD0IIRss+N490MdKd3aJX0GAwGAyGy4eOxdgXv/jFrTwOw2XI0YlKu6LkxwkZ22L/QOGKtJ5eOof18pu3MTrvL7KRb4QJjSCBTDprU2sFMoWqKRzS7RQ8h7OzdVTTGn4hSmn+6cgE7//yMSr1mIJrUWmkj4tVKj48W5JzLVzbopixGZ/3+ejXniFUCktI9g8U+Kk7R7hmqLSh55p3bZTWzPlRmivWwSBWrFJr9EshyFqVMKlBdzhTt5ksFV5bIQczjiRB09iCoTQBzQsIGkukf9eA0Gn11ZaCsHkBQAjRbj1tve+qQUTYfk9oglpMPawxUMzgWJJ6mCClaOa3WdiWpCdv05V1sC3BXC0iTBKESA1uChmbvGsRJYozcw1ilc6mLeW+awcZrUR86J9PMD7vpxW3SJF3LW7aXqK/4HJkokpvfrHxh8FgMBgMVzOmD8SwIkcnKnzkoZPM1EKGuzLk3Cz1MObxc2XOlRv8/L17LmtBtlZFb2n74vi8j2db3LijCyqjqbU3AqnPZ1JFGqTW7OjJcHyqvsxg4OhEhfd94SifeXIMP1QIkc6eaQANJc8mVIqsneaHWVIwUwvxbKtps58QxIqnRuf5+olpfv3+gx25Ni5lpDvLcFeGbz+Tzvms1iK5kIXh0e1cLtIA55VomXNYzT83IjkUix94KWtkjoR1RKetm2qkcKTAs0U72+xiWVjdDJoth46ErCPxHEkQp5EC1w4VeXqsQqLS6ta8HyFFGgBeDWIWFnVbr3kjUozONxguZoiVQiIoNyKCWBEmir6Cyy07usm5Nmdn6zz49ARRrNjVm0vnzBJFLUgYKHiUMg6ff3KCAwPFRZ+545NVDo1VuH5bkRtHSsw3IiYrAZVGxMnpGq4tuX1XT9tV1GAwGAyG5wJGjBmWoZTmM4+PM1MLOThYaLfxFTMOhWYo8eVsPd1pRW9p+2Leten1JJ///KE0gJhmK1hTpbhSIARMVyO2d0WLDAaOTlT4/Y8/yTdPzhJECtdO75sk6UyUSjS1ME5NDKKEgZJH1U/bxBxLMO/HFDIOpawgihWj8z5/9uARdvbkuGbb+hamUgruv36ILz09SRAlxMnaLYiWANeWDBQ8Ts00mlU8sMV59zwBFFxJ1DQlqTct85eGAW+USyXEFIurhVux3zTnS7PC6NSGcaz0vRInqv2+lEJgW6m5jGNJSlmbXX055uohteYc1ulpkBJqQdw2/YBWhe18gHgUa8YrPiPdGVzbphHGqZAjrbalZjZppljBtahqzXQ1JFYKx7IYLGXYP1DAscSKbogPPjXBTC3kmm3F9neK1pr5RsTRySr7BvL8+3v3Ml4NODQ2f8naoq+GVmyDwWAwXLkYMWZYxtm5Bscm0/Y9IRYvSoQQl7X19HorekvbF792NA11zjs2Wkq0BqVTR0XHEkQK5hohtUC152KU0nzqe6M8fna+adgBtiURNM0T4gSlBY4l6cnaNGJNKeOgFeS91Oq7t5m/BKko6s05nCs3+MjDJ/j9H7tpTRv9pdy7v58XXTvIp58YJYgVS10yBGkr3fXDRcbLARlHIoTgxu1d+JFith6254wgFV/dOZfBokc9TPDDmFoUIthaO/qVsK6AdbIirbxtZtvncMkjSDTlRoQrU6OORKfOkL05B0tKhruyDJeyfEfPMlkJmKkFhM2KmVpShYTFAeKQzpb5UUKs0qqoY0n68y5T1ZDp2jS2JZivR8zWI1xLkmjN7r48u3vzFDM2QghipVZ0QzwxVVv2nSKEoCvncs1QkeOTNf74c4eZqgaXrC36amrFvpIwAthgMBjOY8SYYRm1MMaPE3LuygP0l6v19GZU9GbqaSZSzrMoB+dDaVvYKKJE0ZN32wYDZ+caaUWsuZjz47Ta0IxuwrHSeRqtYd9gEUsKXn7TMB//3lnGygGFjNM+1kYYM1OLqEcxjTDhi4cm+C/e0/yr79vR8eIwDBM+/cQYJ6aqlBsRaE3eEYvmlxxLcNfeXm7Z0c23Ts5ydq5O1pH05Bz2DxQYn/fRKGbqMVppihmL4a4ss/WI4a4M09WAqVqIEKLZjplangvWDmC+WBJ9+X9xCS5OiNkCPCcV9NVQYTfLWFlXNtsTU+v6WpB+BpXW9GRt9vXneXK0zJlZn0TptFXSkQSRuuDxCNJzqDVUgoTtXS6eYyEl9BY85uohp6ZraNJssURpQp2AEJyeaTDSnVvwHl7ZDdGPE4ZWcUj0o4TD4xX8KOGaoeIlaYu+0luxr1SMADYYDIbFrO9yu+E5Qd61ydgW9VXE1mqLrWeb9VT0VqM35wBpKLBjCRpRQqzSsNtYaWpBgi0lL7x2oC3oamHMfBCSKI2UaRUtTlS72pAeiibRaVXjjt29PH9vL7ZMbcUd67wQG2uKXCkEWUdiS8GTo2U+8tBJjk5U1nwN/urhE3z/ux7k1z/2KF88PEUtVIQKalHaYubYgoFiWuFqtaztHcgRJZpGlFANInb3ZUm0ZqISEieKKEmo+AnHpmoIIRgqZfBjRdaxyLlpLtTe/gIDRQ/bFlzs9W2rKWIXVsCulGvmFqlYudiL/CrNck6dCiXsHyxw74F+XnjNIM/f00OiNJUgJkg0jUgxXY+oBAlfPTbFlw9PEcYqdU6UEkuIZRcf2u9Nzs+NJU2b/O6sy/XDJZ63M50RqzQizs2l4k4IQdazcSxJlGiU0tT8iGOTVbTWaK0ZLfscGCwsc0Nc7TtFa83TYxXiRHNgoEAx42BJQTHjcHCwwEwt5LNPjKM2scy49MLNpdin4bwAfvxcme6cw77+At05h8fPdf4dZzAYDFcb6xZjn/70p/nKV77S/vf73vc+brvtNl71qlcxOzu7qQdneHYY6c6yf6DAaNlHL8meutBi69nmfEVvZZGYdVMb7QtV9G4d6QagGiYMFj3yrk3cbN2K4jQo+LrhIq+8ZXv7MZOVgLlaTJgoKn5CrBRKa8I4rUYoTZrTpDX9BY+X3jjEzp4c+wcKhLEiihVaa2ZqEVGiyToyNV9wLAqezYGBzhaHf/LZp/mDjz/Vru4tJdapm6NrSYpZh6nmbM7DR2eIlSZONF87PsNXj89QD2M8O91/Wv0SoCFsGozM1NI2xloYE0SKWGmGSh45124v8DeCRdoSuXCWyWpW3lxLXHZXjyTnn6sAhEwX+hebnaaAWpjGInh2GqTcX/CYa4R890yZih/j2TLNCrPSF+vUdJ1jU/X04gGpyUeQKCwpGCi4y9o79YL/WghAojk6WSNWmp6cw9i8Txgn6Rxk8z45VyKlSI1tlGKy4jNablzQDXFvf37F75T5RsRo2We4O0Mp6yz6WacXUdbLZly4MawPI4ANBoNhZda9tvmt3/ot5ufnAXjsscd44xvfyCte8QpOnDixZjC04cpASsHLbhqiN59aTVf8iFgpKn60pdbTSmlOz9Q5NDbP6Zn6un8pb0ZFrzWbVfAcJqshpYzNcFeG7pyDbM7kvO6FB9r3OzpR4VOPjeHYAqfpXGgJkbooaoiVIogSEi3YVsrwxpddw4HB1GXup+4cYaDoMTrvN4NyYywpaEQKW6aiqbfgUco6ay4OHz8zx1/+83GiNV6zsCn8ojhhrOzzlSPTTFQaqYGHI8k4FvUgPY6X3TDICw70M1DMMNydYXuXx2w9ZGzex5aCvGdhyTSLbbIS4EeKvGe1q0L2Bt4eCRAlqRBTNKs2zackRDqDdzlVyWyZhns7TffJWKWid93bEYu/jCVgW4LevEtfweXxs/N8+ekJ/vnIFKNzfjoH2MzI68m7aL1YVFnNP6NEUwtigliRcdb+uheAtCSNMOb4VI2hkkfSFHca0Z49ixUUMjZdWbs5YxgxU4u4eaRr1fa++68fXPE75ehkFdsSXDtUWiaMoLOLKOtlMy7cGNaHEcAGg8GwMuvuMztx4gQ33HADAH/3d3/Hj/7oj/KOd7yDRx55hFe84hWbfoCGZ4fVrN9vHunaEuvpzZgjaFX0Hj9Xbld0WrQqep2Gyb7xJQf5q6+d4eR0jShROJbkhuESr7lnT9tuvnWld7YectfeXsJYcW7OJ0zS1jLRvI8lob/o8cCP3sh127ra+7hmqMSv33+QP3vwCOfKDRph0rQpt3AtSVfWZV9/noqfhjjP1kMq/vKq1+GxCm/82+9S78CrXTXbLcNEUw/jppGHRS2IKTciYqVJlCbjSL5zpsyt27twpGCi7JNoTaLS18KSaXCwLSVSaGpBxMkwTp+3FMjmDFM1SPBjvS7HwoXPol250ZBskkX8ZtFq7wsT1exF3djxZW2BlBKJJlKagufQm08NObTWjPRk+NrxWeYbMVJqhEhFWJBoLKWQTcfLVtVKNQ+uFcmgNFT9GNcSWGKZl0v7ubTiDKarIbv7cszUQgaKHsWMQ5Sk1c9WVELetenNu7i2YKzsM9Kd5T/ct487d/euepFm30Bhxe+UG7aXyDjWqmJxK9qiF164KWacZT+/XFuxr2Su1Flkg8Fg2GrW/ZvGdV3q9ToAn//853n1q18NQG9vb7tiZrg6WMn6fStcrzZrkL5V0TvXbJdaGOY8WvbXVdG779pBXnjdMI+cnmW6FtKXd7l9Z88iV8OFV3qLGYcfvGaAb56Y4dRMgzBJUCqt5FyzrchvvvjaFTPD7r8+bVn88EMn+NLTE9hSUPBsegse/QWX45M1ZuohjShGKfj775zDtWX79Tg6UeF9XzzK6Jzf0WutgXIjQjdSIVbwLGpBQqQWS4laqDg0WuHoeIVYLW1lU1icN+pIg4fBkoJCJm3rnK1HTNbidhvfemXKwu1f1ghB3LR8b4mf9dKINYK0BTbnWvQXXEpZBw3M1kKmqhFdWZt6qAjiBK3T19q10gy7epje1n5bNwWYlAKJRmlItMayLFSosARkbfDj1PK+dX4c20JqnQaG10OklCiVts1mHItYabaVMhQ8G9dO3Tf9KI1NuG1XzwWFWIuVvlOGSxn+/J+OX/Aiyk3bu1Bab5rl/WZeuDF0hhHABoPBsDLr/tZ7wQtewBve8AbuvfdevvGNb/Cxj30MgMOHD7Njx451H8D73vc+/uiP/oixsTFuvfVW/tt/+288//nPX/X+73nPe3j/+9/PqVOn6O/v56d+6qd45zvfSSaTWfe+DWuz1Pp9s7kYB8SV7JE3s6Jn25Ln7+1b9edLr/T25j1eeuM2yo2Ic3MNwlgRxIoHfuSGNEx6Fa7ZVuQP/sVN/JfPPM2To2UODBSIleK7Z8o0woS8ZxHFku6Sw6mZGh956CQ/f+8e9vUX+Mzj40zXApwOF6aatHXNcwS2tPBjTdgsRS0MFW7fVy02eWjdHgOOBUnSzCFrtmQKIkAsuu96aUUCrBY4fTnQHNMibtr/K73RulhK67H1MOHkdI3unEtX1kndDf2Y/qJHuR4RRIJKkOBYaRtrrFLjFVggqpqvXZKklvbtyqLSOJZAaY3nOsQ6RjRDyS0hsISgmE3nePKuxVwjYrTcQApJV9ZpG83Q3GYQxYzNB2wrZfjJO0Y6Fkcrfadc6CKKJQXT1YA//fyRTXPf28wLN4bOMALYYDAYVmbdYuy9730vr3vd6/jbv/1b3v/+9zMyMgLApz71KV7+8peva1sf+9jHeMMb3sAHPvAB7rrrLt7znvfwspe9jKeffprBwcFl9//oRz/Km9/8Zj784Q9zzz33cPjwYV772tcihODd7373ep+K4TJgo5lma7U1XoqKXutKby1IBUiYqGZ7oUN3zqXiR8zVI0q55VeBl2Lbkn/1fTv4yEPpAney4lMP0iDoWhCT82xuGO6iJ+e0BeqP3CLbr91jZ+Y6Pu7urI1jWzSihFojXci3bM1XEhSrVXyEXt5SWI80GUsjSX+mSatcUqbCrhNkczbMFWJRSPHlggVNZ0pJo/mkNnKUrddoKX6sGZsPmKmFbO/Opll1pHOIQqQzekGssaRMZW9z50mzOpaxJbYtqQVJ+/WTwLVDBW4c6eYfHj1HI0orcUmicO109i/jWBQ9hyBOaESKgmfjOZLBkkfFj3CafYyVICaMQ8JYM1zK8Pr7D3LNUGndz3/pxZTX3LObzz0xsegiynBXholKwOi8v+n285e6Ffu5jhHABoPBsDLrFmO7du3i4x//+LLb/+t//a/r3vm73/1ufvEXf5Gf//mfB+ADH/gAn/jEJ/jwhz/Mm9/85mX3f/jhh7n33nt51ateBcCePXv42Z/9Wb7+9a+ve9+Gy4NO5wgqfsTpmTq1MGaqEvDJx0bbmVerLdC2OpB6pDtLd9bhK8emkKSzWLYl6cm57B/IM10L13Wlt7U4/D/fPM0T58rpXFasGCxl2D9QoDfvArQF6vGpKjP1gJofU/E7a+orZWzuOTDA4fFK2t7G2oYYq7XehauIKz9ZvM0E0B0KMUukoddK6WWue5cDArAs0Y46uNhtXYgo0UxXA4qexWwtJNHpfJlrCeqRJonVkvbRdMHrOhZSCGyp2rEMpazNvsEiP3PnTqSALz09SRAnzDdiNJq8Z9Obc5mtR9SCmGLG5vZdvWzvzlEPY45MVJlvRO0WSilSu/2fun0n12xbXbQopTk7mxoynJ1tsKvfRkqx6sWUl9wwxI+526mFMTnH4h8fPcdo2d9wbuBaXKoLN4YUI4ANBoNhOesWY6dOnbrgz3ft2tXRdsIw5Nvf/jZvectb2rdJKXnxi1/MV7/61RUfc8899/A3f/M3fOMb3+D5z38+x48f55Of/CQ/93M/t+p+giAgCIL2v1tzbVEUEUUrW4BvNq39XKr9XUlkJORtgR+EFDLL345BEJPEMf/4yGmmayGNKOH0TGrffcfuHkpeWl8oeZLiQJZjkzU+9/g5dt67d0MLqvWcq+OTVaarDZI4BgFdWQcBTM3XmSjXuGmki/uv7SNJYpIOB6B292R45a1DHJsoU/QcpBR0Zx2KGYnQ6UbyDpysNfi7bzzDU2dmKfsxjrXGhoGerENPzqU/bzNXcJir+fiWXmz88CxjARmpSJrVOCk0rkyF38JWSU/qRX9eKhyZBmZHSZobJzp43S/EWl/AYRwzHaeGBhLQSWovn7M1iVp8ziTgWqDiGD9JYxXc1kxYEvPZx87y0OFxXnTdILftKFFuBDjSYqYeUPVjKo0AlSgGiw537+triv/0s3X7jiLHJmrs6M3wI7dup+imDp9SilU/K8cnqzz41ASnpircnYEPfPEwu/qLXDNU4MtHppithWwrZci5LvUw4alzs4yVa/zbu3axf6DA2dkGz0xVGCm5SBYPLgpgpORyYmKeU1MVRnourrVtW9EB0gr2ej6vVxOX6vfU7p4M//7eXYyW/fNzg2u8lwyLMWuKKwNznq4cnu1zJPQ6Lz9LKVe0H26RdPhb7Ny5c4yMjPDwww9z9913t2//T//pP/HlL3951WrXn/3Zn/H//D//D1pr4jjml3/5l3n/+9+/6n7e9ra38Xu/93vLbv/oRz9KLre1lRODwWAwGAwGg8Fw+VKv13nVq15FuVymVFp/2/3Fsu7K2He+851F/46iiO985zu8+93v5u1vf/umHdhKfOlLX+Id73gH//2//3fuuusujh49ym/8xm/w+7//+zzwwAMrPuYtb3nLovyz+fl5du7cyUtf+tJL9oJHUcTnPvc5XvKSl+A4a88PPdc4Plnlb75+qn2lPOtKGqFirOwzNt+gkHHY158nUmlm0lPn5unO2czVYwZKHs/b2d2+QJAoxTPTdf7Dffu5Zmj9LS+dnquzsw3e98WjdGUdChkbrTVVPyFUCldKQDPvx/zqDx1YdtW+VTE4MVVrt2jt7c9z//XpnOTffO0UjzwzS5Qk9BVcYpVak2ddi509Wb5ydIp608VuLVpmGHv7Crzw2n6eHK2Q92wmKz6nZxpM1cJF95ekTn0L88ou5Ia4cUP382QtgW1Jgjh1dZQiNZTQ6FXnzDyp+f07FQ98SxKoS9NSJoCCIxFSpnbySlMJE1wJxYxNLVT48dbUFy0BriXxF7Qm2hK6Mg5KaypBjCNlM+dNMFcPiRR4lsSzJWGS5stpnRq2uJagmLHxHJvn7ezmX9y2nZ6Cywe/fJy9ffkVq8qdfraU0vzlV07w5Og8+wfySBR7/GOczOyn7Cu+cGiCbV0ed+3tW3Zhr+qnEQu/+kMHABZ9xpay8L4XWxkzmN9TVxLmXF0ZmPN05TA9Pf2s7n/dYuzWW29ddtudd97J9u3b+aM/+iN+4id+oqPt9Pf3Y1kW4+Pji24fHx9n27ZtKz7mgQce4Od+7uf49//+3wNw8803U6vV+KVf+iV++7d/GymX59R4nofnectudxznkn84no19Xglcu72H19xrt+cIgkqIZ1ts78sz3YipBIqHT8wRK4XWUG7EaCnJeA4T1Zj5QFPKpm/lWqSwbYdSLnNRr/Va58pXDWqxZshz0UKAgELu/McpVoqxaoSvWLSdoxMV/vrrZ9o2/kOuTT2MeWy0yplySMaWTNVjnrenl++eKTNZSyhkbPJZl8lKwPHpWSq+ItGdCxCRwHgt4nvnavTmM+wbyPPQ8TlqQYIQEn9JfpelQSnRkchKZefGXRM1YEnJrp48fqKZrYUEcUIQKYIOiuyBEgTJpRFjtoRsNgNoakFCJYiIlCBKIEYRRppkiyKpbQFaCEKVzqsJQWqrj6QWJoSJQEqLapS2KjaS9H6h0syHSfP4BUmz2y/WMJz18CPNY6NVPHeSH755GMd2qEaa4grip9PP1umZOkenGgx25UDa6GZ7rRYWgdJoKZltqEWf2xaeJ6hXQnwF1wwW2TNQ4vFzZQ5m3GXue2fn05nMXf1FM+O1iZjfU1cO5lxdGZjzdPnzbJ+fTQv0uPbaa/nmN7/Z8f1d1+WOO+7gwQcf5Md//McBUErx4IMP8mu/9msrPqZery8TXJaVDm1cjsP+hs5ZaZD+iXNlPv3YGI6VWm47lk0UK2ZrIefmfHb2ZomVSkN3ubT2yBvJzFnLxv+7Z+aYrITctbeHUtbltp2CYxNpzlii0ucYNnOm1lOR0sBkJWS2Ns1IV4anx+axJWwruUxVQiyhFwUBa5Uu6CO13GFRAo4tCBYIONsCpVYOE74Qri2IYk09UlSCCCktso5F0bMYK3eWm3YpSRSMzzfagqaFAhrR1n3/WAJsK3VRZEGemNYQx+n733UsXFsQRIqoeaxSnJ8FtERqOKJJz2Eri6wn7+CHCWfnGjx6ao6+vMtTY/McGChQyjqp9b3WzDcijk5WuXF7F8OlC8eIXMiUx7Uknm0RxOc/twtZ+Lkx7nsGg8FgeC6wbjG2NNhZa83o6Chve9vbOHjw4Lq29YY3vIHXvOY13HnnnTz/+c/nPe95D7Vare2u+OpXv5qRkRHe+c53AvDKV76Sd7/73Tzvec9rtyk+8MADvPKVr2yLMsOVy8L8IaU0//OrzxArTW/exbPT8+s5FiM9WZ6ZrnNmtkF/3kUKqPjRJV2gbSQzZ6GNP8B8I2rb4RczNj05lyMT1Xa+Vm/eo2ePS8WPCRNFuRHypUOTGzLasJoL86laSFBOyLs2c40IpTSuLdtukGGi29v3bIFjWWilsSxBxpY4dhpKPF0NOD3rIyUUXId6GJNswIbebYYPnysHDBY8Eq3xY009vvwurqQZbef/fimQpEIs59pU/NT5sC3GtcZPFEJA1pHESXoutdYrHt/C7DchwJYSx5JUdYwtBZ96fJSenMvpmTrHJ2tp5bYrw3jZZ7TsYzft7//8n45fMOPrQhcqihmbomcz6vvLsvFW+twY9z2DwWAwXO2sW4x1d3cv6/PXWrNz507+9//+3+va1s/8zM8wOTnJ7/7u7zI2NsZtt93Gpz/9aYaGhoDUuXFhJex3fud3EELwO7/zO5w9e5aBgQFe+cpXbvmsmuHSc3auwWQlzRYqNyJc+7xxTM61Ge7yODfnI5uBsBnH3tACbWnW0WC+s4/ERq7atyoGfmTx1Ogss/WQOFFtO/yBoocA5v2InqaNvRCCUtZhphZwcqq+YjVhLSyRLr4dSzQrJFD2U4c+R563RLcEiGaIsUYgtE5FVtPJsCLSebLZesiO7ixdWRulFI043lAeWBCneWQtYTHnx/hhQnKRlvFbyaU8smYnIq4lsS1B1rWoBzFxs1qpFYhEAQI/Uni2Rc610FpRCc6fN0iraK1tasCzLQqeRZQoEqU5NVOn4sdcM1Rkd1+Op8cqnJ5t8PR4haxjsasvx7VDRTKOtWbG17ILFUt+nvNsBooeY/Pp53etz42xnzcYDAbD1cy6xdgXvvCFRWJMSsnAwAAHDhzAttff9fhrv/Zrq7YlfulLX1r0b9u2eetb38pb3/rWde/HcGVRC2OCRHHtthKPnS0zU0ut7x1LEiWKOIGenMvP37uX23f3bGiBtlLW0YH+LCMdPn69V+3zrk0YK779zAyJ0hQyDk7GJko0kxWfmVpAxpbM1kN29eYQQqCU5onROb55YoZ6qNbVCiiAjC0QAsJEEymFEHLRz5VO86zgfBtb6+/xghZFBaAhSTRhknBovIoUkLUllpBYMm2fXM/xLQyTVgoyjsQP40sqeC5HBKnBhhTpbJdWGj9McGwLIQUy0VgWZGwLAfixIowTip5Nb96lGkRs785yZrZx/vVtbluTvu59BQ8hBBU/Ikk0SaLobkYflLIOfXmPrxyd5NRMnZ09We7Z19e+MLZWxtfSCxUjpfTCQtWPOTufvrdfdN0gh0YrHVe7FlbNDQaDwWC4mli3enrhC1+4BYdhMCym1eqUcSS37ezm6ESV2XpINYixpaQ7ny4cf+DgwIYWaUcnKnzkoZNtI41WcPSTo/OMFFPHw2u396y5nfVctR8uZQgixVwjYldPtr249WyBk3M4NdNgsOSRdSTfPTOHIwUPH53acMte1pFYUpAojdIaWwpKGYdGGJDWU1qCTBMlLKpIKb2gqrLK9pVOTR1yjqQn5xDEutlK1xkLC2COLSl6FtPVS1t9utQ4Mq1MrvQc5YJBwFhrdvfkEWgmKkEqjqMExxKUPBvPkWQcq3l+FaPlgHqUMFT0aEQJni0YKnqUGxGJ0sQqnQuUAnKuRc6zmKmF2FKSSI3Wgt6C1zbuqAYxjUgxVMpQjxTVIKGUTd+vQoh28PjZucaKn7+FFypOTs5DBsqNaJHg+qFrB021y2AwGAzPedYtxt75zncyNDTEv/t3/27R7R/+8IeZnJzkTW9606YdnOG5y8JWp4ODBb5vT097dsqRgrF5n1t2dG/IqONCRhpFNw8+fOHQBAe3dXe0OOz0qv3ovI/XFC4ztQghaTvjBVHaLjZZCcg6FqNlnzOzjXULEynAs1Jrek0qsMIkDXYuuBaJ1kiZVqIU50WXas4ZtVoaY9V5EHQQqWaVS1PyJOVg/a2UQawYLfsd2fVfyTiWRAhFmJwXw7Jp0OHaEteyAEXFT8g4gm2lHHfu6aWUsfnmiVmKGYu5RsTofMC8n4bZZxyL7d2ZRQpvrh6xpz/Pvv48QayYqAScna1zbq4BQBAlDBQzFDyLJ87N01/w2D9w/rMQJopYKbqyDuXmbONCsq7F+Hwa2rsarQsVp6YqfPerp/nVHzpgnA8NBoPBYFjCusXYn//5n/PRj3502e033ngj//pf/2sjxgybwkozWTnPQoQwWvbpK3gbNupYaKSxdP6x9e/jk7VVr/pvlFoY49qSoVKGR0/NUQvj9iyPlIJSxibj2Ozty3N0fL5jIdZa0PcXXPoKaUvYsYkqfqSwZCoApJBYUlJpRMvcGBfuxxLQcpRfj6RKZ8b0hmbHIBWFW+lI2AmOgK08BAHUI7Wo4qhJn7ulU+EcC00pYxPEmoxtUQ1iRss+tSBmtp5mwtmWZLDo4dmSINb4UYxnW/TnPV71/bsJY8UnHhsliBI8x6I7n7Ye5lyb3f0FSp5NI0qwZNpW2pv3uGaoQG9zThGac2pS0ggTbClxrcUutiu5ha6ElIKRnizfBUZ6zle+VmoR3j9QuKAxiMFgMBgMVyPrFmNjY2MMDw8vu31gYIDR0dFNOSiDAbbOSe1C1tstgji54FX/hSw1AVmt3Srv2szWQp48N0+kFMWMgyUEtSCdj6v6MVnH5shkhZlGByFbpDNhrm1hS8G/vH0HT5yd5+R0Dc+xiVWMAPKeRRgp5hsRkdLYlqTLtZitR20h1qrShOq8dXqnJLAhJ8XLDSE5r0Q3GUfSnN1buUUxSFIha0tNLYwoZVx29WZ5/FyFqUpAkCj8ZgK2aysmK7CjJ0d3zkZrm/H5AAjZ259nd1+efQP5ZZ+bW3akn5t9/efbanOOxT8+eo4nRufRWi+oEtv0ZB2OT9fY159flDt2sRESq7UIr2UMYjAYDAbD1ci6xdjOnTt56KGH2Lt376LbH3roIbZv375pB2YwwNY4qa2VEQZ0dNUf1neFf6iQOkD6cUJvzkHK1I6cZmthmGhm6gGT842On0uiNBlHIkirYXnP5u59fUgpeHq8wqFzZWZq0aL2P0tAI0qwZWoQsrAC5tmCJNEIsf7csCsdtZHMgA4pNuMP5hrRqq+rhlQsC/j+vT2cnGkwUQkAvciuPog1URwhRIP9A/n2owW6XXVb63OzsOL78pu3MTrvL3MFtW1JKeNgS0k1iDcl42utrL0LGYMYDAaDwXA1sm4x9ou/+Iv8x//4H4miiBe96EUAPPjgg/yn//SfeOMb37jpB2gwbLaT2loZYQD7BvJrXvXv9Ap/q3L29ePT1MKYrGPhxxrX1iitUCqtSDiWwA/VuipTUqbzVgXXRqC5Zihd4M7UAmaqAUqftzVvEcYKSdqGqAVYpEHOUkhKGYupasgGHPS3vM1vq9mqaLO8I5kPEkB3JHB7czYZx+KZ6Xrbej5e8kAFzNZCprIWUkgKGZu+vEc9Ol/a6/Rzs1oF+u59fVy7rbgu18O1WKtFeDVjkE6rzwaDwWAwXGmsW4z91m/9FtPT07zuda8jDNMZhkwmw5ve9Cbe8pa3bPoBGgybzYUywibKdfYV4UXXDV5wsdfpFX6lNZ97YoJjk1VOTteo+jHFrI3QmkaYECeKRGtcS+BIgR9D1rFIwqSjmbEgBktqgjhhuhYxW4/oyTl888QM5+b8NCR4yWMSfb4bzxLgOhIbqMUJSUPhWGBZEqVSo4lOuQxzmp91BKnj5HqohIpvn5pj3o/QTaOVld6JChifD7llR1czSFx0VM1diQtV0lquh5UgourHFDwbz7ZQSq9bEK3VIrySMYiZLzMYDAbD1cy6f3MLIfjDP/xDHnjgAZ566imy2SwHDx7E87ytOD6DYUtYrRpw4/YSVM6xb6Bwwcd3coX/kVOzPD1eIYwVw10Z9pDj8HiFqh8TJ6pdsUo0+JFCWel2LCkWGWxciMGiy7VDJU7P1JisBnz1+DR7+3I8M11HLSmJLd2mRRoEHUSKeutYVKvVTeHaAldqwg61xNWgxaxNbs/cyKYazfk+9MomKvYCN0wBHBzIM1mLNjzD1WK1SpqUgiBO+OJTkxctiNZqEV5qDGLmywwGg8FwtbOxy6hAoVBoG3kYIWa4ElmpGjCYt/n0pw+hlOb0TH3Vtqi1rvBnHItTM3UGix637+pBiLRqkXHSNkABWDIN91U6nQPym6WloIPgYwEMd3ncd80Afqx4amyeOFFESchYuUEjVljiwmIgAZIVylntoGel29b3V4PQ6oTNEmIWF+cFYlupkUqil1fFEpWagVhAojVPjlW4dltpw+6ia7GZgmitFuGFxiBmvsxgMBgMzwXWLcaUUvzBH/wBf/Inf0K1WgWgWCzyxje+kd/+7d9uB9kaDFcCS6sBURQB8F8/d5gjUw0SrejJuhwYLC6qAqx1hX+yEtAIk5UrZ5y3NE9tF/Sin60V0yWAvGuRcWz8SHFkoopSGq1T58SKn7Z4JRoWfho16xNWq3XXtebNDKtzMZou70A9VHi2JApV+7zBgvOp03lBrWFPf2HLKkSbLYgu1CK81Bjk9Ex9Q/NlBoPBYDBcSaxbjP32b/82f/mXf8m73vUu7r33XgC+8pWv8La3vQ3f93n729++6QdpuPyJY8Ujp2eZroX05V1u39mDbV95wvzLT08A8OknxkBaeLak0oiZqoaLqgBrX+FvkHMtBouZ9u2j8w3CWFHMpIvPSKUtaQtZaxFvN6tdiUrnzJ4anafciOgvuMzWo9ScY+GxLHisFMvNPNaD1cHxGToPy14JCeQ9lyBWZDybWhgsih+ANCA661q4lqQ75/BrL9zPnjXaai/EhcwxNmq4cSE6jazYyHyZwWAwGAxXGusWY3/1V3/Fhz70IX7sx36sfdstt9zCyMgIr3vd64wYew7y4FPj/I+HTnJyukaUKBxLsqcvz2vv3cP91w8924fXMYfHKnzgy8f4uR0wUHDBsokSTbkREcTp8nphFeBCV/j7Ch4Zx6IRJRSbgbmNMCFRmlLWRiCYb1awloYAr4ZrpWIqVhAlijOzDfw4QQBzjYicYyGaUqDVJrdwe+oilZQinVeKTVlsRWwp6C84jM2HG3p8wZU4dvqekVJwy0gXR5wqZ2cbKJ3GDQiglHXI2mlw2Q8cHGBXX37F7XXiQLiWOcaFBJHWqcvjZDXg2GR1XQ6HnURWrHe+7GrDOEgaDAbDc4N1/xabmZnhuuuuW3b7ddddx8zMzKYclOHK4cGnxnnnpw5R8SP68m5bkByeqPDOTx0CuCIEmVKav/32aaaq6ULadSxiLfBsgZt3mamF1KOYI+OVdhXgQlf4X3z9EJ97cnxR5SzrWlhSEEaKWpiKKClSkSPFhUWOIJ0VWmj6oXRqg29bkijRzDZNH4TYmuxiSdpmpi9W1V1lSCDnWdy2o5vTs/V1z9h5lqC/4LK9O8u5OZ+5WOEIQTWIecGBPr5+YoaJ+YAwSU1VSp6FbVlcs63Iq+7ateICvRMHwk5mwVYTRDO1gGMTNcYrPo0o4f/9+imeODu/LkOPtaz31zNftpQrXcgYB0mDwWB47rBuMXbrrbfy3ve+lz/7sz9bdPt73/tebr311k07MMPlTxwr/sdDJ6n4Ebt6su15wWJGknctTs02+KuHT3LfwYHLvmWx1Y7l2ssXbEIIChmbih8z1wgXtUVd6Aq/lCyqnA0WMhQ8i8lqiFIaa0GV6UK28AtfOUsCOjX+cCyLpJlTliQLwpu3SCspIE70c8rQYy1cS9Cbd+jNeUgBU9UQS3Ru8+9KKGQcevIekdJYlsBzJBlbMlEJODBY4OaRLh5jnkaUMFTy6C943DLSverCvBORta+/0NEs2C/9wL5lgmimFvDo6TnqQUysYFdPju3dmU13OFzPfNnS538lCxnjIGkwGAzPLdYtxv7Lf/kv/MiP/Aif//znufvuuwH46le/yunTp/nkJz+56QdouHx55PQsJ6dr9OXdZcYtUkr68i4npmo8cnqW5+/t2/B+LsVV7loYk2hwLWvFnzuWJIwjpJDL2qIWXuFfeqyvuWc3n3l8nG+enKEaxPTkXcqNiFqi0erCoqZllKEW/FuKdH8DRQ+lNEGs2i6MW41mc23fr2RabpiWFPTkPSwpODReQWuN50iI1JoCO+da9ORcbFvgxwm2kuzoydFfcJmYDzg9W+fkdI2BQoYfv22EW3Z2MVD0LvgZ6NRw40dukR3Ngo3O+4sE0baSx5HxKvONCNuSlLI2B4eKlLIuxYyz6Q6Hnc6XtbjShYxxkDQYDIbnHusWY/fddx+HDx/mfe97H4cOpW1oP/ETP8HrXvc6tm/fvukHaLh8ma6FRIki664sYLKuxUwtZLq2sRkauHRXufOuTU/WwffT56KXOF2EsSKMFQcGC6tmOa10rBrNM1M1Rst+2mZmSfoKLvUZf83qkmWBIyRZRxIlikaUhjBLoak0QjzXQYrnlvX85YRGYElBtREy14hBCDKOJFGa4AKPG+n2+Mk7dvL0aIXhLg8hZPu9Ucyk1ae+vEtXzuFnn7+L/QOFji9AdGq4cWKq1rE5xnXbSm1B9L2zc5yerZNxLIZKGfYPFOjNu8u2v5kOh53Ml8HVIWS2wjDFYDAYDJc3G5p83r59+zKjjjNnzvBLv/RLfPCDH9yUAzNc/vTlXRxL0ggTipnlbYiNMMGx0grZRrhUV7njWHFuro5SEDVLP7O1CM9rVcQSxuYDtpUy/OQdI+2FXBgmfPbQGGPlAAEcGZ+n7CftY316bJ6Hj0+jlGZ7V4bt+SyNMGFiPkA0nQ1by62VHPiSBFxPkCSK2oLk5URDLVTUogDoPCDasLnYIjXtUDp1t7SEJlKCWpS0z+3C8yKA79vdzd/8wvdj25L3f+kYj58rLxIOkF4IGJsPuHVHNz94cGBdwqFTB0JgXeYYLUH0T0cm+dBXjrOvr0B3zlkmGLbK4XCt+TK4OoSMcZA0GAyG5x6bZkM1PT3NX/7lXxox9hzi9p097OnLc3iiQt61FrUqKqWYroVcO1Tk9p096972pbrKvdAJshEmqCSGnQCaepgQxiFhrBkuZXj9/Qe5ZqgEwP/86kk+9M8nmKz4JFqTKI1jSe7a20sx46CU5umxClppPNsiiDWWEBQzDgLNTD2tFgpoC7OluLZAKUUtSn8oAdeGMD4/ayaFXpeNuhFum4MUkHEkQZzmgCk0Yazb50VAO3Q7zZODrCO5caQL25YbnodailKaM7N1jk/VgPSYPEuuKbL29efXbY4hpWD/QIHBQgbbEssEz8LtPxsOh1eDkHmuO0gaDAbDcxHzjW7YMLYtee29e3jnpw5xaraxyE1xuhZSyji85p49GzLvuBRXuZc6QQ6WPOqNAAiZqIRs78mzsyfL/sECP3X7Tq7Zllbh/udXT/JHn3maIE7IuTaWgPmm/f1Xj8+kbWYFN7Wb92wsKWhECWGs8BwLhWi3F7q2IIz1MoEkSIXnvJ+GUNsSso5FxpHM+zFBcyBpvTNcRohtHrFKRbgU4EhBvORk6AX/tSzpz841ODNbZ1dfft3zUEs5OlHho18/xdeOT1OuR2gB3RmHrGsxVQ153q7uVUXWjp7chsTgxTgcbjVXg5C5nF9fg8FgMGwNl+9vJcMVQcu2vlVdmqmFOJbk2qEir7ln4zljW32VezUnyN6CB1TwHIljC37nR29gd29+UWviX/zTcfwoppRJhZbSGiklGZEaajxyapa79/Y0q2VpG2GkNUmz/GVJgSUgVGllY2m+mNMUXnv6cnzvbBkpwLUtHEvit6pkYuO5YS1jEMPGabWKSlKHyTDWJCoNxpZWKsxaFTFI/5yqBnz75Cwf/spJ/u3duzgwWOx4HmopRycqvOfzR/ju6TksAX1FF4Fgrh4x04hwm9l2B4cKq4qstcTgvv4Cp2fqy45rMyp6W8HVIGQu59fXYDAYDFuDEWOGi+b+64e47+AAj5yeZboW0pd3uX1nz0XZ2W/1Ve4LOUECDBQ8Jishk5WAvf2F9u1//fWTjJYbIATVQCGEQgqBRiOExLYUtSBmthFjSUGUaCyZtnRZzcWhLQQ0HxOsEAgWKbASRb05e+TZAsdKHfeUAltKHAvqQbLuFsWMI8k4FpVG1LH9umF1BBAnCiEFNOMFJGlA81KxHCso+xHfemaaSKn2zGMn81ALUUrz6cfHODxWwbUEfQWvLTyGSpLpWkisNFprZmsh4/Nq1YrbamLw+FSV93/p2KrGORdT0dsqrhYhc7m+vgaDwWDYGjpeyf7ET/zEBX8+Nzd3scdiuIKxbXlR9vVLuZir3HGs1hSGaztBSqJqtMgJ8uhEhU8+NkasIWOdnwlKlE7NP7TClmnGlCMF3VmHqWqAa1sUPBu3eQy2hHANJeTHmv19eWaq6XFWgwTQ2M2Kh9aptbpqzY5x4WqX3QyXtoTAsyW+LYkjUx+7WCw7Pd8WkHHSltPoAonbsYJTMw26Ms66Zx5bsQnHJqt848QMsVKUsotNNIQQFJuZeDnX4mfv2kUp61yw4rZUDHZqnLORit5Wc7UImcv19TUYDAbD5tOxGOvq6lrz569+9asv+oAMBtj4Ve6FhhxRonAsyZ6+PK+9d3HL5NpOkGqRE2TLUERpjQS0FgiZtiAKqdEalNaEMSAg59pcu63I9PEQP04oZWyqQUwYK6aqQUezW187Mc2BgQJPjldRSoEQRIkiUWlL3MJtKJpmIKT/W1qVaf07UoogViuaLxjWTxyDJdMWVNnhazrvxzw1Nk/GtTg7t72jqtjC2ITJqs+hsQpxosm5Nt6Sb3GnKdgbcUIp63DdttKa228JvYof8fffOcd0NeCaoeKaxjmXoyvh1SJkLtfX12AwGAybS8di7CMf+chWHofBsIz1XuVeasjREm+HJyq881NpJl5LkF3ICRJgph6yd6DUdoJsVSRuG+nmxGSNahBjS4kQaQuibUGUpFWyrGMhhSbnuXz/3j4eOzPHRMVnbD51VHTtlatxS5muRfzE7T2cKfvU/Bil9AUNO1pibOF9BDRnziRxokgSTT2IESafbFNQgFZgW4KcZ1MLIpJVKmMWqXNmolNB9uS5eZ4am19zwb20UlXwbE5N15kKQkbLPiPd2UUV3ihJK545x+6ojXeh0JuphxybqDJY9BgoZtoZYnDl2MODETIGg8FguHLY+FCPwXAJODBY5FdeuJ/ffMk1vP7+g/zmS67hl+/bv0yILTXkKGYcbCkpZhx29WSp+BF/9fBJ4qb3eMsJsphxODWbVgRipaj6qSFI0VvsBNkyFClkHW7f3dN0SFREiUZpTZxookSTcy3+w337+I8vvY679vUyWfGxpaA759Jf8NjZl6fTopTW0F/0+Kk7diClWFGIWQs+wQpYqgOkSM1O+vJpO1usIUg0cayxrqxCwWWLJjXxEEB2FaFtCbAsgRDpOdHN8/CtkzOoCzixLI14KGYcunMO27uzuJbAjxJmakE7pFxrTcVP5xVv2bG2WUVL6D1+rkx3zmFbKYMlYa4e8ejpOWaWBLZnXYsgTqiF6cWB0zN1Do3Nc3qmfsHnYTAYDAaDYWWMgYfhsqeTq9wXMuSQMm03PDFV45HTs+3ZtpWcIPNOqlDe8JKDi9oaFxqK3LKjO93nM7PUwrg9I5RzLX7pvn38+v3XcHgstR0fm/fpy3vEiaIWRExXAuYbnTlAWgKGSh7PTCdsK7qcmfVBnK9+WTJd1K80L9ZqV9RAkiRMVTXhAjUXgymLbSIaqPoRrmORcyT1SLUrj7ZI38Napw6LUkCioC/nMDkfXLDKtDDiAdIIhTBRbOvKMFUNGC37zNRDChkb17Yo1yOU1ty6s5uX3bTtgq15K2X5aQ0Zx8azBbUg5thklZ5cT7tdsWWcM1UJePDJiVUNPgwGg8FgMHSGEWOGq4K1DTksZmrhIkMOWO4E2ZORjD/xNe67dnDR/ZYaityyo5sbh0scm6pS8WOqQcyLrh3k137oIEpp/vbbpzk310BrzbHJ6iIh1CndeQfPkTx2psz2nixny0FzIa9TIcbqOWO6+T8NpNrPmHVsJZZIRf9gMcOe/iwPHZlun3PVTH7WpG2KSoFjS67bXiJM1AXjGVoVWT+SHBqtMFMPiZXClpK8Z7O9O8PonN+OlOjKuty9r5efvWvXmqJopSy/YsamN+cyUfHJezYztZCKH1PKOm3jnOGuDJ98bJTZenRBgw+DwWAwGAxrY8SY4bKiZSSw3sH7tQ05kkWGHAtZ6AQZRRGffGL59lczFBnuygI+u/vyvOKWYaQUnJ6p870zc9TCNOg53mD7VtVP+NPPHWamHlHK2AihzwswU9W6ZKw0W7f0NtWsVO7py3HjcIlnpus8M10naTqrtAxWtE7ny/b25+nPu5Qb8QXnuvKuTdjMrosTTSFj41h26rDpx0gpuGVHNz/+vBEGih57+/Ps7Ml19JlZKctPCMH+wTyVIKLqRyQaGlE6Yzha9unNuaBhth61q2mwusGHwWAwGAyGC2PEmOGyYaGRwHpbny5kyKGUYroWcu1QsW3IsRE6NRSpBBHj8wFRcl6ItZal69FQYaI4OZ1W16YqAcaJ/tnBlqklvW7+3ZJpO59oVimVTsVYrBQnp2vM1kNc22JnT47pakCYKOym86ZlWQwUPe7c3cPYfLBmCPFwKUMQKWbri8PJPdvCyQlOzTbYVsrwL28bWXeu32pZfr15j9t2dvPkuXkmKgHj8z49OY+bR7q4eUcX/79Hzi6qprW4kgw+DAaDwWC4XDBi7DlEJ/lbzxaHx+d53xePMV0NGO7KsLcvTyNKOmp9UkozOu/z4hsGOTVb55mZOv0Fr+2mOF0LKWUWG3J0ykLL74ofUQsTrt9WZGdfBktKBgpe+3VUSnNmts4/H5lkrh6iFvQQrreQJQBbChKl8WOjwp5NFsYIxArQzXBn0fw36fnKNcO0z8352JZg/0CBnrzL2dkGsVLkPZvhriwj3Rmma2FHIcSj8z6eI+nOOszWo2ZlTLYrY905F9eWjM776xY/F8ry68m5DBQz3LGnhx9/3ghFz2GkO8vhicqyatpCsq7F+Lx/wdZLg8FgMBgM5zFi7DlCp/lbzwaHxyr8/sef4thklZxrMVUN6cn5HBgscHCwcMHWp6XVtO1dGc41Z2iopZlL1w4Vec0963+erW1/5/QsRyeqzNRCEqXQOt1ud97l4GCB5+3s4brhIg8dneJrx6eZrASU/XjDU1otO3oNBEaIPevYViqKW9o61ufbFFt/aqAeKRwLunM2jmVhW4L+vEd3zqHkOTSipOl+KToOIa6FMa4tuWN3Lyem0qpbK1ZhsJRhd1+O+Ua0IfGzVpZfX8HlX925c9ExrlZNa9Ey+OjEUt9gMBgMBoMRY88J1pO/dak5OlHhfV88yrHJKt05h7xnEyWayYpPNYi5bWf3qq1PS/OXcm6WeimmK+OQaM3dB/q4aXvXhiqAxyer/PXXz3Bqps7pmTrT1YBEp/b1qRxUzNRCnjhX5vhklVqQoLUi69hkXYlVXx68vB6UhmQDph+GzSV91+h2hlvrjLRm9yxBW2B5lmRPf56MY6XGHEHCzdszjFcCDg4V+dFbh2lEybpmIVviJ+NIvm9PDxU/JkwUriUpNoPEg0htWPysN8vvQtW0lsHHWq2XBoPBYDAYzmPE2FXO0vyt1sxJMSPJuxanZhv81cMnue/gwCVvWWxZa0/XArJu6g4nhcCzBU7OYaIS8r0zc9wy0oUfJYuu/q9ky50+L4drtqVGAn6ouHN374aMBB58aoLpakgUJ8w3IiyZujAkaIQQKK1p+DEV//wxSQGujEi0uKj5LiPBLh8Wiq9WJcyWTVkmwJWCTNNkAyEIYoUUAtsSxEoRKc1wV4Zjk1WkEFy3rbSu/S8UP2nOmN0WZPONiLH5oKM8sQtxYLDIvhcWOjLOWaua1knrpcFgMBgMhvMYMXaVs5H8rUtFy1p7e1eWqWpqTe/ZFo0wZqYWUQ1ipmsBs/WQgmczVQlg2+LHXqyRwFL3xsF8+pE4MVWjlLE5MlFJjRssSRDHzT8TVuoeVBr8BESzkmJE1ZVNK78tXJCkrYFoQclTADkhCBOF0qnjoGNJHFuQdSxcS17UHNVC8fOd03PUg5hKEBPECVGsGSh6/PSdOy5a/HSS5ddivdU0g8FgMBgMq2PE2FXORvO3LgUta+29fXl6cy6j5Tq1IKHsRwig4EqEFESxJnY0n3xslG1dGQ4MFle05V5IJwvgldwbD/RnGQH8OCGfSS3E0za11EEv0WpFIbYQI8KuDtp29Cv8zBLpf7HSTNdChAbLEmQciUBQacRoDVGiaIRc1BzVgcEiL7pukD978AiTlQDXlmRsi/6CTc6x+cKhCXb35S6pCFpPNc1gMBgMBsPqGDF2lXMx+VtbTWsephElTFYDTkw3Fv3cjxNsCYOlLN+3p4fpWtg28tiokUCrEvbU6DyfeGyUIErY3p1tB9c+OTrPSBHmGxFBki4stW61qWmC2Eit5xKrne3WOF+rSCYAq/lGCZUi51p4tuTYZJVSxmF3f45KEHF6pr5u0aKU5tBoheGuDM/b2U2kdHtmDHjWsr3WU00zGAwGg8GwMkaMXeVcivytjdKah/nf3zzF6dnGiveJFSRJQl/Bw7Vlu/VwI0YCrUrY0YkKT5ybZ64RMtyVJetYFDybYsYhjlNhd2yySpiks2F+rLBiRWiEmGEBC/1VzjsqJhQzNn15D6U1Ryer9Oc9Eq157xeOris7r0W7nbc7u+KFB5PtZTAYDAbDlcvlETJl2DJsW/Lae/dQzDicmk3zsmKlqPgRp2YbG87f2gykFPzg/j7OrCLEWpyYrhNFEVnXIohTI4/WLE1v3uXIRHXR8zoyUV1mJNByXnz8XJlGlDBdCwgixdGJKl98epIvH57kxFSVx8/OA2lbWVfOwXUsJIJGrC5Z++Fm1DZMs9ilR0rJYNGjK+MQxAn1MCaMFFnHYldvjn39BbpzDo+fK/ORh05ydKLS0XbPt+SufO1s4efCYDAYDAbDlYWpjD0HaNnWt3LGZmrhReVvbSb/99HTa4qcRMO3T5W5YXuJWGnGyj45x8KxJPddM8C3Ts4yWfEZn1crGgksdF7syzt87cQMfqQoeDZSpPlQp2fqjM41KHmpKHVtyf6hIjPViGeocm4uudAhbpiWSQQszqy6WEwN79LSbGglSjQ/cKCXSGm+d6ZMGCtu2dHVrmgVMw4Fz15Xa6HJ9jIYDAaD4erF/PZ+jnD/9UPcd3CAR07PMl0L6cu7G8rf2kyU0jx8ZKaj+05UfGaPRtiW5EP/fIzxckiiNX0Fh5GuLIOlDHfu7eX6baVlMzmtNq9tpQxPj1WIYkXGkSBS50Up0vm0RGkydrrYtaVkoJChO+syWw+ZkMFF2dW3aB2VLSHvSGzHZrYWojWIphrbGtlnWA+WWNyGuOb9JeRdi3IjohYmFDw7zb/rzlLKLhZQ63H7BJPtZTAYDAbD1YwRY88hbFtecvv6C3F6ts6cH3V036lKSHfeZUfB5fBYlWoYgxZM1wJqQcJ0LV0E7+vPL6s0tNq8Cspmph7Sk3NJFMw1QsJYESW6XUmargYA9OQdoiThu2fK1MMYx5JE6uLVmCbNI/NsiZ+AiqPzi36dijRLr08IGDafnCOpRgrd4XlwLIkl05yx6VrI2LyPbQmuHSoti16Aztw+W5hsL4PBYDAYrl6MGDM8a5yYquF0uIDsL9jcONLFN0/OUgsTip7TbjEcK/vtitNKrV+tNq/WXJlj29iWoB4maUWKtCqlNLQ8Oo6MVTg2Wce1BEOlLNUgoR5dvP2/LSHr2jSCuC3MFrYqrmWbb7g0OLakIOWiUO/VyLmSnqxLNYhRWpMkmhu2l8g4VlqBXYGVWguXZt4trPCabC+DwWAwGK5OjBgztLnQYnCrkELQm7OZqa++6O3L2dx37Ta+d7ZMLYwpeBa2lR5XxpHEiaIaxGRciyPjlWWtX602r2+cnMYSgihWzNTC8yKM5YYXM42IIEmPqRKkC+f1tq6thAU0gpi4KQKVNvNdlyODpQy37+zh84cmmKoErKSRW++fgmcz3OVxckaxfzDP7/34jezszvHn/3S849bClTLvlroubkW217PxmTcYDAaDwXAeI8YMQGeLwc1mX3+erpyDEwgsKZmqhsuESdGzuGlHN0rDbC3Ekul9W1gCIg0Zx6Lix8w1wmWtX602r7NzDUbnfE7P1tsVDyHOi6LVmKpF9OTgwGCBIxPVC953LYIFq3ojwi5PsrbgxuESs42Im0a6OHRujiDRxEoTRDGtYpkUkPcs/EhxcqZBT87lV3/oIHv7CwAdtxa2nD5naiHDXZl25t3j58qcKzf4+Xv3tD+Dm5nt9Wx85g0Gg8FgMCzGWNsbFtm+d+ecDVtwr5cdPTm+f18fiYZSxubm7SW2d3n05By6szY7urO8/OZhenMuFT9CAbYUJAsGeRKdGiJ4tiSMFVLIFV3lDgwW+Xcv2MONIyWqzRZBSD8AncwFzdUjthVtPGtrqgYCY0d/uSClZK4RcfNIF//6+Ts5MFTi4GCR3rxLxrHxbIHVNFuphwlBnLC7N8ebf/i6Rc6krdbCm7Z3MVePODlVY66ebrclsBY6fR4cLFDMOFhSUMw4HBwsMNMMOlcXcwVgBZ6tz7zBYDAYDIbFmMrYc5yli8FWO9VGLLjXi5SCV921i4lKwOGxCrUoIetaZF0bSwiu2VbkF1+wj889Oc43Tk6TsQShJdMQZscCNGGs2uIrjBUHBgurusrt6y/QnXEoeQ6TcVqF6zTHWQNfP1nGtiTE6/M7bFnWQ1pNUc0WRUumQjAxrYqXBQJwLcHBwRwFz+HF1w+RdS36Cx5aK2brIcWMw3B3lpxjUfYjyvUISwpe/6KDvPC6wWXbXKu1sOX0OdyVaX/2tNZU/JgwSeMXVmq9vRiezc+8wWAwGAyGxRgx9hxn4WJQa825uQb1KCHnWAx3ZdZlwb0RDgwW+Y8vPsinHx/jsbNl6mFCzrW4ZaS73S4lJe0WQyHS1sRaEINI88DynmS8ErCtlOEn7xhZdQH50LEpvnR4kpxnkw0jGtH6JFCsNHrF6aGVydkw3J3l9GyDKEnFF5yvxC20szc8+7i25J79fdw80sWRiSqff2qcX/qBfewbyPOPj54jUWqRaOrNuWgNlhQ8ca7MD14zsOJ770KthecDndMLCDO1gGMTNWbqIbFSyOa+nhqd37TP30oCsMV6bfcNBoPBYDBcHEaMPcdpLQbH5xMeO1NmrhGRKI0lBd1Zh5t3dLXvtxYbNQM4MFjkdStUDwBOz9SJleYVN2/DswVfenqSMNZYMl04OlJQ8ROGSxlef/9BrhkqrXpsDz41TiNK2NGdpRpE+FG0Lh2kVCrIOkakx+ZYklgpRLNG1ppT07qzFknD1tGqUDqW5J59fdy0oxugLUhG531u3dnN3337DEpDmKg05iBRVP2YnGtzcDDPscnahsTLwkDnKFE8enqORphQyNg4lk0tiJmrR3zisVH2DeQ3ZZZrqQBcynps9w0Gg8FgMFwcRow9x8m7aejwU6PzRIkm61o4UhApzXQt5CtHp7hhuLTiHNZCLtYMYGn1YKXt7RvI88sv3M9T5yqcm6sTJgpLCvYPFvip23dyzbbV93N2rsFo2Sfv2dSjtM3QswVBrDsWZOtxnXct0AjKjQhbAhpirVOzkHVsx7A1tJwxHUvQlbHYP1jkxpGu9s8XCpKBosfO3hxhrJhrRFSDGFtKBksZ9g8UKGVtTk7VNiReWk6fj50tU66HNMKE3ryLEAKt0zbc3X05gijZtNbBhQKwmHGW/Xwl232DwWAwGAxbg/lt+xxnqOBxbs6nESX05hxk06nQkwJHwkw9YrTsM1TwVt3GetzgOmG17T1xbp7evMtr7t5D1rXWVYGrhTFSCAYKHmfnGkSJRghB1hXEiSZcw7M+50jCRJE0ldRaAi5KQCtFpCFR5wWYKYRdHrQKnFGi0UJyYLC4qGVvqSDpL3h0ZW1AECYK15IUM6llfcWPNixeWk6fh8crPDNTpzvnoIEwTqj6MVnX5sBgEccSm9Y62BKAndruGwwGg8Fg2DqMm+JznEfPzhHECVnHwo9T+26t03Y8P9ZkHRs/Snj07NyKj99sN7hOtvf5p8YZ6c5y3bYSO3tzHVUK8q5N1rEY7vJwLEEYJ8SJQnTQKtidS48h79pI0Zmg0qSW+9C5SYjh0tJ610jSNsUWLUHSMoNpiZex+YBixqa/4FHKOu3q1cL7boQDg0V+5NZhChmbRGlm6yF+pBgsZbhtZze9eZesaxHEyaa0DrYEYG/e5chEtR2GXvEjjkxUF9nuGwwGg8Fg2FpMZew5znQtBGB7d5b5RkwjSgh1ahyQ92xKGZuZWti+31I22wxgq8wFRrqzdGcdvnJsqi2+IgWJViidtq15jqTbFUCCJcAWsLM3Q7mRUAkTLHnxoc+GZx9XAFLg2YJGpKmGMTO1gJy3cg4YdJ4ZtlGu31bixuEStiVwbWtR5Q02v3WwZbvfagUen/fxbIubR7p46Y0mZ8xgMBgMhkuFEWPPcfrybrsqsL07QxgrEq2xhMC1JdUgxrEkfXl3xcdvxAwgjhWPnJ5luhbSl3e5fWcPti03vL1OODpZ4eRMLZ3hErCzJ8vpmQZBks5xSQGWEO12xe6sw6yvODnttythyfoc7Q2XIY4E2xJoBEJIihlBohSztYhY1VYVJFstXka6sxwYLPL4uTIHu7KXpHVwLdt9g8FgMBgMW48RY89xbt/Zw56+PIcnKuRdC8+x2j9TSjFdC7l2qMjtO3tWfPx6zADiWPEXXznOP37nHFO1AIHGc2z29OV57b17uP/6oS0xFzg8VuH3P/4UxyareJakHiUEjZisZxPUU0fFSEEUJCROKr2CSBHGysx4XcFIQEqImwN7UqRCLGlGCriWIOtYZD2P175gD9u7sxcUJFspXlqtg1tZfVttv8a+3mAwGAyGZ49nfWbsfe97H3v27CGTyXDXXXfxjW9844L3n5ub41d/9VcZHh7G8zyuueYaPvnJT16io736sG3Ja+/dQzHjcGq2sWh+5NRsg1LG4TX37GlXrpbSmqcZLfvoJcNXC+dpDo3N82Pv+wp//NmnOTReYbYe0ogUaM3hiQrv/NQhHmzOgnWyvU4rBEcnKrzvi0c5NlmlO+cw1JVhR3cWt2lP7tqiPTtki/NzRI04MS2JVzB2s+3UkufPLzo1VgFBwbPIuTY5z2ZXb46Dg8WOZhBb4mU984qd0qq+3bS9i7l6xMmpGnP1iJtHutZtgmMwGAwGg+HK4FmtjH3sYx/jDW94Ax/4wAe46667eM973sPLXvYynn76aQYHB5fdPwxDXvKSlzA4OMjf/u3fMjIywjPPPEN3d/elP/hnAaU0Z2brHJ+qAbC3P8/OnotfEN5//RAA/+Ohk5ycrjFTC3EsybVDRV5zz572z1eikyv6xYzNH37qEM/M1EFDzhEoBH6smKyF7OjOUPEj/urhk9x3cGDTKgQtM5DpWkDWleQ9GwEEsaIWRDTCBEsKenMW80HqkJezNWCE2NXA9q4Mec/mmek6jSgm49hIIci5klLGoSfvYluS23f1XDbOgaZ10GAwGAyG5xbPqhh797vfzS/+4i/y8z//8wB84AMf4BOf+AQf/vCHefOb37zs/h/+8IeZmZnh4YcfxnHSFrY9e/ZccB9BEBAEQfvf8/PzAERRRBRFm/RMLkxrPxezv+OTVf7vt87wzZMzzDcitICujMv37e7hp79vB/sGChd1jD94oJd79nTz3bNzzNQjenMOt450Y9tyzePe3ZPh1Xft4MGnJjgxVWNqPm0lvGV7gR88OMAff/Zpqo0ATypsW2I3F5atEN1yNWBbl8fZmSrfOjnJHbt7V93ei64bZHdPpqPX8vRMnSdOT+MIhUoSpudrzNUiGklq2pEW+zRhpOhyLVxH0vDT94onjRq7nGmdn9XOkwDCKOIHD/RScAXzjZiBostgMYNtSRKlqfoxPQWX+6/tI0niy2omcFvRAdLvuMvt2NbLZnz/GbYec56uHMy5ujIw5+nK4dk+R0Iv7QW7RIRhSC6X42//9m/58R//8fbtr3nNa5ibm+Mf/uEflj3mFa94Bb29veRyOf7hH/6BgYEBXvWqV/GmN70Jy7KW3R/gbW97G7/3e7+37PaPfvSj5HJmVsJgMBgMBoPBYHiuUq/XedWrXkW5XKZUKl3y/T9rlbGpqSmSJGFoaHEL3NDQEIcOHVrxMcePH+cLX/gC/+bf/Bs++clPcvToUV73utcRRRFvfetbV3zMW97yFt7whje0/z0/P8/OnTt56Utfesle8CiK+NznPsdLXvKSdkWvU5TSfOgrx/nk98ZQWtGTd9tOa1prZmsRUgp+5OZt/MIL9l127Uyfe2qcd33yKUqZtF3MkqJdGQNQOg1cHix6CCF4+7+8iTt2917UPo9PVvnvXzrGd0/NUfZDMrZF2Y8u2HooSe3tpdD8/p2KB74lCdTl9VoazuPJlc+TIM14k6SVz5t3dPPAj97AgcEiSmm+fmKaLz09xXi5ASgakaY76/ID1/Tz8hu3rTobuRCl0tnFVhvhcFfmsvvcLeX4ZLVdafbjhIxtsbc/z/3XD150VX0tLub7z3DpMOfpysGcqysDc56uHKanp5/V/V9RbopKKQYHB/ngBz+IZVnccccdnD17lj/6oz9aVYx5nofnectudxznkn84NrLP0zN1vneuiq+gmHFJsBalDnseVPyY756tMlGLLztntP5iFi0sFBbCsqmFCZ5z3lQhVpAomPUVN490ceeegY4WxKuhlOZzh6Z4aqyGtC3yGY9yI6IRi46cEb1mgTVQgiDZ2AJbNhWB2tCjDeth6XmyAGkJHFuyqzfHYClHIZvBcRyOTlT4zFNTzNRCIg1Pj9aYbUREieIzhyb5m2+c4XU/dGDFGUmlNGfnGjw1Os+3Ts4yWfEJEkXGttg/UOBlN12+2VxHJyr89dfPMFMLGe7KMOTa1MOYx0arnJ0PL5k5yLPxnWtYP+Y8XTmYc3VlYM7T5c+zfX6eNTHW39+PZVmMj48vun18fJxt27at+Jjh4WEcx1nUknj99dczNjZGGIa47spZWFcytTCmHsaAbueBLaR1Wz2K1529dSlYaJ0/VHQ5PesTRArbEgg0YayRUtCbu7BrY6ecnWvw2NkyidaUsg71MGGyGlwyi3pJOgv3rNuUXuWsJpOFFHiWZLgryy07u5lvRNTCuG3mMlMLsaXg68fLBHFC1rUoeBa1IOHp8Qrv/GRalV8oyI5OVPjM4+N85/Qsh8cqxEoz3JXh2m0lMo7k8XNlzpUbl6Xj4cLnfXCw0K6qFzMOBc/myESVzz4xzr7+wmVf3TMYDAaD4WrkWVszuq7LHXfcwYMPPti+TSnFgw8+yN13373iY+69916OHj2KUudrDocPH2Z4ePiqFGKQ5njlXBsQRMnyWkvrtpxjryt761Kx0Dp/PkgYLLpk7PS5NGKNEIJrhgr85x+54YKujZ2yULzGiWauHmJdwkVm6wyZZe3WstopzbuSA0MFvn9fH1nHamfSnZ1rcGyyylDR5dHTcwRxQilj49kSS0pyno1rCeYaIX/18EniZjjZ0YkKH3noJI+dLTNbC3EswWAxrbY+drZMlGgODhaYqYV89olxlFq/7FdKc3qmzqGxeU7P1De0jdVoPe/hrsyiIGkAIQTDXRmOTlQ5O9fYtH0aDAaDwWDonGd19f6GN7yB17zmNdx55508//nP5z3veQ+1Wq3trvjqV7+akZER3vnOdwLwK7/yK7z3ve/lN37jN3j961/PkSNHeMc73sGv//qvP5tPY0sZ6c5y80gXJyZrVBoRbkEumhmr+DGWFNyyo+uysedeylLr/HzGIas1fXmPH7ttO7/4gn0XXRFr0RavGqarAVGSzgTFSUC4RX71ArAkKHVejF3B5neXPU4zsBnSubA4SV/3gmvxggN9DBSzFDM2Rydr3DySfi4OT1Tw4wQaUG5EZF1rUSXIEoIIQSljc2KqxiOnZ7lzd2+7qrSt5HFyukYx6+DZFq4tmamFHJuscufunkWiZj2twq2q27HJanuWazPbHmthjB8n5NyVvxuyrsX4vH9ZVtUNBoPBYHgu8KyKsZ/5mZ9hcnKS3/3d32VsbIzbbruNT3/6021Tj1OnTiHl+UX6zp07+cxnPsNv/uZvcssttzAyMsJv/MZv8KY3venZegpbjpSCl9+0jUNjFb57eo7xeZ+unAMIyvUIpTW37uzmZTdtu2zajFrzNQtzku6/foj7Dg7wyOlZpmshfXmX23f2IKXYtEwlpTRaa7aVMjxxtky5EVLMOAgBhYzNbC3aknZFKSHjWNTDBBuIjSv+lhLpdM4QUiHcEr5hkvDw8VkKXoWsa3HNULGdSZd3bTK2xWw9JFEaZ8l7LNFplTbn2kxVA6Zr4aKqUhArYqVwrPQrUwhBIWMzUwup+DE5b/2iplV1a81y5dws9TDe1LbH1vP+/7f353FyndWB//957lZ7V+/d6lZrly1bko0XvGBDIDYYwgCZyUACjjGELBB7gJiw/RK2GQgEEgIhBDJkgMw3GCdkCJMJxMYWtgFjbGws27ItWbJ29b7VXnd9fn/c7lK31Fot9SKf9+ulOKq6t+699VSJe+o8zzlVLyCXPHpOfM0LG9lDIYQQQsy/Bf9f4FtvvZVbb711zufuu+++ox67+uqr+fnPf36Wz2pxWdeZ473Xr+f2h/bz891jjJU9APIph6vXtPLmK1cs6FqVmcHXaMnlsf0TbOsvUvUC0o7F5t48r97UzbrOHFesbmvs9+xgiX959ADPjZQJNbSkbNZ15k4rKzAzwzBadvHCCC/UVOoh6STYhkHSNqj5z6+shiKuujgdcClA6/imNpKiHWedCZimYvqdDnQ8ZTFhKhzLxAsiCpGPccSUvN7mFGs7svx45zCmAj/SOEoRRjqu6BlE5JI2URSvzWzLOLOySloHWIaBH0YkrHjNqm0alN0AL4xQHqcU1MzXWq7p697WXyCbsGZNVdQ6rgo5nT0UQgghxPxb8GBMnJx1nTn+9LUXcnCiyu7RCgCr2zP0taQXNCN2ZBD03EiZuh+RdkwcSwGKPSMVtg+WeO/16xtB1pZnhvjrLTsZKbk4liJhmZRqPqMV75SzAkdmGHqaU6Qdk5/sHMWfau5rGAqlIJ+00GiK9dObSKiZnfmafuvP0gxIMUPWMbBNAzfQOCZASHvGAWXS05zCm8peleohPfkkQRjNCmhu2NTFwYkKu4YrlOsBphFn2MIoLiKTCAKGSiEbe5q4tK+FgWJ9RlbJojXtMFyq42TiqcJ+GGEZBrahTjmoOZW1XM+nQur0dfcXauwcjo+XckxqXshAoU5rxmlkD4UQQggx/yQYW0IMQ7GiLcOKtsyCnsfMMt/ff3IA1w9Zlk+xa6REoeYz3UY8n0pimYpSzefxA5Pc/tB+/vS1F7JrpMRfb9nJYLFOd1MCxzLxw4hCzccN4iDpZLMCx8ownNeVY7LqsWu4Qkc2QW9LkudGKjiWYmCyfsbeCwnC5kfKUqzpyJGwFAcmavi+D0BzysGy44xPwjZJYGKZBhM1n+WtKXYNlzk4UUUpRRBpXntRD8OlOj9+dgw31NgKbFNhKJisBSRtk1de2IVlGbOySus7s6ztzFByfcYrHpmESbke0pyxGSzWacsmTimomc+1XOs6c7z9mlWNH02GinUSlsnm3jyv2rh4S/ILIYQQLwQSjIlTMp0J2zVc4qn+ImU3YGVbmqRt0j9Zx1SKdMKkHkRMVn16mpO0ZRMMFes8tHuM/WMV/uWRQ4yUXJY1JUnY8ZSvhGXiZOKiCFUvYOdQ6aSyAsfKMCilWNeVY7jkMlpxacs6KDSTtZBaIJMJl5qObIJUnA6jKWUzVI+n6oZakzqi5cP09EHTUAwV63z9p3sp1n3qQUjCNHB9zcq2FJNVn0I9wA00KE3CNMgm4gIe0VS27Mis0ubePDsGSwwU6limoiXtcNHy5lMOauZ7Lde6zhxrXp49Y+szhRBCCHFmSDAmTtrM6YC5hIVS0Jy2GSm5DBTqVN2AbNLCMBSOZVDzQ7wgImGb5NM2Y2WPR/ZN8NxIGccysI+ooDhdFKFUD5ic6g91IsfKMIxXXHYPV4i0puaFbB8qUvPi83mey8bEAii6Ic1e3BfMUDAddx+5jgtoTB+cqHgcGK+iFKztyJJ2UgwX6+werZCwDJpTNgkrnvqYtONptYVawL3bh/nVDZ1cu77jqKySG4SsaE1z2aoWLl/VygXdTacV1CzEWi7DUIuuKbwQQgjxQifB2DlirgqGZ/JX7yOnA45VvLixcsImm4CDEzWCSDNdrtBU4GtNOD1ncarzVj0ICXVEwjLwQ03Cmn2OtmngBR6G4qSyAnNlGMYrLlsPTFLzQtKORU+LSXcuyS8PTEggtkR5QRivCbQS1LyQTMICfJpTFgMlv7GOa7rdQ1PS4pmBEoah2NzT1GgUb1sGKdtgohpPc1zbkZlVsdWxTA5O1NjyzDAvWduOYaizklWStVxCCCGEAAnGzglnu1cRHD0d0DGNWVmJfMpmtOxS8QLSWGgdZy/MqRvkQtUnn3JY15nl0b0TlGoBhZqPk3FmZQW8IMQLNGs7syeVFTgywwDw3HCFmhfSkraZqPpxCfKqi2MaSAewpSkII4p1D0NpNIqXrG0HDmBZBqahGCu7JGyTUj2g5odMVDzcIKI14/Do/gLrOrO0Zpz4MzBVRdEwVPyDwIwEbRBpMgmL/snarGmyZyOrJGu5hBBCCCHB2BJ3tnsVTWfctvUXGK+6LMsnAY6qLmfE97jU/QjX91CGImUZ1PyAkguR1ly9ppXLV7Ty6N5JRsvxzfJ4xSObtKYyYhGDxXgt2X+9tA/DUCfM+B2ZYcgmLEYrLo4VZz+Stgk6Pq/WjMP4VEbkTFFwVnqXidmCCGpeRHNKcXFfM7/70tVs/8UBXryylV8eLLJ/vMp4xaPmhzimQXdTkpLr05pxGCnVKbsBL+prpiUdl44fmKyRUMaMzG08PbBcD2jPJjAN5qURsqzlEkIIIV7YJBhbws52r6KZGbfxqsdzwxVKtYALe/K0ZpxGdbnBYp1izcdUCstWeKFGEfdyGii4tGQcLu5r5s1XrsCyjEbwBFD1AkpugBf4eEFEd1OS/3bdes7rzp10xm9mhuGX+8cp1nzyKZvOpiRduQTPDJbIJi3cIMLg1HqBnSjY0iexjXh+FODYcZuEa9a1846XrmZlS5LtwDuuXc3rKgHFqs8/PrSPfWNlluXT+GHEtv4ioOMgvOLx3EiZy1e2sKYjw56RMm6oCSJNGEVU3ZCSG/fF65nK/s5XI2RZyyWEEEK8cEkwtoSdzV5FR2bcluWTlGo+BydruEHEJStaaM0kuHh5M/fvGG70FmtKWYQRU1GKxg00K1pSvPu6dY0AambwtGu4xGTNw1AG6zqz/MZlvZzX1XTKGb/pDMMj+1r4u/t305qxWZZPMVbxCKII27CIDH3SQVPajte0nShwO7IJtDjzHEvRmnJIOCY3XhU3OJ8ubT8dyBygykjZxQ00Tw0Up6Y1+kxWPXqbU/FU1YpHqR6wvDlFSyZB2Q2ouQHDhTp+pLFMhWkonuovcu36dmmELIQQQoizToKxJexs9So6Vsbtwp4m3CBkpOzy9ECBK1e34gYRoYae5hQX9jTRkU2QTZiU3RAvjOJGvGFEyp79UZs5PatU9ylPVWJM2RZBEJ1Wxs8wFJevbOUXKybY1l8AwDENwlCzd6xCxQ1OGIxNv9qVq1rZsnOc6ASNxI5sAi3OPC+I13et7ciSTzlzbvPMYJFnh0rYhkEuZWMnLSxT0T9ZZ994le58gjCCiarHYDFi8/I8xZrPzqESjmXQkbaxTYNiLV5zNlxy2T1alnVbQgghhDirJBhbws5Wr6JjZdxaMwkuWdHC0/1FhosuzwwUMZQil7S4bGUrHblEY9umVFwVIYgi9o5W5gwIDUPhBiH3bh+ZNRWxPeuwe7TCitb0KWf8jlxD5gURA4UabqBRJ5ipOfPpfeNV0BJlLQYaGKt4NKdsljUlj3o+ijSP7BknCDUtaZPEVMuEfMrBNhSHCnVGSx5J26DmhVzc18z1F3Rxx8P7GSzWMYgLd4CmtyXFmvYMYxXvlKb4Hrm2cVlTkoGpH0JkHZgQQgghjkWCsSXsbPUqOl7GrTWT4Mo1bTwzUORNL+6jLePw7Yf2k7SNOV7p+AHhsaYiPj0QF2TozCXJHX3vfcKM3/Q0yC//aBf3bh/GDeLpiceLrUw1+/m9Y1WCSG6eF4uaH/HvTw4QhhG3XLeelS2HPxiHJmtxE/F8kkLNJ2GZje9COmHT12IwUKhx9dp23n3devpa0hyarDFZ87lmbRug8MIIxzTIJePvkWMZJz3F98i1jV4Q4foRCdvAsYyzUt1UCCGEEOeGue+gxZIwnQVqzTjsHC5TqvsEUUSp7rNzuHzavYpmZtzmUvdDWtIOm3ryXL6ylXWdOQYKdfQR0c50QLhujjL1R06FzCVtTEORS9qs68gShJodQ8WjXhNOLuMXRfDMQJEgik7qQx7quLDH9NEiwJJYbNEwFXhBxJ1PD/E//v1pdo+UG89VvAA3jDi/O0fKMRmveLh+SM0LmKx6TFRcEpbJDRu7WdmWwTBU4weHTMKmKWXTnk3QlLIbQVzKMXGD8IRTfKd/UNjWX6A5bdOcsjk4UeXZ4RIHxqs0pxya0zbb+gt844G97BoundX3SQghhBBLiwRjS9x0FmhTT57Jqs/e0QqTVZ/NvfnTLms/nXE7mQDrdAPC4xUfaUrZLMsnGZiMqzQe7/gzRZHmwHiVp/sLfOOB3Rwq1AiiuLPY6cRVvsxSXBQsIGkpbFOBhm2Hitzz9GDj+ekfD5K2yYv6mskmLA5O1tgzWqF/skbZDXEsg7RjHrXPsX5wOLmAf/YPCtmExd7RKkEYF60Jo3itYjZhsb4zy/jU1Mcokg+WEEIIIWIyTfEccKZ7FR257mpZPknKMal5IQOF+qwAK4o0jmmwqaeJR/ZOcHC8imUqkrZ13Oa1x5sKqZTi/O4cI2WXXSNlzuvKHfP4044sw/9Mf5Gqe7jBs9z+Ll2WpUApDCBpxxmrR/dNsrw9fn7mdN22jB03bnZM2jNOXJSjHmAaih88OUB3Psm6ztwZmeJ75A8KxZrPeDXum2cYxqwKjtM/MJxudVMhhBBCnJskGDtHnOleRTPLzz83UmaoWCdhmbMCrF3DJW5/aD8/3z1GoeqjgUzCZGNPnv98aS/XrG0/ZkB4ouIjSdvkvK4ca9qzjJbdOY8/7ci1Z7alePxAXOVRLG0KGi0GkqZBwlR4AVS8wxnT6R8PDk3WeHjPOHU/IJ+28QJNse6TS9q8qK/5qKIcJ/uDw7Ec+YOCF0ZxGwUz/mfVNg3KboAXxg0STre6qRBCCCHOXRKMiWM6XsZt13CJL9yzk8cPTGIqaMs5KBSTVZ9H900QRJplU1mIuZxMZuLSFS38/kvXHLcq3ZFTxSaqHjuHytS8cK7DikXIAFBwrNl7oQYVahJJg0CDUpBxZgfw6zpzvGZzNw/tHqPkhoxXfbSOA6LpqYZHZqZO5geHuUxXThws1AlDTcX1aUo5OKaBZRj4YUTCMvHDCMswcMx4NvjpVjcVQgghxLlL7grEcc2VcYsizZ3bBnl2sIRjKtqyiUYw1dVkMFbxeHaoxF3bBlnz8rlLg59sZsKyjONm/GZOFZuoemw9MEnVC5Eq4ouLAiwjrlg53ZfNBLSKn5vOYipmTynVMx7TGmp+SNI2uWxlM1SGZx2j5se97ZKWiaEUQRihdcSBiSpjFY/LVrWgYFZmak17ltddbLB7tALA6vYMfS3pY2bEZk6HrfkhByaq7BmrcMWqVlozDq1ph+FSHTutKNcDOpuS5JLW86puKoQQQohzlwRj4pQdmqzx5KECodbkZlSgg3i9Vy5pUaoHPHGwcNz1MaebmZhpeqpYyk6yfaBEzQtpyzgUqh5VPzpj1yyeHw34EViGwkRjqjggT9oWacdgqORiKoVhgKEMvCAkjA4XXok0FOsBtmmwqbeJ6y/sZvsvnuXQRI16VCNtm/xi9xhuEBGEIRpF0jExlSKIIspuwMO7x7m4L9/ITB1Zkv5EJeiPnA7b46RI2Qa/2DvB/c+O8OJVLaxqTzNWcdk/UaM57bCyLU3ZDU566qMQQgghXlgkGBOnrOIFU1XoNEkEbTIAAGM2SURBVLZ5dEHO6ceqfnDC9TGnW3zkyKliw8V6o3gCQMI2Af+4ryHmh6XAUHEwFkQaBbRmHFK2iWkqXD9CEX9u0o5JGGk6sg5jZY+qHzamL6Yck+s3dPKuV6wjDOLP1Zfv3UUl0ISRZv9YFa01bqDJpw73GrMMg6Qdrx8bKdXpyiaO2eNuW3+B/kLtqEqkR06HnX7tvtYMacfk4T0TbB8ss6otRV9rms6pPmPFmo/rR6f0A4MQQgghXjgkGBOnLONYpB0LUI31MTP5UwUL0rZ1UutjjjUV8lgB2syMRsWNb6Ar9QDDgK5cglE3YqLinZmLFactDrBUY9ph0gZF/PfOXIJXnN9BxYsYr7j8eOcoURRhKEWkoCXt0NWUpFT3GSy6pGyDz73xYq5e087u0TL/+NB+Ljcgn7LpSjgcmqgyUnap+yGWqaj5EY5lEEWamhdSD0K0hkOTdT539w7CUDNWduluSuIGEVoH5JJxCfqdw+VZhT7g+K0Y2rJJrlnXRn+hzptevIK1HVmWNSWPu9ZRCCGEEAIkGFvUjheQLOS5pG2TTT1N7BmpUKr5OFkDpVSclfDj4gmmoeZcH3My1/TsUJF/eeQQz42UCXVES8phXWeOGzZ1ATQyGpNVj6cOFSi7IdMTEserAZY6vC5JzC8FWKbCVmBbBl1NSZhav2UZCtMwGCzWGSm7lN2QfNohl7RYOV7lueEyZTcgl7KwTIUXRnhBRDZh8coLu7h6TVzL/q5tQ3GwnYNs0kIrRXPaIeuYVN34c2VNNXaueiEacCxjKkNm8sv9EwwW6uRTNnvHqvG5mQYtaYe1HRmyCYtH943zyL4WLl/ZOqtJ9FytGADSifiY3flk44cFKV8vhBBCiBORYGyROtX1LGfT9sEC//DAPnaPVMDQLMslac0mWNacZPdIJV7rZRsUagHlekCkNa3pBOMVj92j5cb5nsw1bXlmiL/espORkotjGSQsg1ItYLTscWiyRtIyGK941LyAR/dNEEYaZ6oyRH2qCoQEYgsn4xj4kcaLNDrU+GGEH8bFNSIdT1GMIk1kMKuH3Jr2DIOFOjUvxDYMJqoeEAdvF/fkeMuVKzAMxYHxKs+NlOluSs6q9JFLWrRmEwyXXdwgpLUphRtEWGZEwjQIgbRtkk1YNCUtnu73qHkhfa1pckkLP9Qcmqyye6RM0jFx/ZC/u383v1gxwQ2buk7YikEqJQohhBDidMidwyJ0qutZzqb/78G9fPneXRRqPgowDcWBZJVl+RTLmlO8eFULjx8scHC8RqQh7Zj0taRZ25lhoFjnGw/s5e3XrAI44TVFEfz1lp0MFussa0piWwZ+qCnUfNwgouYHVL2Iy/vy/OuzIwShJmkrDAUaAxWG0tx5AbWmLbqakvRP1qh4EWGk6S/ERVlSjompDLwwigNxZbGsKclk1W8Ubvm1zcvQOt6n6oWkHZOLeptnBeuHM1QOuIePrZTiwmVN7B0tU6gFuEGZuh9/GuoqzsopoHvqmErRCA4NpYiikJoXf740muaUTWvGbnw+b7561fNuEi2EEEIIcSQJxhaZYxUKyCVtsglrzvUsZ8s9Tw/xhXt2UnZ9cgkb21T4kaZQC6j5cSnwl5/fSUvK5uF9E7RlHNoyDj3NKQzDQGvNzuEyd20bQmt93Gu6a9sQk1WXkZJLd1NiqgAHJCyFk3EYr3iEkWa0XOfHu3xKXogBeKGeKmOvJRBbYGnHwrFMTNPANDRRFKF1PH3VnBqjMNIkLZOmlM3Ktgyve1EPNT9sTFkFjjuN9XCG6ug+ckpByjYp1APqfvx5MJgqja8hCDWTUz8qNCUsSm5IEGkcrRmv+AQRZBMmFS9keUuaZfn4fHYOl7nnmSFeubHzeTWJFkIIIYQ4kgRji8zxCgUopY5qXHu2BEHE392/i4obkE9a2FZcITFhKGxDUawHDBRq/Oy5EUZKPhpNzasxWHTpL7is68zSmnFYlk/yxKFJ0NDbkjrmNT1xaDJef2YpnCMKgiilyCYtRksupXqAOzUP0ZjuURXpYzYMFvNnouqRsuN1YqMll/GKh0JPrf3S+FGE1tDVlGRzbxO7RysYSrGhu2nW6xzvcz3dLPyZ/gk2z0gOa63ZNVQmiKA941BxA+pB1PiMGIYim7AIwoiqH7c/qAcRpbpPpDVVP8A0FG4QFxHpaT78WZ3+zr3u4p7n3YpBCCGEEGImCcYWmRMVCkg5JkNTVdrOpl8emODARA3bUljm7ODIMBQpx6RQC9g+WMZQsLwljTM1rXCkVKfsBryor5mmlDVVREFPVWCc+5qqXogXaRKWOWeFRstQlN0AQylsUzUaASuliCLJii0UA4im/htGmsl6QNaJp7MaKu4P5geaigrIJCyWt6TY3Bt/LvaOVk75czzdLHywEGdmy/WAREIxXHTZN14lm7CI0HTnUwwV4+mOKTvOjwWRJpewKNR8qm7IqvYMKctksFjH9SOStkHSMUnZJh3ZROOYM79zG7qbTqsVgxBCCCHEXCQYW2QWS6GAsYpHEGlsQxFqjXVERssywA1CTANasgmUitfezJxW+NxImfO7sqQdEzTHvaa0Y5KyDHQUrxFzMsasLFrFDXGDiN7mFI4ZFwsJIo2SlNiCiogzlCnbwAs15VpAFEFbxqbuh/hTRVUuWp7n/K4cTVNNwkt1/7Q/x+s6c/z2lSvY/ou9cWBV8qj7IbmkxZqOLDuHSziWQWcuwWCxjhdqbHNqjZgxNWVRazb15GnNOPQXajy6d4Lk1DV0NSXJJQ+f15HfublaMQghhBBCnA4JxhaZ6WlYC10ooG2qKa/rgxdEmLbJzHjM9eOpgV1NSdozCUbKbiOAmp5WOF522W0qrljVhtaabf0Fups0fqRxTKNxwztQqHNRbzNaax7aO44bhIxX4gbOtmngBSFDpTqWoVjXmSVlx8HqvvFao3LidKZMzK+sY5KwDcpunOHKJi2CSFP1QjKOyUTVJ+nEWc7pQOx0PsdHtkRY1ZZhO3DLK9ZRj6BY8/n2Q/uxzbiEvR9GpByL7qYk4xWfshsQRhFuENGVT7KyLc1YxZsK2pK0ZhwOTtboyCZY23F4XaMU5xBCCCHE2STB2CIzPQ1roQsFXNrXwqq2DE8PFDFVXJrcsQxMpQjCkLIXYJuKy1a0YFsGZS+YFUBFOi6WsKYzLl2/b6zK3c8M8cTBAo5l4FgGuUTcPHpFW7rRQ2ygWAeg6gaU3AAv8Kh6EaZSGJbBtkMFbNOgJePQlLLZNVym6keNQGx62pw4+1KWYlV7Bj8MKdaCqWmJcYXCYj0g48R9vZKWwXDJZaLqYZsG/ZNxK4R1XfF0vxNN85urJcK69hS9xOsQbdsmijS/2DPBk4cKtKTsxo8DKcdimWUwXFI0p23yKYsrV7fzyo2d3P3UMM+NlHGDOi0ZhyDSNCXjQjVBFElxDiGEEEKcdRKMLULrOnMLXijAsgzeds0qPv0f25mouCig5gaEWhNGkElYrGxNk05Y5JI2L+pr5rnhCuMVl8nAJwwjckmL37p8BQA/2j5MU8purP1yg5CBekBHLsGvbuhsXNP0de8aLjFZ86l5IYWaT8o2GCzGN/SWYTBadnEsk+XNSQ5O1qgHmqRtkLFgpBpJpmwe+KGm6gb4kca2FEpDqR4QRRoUpJ0EF/c1UfMiDkxU2TtWIWGZuH5EEBl877FD3GkNHrd/3rHaPDw9UKQ3B7tHypzf0zLrR4yKFxfjGCu7JOz4eEnHpClp09eabhxrXUduVrat5geNAE2KcwghhBBiPkgwtkit68wteKGA6y7oon+yxt//ZA8jpTpBpDGUoi1j8/svW0vZDRvTKVszCXQn1PtD6r5LLYjIJW0ePzjBfTt8xisel/Q1A/ENuxdG2IZisOiyfaDEus5so8T5H7xsDQPFOqW6z/ce6+fpgQITFRfQWKYR3+xrzUTVY6LiYZlxwQitNZUgfn+suIFUY82SMRWdSdbszAk0HJys0dWUiDOXhiLlxFnRYKoZ92jZZ11Hhnza5lfO6+ChPeNYRkhPc4q0Yx3Va25N++HPfNo2ufPJwTlbIuScDNTjIH99dzOGoWb9iPHYgQn2j1cp1YOp3ncpLl3RMiuwmmvt15EBmhTnEEIIIcTZJMHYIrbQhQJ2DZfYPlhiQ3eWjT05Ih0HNWGkeXa4zK9uONx3KWUb7BgqUa4HgKK7Kcn6ziwP7BrjwESVTT1x+XKlFE2pw0U8inWf7z/ZzxMHJzEMRaQ1y/JJrrugixWtaUZKdcYrHoMFF6XADyNcPyKckfbyQliWMdCmTdn1AfA1JNBTjarj7QJJlZ1RlhEHweMVH1PFnw3bVNQDTVPSIp+0GC3VGS/Xec3mbsZKLl4QcV5Xbs5ec7c/tJ/WtMPu0Qr1ICSMNAfGa2zozs7ZEgFg90hlVpuHmT9ilOrxWrFs0iKXsE8qsFro75wQQgghXlgkGBNzmtl8+vzupqMKiewcLrNjsMTNL1nJD58a4gdPDjBe8cinbDIJi1zSYt9YlbFyndGyy6P7JinUAtZ35WjNOACMV1x2DJUaU9AqtZCRssvjBya5f8cIF/c1N3qqRVGcbXH9aM7s1kAlJGNr1nVm2TNepVQL8MN4qmIUSUbsTDMV5JMmbqCp+SG5lNVYK2abiqoXMlr28IIIpeA/nhwkaVtc3JefM7BK2Qb3bh9mRVuatR1Z0k6KQxPVqc8IZBJ243MzkxuER5XHnxlQzSz8cTLr04QQQggh5pMEY2JOJ9t8+nUX9/C6i3t48mCBZfm4ct14xWXXcJlIa1K2iWMaeGHEQKFGxQu5eHkey1Q8cbDAZCVuFHxwokYQaRKWQcJyKNYCth6YZLTs4oURJuBF+rhBVcWP2DVcprc1jY5qWAZM1EIJxM6ClG3Slk0yWfOpeh6uH9GWdXBsTbHq44Xxur2kbWAoRc2PGK3EY5x2LNqyCbTWU028Q3aPVqj6Ib3NqUb7g+a0Qz5lU64H7BoucX5XrlGJsykRfyaPVx5/rsIfx1ufJoQQQggx3yQYE3M61ebTVS9gsOhS80PqfohCk3EsvDCeUqijiGzColDz+PGzI1imYrhYR6OwDEXKCbEMg8mqT6Q1WtOYlhgEmugkkxkVP2LfcBlMxfLmNBEuhdrZbZD9QuOYYFsGVS+iLeNQqQdkkhaX9bUwVKqz3SthGoq0YxKEmqofxuMYRvRP1rhvxzCXrGhmrOwzXvWo+SFjZRfHNKh5h0PnXNKiLZNg71iZncNlhoouhgLLNOjImKzphDUdmTlLzh+r8MfM9WkSkAkhhBBioRkLfQJicZrZfHouMxvhDhXrPN1fZN94hbGSy2TFwwsjNJqUbWBOlTwfr7gUa3ExjyCM0Chs08ANIyaqhx+P1x5B3Q/RWqOBUDNrndjx+HF9D4bLHglLPuJnmtbxPxw1P6DiheTTcZVMyzQYKXsopUgnLCKtKbsBmjiTlrQNNJrRssuPnx3l4EQ1roDpmHFRFmDncDxtFeIMbHvOoeqFlOo+oMmnbQwFe8eqAKzvzB417XDmFNv1nVlySRvTUOSSNus7s4xXPH741FDjmEIIIYQQC0XuVMWcpptPDxTqaD37pnW6EW5cATHgyz/ayUTNxws09SAi0FD34z5jfhhXYEzbFug406WBSCtSjklz2iYIItxAU/UjCvWAiarPZNXHUKpRn/5Ubpsj4pv/1W1par5MUjzTgiguvFJxAxxTsaw5RUcuyUChSqnuE4QhYRhPQdRKkUtY2JZB2jExlCII9VSBjiiugOmGOJZJR86h7oc8N1JGa00URRwcr2Gb8edH67i5c6RhVVu8JmzncPmooOpkp9gemqzN23smhBBCCDEXmaYo5nQyzaevv6CLr967iycPFeN9OBw0acALNJM1j3zSxrFMHFPRk7IJQs2lK1ron6zx9GDxqCqHU5XrifwQrU+vX1gQRjwzWKbihc/jXRBz0UAc42qSjsmG7iY2dOf4t62HKNYD/CCi5sfZrUzCxJnKThpG3Ozb9UMcw6BQD0jYFj3NSVrSNmU3IJOwGK94HBivsW+8ws7hEkGkySQsmtM2vS1p2rMOho6AUbYdKnBwosqKtkzj/E51iq0QQgghxEKRzJg4pum+TZt68kxWffaOVpis+mzuzfP2a1ZhW4qf7BoDwDYUCdvANhUzcxFBCNmERcIyiNBEESxrTtHbkqItl5gqhT+38Hn0Bav4kQRiZ5htTPVrm6KBhGnw8vM72D5Yoi2TYF1HloxjNj4HfhBR9UKKVZ/JajwNNWUpVrSlac04bO7N8+JVrWxeniflmJTrPqW6zxOHJhksxL3tMo5JezZBqR6wY7DE1v2T/GLvBABPDxT5+k/3smu41DivU5liK4QQQgixkORuRBzX8ZpPf+eRA5Rdn4xjUffDuOqhoeLCG4FuZLTKbkh3PslY2SWVNljbkQVg72h5zqzXkbU6bGM6EyMWkh/FwZgBGAY0pxzGKj4/fGqIshtwXneOjqYEbhAxMFnD1SGBH1E9YvA8YLTs0pJJ0JpxUErRmknwor5mnuovMDJcwQ0impIWmYRFezauqlh1ffaN17AMxZq2JABJy2TPWJlvPLC3UZRjeortdEPyI9syDBTqbO7Nz1n4QwghhBBiPkkwJk7oWI1wq15AGGm8MCTUEUGoCUKNUnGj5XCqv1cYRXQ1JcinLMbKLntGS+xXBqPFeuPmPpyxNuzIAE3KLCwe0VSVS9s06GxyGC55/HLfBC87r70RVF21ppVf7Blnx9DcwXYEDJU8TMMgmzAp1uJS+LahyCYsupoSbFzWRHPGYcdgiZGSi9aaiWqAIj7+9At3NCW4qDfPrpEKP3xqiDXt2ZOaYvuqjV3Sb0wIIYQQC06CMXFadg2XeGagSBhpSvUwvkk2QE2t8Qqn/msb8DsvXY3W8O2H9jFc8tgxVMGYrs2hwTQUBjq+0SfOvugZUxRDyYrNq+nMVzDjfTenxmu6kqKhFONVn6SpKLv+rDFqSTvkUw5qahynzYihABgo1PnhUwNoZeAFEV4QkUta5FM2y1szmIZiXWeWshs01ng5lsILNRM1H4DV7VkMw5hVlKOvNd2YYjvdZ2yoWCdhmWzuzfOqjdJnTAghhBCLgwRjYpYo0nNOSZxpuofTRMUnYcf9piAOxFAKy4AwjHuDdeQSZByTL9yzCzcIySZMgkjjBVFj6qGO9KziH+FxbuDF2aOA9qxNa8ZhvOJTrAdoHeFPLb0zFDM+C5pyPaCvNU2h6lOs+7RkHABK9YADE1X0HAN3eO/4z97xKt1NSZK2RVvWwVCK0ZJL/2SVvtZMY/riEwcLjFU8okihgfZsfKyWtB2Xzp+jKMfxptgKIYQQQiwGEowtEScTJD1fu4ZLjUxCPQhJWiZrO7LcsOlwJmFmD6dl+SStaQc/dAkjPZXN0ugwnkqWsU36WtP8r5/sxQ1CWtM2hmGgNYSRpljzqE2tLQuJMy5KTVVTnDonCcTmh6niwGZ1e4bmtEMQVrBNg6xjsHesSqTjQMxUAAo3iEjaJsuakihgouqxojWNUgovjLNc02MXZ9IABQqFRjeybhnH4kUrWmlNO+SSFlpr7npqiG39xanPuEFrJsElK5op1XzqQUR3PsmVq5vBHW+c/7GKchxriq0QQgghxGIgwdgScDJB0pk4xjce2NsIstJOiqoXsK2/QH+h1iiOMLOHkxtEJB2TVW1phoouNT+Mez4p6MwmuGRlXL5+rOKSdiwMIy7eGa8pixsD1wN/VsB1so2dxZmTsg3a0g5epMkmba67oIu6f4ihogdoTMvA9yLCQOMDhqExFeRTNqHWXLWmjbofNtZnmUphzvihQDUCMYDZvQps0yCbsGhK2Y3HVrVnePzAJPc9O8z5XTk6pwI+yzQwI82Fy5oanyWQohxCCCGEWLokGFvkTjZIej5mZrvWd2Yb1edySZtswmLncLlRHGFmDyetAyzDIGkbbOh2KLkBXhBS9yNetr6DlGOyf7xKpDUJ6/DNuR9G1Lyo0QAa4uyJaUIk1ejnVcJSWIYiiCLKbsAv9ozjBxFjZZ+qGzBccgm1bkwXNVQ8HTUiXhDW25ziLVeuADj8g4Ef0ppxGC27hDoOsNUcQbZlQFPSwjHjwGq84vLkoQIHxquU6gGlus/+sRotGYd1nVmuXd/OcMllrOKRNOPXKNcDDhU9KcohhBBCiCVJgrFF7FSCpOdzEzoz2zWzDDiAUmpWcYSZPZxySYvWtMNwKa5Q15S0cQMD24xIWAYDhTqr29PsGirhBpq0Ewdi5XpchXF6AZECErbCDeK1Y1KvY35YKi6OUo9CDAUp22xMP6x4AQPFuIqhZSgsC4JQEwJoMNA0pW1u2NTNiuY0Ww9Nsqo9zcr2FKvaMzx1qMjn7tzOaCUutDFXwjOXtOloSpFLWoxVXH66c5SRkgto0o7JsqYUJTegPpVx/a0Xr8Aw4qBv70gRklCo+VKUQwghhBBLlgRji9ipBEnPZ13MzGzXXGYWRzivM9fo4bS+M8vazgwl12e84pFJmJTqIWnHZOdwifZckt+/ZjWPHygyWKyRtBQVN8QNIxQQhtPXAvmExZDvyxqxeaSnytRrIGGbhJEm0hq0JmUbaK0xFJimQuu4XYGONFpBwjaYrHh84e5n+WjFxw3iwbRNg5VtaTqzCVa2ZfCCEkV3drpTASnHIOOYrGnPMF7xuP/ZEYaKdaJIowyFqTSWqehpTjJW8Tg4WePupwd518vX8a6XZ9k/WuLxBw9wyyvWsaI9t2AZsflYyymEEEKIc5cEY4vYqQRJz8fsbJd91PMziyPM1cNpc2+eHYMlDkzUcIOQqmfihREoxXceO8jLz+/gu788yGjFJ5haFDZ9u6qIb/KrgZbKifNMGWAojUZRqgUEUdwjbvtgiZIbYlsKhcIxDTRx4GFEUwGaoaj7ETuGSoShJp2wWNacBB3v/0tvkuaUTUcuQXsORksu9amALWGZNKcd1ndl2Tde5dBEldGyC1rHbQ4MBUoxVHLpbkqSS1qU6gFPHCw0fnjobUnxONDbsnDBz3ys5RRCCCHEuU2CsUXsVIKk56O3OdXIdmUT1qws3FzFEY7s4eQGIS0Zm6oXknIM2rMJhop19o9W2DFYIpuw2NiT54mDk/jh4aDLMRVtWQc/jCi5IRopZT+fHBPcACKtidCYpkFz2iKdMBmt+GgNlqHoakpimwajZZeqFxJFEVUvJIzi8Uo7RlwdsxrQ05xEodk+WGZCa3qW5TBNk/ZsAi+ICKKIYj0gZZu88bI+bn9oP6V6/GOCVpCwDFKOiWUoan7EeMWjO58AoOoHz/uHhzNlPtZyCiGEEOLcJ8HYInaqQdLpmivblXJMal7IQKE+Z3GEmT2cSnWf7z3Wj2UY2Kbi4T0TVLwAc6oU+mTVxw9CmtM2ST8iaRmkHIuWtIVpmtS8ALPkMuy7sl5sHtX8w4FvBGQsRXdTamr9mKLighdGJMw4W1X3Q/wwIgyjRvsByyAOsHVcAMQLIiIUSsXrAyteRFPKRClFwjZJYBLpuBdZqCPasw5XrGrl8UMFijWfpG1gTxX0cCyDmh9S8eKMWtq2nvcPD2fCfK3lFEIIIcS5zzjxJmKhTAdJrRmHncNlSnWfIIoo1X12DpfPaAW56WzXpp48k1WfvaMVJqtxcYS3X7OKNe1ZDoxX2T5Y5MB4NZ6yNtXDKZe0GS27pGyTh/dMMF52qbohxVrAZDWg7voUagFVLyTjWKQck7asg2maU+uSFK0Zm6RtILeu88NUzHqvlQI/0tT9EMcySDtWXI5ewWTNp+qH1Lyw0ax7uqFzRByUhWFE3Q8JIh2Xto+LLeKHs8NrrTWuH68rVErhhhGr2jP0taSwDIUfRI3XNlWctSvV48D+ouWLo3T9qazlFEIIIYQ4noX/mRn48pe/zOc+9zkGBwe5+OKL+dKXvsQVV1xxwv3uuOMO3vzmN/OGN7yB733ve2f/RBfAkVMCh4p1EpZ5VirIzcx2zSxIsHu0zFfue+6Ya2MqXkDVC9g3XmWo5M56TQ3UI8ALyTomGo1pxGvdDKWoeiFuEFLzQ/ypBtDi7ItmvNEGgAYviDg4WUWpeB1W2jbj7JdpMFio4c5oAmeb4IfxfqGeqowZRYSRJmEZ2JZB6EfUvAA3sLBNo1FJ0zIN+lrTdGQTJC2Tmh+yrjPLaNmL10C6AQnbIIyIqztaEed1NXPDpu5FkWmar7WcQgghhDj3LXgw9k//9E/cdtttfPWrX+XKK6/kC1/4AjfccAM7duygs7PzmPvt3buXP/7jP+alL33pPJ7twjhWkHQ2bkyns13TTmZtzEjJ5bmRMjuHK8d97bIXYihFOmFSnOojNR0UaC1rxeaLCaDiwilKKRRxViyKoO5HDJc8NnTnyHbl2D1aoe7H4zb9aXNMUMogVPF0xTiDFa/5MpTGNuMuz5mETW9zkrFqgB9G2KZBZzaBZRlcuqKFS/ta+MWeiUZlzqvWtPLkoQIHJ2pU3YBAQ1PS5jWbunnLVSsXzRqs+VrLKYQQQohz34LfLXz+85/n937v93j7298OwFe/+lW+//3v8/Wvf50PfehDc+4ThiE33ngjn/jEJ/jJT37C5OTkPJ7xwjgySDobjizTvawpecK1Mbc/tJ+aF1I9ySxA0Q2wS3Gj4YRp4IYRYSSB2HxLWAaRBkMpoqlS9p4fglJkHJPzOrMMlTx68kmUitdvPbBrjDCKUMpoVFQMQk0wlRhTCspuyGjFpy2ToCPnMFELCIKICE0QRIxWPM7vzvGqjV1YlnHUWsWXrG1npFRnz2iFXNLmbdes4qXrOhZFRmzafK3lFEIIIcS5b0GDMc/zePTRR/nwhz/ceMwwDK6//noefPDBY+733//7f6ezs5N3vOMd/OQnPznuMVzXxXUPT50rFosA+L6P7/vP8wpOzvRx5ut4p2P3SJktzwyzZ7TSmIrYlrHZO1ZleUsag9kRkwJ6cjaP7BmlPefgez4J8+RCqt68w8BkDYMIS2lM8+xc0+lIGHrWf88ljXYCCiwVB14p28T1I6IIEqZGo1E6ZN9YidZMAiLF8pY0bhCSTxiEYfwxCCKNhSY04kbdQdzJgCgM2Nid4xXnd/LIvglqro9lq8bRDUNjEhEGAb7vs7IlyVuvXN747I0GcVbpuvPb+dUNnazpyBKGQaMn3bSF/k5dv6GNwUKF3cNFupuSpByDmhcxWKzTnnG47vw2fN9noFA//ONGPrmogsr5stBjJU6OjNPSIWO1NMg4LR0LPUZKa71gd539/f309vbys5/9jKuvvrrx+Ac+8AHuv/9+HnrooaP2+elPf8pv/dZvsXXrVtrb23nb297G5OTkMdeMffzjH+cTn/jEUY/ffvvtpNNnN9MkhBBCCCGEWLyq1SpvectbKBQKNDU1zfvxF3ya4qkolUrcdNNNfO1rX6O9vf2k9vnwhz/Mbbfd1vh7sVikr6+PV73qVfP2hvu+z913380rX/lKbPvoNSYLKYo0/+une3h6oMjajsysKVfFms+Ptg/TnU9w5eq2oyrHHRiv8uj+CV5+Xif3PD3ASOXEvyxYwObeJp7sLxIswuRTwtD8j8sjPvKIgRudO1kMBRhTFQ6n6xvaCsKptXpx822FoaA9m+Ala9t51cYu/t/jA+RTNpmEyWP7J+kv1IgiTc2PS9xrYHV7mkzS5opVrfzONasZKNT58r27yKdsssmj/4kp1wMKNZ9bXrGO3pbTm8q3WL5TUaSPyn7tHavwjw/tZ6Li0d2UJO2YVL2QwWKdlozDb1+5gjUd2QU75/m2WMZKHJ+M09IhY7U0yDgtHWNjYwt6/AUNxtrb2zFNk6GhoVmPDw0N0d3dfdT2zz33HHv37uV1r3td47Eoim8tLctix44drF27dtY+iUSCRCJx1GvZtj3vX46FOOaJHBivsmu0Rmc+DYY1a+1WNmXQlkuxf6LOhmUR+bTTeE5rzWg1IJ1IcHCyjlYmbnjidWMu8PP9JVjkRezdSOGGi/scT5bJ4RL001dkEI/FNAUYEbRlHa5e18lY1efJ/gor23M8NVBkfWeWlR05JuohVS+k2VGU6nHvuJZsivZcgldu6iGRcKhHdSqBpivhoNXR72EioaiWPOoRz/v7sBi+U6s6D38vokhzz/YxRisB6zubGj9gZFIWa5Jxi4otO8ZY3938gpuyuBjGSpyYjNPSIWO1NMg4LX4LPT4L2mfMcRwuu+wytmzZ0ngsiiK2bNkya9ritA0bNvDkk0+ydevWxp/Xv/71vOIVr2Dr1q309fXN5+mfdVGkj+rtdaYdLtM9Oy7XU/2dOpuSAOwcLjX6nBVrHo8fnCTjWDSnLX62e4yJqr/Iw6sXrunlVoq48qGpji6Yoqf+rOnI0p5Lsiyf5LmRMi9a0dzoc2ebBpt78zSnbAq1AMOA7nySi/uaefs1qxrVDmdWG5zLYqw2eKa+a9KDTAghhBCnYsHvhm677TZuvvlmLr/8cq644gq+8IUvUKlUGtUV3/rWt9Lb28unP/1pkskkmzZtmrV/c3MzwFGPL3W7hkuN3mJz9fZ6vqYrJw4W6oShpuL6NKXiX/hHy3UeP1BgouIRaA1a4wWa/eNVvCBitOwCiiij2T5QwgsiUrZFZ5PNcNGVyoiLkGWAH3HMqaGWAtNU1LwQrXWjV1Z7LjGrz50bhKxoTXPZqhYuX9XKBd1NR7VZWGrVBs/kd016kAkhhBDiVCx4MPabv/mbjIyM8NGPfpTBwUFe9KIXceedd9LV1QXA/v37MYwFTeDNu5Pp7fV8ArKZN581P+TARJU9YxWuWNXKockaD+8Zxw1Cpku7pB2TIIqIph7oyCVY056hUPPxwoiEZRJpTd2PaEnbaKBc9/GjY5+DmD+WEbdG4DjZnkCD0jBW9ijVA5Sikb3qa02fUp87w1BHlaxPOSY1L2SgUKc14/CqjV2LYpremf6uSQ8yIYQQQpyKRXFHcOutt3LrrbfO+dx999133H2/+c1vnvkTWkBRpE/Y2+uHTw2xpj17WjezR9589jgpUrbBL/ZOcOdTgxRqHmEIthW/tkIRRJq9Y1WGii7ZpMUNF3ZhGAbDJRetIZc0cQNNwjJozzpYhoFjGXG/qDGZjjVfLCMuL++H8fzjiHhKYjZhUqyFJ9qdYCpDWvcDJmvBrOzVqfa5W9eZm5VRGyrWSVgmm3vzvGrjmcnuPl9n47u21LKCQgghhFhYiyIYE4edypqTU20Cfaybz77WDEnL5LuPHcQPwTbiZsC2oUg6JqZSTNZ8qm6AIr6h7GlOkXLMuPFvFDcF9kONZRgkbJOaFzBUck9wRuJMOdzJSwGaiDggMxS4QXRSU0cNwA0jdo9WWNeZe97Zq3WduVPKqM23s/FdW0pZQSGEEEIsPAnGFpmzuebkeDefXhShpoo75JI2tqkwDYVSCj+Mpyi6oWa04vHIvgmWTdZZ056hOWUzVvFoSpporQm1puYF7B+vUvVknuJ8cUxAqUbhCTXj/xgqDtDi/3tsIaAjzfKWFDdfvYo17VkOjFcp1X3KbkA2aZFL2KcUUJ1qRm0+na3v2lLICgohhBBicZBgbJE5m2tOjnfzGRduiDMpKLDMeJ2eH0aU3aBxk28qRcoxGSnVKbsBazoyFOo+41Uf2zQII81IyaVQkwIF88E2IetY+FEcbBlWHDz7oUYpWJZPYpkG+8YqBCcRGydsk1It4I5f7AcN+yeq7B+rUvNDUo7JitY0l/S1nLFCMgvpbH7XFntWUAghhBCLgwRji8zZXHNyvJvPlGNimQZaRwShRk99MmpeRBSBZcZrx0ylyDomKmExWKxTqHpkbIOqF1LzQ54dLC3KZs7nqgu6m7hoeTNPDxSZqHhxLzEFExWPqheSciyyCZOhokHZPX40ZhtwXmeWTMLk7qeH8CNNyjYwgFzSwvUjDoxXcf3ojBSSWWhne33XYs4KCiGEEGJxkGBskZm55uTZoRK5pIVpKMIo7vvVlk2c9pqT4918dueSJCyTMNLYBtT8ENNQ+FHcLtgLNEnboLPJYaLqY5mKcj3OtGUTFi1pm7ofUapLRuxsU4BjKiKtWdeRoy2b4Np17ZTqAV4Y4ZgGo2WX+3aMUHYDgigim7CpuMduO2AqSNomPS1phooutqmouAGeH7K2I4NhGGQTmvGKRxBFjJXd51VIZjGQ9V1CCCGEWGgSjC1C6zpz/OqGTr75wF6e6i/ihxG2abCqPcMbN3SedjZi+ubz0GSVxw9O0pJ2yCUtLEMxWHTZ3Jtn92iFihtgK6j7If7U3DbHMrhqTRvLW1LsGi6zc7hM3Y8r9AWRJmNbVL0TV+wTz589FYiZpkGk4/FRStGUmpnt1PS1pMilLNZ2ZKl4IQ/tHqVUD6h6swt62IaiJWOTtE20hvGqR9I2ibQ/VZ1RkzDiY2STFhNVv/E5OJ1CMouJrO8SQgghxEKSYGwR2jVc4kfbh8kkLK5e04ZhxIUZivWAH20fZmVb+nndJCYsg0MTNZ7pL6IUtGYSvGRtG2++cgX7xqp884G97B2rEEQa29TkUzZXrG5lTUcWgPO7FcNFl6apG/OMY+JYCrcoBTvmgxfG2cumpMXTgyX6WtOzevFprRksuly1to26HzJR9ckmLJpSDq3pBIW6j9aQT1lMVD06c3FBFzeIQEEQxb3j4nIfilAfDt1s06DsBpiGouoF50TzYlnfJYQQQoiFIsHYIjOz/Px5XdnZUwm1fl59xnYNl/jCPTt5dqiE1vE6sUjHUxIHi3EZ+usu6OJX1nfwywMTjJRcfrpzlPGKy+r2TON1/DDuEByEGq3jQgdjFQ//OE2FxZllmwYp26TiBjxxqMDajuxRU+zecuUKIq35l0cOsWu4hB9GlLyQVe0Z1nXmsAzFg7vHUAoqbkBnU5KWlI1lGGgdB2IQF22Z5ocRlhEXajmXmhfL+i4hhBBCLATjxJuI+XQqvY9ORRRpbv/5fh4/MEkYaXIpm86mJG3ZBKaheOLgJN9+aD9RpLEsgytWt/Hai3p4x0tX055LsnO4TKnuE0QRXhBR8yLqfkjF9SnUAnyp2nFWJSxF0jJIWIqEFTfVjnRcMTGftDg0WWPPSIXJqs/m3jxvv2YVAHc/NcxIqU4EdOWS5NMOpqGwzbgqZsYxGSy6JG2TtR1ZmlI2rWmHuh9iqDgcs834c6i1plwPaEnblOoB6zqz0rxYCCGEEOJ5kGBskTlcfn7ujEPKMXGD8JSnhx2YqPLzPeMYStGWcUhYBoaKb+zbMg6GUjy4e5wDE9VZ+02vqdnUk2ey6rN3tEIQRnTnk5TrAeFUpcWEZSCTus4OxwTHNLCm1m05ZlzQpVALGC66bB8qUa0HdGQT/JdLe3nnr6wF4BsP7GVbf4HmtMOKljTL8kmakhaletwHbt9YhZaMQ3dTkpa0g23GUxK78wn8UOPYJk0pm/GKR6nuM1b2ME0DyzCeVyEZIYQQQggROzfmGJ1Dzlbvoz2jFSZrHh3ZxJwZt3zaZqzssme0wsq2zKznj1xTk7ZN/nrLs2wfLMXFJCKNYcYV/txQMmRnkqVATa3bijQYU42dq/7hIhz7x6qMljz2T9QYm1oDdvfT8VTXtozNjsES49W4CqKpFJGGlozD269ZRS5hU/MD7n5qeFYBi1de2HW4z9h4lVI9IO2Y9LWkuHRFixS3EEIIIYQ4AyQYW2TOZu8jpUEfs7j5yQdRg8U64xWP5pRFGMFkzcMLNIahcMy4wIR4/hIGmKbCjyAKNZahCMMQd0bRSgMaLQkmqh6P7J3gaz99jiCIe4Q9frBAzQvJJi1s08IPIyYqHo/sGecNF/dw4bI8AOs6ckcVsIB42myp7lN2A7JJi1zCluIWQgghhBBniARji8zZ6H0UTTXvTdgGYyWXnmbjqOp7hapPc8pmTXvmqP13DZcapb/rQYjrh+wZq+JYJvlU3GNsourjBhEKjRNGlD2prPh8+REkHZNMQhFFUKz7BDPeVkXc4NkLI7KJONCq+yGP7p1geUsKL9DUvJDWjNMI6hOWSUcuwcGJGlueGeYla9sxDHXMAhZS1EIIIYQQ4uyRYGwROpO9j6YDqV3DJSINE1Wfqh/R1ZQgl7TjCns1n0jDFavb0MD2wWIjO7J7tMw3HtjLeMVjWT5J2kkxXKzzzEAJL4zwgpDWTILufBKASGtqXsChiTpNKYvhkncKOTcxUwSU6nFGKoz0VEA1+91UxMG264ckHRM3iCjWA8puQKEWkEtaR01LDSJNJmHRP1nj0GSN3uaUlHUXQgghhFgAEowtUmei99Gu4dKsQOola9v42XNjjJZd+ifr5FMBjmViGgZrW1OgNX/2/Weo+AEZ22JTbxMTFZ/xisf6zsNl9rvzSdqzNjsGy4SRZrTskrJNskmbbMJkourj2AaXrGjh8YOTTFQ83ODYEyTF3BRxQOaHEfmkSaEeMd1XWwGmAq3i4IowIomJ1hpF3Duuf7JOS3r2usPpiojt2QSmAc8MFPm3rf2NrGfSiqsq3rBJ1oQJIYQQQpxtEowtYs+n99HMfmXTgVQuafPS9e3sHCqxe7SKYRis7cjQ25Ji90iFB3aPEc7oFbZjqIQfRFy5pnVWduXJQwV2DlcaxTpUCGEUUvVDJiom3c1JunIJmpI2+ZRD0jLpL9Rwpfz9KTEVhBrqfkTSMsg6JoaKqHpRPEXRUCitCYEw1ARBRBBputIOr97UzXPDZUZKHi0ZG9s08MOIcj0g5Vj0Nicp1gO+/+QAXhA1sp5VL2Bbf4H+Qo23X7NKAjIhhBBCiLNIStufo47Vr6w1k+CK1W38ynkdrOvI8I5rV0MEu0cqcf+xpE1rxiGXtAkjzXjV47nhylQTYNg9Uubnu8fwggjHjAMGc+pTpLUikzB52bp2rlnbTrEe0JyyKNR8DMmLnTYNFOsB9WC6omL8WBTFjZkVEGkoewGWaXDt+jZ+/eJeXnF+JwnboO6FTFQ96n5EZ1OSi5fnqXoRrh/h+iHrO7PkkjamEQfs6zuzjFc8fvjU0NQxhBBCCCHE2SCZsXPU4X5lR1ddVErR2ZSg6gVM1Dy+/+QAFTdeX5QywTHjPmSt6bjH1IGJKsWaTy5p8+i+cbwgImEp4lAA0o6FoTQVP64ucWiyyq9e0MlAsc5YxWWi6ksodhoiDq8Qs0wwlcKxDYJIoyON1hDqePqnBmzT4NK+Fm68ciWWZfCWq1ZQD0IOTdZoSTvkkhaWoRgsuiRsgyAy6GlOnbC5uBTxEEIIIYQ4OyQzdo6a2a9Ma02x5jNadinWfLSOq+ztGi7z7m9vZbjsUfEjBkse2wYrPN1fjPuZ2SbZpEXVCxmruOwaKTFeiQtyuL6mHmiCSFPxfEpuSBhGjFc9Hts/yY+eGeFXN3Sya7gkgdhpMIizXQCOAZ0ZBxT4kSZtG1iGYjqGMhSkbYPXbFrGn/6nCxpTC9d15vida1dzxao2DKUYr3gUagGbe/O89qJlOJZxxpuLCyGEEEKIkyeZsXPUdL+yn+8ZIwgiJmo+QRRhGQYtKZuDkzUOTtTmDJTKXsiu4RLrOnPkUxaFqs8v901SD0LqM9d9TaXGghBQmulif6ZpsGeszKGHq0xU5Wb+dExXsDeA87pzFGsBXhAQ6bhipQIcy2BZPkXKMbhmbTsfuGEDlmUQRXpW4Zc/eNkaBor1WYVgDk3WuNMaPOPNxYUQQgghxMmTO60l7sgb7+mKi4ah2LAsx79uPUSp7tOWccinbGpeyHOjFUbLbmOa4VwBWdWPGC5UUIaBbRpo4mzakUIdv0Yjk6MhlzDpbU7yL48eOqvX/kKwpiMdTyNUcbYqjDSGUnhBRIQmn7K4sCfPm17ch2UZR/WEm1kdcUN3U+N1z2ZzcSGEEEIIcXIkGFvCjnfjvaY9y/aBEsuaknRkHSaqPoWaj2UYpB2jMQXOVHEQNVeL5tFKQNIxacsk6M45KMAtuBwZkmnioGxazQ340fYRvFAmKJ6IQfz+OabCMRVuqAkjjWUo0o5J1QvRGrqbktT9kPGKT80PMQ0Iw3id2M0vWcm6ztxRrQyOVx3xbDQXF0IIIYQQp0aCsSXqRDfer97UzXMjZdZ3ZckmLEr1AC+McEyDrQcm2DtWi19ITVXni47OkAU6Lpne25zgwEQdrcG2FZEfbzlXqJWyDZRhMlKondXrP1dExAGxG2q8MJ5+qFRcmCMCxiseq9szKKVIORY9tonrh4xXfdKOQSZhMln1CYLoqFYGALmkTTZhsXO4zA+fGmJNe7YRYB3ZXHywUCfUmmX5JNddEAf0QgghhBDi7JFgbAmaq4cYzL7x3vLMEDU/oGeOaopNyRnDPj1X8Rj8MOLR/ZP4ocZQEEZx8KaIg7WZsraBMhTDxRqSFDt50++VASgjzlTqCGpTxTNK9ZCEHTZ6hY3X4uxYGEVsO1Tkq/c9x7rOLLtHK6xoTZ9SdcTp5uI/e26Ue54eZqBQY6hY519/eYgnDhSk+bMQQgghxFkkwdgSdKweYnD4xvvQZA009E9WGSi4TFQ9gjDCMg3ySQtbga/jgGp6qtyRMhZUAgimoi6T2VX+jlT255rsKE6GAowZtU3VVOBrm4p8yqLuxw2by25Aoeaj0dhTOwyX6kzWPEbLHp25JLnk0a+fckyGpop4HGn3aJn/2DbIeMWjtyVF2rGk+bMQQgghxDyQ0vZL0OEeYscuS24qRco2+cXeCYaLNepT+9S9gNGySypxeN+5gqukpXCPiK18TaOnlSS+ziwFaD09bVFhTU0lTFomacdgWT5BGGnGyi6uH6GjuMx9wo7XeZXdkJoXsmOo2GjQPdOxqiMemWWV5s9CCCGEEPNHMmNL0MweYscqS24bqjHlbGYhDUU8zTCXtLl4eZ5nh4rU/Nk32mnbABRBdHT1RMl9nR3TAa6pFIZSBGGEYSgsU7FvvEapXsIPo6lsGTBdeCXScSbLD7FNRf9EjWLNJ592Dr/2caojnkyWVZo/CyGEEEKcHZIZW4Kmy5IPFOpHZUG01vx8zxj3bB/mob0Tc1Y0jDSUXR+A//nWy9nQlSGbMOnI2mzsStOStvHDOOySWnpnj2WAbUDSViSnGjlD3EfMNOPsmGEomhI2tqka6chQg6UU+aSJBiaqPtmEiWOZKAW7RsqU6nFfuVLdZ+dw+ZjVEU8myyrNn4UQQgghzg7JjC1BxytL/vM9Yzw7WCY4zrSylK2o+5qdQyVylsVLz+vkh08NorXGtCyqvt+Iwqbre5gqXtN0ZKsxSx1dyEOcmG0qkpZJFEWYpsI2DYIorqaYsAxqfojrxyUuJ6ouLSmLKNJ4YQTEQZptGhgG1PwQreMM6YrWDBcuyzNadhkq1klYJpt787xq49yFOE4myyrNn4UQQgghzg65w1qijixLPlSsYxkGhyZqRJGOC23MsZ8GXF9jGVD3I750/y6UUrhBRLHmM1718cOQMJy9j1JMTWObHXlZpiIMtKwhO0mKOBBzLAPTUDiWSRBqEqaiNeNgKkV/oUaxHg/AcNkDYLwaYBrgmAZhpAmiuB+ZaSh8rakHEV4QcXFfM+9/1fkMTBXrmNkIfC7S/FkIIYQQYuFIMLaETZclPzRZo+IFPLR7lDufGsAyQCtFdIz68iFxUBABB8ervGRdBxnH5MlDBfona7hTM9Jmhl7h1PqkmUzANAyUihsTi+ObnhMcRBoVRmituXBZM0MlFwVU3JDRsjvn1FINBBFEUYRtQhDGBTxAEWkdV0JsTvEbl/ViWcZJr++S5s9CCCGEEAtH1owtcYah6GtNs6G7CT+cqoyo4mmFxxNEcbDVkUuwY7DEjqEyhlIkbRNTxWuZMraiJWXhmIpIx6XWZwqBihces9S9mC2a/qPBDTSuH2FbBud35XjrS1ZiKOYMxI58jSCMi7AEYbwmLIw0vfkk775uPed1NZ3yeU1nWTf15Jms+uwdrTBZ9dncm5ey9kIIIYQQZ5Fkxs4h3fkEphFnxEzTQBEed/qgoeDQRJWIOAizpvpWJWwDP4gIUZgaHMvAC4+urCiev+eGy5iG4sfPjlA+ckHecSRtE8tU5JImL1nXxu9du5bzuk8/aDoyy3qi6Y1CCCGEEOL5k2DsHPKqDd105XZwcKIGQYhjKtxjZFoU8XqvwZJHyjYo1gP01NqjrGNS0ZBOmBBBoXq4kp6pIJOw0FpTdo8f7InjMw3on6yfcrsAZcDqjgwv6mvmugu6uGZt+xkJmqazrEIIIYQQYn7INMVziOOY/O5L15ByLAIN4THmD6Ztg/xUdb64n5VBaiozFkWakhtgm4p80mZdVxZDgW0oklZceCJlG+SStgRiz5Mbnl7ftpVtGT78mg28/ZrVdOQSHJqsSVNmIYQQQoglSDJj55ibrl4FwFfu28VQ0W08bijIpyw29eTZ3NPEd7f2U6wF2JbCUHG1RMcycExFzY9ImAAaP9RTFQAhQmEbCtNQVKXv1IKwFFy7tpVf7Jlg9+hB6kFI0jJZ25Hlhk1zl68XQgghhBCLkwRjS0gU6ZNa03PT1av4zcv6+NYv9nH7Q/tJ2QYrWtO0Z5M0pWxK9QDbNDCN+DUjHZcxDzUYhoFlghdFBBFkHBMAPwLHVCQdE1DUfFlDthDWdqQYqwYMl4ssyydJOymqXsC2/gL9hZoU3BBCCCGEWEIkGFsidg2XuPPJQZ48VKDiB2Rsi829eV69uXvWzffMgG1te46uXJLdoxWqXhnLrNKSdmjLOjimIuVY+GFEFEVUgjg7lnZMOrIOAwWXaKriomMZeEFEKmnEzYlDfcwpkOLsWdWa4pp1XQwU66zvzDZ6guWSNtmExc7hMj98aog17VkpvCGEEEIIsQRIMLYE7Bou8YV7dvLsUGlWELRnrML2oRLvvX496zpz7BouNZpAj5ZdDoxXiYirI2qtMRQMF+sMFeuU6gFtGYcw0hTqPhrQGipuwETVw1CK1oyNbRlcuqKFx/ZPUHIDwgg0cdZMzA9TwZuv7OP3XrqWL96zk2X55KzmzBA35F6WT7JruMyhyZoU4hBCCCGEWAIkGFvkokhz+8/38/iBSRwrLpxhmwo/1JTqPo8fmOTbD+3nN6/o4x9+to/xikd3U5L+yRpaayzTwFCKjGNSDyKU0tS8iLRj0ZKxKdYCTKXQCsIoou7HfcNySYuNPXlSjslAoc6FPXl2j5Qp1HxO0ApLnEGv2djBX73xUpJJi+2DRepBSNpJzbltyjEZKtapyHo+IYQQQoglQYKxRe7ARJWf7xnHUIq2jNPIiCQshZNxGCq6PPjcGIZSjFc81ndmKdUDxisufqSp+T5hBK3pNC/qa8WPNF4QMV5x6Z+sU3ID+lrTRFozMFnHNzXZhEXKNtk3XuXylS20ZWye7i8Sao1jKWq+RGPz4Q9ftpoP/NqFjb9nHIukZVL1AnJJ+6jta15IwjLJOPK1FkIIIYRYCuSubRHzvJA7Ht7PwYkKzSmHKIowTbPxvFKKfNpmqFDnqf4iG5blUErxi71j7ByuzCo9P17xqPsRL9/QRRBFjJZd8imbtGNS8ULqfogXRuTTNp3ZJIYR77N/vMrTA0VGSnW8IEKWip08y4BNPTl2DlepnEJDZ4A3XbpsViAG0NucYm1Hlm39BbIJa9ZURa01A4U6m3vz9DbPnTkTQgghhBCLiwRji9T/9+Be/v4nexgo1PBCTaleY6hYpzufojufnLGlRmuNF4WkHYsfbR9iW3/pqNeLgCcOFUnYJpt68xgKko7BZctaqHohQ6U6T/UX6MgmMFRcLXGs7HL/jhpVLzxm82gxt6RtsHFZjua0w0DRO6Vg7GXr2vnsmy496nHDUNywqYv+Qo2dw2WW5ZOkHJOaFzJQqNOacXjVxi4p3iGEEEIIsURIMLYI/cPP9vC5O3fghiFJ08QP4xt5L9QcnKwC0J1PorWmUPXJp21aUw4Hxys81V885utq4PEDk+STJms7c4wUXWp+SFMqnvK2265QdgMmqj6TFU8CsBkMFff4Mk0DBQRRRKjjcv9hqAmm3qq0Y7K5N88f/MoauvMpijWfv7p7B6Ml96TW2r3rZav54BEZsZnWdeZ4+zWrGoVahop1ElZ8zFdtlD5jQgghhBBLiQRji8zThwp8/oc7qXohtqUItMYyFMHU/MAogsFCjaakQdWLpw1eu66D1ozN//75vsY0QgXMde/vhprBostvXL6CX+6bZP94hfO6LHJJi6RtsHOohOvHgcaxXuOFRAFpx2BTT57u5hTNSYutBybZP1HF9SMs08A2wTIV+WRcffLCZU28bH0nhqGIIs2m3maeOFikNlUc5VjH+dpvX8r1m5ad8JzWdeZY8/LsSfWcE0IIIYQQi5cEY4vIruESf/Yfz1B2fWxTYRsKTZyVMVRcel4TB1RDJY/mlMPFPTluvGoFA4U6f/+TPSd1nEI94N8e78cLIkZKLhU3ZF1nhqobUPMipGr9YY6luHptOxt78gAUaz5uqFnWlMINIjb2NuGYBo5lkLBMQDNR9Rvl5Q1D8cbL+vjxsyPsHasS6YgwpPEeKyBlG7x6Uze/emH3SZ+XYSgpXy+EEEIIscRJMLZIRJHmrm1DjJY9tI4zLUopFGBP1ezQgI7iKXHNaZs3XNzLDZviqWlBpGnPOhTqh8uaK+JGzgYQ6cMBwLJ8kjXtWapegBtEFGs+Ww9Osn+iBjO2fyFLmIpswqY167ChK0ux5uOFEWU3wAsCFAZd+SSr2jKzCmkEUcRwyaVU9zkwXm1krv74hvP5i7t20D9ZIzA0GrAMRVPS4tIVrfzhK9ZJZksIIYQQ4gVGgrFF4tBkjedGynTnE+waLhFGYEwFYUopLDMOkCzTwI80N165gpuuWt24gc84Fpt68uwfq+LruFFwI0ZQimhqwZJjwOUrmjENRS5pc0lfMzsGiuwYKhGFESYSiAE0pWzOW9ZMGGl++Mxwo9m2H0ZMVDzacgnWdmSPar5c80LcIOJ7j/UzWnapByFJy2RtR5b333A+D+0e56n+An4U0ZJyuLivmRs2dctaLyGEEEKIFyAJxhaJihdQD0Iu6GrisX2TlN0AyzAaN/sKiLSmHkX0Nqd58+UrZ2VSeptTnNfdxLaBIrtHKgQaTOKALJxROeKi5XksKx52rTXb+gs8tHuMiieTE2da3pzkzVes4P9u7Wdgsg6AQmMbCscyqXsRWs+OWrXW7BwuU6z5WIaipzlF2klR9QK29RfoL9S4+epVvNVZJWu9hBBCCCGEBGOLxXRDXzeMuHRlCw/sGqXmR9imgWlAEGr8UJNxTN5x7Wocx5y1/8yy5wAHxqp4kZ5VgaMpaWFbFuMVD9D8aPswh6YCDREzpv57YU8zOwZKhJHmho1dlN24D5tjGnhhyI+fHeXhveNcs7aNdMKi5oX0T9Yp1nyakjbndeUagXQuaZNNWOwcLnPPM0O881fWSgAmhBBCCCEkGFssZjb03dwbF4v45b4JKl6AF8QZrqaUzR+9cj03Xb1qzteYLnvek0/xyP4xtu6bxAsjckkbW2ly6QQjpTqjZZey6zNQcOfxCpeGjGMCIUPFOocKHr0tKQzDoCllzNjK5sWrWtg+WKJ/so5lKhKWyYq2FEEUsaI1fdT0RaUUy/JJdg2XG8U9hBBCCCHEC5sEYwsoivSs8uSv3NjZaOi7uj3Dhq4czwwWGSy6tGcd/n+vuYALpwK1Y1nXmWPVyzIU7vLxA826jiy5pMWj+yYZLtVpSds8N1phsurP01UuLR1ZG/Co+iG+jkg7c39FljWnqPsRb3pxH935JBnHouT6/M2Pdh1zn5RjMlSsU/GCOZ8XQgghhBAvLBKMLZBdw6VG496ZRR5+dUMn2wdKPDdSxg1CWjMJrljddkoNfQeKcfbrvK4cuWTc0HltZ4aS6zNccqkfp9/VC1l7xm5UTUnbJoE2qHpB4z2cqeaFJO14zKazXAfGqyQt87j7JCyTzDGCNSGEEEII8cIid4ULYNdwiW88sJfxiseyfHLOIg+vd3qOWeThyIxaVzbB1kOTjFU8WtM2JTdguFwnm7DQWqOUojWT4EV9zTxxcJL+ydoCXv3ilLYNeltSREEIwMaePBEGTw0UySasWdMOtdYMFOps7s3T25xqPD5zqunJ7iOEEEIIIV64FkUw9uUvf5nPfe5zDA4OcvHFF/OlL32JK664Ys5tv/a1r/G///f/Ztu2bQBcdtll/Nmf/dkxt19spvuJjVc81ndmT7nIw5EZtYmKR/9kHTcICUKNF0bYpkJrGJis05lLsq4zS2vGAcA0DCQpNlvWMVjZnmGyGpB14vf8ugs6MS2LgWKdncNlluWTpByTmhcyUKjTmnF41cauWWM0s4jKye4jhBBCCCFeuIwTb3J2/dM//RO33XYbH/vYx/jlL3/JxRdfzA033MDw8PCc29933328+c1v5t577+XBBx+kr6+PV73qVRw6dGiez/z0TPcTW5ZPnrDIw5GmM2rb+gs0p20M4On+IoPFGlUvJIjicutVL6LqhxRrHsOlOlsPTLJntMzWA5OMFOvEraTPfceLeWwTMrZB0jLIJG3Gyh6GUrxkTRsAazqyjYIom3ryTFZ99k6ttdvcm+ft16yac9ro6ewjhBBCCCFemBY8M/b5z3+e3/u93+Ptb387AF/96lf5/ve/z9e//nU+9KEPHbX9t771rVl///u//3v+z//5P2zZsoW3vvWt83LOz8d0P7G0M/dUtWMVeTgyo6Y1PHGwQBBpWtM2k7UADbSk4rVKkzWfqh+RdUKKtYjH9nkopal4IY5l4Hvh2b7UBWMb0Jx2iCJN0Q0wFZiGIog0CcvkilXNjJR9JisefhSxrjPHmo4M113QxRUr8tx55+7Ga63rzLHm5dlZ00JP1BvsdPYRQgghhBAvPAsajHmex6OPPsqHP/zhxmOGYXD99dfz4IMPntRrVKtVfN+ntbV1zudd18V1D5dwLxaLAPi+j+/PT0XB6eP4vk/SgIylqLse2eTRb7/rBqQtRdJg1vkdmqixd6RIb5ODQcRAsUbN9cgnDUwFtooAhaUiLNOgJWVS90LySYuKFzBScWlJ2yQt6Mo6DBTq1IJzp9GzpaAjl8CxDCYqPqaOC2zoCLwwImmaKMtgVXuKjctyaK15aqDIytYMv33VykawNHOsZurO2UAc6IZhQHgSsezp7CNOzrHGSSw+MlZLg4zT0iFjtTTIOC0dCz1GSmu9YEuI+vv76e3t5Wc/+xlXX3114/EPfOAD3H///Tz00EMnfI0//MM/5K677uKpp54imUwe9fzHP/5xPvGJTxz1+O233046Lb2ehBBCCCGEeKGqVqu85S1voVAo0NTUNO/HX/Bpis/HZz7zGe644w7uu+++OQMxgA9/+MPcdtttjb8Xi8XGOrP5esN93+fuu+/mla98JbZts3ukzD8+tJ+Jikd3U5KUY1DzIgaLdVoyDr995QrWdGRnvcahiRpfvncX+ZRNNmkxUKjxo2eGsS0DUykKNQ9Q5FMWlmngBhF+EPGrGzpRSvGLveNsXNbEnrEqhoLhUp2aFy7J7JgCljcn+S+XLmdTb57/9/hA430BmKh67B4pM1ryqPkBfqhZ2ZqiKeXgWAYJy2RNR4Zf3dB51Pt85FiJxUnGaemQsVoaZJyWDhmrpUHGaekYGxtb0OMvaDDW3t6OaZoMDQ3NenxoaIju7u7j7vsXf/EXfOYzn+Gee+7hoosuOuZ2iUSCRCJx1OO2bc/7l2P6mOf3tHDzNVajKqJb8khYJhf2thyzn9iKdotVHU1s6y+wPunQ2ZQhlXAYq3g0JU18HVdJDLRBEECxHtGWdehoSrFrpEpPSxY3UmSTDsNlF9O0iBREWuMvonhMwZzVHk3AMOM1Vz3NKT7yho28dF0HAE/2Vxrvi1KK5kyKS9JJijWfXSNlLuxp4o+vP5+hsnvSa7gW4vMhTp2M09IhY7U0yDgtHTJWS4OM0+K30OOzoMGY4zhcdtllbNmyhV//9V8HIIoitmzZwq233nrM/T772c/yqU99irvuuovLL798ns72zDrVIg9zlU3fvDzPT3eOMl71SVgmhoKyGxBEkLQNzuvK8txolbaswxsvX86Ptg9T9UMsQ+GpuNqgZRpoIs52gsyauiytAQW2adCUNEknbCxTMVnxCDXkkxZ+GDFSchtBomXE52kZip7mFB949QZ+5bzOxmsfq5z8UMllZVuGN13eh+OYjebMQgghhBBCLAYLPk3xtttu4+abb+byyy/niiuu4Atf+AKVSqVRXfGtb30rvb29fPrTnwbgz//8z/noRz/K7bffzqpVqxgcHAQgm82SzWaPeZzFyDDUKQUI02XTpzNqABf2NM3qMxbpiFzSZEVriqakw7rObCPbtrItzV3bhnjswAT7x6v4ocY0jLgvGVCq+XhhBBEYBiRskyCKqPmnv6zQUpBJWLRnE7RkbArVgEhr2rI2vc1p1nfleNXGLvaNVfnmA3vZO1Yh1NCaTZCwDFrSDralsA2TTb1N/NfLl3Ne1+zppUe+L0PFOgnLZHNv/piZRiGEEEIIIRbaggdjv/mbv8nIyAgf/ehHGRwc5EUvehF33nknXV1dAOzfvx/DONwO7Stf+Qqe5/Ff/+t/nfU6H/vYx/j4xz8+n6e+IObKqHVlE2w9NMlYxaM1bdPZlMQNoqOybTP3LdV9ym5A2jGpeiHZpEXaMukv1nhuuELSNnnxqhZ6mlI8vGecf3viECMlF6U0Kcsk1JCyDUxTkbEtvCgin7LpzCVJOAajZY+EabKuK8u6jri5dc0PSdsmGqj54azzW9eZ41fWd/DLAxOMVTzaMg4v6m0+6amFUk5eCCGEEEIsNQsejAHceuutx5yWeN999836+969e8/+CS1yc2XUrljddtr7zrSqI8tL1nbMeuza8zu49vyOY+xx5liWcdR1nErm8FQzjUIIIYQQQiwk48SbCCGEEEIIIYQ40yQYE0IIIYQQQogFIMGYEEIIIYQQQiwACcaEEEIIIYQQYgFIMCaEEEIIIYQQC0CCMSGEEEIIIYRYABKMCSGEEEIIIcQCkGBMCCGEEEIIIRaABGNCCCGEEEIIsQAkGBNCCCGEEEKIBSDBmBBCCCGEEEIsAAnGhBBCCCGEEGIBSDAmhBBCCCGEEAvAWugTmG9aawCKxeK8HdP3farVKsViEdu25+244tTJWC0NMk5Lh4zV0iDjtHTIWC0NMk5LR6lUAg7HCPPtBReMTb/hfX19C3wmQgghhBBCiMVgbGyMfD4/78dVeqHCwAUSRRH9/f3kcjmUUvNyzGKxSF9fHwcOHKCpqWlejilOj4zV0iDjtHTIWC0NMk5Lh4zV0iDjtHQUCgVWrFjBxMQEzc3N8378F1xmzDAMli9fviDHbmpqki/kEiFjtTTIOC0dMlZLg4zT0iFjtTTIOC0dhrEwpTSkgIcQQgghhBBCLAAJxoQQQgghhBBiAUgwNg8SiQQf+9jHSCQSC30q4gRkrJYGGaelQ8ZqaZBxWjpkrJYGGaelY6HH6gVXwEMIIYQQQgghFgPJjAkhhBBCCCHEApBgTAghhBBCCCEWgARjQgghhBBCCLEAJBgTQgghhBBCiAUgwdg8+PKXv8yqVatIJpNceeWVPPzwwwt9SuesT3/607z4xS8ml8vR2dnJr//6r7Njx45Z29TrdW655Rba2trIZrP8xm/8BkNDQ7O22b9/P6997WtJp9N0dnby/ve/nyAIZm1z3333cemll5JIJFi3bh3f/OY3z/blnbM+85nPoJTive99b+MxGafF49ChQ/z2b/82bW1tpFIpNm/ezCOPPNJ4XmvNRz/6UZYtW0YqleL6669n586ds15jfHycG2+8kaamJpqbm3nHO95BuVyetc0TTzzBS1/6UpLJJH19fXz2s5+dl+s7V4RhyEc+8hFWr15NKpVi7dq1/I//8T+YWadLxmr+/fjHP+Z1r3sdPT09KKX43ve+N+v5+RyT73znO2zYsIFkMsnmzZv5wQ9+cMavdyk73lj5vs8HP/hBNm/eTCaToaenh7e+9a309/fPeg0Zq7PvRN+pmd75zneilOILX/jCrMcX1ThpcVbdcccd2nEc/fWvf10/9dRT+vd+7/d0c3OzHhoaWuhTOyfdcMMN+hvf+Ibetm2b3rp1q/61X/s1vWLFCl0ulxvbvPOd79R9fX16y5Yt+pFHHtFXXXWVfslLXtJ4PggCvWnTJn399dfrxx57TP/gBz/Q7e3t+sMf/nBjm927d+t0Oq1vu+02/fTTT+svfelL2jRNfeedd87r9Z4LHn74Yb1q1Sp90UUX6fe85z2Nx2WcFofx8XG9cuVK/ba3vU0/9NBDevfu3fquu+7Su3btamzzmc98Rufzef29731PP/744/r1r3+9Xr16ta7Vao1tXv3qV+uLL75Y//znP9c/+clP9Lp16/Sb3/zmxvOFQkF3dXXpG2+8UW/btk1/+9vf1qlUSv/d3/3dvF7vUvapT31Kt7W16X//93/Xe/bs0d/5znd0NpvVX/ziFxvbyFjNvx/84Af6T/7kT/R3v/tdDeh//dd/nfX8fI3JAw88oE3T1J/97Gf1008/rf/0T/9U27atn3zyybP+HiwVxxuryclJff311+t/+qd/0tu3b9cPPvigvuKKK/Rll1026zVkrM6+E32npn33u9/VF198se7p6dF/9Vd/Neu5xTROEoydZVdccYW+5ZZbGn8Pw1D39PToT3/60wt4Vi8cw8PDGtD333+/1jr+x9S2bf2d73ynsc0zzzyjAf3ggw9qreMvuWEYenBwsLHNV77yFd3U1KRd19Vaa/2BD3xAb9y4cdaxfvM3f1PfcMMNZ/uSzimlUkmvX79e33333fpXfuVXGsGYjNPi8cEPflBfe+21x3w+iiLd3d2tP/e5zzUem5yc1IlEQn/729/WWmv99NNPa0D/4he/aGzzH//xH1oppQ8dOqS11vpv//ZvdUtLS2Pspo99/vnnn+lLOme99rWv1b/zO78z67H/8l/+i77xxhu11jJWi8GRN47zOSZvetOb9Gtf+9pZ53PllVfqP/iDPzij13iuON5N/rSHH35YA3rfvn1aaxmrhXCscTp48KDu7e3V27Zt0ytXrpwVjC22cZJpimeR53k8+uijXH/99Y3HDMPg+uuv58EHH1zAM3vhKBQKALS2tgLw6KOP4vv+rDHZsGEDK1asaIzJgw8+yObNm+nq6mpsc8MNN1AsFnnqqaca28x8jeltZFxPzS233MJrX/vao95LGafF49/+7d+4/PLLeeMb30hnZyeXXHIJX/va1xrP79mzh8HBwVnvcz6f58orr5w1Vs3NzVx++eWNba6//noMw+Chhx5qbPOyl70Mx3Ea29xwww3s2LGDiYmJs32Z54SXvOQlbNmyhWeffRaAxx9/nJ/+9Ke85jWvAWSsFqP5HBP59/DMKxQKKKVobm4GZKwWiyiKuOmmm3j/+9/Pxo0bj3p+sY2TBGNn0ejoKGEYzrpZBOjq6mJwcHCBzuqFI4oi3vve93LNNdewadMmAAYHB3Ecp/EP57SZYzI4ODjnmE0/d7xtisUitVrtbFzOOeeOO+7gl7/8JZ/+9KePek7GafHYvXs3X/nKV1i/fj133XUX73rXu3j3u9/NP/zDPwCH3+vj/Ts3ODhIZ2fnrOcty6K1tfWUxlMc34c+9CF+67d+iw0bNmDbNpdccgnvfe97ufHGGwEZq8VoPsfkWNvImJ2eer3OBz/4Qd785jfT1NQEyFgtFn/+53+OZVm8+93vnvP5xTZO1iltLcQScsstt7Bt2zZ++tOfLvSpiCMcOHCA97znPdx9990kk8mFPh1xHFEUcfnll/Nnf/ZnAFxyySVs27aNr371q9x8880LfHZipn/+53/mW9/6FrfffjsbN25k69atvPe976Wnp0fGSogzyPd93vSmN6G15itf+cpCn46Y4dFHH+WLX/wiv/zlL1FKLfTpnBTJjJ1F7e3tmKZ5VAW4oaEhuru7F+isXhhuvfVW/v3f/517772X5cuXNx7v7u7G8zwmJydnbT9zTLq7u+ccs+nnjrdNU1MTqVTqTF/OOefRRx9leHiYSy+9FMuysCyL+++/n7/+67/Gsiy6urpknBaJZcuWceGFF8567IILLmD//v3A4ff6eP/OdXd3Mzw8POv5IAgYHx8/pfEUx/f+97+/kR3bvHkzN910E3/0R3/UyD7LWC0+8zkmx9pGxuzUTAdi+/bt4+67725kxUDGajH4yU9+wvDwMCtWrGjcX+zbt4/3ve99rFq1Clh84yTB2FnkOA6XXXYZW7ZsaTwWRRFbtmzh6quvXsAzO3dprbn11lv513/9V370ox+xevXqWc9fdtll2LY9a0x27NjB/v37G2Ny9dVX8+STT876ok7/gzt9U3r11VfPeo3pbWRcT851113Hk08+ydatWxt/Lr/8cm688cbG/y/jtDhcc801R7WHePbZZ1m5ciUAq1evpru7e9b7XCwWeeihh2aN1eTkJI8++mhjmx/96EdEUcSVV17Z2ObHP/4xvu83trn77rs5//zzaWlpOWvXdy6pVqsYxuz/WTdNkyiKABmrxWg+x0T+PXz+pgOxnTt3cs8999DW1jbreRmrhXfTTTfxxBNPzLq/6Onp4f3vfz933XUXsAjH6ZTKfYhTdscdd+hEIqG/+c1v6qefflr//u//vm5ubp5VAU6cOe9617t0Pp/X9913nx4YGGj8qVarjW3e+c536hUrVugf/ehH+pFHHtFXX321vvrqqxvPT5dMf9WrXqW3bt2q77zzTt3R0TFnyfT3v//9+plnntFf/vKXpWT68zSzmqLWMk6LxcMPP6wty9Kf+tSn9M6dO/W3vvUtnU6n9T/+4z82tvnMZz6jm5ub9f/9v/9XP/HEE/oNb3jDnKW5L7nkEv3QQw/pn/70p3r9+vWzyghPTk7qrq4ufdNNN+lt27bpO+64Q6fTaSmXfgpuvvlm3dvb2yht/93vfle3t7frD3zgA41tZKzmX6lU0o899ph+7LHHNKA///nP68cee6xRgW++xuSBBx7QlmXpv/iLv9DPPPOM/tjHPibl0o9wvLHyPE+//vWv18uXL9dbt26ddY8xs+KejNXZd6Lv1JGOrKao9eIaJwnG5sGXvvQlvWLFCu04jr7iiiv0z3/+84U+pXMWMOefb3zjG41tarWa/sM//EPd0tKi0+m0/s//+T/rgYGBWa+zd+9e/ZrXvEanUind3t6u3/e+92nf92dtc++99+oXvehF2nEcvWbNmlnHEKfuyGBMxmnx+H//7//pTZs26UQioTds2KD/5//8n7Oej6JIf+QjH9FdXV06kUjo6667Tu/YsWPWNmNjY/rNb36zzmazuqmpSb/97W/XpVJp1jaPP/64vvbaa3UikdC9vb36M5/5zFm/tnNJsVjU73nPe/SKFSt0MpnUa9as0X/yJ38y60ZRxmr+3XvvvXP+79LNN9+stZ7fMfnnf/5nfd5552nHcfTGjRv197///bN23UvR8cZqz549x7zHuPfeexuvIWN19p3oO3WkuYKxxTROSmutTy2XJoQQQgghhBDi+ZI1Y0IIIYQQQgixACQYE0IIIYQQQogFIMGYEEIIIYQQQiwACcaEEEIIIYQQYgFIMCaEEEIIIYQQC0CCMSGEEEIIIYRYABKMCSGEEEIIIcQCkGBMCCGEEEIIIRaABGNCCCHE87B3716UUmzdunWhT0UIIcQSI8GYEEKIefW2t72NX//1X1/o0zhpe/bs4S1veQs9PT0kk0mWL1/OG97wBrZv3w5AX18fAwMDbNq0aYHPVAghxFJjLfQJCCGEEIuV7/u88pWv5Pzzz+e73/0uy5Yt4+DBg/zHf/wHk5OTAJimSXd398KeqBBCiCVJMmNCCCEWlfvvv58rrriCRCLBsmXL+NCHPkQQBI3noyjis5/9LOvWrSORSLBixQo+9alPAXDfffehlGoESgBbt25FKcXevXsB2LdvH6973etoaWkhk8mwceNGfvCDH8x5Lk899RTPPfccf/u3f8tVV13FypUrueaaa/jkJz/JVVddBRw9TfFtb3sbSqmj/tx3330AuK7LH//xH9Pb20smk+HKK69sPCeEEOKFRYIxIYQQi8ahQ4f4tV/7NV784hfz+OOP85WvfIX/9b/+F5/85Ccb23z4wx/mM5/5DB/5yEd4+umnuf322+nq6jrpY9xyyy24rsuPf/xjnnzySf78z/+cbDY757YdHR0YhsG//Mu/EIbhSb3+F7/4RQYGBhp/3vOe99DZ2cmGDRsAuPXWW3nwwQe54447eOKJJ3jjG9/Iq1/9anbu3HnS1yCEEOLcINMUhRBCLBp/+7d/S19fH3/zN3+DUooNGzbQ39/PBz/4QT760Y9SqVT44he/yN/8zd9w8803A7B27Vquvfbakz7G/v37+Y3f+A02b94MwJo1a465bW9vL3/913/NBz7wAT7xiU9w+eWX84pXvIIbb7zxmPvl83ny+TwA3/3ud/m7v/s77rnnHrq7u9m/fz/f+MY32L9/Pz09PQD88R//MXfeeSff+MY3+LM/+7OTvg4hhBBLn2TGhBBCLBrPPPMMV199NUqpxmPXXHMN5XKZgwcP8swzz+C6Ltddd91pH+Pd7343n/zkJ7nmmmv42Mc+xhNPPHHc7W+55RYGBwf51re+xdVXX813vvMdNm7cyN13333c/R577DFuuukm/uZv/oZrrrkGgCeffJIwDDnvvPPIZrONP/fffz/PPffcaV+TEEKIpUmCMSGEEEtGKpU67vOGEf/Pmta68Zjv+7O2+d3f/V12797NTTfdxJNPPsnll1/Ol770peO+bi6X43Wvex2f+tSnePzxx3npS186a+rkkQYHB3n961/P7/7u7/KOd7yj8Xi5XMY0TR599FG2bt3a+PPMM8/wxS9+8bjnIIQQ4twjwZgQQohF44ILLuDBBx+cFUw98MAD5HI5li9fzvr160mlUmzZsmXO/Ts6OgAYGBhoPDZX/6++vj7e+c538t3vfpf3ve99fO1rXzvpc5yePlmpVOZ8vl6v84Y3vIENGzbw+c9/ftZzl1xyCWEYMjw8zLp162b9kYqMQgjxwiNrxoQQQsy7QqFwVJDU1tbGH/7hH/KFL3yB//bf/hu33norO3bs4GMf+xi33XYbhmGQTCb54Ac/yAc+8AEcx+Gaa65hZGSEp556ine84x2sW7eOvr4+Pv7xj/OpT32KZ599lr/8y7+cdZz3vve9vOY1r+G8885jYmKCe++9lwsuuGDO89y6dSsf+9jHuOmmm7jwwgtxHIf777+fr3/963zwgx+cc58/+IM/4MCBA2zZsoWRkZHG462trZx33nnceOONvPWtb+Uv//IvueSSSxgZGWHLli1cdNFFvPa1r31+b6wQQoglRYIxIYQQ8+6+++7jkksumfXYO97xDv7+7/+eH/zgB7z//e/n4osvprW1lXe84x386Z/+aWO7j3zkI1iWxUc/+lH6+/tZtmwZ73znOwGwbZtvf/vbvOtd7+Kiiy7ixS9+MZ/85Cd54xvf2Ng/DENuueUWDh48SFNTE69+9av5q7/6qznPc/ny5axatYpPfOITjRL203//oz/6ozn3uf/++xkYGODCCy+c9fi9997Ly1/+cr7xjW/wyU9+kve9730cOnSI9vZ2rrrqKv7Tf/pPp/VeCiGEWLqUnjkXRAghhBBCCCHEvJA1Y0IIIYQQQgixACQYE0IIIYQQQogFIMGYEEIIIYQQQiwACcaEEEIIIYQQYgFIMCaEEEIIIYQQC0CCMSGEEEIIIYRYABKMCSGEEEIIIcQCkGBMCCGEEEIIIRaABGNCCCGEEEIIsQAkGBNCCCGEEEKIBSDBmBBCCCGEEEIsgP8/2kIbanaDmocAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plt.figure(figsize=(10, 6))\n", + "# plt.scatter(panda_df[\"locusSize\"], panda_df[\"locusLength\"], alpha=0.5)\n", + "# plt.title(\"Scatter Plot of Locus Size vs Locus Length\")\n", + "# plt.xlabel(\"Locus Size\")\n", + "# plt.ylabel(\"Locus Length\")\n", + "# plt.grid(True)\n", + "# plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gentropy-iQynFIia-py3.10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/poetry.lock b/poetry.lock index d70afbb00..edba6ef99 100644 --- a/poetry.lock +++ b/poetry.lock @@ -15,111 +15,127 @@ files = [ pycares = ">=3.0.0" 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">=0.2.0" [metadata] lock-version = "2.0" python-versions = "^3.10, <3.11" -content-hash = "8d85933db4bf8f4ca92ef227e9a767d26f4f7d2b360083449cfafc54878885fc" +content-hash = "c22ab3de1b76f7549448f4204d52fe0a2d9e68cbbd4d4e873fad667a075dffe3" diff --git a/pyproject.toml b/pyproject.toml index 3f4f44545..54b5a20ca 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,24 +24,27 @@ pyspark = "3.3.4" scipy = "^1.11.4" hydra-core = "^1.3.2" pyliftover = "^0.4" -xgboost = "^1.7.3" numpy = "^1.26.2" hail = "0.2.127" -wandb = "^0.16.2" +wandb = ">=0.16.2,<0.19.0" google = "^3.0.0" omegaconf = "^2.3.0" typing-extensions = "^4.9.0" scikit-learn = "^1.3.2" -pandas = "^2.1.4" +pandas = { extras = ["gcp", "parquet"], version = "^2.2.2" } +skops = ">=0.9,<0.11" +google-cloud-secret-manager = "^2.20.0" +shap = "^0.46.0" +matplotlib = "3.7.3" [tool.poetry.dev-dependencies] -pre-commit = "^3.6.0" -mypy = "^1.8" -pep8-naming = "^0.13.2" -interrogate = "^1.5.0" +pre-commit = "^4.0.0" +mypy = "^1.13" +pep8-naming = "^0.14.1" +interrogate = "^1.7.0" isort = "^5.13.2" darglint = "^1.8.1" -ruff = "^0.2.0" +ruff = "^0.7.0" [tool.poetry.group.docs.dependencies] mkdocs = "^1.5.3" @@ -58,9 +61,9 @@ pymdown-extensions = "^10.7" [tool.poetry.group.tests.dependencies] -pytest-cov = "^4.1.0" +pytest-cov = ">=4.1,<6.0" pytest-sugar = ">=0.9.5,<1.1.0" -dbldatagen = "^0.3.1" +dbldatagen = ">=0.3.1,<0.5.0" pyparsing = "^3.1.1" pytest = ">=7.4.4,<9.0.0" pytest-xdist = "^3.5.0" @@ -70,12 +73,9 @@ pytest-xdist = "^3.5.0" ipython = "^8.19.0" ipykernel = "^6.28.0" google-cloud-dataproc = "^5.8.0" -apache-airflow = "^2.8.0" -apache-airflow-providers-google = "^10.13.1" -pydoclint = ">=0.3.8,<0.5.0" +pydoclint = ">=0.3.8,<0.6.0" prettier = "^0.0.7" -deptry = "^0.12.0" -python-semantic-release = ">=8.7,<10.0" +deptry = ">=0.12,<0.21" yamllint = "^1.33.0" [tool.semantic_release] @@ -113,7 +113,7 @@ requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.deptry] -extend_exclude = ["src/conftest.py", "src/airflow", "src/utils"] +extend_exclude = ["src/conftest.py", "src/utils"] [tool.deptry.per_rule_ignores] DEP001 = ["gentropy"] @@ -126,8 +126,9 @@ exclude = ["dist"] [tool.pytest.ini_options] addopts = "-n auto --doctest-modules --cov=src/ --cov-report=xml" -pythonpath = [".", "./src/airflow/dags"] -testpaths = ["tests/gentropy", "src/gentropy/"] +pythonpath = ["."] +testpaths = ["tests/gentropy", "src/gentropy"] +markers = ["step_test"] # Semi-strict mode for mypy [tool.mypy] @@ -158,13 +159,14 @@ module = [ ignore_missing_imports = true -[tool.ruff] +[tool.ruff.lint] select = [ "B002", # Python does not support the unary prefix increment "B007", # Loop control variable {name} not used within loop body "B014", # Exception handler with duplicate exception "B023", # Function definition does not bind loop variable {name} "B026", # Star-arg unpacking after a keyword argument is strongly discouraged + "B904", # Checks for raise statements in exception handlers that lack a from clause "C", # complexity "COM818", # Trailing comma on bare tuple prohibited "D", # docstrings @@ -179,7 +181,6 @@ select = [ "N804", # First argument of a class method should be named cls "N805", # First argument of a method should be named self "N815", # Variable {name} in class scope should not be mixedCase - "PGH001", # No builtin eval() allowed "PGH004", # Use specific rule codes when using noqa "PLC0414", # Useless import alias. Import alias does not rename original package. "PLC", # pylint @@ -218,7 +219,6 @@ select = [ "T20", # flake8-print "TID251", # Banned imports "TRY004", # Prefer TypeError exception for invalid type - "TRY200", # Use raise from to specify exception cause "TRY302", # Remove exception handler; error is immediately re-raised "UP", # pyupgrade "W", # pycodestyle @@ -248,15 +248,15 @@ ignore = [ ] -[tool.ruff.per-file-ignores] +[tool.ruff.lint.per-file-ignores] "__init__.py" = ["E402"] "path/to/file.py" = ["E402"] "**/{tests,docs,tools}/*" = ["E402"] -[tool.ruff.flake8-quotes] +[tool.ruff.lint.flake8-quotes] docstring-quotes = "double" -[tool.ruff.pydocstyle] +[tool.ruff.lint.pydocstyle] convention = "google" [tool.pydoclint] @@ -267,3 +267,4 @@ check-return-types = true allow-init-docstring = true arg-type-hints-in-docstring = true skip-checking-short-docstrings = false +check-class-attributes = false diff --git a/src/airflow/.env b/src/airflow/.env deleted file mode 100644 index 2bcd8244f..000000000 --- a/src/airflow/.env +++ /dev/null @@ -1,6 +0,0 @@ -AIRFLOW_IMAGE_NAME=extending_airflow:latest -GOOGLE_LOCAL_CREDENTIALS_PATH=~/.config/gcloud -GOOGLE_DOCKER_CREDENTIALS_PATH=/.config/gcloud -GOOGLE_APPLICATION_CREDENTIALS=/.config/gcloud/service_account_credentials.json -AIRFLOW_CONN_GOOGLE_CLOUD_DEFAULT='google-cloud-platform://?extra__google_cloud_platform__key_path=/.config/gcloud/service_account_credentials.json' -GCP_PROJECT_ID=open-targets-genetics-dev diff --git a/src/airflow/Dockerfile b/src/airflow/Dockerfile deleted file mode 100644 index 9ca7c9193..000000000 --- a/src/airflow/Dockerfile +++ /dev/null @@ -1,33 +0,0 @@ -FROM apache/airflow:slim-2.7.3-python3.10 - -# Install additional Python requirements. -# --no-cache-dir is a good practice when installing packages using pip, because it helps to keep the image lightweight. -COPY requirements.txt /requirements.txt -RUN pip install --quiet --user --no-cache-dir --upgrade pip setuptools && \ - pip install --quiet --user --no-cache-dir -r /requirements.txt - -# Source: https://airflow.apache.org/docs/docker-stack/recipes.html -# Installing the GCP CLI in the container -SHELL ["/bin/bash", "-o", "pipefail", "-e", "-u", "-x", "-c"] - -USER 0 -ARG CLOUD_SDK_VERSION=452.0.0 -ENV GCLOUD_HOME=/home/google-cloud-sdk - -ENV PATH="${GCLOUD_HOME}/bin/:${PATH}" - -RUN DOWNLOAD_URL="https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-sdk-${CLOUD_SDK_VERSION}-linux-x86_64.tar.gz" \ - && TMP_DIR="$(mktemp -d)" \ - && curl -fL "${DOWNLOAD_URL}" --output "${TMP_DIR}/google-cloud-sdk.tar.gz" \ - && mkdir -p "${GCLOUD_HOME}" \ - && tar xzf "${TMP_DIR}/google-cloud-sdk.tar.gz" -C "${GCLOUD_HOME}" --strip-components=1 \ - && "${GCLOUD_HOME}/install.sh" \ - --bash-completion=false \ - --path-update=false \ - --usage-reporting=false \ - --quiet \ - && rm -rf "${TMP_DIR}" \ - && gcloud --version - -# Switch back to a non-root user for security purposes -USER airflow diff --git a/src/airflow/dags/common_airflow.py b/src/airflow/dags/common_airflow.py deleted file mode 100644 index e3dc56ccb..000000000 --- a/src/airflow/dags/common_airflow.py +++ /dev/null @@ -1,353 +0,0 @@ -"""Airflow boilerplate code which can be shared by several DAGs.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING, Any, Optional - -import pendulum -import yaml -from airflow.providers.google.cloud.operators.dataproc import ( - ClusterGenerator, - DataprocCreateClusterOperator, - DataprocDeleteClusterOperator, - DataprocSubmitJobOperator, -) -from airflow.utils.trigger_rule import TriggerRule -from google.cloud import dataproc_v1 - -if TYPE_CHECKING: - from pathlib import Path - -# Code version. It has to be repeated here as well as in `pyproject.toml`, because Airflow isn't able to look at files outside of its `dags/` directory. -GENTROPY_VERSION = "0.0.0" - -# Cloud configuration. -GCP_PROJECT = "open-targets-genetics-dev" -GCP_REGION = "europe-west1" -GCP_ZONE = "europe-west1-d" -GCP_DATAPROC_IMAGE = "2.1" -GCP_AUTOSCALING_POLICY = "otg-etl" - -# Cluster init configuration. -INITIALISATION_BASE_PATH = ( - f"gs://genetics_etl_python_playground/initialisation/{GENTROPY_VERSION}" -) -CONFIG_TAG = f"{INITIALISATION_BASE_PATH}/config.tar.gz" -PACKAGE_WHEEL = ( - f"{INITIALISATION_BASE_PATH}/gentropy-{GENTROPY_VERSION}-py3-none-any.whl" -) -INITIALISATION_EXECUTABLE_FILE = [ - f"{INITIALISATION_BASE_PATH}/install_dependencies_on_cluster.sh" -] - -# CLI configuration. -CLUSTER_CONFIG_DIR = "/config" -CONFIG_NAME = "ot_config" -PYTHON_CLI = "cli.py" - -# Shared DAG construction parameters. -shared_dag_args = { - "owner": "Open Targets Data Team", - "retries": 0, -} - -shared_dag_kwargs = { - "tags": ["genetics_etl", "experimental"], - "start_date": pendulum.now(tz="Europe/London").subtract(days=1), - "schedule": "@once", - "catchup": False, -} - - -def create_cluster( - cluster_name: str, - master_machine_type: str = "n1-highmem-16", - worker_machine_type: str = "n1-standard-16", - num_workers: int = 2, - num_preemptible_workers: int = 0, - num_local_ssds: int = 1, - autoscaling_policy: str = GCP_AUTOSCALING_POLICY, - master_disk_size: int = 500, -) -> DataprocCreateClusterOperator: - """Generate an Airflow task to create a Dataproc cluster. Common parameters are reused, and varying parameters can be specified as needed. - - Args: - cluster_name (str): Name of the cluster. - master_machine_type (str): Machine type for the master node. Defaults to "n1-highmem-8". - worker_machine_type (str): Machine type for the worker nodes. Defaults to "n1-standard-16". - num_workers (int): Number of worker nodes. Defaults to 2. - num_preemptible_workers (int): Number of preemptible worker nodes. Defaults to 0. - num_local_ssds (int): How many local SSDs to attach to each worker node, both primary and secondary. Defaults to 1. - autoscaling_policy (str): Name of the autoscaling policy to use. Defaults to GCP_AUTOSCALING_POLICY. - master_disk_size (int): Size of the master node's boot disk in GB. Defaults to 500. - - Returns: - DataprocCreateClusterOperator: Airflow task to create a Dataproc cluster. - """ - # Create base cluster configuration. - cluster_config = ClusterGenerator( - project_id=GCP_PROJECT, - zone=GCP_ZONE, - master_machine_type=master_machine_type, - worker_machine_type=worker_machine_type, - master_disk_size=master_disk_size, - worker_disk_size=500, - num_preemptible_workers=num_preemptible_workers, - num_workers=num_workers, - image_version=GCP_DATAPROC_IMAGE, - enable_component_gateway=True, - init_actions_uris=INITIALISATION_EXECUTABLE_FILE, - metadata={ - "CONFIGTAR": CONFIG_TAG, - "PACKAGE": PACKAGE_WHEEL, - }, - idle_delete_ttl=30 * 60, # In seconds. - autoscaling_policy=f"projects/{GCP_PROJECT}/regions/{GCP_REGION}/autoscalingPolicies/{autoscaling_policy}", - ).make() - - # If specified, amend the configuration to include local SSDs for worker nodes. - if num_local_ssds: - for worker_section in ("worker_config", "secondary_worker_config"): - # Create a disk config section if it does not exist. - cluster_config[worker_section].setdefault("disk_config", {}) - # Specify the number of local SSDs. - cluster_config[worker_section]["disk_config"][ - "num_local_ssds" - ] = num_local_ssds - - # Return the cluster creation operator. - return DataprocCreateClusterOperator( - task_id="create_cluster", - project_id=GCP_PROJECT, - cluster_config=cluster_config, - region=GCP_REGION, - cluster_name=cluster_name, - trigger_rule=TriggerRule.ALL_SUCCESS, - ) - - -def submit_job( - cluster_name: str, - task_id: str, - job_type: str, - job_specification: dict[str, Any], - trigger_rule: TriggerRule = TriggerRule.ALL_SUCCESS, -) -> DataprocSubmitJobOperator: - """Submit an arbitrary job to a Dataproc cluster. - - Args: - cluster_name (str): Name of the cluster. - task_id (str): Name of the task. - job_type (str): Type of the job to submit. - job_specification (dict[str, Any]): Specification of the job to submit. - trigger_rule (TriggerRule): Trigger rule for the task. Defaults to TriggerRule.ALL_SUCCESS. - - Returns: - DataprocSubmitJobOperator: Airflow task to submit an arbitrary job to a Dataproc cluster. - """ - return DataprocSubmitJobOperator( - task_id=task_id, - region=GCP_REGION, - project_id=GCP_PROJECT, - job={ - "job_uuid": f"airflow-{task_id}", - "reference": {"project_id": GCP_PROJECT}, - "placement": {"cluster_name": cluster_name}, - job_type: job_specification, - }, - trigger_rule=trigger_rule, - ) - - -def submit_pyspark_job( - cluster_name: str, - task_id: str, - python_module_path: str, - args: list[str], - trigger_rule: TriggerRule = TriggerRule.ALL_SUCCESS, -) -> DataprocSubmitJobOperator: - """Submit a PySpark job to a Dataproc cluster. - - Args: - cluster_name (str): Name of the cluster. - task_id (str): Name of the task. - python_module_path (str): Path to the Python module to run. - args (list[str]): Arguments to pass to the Python module. - trigger_rule (TriggerRule): Trigger rule for the task. Defaults to TriggerRule.ALL_SUCCESS. - - Returns: - DataprocSubmitJobOperator: Airflow task to submit a PySpark job to a Dataproc cluster. - """ - return submit_job( - cluster_name=cluster_name, - task_id=task_id, - job_type="pyspark_job", - trigger_rule=trigger_rule, - job_specification={ - "main_python_file_uri": python_module_path, - "args": args, - "properties": { - "spark.jars": "/opt/conda/miniconda3/lib/python3.10/site-packages/hail/backend/hail-all-spark.jar", - "spark.driver.extraClassPath": "/opt/conda/miniconda3/lib/python3.10/site-packages/hail/backend/hail-all-spark.jar", - "spark.executor.extraClassPath": "./hail-all-spark.jar", - "spark.serializer": "org.apache.spark.serializer.KryoSerializer", - "spark.kryo.registrator": "is.hail.kryo.HailKryoRegistrator", - }, - }, - ) - - -def submit_step( - cluster_name: str, - step_id: str, - task_id: str = "", - trigger_rule: TriggerRule = TriggerRule.ALL_SUCCESS, - other_args: Optional[list[str]] = None, -) -> DataprocSubmitJobOperator: - """Submit a PySpark job to execute a specific CLI step. - - Args: - cluster_name (str): Name of the cluster. - step_id (str): Name of the step in gentropy. - task_id (str): Name of the task. Defaults to step_id. - trigger_rule (TriggerRule): Trigger rule for the task. Defaults to TriggerRule.ALL_SUCCESS. - other_args (Optional[list[str]]): Other arguments to pass to the CLI step. Defaults to None. - - Returns: - DataprocSubmitJobOperator: Airflow task to submit a PySpark job to execute a specific CLI step. - """ - if task_id == "": - task_id = step_id - return submit_pyspark_job( - cluster_name=cluster_name, - task_id=task_id, - python_module_path=f"{INITIALISATION_BASE_PATH}/{PYTHON_CLI}", - trigger_rule=trigger_rule, - args=[f"step={step_id}"] - + (other_args if other_args is not None else []) - + [ - f"--config-dir={CLUSTER_CONFIG_DIR}", - f"--config-name={CONFIG_NAME}", - ], - ) - - -def install_dependencies(cluster_name: str) -> DataprocSubmitJobOperator: - """Install dependencies on a Dataproc cluster. - - Args: - cluster_name (str): Name of the cluster. - - Returns: - DataprocSubmitJobOperator: Airflow task to install dependencies on a Dataproc cluster. - """ - return submit_job( - cluster_name=cluster_name, - task_id="install_dependencies", - job_type="pig_job", - job_specification={ - "jar_file_uris": [ - f"gs://genetics_etl_python_playground/initialisation/{GENTROPY_VERSION}/install_dependencies_on_cluster.sh" - ], - "query_list": { - "queries": [ - "sh chmod 750 ${PWD}/install_dependencies_on_cluster.sh", - "sh ${PWD}/install_dependencies_on_cluster.sh", - ] - }, - }, - ) - - -def delete_cluster(cluster_name: str) -> DataprocDeleteClusterOperator: - """Generate an Airflow task to delete a Dataproc cluster. - - Args: - cluster_name (str): Name of the cluster. - - Returns: - DataprocDeleteClusterOperator: Airflow task to delete a Dataproc cluster. - """ - return DataprocDeleteClusterOperator( - task_id="delete_cluster", - project_id=GCP_PROJECT, - cluster_name=cluster_name, - region=GCP_REGION, - trigger_rule=TriggerRule.ALL_DONE, - ) - - -def read_yaml_config(config_path: Path) -> Any: - """Parse a YAMl config file and do all necessary checks. - - Args: - config_path (Path): Path to the YAML config file. - - Returns: - Any: Parsed YAML config file. - """ - assert config_path.exists(), f"YAML config path {config_path} does not exist." - with open(config_path) as config_file: - return yaml.safe_load(config_file) - - -def generate_dag(cluster_name: str, tasks: list[DataprocSubmitJobOperator]) -> Any: - """For a list of tasks, generate a complete DAG. - - Args: - cluster_name (str): Name of the cluster. - tasks (list[DataprocSubmitJobOperator]): List of tasks to execute. - - Returns: - Any: Airflow DAG. - """ - return ( - create_cluster(cluster_name) - >> install_dependencies(cluster_name) - >> tasks - >> delete_cluster(cluster_name) - ) - - -def submit_pyspark_job_no_operator( - cluster_name: str, - step_id: str, - other_args: Optional[list[str]] = None, -) -> None: - """Submits the Pyspark job to the cluster. - - Args: - cluster_name (str): Cluster name - step_id (str): Step id - other_args (Optional[list[str]]): Other arguments to pass to the CLI step. Defaults to None. - """ - # Create the job client. - job_client = dataproc_v1.JobControllerClient( - client_options={"api_endpoint": f"{GCP_REGION}-dataproc.googleapis.com:443"} - ) - - python_uri = f"{INITIALISATION_BASE_PATH}/{PYTHON_CLI}" - # Create the job config. 'main_jar_file_uri' can also be a - # Google Cloud Storage URL. - job_description = { - "placement": {"cluster_name": cluster_name}, - "pyspark_job": { - "main_python_file_uri": python_uri, - "args": [f"step={step_id}"] - + (other_args if other_args is not None else []) - + [ - f"--config-dir={CLUSTER_CONFIG_DIR}", - f"--config-name={CONFIG_NAME}", - ], - "properties": { - "spark.jars": "/opt/conda/miniconda3/lib/python3.10/site-packages/hail/backend/hail-all-spark.jar", - "spark.driver.extraClassPath": "/opt/conda/miniconda3/lib/python3.10/site-packages/hail/backend/hail-all-spark.jar", - "spark.executor.extraClassPath": "./hail-all-spark.jar", - "spark.serializer": "org.apache.spark.serializer.KryoSerializer", - "spark.kryo.registrator": "is.hail.kryo.HailKryoRegistrator", - }, - }, - } - job_client.submit_job( - project_id=GCP_PROJECT, region=GCP_REGION, job=job_description - ) diff --git a/src/airflow/dags/configs/dag.yaml b/src/airflow/dags/configs/dag.yaml deleted file mode 100644 index 0b634caaa..000000000 --- a/src/airflow/dags/configs/dag.yaml +++ /dev/null @@ -1,18 +0,0 @@ -- id: "ot_gene_index" -- id: "ot_variant_index" -- id: "ot_variant_to_gene" - prerequisites: - - "ot_variant_index" - - "ot_gene_index" -- id: "ot_colocalisation" -- id: "ot_locus_to_gene_train" - prerequisites: - - "ot_variant_index" - - "ot_variant_to_gene" - - "ot_colocalisation" -- id: "ot_locus_to_gene_predict" - prerequisites: - - "ot_locus_to_gene_train" - - "ot_variant_index" - - "ot_variant_to_gene" - - "ot_colocalisation" diff --git a/src/airflow/dags/dag_genetics_etl.py b/src/airflow/dags/dag_genetics_etl.py deleted file mode 100644 index 1d27b8bba..000000000 --- a/src/airflow/dags/dag_genetics_etl.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Airflow DAG for the ETL part of the pipeline.""" -from __future__ import annotations - -from pathlib import Path - -import common_airflow as common -from airflow.models.dag import DAG - -CLUSTER_NAME = "otg-etl" -SOURCE_CONFIG_FILE_PATH = Path(__file__).parent / "configs" / "dag.yaml" - - -with DAG( - dag_id=Path(__file__).stem, - description="Open Targets Genetics ETL workflow", - default_args=common.shared_dag_args, - **common.shared_dag_kwargs, -): - # Parse and define all steps and their prerequisites. - tasks = {} - steps = common.read_yaml_config(SOURCE_CONFIG_FILE_PATH) - for step in steps: - # Define task for the current step. - step_id = step["id"] - this_task = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id=step_id, - task_id=step_id, - ) - # Chain prerequisites. - tasks[step_id] = this_task - for prerequisite in step.get("prerequisites", []): - this_task.set_upstream(tasks[prerequisite]) - # Construct the DAG with all tasks. - dag = common.generate_dag(cluster_name=CLUSTER_NAME, tasks=list(tasks.values())) diff --git a/src/airflow/dags/dag_preprocess.py b/src/airflow/dags/dag_preprocess.py deleted file mode 100644 index 4439914c5..000000000 --- a/src/airflow/dags/dag_preprocess.py +++ /dev/null @@ -1,27 +0,0 @@ -"""Airflow DAG for the Preprocess part of the pipeline.""" -from __future__ import annotations - -from pathlib import Path - -import common_airflow as common -from airflow.models.dag import DAG - -CLUSTER_NAME = "otg-preprocess" - -ALL_STEPS = [ - "ot_ld_index", - "ot_variant_annotation", -] - - -with DAG( - dag_id=Path(__file__).stem, - description="Open Targets Genetics β€” Preprocess", - default_args=common.shared_dag_args, - **common.shared_dag_kwargs, -): - all_tasks = [ - common.submit_step(cluster_name=CLUSTER_NAME, step_id=step, task_id=step) - for step in ALL_STEPS - ] - dag = common.generate_dag(cluster_name=CLUSTER_NAME, tasks=all_tasks) diff --git a/src/airflow/dags/finngen_harmonisation.py b/src/airflow/dags/finngen_harmonisation.py deleted file mode 100644 index e2f2d1b4a..000000000 --- a/src/airflow/dags/finngen_harmonisation.py +++ /dev/null @@ -1,77 +0,0 @@ -"""Airflow DAG for the harmonisation part of the pipeline.""" -from __future__ import annotations - -import re -import time -from pathlib import Path -from typing import Any - -import common_airflow as common -from airflow.decorators import task -from airflow.models.dag import DAG -from airflow.providers.google.cloud.operators.gcs import GCSListObjectsOperator - -CLUSTER_NAME = "otg-finngen-harmonisation" -AUTOSCALING = "gwascatalog-harmonisation" # same as GWAS Catalog harmonisation -SUMMARY_STATS_BUCKET_NAME = "finngen-public-data-r10" -RELEASEBUCKET = "gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX" -SUMSTATS_PARQUET = f"{RELEASEBUCKET}/summary_statistics/finngen" - -with DAG( - dag_id=Path(__file__).stem, - description="Open Targets Genetics β€” Finngen harmonisation", - default_args=common.shared_dag_args, - **common.shared_dag_kwargs, -): - # List raw harmonised files from GWAS Catalog - list_inputs = GCSListObjectsOperator( - task_id="list_raw_sumstats", - bucket=SUMMARY_STATS_BUCKET_NAME, - prefix="summary_stats", - match_glob="**/*.gz", - ) - - # Submit jobs to dataproc - @task(task_id="submit_jobs") - def submit_jobs(**kwargs: Any) -> None: - """Submit jobs to dataproc. - - Args: - **kwargs (Any): Keyword arguments. - """ - ti = kwargs["ti"] - todo = ti.xcom_pull(task_ids="list_raw_sumstats", key="return_value") - print("Number of jobs to submit: ", len(todo)) # noqa: T201 - for i in range(len(todo)): - # Not to exceed default quota 400 jobs per minute - if i > 0 and i % 399 == 0: - time.sleep(60) - input_path = todo[i] - match_result = re.search(r"summary_stats/finngen_(.*).gz", input_path) - if match_result: - study_id = match_result.group(1) - print("Submitting job for study: ", study_id) # noqa: T201 - common.submit_pyspark_job_no_operator( - cluster_name=CLUSTER_NAME, - step_id="ot_finngen_sumstat_preprocess", - other_args=[ - f"step.raw_sumstats_path=gs://{SUMMARY_STATS_BUCKET_NAME}/{input_path}", - f"step.out_sumstats_path={SUMSTATS_PARQUET}/{study_id}.parquet", - ], - ) - - # list_inputs >> - ( - list_inputs - >> common.create_cluster( - CLUSTER_NAME, - autoscaling_policy=AUTOSCALING, - num_workers=8, - # num_preemptible_workers=8, - master_machine_type="n1-highmem-32", - worker_machine_type="n1-standard-2", - ) - >> common.install_dependencies(CLUSTER_NAME) - >> submit_jobs() - >> common.delete_cluster(CLUSTER_NAME) - ) diff --git a/src/airflow/dags/finngen_preprocess.py b/src/airflow/dags/finngen_preprocess.py deleted file mode 100644 index 38aeb2510..000000000 --- a/src/airflow/dags/finngen_preprocess.py +++ /dev/null @@ -1,105 +0,0 @@ -"""Airflow DAG for the Preprocess part of the pipeline.""" -from __future__ import annotations - -from pathlib import Path - -import common_airflow as common -from airflow.models.dag import DAG -from airflow.utils.task_group import TaskGroup -from airflow.utils.trigger_rule import TriggerRule - -CLUSTER_NAME = "otg-preprocess-finngen" -AUTOSCALING = "finngen-preprocess" - -# Get all parameters for the DAG: -FINNGEN_VERSION = "r10" -FINNGEN_BUCKET = f"gs://finngen_data/{FINNGEN_VERSION}" - -STUDY_INDEX = f"{FINNGEN_BUCKET}/study_index" -SUMMARY_STATISTICS = f"{FINNGEN_BUCKET}/harmonised_summary_statistics" -WINDOW_BASED_CLUMPED = f"{FINNGEN_BUCKET}/study_locus_datasets/finngen_window_clumped" -LD_CLUMPED = f"{FINNGEN_BUCKET}/study_locus_datasets/finngen_ld_clumped" -PICSED_CREDIBLE_SET = f"{FINNGEN_BUCKET}/credible_set_datasets/finngen_pics" - -FINNGEN_FINEMAPPING = ( - "gs://genetics_etl_python_playground/input/Finngen_susie_finemapping_r10/full" -) -FINNGEN_FM_SUMMARIES = "gs://genetics_etl_python_playground/input/Finngen_susie_finemapping_r10/Finngen_susie_credset_summary_r10.tsv" -FINNGEN_PREFIX = "FINNGEN_R10_" -FINNGEN_FM_OUT = "gs://genetics_etl_python_playground/output/python_etl/parquet/XX.XX/finngen_susie_processed" - -with DAG( - dag_id=Path(__file__).stem, - description="Open Targets Genetics β€” Finngen preprocess", - default_args=common.shared_dag_args, - **common.shared_dag_kwargs, -): - finngen_finemapping_ingestion = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_finngen_finemapping_ingestion", - task_id="finngen_finemapping_ingestion", - other_args=[ - f"step.finngen_finemapping_out={FINNGEN_FM_OUT}", - f"step.finngen_release_prefix={FINNGEN_PREFIX}", - f"step.finngen_finemapping_results_path={FINNGEN_FINEMAPPING}", - f"step.finngen_finemapping_summaries_path={FINNGEN_FM_SUMMARIES}", - ], - # This allows to attempt running the task when above step fails do to failifexists - trigger_rule=TriggerRule.ALL_DONE, - ) - with TaskGroup( - group_id="finngen_summary_stats_preprocess" - ) as finngen_summary_stats_preprocess: - study_index = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_finngen_studies", - task_id="finngen_studies", - other_args=[ - f"step.finngen_study_index_out={STUDY_INDEX}", - ], - ) - - window_based_clumping = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="window_based_clumping", - task_id="finngen_window_based_clumping", - other_args=[ - f"step.summary_statistics_input_path={SUMMARY_STATISTICS}", - f"step.study_locus_output_path={WINDOW_BASED_CLUMPED}", - ], - ) - ld_clumping = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_ld_based_clumping", - task_id="finngen_ld_clumping", - other_args=[ - f"step.study_locus_input_path={WINDOW_BASED_CLUMPED}", - f"step.study_index_path={STUDY_INDEX}", - f"step.clumped_study_locus_output_path={LD_CLUMPED}", - ], - trigger_rule=TriggerRule.ALL_DONE, - ) - pics = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_pics", - task_id="finngen_pics", - other_args=[ - f"step.study_locus_ld_annotated_in={LD_CLUMPED}", - f"step.picsed_study_locus_out={PICSED_CREDIBLE_SET}", - ], - # This allows to attempt running the task when above step fails do to failifexists - trigger_rule=TriggerRule.ALL_DONE, - ) - # Define order of steps: - (study_index >> window_based_clumping >> ld_clumping >> pics) - ( - common.create_cluster( - CLUSTER_NAME, - autoscaling_policy=AUTOSCALING, - master_disk_size=2000, - num_workers=6, - ) - >> common.install_dependencies(CLUSTER_NAME) - >> [finngen_summary_stats_preprocess, finngen_finemapping_ingestion] - >> common.delete_cluster(CLUSTER_NAME) - ) diff --git a/src/airflow/dags/gwas_catalog_harmonisation.py b/src/airflow/dags/gwas_catalog_harmonisation.py deleted file mode 100644 index 5713e223d..000000000 --- a/src/airflow/dags/gwas_catalog_harmonisation.py +++ /dev/null @@ -1,123 +0,0 @@ -"""Airflow DAG for the harmonisation part of the pipeline.""" -from __future__ import annotations - -import re -import time -from pathlib import Path -from typing import Any - -import common_airflow as common -from airflow.decorators import task -from airflow.models.dag import DAG -from airflow.providers.google.cloud.operators.gcs import GCSListObjectsOperator - -CLUSTER_NAME = "otg-gwascatalog-harmonisation" -AUTOSCALING = "gwascatalog-harmonisation" - -SUMMARY_STATS_BUCKET_NAME = "gwas_catalog_data" -RAW_SUMMARY_STATISTICS_PREFIX = "raw_summary_statistics" -HARMONISED_SUMMARY_STATISTICS_PREFIX = "harmonised_summary_statistics" - -with DAG( - dag_id=Path(__file__).stem, - description="Open Targets Genetics β€” GWAS Catalog harmonisation", - default_args=common.shared_dag_args, - **common.shared_dag_kwargs, -): - # List raw harmonised files from GWAS Catalog - list_inputs = GCSListObjectsOperator( - task_id="list_raw_harmonised", - bucket=SUMMARY_STATS_BUCKET_NAME, - prefix=RAW_SUMMARY_STATISTICS_PREFIX, - match_glob="**/*.h.tsv.gz", - ) - # List parquet files that have been previously processed - list_outputs = GCSListObjectsOperator( - task_id="list_harmonised_parquet", - bucket=SUMMARY_STATS_BUCKET_NAME, - prefix=HARMONISED_SUMMARY_STATISTICS_PREFIX, - match_glob="**/_SUCCESS", - ) - - # Create list of pending jobs - @task(task_id="create_to_do_list") - def create_to_do_list(**kwargs: Any) -> Any: - """Create the to-do list of studies. - - Args: - **kwargs (Any): Keyword arguments. - - Returns: - Any: To-do list. - """ - ti = kwargs["ti"] - raw_harmonised = ti.xcom_pull( - task_ids="list_raw_harmonised", key="return_value" - ) - print("Number of raw harmonised files: ", len(raw_harmonised)) # noqa: T201 - to_do_list = [] - # Remove the ones that have been processed - parquets = ti.xcom_pull(task_ids="list_harmonised_parquet", key="return_value") - print("Number of parquet files: ", len(parquets)) # noqa: T201 - for path in raw_harmonised: - match_result = re.search( - rf"{RAW_SUMMARY_STATISTICS_PREFIX}/(.*)/(GCST\d+)/harmonised/(.*)\.h\.tsv\.gz", - path, - ) - if match_result: - study_id = match_result.group(2) - if ( - f"{HARMONISED_SUMMARY_STATISTICS_PREFIX}/{study_id}.parquet/_SUCCESS" - not in parquets - ): - to_do_list.append(path) - print("Number of jobs to submit: ", len(to_do_list)) # noqa: T201 - ti.xcom_push(key="to_do_list", value=to_do_list) - - # Submit jobs to dataproc - @task(task_id="submit_jobs") - def submit_jobs(**kwargs: Any) -> None: - """Submit jobs to dataproc. - - Args: - **kwargs (Any): Keyword arguments. - """ - ti = kwargs["ti"] - todo = ti.xcom_pull(task_ids="create_to_do_list", key="to_do_list") - print("Number of jobs to submit: ", len(todo)) # noqa: T201 - for i in range(len(todo)): - # Not to exceed default quota 400 jobs per minute - if i > 0 and i % 399 == 0: - time.sleep(60) - input_path = todo[i] - match_result = re.search( - rf"{RAW_SUMMARY_STATISTICS_PREFIX}/(.*)/(GCST\d+)/harmonised/(.*)\.h\.tsv\.gz", - input_path, - ) - if match_result: - study_id = match_result.group(2) - print("Submitting job for study: ", study_id) # noqa: T201 - common.submit_pyspark_job_no_operator( - cluster_name=CLUSTER_NAME, - step_id="ot_gwas_catalog_sumstat_preprocess", - other_args=[ - f"step.raw_sumstats_path=gs://{SUMMARY_STATS_BUCKET_NAME}/{input_path}", - f"step.out_sumstats_path=gs://{SUMMARY_STATS_BUCKET_NAME}/{HARMONISED_SUMMARY_STATISTICS_PREFIX}/{study_id}.parquet", - ], - ) - - ( - [list_inputs, list_outputs] - >> create_to_do_list() - >> common.create_cluster( - CLUSTER_NAME, - autoscaling_policy=AUTOSCALING, - num_workers=8, - num_preemptible_workers=8, - master_machine_type="n1-highmem-64", - worker_machine_type="n1-standard-2", - ) - >> common.install_dependencies(CLUSTER_NAME) - >> submit_jobs() - # >> common.delete_cluster(CLUSTER_NAME) - ) diff --git a/src/airflow/dags/gwas_catalog_preprocess.py b/src/airflow/dags/gwas_catalog_preprocess.py deleted file mode 100644 index 36130c87e..000000000 --- a/src/airflow/dags/gwas_catalog_preprocess.py +++ /dev/null @@ -1,225 +0,0 @@ -"""Airflow DAG for the preprocessing of GWAS Catalog's harmonised summary statistics and curated associations.""" -from __future__ import annotations - -from pathlib import Path - -import common_airflow as common -from airflow.models.dag import DAG -from airflow.operators.python import PythonOperator -from airflow.providers.google.cloud.hooks.gcs import GCSHook -from airflow.providers.google.cloud.operators.gcs import GCSListObjectsOperator -from airflow.utils.task_group import TaskGroup - -CLUSTER_NAME = "otg-preprocess-gwascatalog" -AUTOSCALING = "otg-preprocess-gwascatalog" - -# Setting up bucket name and output object names: -GWAS_CATALOG_BUCKET_NAME = "gwas_catalog_data" -HARMONISED_SUMSTATS_PREFIX = "harmonised_summary_statistics" - -# Manifest paths: -MANIFESTS_PATH = f"gs://{GWAS_CATALOG_BUCKET_NAME}/manifests/" - -# The name of the manifest files have to be consistent with the config file: -HARMONISED_SUMSTATS_LIST_OBJECT_NAME = ( - "manifests/gwas_catalog_harmonised_summary_statistics_list.txt" -) -HARMONISED_SUMSTATS_LIST_FULL_NAME = ( - f"gs://{GWAS_CATALOG_BUCKET_NAME}/{HARMONISED_SUMSTATS_LIST_OBJECT_NAME}" -) -CURATION_INCLUSION_NAME = f"{MANIFESTS_PATH}/gwas_catalog_curation_included_studies" -CURATION_EXCLUSION_NAME = f"{MANIFESTS_PATH}/gwas_catalog_curation_excluded_studies" -SUMMARY_STATISTICS_INCLUSION_NAME = ( - f"{MANIFESTS_PATH}/gwas_catalog_summary_statistics_included_studies" -) -SUMMARY_STATISTICS_EXCLUSION_NAME = ( - f"{MANIFESTS_PATH}/gwas_catalog_summary_statistics_excluded_studies" -) - -# Study index: -STUDY_INDEX = f"gs://{GWAS_CATALOG_BUCKET_NAME}/study_index" - -# Study loci: -CURATED_STUDY_LOCI = f"gs://{GWAS_CATALOG_BUCKET_NAME}/study_locus_datasets/gwas_catalog_curated_associations" -CURATED_LD_CLUMPED = f"gs://{GWAS_CATALOG_BUCKET_NAME}/study_locus_datasets/gwas_catalog_curated_associations_ld_clumped" -WINDOW_BASED_CLUMPED = f"gs://{GWAS_CATALOG_BUCKET_NAME}/study_locus_datasets/gwas_catalog_summary_stats_window_clumped" -LD_BASED_CLUMPED = f"gs://{GWAS_CATALOG_BUCKET_NAME}/study_locus_datasets/gwas_catalog_summary_stats_ld_clumped" -# Credible sets: -CURATED_CREDIBLE_SETS = ( - f"gs://{GWAS_CATALOG_BUCKET_NAME}/credible_set_datasets/gwas_catalog_curated" -) -SUMMARY_STATISTICS_CREDIBLE_SETS = ( - f"gs://{GWAS_CATALOG_BUCKET_NAME}/credible_set_datasets/gwas_catalog_summary_stats" -) - - -def upload_harmonized_study_list( - concatenated_studies: str, bucket_name: str, object_name: str -) -> None: - """This function uploads file to GCP. - - Args: - concatenated_studies (str): Concatenated list of harmonized summary statistics. - bucket_name (str): Bucket name - object_name (str): Name of the object - """ - hook = GCSHook(gcp_conn_id="google_cloud_default") - hook.upload( - bucket_name=bucket_name, - object_name=object_name, - data=concatenated_studies, - encoding="utf-8", - ) - - -with DAG( - dag_id=Path(__file__).stem, - description="Open Targets Genetics β€” GWAS Catalog preprocess", - default_args=common.shared_dag_args, - **common.shared_dag_kwargs, -): - # Getting list of folders (each a gwas study with summary statistics) - list_harmonised_sumstats = GCSListObjectsOperator( - task_id="list_harmonised_parquet", - bucket=GWAS_CATALOG_BUCKET_NAME, - prefix=HARMONISED_SUMSTATS_PREFIX, - match_glob="**/_SUCCESS", - ) - - # Upload resuling list to a bucket: - upload_task = PythonOperator( - task_id="uploader", - python_callable=upload_harmonized_study_list, - op_kwargs={ - "concatenated_studies": '{{ "\n".join(ti.xcom_pull( key="return_value", task_ids="list_harmonised_parquet")) }}', - "bucket_name": GWAS_CATALOG_BUCKET_NAME, - "object_name": HARMONISED_SUMSTATS_LIST_OBJECT_NAME, - }, - ) - - # Processing curated GWAS Catalog top-bottom: - with TaskGroup(group_id="curation_processing") as curation_processing: - # Generate inclusion list: - curation_calculate_inclusion_list = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_gwas_catalog_study_inclusion", - task_id="catalog_curation_inclusion_list", - other_args=[ - "step.criteria=curation", - f"step.inclusion_list_path={CURATION_INCLUSION_NAME}", - f"step.exclusion_list_path={CURATION_EXCLUSION_NAME}", - f"step.harmonised_study_file={HARMONISED_SUMSTATS_LIST_FULL_NAME}", - ], - ) - - # Ingest curated associations from GWAS Catalog: - curation_ingest_data = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_gwas_catalog_ingestion", - task_id="ingest_curated_gwas_catalog_data", - other_args=[f"step.inclusion_list_path={CURATION_INCLUSION_NAME}"], - ) - - # Run LD-annotation and clumping on curated data: - curation_ld_clumping = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_ld_based_clumping", - task_id="catalog_curation_ld_clumping", - other_args=[ - f"step.study_locus_input_path={CURATED_STUDY_LOCI}", - f"step.study_index_path={STUDY_INDEX}", - f"step.clumped_study_locus_output_path={CURATED_LD_CLUMPED}", - ], - ) - - # Do PICS based finemapping: - curation_pics = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_pics", - task_id="catalog_curation_pics", - other_args=[ - f"step.study_locus_ld_annotated_in={CURATED_LD_CLUMPED}", - f"step.picsed_study_locus_out={CURATED_CREDIBLE_SETS}", - ], - ) - - # Define order of steps: - ( - curation_calculate_inclusion_list - >> curation_ingest_data - >> curation_ld_clumping - >> curation_pics - ) - - # Processing summary statistics from GWAS Catalog: - with TaskGroup( - group_id="summary_statistics_processing" - ) as summary_statistics_processing: - # Generate inclusion study lists: - summary_stats_calculate_inclusion_list = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_gwas_catalog_study_inclusion", - task_id="catalog_sumstats_inclusion_list", - other_args=[ - "step.criteria=summary_stats", - f"step.inclusion_list_path={SUMMARY_STATISTICS_INCLUSION_NAME}", - f"step.exclusion_list_path={SUMMARY_STATISTICS_EXCLUSION_NAME}", - f"step.harmonised_study_file={HARMONISED_SUMSTATS_LIST_FULL_NAME}", - ], - ) - - # Run window-based clumping: - summary_stats_window_based_clumping = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_window_based_clumping", - task_id="catalog_sumstats_window_clumping", - other_args=[ - f"step.summary_statistics_input_path=gs://{GWAS_CATALOG_BUCKET_NAME}/{HARMONISED_SUMSTATS_PREFIX}", - f"step.inclusion_list_path={SUMMARY_STATISTICS_INCLUSION_NAME}", - f"step.study_locus_output_path={WINDOW_BASED_CLUMPED}", - ], - ) - - # Run LD based clumping: - summary_stats_ld_clumping = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_ld_based_clumping", - task_id="catalog_sumstats_ld_clumping", - other_args=[ - f"step.study_locus_input_path={WINDOW_BASED_CLUMPED}", - f"step.study_index_path={STUDY_INDEX}", - f"step.clumped_study_locus_output_path={LD_BASED_CLUMPED}", - ], - ) - - # Run PICS finemapping: - summary_stats_pics = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="ot_pics", - task_id="catalog_sumstats_pics", - other_args=[ - f"step.study_locus_ld_annotated_in={LD_BASED_CLUMPED}", - f"step.picsed_study_locus_out={SUMMARY_STATISTICS_CREDIBLE_SETS}", - ], - ) - - # Order of steps within the group: - ( - summary_stats_calculate_inclusion_list - >> summary_stats_window_based_clumping - >> summary_stats_ld_clumping - >> summary_stats_pics - ) - - # DAG description: - ( - common.create_cluster( - CLUSTER_NAME, autoscaling_policy=AUTOSCALING, num_workers=5 - ) - >> common.install_dependencies(CLUSTER_NAME) - >> list_harmonised_sumstats - >> upload_task - >> curation_processing - >> summary_statistics_processing - >> common.delete_cluster(CLUSTER_NAME) - ) diff --git a/src/airflow/dags/gwas_curation_update.py b/src/airflow/dags/gwas_curation_update.py deleted file mode 100644 index 1ef0f39f9..000000000 --- a/src/airflow/dags/gwas_curation_update.py +++ /dev/null @@ -1,33 +0,0 @@ -"""DAG for updating GWAS Catalog curation table.""" -from __future__ import annotations - -from datetime import datetime -from pathlib import Path - -import common_airflow as common -from airflow.models.dag import DAG - -CLUSTER_NAME = "otg-gwascatalog-curation" -RUN_DATE = datetime.today().strftime("%Y-%m-%d") - -with DAG( - dag_id=Path(__file__).stem, - description="Open Targets Genetics β€” GWAS Catalog curation update", - default_args=common.shared_dag_args, - **common.shared_dag_kwargs, -): - update_gwas_curation = common.submit_step( - cluster_name=CLUSTER_NAME, - step_id="gwas_catalog_curation_update", - task_id="gwas_catalog_curation_update", - other_args=[ - f"step.gwas_catalog_study_curation_out=gs://genetics_etl_python_playground/input/v2d/GWAS_Catalog_study_curation_{RUN_DATE}.tsv", - ], - ) - - # DAG description: - ( - common.create_cluster(CLUSTER_NAME, num_workers=2) - >> common.install_dependencies(CLUSTER_NAME) - >> update_gwas_curation - ) diff --git a/src/airflow/docker-compose.yaml b/src/airflow/docker-compose.yaml deleted file mode 100644 index 8e8523490..000000000 --- a/src/airflow/docker-compose.yaml +++ /dev/null @@ -1,228 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -# Basic Airflow cluster configuration for LocalExecutor with PostgreSQL. -# -# WARNING: This configuration is for local development. Do not use it in a production deployment. -# -# This configuration supports basic configuration using environment variables or an .env file -# The following variables are supported: -# -# AIRFLOW_IMAGE_NAME - Docker image name used to run Airflow. -# Default: apache/airflow:slim-2.7.2-python3.10 -# AIRFLOW_UID - User ID in Airflow containers -# Default: 50000 -# AIRFLOW_PROJ_DIR - Base path to which all the files will be volumed. -# Default: . -# Those configurations are useful mostly in case of standalone testing/running Airflow in test/try-out mode -# -# _AIRFLOW_WWW_USER_USERNAME - Username for the administrator account (if requested). -# Default: airflow -# _AIRFLOW_WWW_USER_PASSWORD - Password for the administrator account (if requested). -# Default: airflow -# _PIP_ADDITIONAL_REQUIREMENTS - Additional PIP requirements to add when starting all containers. -# Use this option ONLY for quick checks. Installing requirements at container -# startup is done EVERY TIME the service is started. -# A better way is to build a custom image or extend the official image -# as described in https://airflow.apache.org/docs/docker-stack/build.html. -# Default: '' -# -# Feel free to modify this file to suit your needs. -version: "3.8" -x-airflow-common: &airflow-common - # In order to add custom dependencies or upgrade provider packages you can use your extended image. - # Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml - # and uncomment the "build" line below, Then run `docker-compose build` to build the images. - image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:slim-2.7.2-python3.10} - # build: . - environment: &airflow-common-env - AIRFLOW__CORE__EXECUTOR: LocalExecutor - AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow - # For backward compatibility, with Airflow <2.3 - AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow - AIRFLOW__CORE__FERNET_KEY: "" - AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: "true" - AIRFLOW__CORE__LOAD_EXAMPLES: "false" - AIRFLOW__API__AUTH_BACKENDS: "airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session" - # yamllint disable rule:line-length - # Use simple http server on scheduler for health checks - # See https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/logging-monitoring/check-health.html#scheduler-health-check-server - # yamllint enable rule:line-length - AIRFLOW__SCHEDULER__ENABLE_HEALTH_CHECK: "true" - # WARNING: Use _PIP_ADDITIONAL_REQUIREMENTS option ONLY for a quick checks - # for other purpose (development, test and especially production usage) build/extend Airflow image. - _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-} - # GCLOUD Authentication - GOOGLE_APPLICATION_CREDENTIALS: ${GOOGLE_APPLICATION_CREDENTIALS:-} - AIRFLOW_CONN_GOOGLE_CLOUD_DEFAULT: ${AIRFLOW_CONN_GOOGLE_CLOUD_DEFAULT:-} - GCP_PROJECT_ID: ${GCP_PROJECT_ID:-} - GCP_GCS_BUCKET: ${GCP_GCS_BUCKET:-} - - volumes: - - ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags - - ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs - - ${AIRFLOW_PROJ_DIR:-.}/config:/opt/airflow/config - - ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins - # GCLOUD Authentication - - ${GOOGLE_LOCAL_CREDENTIALS_PATH}:/${GOOGLE_DOCKER_CREDENTIALS_PATH}:ro - user: "${AIRFLOW_UID:-50000}:0" - depends_on: &airflow-common-depends-on - postgres: - condition: service_healthy - -services: - postgres: - image: postgres:13 - environment: - POSTGRES_USER: airflow - POSTGRES_PASSWORD: airflow - POSTGRES_DB: airflow - volumes: - - postgres-db-volume:/var/lib/postgresql/data - healthcheck: - test: ["CMD", "pg_isready", "-U", "airflow"] - interval: 10s - retries: 5 - start_period: 5s - restart: always - - airflow-webserver: - <<: *airflow-common - command: webserver - ports: - - "8080:8080" - healthcheck: - test: ["CMD", "curl", "--fail", "http://localhost:8080/health"] - interval: 30s - timeout: 10s - retries: 5 - start_period: 30s - restart: always - depends_on: - <<: *airflow-common-depends-on - airflow-init: - condition: service_completed_successfully - - airflow-scheduler: - <<: *airflow-common - command: scheduler - healthcheck: - test: ["CMD", "curl", "--fail", "http://localhost:8974/health"] - interval: 30s - timeout: 10s - retries: 5 - start_period: 30s - restart: always - depends_on: - <<: *airflow-common-depends-on - airflow-init: - condition: service_completed_successfully - - airflow-init: - <<: *airflow-common - entrypoint: /bin/bash - # yamllint disable rule:line-length - command: - - -c - - | - function ver() { - printf "%04d%04d%04d%04d" $${1//./ } - } - airflow_version=$$(AIRFLOW__LOGGING__LOGGING_LEVEL=INFO && gosu airflow airflow version) - airflow_version_comparable=$$(ver $${airflow_version}) - min_airflow_version=2.2.0 - min_airflow_version_comparable=$$(ver $${min_airflow_version}) - if (( airflow_version_comparable < min_airflow_version_comparable )); then - echo - echo -e "\033[1;31mERROR!!!: Too old Airflow version $${airflow_version}!\e[0m" - echo "The minimum Airflow version supported: $${min_airflow_version}. Only use this or higher!" - echo - exit 1 - fi - if [[ -z "${AIRFLOW_UID}" ]]; then - echo - echo -e "\033[1;33mWARNING!!!: AIRFLOW_UID not set!\e[0m" - echo "If you are on Linux, you SHOULD follow the instructions below to set " - echo "AIRFLOW_UID environment variable, otherwise files will be owned by root." - echo "For other operating systems you can get rid of the warning with manually created .env file:" - echo " See: https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#setting-the-right-airflow-user" - echo - fi - one_meg=1048576 - mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg)) - cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat) - disk_available=$$(df / | tail -1 | awk '{print $$4}') - warning_resources="false" - if (( mem_available < 4000 )) ; then - echo - echo -e "\033[1;33mWARNING!!!: Not enough memory available for Docker.\e[0m" - echo "At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))" - echo - warning_resources="true" - fi - if (( cpus_available < 2 )); then - echo - echo -e "\033[1;33mWARNING!!!: Not enough CPUS available for Docker.\e[0m" - echo "At least 2 CPUs recommended. You have $${cpus_available}" - echo - warning_resources="true" - fi - if (( disk_available < one_meg * 10 )); then - echo - echo -e "\033[1;33mWARNING!!!: Not enough Disk space available for Docker.\e[0m" - echo "At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))" - echo - warning_resources="true" - fi - if [[ $${warning_resources} == "true" ]]; then - echo - echo -e "\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\e[0m" - echo "Please follow the instructions to increase amount of resources available:" - echo " https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#before-you-begin" - echo - fi - mkdir -p /sources/logs /sources/dags /sources/plugins - chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins} - exec /entrypoint airflow version - # yamllint enable rule:line-length - environment: - <<: *airflow-common-env - _AIRFLOW_DB_MIGRATE: "true" - _AIRFLOW_WWW_USER_CREATE: "true" - _AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow} - _AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow} - _PIP_ADDITIONAL_REQUIREMENTS: "" - user: "0:0" - volumes: - - ${AIRFLOW_PROJ_DIR:-.}:/sources - - airflow-cli: - <<: *airflow-common - profiles: - - debug - environment: - <<: *airflow-common-env - CONNECTION_CHECK_MAX_COUNT: "0" - # Workaround for entrypoint issue. See: https://github.com/apache/airflow/issues/16252 - command: - - bash - - -c - - airflow - -volumes: - postgres-db-volume: diff --git a/src/airflow/logs/.gitkeep b/src/airflow/logs/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/airflow/plugins/.gitkeep b/src/airflow/plugins/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/airflow/requirements.txt b/src/airflow/requirements.txt deleted file mode 100644 index e9fd45142..000000000 --- a/src/airflow/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -apache-airflow-providers-google==10.10.1 -psycopg2-binary==2.9.9 diff --git a/src/gentropy/assets/data/gwas_population_2_LD_panel_map.json b/src/gentropy/assets/data/gwas_population_2_LD_panel_map.json index 52cee5140..fa00089c6 100644 --- a/src/gentropy/assets/data/gwas_population_2_LD_panel_map.json +++ b/src/gentropy/assets/data/gwas_population_2_LD_panel_map.json @@ -5,13 +5,13 @@ "Asian unspecified": "eas", "Hispanic or Latin American": "amr", "East Asian": "eas", - "Central Asian": "seu", + "Central Asian": "eas", "Oceanian": "eas", "South East Asian": "eas", "Other admixed ancestry": "nfe", "African unspecified": "afr", "Sub-Saharan African": "afr", - "Greater Middle Eastern (Middle Eastern, North African or Persian)": "seu", + "Greater Middle Eastern (Middle Eastern, North African or Persian)": "eas", "Aboriginal Australian": "eas", "Other": "nfe", "South Asian": "eas", diff --git a/src/gentropy/assets/schemas/biosample_index.json b/src/gentropy/assets/schemas/biosample_index.json new file mode 100644 index 000000000..6309ca2c7 --- /dev/null +++ b/src/gentropy/assets/schemas/biosample_index.json @@ -0,0 +1,83 @@ +{ + "type": "struct", + "fields": [ + { + "name": "biosampleId", + "type": "string", + "nullable": false, + "metadata": {} + }, + { + "name": "biosampleName", + "type": "string", + "nullable": false, + "metadata": {} + }, + { + "name": "description", + "type": "string", + "nullable": true, + "metadata": {} + }, + { + "name": "xrefs", + "type": { + "type": "array", + "elementType": "string", + "containsNull": true + }, + "nullable": true, + "metadata": {} + }, + { + "name": "synonyms", + "type": { + "type": "array", + "elementType": "string", + "containsNull": true + }, + "nullable": true, + "metadata": {} + }, + { + "name": "parents", + "type": { + "type": "array", + "elementType": "string", + "containsNull": true + }, + "nullable": true, + "metadata": {} + }, + { + "name": "ancestors", + "type": { + "type": "array", + "elementType": "string", + "containsNull": true + }, + "nullable": true, + "metadata": {} + }, + { + "name": "descendants", + "type": { + "type": "array", + "elementType": "string", + "containsNull": true + }, + "nullable": true, + "metadata": {} + }, + { + "name": "children", + "type": { + "type": "array", + "elementType": "string", + "containsNull": true + }, + "nullable": true, + "metadata": {} + } + ] +} diff --git a/src/gentropy/assets/schemas/colocalisation.json b/src/gentropy/assets/schemas/colocalisation.json index 7ff7453b9..a065dc947 100644 --- a/src/gentropy/assets/schemas/colocalisation.json +++ b/src/gentropy/assets/schemas/colocalisation.json @@ -4,13 +4,19 @@ { "name": "leftStudyLocusId", "nullable": false, - "type": "long", + "type": "string", "metadata": {} }, { "name": "rightStudyLocusId", "nullable": false, - "type": "long", + "type": "string", + "metadata": {} + }, + { + "name": "rightStudyType", + "nullable": false, + "type": "string", "metadata": {} }, { @@ -62,13 +68,13 @@ "metadata": {} }, { - "name": "log2h4h3", + "name": "clpp", "type": "double", "nullable": true, "metadata": {} }, { - "name": "clpp", + "name": "betaRatioSignAverage", "type": "double", "nullable": true, "metadata": {} diff --git a/src/gentropy/assets/schemas/l2g_feature.json b/src/gentropy/assets/schemas/l2g_feature.json index 3139a57e4..314b4dde0 100644 --- a/src/gentropy/assets/schemas/l2g_feature.json +++ b/src/gentropy/assets/schemas/l2g_feature.json @@ -3,7 +3,7 @@ "fields": [ { "name": "studyLocusId", - "type": "long", + "type": "string", "nullable": false, "metadata": {} }, diff --git a/src/gentropy/assets/schemas/l2g_feature_matrix.json b/src/gentropy/assets/schemas/l2g_feature_matrix.json deleted file mode 100644 index a1b58040b..000000000 --- a/src/gentropy/assets/schemas/l2g_feature_matrix.json +++ /dev/null @@ -1,131 +0,0 @@ -{ - "fields": [ - { - "metadata": {}, - "name": "studyLocusId", - "nullable": false, - "type": "long" - }, - { - "metadata": {}, - "name": "geneId", - "nullable": false, - "type": "string" - }, - { - "metadata": {}, - "name": "goldStandardSet", - "nullable": true, - "type": "string" - }, - { - "metadata": {}, - "name": "distanceTssMean", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "distanceTssMinimum", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "vepMaximumNeighborhood", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "vepMaximum", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "vepMeanNeighborhood", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "vepMean", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "eqtlColocClppMaximum", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "eqtlColocClppMaximumNeighborhood", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "eqtlColocLlrMaximum", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "eqtlColocLlrMaximumNeighborhood", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "pqtlColocClppMaximum", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "pqtlColocClppMaximumNeighborhood", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "pqtlColocLlrMaximum", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "pqtlColocLlrMaximumNeighborhood", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "sqtlColocClppMaximum", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "sqtlColocClppMaximumNeighborhood", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "sqtlColocLlrMaximum", - "nullable": true, - "type": "float" - }, - { - "metadata": {}, - "name": "sqtlColocLlrMaximumNeighborhood", - "nullable": true, - "type": "float" - } - ], - "type": "struct" -} diff --git a/src/gentropy/assets/schemas/l2g_gold_standard.json b/src/gentropy/assets/schemas/l2g_gold_standard.json index cf19d6b52..6af921d61 100644 --- a/src/gentropy/assets/schemas/l2g_gold_standard.json +++ b/src/gentropy/assets/schemas/l2g_gold_standard.json @@ -3,7 +3,7 @@ "fields": [ { "name": "studyLocusId", - "type": "long", + "type": "string", "nullable": false, "metadata": {} }, diff --git a/src/gentropy/assets/schemas/l2g_predictions.json b/src/gentropy/assets/schemas/l2g_predictions.json index 16b274207..57247a49a 100644 --- a/src/gentropy/assets/schemas/l2g_predictions.json +++ b/src/gentropy/assets/schemas/l2g_predictions.json @@ -3,7 +3,7 @@ "fields": [ { "name": "studyLocusId", - "type": "long", + "type": "string", "nullable": false, "metadata": {} }, @@ -18,6 +18,17 @@ "type": "double", "nullable": false, "metadata": {} + }, + { + "metadata": {}, + "name": "locusToGeneFeatures", + "nullable": true, + "type": { + "keyType": "string", + "type": "map", + "valueContainsNull": true, + "valueType": "float" + } } ] } diff --git a/src/gentropy/assets/schemas/pairwise_ld.json b/src/gentropy/assets/schemas/pairwise_ld.json new file mode 100644 index 000000000..bac781ac3 --- /dev/null +++ b/src/gentropy/assets/schemas/pairwise_ld.json @@ -0,0 +1,23 @@ +{ + "fields": [ + { + "metadata": {}, + "name": "variantIdI", + "nullable": false, + "type": "string" + }, + { + "metadata": {}, + "name": "variantIdJ", + "nullable": false, + "type": "string" + }, + { + "metadata": {}, + "name": "r", + "nullable": false, + "type": "double" + } + ], + "type": "struct" +} diff --git a/src/gentropy/assets/schemas/study_index.json b/src/gentropy/assets/schemas/study_index.json index 08f61c072..9c50d4a19 100644 --- a/src/gentropy/assets/schemas/study_index.json +++ b/src/gentropy/assets/schemas/study_index.json @@ -22,7 +22,7 @@ { "name": "traitFromSource", "type": "string", - "nullable": false, + "nullable": true, "metadata": {} }, { @@ -35,12 +35,34 @@ "nullable": true, "metadata": {} }, + { + "name": "diseaseIds", + "type": { + "type": "array", + "elementType": "string", + "containsNull": true + }, + "nullable": true, + "metadata": {} + }, { "name": "geneId", "type": "string", "nullable": true, "metadata": {} }, + { + "name": "biosampleFromSourceId", + "type": "string", + "nullable": true, + "metadata": {} + }, + { + "name": "biosampleId", + "type": "string", + "nullable": true, + "metadata": {} + }, { "name": "pubmedId", "type": "string", @@ -81,6 +103,16 @@ "nullable": true, "metadata": {} }, + { + "name": "backgroundDiseaseIds", + "type": { + "type": "array", + "elementType": "string", + "containsNull": true + }, + "nullable": true, + "metadata": {} + }, { "name": "initialSampleSize", "type": "string", @@ -224,6 +256,23 @@ "type": "boolean", "nullable": true, "metadata": {} + }, + { + "name": "condition", + "type": "string", + "nullable": true, + "metadata": {} + }, + { + "name": "sumstatQCValues", + "type": { + "type": "map", + "keyType": "string", + "valueType": "float", + "valueContainsNull": true + }, + "nullable": true, + "metadata": {} } ] } diff --git a/src/gentropy/assets/schemas/study_locus.json b/src/gentropy/assets/schemas/study_locus.json index e0f366d68..5c7bf1178 100644 --- a/src/gentropy/assets/schemas/study_locus.json +++ b/src/gentropy/assets/schemas/study_locus.json @@ -4,7 +4,13 @@ "metadata": {}, "name": "studyLocusId", "nullable": false, - "type": "long" + "type": "string" + }, + { + "metadata": {}, + "name": "studyType", + "nullable": true, + "type": "string" }, { "metadata": {}, @@ -42,6 +48,12 @@ "nullable": true, "type": "double" }, + { + "metadata": {}, + "name": "zScore", + "nullable": true, + "type": "double" + }, { "metadata": {}, "name": "pValueMantissa", @@ -100,6 +112,30 @@ "nullable": true, "type": "double" }, + { + "metadata": {}, + "name": "purityMeanR2", + "nullable": true, + "type": "double" + }, + { + "metadata": {}, + "name": "purityMinR2", + "nullable": true, + "type": "double" + }, + { + "metadata": {}, + "name": "locusStart", + "nullable": true, + "type": "integer" + }, + { + "metadata": {}, + "name": "locusEnd", + "nullable": true, + "type": "integer" + }, { "metadata": {}, "name": "sampleSize", @@ -205,6 +241,12 @@ }, "type": "array" } + }, + { + "metadata": {}, + "name": "confidence", + "nullable": true, + "type": "string" } ], "type": "struct" diff --git a/src/gentropy/assets/schemas/study_locus_overlap.json b/src/gentropy/assets/schemas/study_locus_overlap.json index 9a8e123cd..0c4319827 100644 --- a/src/gentropy/assets/schemas/study_locus_overlap.json +++ b/src/gentropy/assets/schemas/study_locus_overlap.json @@ -4,13 +4,19 @@ "metadata": {}, "name": "leftStudyLocusId", "nullable": false, - "type": "long" + "type": "string" }, { "metadata": {}, "name": "rightStudyLocusId", "nullable": false, - "type": "long" + "type": "string" + }, + { + "metadata": {}, + "name": "rightStudyType", + "nullable": false, + "type": "string" }, { "metadata": {}, diff --git a/src/gentropy/assets/schemas/v2g.json b/src/gentropy/assets/schemas/v2g.json deleted file mode 100644 index afbe401dd..000000000 --- a/src/gentropy/assets/schemas/v2g.json +++ /dev/null @@ -1,77 +0,0 @@ -{ - "type": "struct", - "fields": [ - { - "name": "geneId", - "type": "string", - "nullable": false, - "metadata": {} - }, - { - "name": "variantId", - "type": "string", - "nullable": false, - "metadata": {} - }, - { - "name": "distance", - "type": "long", - "nullable": true, - "metadata": {} - }, - { - "name": "chromosome", - "type": "string", - "nullable": false, - "metadata": {} - }, - { - "name": "datatypeId", - "type": "string", - "nullable": false, - "metadata": {} - }, - { - "name": "datasourceId", - "type": "string", - "nullable": false, - "metadata": {} - }, - { - "name": "score", - "type": "double", - "nullable": true, - "metadata": {} - }, - { - "name": "resourceScore", - "type": "double", - "nullable": true, - "metadata": {} - }, - { - "name": "pmid", - "type": "string", - "nullable": true, - "metadata": {} - }, - { - "name": "biofeature", - "type": "string", - "nullable": true, - "metadata": {} - }, - { - "name": "variantFunctionalConsequenceId", - "type": "string", - "nullable": true, - "metadata": {} - }, - { - "name": "isHighQualityPlof", - "type": "boolean", - "nullable": true, - "metadata": {} - } - ] -} diff --git a/src/gentropy/assets/schemas/variant_annotation.json b/src/gentropy/assets/schemas/variant_annotation.json deleted file mode 100644 index 826eabaf9..000000000 --- a/src/gentropy/assets/schemas/variant_annotation.json +++ /dev/null @@ -1,221 +0,0 @@ -{ - "type": "struct", - "fields": [ - { - "name": "variantId", - "type": "string", - "nullable": false, - "metadata": {} - }, - { - "name": "chromosome", - "type": "string", - "nullable": false, - "metadata": {} - }, - { - "name": "position", - "type": "integer", - "nullable": false, - "metadata": {} - }, - { - "name": "gnomadVariantId", - "type": "string", - "nullable": false, - "metadata": {} - }, - { - "name": "referenceAllele", - "type": "string", - "nullable": false, - "metadata": {} - }, - { - "name": "alternateAllele", - "type": "string", - "nullable": false, - "metadata": {} - }, - { - "name": "chromosomeB37", - "type": "string", - "nullable": true, - "metadata": {} - }, - { - "name": "positionB37", - "type": "integer", - "nullable": true, - "metadata": {} - }, - { - "name": "alleleType", - "type": "string", - "nullable": true, - "metadata": {} - }, - { - "name": "rsIds", - "type": { - "type": "array", - "elementType": "string", - "containsNull": true - }, - "nullable": true, - "metadata": {} - }, - { - "name": "alleleFrequencies", - "type": { - "type": "array", - "elementType": { - "type": "struct", - "fields": [ - { - "name": "populationName", - "type": "string", - "nullable": true, - "metadata": {} - }, - { - "name": "alleleFrequency", - "type": "double", - "nullable": true, - "metadata": {} - } - ] - }, - "containsNull": true - }, - "nullable": false, - "metadata": {} - }, - { - "name": "inSilicoPredictors", - "nullable": false, - "metadata": {}, - "type": { - "type": "struct", - "fields": [ - { - "name": "cadd", - "nullable": true, - "metadata": {}, - "type": { - "type": "struct", - "fields": [ - { - "name": "raw", - "type": "float", - "nullable": true, - "metadata": {} - }, - { - "name": "phred", - "type": "float", - "nullable": true, - "metadata": {} - } - ] - } - }, - { - "name": "revelMax", - "type": "double", - "nullable": true, - "metadata": {} - }, - { - "name": "spliceaiDsMax", - "type": "float", - "nullable": true, - "metadata": {} - }, - { - "name": "pangolinLargestDs", - "type": "double", - "nullable": true, - "metadata": {} - }, - { - "name": "phylop", - "type": "double", - "nullable": true, - "metadata": {} - }, - { - "name": "siftMax", - "type": "double", - "nullable": true, - "metadata": {} - }, - { - "name": "polyphenMax", - "type": "double", - "nullable": true, - "metadata": {} - } - ] - } - }, - { - "name": "vep", - "type": { - "type": "struct", - "fields": [ - { - "name": "mostSevereConsequence", - "type": "string", - "nullable": true, - "metadata": {} - }, - { - "name": "transcriptConsequences", - "type": { - "type": "array", - "elementType": { - "type": "struct", - "fields": [ - { - "name": "aminoAcids", - "type": "string", - "nullable": true, - "metadata": {} - }, - { - "name": "consequenceTerms", - "type": { - "type": "array", - "elementType": "string", - "containsNull": true - }, - "nullable": true, - "metadata": {} - }, - { - "name": "geneId", - "type": "string", - "nullable": true, - "metadata": {} - }, - { - "name": "lof", - "type": "string", - "nullable": true, - "metadata": {} - } - ] - }, - "containsNull": true - }, - "nullable": true, - "metadata": {} - } - ] - }, - "nullable": false, - "metadata": {} - } - ] -} diff --git a/src/gentropy/assets/schemas/variant_index.json b/src/gentropy/assets/schemas/variant_index.json index c6a3702c9..6d5e211ac 100644 --- a/src/gentropy/assets/schemas/variant_index.json +++ b/src/gentropy/assets/schemas/variant_index.json @@ -1,53 +1,224 @@ { - "type": "struct", "fields": [ { + "metadata": {}, "name": "variantId", - "type": "string", "nullable": false, - "metadata": {} + "type": "string" }, { + "metadata": {}, "name": "chromosome", - "type": "string", "nullable": false, - "metadata": {} + "type": "string" }, { + "metadata": {}, "name": "position", - "type": "integer", "nullable": false, - "metadata": {} + "type": "integer" }, { + "metadata": {}, "name": "referenceAllele", - "type": "string", "nullable": false, - "metadata": {} + "type": "string" }, { + "metadata": {}, "name": "alternateAllele", - "type": "string", "nullable": false, - "metadata": {} + "type": "string" }, { - "name": "chromosomeB37", - "type": "string", + "metadata": {}, + "name": "inSilicoPredictors", "nullable": true, - "metadata": {} + "type": { + "containsNull": true, + "elementType": { + "fields": [ + { + "metadata": {}, + "name": "method", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "assessment", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "score", + "nullable": true, + "type": "float" + }, + { + "metadata": {}, + "name": "assessmentFlag", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "targetId", + "nullable": true, + "type": "string" + } + ], + "type": "struct" + }, + "type": "array" + } }, { - "name": "positionB37", - "type": "integer", + "metadata": {}, + "name": "mostSevereConsequenceId", "nullable": true, - "metadata": {} + "type": "string" }, { - "name": "alleleType", - "type": "string", - "nullable": false, - "metadata": {} + "metadata": {}, + "name": "transcriptConsequences", + "nullable": true, + "type": { + "containsNull": false, + "elementType": { + "fields": [ + { + "metadata": {}, + "name": "variantFunctionalConsequenceIds", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "aminoAcidChange", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "uniprotAccessions", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "isEnsemblCanonical", + "nullable": false, + "type": "boolean" + }, + { + "metadata": {}, + "name": "codons", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "distanceFromFootprint", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "distanceFromTss", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "appris", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "maneSelect", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "targetId", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "impact", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "lofteePrediction", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "siftPrediction", + "nullable": true, + "type": "float" + }, + { + "metadata": {}, + "name": "polyphenPrediction", + "nullable": true, + "type": "float" + }, + { + "metadata": {}, + "name": "consequenceScore", + "nullable": true, + "type": "float" + }, + { + "metadata": {}, + "name": "transcriptIndex", + "nullable": true, + "type": "integer" + }, + { + "metadata": {}, + "name": "transcriptId", + "nullable": true, + "type": "string" + } + ], + "type": "struct" + }, + "type": "array" + } + }, + { + "metadata": {}, + "name": "rsIds", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "hgvsId", + "nullable": true, + "type": "string" }, { "name": "alleleFrequencies", @@ -72,92 +243,35 @@ }, "containsNull": true }, - "nullable": false, + "nullable": true, "metadata": {} }, { - "name": "inSilicoPredictors", - "nullable": false, "metadata": {}, + "name": "dbXrefs", + "nullable": true, "type": { - "type": "struct", - "fields": [ - { - "name": "cadd", - "nullable": true, - "metadata": {}, - "type": { - "type": "struct", - "fields": [ - { - "name": "raw", - "type": "float", - "nullable": true, - "metadata": {} - }, - { - "name": "phred", - "type": "float", - "nullable": true, - "metadata": {} - } - ] + "containsNull": true, + "elementType": { + "fields": [ + { + "metadata": {}, + "name": "id", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "source", + "nullable": true, + "type": "string" } - }, - { - "name": "revelMax", - "type": "double", - "nullable": true, - "metadata": {} - }, - { - "name": "spliceaiDsMax", - "type": "float", - "nullable": true, - "metadata": {} - }, - { - "name": "pangolinLargestDs", - "type": "double", - "nullable": true, - "metadata": {} - }, - { - "name": "phylop", - "type": "double", - "nullable": true, - "metadata": {} - }, - { - "name": "siftMax", - "type": "double", - "nullable": true, - "metadata": {} - }, - { - "name": "polyphenMax", - "type": "double", - "nullable": true, - "metadata": {} - } - ] + ], + "type": "struct" + }, + "type": "array" } - }, - { - "name": "mostSevereConsequence", - "type": "string", - "nullable": true, - "metadata": {} - }, - { - "name": "rsIds", - "type": { - "type": "array", - "elementType": "string", - "containsNull": true - }, - "nullable": true, - "metadata": {} } - ] + ], + "type": "struct" } diff --git a/src/gentropy/assets/schemas/vep_json_output.json b/src/gentropy/assets/schemas/vep_json_output.json new file mode 100644 index 000000000..43c3f4ad7 --- /dev/null +++ b/src/gentropy/assets/schemas/vep_json_output.json @@ -0,0 +1,573 @@ +{ + "fields": [ + { + "metadata": {}, + "name": "allele_string", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "assembly_name", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "intergenic_consequences", + "nullable": true, + "type": { + "containsNull": true, + "elementType": { + "fields": [ + { + "metadata": {}, + "name": "hgvsg", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "cadd_phred", + "nullable": true, + "type": "double" + }, + { + "metadata": {}, + "name": "cadd_raw", + "nullable": true, + "type": "double" + }, + { + "metadata": {}, + "name": "consequence_terms", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "impact", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "variant_allele", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "gene_id", + "nullable": true, + "type": "string" + } + ], + "type": "struct" + }, + "type": "array" + } + }, + { + "metadata": {}, + "name": "colocated_variants", + "nullable": true, + "type": { + "containsNull": true, + "elementType": { + "fields": [ + { + "metadata": {}, + "name": "allele_string", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "clin_sig", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "clin_sig_allele", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "end", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "id", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "phenotype_or_disease", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "pubmed", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "long", + "type": "array" + } + }, + { + "metadata": {}, + "name": "seq_region_name", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "start", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "strand", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "var_synonyms", + "nullable": true, + "type": { + "fields": [ + { + "metadata": {}, + "name": "ClinVar", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "LMDD", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "OIVD", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "OMIM", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "double", + "type": "array" + } + }, + { + "metadata": {}, + "name": "PharmGKB", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "PhenCode", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "UniProt", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "dbPEX", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + } + ], + "type": "struct" + } + } + ], + "type": "struct" + }, + "type": "array" + } + }, + { + "metadata": {}, + "name": "end", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "id", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "input", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "most_severe_consequence", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "seq_region_name", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "start", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "strand", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "transcript_consequences", + "nullable": true, + "type": { + "containsNull": true, + "elementType": { + "fields": [ + { + "metadata": {}, + "name": "alphamissense", + "nullable": true, + "type": { + "fields": [ + { + "metadata": {}, + "name": "am_class", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "am_pathogenicity", + "nullable": true, + "type": "double" + } + ], + "type": "struct" + } + }, + { + "metadata": {}, + "name": "amino_acids", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "hgvsg", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "hgvsc", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "hgvsp", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "appris", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "mane_select", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "tssdistance", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "cadd_phred", + "nullable": true, + "type": "double" + }, + { + "metadata": {}, + "name": "cadd_raw", + "nullable": true, + "type": "double" + }, + { + "metadata": {}, + "name": "canonical", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "cdna_end", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "cdna_start", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "cds_end", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "cds_start", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "codons", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "consequence_terms", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "distance", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "flags", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "gene_id", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "impact", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "lof", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "lof_filter", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "lof_flags", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "lof_info", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "polyphen_prediction", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "polyphen_score", + "nullable": true, + "type": "double" + }, + { + "metadata": {}, + "name": "protein_end", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "protein_start", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "sift_prediction", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "sift_score", + "nullable": true, + "type": "double" + }, + { + "metadata": {}, + "name": "strand", + "nullable": true, + "type": "long" + }, + { + "metadata": {}, + "name": "swissprot", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "transcript_id", + "nullable": true, + "type": "string" + }, + { + "metadata": {}, + "name": "trembl", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "uniparc", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "uniprot_isoform", + "nullable": true, + "type": { + "containsNull": true, + "elementType": "string", + "type": "array" + } + }, + { + "metadata": {}, + "name": "variant_allele", + "nullable": true, + "type": "string" + } + ], + "type": "struct" + }, + "type": "array" + } + } + ], + "type": "struct" +} diff --git a/src/gentropy/biosample_index.py b/src/gentropy/biosample_index.py new file mode 100644 index 000000000..e0b5e9b10 --- /dev/null +++ b/src/gentropy/biosample_index.py @@ -0,0 +1,37 @@ +"""Step to generate biosample index dataset.""" +from __future__ import annotations + +from gentropy.common.session import Session +from gentropy.datasource.biosample_ontologies.utils import extract_ontology_from_json + + +class BiosampleIndexStep: + """Biosample index step. + + This step generates a Biosample index dataset from the various ontology sources. Currently Cell Ontology and Uberon are supported. + """ + + def __init__( + self, + session: Session, + cell_ontology_input_path: str, + uberon_input_path: str, + efo_input_path: str, + biosample_index_path: str, + ) -> None: + """Run Biosample index generation step. + + Args: + session (Session): Session object. + cell_ontology_input_path (str): Input cell ontology dataset path. + uberon_input_path (str): Input uberon dataset path. + efo_input_path (str): Input efo dataset path. + biosample_index_path (str): Output gene index dataset path. + """ + cell_ontology_index = extract_ontology_from_json(cell_ontology_input_path, session.spark) + uberon_index = extract_ontology_from_json(uberon_input_path, session.spark) + efo_index = extract_ontology_from_json(efo_input_path, session.spark).retain_rows_with_ancestor_id(["CL_0000000"]) + + biosample_index = cell_ontology_index.merge_indices([uberon_index, efo_index]) + + biosample_index.df.write.mode(session.write_mode).parquet(biosample_index_path) diff --git a/src/gentropy/clump.py b/src/gentropy/clump.py deleted file mode 100644 index 9ea3306ac..000000000 --- a/src/gentropy/clump.py +++ /dev/null @@ -1,70 +0,0 @@ -"""Step to run clump associations from summary statistics or study locus.""" -from __future__ import annotations - -from typing import Optional - -from gentropy.common.session import Session -from gentropy.dataset.ld_index import LDIndex -from gentropy.dataset.study_index import StudyIndex -from gentropy.dataset.study_locus import StudyLocus -from gentropy.dataset.summary_statistics import SummaryStatistics - - -class ClumpStep: - """Perform clumping of an association dataset to identify independent signals. - - Two types of clumping are supported and are applied based on the input dataset: - - Clumping of summary statistics based on a window-based approach. - - Clumping of study locus based on LD. - - Both approaches yield a StudyLocus dataset. - """ - - def __init__( - self, - session: Session, - input_path: str, - clumped_study_locus_path: str, - study_index_path: Optional[str] = None, - ld_index_path: Optional[str] = None, - locus_collect_distance: Optional[int] = None, - ) -> None: - """Run the clumping step. - - Args: - session (Session): Session object. - input_path (str): Input path for the study locus or summary statistics files. - clumped_study_locus_path (str): Output path for the clumped study locus dataset. - study_index_path (Optional[str]): Input path for the study index dataset. - ld_index_path (Optional[str]): Input path for the LD index dataset. - locus_collect_distance (Optional[int]): Distance in base pairs to collect variants around the study locus. - - Raises: - ValueError: If study index and LD index paths are not provided for study locus. - """ - input_cols = session.spark.read.parquet( - input_path, recursiveFileLookup=True - ).columns - if "studyLocusId" in input_cols: - if study_index_path is None or ld_index_path is None: - raise ValueError( - "Study index and LD index paths are required for clumping study locus." - ) - study_locus = StudyLocus.from_parquet(session, input_path) - ld_index = LDIndex.from_parquet(session, ld_index_path) - study_index = StudyIndex.from_parquet(session, study_index_path) - - clumped_study_locus = study_locus.annotate_ld( - study_index=study_index, ld_index=ld_index - ).clump() - else: - sumstats = SummaryStatistics.from_parquet( - session, input_path, recursiveFileLookup=True - ).coalesce(4000) - clumped_study_locus = sumstats.window_based_clumping( - locus_collect_distance=locus_collect_distance - ) - - clumped_study_locus.df.write.mode(session.write_mode).parquet( - clumped_study_locus_path - ) diff --git a/src/gentropy/colocalisation.py b/src/gentropy/colocalisation.py index 010827713..a45a9a6a1 100644 --- a/src/gentropy/colocalisation.py +++ b/src/gentropy/colocalisation.py @@ -1,10 +1,15 @@ """Step to generate colocalisation results.""" + from __future__ import annotations +from functools import partial +from typing import Any, Type + +from pyspark.sql.functions import col + from gentropy.common.session import Session -from gentropy.dataset.study_index import StudyIndex -from gentropy.dataset.study_locus import CredibleInterval, StudyLocus -from gentropy.method.colocalisation import ECaviar +from gentropy.dataset.study_locus import StudyLocus +from gentropy.method.colocalisation import Coloc, ColocalisationMethodInterface class ColocalisationStep: @@ -13,34 +18,81 @@ class ColocalisationStep: This workflow runs colocalisation analyses that assess the degree to which independent signals of the association share the same causal variant in a region of the genome, typically limited by linkage disequilibrium (LD). """ + __coloc_methods__ = { + method.METHOD_NAME.lower(): method + for method in ColocalisationMethodInterface.__subclasses__() + } + def __init__( self, session: Session, credible_set_path: str, - study_index_path: str, coloc_path: str, + colocalisation_method: str, + colocalisation_method_params: dict[str, Any] | None = None, ) -> None: """Run Colocalisation step. + This step allows for running two colocalisation methods: ecaviar and coloc. + Args: session (Session): Session object. credible_set_path (str): Input credible sets path. - study_index_path (str): Input study index path. coloc_path (str): Output Colocalisation path. + colocalisation_method (str): Colocalisation method. + colocalisation_method_params (dict[str, Any] | None): Keyword arguments passed to the colocalise method of Colocalisation class. Defaults to None + + Keyword Args: + priorc1 (float): Prior on variant being causal for trait 1. Defaults to 1e-4. For coloc method only. + priorc2 (float): Prior on variant being causal for trait 2. Defaults to 1e-4. For coloc method only. + priorc12 (float): Prior on variant being causal for both traits. Defaults to 1e-5. For coloc method only. """ + colocalisation_method = colocalisation_method.lower() + colocalisation_class = self._get_colocalisation_class(colocalisation_method) + # Extract credible_set = StudyLocus.from_parquet( - session, credible_set_path, recursiveFileLookup=True - ) - si = StudyIndex.from_parquet( - session, study_index_path, recursiveFileLookup=True + session, credible_set_path, recusiveFileLookup=True ) + if colocalisation_method == Coloc.METHOD_NAME.lower(): + credible_set = credible_set.filter( + col("finemappingMethod").isin("SuSie", "SuSiE-inf") + ) # Transform - overlaps = credible_set.filter_credible_set( - CredibleInterval.IS95 - ).find_overlaps(si) - ecaviar_results = ECaviar.colocalise(overlaps) + overlaps = credible_set.find_overlaps() + # Make a partial caller to ensure that colocalisation_method_params are added to the call only when dict is not empty + coloc = colocalisation_class.colocalise + if colocalisation_method_params: + coloc = partial(coloc, **colocalisation_method_params) + colocalisation_results = coloc(overlaps) # Load - ecaviar_results.df.write.mode(session.write_mode).parquet(coloc_path) + colocalisation_results.df.write.mode(session.write_mode).parquet( + f"{coloc_path}/{colocalisation_method.lower()}" + ) + + @classmethod + def _get_colocalisation_class( + cls, method: str + ) -> Type[ColocalisationMethodInterface]: + """Get colocalisation class. + + Args: + method (str): Colocalisation method. + + Returns: + Type[ColocalisationMethodInterface]: Class that implements the ColocalisationMethodInterface. + + Raises: + ValueError: if method not available. + + Examples: + >>> ColocalisationStep._get_colocalisation_class("ECaviar") + + """ + method = method.lower() + if method not in cls.__coloc_methods__: + raise ValueError(f"Colocalisation method {method} not available.") + coloc_method = cls.__coloc_methods__[method] + return coloc_method diff --git a/src/gentropy/common/Liftover.py b/src/gentropy/common/Liftover.py index e0ac577c9..b7841c190 100644 --- a/src/gentropy/common/Liftover.py +++ b/src/gentropy/common/Liftover.py @@ -1,4 +1,5 @@ """LiftOver support.""" + from __future__ import annotations import tempfile @@ -24,17 +25,19 @@ class LiftOverSpark: - The mapping is dropped if the mapped chromosome is not on the same as the source. - The mapping is dropped if the mapping is ambiguous (more than one mapping is available). - If regions are provided, the mapping is dropped if the new region is reversed (mapped_start > mapped_end). - - If regions are provided, the mapping is dropped if the difference of the lenght of the mapped region and original is larger than a threshold. + - If regions are provided, the mapping is dropped if the difference of the length of the mapped region and original is larger than a threshold. - When lifting over intervals, only unique coordinates are lifted, they joined back to the original dataframe. - - Args: - chain_file (str): Path to the chain file - max_difference (int): Maximum difference between the length of the mapped region and the original region. Defaults to 100. """ - def __init__( # noqa: D107 + def __init__( self: LiftOverSpark, chain_file: str, max_difference: int = 100 ) -> None: + """Initialize the LiftOver object. + + Args: + chain_file (str): Path to the chain file + max_difference (int, optional): The maximum tolerated difference in the resulting length. Defaults to 100. + """ self.chain_file = chain_file self.max_difference = max_difference diff --git a/src/gentropy/common/genomic_region.py b/src/gentropy/common/genomic_region.py new file mode 100644 index 000000000..ffa298f86 --- /dev/null +++ b/src/gentropy/common/genomic_region.py @@ -0,0 +1,103 @@ +"""Genomic Region class.""" + +from enum import Enum + + +class KnownGenomicRegions(Enum): + """Known genomic regions in the human genome in string format.""" + + MHC = "chr6:25726063-33400556" + + +class GenomicRegion: + """Genomic regions of interest. + + Attributes: + chromosome (str): Chromosome. + start (int): Start position. + end (int): + """ + + def __init__(self, chromosome: str, start: int, end: int) -> None: + """Class constructor. + + Args: + chromosome (str): Chromosome. + start (int): Start position. + end (int): End position. + """ + self.chromosome = chromosome + self.start = start + self.end = end + + def __str__(self) -> str: + """String representation of the genomic region. + + Returns: + str: Genomic region in chr:start-end format. + """ + return f"{self.chromosome}:{self.start}-{self.end}" + + @classmethod + def from_string(cls: type["GenomicRegion"], region: str) -> "GenomicRegion": + """Parse region string to chr:start-end. + + Args: + region (str): Genomic region expected to follow chr##:#,###-#,### format or ##:####-#####. + + Returns: + GenomicRegion: Genomic region object. + + Raises: + ValueError: If the end and start positions cannot be casted to integer or not all three values value error is raised. + + Examples: + >>> print(GenomicRegion.from_string('chr6:28,510,120-33,480,577')) + 6:28510120-33480577 + >>> print(GenomicRegion.from_string('6:28510120-33480577')) + 6:28510120-33480577 + >>> print(GenomicRegion.from_string('6:28510120')) + Traceback (most recent call last): + ... + ValueError: Genomic region should follow a ##:####-#### format. + >>> print(GenomicRegion.from_string('6:28510120-foo')) + Traceback (most recent call last): + ... + ValueError: Start and the end position of the region has to be integer. + """ + region = region.replace(":", "-").replace(",", "") + try: + chromosome, start_position, end_position = region.split("-") + except ValueError as err: + raise ValueError( + "Genomic region should follow a ##:####-#### format." + ) from err + + try: + return cls( + chromosome=chromosome.replace("chr", ""), + start=int(start_position), + end=int(end_position), + ) + except ValueError as err: + raise ValueError( + "Start and the end position of the region has to be integer." + ) from err + + @classmethod + def from_known_genomic_region( + cls: type["GenomicRegion"], region: KnownGenomicRegions + ) -> "GenomicRegion": + """Get known genomic region. + + Args: + region (KnownGenomicRegions): Known genomic region. + + Returns: + GenomicRegion: Genomic region object. + + Examples: + >>> print(GenomicRegion.from_known_genomic_region(KnownGenomicRegions.MHC)) + 6:25726063-33400556 + """ + return GenomicRegion.from_string(region.value) diff --git a/src/gentropy/common/harmonise.py b/src/gentropy/common/harmonise.py new file mode 100644 index 000000000..6420245bd --- /dev/null +++ b/src/gentropy/common/harmonise.py @@ -0,0 +1,191 @@ +"""Variant harmonisation utilities.""" + +import pyspark.sql.functions as f +import pyspark.sql.types as t +from pyspark.sql import DataFrame, SparkSession + +from gentropy.common.spark_helpers import neglog_pvalue_to_mantissa_and_exponent + + +def harmonise_summary_stats( + spark: SparkSession, + raw_summary_stats_path: str, + tmp_variant_annotation_path: str, + chromosome: str, + colname_position: str, + colname_allele0: str, + colname_allele1: str, + colname_a1freq: str | None, + colname_info: str | None, + colname_beta: str, + colname_se: str, + colname_mlog10p: str, + colname_n: str | None, +) -> DataFrame: + """Ingest and harmonise the summary stats. + + 1. Rename chromosome 23 to X. + 2. Filter out low INFO rows. + 3. Filter out low frequency rows. + 4. Assign variant types. + 5. Create variant ID for joining the variant annotation dataset. + 6. Join with the Variant Annotation dataset. + 7. Drop bad quality variants. + + Args: + spark (SparkSession): Spark session object. + raw_summary_stats_path (str): Input raw summary stats path. + tmp_variant_annotation_path (str): Path to the Variant Annotation dataset which has been further prepared and processed by the per_chromosome module (previous PR in the chain) to speed up the joins in the harmonisation phase. It includes all variants in both the direct (A0/A1) and reverse (A1/A0) orientations, so that the direction of the variant can be easily determined on joining. + chromosome (str): Which chromosome to process. + colname_position (str): Column name for position. + colname_allele0 (str): Column name for allele0. + colname_allele1 (str): Column name for allele1. + colname_a1freq (str | None): Column name for allele1 frequency (optional). + colname_info (str | None): Column name for INFO, reflecting variant quality (optional). + colname_beta (str): Column name for beta. + colname_se (str): Column name for beta standard error. + colname_mlog10p (str): Column name for -log10(p). + colname_n (str | None): Column name for the number of samples (optional). + + Returns: + DataFrame: A harmonised summary stats dataframe. + """ + # Read the precomputed variant annotation dataset. + va_df = ( + spark + .read + .parquet(tmp_variant_annotation_path) + .filter(f.col("vaChromosome") == ("X" if chromosome == "23" else chromosome)) + .persist() + ) + + # Read and process the summary stats dataset. + df = ( + spark + .read + .parquet(raw_summary_stats_path) + .filter(f.col("chromosome") == chromosome) + # Harmonise, 1: Rename chromosome 23 to X. + .withColumn( + "chromosome", + f.when( + f.col("chromosome") == "23", "X" + ).otherwise(f.col("chromosome")) + ) + ) + if colname_info: + # Harmonise, 2: Filter out low INFO rows. + df = df.filter(f.col(colname_info) >= 0.8) + if colname_a1freq: + # Harmonise, 3: Filter out low frequency rows. + df = ( + df + .withColumn( + "MAF", + f.when(f.col(colname_a1freq) < 0.5, f.col(colname_a1freq)) + .otherwise(1 - f.col(colname_a1freq)) + ) + .filter(f.col("MAF") >= 0.0001) + .drop("MAF") + ) + df = ( + df + # Harmonise, 4: Assign variant types. + # There are three possible variant types: + # 1. `snp_c` means an SNP converting a base into its complementary base: A<>T or G> None: + """Prepare the Variant Annotation dataset for efficient per-chromosome joins. + + Args: + session (SparkSession): The Spark session to be used for reading and writing data. + variant_annotation_path (str): The path to the input variant annotation dataset. + tmp_variant_annotation_path (str): The path to store the temporary output for the repartitioned annotation dataset. + """ + va_df = ( + session + .spark + .read + .parquet(variant_annotation_path) + ) + va_df_direct = ( + va_df. + select( + f.col("chromosome").alias("vaChromosome"), + f.col("variantId"), + f.concat_ws( + "_", + f.col("chromosome"), + f.col("position"), + f.col("referenceAllele"), + f.col("alternateAllele") + ).alias("summary_stats_id"), + f.lit("direct").alias("direction") + ) + ) + va_df_flip = ( + va_df. + select( + f.col("chromosome").alias("vaChromosome"), + f.col("variantId"), + f.concat_ws( + "_", + f.col("chromosome"), + f.col("position"), + f.col("alternateAllele"), + f.col("referenceAllele") + ).alias("summary_stats_id"), + f.lit("flip").alias("direction") + ) + ) + ( + va_df_direct.union(va_df_flip) + .coalesce(1) + .repartition("vaChromosome") + .write + .partitionBy("vaChromosome") + .mode("overwrite") + .parquet(tmp_variant_annotation_path) + ) + + +def process_summary_stats_per_chromosome( + session: SparkSession, + ingestion_class: type[UkbPppEurSummaryStats] | type[FinngenUkbMetaSummaryStats], + raw_summary_stats_path: str, + tmp_variant_annotation_path: str, + summary_stats_output_path: str, + study_index_path: str, + ) -> None: + """Processes summary statistics for each chromosome, partitioning and writing results. + + Args: + session (SparkSession): The Spark session to use for distributed data processing. + ingestion_class (type[UkbPppEurSummaryStats] | type[FinngenUkbMetaSummaryStats]): The class used to handle ingestion of source data. Must have a `from_source` method returning a DataFrame. + raw_summary_stats_path (str): The path to the raw summary statistics files. + tmp_variant_annotation_path (str): The path to temporary variant annotation data, used for chromosome joins. + summary_stats_output_path (str): The output path to write processed summary statistics as parquet files. + study_index_path (str): The path to study index, which is necessary in some cases to populate the sample size column. + """ + # Set mode to overwrite for processing the first chromosome. + write_mode = "overwrite" + # Chromosome 23 is X, this is handled downstream. + for chromosome in list(range(1, 24)): + logging_message = f" Processing chromosome {chromosome}" + session.logger.info(logging_message) + ( + ingestion_class.from_source( + spark=session.spark, + raw_summary_stats_path=raw_summary_stats_path, + tmp_variant_annotation_path=tmp_variant_annotation_path, + chromosome=str(chromosome), + study_index_path=study_index_path, + ) + .df + .coalesce(1) + .repartition("studyId", "chromosome") + .write + .partitionBy("studyId", "chromosome") + .mode(write_mode) + .parquet(summary_stats_output_path) + ) + # Now that we have written the first chromosome, change mode to append for subsequent operations. + write_mode = "append" diff --git a/src/gentropy/common/schemas.py b/src/gentropy/common/schemas.py index 1dcd75a22..624e3e0e1 100644 --- a/src/gentropy/common/schemas.py +++ b/src/gentropy/common/schemas.py @@ -1,66 +1,212 @@ """Methods for handling schemas.""" + from __future__ import annotations import importlib.resources as pkg_resources import json -from collections import namedtuple -from typing import Any +from collections import defaultdict -import pyspark.sql.types as t +from pyspark.sql.types import ArrayType, StructType from gentropy.assets import schemas -def parse_spark_schema(schema_json: str) -> t.StructType: +class SchemaValidationError(Exception): + """This exception is raised when a schema validation fails.""" + + def __init__( + self: SchemaValidationError, message: str, errors: defaultdict[str, list[str]] + ) -> None: + """Initialize the SchemaValidationError. + + Args: + message (str): The message to be displayed. + errors (defaultdict[str, list[str]]): The collection of observed discrepancies + """ + super().__init__(message) + self.message = message # Explicitly set the message attribute + self.errors = errors + + def __str__(self: SchemaValidationError) -> str: + """Return a string representation of the exception. + + Returns: + str: The string representation of the exception. + """ + stringified_errors = "\n ".join( + [f'{k}: {",".join(v)}' for k, v in self.errors.items()] + ) + return f"{self.message}\nErrors:\n {stringified_errors}" + + +def parse_spark_schema(schema_json: str) -> StructType: """Parse Spark schema from JSON. Args: schema_json (str): JSON filename containing spark schema in the schemas package Returns: - t.StructType: Spark schema + StructType: Spark schema """ core_schema = json.loads( pkg_resources.read_text(schemas, schema_json, encoding="utf-8") ) - return t.StructType.fromJson(core_schema) + return StructType.fromJson(core_schema) -def flatten_schema(schema: t.StructType, prefix: str = "") -> list[Any]: - """It takes a Spark schema and returns a list of all fields in the schema once flattened. +def compare_array_schemas( + observed_schema: ArrayType, + expected_schema: ArrayType, + parent_field_name: str | None = None, + schema_issues: defaultdict[str, list[str]] | None = None, +) -> defaultdict[str, list[str]]: + """Compare two array schemas. + + The comparison is done recursively, so nested structs are also compared. Args: - schema (t.StructType): The schema of the dataframe - prefix (str): The prefix to prepend to the field names. Defaults to "". + observed_schema (ArrayType): The observed schema. + expected_schema (ArrayType): The expected schema. + parent_field_name (str | None): The parent field name. Defaults to None. + schema_issues (defaultdict[str, list[str]] | None): The schema issues. Defaults to None. Returns: - list[Any]: A list of all the columns in the dataframe. - - Examples: - >>> from pyspark.sql.types import ArrayType, StringType, StructField, StructType - >>> schema = StructType( - ... [ - ... StructField("studyLocusId", StringType(), False), - ... StructField("locus", ArrayType(StructType([StructField("variantId", StringType(), False)])), False) - ... ] - ... ) - >>> df = spark.createDataFrame([("A", [{"variantId": "varA"}]), ("B", [{"variantId": "varB"}])], schema) - >>> flatten_schema(df.schema) - [Field(name='studyLocusId', dataType=StringType()), Field(name='locus', dataType=ArrayType(StructType([]), True)), Field(name='locus.variantId', dataType=StringType())] + defaultdict[str, list[str]]: The schema issues. """ - Field = namedtuple("Field", ["name", "dataType"]) - fields = [] - for field in schema.fields: - name = f"{prefix}.{field.name}" if prefix else field.name - dtype = field.dataType - if isinstance(dtype, t.StructType): - fields.append(Field(name, t.ArrayType(t.StructType()))) - fields += flatten_schema(dtype, prefix=name) - elif isinstance(dtype, t.ArrayType) and isinstance( - dtype.elementType, t.StructType - ): - fields.append(Field(name, t.ArrayType(t.StructType()))) - fields += flatten_schema(dtype.elementType, prefix=name) - else: - fields.append(Field(name, dtype)) - return fields + # Create default values if not provided: + if schema_issues is None: + schema_issues = defaultdict(list) + + if parent_field_name is None: + parent_field_name = "" + + observed_type = observed_schema.elementType.typeName() + expected_type = expected_schema.elementType.typeName() + + # If element types are not matching, no further tests are needed: + if observed_type != expected_type: + schema_issues["columns_with_non_matching_type"].append( + f'For column "{parent_field_name}[]" found {observed_type} instead of {expected_type}' + ) + + # If element type is a struct, resolve nesting: + elif (observed_type == "struct") and (expected_type == "struct"): + schema_issues = compare_struct_schemas( + observed_schema.elementType, + expected_schema.elementType, + f"{parent_field_name}[].", + schema_issues, + ) + + # If element type is an array, resolve nesting: + elif (observed_type == "array") and (expected_type == "array"): + schema_issues = compare_array_schemas( + observed_schema.elementType, + expected_schema.elementType, + parent_field_name, + schema_issues, + ) + + return schema_issues + + +def compare_struct_schemas( + observed_schema: StructType, + expected_schema: StructType, + parent_field_name: str | None = None, + schema_issues: defaultdict[str, list[str]] | None = None, +) -> defaultdict[str, list[str]]: + """Compare two struct schemas. + + The comparison is done recursively, so nested structs are also compared. + + Checking logic: + 1. Checking for duplicated columns in the observed schema. + 2. Checking for missing mandatory columns in the observed schema. + 3. Now we know that all mandatory columns are present, we can iterate over the observed schema and compare the types. + 4. Flagging unexpected columns in the observed schema. + 5. Flagging columns with non-matching types. + 6. If a column is a struct -> call compare_struct_schemas + 7. If a column is an array -> call compare_array_schemas + 8. Return dictionary with issues. + + Args: + observed_schema (StructType): The observed schema. + expected_schema (StructType): The expected schema. + parent_field_name (str | None): The parent field name. Defaults to None. + schema_issues (defaultdict[str, list[str]] | None): The schema issues. Defaults to None. + + Returns: + defaultdict[str, list[str]]: The schema issues. + """ + # Create default values if not provided: + if schema_issues is None: + schema_issues = defaultdict(list) + + if parent_field_name is None: + parent_field_name = "" + + # Flagging duplicated columns if present: + if duplicated_columns := list( + { + f"{parent_field_name}{field.name}" + for field in observed_schema + if list(observed_schema).count(field) > 1 + } + ): + schema_issues["duplicated_columns"] += duplicated_columns + + # Testing mandatory fields: + required_fields = [x.name for x in expected_schema if not x.nullable] + if missing_required_fields := [ + f"{parent_field_name}{req}" + for req in required_fields + if not any(field.name == req for field in observed_schema) + ]: + schema_issues["missing_mandatory_columns"] += missing_required_fields + + # Converting schema to dictionaries for easier comparison: + observed_schema_dict = {field.name: field for field in observed_schema} + expected_schema_dict = {field.name: field for field in expected_schema} + + # Testing optional fields and types: + for field_name, field in observed_schema_dict.items(): + # Testing observed field name, if name is not matched, no further tests are needed: + if field_name not in expected_schema_dict: + schema_issues["unexpected_columns"].append( + f"{parent_field_name}{field_name}" + ) + continue + + # When we made sure the field is in both schemas, extracting field type information: + observed_type = field.dataType + observed_type_name = field.dataType.typeName() + + expected_type = expected_schema_dict[field_name].dataType + expected_type_name = expected_schema_dict[field_name].dataType.typeName() + + # Flagging non-matching types if types don't match, jumping to next field: + if observed_type_name != expected_type_name: + schema_issues["columns_with_non_matching_type"].append( + f'For column "{parent_field_name}{field_name}" found {observed_type_name} instead of {expected_type_name}' + ) + continue + + # If column is a struct, resolve nesting: + if observed_type_name == "struct": + schema_issues = compare_struct_schemas( + observed_type, + expected_type, + f"{parent_field_name}{field_name}.", + schema_issues, + ) + # If column is an array, resolve nesting: + elif observed_type_name == "array": + schema_issues = compare_array_schemas( + observed_type, + expected_type, + f"{parent_field_name}{field_name}[]", + schema_issues, + ) + + return schema_issues diff --git a/src/gentropy/common/session.py b/src/gentropy/common/session.py index 1837aec6b..297903629 100644 --- a/src/gentropy/common/session.py +++ b/src/gentropy/common/session.py @@ -4,6 +4,7 @@ from typing import TYPE_CHECKING +import hail as hl from pyspark.conf import SparkConf from pyspark.sql import SparkSession @@ -50,6 +51,8 @@ def __init__( # noqa: D107 self.hail_home = hail_home self.start_hail = start_hail + if start_hail: + hl.init(sc=self.spark.sparkContext, log="/dev/null") def _default_config(self: Session) -> SparkConf: """Default spark configuration. @@ -122,24 +125,32 @@ def _create_merged_config( all_settings += list(extended_spark_conf.items()) return SparkConf().setAll(all_settings) - def read_parquet( + def load_data( self: Session, path: str | list[str], - schema: StructType, + format: str = "parquet", + schema: StructType | str | None = None, **kwargs: bool | float | int | str | None, ) -> DataFrame: - """Reads parquet dataset (provided as a single path or a list of paths) with a provided schema. + """Generic function to read a file or folder into a Spark dataframe. + + The `recursiveFileLookup` flag when set to True will skip all partition columns, but read files from all subdirectories. Args: - path (str | list[str]): path to the parquet dataset - schema (StructType): Spark schema - **kwargs (bool | float | int | str | None): Additional arguments to pass to spark.read.parquet + path (str | list[str]): path to the dataset + format (str): file format. Defaults to parquet. + schema (StructType | str | None): Schema to use when reading the data. + **kwargs (bool | float | int | str | None): Additional arguments to pass to spark.read.load. `mergeSchema` is set to True, `recursiveFileLookup` is set to False by default. Returns: - DataFrame: Dataframe with provided schema + DataFrame: Dataframe """ - path = [path] if isinstance(path, str) else path - return self.spark.read.schema(schema).parquet(*path, **kwargs) + # Set default kwargs + if schema is None: + kwargs["inferSchema"] = kwargs.get("inferSchema", True) + kwargs["mergeSchema"] = kwargs.get("mergeSchema", True) + kwargs["recursiveFileLookup"] = kwargs.get("recursiveFileLookup", False) + return self.spark.read.load(path, format=format, schema=schema, **kwargs) class Log4j: diff --git a/src/gentropy/common/spark_helpers.py b/src/gentropy/common/spark_helpers.py index bd22d91c9..a1bf9670a 100644 --- a/src/gentropy/common/spark_helpers.py +++ b/src/gentropy/common/spark_helpers.py @@ -1,21 +1,23 @@ """Common utilities in Spark that can be used across the project.""" + from __future__ import annotations import re import sys -from typing import TYPE_CHECKING, Any, Iterable, Optional +from functools import reduce, wraps +from itertools import chain +from typing import TYPE_CHECKING, Any, Callable, Iterable, Optional, TypeVar import pyspark.sql.functions as f import pyspark.sql.types as t from pyspark.ml import Pipeline from pyspark.ml.feature import MinMaxScaler, VectorAssembler from pyspark.ml.functions import vector_to_array -from pyspark.sql import Row, Window -from pyspark.sql.types import FloatType +from pyspark.sql import Column, Row, Window from scipy.stats import norm if TYPE_CHECKING: - from pyspark.sql import Column, DataFrame, WindowSpec + from pyspark.sql import DataFrame, WindowSpec def convert_from_wide_to_long( @@ -53,7 +55,7 @@ def convert_from_wide_to_long( _vars_and_vals = f.array( *( f.struct( - f.lit(c).alias(var_name), f.col(c).cast(FloatType()).alias(value_name) + f.lit(c).alias(var_name), f.col(c).cast(t.FloatType()).alias(value_name) ) for c in value_vars ) @@ -265,19 +267,19 @@ def neglog_pvalue_to_mantissa_and_exponent(p_value: Column) -> tuple[Column, Col ... .select('negLogPv',*neglog_pvalue_to_mantissa_and_exponent(f.col('negLogPv'))) ... .show() ... ) - +--------+------------------+--------------+ - |negLogPv| pValueMantissa|pValueExponent| - +--------+------------------+--------------+ - | 4.56| 3.63078054770101| -5| - | 2109.23|1.6982436524618154| -2110| - +--------+------------------+--------------+ + +--------+--------------+--------------+ + |negLogPv|pValueMantissa|pValueExponent| + +--------+--------------+--------------+ + | 4.56| 2.7542286| -5| + | 2109.23| 5.8884363| -2110| + +--------+--------------+--------------+ """ exponent: Column = f.ceil(p_value) - mantissa: Column = f.pow(f.lit(10), (p_value - exponent + f.lit(1))) + mantissa: Column = f.pow(f.lit(10), (exponent - p_value)) return ( - mantissa.cast(t.DoubleType()).alias("pValueMantissa"), + mantissa.cast(t.FloatType()).alias("pValueMantissa"), (-1 * exponent).cast(t.IntegerType()).alias("pValueExponent"), ) @@ -363,6 +365,7 @@ def order_array_of_structs_by_field(column_name: str, field_name: str) -> Column array_sort( {column_name}, (left, right) -> case + when left.{field_name} is null and right.{field_name} is null then 0 when left.{field_name} is null then 1 when right.{field_name} is null then -1 when left.{field_name} < right.{field_name} then 1 @@ -373,6 +376,111 @@ def order_array_of_structs_by_field(column_name: str, field_name: str) -> Column ) +def order_array_of_structs_by_two_fields( + array_name: str, descending_column: str, ascending_column: str +) -> Column: + """Sort array of structs by a field in descending order and by an other field in an ascending order. + + This function doesn't deal with null values, assumes the sort columns are not nullable. + The sorting function compares the descending_column first, in case when two values from descending_column are equal + it compares the ascending_column. When values in both columns are equal, the rows order is preserved. + + Args: + array_name (str): Column name with array of structs + descending_column (str): Name of the first keys sorted in descending order + ascending_column (str): Name of the second keys sorted in ascending order + + Returns: + Column: Sorted column + + Examples: + >>> data = [(1.0, 45, 'First'), (0.5, 232, 'Third'), (0.5, 233, 'Fourth'), (1.0, 125, 'Second'),] + >>> ( + ... spark.createDataFrame(data, ['col1', 'col2', 'ranking']) + ... .groupBy(f.lit('c')) + ... .agg(f.collect_list(f.struct('col1','col2', 'ranking')).alias('list')) + ... .select(order_array_of_structs_by_two_fields('list', 'col1', 'col2').alias('sorted_list')) + ... .show(truncate=False) + ... ) + +-----------------------------------------------------------------------------+ + |sorted_list | + +-----------------------------------------------------------------------------+ + |[{1.0, 45, First}, {1.0, 125, Second}, {0.5, 232, Third}, {0.5, 233, Fourth}]| + +-----------------------------------------------------------------------------+ + + >>> data = [(1.0, 45, 'First'), (1.0, 45, 'Second'), (0.5, 233, 'Fourth'), (1.0, 125, 'Third'),] + >>> ( + ... spark.createDataFrame(data, ['col1', 'col2', 'ranking']) + ... .groupBy(f.lit('c')) + ... .agg(f.collect_list(f.struct('col1','col2', 'ranking')).alias('list')) + ... .select(order_array_of_structs_by_two_fields('list', 'col1', 'col2').alias('sorted_list')) + ... .show(truncate=False) + ... ) + +----------------------------------------------------------------------------+ + |sorted_list | + +----------------------------------------------------------------------------+ + |[{1.0, 45, First}, {1.0, 45, Second}, {1.0, 125, Third}, {0.5, 233, Fourth}]| + +----------------------------------------------------------------------------+ + + """ + return f.expr( + f""" + array_sort( + {array_name}, + (left, right) -> case + when left.{descending_column} is null and right.{descending_column} is null then 0 + when left.{ascending_column} is null and right.{ascending_column} is null then 0 + + when left.{descending_column} is null then 1 + when right.{descending_column} is null then -1 + + when left.{ascending_column} is null then 1 + when right.{ascending_column} is null then -1 + + when left.{descending_column} < right.{descending_column} then 1 + when left.{descending_column} > right.{descending_column} then -1 + when left.{descending_column} == right.{descending_column} and left.{ascending_column} > right.{ascending_column} then 1 + when left.{descending_column} == right.{descending_column} and left.{ascending_column} < right.{ascending_column} then -1 + when left.{ascending_column} == right.{ascending_column} and left.{descending_column} == right.{descending_column} then 0 + end) + """ + ) + + +def map_column_by_dictionary(col: Column, mapping_dict: dict[str, Any]) -> Column: + """Map column values to dictionary values by key. + + Missing consequence label will be converted to None, unmapped consequences will be mapped as None. + + Args: + col (Column): Column containing labels to map. + mapping_dict (dict[str, Any]): Dictionary with mapping key/value pairs. + + Returns: + Column: Column with mapped values. + + Examples: + >>> data = [('consequence_1',),('unmapped_consequence',),(None,)] + >>> m = {'consequence_1': 'SO:000000'} + >>> ( + ... spark.createDataFrame(data, ['label']) + ... .select('label',map_column_by_dictionary(f.col('label'),m).alias('id')) + ... .show() + ... ) + +--------------------+---------+ + | label| id| + +--------------------+---------+ + | consequence_1|SO:000000| + |unmapped_consequence| null| + | null| null| + +--------------------+---------+ + + """ + map_expr = f.create_map(*[f.lit(x) for x in chain(*mapping_dict.items())]) + + return map_expr[col] + + def pivot_df( df: DataFrame, pivot_col: str, @@ -431,3 +539,346 @@ def get_value_from_row(row: Row, column: str) -> Any: if column not in row: raise ValueError(f"Column {column} not found in row {row}") return row[column] + + +def enforce_schema( + expected_schema: t.ArrayType | t.StructType | Column | str, +) -> Callable[..., Any]: + """A function to enforce the schema of a function output follows expectation. + + Behaviour: + - Fields that are not present in the expected schema will be dropped. + - Expected but missing fields will be added with Null values. + - Fields with incorrect data types will be casted to the expected data type. + + This is a decorator function and expected to be used like this: + + @enforce_schema(spark_schema) + def my_function() -> t.StructType: + return ... + + Args: + expected_schema (t.ArrayType | t.StructType | Column | str): The expected schema of the output. + + Returns: + Callable[..., Any]: A decorator function. + """ + T = TypeVar("T", str, Column) + + def decorator(function: Callable[..., T]) -> Callable[..., T]: + """A decorator function to enforce the schema of a function output follows expectation. + + Args: + function (Callable[..., T]): The function to be decorated. + + Returns: + Callable[..., T]: The decorated function. + """ + + @wraps(function) + def wrapper(*args: str, **kwargs: str) -> Any: + return f.from_json(f.to_json(function(*args, **kwargs)), expected_schema) + + return wrapper + + return decorator + + +def rename_all_columns(df: DataFrame, prefix: str) -> DataFrame: + """Given a prefix, rename all columns of a DataFrame. + + Args: + df (DataFrame): The DataFrame to be processed. + prefix (str): The prefix to be added to the column names. + + Returns: + DataFrame: The DataFrame with all columns renamed. + + Examples: + >>> data = [('a', 1.2, True),('b', 0.0, False),('c', None, None),] + >>> prefix = 'prefix_' + >>> rename_all_columns(spark.createDataFrame(data, ['col1', 'col2', 'col3']), prefix).show() + +-----------+-----------+-----------+ + |prefix_col1|prefix_col2|prefix_col3| + +-----------+-----------+-----------+ + | a| 1.2| true| + | b| 0.0| false| + | c| null| null| + +-----------+-----------+-----------+ + + """ + return reduce( + lambda df, col: df.withColumnRenamed(col, f"{prefix}{col}"), + df.columns, + df, + ) + + +def safe_array_union( + a: Column, b: Column, fields_order: list[str] | None = None +) -> Column: + """Merge the content of two optional columns. + + The function assumes the array columns have the same schema. + If the `fields_order` is passed, the function assumes that it deals with array of structs and sorts the nested + struct fields by the provided `fields_order` before conducting array_merge. + If the `fields_order` is not passed and both columns are > type then function assumes struct fields have the same order, + otherwise the function will raise an AnalysisException. + + Args: + a (Column): One optional array column. + b (Column): The other optional array column. + fields_order (list[str] | None): The order of the fields in the struct. Defaults to None. + + Returns: + Column: array column with merged content. + + Examples: + >>> data = [(['a'], ['b']), (['c'], None), (None, ['d']), (None, None)] + >>> ( + ... spark.createDataFrame(data, ['col1', 'col2']) + ... .select( + ... safe_array_union(f.col('col1'), f.col('col2')).alias('merged') + ... ) + ... .show() + ... ) + +------+ + |merged| + +------+ + |[a, b]| + | [c]| + | [d]| + | null| + +------+ + + >>> schema="arr2: array>, arr: array>" + >>> data = [([(1,"a",), (2, "c")],[("a", 1,)]),] + >>> df = spark.createDataFrame(data=data, schema=schema) + >>> df.select(safe_array_union(f.col("arr"), f.col("arr2"), fields_order=["a", "b"]).alias("merged")).show() + +----------------+ + | merged| + +----------------+ + |[{a, 1}, {c, 2}]| + +----------------+ + + >>> schema="arr2: array>, arr: array>" + >>> data = [([(1,"a",), (2, "c")],[("a", 1,)]),] + >>> df = spark.createDataFrame(data=data, schema=schema) + >>> df.select(safe_array_union(f.col("arr"), f.col("arr2")).alias("merged")).show() # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + pyspark.sql.utils.AnalysisException: ... + """ + if fields_order: + # sort the nested struct fields by the provided order + a = sort_array_struct_by_columns(a, fields_order) + b = sort_array_struct_by_columns(b, fields_order) + return f.when(a.isNotNull() & b.isNotNull(), f.array_union(a, b)).otherwise( + f.coalesce(a, b) + ) + + +def sort_array_struct_by_columns(column: Column, fields_order: list[str]) -> Column: + """Sort nested struct fields by provided fields order. + + Args: + column (Column): Column with array of structs. + fields_order (list[str]): List of field names to sort by. + + Returns: + Column: Sorted column. + + Examples: + >>> schema="arr: array>" + >>> data = [([(1,"a",), (2, "c")],)] + >>> fields_order = ["a", "b"] + >>> df = spark.createDataFrame(data=data, schema=schema) + >>> df.select(sort_array_struct_by_columns(f.col("arr"), fields_order).alias("sorted")).show() + +----------------+ + | sorted| + +----------------+ + |[{c, 2}, {a, 1}]| + +----------------+ + + """ + column_name = extract_column_name(column) + fields_order_expr = ", ".join([f"x.{field}" for field in fields_order]) + return f.expr( + f"sort_array(transform({column_name}, x -> struct({fields_order_expr})), False)" + ).alias(column_name) + + +def extract_column_name(column: Column) -> str: + """Extract column name from a column expression. + + Args: + column (Column): Column expression. + + Returns: + str: Column name. + + Raises: + ValueError: If the column name cannot be extracted. + + Examples: + >>> extract_column_name(f.col('col1')) + 'col1' + >>> extract_column_name(f.sort_array(f.col('col1'))) + 'sort_array(col1, true)' + """ + pattern = re.compile("^Column<'(?P.*)'>?") + + _match = pattern.search(str(column)) + if not _match: + raise ValueError(f"Cannot extract column name from {column}") + return _match.group("name") + + +def create_empty_column_if_not_exists( + col_name: str, col_schema: t.DataType = t.NullType() +) -> Column: + """Create a column if it does not exist in the DataFrame. + + Args: + col_name (str): The name of the column to be created. + col_schema (t.DataType): The schema of the column to be created. Defaults to NullType. + + Returns: + Column: The expression to create the column. + + Examples: + >>> df = spark.createDataFrame([(1, 2),], ['col1', 'col2']) + >>> df.select("*", create_empty_column_if_not_exists('col3', t.IntegerType())).show() + +----+----+----+ + |col1|col2|col3| + +----+----+----+ + | 1| 2|null| + +----+----+----+ + + """ + return f.lit(None).cast(col_schema).alias(col_name) + + +def get_standard_error_from_confidence_interval(lower: Column, upper: Column) -> Column: + """Compute the standard error from the confidence interval. + + Args: + lower (Column): The lower bound of the confidence interval. + upper (Column): The upper bound of the confidence interval. + + Returns: + Column: The standard error. + + Examples: + >>> data = [(0.5, 1.5), (None, 2.5), (None, None)] + >>> ( + ... spark.createDataFrame(data, ['lower', 'upper']) + ... .select( + ... get_standard_error_from_confidence_interval(f.col('lower'), f.col('upper')).alias('standard_error') + ... ) + ... .show() + ... ) + +-------------------+ + | standard_error| + +-------------------+ + |0.25510204081632654| + | null| + | null| + +-------------------+ + + """ + return (upper - lower) / (2 * 1.96) + + +def get_nested_struct_schema(dtype: t.DataType) -> t.StructType: + """Get the bottom StructType from a nested ArrayType type. + + Args: + dtype (t.DataType): The nested data structure. + + Returns: + t.StructType: The nested struct schema. + + Raises: + TypeError: If the input data type is not a nested struct. + + Examples: + >>> get_nested_struct_schema(t.ArrayType(t.StructType([t.StructField('a', t.StringType())]))) + StructType([StructField('a', StringType(), True)]) + + >>> get_nested_struct_schema(t.ArrayType(t.ArrayType(t.StructType([t.StructField("a", t.StringType())])))) + StructType([StructField('a', StringType(), True)]) + """ + if isinstance(dtype, t.StructField): + dtype = dtype.dataType + + match dtype: + case t.StructType(fields=_): + return dtype + case t.ArrayType(elementType=dtype): + return get_nested_struct_schema(dtype) + case _: + raise TypeError("The input data type must be a nested struct.") + + +def get_struct_field_schema(schema: t.StructType, name: str) -> t.DataType: + """Get schema for underlying struct field. + + Args: + schema (t.StructType): Provided schema where the name should be looked in. + name (str): Name of the field to look in the schema + + Returns: + t.DataType: Data type of the StructField with provided name + + Raises: + ValueError: If provided name is not present in the input schema + + Examples: + >>> get_struct_field_schema(t.StructType([t.StructField("a", t.StringType())]), "a") + StringType() + + >>> get_struct_field_schema(t.StructType([t.StructField("a", t.StringType())]), "b") # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + ValueError: Provided name b is not present in the schema + + """ + matching_fields = [f for f in schema.fields if f.name == name] + if not matching_fields: + raise ValueError("Provided name %s is not present in the schema.", name) + return matching_fields[0].dataType + +def calculate_harmonic_sum(input_array: Column) -> Column: + """Calculate the harmonic sum of an array. + + Args: + input_array (Column): input array of doubles + + Returns: + Column: column of harmonic sums + + Examples: + >>> from pyspark.sql import Row + >>> df = spark.createDataFrame([ + ... Row([0.3, 0.8, 1.0]), + ... Row([0.7, 0.2, 0.9]), + ... ], ["input_array"] + ... ) + >>> df.select("*", calculate_harmonic_sum(f.col("input_array")).alias("harmonic_sum")).show() + +---------------+------------------+ + | input_array| harmonic_sum| + +---------------+------------------+ + |[0.3, 0.8, 1.0]|0.7502326177269538| + |[0.7, 0.2, 0.9]|0.6674366756805108| + +---------------+------------------+ + + """ + return f.aggregate( + f.arrays_zip( + f.sort_array(input_array, False).alias("score"), + f.sequence(f.lit(1), f.size(input_array)).alias("pos") + ), + f.lit(0.0), + lambda acc, x: acc + + x["score"]/f.pow(x["pos"], 2)/f.lit(sum(1 / ((i + 1)**2) for i in range(1000))) + ) diff --git a/src/gentropy/common/types.py b/src/gentropy/common/types.py new file mode 100644 index 000000000..7adbad9c4 --- /dev/null +++ b/src/gentropy/common/types.py @@ -0,0 +1,18 @@ +"""Types and type aliases used in the package.""" + +from typing import Literal + +LD_Population = Literal["afr", "amr", "asj", "eas", "est", "fin", "nfe", "nwe", "seu"] + +VariantPopulation = Literal[ + "afr", "amr", "ami", "asj", "eas", "fin", "nfe", "mid", "sas", "remaining" +] +DataSourceType = Literal[ + "gnomad", + "finngen", + "gwas_catalog", + "eqtl_catalog", + "ukbiobank", + "open_targets", + "intervals", +] diff --git a/src/gentropy/common/utils.py b/src/gentropy/common/utils.py index 05704a299..ca6e8e7f2 100644 --- a/src/gentropy/common/utils.py +++ b/src/gentropy/common/utils.py @@ -1,11 +1,13 @@ """Common functions in the Genetics datasets.""" + from __future__ import annotations import sys from math import floor, log10 -from typing import TYPE_CHECKING, Tuple +from typing import TYPE_CHECKING import hail as hl +import numpy as np from pyspark.sql import functions as f from pyspark.sql import types as t @@ -13,49 +15,10 @@ if TYPE_CHECKING: from hail.table import Table + from numpy.typing import NDArray from pyspark.sql import Column -def parse_region(region: str) -> Tuple[str, int, int]: - """Parse region string to chr:start-end. - - Args: - region (str): Genomic region expected to follow chr##:#,###-#,### format or ##:####-#####. - - Returns: - Tuple[str, int, int]: Chromosome, start position, end position - - Raises: - ValueError: If the end and start positions cannot be casted to integer or not all three values value error is raised. - - Examples: - >>> parse_region('chr6:28,510,120-33,480,577') - ('6', 28510120, 33480577) - >>> parse_region('6:28510120-33480577') - ('6', 28510120, 33480577) - >>> parse_region('6:28510120') - Traceback (most recent call last): - ... - ValueError: Genomic region should follow a ##:####-#### format. - >>> parse_region('6:28510120-foo') - Traceback (most recent call last): - ... - ValueError: Start and the end position of the region has to be integer. - """ - region = region.replace(":", "-").replace(",", "") - try: - (chromosome, start_position, end_position) = region.split("-") - except ValueError as err: - raise ValueError("Genomic region should follow a ##:####-#### format.") from err - - try: - return (chromosome.replace("chr", ""), int(start_position), int(end_position)) - except ValueError as err: - raise ValueError( - "Start and the end position of the region has to be integer." - ) from err - - def calculate_confidence_interval( pvalue_mantissa: Column, pvalue_exponent: Column, @@ -206,41 +169,6 @@ def parse_pvalue(pv: Column) -> list[Column]: ] -def convert_gnomad_position_to_ensembl( - position: Column, reference: Column, alternate: Column -) -> Column: - """Convert GnomAD variant position to Ensembl variant position. - - For indels (the reference or alternate allele is longer than 1), then adding 1 to the position, for SNPs, - the position is unchanged. More info about the problem: https://www.biostars.org/p/84686/ - - Args: - position (Column): Position of the variant in GnomAD's coordinates system. - reference (Column): The reference allele in GnomAD's coordinates system. - alternate (Column): The alternate allele in GnomAD's coordinates system. - - Returns: - Column: The position of the variant in the Ensembl genome. - - Examples: - >>> d = [(1, "A", "C"), (2, "AA", "C"), (3, "A", "AA")] - >>> df = spark.createDataFrame(d).toDF("position", "reference", "alternate") - >>> df.withColumn("new_position", convert_gnomad_position_to_ensembl(f.col("position"), f.col("reference"), f.col("alternate"))).show() - +--------+---------+---------+------------+ - |position|reference|alternate|new_position| - +--------+---------+---------+------------+ - | 1| A| C| 1| - | 2| AA| C| 3| - | 3| A| AA| 4| - +--------+---------+---------+------------+ - - - """ - return f.when( - (f.length(reference) > 1) | (f.length(alternate) > 1), position + 1 - ).otherwise(position) - - def _liftover_loci( variant_index: Table, chain_path: str, dest_reference_genome: str ) -> Table: @@ -325,3 +253,65 @@ def parse_efos(efo_uri: Column) -> Column: """ colname = efo_uri._jc.toString() return f.array_sort(f.expr(f"regexp_extract_all(`{colname}`, '([A-Z]+_[0-9]+)')")) + + +def get_logsum(arr: NDArray[np.float64]) -> float: + """Calculates logarithm of the sum of exponentials of a vector. The max is extracted to ensure that the sum is not Inf. + + This function emulates scipy's logsumexp expression. + + Args: + arr (NDArray[np.float64]): input array + + Returns: + float: logsumexp of the input array + + Example: + >>> l = [0.2, 0.1, 0.05, 0] + >>> round(get_logsum(l), 6) + 1.476557 + """ + themax = np.max(arr) + result = themax + np.log(np.sum(np.exp(arr - themax))) + return float(result) + + +def access_gcp_secret(secret_id: str, project_id: str) -> str: + """Access GCP secret manager to get the secret value. + + Args: + secret_id (str): ID of the secret + project_id (str): ID of the GCP project + + Returns: + str: secret value + """ + from google.cloud import secretmanager + + client = secretmanager.SecretManagerServiceClient() + name = f"projects/{project_id}/secrets/{secret_id}/versions/latest" + response = client.access_secret_version(name=name) + return response.payload.data.decode("UTF-8") + + +def copy_to_gcs(source_path: str, destination_blob: str) -> None: + """Copy a file to a Google Cloud Storage bucket. + + Args: + source_path (str): Path to the local file to copy + destination_blob (str): GS path to the destination blob in the GCS bucket + + Raises: + ValueError: If the path is a directory + """ + import os + from urllib.parse import urlparse + + from google.cloud import storage + + if os.path.isdir(source_path): + raise ValueError("Path should be a file, not a directory.") + client = storage.Client() + bucket = client.bucket(bucket_name=urlparse(destination_blob).hostname) + blob = bucket.blob(blob_name=urlparse(destination_blob).path.lstrip("/")) + blob.upload_from_filename(source_path) diff --git a/src/gentropy/common/version_engine.py b/src/gentropy/common/version_engine.py new file mode 100644 index 000000000..d852d8f5d --- /dev/null +++ b/src/gentropy/common/version_engine.py @@ -0,0 +1,158 @@ +"""Mechanism to seek version from specific datasource.""" + +from __future__ import annotations + +import re +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Callable + +from gentropy.common.types import DataSourceType + + +class VersionEngine: + """Seek version from the datasource.""" + + def __init__(self, datasource: DataSourceType) -> None: + """Initialize VersionEngine. + + Args: + datasource (DataSourceType): datasource to seek the version from + """ + self.datasource = datasource + + @staticmethod + def version_seekers() -> dict[DataSourceType, DatasourceVersionSeeker]: + """List version seekers. + + Returns: + dict[DataSourceType, DatasourceVersionSeeker]: list of available data sources. + """ + return { + "gnomad": GnomADVersionSeeker(), + } + + def seek(self, text: str | Path) -> str: + """Interface for inferring the version from text by using registered data source version iner method. + + Args: + text (str | Path): text to seek version from + + Returns: + str: inferred version + + Raises: + TypeError: if version can not be found in the text + + Examples: + >>> VersionEngine("gnomad").seek("gs://gcp-public-data--gnomad/release/2.1.1/vcf/genomes/gnomad.genomes.r2.1.1.sites.vcf.bgz") + '2.1.1' + """ + match text: + case Path() | str(): + text = str(text) + case _: + msg = f"Can not find version in {text}" + raise TypeError(msg) + infer_method = self._get_version_seek_method() + return infer_method(text) + + def _get_version_seek_method(self) -> Callable[[str], str]: + """Method that gets the version seeker for the datasource. + + Returns: + Callable[[str], str]: Method to seek version based on the initialized datasource + + Raises: + ValueError: if datasource is not registered in the list of version seekers + """ + if self.datasource not in self.version_seekers(): + raise ValueError(f"Invalid datasource {self.datasource}") + return self.version_seekers()[self.datasource].seek_version + + def amend_version( + self, analysis_input_path: str | Path, analysis_output_path: str | Path + ) -> str: + """Amend version to the analysis output path if it is not already present. + + Path can be path to g3:// or Path object, absolute or relative. + The analysis_input_path has to contain the version number. + If the analysis_output_path contains the same version as inferred from input version already, + then it will not be appended. + + Args: + analysis_input_path (str | Path): step input path + analysis_output_path (str | Path): step output path + + Returns: + str: Path with the ammended version, does not return Path object! + + Examples: + >>> VersionEngine("gnomad").amend_version("gs://gcp-public-data--gnomad/release/2.1.1/vcf/genomes/gnomad.genomes.r2.1.1.sites.vcf.bgz", "/some/path/without/version") + '/some/path/without/version/2.1.1' + """ + version = self.seek(analysis_input_path) + output_path = str(analysis_output_path) + if version in output_path: + return output_path + if output_path.endswith("/"): + return f"{analysis_output_path}{version}" + return f"{analysis_output_path}/{version}" + + +class DatasourceVersionSeeker(ABC): + """Interface for datasource version seeker. + + Raises: + NotImplementedError: if method is not implemented in the subclass + """ + + @staticmethod + @abstractmethod + def seek_version(text: str) -> str: + """Seek version from text. Implement this method in the subclass. + + Args: + text (str): text to seek version from + + Returns: + str: seeked version + + Raises: + NotImplementedError: if method is not implemented in the subclass + + """ + raise NotImplementedError + + +class GnomADVersionSeeker(DatasourceVersionSeeker): + """Seek version from GnomAD datasource.""" + + @staticmethod + def seek_version(text: str) -> str: + """Seek GnomAD version from provided text by using regex. + + Up to 3 digits are allowed in the version number. + Historically gnomAD version numbers have been in the format + 2.1.1, 3.1, etc. as of 2024-05. GnomAD versions can be found by + running `"gs://gcp-public-data--gnomad/release/*/*/*"` + + Args: + text (str): text to seek version from + + Raises: + ValueError: if version can not be seeked + + Returns: + str: seeked version + + Examples: + >>> GnomADVersionSeeker.seek_version("gs://gcp-public-data--gnomad/release/2.1.1/vcf/genomes/gnomad.genomes.r2.1.1.sites.vcf.bgz") + '2.1.1' + """ + result = re.search(r"v?((\d+){1}\.(\d+){1}\.?(\d+)?)", text) + match result: + case None: + raise ValueError(f"No GnomAD version found in provided text: {text}") + case _: + return result.group(1) diff --git a/src/gentropy/config.py b/src/gentropy/config.py index 552c50d1d..82ead9ed0 100644 --- a/src/gentropy/config.py +++ b/src/gentropy/config.py @@ -1,7 +1,8 @@ """Interface for application configuration.""" + import os from dataclasses import dataclass, field -from typing import Any, Dict, List +from typing import Any, ClassVar, List, TypedDict from hail import __file__ as hail_location from hydra.core.config_store import ConfigStore @@ -16,7 +17,7 @@ class SessionConfig: write_mode: str = "errorifexists" spark_uri: str = "local[*]" hail_home: str = os.path.dirname(hail_location) - extended_spark_conf: dict[str, str] | None = None + extended_spark_conf: dict[str, str] | None = field(default_factory=dict[str, str]) _target_: str = "gentropy.common.session.Session" @@ -35,8 +36,9 @@ class ColocalisationConfig(StepConfig): """Colocalisation step configuration.""" credible_set_path: str = MISSING - study_index_path: str = MISSING coloc_path: str = MISSING + colocalisation_method: str = MISSING + colocalisation_method_params: dict[str, Any] = field(default_factory=dict[str, Any]) _target_: str = "gentropy.colocalisation.ColocalisationStep" @@ -49,50 +51,53 @@ class GeneIndexConfig(StepConfig): _target_: str = "gentropy.gene_index.GeneIndexStep" +@dataclass +class BiosampleIndexConfig(StepConfig): + """Biosample index step configuration.""" + + cell_ontology_input_path: str = MISSING + uberon_input_path: str = MISSING + efo_input_path: str = MISSING + biosample_index_path: str = MISSING + _target_: str = "gentropy.biosample_index.BiosampleIndexStep" + + @dataclass class GWASCatalogStudyCurationConfig(StepConfig): """GWAS Catalog study curation step configuration.""" catalog_study_files: list[str] = MISSING catalog_ancestry_files: list[str] = MISSING - catalog_sumstats_lut: str = MISSING gwas_catalog_study_curation_out: str = MISSING gwas_catalog_study_curation_file: str = MISSING _target_: str = "gentropy.gwas_catalog_study_curation.GWASCatalogStudyCurationStep" @dataclass -class GWASCatalogStudyInclusionConfig(StepConfig): - """GWAS Catalog study inclusion step configuration.""" +class GWASCatalogStudyIndexGenerationStep(StepConfig): + """GWAS Catalog study index generation.""" catalog_study_files: list[str] = MISSING catalog_ancestry_files: list[str] = MISSING - catalog_associations_file: str = MISSING - gwas_catalog_study_curation_file: str = MISSING - variant_annotation_path: str = MISSING - harmonised_study_file: str = MISSING - criteria: str = MISSING - inclusion_list_path: str = MISSING - exclusion_list_path: str = MISSING + study_index_path: str = MISSING + gwas_catalog_study_curation_file: str | None = None + sumstats_qc_path: str | None = None _target_: str = ( - "gentropy.gwas_catalog_study_inclusion.GWASCatalogStudyInclusionGenerator" + "gentropy.gwas_catalog_study_index.GWASCatalogStudyIndexGenerationStep" ) @dataclass -class GWASCatalogIngestionConfig(StepConfig): +class GWASCatalogTopHitIngestionConfig(StepConfig): """GWAS Catalog ingestion step configuration.""" catalog_study_files: list[str] = MISSING catalog_ancestry_files: list[str] = MISSING - catalog_sumstats_lut: str = MISSING catalog_associations_file: str = MISSING variant_annotation_path: str = MISSING catalog_studies_out: str = MISSING catalog_associations_out: str = MISSING - gwas_catalog_study_curation_file: str | None = None - inclusion_list_path: str | None = None - _target_: str = "gentropy.gwas_catalog_ingestion.GWASCatalogIngestionStep" + _target_: str = "gentropy.gwas_catalog_top_hits.GWASCatalogTopHitIngestionStep" @dataclass @@ -110,9 +115,16 @@ class GWASCatalogSumstatsPreprocessConfig(StepConfig): class EqtlCatalogueConfig(StepConfig): """eQTL Catalogue step configuration.""" + session: Any = field( + default_factory=lambda: { + "start_hail": True, + } + ) eqtl_catalogue_paths_imported: str = MISSING eqtl_catalogue_study_index_out: str = MISSING - eqtl_catalogue_summary_stats_out: str = MISSING + eqtl_catalogue_credible_sets_out: str = MISSING + mqtl_quantification_methods_blacklist: list[str] = field(default_factory=lambda: []) + eqtl_lead_pvalue_threshold: float = 1e-3 _target_: str = "gentropy.eqtl_catalogue.EqtlCatalogueStep" @@ -120,27 +132,41 @@ class EqtlCatalogueConfig(StepConfig): class FinngenStudiesConfig(StepConfig): """FinnGen study index step configuration.""" + session: Any = field( + default_factory=lambda: { + "start_hail": True, + } + ) finngen_study_index_out: str = MISSING + finngen_phenotype_table_url: str = "https://r11.finngen.fi/api/phenos" + finngen_release_prefix: str = "FINNGEN_R11_" + finngen_summary_stats_url_prefix: str = ( + "gs://finngen-public-data-r11/summary_stats/finngen_R11_" + ) + finngen_summary_stats_url_suffix: str = ".gz" + efo_curation_mapping_url: str = "https://raw.githubusercontent.com/opentargets/curation/24.09.1/mappings/disease/manual_string.tsv" + sample_size: int = 453733 # https://www.finngen.fi/en/access_results#:~:text=Total%20sample%20size%3A%C2%A0453%2C733%C2%A0(254%2C618%C2%A0females%20and%C2%A0199%2C115%20males) _target_: str = "gentropy.finngen_studies.FinnGenStudiesStep" -@dataclass -class FinngenSumstatPreprocessConfig(StepConfig): - """FinnGen study index step configuration.""" - - raw_sumstats_path: str = MISSING - out_sumstats_path: str = MISSING - _target_: str = "gentropy.finngen_sumstat_preprocess.FinnGenSumstatPreprocessStep" - - @dataclass class FinngenFinemappingConfig(StepConfig): """FinnGen fine mapping ingestion step configuration.""" - finngen_finemapping_results_path: str = MISSING - finngen_finemapping_summaries_path: str = MISSING - finngen_release_prefix: str = MISSING + session: Any = field( + default_factory=lambda: { + "start_hail": True, + } + ) + finngen_susie_finemapping_snp_files: str = ( + "gs://finngen-public-data-r11/finemap/full/susie/*.snp.bgz" + ) + finngen_susie_finemapping_cs_summary_files: str = ( + "gs://finngen-public-data-r11/finemap/summary/*SUSIE.cred.summary.tsv" + ) finngen_finemapping_out: str = MISSING + finngen_finemapping_lead_pvalue_threshold: float = 1e-5 + finngen_release_prefix: str = "FINNGEN_R11" _target_: str = ( "gentropy.finngen_finemapping_ingestion.FinnGenFinemappingIngestionStep" ) @@ -155,9 +181,25 @@ class LDIndexConfig(StepConfig): "start_hail": True, } ) - min_r2: float = 0.5 ld_index_out: str = MISSING - _target_: str = "gentropy.ld_index.LDIndexStep" + min_r2: float = 0.5 + ld_matrix_template: str = "gs://gcp-public-data--gnomad/release/2.1.1/ld/gnomad.genomes.r2.1.1.{POP}.common.adj.ld.bm" + ld_index_raw_template: str = "gs://gcp-public-data--gnomad/release/2.1.1/ld/gnomad.genomes.r2.1.1.{POP}.common.ld.variant_indices.ht" + liftover_ht_path: str = "gs://gcp-public-data--gnomad/release/2.1.1/liftover_grch38/ht/genomes/gnomad.genomes.r2.1.1.sites.liftover_grch38.ht" + grch37_to_grch38_chain_path: str = ( + "gs://hail-common/references/grch37_to_grch38.over.chain.gz" + ) + ld_populations: list[str] = field( + default_factory=lambda: [ + "afr", # African-American + "amr", # American Admixed/Latino + "eas", # East Asian + "fin", # Finnish + "nfe", # Non-Finnish European + ] + ) + use_version_from_input: bool = False + _target_: str = "gentropy.gnomad_ingestion.LDIndexStep" @dataclass @@ -175,75 +217,129 @@ class LDBasedClumpingConfig(StepConfig): class LocusToGeneConfig(StepConfig): """Locus to gene step configuration.""" - session: Any = field( - default_factory=lambda: { - "extended_spark_conf": { - "spark.dynamicAllocation.enabled": "false", - "spark.driver.memory": "48g", - "spark.executor.memory": "48g", - } - } - ) run_mode: str = MISSING - model_path: str = MISSING - predictions_path: str = MISSING credible_set_path: str = MISSING - variant_gene_path: str = MISSING - colocalisation_path: str = MISSING - study_index_path: str = MISSING + feature_matrix_path: str = MISSING + predictions_path: str | None = None + variant_index_path: str | None = None + model_path: str | None = None gold_standard_curation_path: str | None = None gene_interactions_path: str | None = None features_list: list[str] = field( default_factory=lambda: [ - # average distance of all tagging variants to gene TSS + # max CLPP for each (study, locus, gene) aggregating over a specific qtl type + "eQtlColocClppMaximum", + "pQtlColocClppMaximum", + "sQtlColocClppMaximum", + # max H4 for each (study, locus, gene) aggregating over a specific qtl type + "eQtlColocH4Maximum", + "pQtlColocH4Maximum", + "sQtlColocH4Maximum", + # max CLPP for each (study, locus, gene) aggregating over a specific qtl type and in relation with the mean in the vicinity + "eQtlColocClppMaximumNeighbourhood", + "pQtlColocClppMaximumNeighbourhood", + "sQtlColocClppMaximumNeighbourhood", + # max H4 for each (study, locus, gene) aggregating over a specific qtl type and in relation with the mean in the vicinity + "eQtlColocH4MaximumNeighbourhood", + "pQtlColocH4MaximumNeighbourhood", + "sQtlColocH4MaximumNeighbourhood", + # distance to gene footprint + "distanceSentinelFootprint", + "distanceSentinelFootprintNeighbourhood", + "distanceFootprintMean", + "distanceFootprintMeanNeighbourhood", + # distance to gene tss "distanceTssMean", - # minimum distance of all tagging variants to gene TSS - "distanceTssMinimum", - # maximum vep consequence score of the locus 95% credible set among all genes in the vicinity - "vepMaximumNeighborhood", - # maximum vep consequence score of the locus 95% credible set split by gene + "distanceTssMeanNeighbourhood", + "distanceSentinelTss", + "distanceSentinelTssNeighbourhood", + # vep "vepMaximum", - # mean vep consequence score of the locus 95% credible set among all genes in the vicinity - "vepMeanNeighborhood", - # mean vep consequence score of the locus 95% credible set split by gene + "vepMaximumNeighbourhood", "vepMean", - # max clpp for each (study, locus, gene) aggregating over all eQTLs - "eqtlColocClppMaximum", - # max clpp for each (study, locus) aggregating over all eQTLs - "eqtlColocClppMaximumNeighborhood", - # max clpp for each (study, locus, gene) aggregating over all pQTLs - # "pqtlColocClppMaximum", - # max clpp for each (study, locus) aggregating over all pQTLs - # "pqtlColocClppMaximumNeighborhood", - # max clpp for each (study, locus, gene) aggregating over all sQTLs - # "sqtlColocClppMaximum", - # max clpp for each (study, locus) aggregating over all sQTLs - # "sqtlColocClppMaximumNeighborhood", - # # max log-likelihood ratio value for each (study, locus, gene) aggregating over all eQTLs - # "eqtlColocLlrLocalMaximum", - # # max log-likelihood ratio value for each (study, locus) aggregating over all eQTLs - # "eqtlColocLlpMaximumNeighborhood", - # # max log-likelihood ratio value for each (study, locus, gene) aggregating over all pQTLs - # "pqtlColocLlrLocalMaximum", - # # max log-likelihood ratio value for each (study, locus) aggregating over all pQTLs - # "pqtlColocLlpMaximumNeighborhood", - # # max log-likelihood ratio value for each (study, locus, gene) aggregating over all sQTLs - # "sqtlColocLlrLocalMaximum", - # # max log-likelihood ratio value for each (study, locus) aggregating over all sQTLs - # "sqtlColocLlpMaximumNeighborhood", + "vepMeanNeighbourhood", + # other + "geneCount500kb", + "proteinGeneCount500kb", + "credibleSetConfidence", + "isProteinCoding", ] ) hyperparameters: dict[str, Any] = field( default_factory=lambda: { + "n_estimators": 100, "max_depth": 5, - "loss_function": "binary:logistic", + "loss": "log_loss", } ) wandb_run_name: str | None = None - perform_cross_validation: bool = False + hf_hub_repo_id: str | None = "opentargets/locus_to_gene" + download_from_hub: bool = True _target_: str = "gentropy.l2g.LocusToGeneStep" +@dataclass +class LocusToGeneFeatureMatrixConfig(StepConfig): + """Locus to gene feature matrix step configuration.""" + + session: Any = field( + default_factory=lambda: { + "extended_spark_conf": { + "spark.driver.memory": "48g", + "spark.executor.memory": "48g", + "spark.sql.shuffle.partitions": "800", + } + } + ) + credible_set_path: str = MISSING + variant_index_path: str | None = None + colocalisation_path: str | None = None + study_index_path: str | None = None + gene_index_path: str | None = None + feature_matrix_path: str = MISSING + features_list: list[str] = field( + default_factory=lambda: [ + # max CLPP for each (study, locus, gene) aggregating over a specific qtl type + "eQtlColocClppMaximum", + "pQtlColocClppMaximum", + "sQtlColocClppMaximum", + # max H4 for each (study, locus, gene) aggregating over a specific qtl type + "eQtlColocH4Maximum", + "pQtlColocH4Maximum", + "sQtlColocH4Maximum", + # max CLPP for each (study, locus, gene) aggregating over a specific qtl type and in relation with the mean in the vicinity + "eQtlColocClppMaximumNeighbourhood", + "pQtlColocClppMaximumNeighbourhood", + "sQtlColocClppMaximumNeighbourhood", + # max H4 for each (study, locus, gene) aggregating over a specific qtl type and in relation with the mean in the vicinity + "eQtlColocH4MaximumNeighbourhood", + "pQtlColocH4MaximumNeighbourhood", + "sQtlColocH4MaximumNeighbourhood", + # distance to gene footprint + "distanceSentinelFootprint", + "distanceSentinelFootprintNeighbourhood", + "distanceFootprintMean", + "distanceFootprintMeanNeighbourhood", + # distance to gene tss + "distanceTssMean", + "distanceTssMeanNeighbourhood", + "distanceSentinelTss", + "distanceSentinelTssNeighbourhood", + # vep + "vepMaximum", + "vepMaximumNeighbourhood", + "vepMean", + "vepMeanNeighbourhood", + # other + "geneCount500kb", + "proteinGeneCount500kb", + "credibleSetConfidence", + "isProteinCoding", + ] + ) + _target_: str = "gentropy.l2g.LocusToGeneFeatureMatrixStep" + + @dataclass class PICSConfig(StepConfig): """PICS step configuration.""" @@ -254,8 +350,34 @@ class PICSConfig(StepConfig): @dataclass -class VariantAnnotationConfig(StepConfig): - """Variant annotation step configuration.""" +class UkbPppEurConfig(StepConfig): + """UKB PPP (EUR) ingestion step configuration.""" + + raw_study_index_path_from_tsv: str = MISSING + raw_summary_stats_path: str = MISSING + tmp_variant_annotation_path: str = MISSING + variant_annotation_path: str = MISSING + study_index_output_path: str = MISSING + summary_stats_output_path: str = MISSING + _target_: str = "gentropy.ukb_ppp_eur_sumstat_preprocess.UkbPppEurStep" + + +@dataclass +class FinngenUkbMetaConfig(StepConfig): + """FinnGen UKB meta-analysis ingestion step configuration.""" + + raw_study_index_path_from_tsv: str = MISSING + raw_summary_stats_path: str = MISSING + tmp_variant_annotation_path: str = MISSING + variant_annotation_path: str = MISSING + study_index_output_path: str = MISSING + summary_stats_output_path: str = MISSING + _target_: str = "gentropy.finngen_ukb_meta.FinngenUkbMetaIngestionStep" + + +@dataclass +class GnomadVariantConfig(StepConfig): + """Gnomad variant ingestion step configuration.""" session: Any = field( default_factory=lambda: { @@ -263,64 +385,279 @@ class VariantAnnotationConfig(StepConfig): } ) variant_annotation_path: str = MISSING - _target_: str = "gentropytropy.variant_annotation.VariantAnnotationStep" + gnomad_genomes_path: str = "gs://gcp-public-data--gnomad/release/4.0/ht/genomes/gnomad.genomes.v4.0.sites.ht/" + gnomad_variant_populations: list[str] = field( + default_factory=lambda: [ + "afr", # African-American + "amr", # American Admixed/Latino + "ami", # Amish ancestry + "asj", # Ashkenazi Jewish + "eas", # East Asian + "fin", # Finnish + "nfe", # Non-Finnish European + "mid", # Middle Eastern + "sas", # South Asian + "remaining", # Other + ] + ) + use_version_from_input: bool = False + _target_: str = "gentropy.gnomad_ingestion.GnomadVariantIndexStep" + + +@dataclass +class PanUKBBConfig(StepConfig): + """Pan UKB variant ingestion step configuration.""" + + session: Any = field( + default_factory=lambda: { + "start_hail": True, + } + ) + pan_ukbb_ht_path: str = "gs://panukbb-ld-matrixes/ukb-diverse-pops-public-build-38/UKBB.{POP}.ldadj.variant.b38" + pan_ukbb_bm_path: str = "gs://panukbb-ld-matrixes/UKBB.{POP}.ldadj" + ukbb_annotation_path: str = "gs://panukbb-ld-matrixes/UKBB.{POP}.aligned.parquet" + pan_ukbb_pops: list[str] = field( + default_factory=lambda: [ + "AFR", # African + "CSA", # Central/South Asian + "EUR", # European + ] + ) + use_version_from_input: bool = False + _target_: str = "gentropy.pan_ukb_ingestion.PanUKBBVariantIndexStep" @dataclass class VariantIndexConfig(StepConfig): """Variant index step configuration.""" - variant_annotation_path: str = MISSING - credible_set_path: str = MISSING + class _ConsequenceToPathogenicityScoreMap(TypedDict): + """Typing definition for CONSEQUENCE_TO_PATHOGENICITY_SCORE.""" + + id: str + label: str + score: float + + session: SessionConfig = SessionConfig() + vep_output_json_path: str = MISSING variant_index_path: str = MISSING + gnomad_variant_annotations_path: str | None = None + hash_threshold: int = 300 + consequence_to_pathogenicity_score: ClassVar[ + list[_ConsequenceToPathogenicityScoreMap] + ] = [ + {"id": "SO_0001575", "label": "splice_donor_variant", "score": 1.0}, + {"id": "SO_0001589", "label": "frameshift_variant", "score": 1.0}, + {"id": "SO_0001574", "label": "splice_acceptor_variant", "score": 1.0}, + {"id": "SO_0001587", "label": "stop_gained", "score": 1.0}, + {"id": "SO_0002012", "label": "start_lost", "score": 1.0}, + {"id": "SO_0001578", "label": "stop_lost", "score": 1.0}, + {"id": "SO_0001893", "label": "transcript_ablation", "score": 1.0}, + {"id": "SO_0001822", "label": "inframe_deletion", "score": 0.66}, + { + "id": "SO_0001818", + "label": "protein_altering_variant", + "score": 0.66, + }, + {"id": "SO_0001821", "label": "inframe_insertion", "score": 0.66}, + { + "id": "SO_0001787", + "label": "splice_donor_5th_base_variant", + "score": 0.66, + }, + {"id": "SO_0001583", "label": "missense_variant", "score": 0.66}, + {"id": "SO_0001567", "label": "stop_retained_variant", "score": 0.33}, + {"id": "SO_0001630", "label": "splice_region_variant", "score": 0.33}, + {"id": "SO_0002019", "label": "start_retained_variant", "score": 0.33}, + { + "id": "SO_0002169", + "label": "splice_polypyrimidine_tract_variant", + "score": 0.33, + }, + {"id": "SO_0001819", "label": "synonymous_variant", "score": 0.33}, + { + "id": "SO_0002170", + "label": "splice_donor_region_variant", + "score": 0.33, + }, + {"id": "SO_0001624", "label": "3_prime_UTR_variant", "score": 0.1}, + {"id": "SO_0001623", "label": "5_prime_UTR_variant", "score": 0.1}, + {"id": "SO_0001627", "label": "intron_variant", "score": 0.1}, + { + "id": "SO_0001619", + "label": "non_coding_transcript_variant", + "score": 0.0, + }, + {"id": "SO_0001580", "label": "coding_sequence_variant", "score": 0.0}, + {"id": "SO_0001632", "label": "downstream_gene_variant", "score": 0.0}, + {"id": "SO_0001631", "label": "upstream_gene_variant", "score": 0.0}, + { + "id": "SO_0001792", + "label": "non_coding_transcript_exon_variant", + "score": 0.0, + }, + {"id": "SO_0001620", "label": "mature_miRNA_variant", "score": 0.0}, + ] + _target_: str = "gentropy.variant_index.VariantIndexStep" @dataclass -class VariantToGeneConfig(StepConfig): - """V2G step configuration.""" +class ConvertToVcfStepConfig(StepConfig): + """Variant to VCF step configuration.""" - variant_index_path: str = MISSING - variant_annotation_path: str = MISSING - gene_index_path: str = MISSING - vep_consequences_path: str = MISSING - liftover_chain_file_path: str = MISSING - liftover_max_length_difference: int = 100 - max_distance: int = 500_000 - approved_biotypes: List[str] = field( - default_factory=lambda: [ - "protein_coding", - "3prime_overlapping_ncRNA", - "antisense", - "bidirectional_promoter_lncRNA", - "IG_C_gene", - "IG_D_gene", - "IG_J_gene", - "IG_V_gene", - "lincRNA", - "macro_lncRNA", - "non_coding", - "sense_intronic", - "sense_overlapping", - ] - ) - interval_sources: Dict[str, str] = field(default_factory=dict) - v2g_path: str = MISSING - _target_: str = "gentropy.v2g.V2GStep" + source_paths: list[str] = MISSING + source_formats: list[str] = MISSING + output_path: str = MISSING + partition_size: int = 2000 + _target_: str = "gentropy.variant_index.ConvertToVcfStep" + + +@dataclass +class LocusBreakerClumpingConfig(StepConfig): + """Locus breaker clumping step configuration.""" + + summary_statistics_input_path: str = MISSING + clumped_study_locus_output_path: str = MISSING + lbc_baseline_pvalue: float = 1e-5 + lbc_distance_cutoff: int = 250_000 + lbc_pvalue_threshold: float = 1e-8 + lbc_flanking_distance: int = 100_000 + large_loci_size: int = 1_500_000 + wbc_clump_distance: int = 500_000 + wbc_pvalue_threshold: float = MISSING + collect_locus: bool = False + remove_mhc: bool = True + _target_: str = "gentropy.locus_breaker_clumping.LocusBreakerClumpingStep" @dataclass -class WindowBasedClumpingStep(StepConfig): +class WindowBasedClumpingStepConfig(StepConfig): """Window-based clumping step configuration.""" + session: Any = field( + default_factory=lambda: { + "start_hail": True, + } + ) summary_statistics_input_path: str = MISSING study_locus_output_path: str = MISSING + gwas_significance: float = 1e-8 + distance: int = 500_000 + collect_locus: bool = False + collect_locus_distance: int = 500_000 inclusion_list_path: str | None = None - locus_collect_distance: str | None = None - _target_: str = "gentropy.window_based_clumping.WindowBasedClumpingStep" +@dataclass +class FinemapperConfig(StepConfig): + """SuSiE fine-mapper step configuration.""" + + session: Any = field( + default_factory=lambda: { + "start_hail": True, + } + ) + study_index_path: str = MISSING + study_locus_manifest_path: str = MISSING + study_locus_index: int = MISSING + max_causal_snps: int = MISSING + lead_pval_threshold: float = MISSING + purity_mean_r2_threshold: float = MISSING + purity_min_r2_threshold: float = MISSING + cs_lbf_thr: float = MISSING + sum_pips: float = MISSING + susie_est_tausq: bool = MISSING + run_carma: bool = MISSING + carma_tau: float = MISSING + run_sumstat_imputation: bool = MISSING + carma_time_limit: int = MISSING + imputed_r2_threshold: float = MISSING + ld_score_threshold: float = MISSING + ld_min_r2: float = MISSING + _target_: str = "gentropy.susie_finemapper.SusieFineMapperStep" + + +@dataclass +class SummaryStatisticsQCStepConfig(StepConfig): + """GWAS QC step configuration.""" + + gwas_path: str = MISSING + output_path: str = MISSING + pval_threshold: float = MISSING + _target_: str = "gentropy.sumstat_qc_step.SummaryStatisticsQCStep" + + +@dataclass +class CredibleSetQCStepConfig(StepConfig): + """Credible set quality control step configuration.""" + + credible_sets_path: str = MISSING + output_path: str = MISSING + p_value_threshold: float = 1e-5 + purity_min_r2: float = 0.01 + clump: bool = False + ld_index_path: str | None = None + study_index_path: str | None = None + ld_min_r2: float | None = 0.8 + n_partitions: int | None = 200 + _target_: str = "gentropy.credible_set_qc.CredibleSetQCStep" + + +@dataclass +class StudyValidationStepConfig(StepConfig): + """Configuration of the study index validation step. + + The study indices are read from multiple location, therefore we are expecting a list of paths. + """ + + study_index_path: list[str] = MISSING + target_index_path: str = MISSING + disease_index_path: str = MISSING + biosample_index_path: str = MISSING + valid_study_index_path: str = MISSING + invalid_study_index_path: str = MISSING + invalid_qc_reasons: list[str] = MISSING + _target_: str = "gentropy.study_validation.StudyValidationStep" + + +@dataclass +class LocusToGeneEvidenceStepConfig(StepConfig): + """Configuration of the locus to gene evidence step.""" + + locus_to_gene_predictions_path: str = MISSING + credible_set_path: str = MISSING + study_index_path: str = MISSING + evidence_output_path: str = MISSING + locus_to_gene_threshold: float = 0.05 + _target_: str = "gentropy.l2g.LocusToGeneEvidenceStep" + +@dataclass +class LocusToGeneAssociationsStepConfig(StepConfig): + """Configuration of the locus to gene association step.""" + + evidence_input_path: str = MISSING + disease_index_path: str = MISSING + direct_associations_output_path: str = MISSING + indirect_associations_output_path: str = MISSING + _target_: str = "gentropy.l2g.LocusToGeneAssociationsStep" + +@dataclass +class StudyLocusValidationStepConfig(StepConfig): + """Configuration of the study index validation step. + + The study locus datasets are read from multiple location, therefore we are expecting a list of paths. + """ + + study_index_path: str = MISSING + study_locus_path: list[str] = MISSING + valid_study_locus_path: str = MISSING + invalid_study_locus_path: str = MISSING + invalid_qc_reasons: list[str] = MISSING + _target_: str = "gentropy.study_locus_validation.StudyLocusValidationStep" + + @dataclass class Config: """Application configuration.""" @@ -339,6 +676,7 @@ def register_config() -> None: cs.store(group="step", name="colocalisation", node=ColocalisationConfig) cs.store(group="step", name="eqtl_catalogue", node=EqtlCatalogueConfig) cs.store(group="step", name="gene_index", node=GeneIndexConfig) + cs.store(group="step", name="biosample_index", node=BiosampleIndexConfig) cs.store( group="step", name="gwas_catalog_study_curation", @@ -346,26 +684,28 @@ def register_config() -> None: ) cs.store( group="step", - name="gwas_catalog_study_inclusion", - node=GWASCatalogStudyInclusionConfig, - ) - cs.store( - group="step", name="gwas_catalog_ingestion", node=GWASCatalogIngestionConfig + name="gwas_catalog_study_index", + node=GWASCatalogStudyIndexGenerationStep, ) cs.store( group="step", name="gwas_catalog_sumstat_preprocess", node=GWASCatalogSumstatsPreprocessConfig, ) + cs.store( + group="step", + name="gwas_catalog_top_hit_ingestion", + node=GWASCatalogTopHitIngestionConfig, + ) cs.store(group="step", name="ld_based_clumping", node=LDBasedClumpingConfig) cs.store(group="step", name="ld_index", node=LDIndexConfig) cs.store(group="step", name="locus_to_gene", node=LocusToGeneConfig) - cs.store(group="step", name="finngen_studies", node=FinngenStudiesConfig) cs.store( group="step", - name="finngen_sumstat_preprocess", - node=FinngenSumstatPreprocessConfig, + name="locus_to_gene_feature_matrix", + node=LocusToGeneFeatureMatrixConfig, ) + cs.store(group="step", name="finngen_studies", node=FinngenStudiesConfig) cs.store( group="step", @@ -374,7 +714,39 @@ def register_config() -> None: ) cs.store(group="step", name="pics", node=PICSConfig) - cs.store(group="step", name="variant_annotation", node=VariantAnnotationConfig) + cs.store(group="step", name="gnomad_variants", node=GnomadVariantConfig) + cs.store(group="step", name="ukb_ppp_eur_sumstat_preprocess", node=UkbPppEurConfig) cs.store(group="step", name="variant_index", node=VariantIndexConfig) - cs.store(group="step", name="variant_to_gene", node=VariantToGeneConfig) - cs.store(group="step", name="window_based_clumping", node=WindowBasedClumpingStep) + cs.store(group="step", name="variant_to_vcf", node=ConvertToVcfStepConfig) + cs.store( + group="step", name="window_based_clumping", node=WindowBasedClumpingStepConfig + ) + cs.store(group="step", name="susie_finemapping", node=FinemapperConfig) + cs.store( + group="step", name="summary_statistics_qc", node=SummaryStatisticsQCStepConfig + ) + cs.store( + group="step", name="locus_breaker_clumping", node=LocusBreakerClumpingConfig + ) + cs.store( + group="step", + name="credible_set_validation", + node=StudyLocusValidationStepConfig, + ) + cs.store( + group="step", + name="study_validation", + node=StudyValidationStepConfig, + ) + cs.store( + group="step", + name="locus_to_gene_evidence", + node=LocusToGeneEvidenceStepConfig, + ) + cs.store( + group="step", + name="locus_to_gene_associations", + node=LocusToGeneAssociationsStepConfig, + ) + cs.store(group="step", name="finngen_ukb_meta_ingestion", node=FinngenUkbMetaConfig) + cs.store(group="step", name="credible_set_qc", node=CredibleSetQCStepConfig) diff --git a/src/gentropy/credible_set_qc.py b/src/gentropy/credible_set_qc.py new file mode 100644 index 000000000..5b89faf59 --- /dev/null +++ b/src/gentropy/credible_set_qc.py @@ -0,0 +1,77 @@ +"""Step to run credible set quality control on finemapping output StudyLoci.""" + +from __future__ import annotations + +from gentropy.common.session import Session +from gentropy.dataset.ld_index import LDIndex +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_locus import StudyLocus +from gentropy.method.susie_inf import SUSIE_inf + + +class CredibleSetQCStep: + """Credible set quality control step for fine mapped StudyLoci.""" + + def __init__( + self, + session: Session, + credible_sets_path: str, + output_path: str, + p_value_threshold: float, + purity_min_r2: float, + clump: bool, + ld_index_path: str | None, + study_index_path: str | None, + ld_min_r2: float | None, + n_partitions: int | None, + ) -> None: + """Run credible set quality control step. + + Check defaults used by steps in hydra configuration `gentropy.config.CredibleSetQCStepConfig` + + Due to the large number of partitions at the input credible_set_path after finemapping, the + best strategy it is to repartition and save the dataset after deduplication. + + The `clump` mode will perform additional LD based clumping on the input credible sets. + Enabling `clump` mode requires providing `ld_index_path`, `study_index_path` and `ld_min_r2`. + + Args: + session (Session): Session object. + credible_sets_path (str): Path to credible sets file. + output_path (str): Path to write the output file. + p_value_threshold (float): P-value threshold for credible set quality control. + purity_min_r2 (float): Minimum R2 for purity estimation. + clump (bool): Whether to clump the credible sets by LD. + ld_index_path (str | None): Path to LD index file. + study_index_path (str | None): Path to study index file. + ld_min_r2 (float | None): Minimum R2 for LD estimation. + n_partitions (int | None): Number of partitions to coalesce the dataset after reading. Defaults to 200 + """ + n_partitions = n_partitions or 200 + + ld_index = ( + LDIndex.from_parquet(session, ld_index_path) if ld_index_path else None + ) + study_index = ( + StudyIndex.from_parquet(session, study_index_path) + if study_index_path + else None + ) + + cred_sets = StudyLocus.from_parquet( + session, credible_sets_path, recursiveFileLookup=True + ).coalesce(n_partitions) + + cred_sets_clean = SUSIE_inf.credible_set_qc( + cred_sets, + p_value_threshold, + purity_min_r2, + clump, + ld_index, + study_index, + ld_min_r2, + ) + # ensure the saved object is still a valid StudyLocus + StudyLocus( + _df=cred_sets_clean.df, _schema=StudyLocus.get_schema() + ).df.write.mode(session.write_mode).parquet(output_path) diff --git a/src/gentropy/dataset/biosample_index.py b/src/gentropy/dataset/biosample_index.py new file mode 100644 index 000000000..bae3c0a4f --- /dev/null +++ b/src/gentropy/dataset/biosample_index.py @@ -0,0 +1,94 @@ +"""Biosample index dataset.""" + +from __future__ import annotations + +from dataclasses import dataclass +from functools import reduce +from typing import TYPE_CHECKING + +import pyspark.sql.functions as f +from pyspark.sql import DataFrame +from pyspark.sql.types import ArrayType, StringType + +from gentropy.common.schemas import parse_spark_schema +from gentropy.dataset.dataset import Dataset + +if TYPE_CHECKING: + from pyspark.sql.types import StructType + + +@dataclass +class BiosampleIndex(Dataset): + """Biosample index dataset. + + A Biosample index dataset captures the metadata of the biosamples (e.g. tissues, cell types, cell lines, etc) such as alternate names and relationships with other biosamples. + """ + + @classmethod + def get_schema(cls: type[BiosampleIndex]) -> StructType: + """Provide the schema for the BiosampleIndex dataset. + + Returns: + StructType: The schema of the BiosampleIndex dataset. + """ + return parse_spark_schema("biosample_index.json") + + def merge_indices( + self: BiosampleIndex, + biosample_indices : list[BiosampleIndex] + ) -> BiosampleIndex: + """Merge a list of biosample indices into a single biosample index. + + Where there are conflicts, in single values - the first value is taken. In list values, the union of all values is taken. + + Args: + biosample_indices (list[BiosampleIndex]): Biosample indices to merge. + + Returns: + BiosampleIndex: Merged biosample index. + """ + # Extract the DataFrames from the BiosampleIndex objects + biosample_dfs = [biosample_index.df for biosample_index in biosample_indices] + [self.df] + + # Merge the DataFrames + merged_df = reduce(DataFrame.unionAll, biosample_dfs) + + # Determine aggregation functions for each column + # Currently this will take the first value for single values and merge lists for list values + agg_funcs = [] + for field in merged_df.schema.fields: + if field.name != "biosampleId": # Skip the grouping column + if field.dataType == ArrayType(StringType()): + agg_funcs.append(f.array_distinct(f.flatten(f.collect_list(field.name))).alias(field.name)) + else: + agg_funcs.append(f.first(f.col(field.name), ignorenulls=True).alias(field.name)) + + # Perform aggregation + aggregated_df = merged_df.groupBy("biosampleId").agg(*agg_funcs) + + return BiosampleIndex( + _df=aggregated_df, + _schema=BiosampleIndex.get_schema() + ) + + def retain_rows_with_ancestor_id( + self: BiosampleIndex, + ancestor_ids : list[str] + ) -> BiosampleIndex: + """Filter the biosample index to retain only rows with the given ancestor IDs. + + Args: + ancestor_ids (list[str]): Ancestor IDs to filter on. + + Returns: + BiosampleIndex: Filtered biosample index. + """ + # Create a Spark array of ancestor IDs prior to filtering + ancestor_ids_array = f.array(*[f.lit(id) for id in ancestor_ids]) + + return BiosampleIndex( + _df=self.df.filter( + f.size(f.array_intersect(f.col("ancestors"), ancestor_ids_array)) > 0 + ), + _schema=BiosampleIndex.get_schema() + ) diff --git a/src/gentropy/dataset/colocalisation.py b/src/gentropy/dataset/colocalisation.py index e72543cb2..db0040652 100644 --- a/src/gentropy/dataset/colocalisation.py +++ b/src/gentropy/dataset/colocalisation.py @@ -1,15 +1,27 @@ """Colocalisation dataset.""" + from __future__ import annotations from dataclasses import dataclass from typing import TYPE_CHECKING +import pyspark.sql.functions as f + from gentropy.common.schemas import parse_spark_schema +from gentropy.common.spark_helpers import get_record_with_maximum_value from gentropy.dataset.dataset import Dataset +from gentropy.dataset.study_locus import StudyLocus +from gentropy.datasource.eqtl_catalogue.study_index import EqtlCatalogueStudyIndex if TYPE_CHECKING: + from pyspark.sql import DataFrame from pyspark.sql.types import StructType + from gentropy.dataset.study_index import StudyIndex + from gentropy.dataset.study_locus import StudyLocus + +from functools import reduce + @dataclass class Colocalisation(Dataset): @@ -23,3 +35,143 @@ def get_schema(cls: type[Colocalisation]) -> StructType: StructType: Schema for the Colocalisation dataset """ return parse_spark_schema("colocalisation.json") + + def extract_maximum_coloc_probability_per_region_and_gene( + self: Colocalisation, + study_locus: StudyLocus, + study_index: StudyIndex, + *, + filter_by_colocalisation_method: str, + filter_by_qtls: str | list[str] | None = None, + ) -> DataFrame: + """Get maximum colocalisation probability for a (studyLocus, gene) window. + + Args: + study_locus (StudyLocus): Dataset containing study loci to filter the colocalisation dataset on and the geneId linked to the region + study_index (StudyIndex): Study index to use to get study metadata + filter_by_colocalisation_method (str): optional filter to apply on the colocalisation dataset + filter_by_qtls (str | list[str] | None): optional filter to apply on the colocalisation dataset + + Returns: + DataFrame: table with the maximum colocalisation scores for the provided study loci + + Raises: + ValueError: if filter_by_qtl is not in the list of valid QTL types or is not in the list of valid colocalisation methods + """ + from gentropy.colocalisation import ColocalisationStep + + valid_qtls = list( + set(EqtlCatalogueStudyIndex.method_to_study_type_mapping.values()) + ) + [ + f"sc{qtl}" + for qtl in set( + EqtlCatalogueStudyIndex.method_to_study_type_mapping.values() + ) + ] + + if filter_by_qtls: + filter_by_qtls = ( + list(map(str.lower, [filter_by_qtls])) + if isinstance(filter_by_qtls, str) + else list(map(str.lower, filter_by_qtls)) + ) + if any(qtl not in valid_qtls for qtl in filter_by_qtls): + raise ValueError(f"There are no studies with QTL type {filter_by_qtls}") + + if filter_by_colocalisation_method not in [ + "ECaviar", + "Coloc", + ]: # TODO: Write helper class to retrieve coloc method names + raise ValueError( + f"Colocalisation method {filter_by_colocalisation_method} is not supported." + ) + + method_colocalisation_metric = ColocalisationStep._get_colocalisation_class( + filter_by_colocalisation_method + ).METHOD_METRIC + + coloc_filtering_expr = [ + f.col("rightGeneId").isNotNull(), + f.lower("colocalisationMethod") == filter_by_colocalisation_method.lower(), + ] + if filter_by_qtls: + coloc_filtering_expr.append(f.lower("rightStudyType").isin(filter_by_qtls)) + + filtered_colocalisation = ( + # Bring rightStudyType and rightGeneId and filter by rows where the gene is null, + # which is equivalent to filtering studyloci from gwas on the right side + self.append_study_metadata( + study_locus, + study_index, + metadata_cols=["geneId", "studyType"], + colocalisation_side="right", + ) + # it also filters based on method and qtl type + .filter(reduce(lambda a, b: a & b, coloc_filtering_expr)) + # and filters colocalisation results to only include the subset of studylocus that contains gwas studylocusid + .join( + study_locus.df.selectExpr("studyLocusId as leftStudyLocusId"), + "leftStudyLocusId", + ) + ) + + return get_record_with_maximum_value( + filtered_colocalisation.withColumnRenamed( + "leftStudyLocusId", "studyLocusId" + ).withColumnRenamed("rightGeneId", "geneId"), + ["studyLocusId", "geneId"], + method_colocalisation_metric, + ) + + def append_study_metadata( + self: Colocalisation, + study_locus: StudyLocus, + study_index: StudyIndex, + *, + metadata_cols: list[str], + colocalisation_side: str = "right", + ) -> DataFrame: + """Appends metadata from the study to the requested side of the colocalisation dataset. + + Args: + study_locus (StudyLocus): Dataset containing study loci that links the colocalisation dataset and the study index via the studyId + study_index (StudyIndex): Dataset containing study index that contains the metadata + metadata_cols (list[str]): List of study columns to append + colocalisation_side (str): Which side of the colocalisation dataset to append metadata to. Must be either 'right' or 'left' + + Returns: + DataFrame: Colocalisation dataset with appended metadata of the study from the requested side + + Raises: + ValueError: if colocalisation_side is not 'right' or 'left' + """ + metadata_cols = ["studyId", *metadata_cols] + if colocalisation_side not in ["right", "left"]: + raise ValueError( + f"colocalisation_side must be either 'right' or 'left', got {colocalisation_side}" + ) + + study_loci_w_metadata = ( + study_locus.df.select("studyLocusId", "studyId") + .join( + f.broadcast(study_index.df.select("studyId", *metadata_cols)), + "studyId", + ) + .distinct() + ) + coloc_df = ( + # drop `rightStudyType` in case it is requested + self.df.drop("rightStudyType") + if "studyType" in metadata_cols and colocalisation_side == "right" + else self.df + ) + return ( + # Append that to the respective side of the colocalisation dataset + study_loci_w_metadata.selectExpr( + f"studyLocusId as {colocalisation_side}StudyLocusId", + *[ + f"{col} as {colocalisation_side}{col[0].upper() + col[1:]}" + for col in metadata_cols + ], + ).join(coloc_df, f"{colocalisation_side}StudyLocusId", "right") + ) diff --git a/src/gentropy/dataset/dataset.py b/src/gentropy/dataset/dataset.py index 856357afe..67fe05eaf 100644 --- a/src/gentropy/dataset/dataset.py +++ b/src/gentropy/dataset/dataset.py @@ -1,15 +1,23 @@ """Dataset class for gentropy.""" + from __future__ import annotations from abc import ABC, abstractmethod from dataclasses import dataclass +from enum import Enum +from functools import reduce from typing import TYPE_CHECKING, Any +import pyspark.sql.functions as f +from pyspark.sql.types import DoubleType +from pyspark.sql.window import Window from typing_extensions import Self -from gentropy.common.schemas import flatten_schema +from gentropy.common.schemas import SchemaValidationError, compare_struct_schemas if TYPE_CHECKING: + from enum import Enum + from pyspark.sql import Column, DataFrame from pyspark.sql.types import StructType @@ -58,6 +66,30 @@ def schema(self: Dataset) -> StructType: """ return self._schema + @classmethod + def _process_class_params( + cls, params: dict[str, Any] + ) -> tuple[dict[str, Any], dict[str, Any]]: + """Separate class initialization parameters from spark session parameters. + + Args: + params (dict[str, Any]): Combined parameters dictionary + + Returns: + tuple[dict[str, Any], dict[str, Any]]: (class_params, spark_params) + """ + # Get all field names from the class (including parent classes) + class_field_names = { + field.name + for cls_ in cls.__mro__ + if hasattr(cls_, "__dataclass_fields__") + for field in cls_.__dataclass_fields__.values() + } + # Separate parameters + class_params = {k: v for k, v in params.items() if k in class_field_names} + spark_params = {k: v for k, v in params.items() if k not in class_field_names} + return class_params, spark_params + @classmethod @abstractmethod def get_schema(cls: type[Self]) -> StructType: @@ -65,8 +97,31 @@ def get_schema(cls: type[Self]) -> StructType: Returns: StructType: Schema for the Dataset + + Raises: + NotImplementedError: Must be implemented in the child classes + """ + raise NotImplementedError("Must be implemented in the child classes") + + @classmethod + def get_QC_column_name(cls: type[Self]) -> str | None: + """Abstract method to get the QC column name. Assumes None unless overriden by child classes. + + Returns: + str | None: Column name + """ + return None + + @classmethod + def get_QC_mappings(cls: type[Self]) -> dict[str, str]: + """Method to get the mapping between QC flag and corresponding QC category value. + + Returns empty dict unless overriden by child classes. + + Returns: + dict[str, str]: Mapping between flag name and QC column category value. """ - pass + return {} @classmethod def from_parquet( @@ -89,10 +144,14 @@ def from_parquet( ValueError: Parquet file is empty """ schema = cls.get_schema() - df = session.read_parquet(path, schema=schema, **kwargs) + + # Separate class params from spark params + class_params, spark_params = cls._process_class_params(kwargs) + + df = session.load_data(path, format="parquet", schema=schema, **spark_params) if df.isEmpty(): raise ValueError(f"Parquet file is empty: {path}") - return cls(_df=df, _schema=schema) + return cls(_df=df, _schema=schema, **class_params) def filter(self: Self, condition: Column) -> Self: """Creates a new instance of a Dataset with the DataFrame filtered by the condition. @@ -111,58 +170,87 @@ def validate_schema(self: Dataset) -> None: """Validate DataFrame schema against expected class schema. Raises: - ValueError: DataFrame schema is not valid + SchemaValidationError: If the DataFrame schema does not match the expected schema """ expected_schema = self._schema - expected_fields = flatten_schema(expected_schema) observed_schema = self._df.schema - observed_fields = flatten_schema(observed_schema) # Unexpected fields in dataset - if unexpected_field_names := [ - x.name - for x in observed_fields - if x.name not in [y.name for y in expected_fields] - ]: - raise ValueError( - f"The {unexpected_field_names} fields are not included in DataFrame schema: {expected_fields}" + if discrepancies := compare_struct_schemas(observed_schema, expected_schema): + raise SchemaValidationError( + f"Schema validation failed for {type(self).__name__}", discrepancies ) - # Required fields not in dataset - required_fields = [x.name for x in expected_schema if not x.nullable] - if missing_required_fields := [ - req - for req in required_fields - if not any(field.name == req for field in observed_fields) - ]: - raise ValueError( - f"The {missing_required_fields} fields are required but missing: {required_fields}" - ) + def valid_rows(self: Self, invalid_flags: list[str], invalid: bool = False) -> Self: + """Filters `Dataset` according to a list of quality control flags. Only `Dataset` classes with a QC column can be validated. - # Fields with duplicated names - if duplicated_fields := [ - x for x in set(observed_fields) if observed_fields.count(x) > 1 - ]: - raise ValueError( - f"The following fields are duplicated in DataFrame schema: {duplicated_fields}" - ) + This method checks do following steps: + - Check if the Dataset contains a QC column. + - Check if the invalid_flags exist in the QC mappings flags. + - Filter the Dataset according to the invalid_flags and invalid parameters. - # Fields with different datatype - observed_field_types = { - field.name: type(field.dataType) for field in observed_fields - } - expected_field_types = { - field.name: type(field.dataType) for field in expected_fields - } - if fields_with_different_observed_datatype := [ - name - for name, observed_type in observed_field_types.items() - if name in expected_field_types - and observed_type != expected_field_types[name] - ]: + Args: + invalid_flags (list[str]): List of quality control flags to be excluded. + invalid (bool): If True returns the invalid rows, instead of the valid. Defaults to False. + + Returns: + Self: filtered dataset. + + Raises: + ValueError: If the Dataset does not contain a QC column or if the invalid_flags elements do not exist in QC mappings flags. + """ + # If the invalid flags are not valid quality checks (enum) for this Dataset we raise an error: + invalid_reasons = [] + for flag in invalid_flags: + if flag not in self.get_QC_mappings(): + raise ValueError( + f"{flag} is not a valid QC flag for {type(self).__name__} ({self.get_QC_mappings()})." + ) + reason = self.get_QC_mappings()[flag] + invalid_reasons.append(reason) + + qc_column_name = self.get_QC_column_name() + # If Dataset (class) does not contain QC column we raise an error: + if not qc_column_name: raise ValueError( - f"The following fields present differences in their datatypes: {fields_with_different_observed_datatype}." + f"{type(self).__name__} objects do not contain a QC column to filter by." ) + else: + column: str = qc_column_name + # If QC column (nullable) is not available in the dataframe we create an empty array: + qc = f.when(f.col(column).isNull(), f.array()).otherwise(f.col(column)) + + filterCondition = ~f.arrays_overlap( + f.array([f.lit(i) for i in invalid_reasons]), qc + ) + # Returning the filtered dataset: + if invalid: + return self.filter(~filterCondition) + else: + return self.filter(filterCondition) + + def drop_infinity_values(self: Self, *cols: str) -> Self: + """Drop infinity values from Double typed column. + + Infinity type reference - https://spark.apache.org/docs/latest/sql-ref-datatypes.html#floating-point-special-values + The implementation comes from https://stackoverflow.com/questions/34432998/how-to-replace-infinity-in-pyspark-dataframe + + Args: + *cols (str): names of the columns to check for infinite values, these should be of DoubleType only! + + Returns: + Self: Dataset after removing infinite values + """ + if len(cols) == 0: + return self + inf_strings = ("Inf", "+Inf", "-Inf", "Infinity", "+Infinity", "-Infinity") + inf_values = [f.lit(v).cast(DoubleType()) for v in inf_strings] + conditions = [f.col(c).isin(inf_values) for c in cols] + # reduce individual filter expressions with or statement + # to col("beta").isin([lit(Inf)]) | col("beta").isin([lit(Inf)])... + condition = reduce(lambda a, b: a | b, conditions) + self.df = self._df.filter(~condition) + return self def persist(self: Self) -> Self: """Persist in memory the DataFrame included in the Dataset. @@ -211,3 +299,57 @@ def repartition(self: Self, num_partitions: int, **kwargs: Any) -> Self: """ self.df = self._df.repartition(num_partitions, **kwargs) return self + + @staticmethod + def update_quality_flag( + qc: Column, flag_condition: Column, flag_text: Enum + ) -> Column: + """Update the provided quality control list with a new flag if condition is met. + + Args: + qc (Column): Array column with the current list of qc flags. + flag_condition (Column): This is a column of booleans, signing which row should be flagged + flag_text (Enum): Text for the new quality control flag + + Returns: + Column: Array column with the updated list of qc flags. + """ + qc = f.when(qc.isNull(), f.array()).otherwise(qc) + return f.when( + flag_condition, + f.array_union(qc, f.array(f.lit(flag_text.value))), + ).otherwise(qc) + + @staticmethod + def flag_duplicates(test_column: Column) -> Column: + """Return True for rows, where the value was already seen in column. + + This implementation allows keeping the first occurrence of the value. + + Args: + test_column (Column): Column to check for duplicates + + Returns: + Column: Column with a boolean flag for duplicates + """ + return ( + f.row_number().over(Window.partitionBy(test_column).orderBy(f.rand())) > 1 + ) + + @staticmethod + def generate_identifier(uniqueness_defining_columns: list[str]) -> Column: + """Hashes the provided columns to generate a unique identifier. + + Args: + uniqueness_defining_columns (list[str]): list of columns defining uniqueness + + Returns: + Column: column with a unique identifier + """ + hashable_columns = [ + f.when(f.col(column).cast("string").isNull(), f.lit("None")).otherwise( + f.col(column).cast("string") + ) + for column in uniqueness_defining_columns + ] + return f.md5(f.concat(*hashable_columns)) diff --git a/src/gentropy/dataset/intervals.py b/src/gentropy/dataset/intervals.py index c3b9136c9..37158810b 100644 --- a/src/gentropy/dataset/intervals.py +++ b/src/gentropy/dataset/intervals.py @@ -1,22 +1,19 @@ """Interval dataset.""" + from __future__ import annotations from dataclasses import dataclass from typing import TYPE_CHECKING -import pyspark.sql.functions as f - from gentropy.common.Liftover import LiftOverSpark from gentropy.common.schemas import parse_spark_schema from gentropy.dataset.dataset import Dataset from gentropy.dataset.gene_index import GeneIndex -from gentropy.dataset.v2g import V2G if TYPE_CHECKING: from pyspark.sql import SparkSession from pyspark.sql.types import StructType - from gentropy.dataset.variant_index import VariantIndex @dataclass @@ -74,32 +71,3 @@ def from_source( source_class = source_to_class[source_name] data = source_class.read(spark, source_path) # type: ignore return source_class.parse(data, gene_index, lift) # type: ignore - - def v2g(self: Intervals, variant_index: VariantIndex) -> V2G: - """Convert intervals into V2G by intersecting with a variant index. - - Args: - variant_index (VariantIndex): Variant index dataset - - Returns: - V2G: Variant-to-gene evidence dataset - """ - return V2G( - _df=( - self.df.alias("interval") - .join( - variant_index.df.selectExpr( - "chromosome as vi_chromosome", "variantId", "position" - ).alias("vi"), - on=[ - f.col("vi.vi_chromosome") == f.col("interval.chromosome"), - f.col("vi.position").between( - f.col("interval.start"), f.col("interval.end") - ), - ], - how="inner", - ) - .drop("start", "end", "vi_chromosome", "position") - ), - _schema=V2G.get_schema(), - ) diff --git a/src/gentropy/dataset/l2g_feature.py b/src/gentropy/dataset/l2g_feature.py deleted file mode 100644 index 2e9f19d61..000000000 --- a/src/gentropy/dataset/l2g_feature.py +++ /dev/null @@ -1,25 +0,0 @@ -"""L2G Feature Dataset.""" -from __future__ import annotations - -from dataclasses import dataclass -from typing import TYPE_CHECKING - -from gentropy.common.schemas import parse_spark_schema -from gentropy.dataset.dataset import Dataset - -if TYPE_CHECKING: - from pyspark.sql.types import StructType - - -@dataclass -class L2GFeature(Dataset): - """Locus-to-gene feature dataset.""" - - @classmethod - def get_schema(cls: type[L2GFeature]) -> StructType: - """Provides the schema for the L2GFeature dataset. - - Returns: - StructType: Schema for the L2GFeature dataset - """ - return parse_spark_schema("l2g_feature.json") diff --git a/src/gentropy/dataset/l2g_feature_matrix.py b/src/gentropy/dataset/l2g_feature_matrix.py index fa84499dc..f59e1e725 100644 --- a/src/gentropy/dataset/l2g_feature_matrix.py +++ b/src/gentropy/dataset/l2g_feature_matrix.py @@ -1,98 +1,109 @@ """Feature matrix of study locus pairs annotated with their functional genomics features.""" + from __future__ import annotations -from dataclasses import dataclass from functools import reduce from typing import TYPE_CHECKING, Type -from gentropy.common.schemas import parse_spark_schema +import pyspark.sql.functions as f +from pyspark.sql import Window +from typing_extensions import Self + from gentropy.common.spark_helpers import convert_from_long_to_wide -from gentropy.dataset.dataset import Dataset -from gentropy.method.l2g.feature_factory import ColocalisationFactory, StudyLocusFactory +from gentropy.dataset.l2g_gold_standard import L2GGoldStandard +from gentropy.method.l2g.feature_factory import FeatureFactory, L2GFeatureInputLoader if TYPE_CHECKING: - from pyspark.sql.types import StructType + from pyspark.sql import DataFrame - from gentropy.dataset.colocalisation import Colocalisation - from gentropy.dataset.study_index import StudyIndex from gentropy.dataset.study_locus import StudyLocus - from gentropy.dataset.v2g import V2G -@dataclass -class L2GFeatureMatrix(Dataset): - """Dataset with features for Locus to Gene prediction. +class L2GFeatureMatrix: + """Dataset with features for Locus to Gene prediction.""" - Attributes: - features_list (list[str] | None): List of features to use. If None, all possible features are used. - """ + def __init__( + self, + _df: DataFrame, + features_list: list[str] | None = None, + with_gold_standard: bool = False, + ) -> None: + """Post-initialisation to set the features list. If not provided, all columns except the fixed ones are used. - features_list: list[str] | None = None + Args: + _df (DataFrame): Feature matrix dataset + features_list (list[str] | None): List of features to use. If None, all possible features are used. + with_gold_standard (bool): Whether to include the gold standard set in the feature matrix. + """ + self.with_gold_standard = with_gold_standard + self.fixed_cols = ["studyLocusId", "geneId"] + if self.with_gold_standard: + self.fixed_cols.append("goldStandardSet") - def __post_init__(self: L2GFeatureMatrix) -> None: - """Post-initialisation to set the features list. If not provided, all columns except the fixed ones are used.""" - fixed_cols = ["studyLocusId", "geneId", "goldStandardSet"] - self.features_list = self.features_list or [ - col for col in self._df.columns if col not in fixed_cols + self.features_list = features_list or [ + col for col in _df.columns if col not in self.fixed_cols ] + self._df = _df.selectExpr( + self.fixed_cols + + [ + f"CAST({feature} AS FLOAT) AS {feature}" + for feature in self.features_list + ] + ) @classmethod - def generate_features( + def from_features_list( cls: Type[L2GFeatureMatrix], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, features_list: list[str], - study_locus: StudyLocus, - study_index: StudyIndex, - variant_gene: V2G, - colocalisation: Colocalisation, + features_input_loader: L2GFeatureInputLoader, ) -> L2GFeatureMatrix: - """Generate features from the gentropy datasets. + """Generate features from the gentropy datasets by calling the feature factory that will instantiate the corresponding features. Args: - features_list (list[str]): List of features to generate - study_locus (StudyLocus): Study locus dataset - study_index (StudyIndex): Study index dataset - variant_gene (V2G): Variant to gene dataset - colocalisation (Colocalisation): Colocalisation dataset + study_loci_to_annotate (StudyLocus | L2GGoldStandard): Study locus pairs to annotate + features_list (list[str]): List of feature names to be computed. + features_input_loader (L2GFeatureInputLoader): Object that contais features input. Returns: L2GFeatureMatrix: L2G feature matrix dataset - - Raises: - ValueError: If the feature matrix is empty """ - if features_dfs := [ - # Extract features - ColocalisationFactory._get_coloc_features( - study_locus, study_index, colocalisation - ).df, - StudyLocusFactory._get_tss_distance_features(study_locus, variant_gene).df, - StudyLocusFactory._get_vep_features(study_locus, variant_gene).df, - ]: - fm = reduce( - lambda x, y: x.unionByName(y), - features_dfs, + features_long_df = reduce( + lambda x, y: x.unionByName(y, allowMissingColumns=True), + [ + # Compute all features and merge them into a single dataframe + feature.df + for feature in FeatureFactory( + study_loci_to_annotate, features_list + ).generate_features(features_input_loader) + ], + ) + if isinstance(study_loci_to_annotate, L2GGoldStandard): + return cls( + _df=convert_from_long_to_wide( + # Add gold standard set to the feature matrix + features_long_df.join( + study_loci_to_annotate.df.select( + "studyLocusId", "geneId", "goldStandardSet" + ), + ["studyLocusId", "geneId"], + ), + ["studyLocusId", "geneId", "goldStandardSet"], + "featureName", + "featureValue", + ), + with_gold_standard=True, ) - else: - raise ValueError("No features found") - - # raise error if the feature matrix is empty return cls( _df=convert_from_long_to_wide( - fm, ["studyLocusId", "geneId"], "featureName", "featureValue" + features_long_df, + ["studyLocusId", "geneId"], + "featureName", + "featureValue", ), - _schema=cls.get_schema(), - features_list=features_list, + with_gold_standard=False, ) - @classmethod - def get_schema(cls: type[L2GFeatureMatrix]) -> StructType: - """Provides the schema for the L2gFeatureMatrix dataset. - - Returns: - StructType: Schema for the L2gFeatureMatrix dataset - """ - return parse_spark_schema("l2g_feature_matrix.json") - def calculate_feature_missingness_rate( self: L2GFeatureMatrix, ) -> dict[str, float]: @@ -119,51 +130,72 @@ def calculate_feature_missingness_rate( } def fill_na( - self: L2GFeatureMatrix, value: float = 0.0, subset: list[str] | None = None + self: L2GFeatureMatrix, na_value: float = 0.0, subset: list[str] | None = None ) -> L2GFeatureMatrix: """Fill missing values in a column with a given value. + For features that correspond to gene attributes, missing values are imputed using the mean of the column. + Args: - value (float): Value to replace missing values with. Defaults to 0.0. + na_value (float): Value to replace missing values with. Defaults to 0.0. subset (list[str] | None): Subset of columns to consider. Defaults to None. Returns: L2GFeatureMatrix: L2G feature matrix dataset """ - self.df = self._df.fillna(value, subset=subset) + cols_to_impute = [ + "proteinGeneCount500kb", + "geneCount500kb", + ] + for col in cols_to_impute: + if col not in self._df.columns: + continue + else: + self._df = self._df.withColumn( + col, + f.when( + f.col(col).isNull(), + f.mean(f.col(col)).over(Window.partitionBy("studyLocusId")), + ).otherwise(f.col(col)), + ) + self._df = self._df.fillna(na_value, subset=subset) return self def select_features( - self: L2GFeatureMatrix, features_list: list[str] | None + self: L2GFeatureMatrix, + features_list: list[str] | None, ) -> L2GFeatureMatrix: - """Select a subset of features from the feature matrix. + """Returns a new object with a subset of features from the original feature matrix. Args: features_list (list[str] | None): List of features to select Returns: L2GFeatureMatrix: L2G feature matrix dataset - """ - features_list = features_list or self.features_list - fixed_cols = ["studyLocusId", "geneId", "goldStandardSet"] - self.df = self._df.select(fixed_cols + features_list) # type: ignore - return self - def train_test_split( - self: L2GFeatureMatrix, fraction: float - ) -> tuple[L2GFeatureMatrix, L2GFeatureMatrix]: - """Split the dataset into training and test sets. + Raises: + ValueError: If no features have been selected. + """ + if features_list := features_list or self.features_list: + # cast to float every feature in the features_list + return L2GFeatureMatrix( + _df=self._df.selectExpr( + self.fixed_cols + + [ + f"CAST({feature} AS FLOAT) AS {feature}" + for feature in features_list + ] + ), + features_list=features_list, + with_gold_standard=self.with_gold_standard, + ) + raise ValueError("features_list cannot be None") - Args: - fraction (float): Fraction of the dataset to use for training + def persist(self: Self) -> Self: + """Persist the feature matrix in memory. Returns: - tuple[L2GFeatureMatrix, L2GFeatureMatrix]: Training and test datasets + Self: Persisted Dataset """ - train, test = self._df.randomSplit([fraction, 1 - fraction], seed=42) - return ( - L2GFeatureMatrix( - _df=train, _schema=L2GFeatureMatrix.get_schema() - ).persist(), - L2GFeatureMatrix(_df=test, _schema=L2GFeatureMatrix.get_schema()).persist(), - ) + self._df = self._df.persist() + return self diff --git a/src/gentropy/dataset/l2g_features/__init__.py b/src/gentropy/dataset/l2g_features/__init__.py new file mode 100644 index 000000000..ce15cedfe --- /dev/null +++ b/src/gentropy/dataset/l2g_features/__init__.py @@ -0,0 +1,3 @@ +"""Feature factories for L2G.""" + +from __future__ import annotations diff --git a/src/gentropy/dataset/l2g_features/colocalisation.py b/src/gentropy/dataset/l2g_features/colocalisation.py new file mode 100644 index 000000000..fdbf3ed18 --- /dev/null +++ b/src/gentropy/dataset/l2g_features/colocalisation.py @@ -0,0 +1,730 @@ +"""Collection of methods that extract features from the colocalisation datasets.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +import pyspark.sql.functions as f + +from gentropy.common.spark_helpers import convert_from_wide_to_long +from gentropy.dataset.colocalisation import Colocalisation +from gentropy.dataset.gene_index import GeneIndex +from gentropy.dataset.l2g_features.l2g_feature import L2GFeature +from gentropy.dataset.l2g_gold_standard import L2GGoldStandard +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_locus import StudyLocus +from gentropy.dataset.variant_index import VariantIndex + +if TYPE_CHECKING: + from pyspark.sql import DataFrame + + +def common_colocalisation_feature_logic( + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + colocalisation_method: str, + colocalisation_metric: str, + feature_name: str, + qtl_types: list[str] | str, + *, + colocalisation: Colocalisation, + study_index: StudyIndex, + study_locus: StudyLocus, +) -> DataFrame: + """Wrapper to call the logic that creates a type of colocalisation features. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + colocalisation_method (str): The colocalisation method to filter the data by + colocalisation_metric (str): The colocalisation metric to use + feature_name (str): The name of the feature to create + qtl_types (list[str] | str): The types of QTL to filter the data by + colocalisation (Colocalisation): Dataset with the colocalisation results + study_index (StudyIndex): Study index to fetch study type and gene + study_locus (StudyLocus): Study locus to traverse between colocalisation and study index + + Returns: + DataFrame: Feature annotation in long format with the columns: studyLocusId, geneId, featureName, featureValue + """ + joining_cols = ( + ["studyLocusId", "geneId"] + if isinstance(study_loci_to_annotate, L2GGoldStandard) + else ["studyLocusId"] + ) + return ( + study_loci_to_annotate.df.join( + colocalisation.extract_maximum_coloc_probability_per_region_and_gene( + study_locus, + study_index, + filter_by_colocalisation_method=colocalisation_method, + filter_by_qtls=qtl_types, + ), + on=joining_cols, + ) + .selectExpr( + "studyLocusId", + "geneId", + f"{colocalisation_metric} as {feature_name}", + ) + .distinct() + ) + + +def extend_missing_colocalisation_to_neighbourhood_genes( + feature_name: str, + local_features: DataFrame, + variant_index: VariantIndex, + gene_index: GeneIndex, + study_locus: StudyLocus, +) -> DataFrame: + """This function creates an artificial dataset of features that represents the missing colocalisation to the neighbourhood genes. + + Args: + feature_name (str): The name of the feature to extend + local_features (DataFrame): The dataframe of features to extend + variant_index (VariantIndex): Variant index containing all variant/gene relationships + gene_index (GeneIndex): Gene index to fetch the gene information + study_locus (StudyLocus): Study locus to traverse between colocalisation and variant index + + Returns: + DataFrame: Dataframe of features that include genes in the neighbourhood not present in the colocalisation results. For these genes, the feature value is set to 0. + """ + coding_variant_gene_lut = ( + variant_index.df.select( + "variantId", f.explode("transcriptConsequences").alias("tc") + ) + .select(f.col("tc.targetId").alias("geneId"), "variantId") + .join(gene_index.df.select("geneId", "biotype"), "geneId", "left") + .filter(f.col("biotype") == "protein_coding") + .drop("biotype") + .distinct() + ) + local_features_w_variant = local_features.join( + study_locus.df.select("studyLocusId", "variantId"), "studyLocusId" + ) + return ( + # Get the genes that are not present in the colocalisation results + coding_variant_gene_lut.join( + local_features_w_variant, ["variantId", "geneId"], "left_anti" + ) + # We now link the missing variant/gene to the study locus from the original dataframe + .join( + local_features_w_variant.select("studyLocusId", "variantId").distinct(), + "variantId", + ) + .drop("variantId") + # Fill the information for missing genes with 0 + .withColumn(feature_name, f.lit(0.0)) + ) + + +def common_neighbourhood_colocalisation_feature_logic( + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + colocalisation_method: str, + colocalisation_metric: str, + feature_name: str, + qtl_types: list[str] | str, + *, + colocalisation: Colocalisation, + study_index: StudyIndex, + gene_index: GeneIndex, + study_locus: StudyLocus, + variant_index: VariantIndex, +) -> DataFrame: + """Wrapper to call the logic that creates a type of colocalisation features. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + colocalisation_method (str): The colocalisation method to filter the data by + colocalisation_metric (str): The colocalisation metric to use + feature_name (str): The name of the feature to create + qtl_types (list[str] | str): The types of QTL to filter the data by + colocalisation (Colocalisation): Dataset with the colocalisation results + study_index (StudyIndex): Study index to fetch study type and gene + gene_index (GeneIndex): Gene index to add gene type + study_locus (StudyLocus): Study locus to traverse between colocalisation and study index + variant_index (VariantIndex): Variant index to annotate all overlapping genes + + Returns: + DataFrame: Feature annotation in long format with the columns: studyLocusId, geneId, featureName, featureValue + """ + # First maximum colocalisation score for each studylocus, gene + local_feature_name = feature_name.replace("Neighbourhood", "") + local_max = common_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + local_feature_name, + qtl_types, + colocalisation=colocalisation, + study_index=study_index, + study_locus=study_locus, + ) + extended_local_max = local_max.unionByName( + extend_missing_colocalisation_to_neighbourhood_genes( + local_feature_name, + local_max, + variant_index, + gene_index, + study_locus, + ) + ) + # Compute average score in the vicinity (feature will be the same for any gene associated with a studyLocus) + # (non protein coding genes in the vicinity are excluded see #3552) + regional_mean_per_study_locus = ( + extended_local_max.join( + gene_index.df.select("geneId", "biotype"), "geneId", "left" + ) + .filter(f.col("biotype") == "protein_coding") + .groupBy("studyLocusId") + .agg(f.mean(local_feature_name).alias("regional_mean")) + ) + return ( + local_max.join(regional_mean_per_study_locus, "studyLocusId", "left") + .withColumn( + feature_name, + f.col(local_feature_name) - f.coalesce(f.col("regional_mean"), f.lit(0.0)), + ) + .drop("regional_mean", local_feature_name) + ) + + +class EQtlColocClppMaximumFeature(L2GFeature): + """Max CLPP for each (study, locus, gene) aggregating over all eQTLs.""" + + feature_dependency_type = [Colocalisation, StudyIndex, StudyLocus] + feature_name = "eQtlColocClppMaximum" + + @classmethod + def compute( + cls: type[EQtlColocClppMaximumFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> EQtlColocClppMaximumFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dictionary with the dependencies required. They are passed as keyword arguments. + + Returns: + EQtlColocClppMaximumFeature: Feature dataset + """ + colocalisation_method = "ECaviar" + colocalisation_metric = "clpp" + qtl_type = ["eqtl", "sceqtl"] + + return cls( + _df=convert_from_wide_to_long( + common_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class EQtlColocClppMaximumNeighbourhoodFeature(L2GFeature): + """Max CLPP for each (study, locus) aggregating over all eQTLs.""" + + feature_dependency_type = [ + Colocalisation, + StudyIndex, + GeneIndex, + StudyLocus, + VariantIndex, + ] + feature_name = "eQtlColocClppMaximumNeighbourhood" + + @classmethod + def compute( + cls: type[EQtlColocClppMaximumNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> EQtlColocClppMaximumNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dictionary with the dependencies required. They are passed as keyword arguments. + + Returns: + EQtlColocClppMaximumNeighbourhoodFeature: Feature dataset + """ + colocalisation_method = "ECaviar" + colocalisation_metric = "clpp" + qtl_type = ["eqtl", "sceqtl"] + + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class PQtlColocClppMaximumFeature(L2GFeature): + """Max CLPP for each (study, locus, gene) aggregating over all pQTLs.""" + + feature_dependency_type = [Colocalisation, StudyIndex, StudyLocus] + feature_name = "pQtlColocClppMaximum" + + @classmethod + def compute( + cls: type[PQtlColocClppMaximumFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> PQtlColocClppMaximumFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset with the colocalisation results + + Returns: + PQtlColocClppMaximumFeature: Feature dataset + """ + colocalisation_method = "ECaviar" + colocalisation_metric = "clpp" + qtl_type = "pqtl" + return cls( + _df=convert_from_wide_to_long( + common_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class PQtlColocClppMaximumNeighbourhoodFeature(L2GFeature): + """Max CLPP for each (study, locus, gene) aggregating over all pQTLs.""" + + feature_dependency_type = [ + Colocalisation, + StudyIndex, + GeneIndex, + StudyLocus, + VariantIndex, + ] + feature_name = "pQtlColocClppMaximumNeighbourhood" + + @classmethod + def compute( + cls: type[PQtlColocClppMaximumNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> PQtlColocClppMaximumNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset with the colocalisation results + + Returns: + PQtlColocClppMaximumNeighbourhoodFeature: Feature dataset + """ + colocalisation_method = "ECaviar" + colocalisation_metric = "clpp" + qtl_type = "pqtl" + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class SQtlColocClppMaximumFeature(L2GFeature): + """Max CLPP for each (study, locus, gene) aggregating over all sQTLs.""" + + feature_dependency_type = [Colocalisation, StudyIndex, StudyLocus] + feature_name = "sQtlColocClppMaximum" + + @classmethod + def compute( + cls: type[SQtlColocClppMaximumFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> SQtlColocClppMaximumFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset with the colocalisation results + + Returns: + SQtlColocClppMaximumFeature: Feature dataset + """ + colocalisation_method = "ECaviar" + colocalisation_metric = "clpp" + qtl_types = ["sqtl", "tuqtl", "scsqtl", "sctuqtl"] + return cls( + _df=convert_from_wide_to_long( + common_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_types, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class SQtlColocClppMaximumNeighbourhoodFeature(L2GFeature): + """Max CLPP for each (study, locus, gene) aggregating over all sQTLs.""" + + feature_dependency_type = [ + Colocalisation, + StudyIndex, + GeneIndex, + StudyLocus, + VariantIndex, + ] + feature_name = "sQtlColocClppMaximumNeighbourhood" + + @classmethod + def compute( + cls: type[SQtlColocClppMaximumNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> SQtlColocClppMaximumNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset with the colocalisation results + + Returns: + SQtlColocClppMaximumNeighbourhoodFeature: Feature dataset + """ + colocalisation_method = "ECaviar" + colocalisation_metric = "clpp" + qtl_types = ["sqtl", "tuqtl", "scsqtl", "sctuqtl"] + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_types, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class EQtlColocH4MaximumFeature(L2GFeature): + """Max H4 for each (study, locus, gene) aggregating over all eQTLs.""" + + feature_dependency_type = [Colocalisation, StudyIndex, StudyLocus] + feature_name = "eQtlColocH4Maximum" + + @classmethod + def compute( + cls: type[EQtlColocH4MaximumFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> EQtlColocH4MaximumFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset with the colocalisation results + + Returns: + EQtlColocH4MaximumFeature: Feature dataset + """ + colocalisation_method = "Coloc" + colocalisation_metric = "h4" + qtl_type = ["eqtl", "sceqtl"] + return cls( + _df=convert_from_wide_to_long( + common_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class EQtlColocH4MaximumNeighbourhoodFeature(L2GFeature): + """Max H4 for each (study, locus) aggregating over all eQTLs.""" + + feature_dependency_type = [ + Colocalisation, + StudyIndex, + GeneIndex, + StudyLocus, + VariantIndex, + ] + feature_name = "eQtlColocH4MaximumNeighbourhood" + + @classmethod + def compute( + cls: type[EQtlColocH4MaximumNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> EQtlColocH4MaximumNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset with the colocalisation results + + Returns: + EQtlColocH4MaximumNeighbourhoodFeature: Feature dataset + """ + colocalisation_method = "Coloc" + colocalisation_metric = "h4" + qtl_type = ["eqtl", "sceqtl"] + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class PQtlColocH4MaximumFeature(L2GFeature): + """Max H4 for each (study, locus, gene) aggregating over all pQTLs.""" + + feature_dependency_type = [Colocalisation, StudyIndex, StudyLocus] + feature_name = "pQtlColocH4Maximum" + + @classmethod + def compute( + cls: type[PQtlColocH4MaximumFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> PQtlColocH4MaximumFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset with the colocalisation results + + Returns: + PQtlColocH4MaximumFeature: Feature dataset + """ + colocalisation_method = "Coloc" + colocalisation_metric = "h4" + qtl_type = "pqtl" + return cls( + _df=convert_from_wide_to_long( + common_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class PQtlColocH4MaximumNeighbourhoodFeature(L2GFeature): + """Max H4 for each (study, locus) aggregating over all pQTLs.""" + + feature_dependency_type = [ + Colocalisation, + StudyIndex, + GeneIndex, + StudyLocus, + VariantIndex, + ] + feature_name = "pQtlColocH4MaximumNeighbourhood" + + @classmethod + def compute( + cls: type[PQtlColocH4MaximumNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> PQtlColocH4MaximumNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset with the colocalisation results + + Returns: + PQtlColocH4MaximumNeighbourhoodFeature: Feature dataset + """ + colocalisation_method = "Coloc" + colocalisation_metric = "h4" + qtl_type = "pqtl" + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class SQtlColocH4MaximumFeature(L2GFeature): + """Max H4 for each (study, locus, gene) aggregating over all sQTLs.""" + + feature_dependency_type = [Colocalisation, StudyIndex, StudyLocus] + feature_name = "sQtlColocH4Maximum" + + @classmethod + def compute( + cls: type[SQtlColocH4MaximumFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> SQtlColocH4MaximumFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset with the colocalisation results + + Returns: + SQtlColocH4MaximumFeature: Feature dataset + """ + colocalisation_method = "Coloc" + colocalisation_metric = "h4" + qtl_types = ["sqtl", "tuqtl", "scsqtl", "sctuqtl"] + return cls( + _df=convert_from_wide_to_long( + common_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_types, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class SQtlColocH4MaximumNeighbourhoodFeature(L2GFeature): + """Max H4 for each (study, locus) aggregating over all sQTLs.""" + + feature_dependency_type = [ + Colocalisation, + StudyIndex, + GeneIndex, + StudyLocus, + VariantIndex, + ] + feature_name = "sQtlColocH4MaximumNeighbourhood" + + @classmethod + def compute( + cls: type[SQtlColocH4MaximumNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> SQtlColocH4MaximumNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset with the colocalisation results + + Returns: + SQtlColocH4MaximumNeighbourhoodFeature: Feature dataset + """ + colocalisation_method = "Coloc" + colocalisation_metric = "h4" + qtl_types = ["sqtl", "tuqtl", "scsqtl", "sctuqtl"] + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_colocalisation_feature_logic( + study_loci_to_annotate, + colocalisation_method, + colocalisation_metric, + cls.feature_name, + qtl_types, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) diff --git a/src/gentropy/dataset/l2g_features/distance.py b/src/gentropy/dataset/l2g_features/distance.py new file mode 100644 index 000000000..08ffc7c5e --- /dev/null +++ b/src/gentropy/dataset/l2g_features/distance.py @@ -0,0 +1,436 @@ +"""Collection of methods that extract distance features from the variant index dataset.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +import pyspark.sql.functions as f +from pyspark.sql import Window + +from gentropy.common.spark_helpers import convert_from_wide_to_long +from gentropy.dataset.l2g_features.l2g_feature import L2GFeature +from gentropy.dataset.l2g_gold_standard import L2GGoldStandard +from gentropy.dataset.study_locus import StudyLocus +from gentropy.dataset.variant_index import VariantIndex + +if TYPE_CHECKING: + from pyspark.sql import DataFrame + + +def common_distance_feature_logic( + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + *, + variant_index: VariantIndex, + feature_name: str, + distance_type: str, + genomic_window: int = 500_000, +) -> DataFrame: + """Calculate the distance feature that correlates a variant in a credible set with a gene. + + The distance is weighted by the posterior probability of the variant to factor in its contribution to the trait when we look at the average distance score for all variants in the credible set. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + variant_index (VariantIndex): The dataset containing distance to gene information + feature_name (str): The name of the feature + distance_type (str): The type of distance to gene + genomic_window (int): The maximum window size to consider + + Returns: + DataFrame: Feature dataset + """ + distances_dataset = variant_index.get_distance_to_gene(distance_type=distance_type) + if "Mean" in feature_name: + # Weighting by the SNP contribution is only applied when we are averaging all distances + df = study_loci_to_annotate.df.withColumn( + "variantInLocus", f.explode_outer("locus") + ).select( + "studyLocusId", + f.col("variantInLocus.variantId").alias("variantId"), + f.col("variantInLocus.posteriorProbability").alias("posteriorProbability"), + ) + distance_score_expr = ( + f.lit(genomic_window) - f.col(distance_type) + f.lit(1) + ) * f.col("posteriorProbability") + agg_expr = f.sum(f.col("distance_score")) + elif "Sentinel" in feature_name: + df = study_loci_to_annotate.df.select("studyLocusId", "variantId") + # For minimum distances we calculate the unweighted distance between the sentinel (lead) and the gene. This + distance_score_expr = f.lit(genomic_window) - f.col(distance_type) + f.lit(1) + agg_expr = f.first(f.col("distance_score")) + return ( + df.join( + distances_dataset.withColumnRenamed("targetId", "geneId"), + on="variantId", + how="inner", + ) + .withColumn( + "distance_score", + distance_score_expr, + ) + .groupBy("studyLocusId", "geneId") + .agg(agg_expr.alias("distance_score_agg")) + .withColumn( + feature_name, + f.log10(f.col("distance_score_agg")) / f.log10(f.lit(genomic_window + 1)), + ) + .drop("distance_score_agg") + ) + + +def common_neighbourhood_distance_feature_logic( + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + *, + variant_index: VariantIndex, + feature_name: str, + distance_type: str, + genomic_window: int = 500_000, +) -> DataFrame: + """Calculate the distance feature that correlates any variant in a credible set with any gene nearby the locus. The distance is weighted by the posterior probability of the variant to factor in its contribution to the trait. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + variant_index (VariantIndex): The dataset containing distance to gene information + feature_name (str): The name of the feature + distance_type (str): The type of distance to gene + genomic_window (int): The maximum window size to consider + + Returns: + DataFrame: Feature dataset + """ + local_feature_name = feature_name.replace("Neighbourhood", "") + # First compute mean distances to a gene + local_metric = common_distance_feature_logic( + study_loci_to_annotate, + feature_name=local_feature_name, + distance_type=distance_type, + variant_index=variant_index, + genomic_window=genomic_window, + ) + return ( + # Then compute mean distance in the vicinity (feature will be the same for any gene associated with a studyLocus) + local_metric.withColumn( + "regional_metric", + f.mean(f.col(local_feature_name)).over(Window.partitionBy("studyLocusId")), + ) + .withColumn( + feature_name, + (f.col(local_feature_name) - f.col("regional_metric")) + / f.log10(f.lit(genomic_window + 1)), + ) + .drop("regional_metric", local_feature_name) + ) + + +class DistanceTssMeanFeature(L2GFeature): + """Average distance of all tagging variants to gene TSS.""" + + feature_dependency_type = VariantIndex + feature_name = "distanceTssMean" + + @classmethod + def compute( + cls: type[DistanceTssMeanFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> DistanceTssMeanFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the distance information + + Returns: + DistanceTssMeanFeature: Feature dataset + """ + distance_type = "distanceFromTss" + return cls( + _df=convert_from_wide_to_long( + common_distance_feature_logic( + study_loci_to_annotate, + feature_name=cls.feature_name, + distance_type=distance_type, + **feature_dependency, + ).withColumn( + cls.feature_name, + f.when(f.col(cls.feature_name) < 0, f.lit(0.0)).otherwise( + f.col(cls.feature_name) + ), + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class DistanceTssMeanNeighbourhoodFeature(L2GFeature): + """Minimum mean distance to TSS for all genes in the vicinity of a studyLocus.""" + + feature_dependency_type = VariantIndex + feature_name = "distanceTssMeanNeighbourhood" + + @classmethod + def compute( + cls: type[DistanceTssMeanNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> DistanceTssMeanNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the distance information + + Returns: + DistanceTssMeanNeighbourhoodFeature: Feature dataset + """ + distance_type = "distanceFromTss" + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_distance_feature_logic( + study_loci_to_annotate, + feature_name=cls.feature_name, + distance_type=distance_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class DistanceSentinelTssFeature(L2GFeature): + """Distance of the sentinel variant to gene TSS. This is not weighted by the causal probability.""" + + feature_dependency_type = VariantIndex + feature_name = "distanceSentinelTss" + + @classmethod + def compute( + cls: type[DistanceSentinelTssFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> DistanceSentinelTssFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the distance information + + Returns: + DistanceSentinelTssFeature: Feature dataset + """ + distance_type = "distanceFromTss" + return cls( + _df=convert_from_wide_to_long( + common_distance_feature_logic( + study_loci_to_annotate, + feature_name=cls.feature_name, + distance_type=distance_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class DistanceSentinelTssNeighbourhoodFeature(L2GFeature): + """Distance between the sentinel variant and a gene TSS as a relation of the distnace with all the genes in the vicinity of a studyLocus. This is not weighted by the causal probability.""" + + feature_dependency_type = VariantIndex + feature_name = "distanceSentinelTssNeighbourhood" + + @classmethod + def compute( + cls: type[DistanceSentinelTssNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> DistanceSentinelTssNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the distance information + + Returns: + DistanceSentinelTssNeighbourhoodFeature: Feature dataset + """ + distance_type = "distanceFromTss" + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_distance_feature_logic( + study_loci_to_annotate, + feature_name=cls.feature_name, + distance_type=distance_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class DistanceFootprintMeanFeature(L2GFeature): + """Average distance of all tagging variants to the footprint of a gene.""" + + feature_dependency_type = VariantIndex + feature_name = "distanceFootprintMean" + + @classmethod + def compute( + cls: type[DistanceFootprintMeanFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> DistanceFootprintMeanFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the distance information + + Returns: + DistanceFootprintMeanFeature: Feature dataset + """ + distance_type = "distanceFromFootprint" + return cls( + _df=convert_from_wide_to_long( + common_distance_feature_logic( + study_loci_to_annotate, + feature_name=cls.feature_name, + distance_type=distance_type, + **feature_dependency, + ).withColumn( + cls.feature_name, + f.when(f.col(cls.feature_name) < 0, f.lit(0.0)).otherwise( + f.col(cls.feature_name) + ), + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class DistanceFootprintMeanNeighbourhoodFeature(L2GFeature): + """Minimum mean distance to footprint for all genes in the vicinity of a studyLocus.""" + + feature_dependency_type = VariantIndex + feature_name = "distanceFootprintMeanNeighbourhood" + + @classmethod + def compute( + cls: type[DistanceFootprintMeanNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> DistanceFootprintMeanNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the distance information + + Returns: + DistanceFootprintMeanNeighbourhoodFeature: Feature dataset + """ + distance_type = "distanceFromFootprint" + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_distance_feature_logic( + study_loci_to_annotate, + feature_name=cls.feature_name, + distance_type=distance_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class DistanceSentinelFootprintFeature(L2GFeature): + """Distance between the sentinel variant and the footprint of a gene.""" + + feature_dependency_type = VariantIndex + feature_name = "distanceSentinelFootprint" + + @classmethod + def compute( + cls: type[DistanceSentinelFootprintFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> DistanceSentinelFootprintFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the distance information + + Returns: + DistanceSentinelFootprintFeature: Feature dataset + """ + distance_type = "distanceFromFootprint" + return cls( + _df=convert_from_wide_to_long( + common_distance_feature_logic( + study_loci_to_annotate, + feature_name=cls.feature_name, + distance_type=distance_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class DistanceSentinelFootprintNeighbourhoodFeature(L2GFeature): + """Distance between the sentinel variant and a gene footprint as a relation of the distnace with all the genes in the vicinity of a studyLocus. This is not weighted by the causal probability.""" + + feature_dependency_type = VariantIndex + feature_name = "distanceSentinelFootprintNeighbourhood" + + @classmethod + def compute( + cls: type[DistanceSentinelFootprintNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> DistanceSentinelFootprintNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the distance information + + Returns: + DistanceSentinelFootprintNeighbourhoodFeature: Feature dataset + """ + distance_type = "distanceFromFootprint" + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_distance_feature_logic( + study_loci_to_annotate, + feature_name=cls.feature_name, + distance_type=distance_type, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) diff --git a/src/gentropy/dataset/l2g_features/l2g_feature.py b/src/gentropy/dataset/l2g_features/l2g_feature.py new file mode 100644 index 000000000..7073ca758 --- /dev/null +++ b/src/gentropy/dataset/l2g_features/l2g_feature.py @@ -0,0 +1,65 @@ +"""L2G Feature Dataset with a collection of methods that extract features from the gentropy datasets to be fed in L2G.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any + +from gentropy.common.schemas import parse_spark_schema +from gentropy.dataset.dataset import Dataset + +if TYPE_CHECKING: + from pyspark.sql.types import StructType + + from gentropy.dataset.l2g_gold_standard import L2GGoldStandard + from gentropy.dataset.study_locus import StudyLocus + + +@dataclass +class L2GFeature(Dataset, ABC): + """Locus-to-gene feature dataset that serves as template to generate each of the features that inform about locus to gene assignments.""" + + def __post_init__( + self: L2GFeature, + feature_dependency_type: Any = None, + credible_set: StudyLocus | None = None, + ) -> None: + """Initializes a L2GFeature dataset. Any child class of L2GFeature must implement the `compute` method. + + Args: + feature_dependency_type (Any): The dependency that the L2GFeature dataset depends on. Defaults to None. + credible_set (StudyLocus | None): The credible set that the L2GFeature dataset is based on. Defaults to None. + """ + super().__post_init__() + self.feature_dependency_type = feature_dependency_type + self.credible_set = credible_set + + @classmethod + def get_schema(cls: type[L2GFeature]) -> StructType: + """Provides the schema for the L2GFeature dataset. + + Returns: + StructType: Schema for the L2GFeature dataset + """ + return parse_spark_schema("l2g_feature.json") + + @classmethod + @abstractmethod + def compute( + cls: type[L2GFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: Any, + ) -> L2GFeature: + """Computes the L2GFeature dataset. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (Any): The dependency that the L2GFeature class needs to compute the feature + Returns: + L2GFeature: a L2GFeature dataset + + Raises: + NotImplementedError: This method must be implemented in the child classes + """ + raise NotImplementedError("Must be implemented in the child classes") diff --git a/src/gentropy/dataset/l2g_features/other.py b/src/gentropy/dataset/l2g_features/other.py new file mode 100644 index 000000000..2fc32592b --- /dev/null +++ b/src/gentropy/dataset/l2g_features/other.py @@ -0,0 +1,345 @@ +"""Methods to generate features which are not obviously categorised.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +import pyspark.sql.functions as f + +from gentropy.common.spark_helpers import convert_from_wide_to_long +from gentropy.dataset.gene_index import GeneIndex +from gentropy.dataset.l2g_features.l2g_feature import L2GFeature +from gentropy.dataset.l2g_gold_standard import L2GGoldStandard +from gentropy.dataset.study_locus import CredibleSetConfidenceClasses, StudyLocus +from gentropy.dataset.variant_index import VariantIndex + +if TYPE_CHECKING: + from pyspark.sql import Column, DataFrame + + +def common_genecount_feature_logic( + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + *, + gene_index: GeneIndex, + feature_name: str, + genomic_window: int, + protein_coding_only: bool = False, +) -> DataFrame: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci + that will be used for annotation + gene_index (GeneIndex): Dataset containing information related to all genes in release. + feature_name (str): The name of the feature + genomic_window (int): The maximum window size to consider + protein_coding_only (bool): Whether to only consider protein coding genes in calculation. + + Returns: + DataFrame: Feature dataset + """ + study_loci_window = ( + study_loci_to_annotate.df.withColumn( + "window_start", f.col("position") - (genomic_window / 2) + ) + .withColumn("window_end", f.col("position") + (genomic_window / 2)) + .withColumnRenamed("chromosome", "SL_chromosome") + ) + gene_index_filter = gene_index.df + + if protein_coding_only: + gene_index_filter = gene_index_filter.filter( + f.col("biotype") == "protein_coding" + ) + + distinct_gene_counts = ( + study_loci_window.join( + gene_index_filter.alias("genes"), + on=( + (f.col("SL_chromosome") == f.col("genes.chromosome")) + & (f.col("genes.tss") >= f.col("window_start")) + & (f.col("genes.tss") <= f.col("window_end")) + ), + how="inner", + ) + .groupBy("studyLocusId") + .agg(f.approx_count_distinct("geneId").alias(feature_name)) + ) + + return ( + study_loci_window.join( + gene_index_filter.alias("genes"), + on=( + (f.col("SL_chromosome") == f.col("genes.chromosome")) + & (f.col("genes.tss") >= f.col("window_start")) + & (f.col("genes.tss") <= f.col("window_end")) + ), + how="inner", + ) + .join(distinct_gene_counts, on="studyLocusId", how="inner") + .select("studyLocusId", "geneId", feature_name) + .distinct() + ) + + +def is_protein_coding_feature_logic( + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + *, + gene_index: GeneIndex, + feature_name: str, + genomic_window: int, +) -> DataFrame: + """Computes the feature to indicate if a gene is protein-coding or not. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci + that will be used for annotation + gene_index (GeneIndex): Dataset containing information related to all genes in release. + feature_name (str): The name of the feature + genomic_window (int): The maximum window size to consider + + Returns: + DataFrame: Feature dataset, with 1 if the gene is protein-coding, 0 if not. + """ + study_loci_window = ( + study_loci_to_annotate.df.withColumn( + "window_start", f.col("position") - (genomic_window / 2) + ) + .withColumn("window_end", f.col("position") + (genomic_window / 2)) + .withColumnRenamed("chromosome", "SL_chromosome") + ) + return ( + study_loci_window.join( + gene_index.df.alias("genes"), + on=( + (f.col("SL_chromosome") == f.col("genes.chromosome")) + & (f.col("genes.tss") >= f.col("window_start")) + & (f.col("genes.tss") <= f.col("window_end")) + ), + how="inner", + ) + .withColumn( + feature_name, + f.when(f.col("biotype") == "protein_coding", f.lit(1)).otherwise(f.lit(0)), + ) + .select("studyLocusId", "geneId", feature_name) + .distinct() + ) + + +class GeneCountFeature(L2GFeature): + """Counts the number of genes within a specified window size from the study locus.""" + + feature_dependency_type = GeneIndex + feature_name = "geneCount500kb" + + @classmethod + def compute( + cls: type[GeneCountFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> GeneCountFeature: + """Computes the gene count feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dictionary containing dependencies, with gene index and window size + + Returns: + GeneCountFeature: Feature dataset + """ + genomic_window = 500000 + gene_count_df = common_genecount_feature_logic( + study_loci_to_annotate=study_loci_to_annotate, + feature_name=cls.feature_name, + genomic_window=genomic_window, + **feature_dependency, + ) + + return cls( + _df=convert_from_wide_to_long( + gene_count_df, + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class ProteinGeneCountFeature(L2GFeature): + """Counts the number of protein coding genes within a specified window size from the study locus.""" + + feature_dependency_type = GeneIndex + feature_name = "proteinGeneCount500kb" + + @classmethod + def compute( + cls: type[ProteinGeneCountFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> ProteinGeneCountFeature: + """Computes the gene count feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dictionary containing dependencies, with gene index and window size + + Returns: + ProteinGeneCountFeature: Feature dataset + """ + genomic_window = 500000 + gene_count_df = common_genecount_feature_logic( + study_loci_to_annotate=study_loci_to_annotate, + feature_name=cls.feature_name, + genomic_window=genomic_window, + protein_coding_only=True, + **feature_dependency, + ) + + return cls( + _df=convert_from_wide_to_long( + gene_count_df, + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class ProteinCodingFeature(L2GFeature): + """Indicates whether a gene is protein-coding within a specified window size from the study locus.""" + + feature_dependency_type = GeneIndex + feature_name = "isProteinCoding" + + @classmethod + def compute( + cls: type[ProteinCodingFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> ProteinCodingFeature: + """Computes the protein coding feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dictionary containing dependencies, including gene index + + Returns: + ProteinCodingFeature: Feature dataset with 1 if the gene is protein-coding, 0 otherwise + """ + genomic_window = 1000000 + protein_coding_df = is_protein_coding_feature_logic( + study_loci_to_annotate=study_loci_to_annotate, + feature_name=cls.feature_name, + genomic_window=genomic_window, + **feature_dependency, + ) + + return cls( + _df=convert_from_wide_to_long( + protein_coding_df, + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class CredibleSetConfidenceFeature(L2GFeature): + """Distance of the sentinel variant to gene TSS. This is not weighted by the causal probability.""" + + feature_dependency_type = [StudyLocus, VariantIndex] + feature_name = "credibleSetConfidence" + + @classmethod + def compute( + cls: type[CredibleSetConfidenceFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> CredibleSetConfidenceFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the distance information + + Returns: + CredibleSetConfidenceFeature: Feature dataset + """ + full_credible_set = feature_dependency["study_locus"].df.select( + "studyLocusId", + "studyId", + f.explode("locus.variantId").alias("variantId"), + cls.score_credible_set_confidence(f.col("confidence")).alias( + cls.feature_name + ), + ) + + return cls( + _df=convert_from_wide_to_long( + ( + study_loci_to_annotate.df.drop("studyLocusId") + # Annotate genes + .join( + feature_dependency["variant_index"].df.select( + "variantId", + f.explode("transcriptConsequences.targetId").alias( + "geneId" + ), + ), + on="variantId", + how="inner", + ) + # Annotate credible set confidence + .join(full_credible_set, ["variantId", "studyId"]) + .select("studyLocusId", "geneId", cls.feature_name) + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + @classmethod + def score_credible_set_confidence( + cls: type[CredibleSetConfidenceFeature], + confidence_column: Column, + ) -> Column: + """Expression that assigns a score to the credible set confidence. + + Args: + confidence_column (Column): Confidence column in the StudyLocus object + + Returns: + Column: A confidence score between 0 and 1 + """ + return ( + f.when( + f.col("confidence") + == CredibleSetConfidenceClasses.FINEMAPPED_IN_SAMPLE_LD.value, + f.lit(1.0), + ) + .when( + f.col("confidence") + == CredibleSetConfidenceClasses.FINEMAPPED_OUT_OF_SAMPLE_LD.value, + f.lit(0.75), + ) + .when( + f.col("confidence") + == CredibleSetConfidenceClasses.PICSED_SUMMARY_STATS.value, + f.lit(0.5), + ) + .when( + f.col("confidence") + == CredibleSetConfidenceClasses.PICSED_TOP_HIT.value, + f.lit(0.25), + ) + .when( + f.col("confidence") == CredibleSetConfidenceClasses.UNKNOWN.value, + f.lit(0.0), + ) + ) diff --git a/src/gentropy/dataset/l2g_features/vep.py b/src/gentropy/dataset/l2g_features/vep.py new file mode 100644 index 000000000..91b03d57b --- /dev/null +++ b/src/gentropy/dataset/l2g_features/vep.py @@ -0,0 +1,268 @@ +"""Collection of methods that extract distance features from the variant index dataset.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +import pyspark.sql.functions as f + +from gentropy.common.spark_helpers import convert_from_wide_to_long +from gentropy.dataset.gene_index import GeneIndex +from gentropy.dataset.l2g_features.l2g_feature import L2GFeature +from gentropy.dataset.l2g_gold_standard import L2GGoldStandard +from gentropy.dataset.study_locus import StudyLocus +from gentropy.dataset.variant_index import VariantIndex + +if TYPE_CHECKING: + from pyspark.sql import DataFrame + + +def common_vep_feature_logic( + study_loci_to_annotate: L2GGoldStandard | StudyLocus, + *, + variant_index: VariantIndex, + feature_name: str, +) -> DataFrame: + """Extracts variant severity score computed from VEP. + + Args: + study_loci_to_annotate (L2GGoldStandard | StudyLocus): The dataset containing study loci that will be used for annotation + variant_index (VariantIndex): The dataset containing functional consequence information + feature_name (str): The name of the feature + + Returns: + DataFrame: Feature dataset + """ + # Variant/Target/Severity dataframe + consequences_dataset = variant_index.df.withColumn( + "transcriptConsequence", f.explode("transcriptConsequences") + ).select( + "variantId", + f.col("transcriptConsequence.targetId").alias("geneId"), + f.col("transcriptConsequence.consequenceScore").alias("severityScore"), + ) + if isinstance(study_loci_to_annotate, StudyLocus): + variants_df = ( + study_loci_to_annotate.df.withColumn( + "variantInLocus", f.explode_outer("locus") + ) + .select( + "studyLocusId", + f.col("variantInLocus.variantId").alias("variantId"), + f.col("variantInLocus.posteriorProbability").alias( + "posteriorProbability" + ), + ) + .join(consequences_dataset, "variantId") + ) + elif isinstance(study_loci_to_annotate, L2GGoldStandard): + variants_df = study_loci_to_annotate.df.select( + "studyLocusId", "variantId", f.lit(1.0).alias("posteriorProbability") + ).join(consequences_dataset, "variantId") + + if "Maximum" in feature_name: + agg_expr = f.max("severityScore") + elif "Mean" in feature_name: + variants_df = variants_df.withColumn( + "weightedScore", f.col("severityScore") * f.col("posteriorProbability") + ) + agg_expr = f.mean("weightedScore") + return variants_df.groupBy("studyLocusId", "geneId").agg( + agg_expr.alias(feature_name) + ) + + +def common_neighbourhood_vep_feature_logic( + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + *, + variant_index: VariantIndex, + gene_index: GeneIndex, + feature_name: str, +) -> DataFrame: + """Extracts variant severity score computed from VEP for any gene, based on what is the mean score for protein coding genes that are nearby the locus. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + variant_index (VariantIndex): The dataset containing functional consequence information + gene_index (GeneIndex): The dataset containing the gene biotype + feature_name (str): The name of the feature + + Returns: + DataFrame: Feature dataset + """ + local_feature_name = feature_name.replace("Neighbourhood", "") + local_metric = common_vep_feature_logic( + study_loci_to_annotate, + feature_name=local_feature_name, + variant_index=variant_index, + ).join( + # Bring gene classification + gene_index.df.select("geneId", "biotype"), + "geneId", + "inner", + ) + # Compute average score in the vicinity (feature will be the same for any gene associated with a studyLocus) + # (non protein coding genes in the vicinity are excluded see #3552) + regional_mean_per_study_locus = ( + local_metric.filter(f.col("biotype") == "protein_coding") + .groupBy("studyLocusId") + .agg(f.mean(local_feature_name).alias("regional_mean")) + ) + return ( + local_metric.join(regional_mean_per_study_locus, "studyLocusId", "left") + .withColumn( + feature_name, + f.col(local_feature_name) - f.coalesce(f.col("regional_mean"), f.lit(0.0)), + ) + .drop("regional_mean", local_feature_name, "biotype") + ) + + +class VepMaximumFeature(L2GFeature): + """Maximum functional consequence score among all variants in a credible set for a studyLocus/gene.""" + + feature_dependency_type = VariantIndex + feature_name = "vepMaximum" + + @classmethod + def compute( + cls: type[VepMaximumFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> VepMaximumFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the functional consequence information + + Returns: + VepMaximumFeature: Feature dataset + """ + return cls( + _df=convert_from_wide_to_long( + common_vep_feature_logic( + study_loci_to_annotate=study_loci_to_annotate, + feature_name=cls.feature_name, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class VepMaximumNeighbourhoodFeature(L2GFeature): + """Maximum functional consequence score among all variants in a credible set for a studyLocus/gene relative to the mean VEP score across all protein coding genes in the vicinity.""" + + feature_dependency_type = [VariantIndex, GeneIndex] + feature_name = "vepMaximumNeighbourhood" + + @classmethod + def compute( + cls: type[VepMaximumNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> VepMaximumNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the functional consequence information + + Returns: + VepMaximumNeighbourhoodFeature: Feature dataset + """ + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_vep_feature_logic( + study_loci_to_annotate, + feature_name=cls.feature_name, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class VepMeanFeature(L2GFeature): + """Average functional consequence score among all variants in a credible set for a studyLocus/gene. + + The mean severity score is weighted by the posterior probability of each variant. + """ + + feature_dependency_type = VariantIndex + feature_name = "vepMean" + + @classmethod + def compute( + cls: type[VepMeanFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> VepMeanFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the functional consequence information + + Returns: + VepMeanFeature: Feature dataset + """ + return cls( + _df=convert_from_wide_to_long( + common_vep_feature_logic( + study_loci_to_annotate=study_loci_to_annotate, + feature_name=cls.feature_name, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) + + +class VepMeanNeighbourhoodFeature(L2GFeature): + """Mean functional consequence score among all variants in a credible set for a studyLocus/gene relative to the mean VEP score across all protein coding genes in the vicinity. + + The mean severity score is weighted by the posterior probability of each variant. + """ + + feature_dependency_type = [VariantIndex, GeneIndex] + feature_name = "vepMeanNeighbourhood" + + @classmethod + def compute( + cls: type[VepMeanNeighbourhoodFeature], + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + feature_dependency: dict[str, Any], + ) -> VepMeanNeighbourhoodFeature: + """Computes the feature. + + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + feature_dependency (dict[str, Any]): Dataset that contains the functional consequence information + + Returns: + VepMeanNeighbourhoodFeature: Feature dataset + """ + return cls( + _df=convert_from_wide_to_long( + common_neighbourhood_vep_feature_logic( + study_loci_to_annotate, + feature_name=cls.feature_name, + **feature_dependency, + ), + id_vars=("studyLocusId", "geneId"), + var_name="featureName", + value_name="featureValue", + ), + _schema=cls.get_schema(), + ) diff --git a/src/gentropy/dataset/l2g_gold_standard.py b/src/gentropy/dataset/l2g_gold_standard.py index 5bc48413c..ec99f7141 100644 --- a/src/gentropy/dataset/l2g_gold_standard.py +++ b/src/gentropy/dataset/l2g_gold_standard.py @@ -1,4 +1,5 @@ """L2G gold standard dataset.""" + from __future__ import annotations from dataclasses import dataclass @@ -10,13 +11,15 @@ from gentropy.common.schemas import parse_spark_schema from gentropy.common.spark_helpers import get_record_with_maximum_value from gentropy.dataset.dataset import Dataset +from gentropy.dataset.study_locus import StudyLocus if TYPE_CHECKING: from pyspark.sql import DataFrame from pyspark.sql.types import StructType + from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix from gentropy.dataset.study_locus_overlap import StudyLocusOverlap - from gentropy.dataset.v2g import V2G + from gentropy.dataset.variant_index import VariantIndex @dataclass @@ -31,16 +34,16 @@ class L2GGoldStandard(Dataset): def from_otg_curation( cls: type[L2GGoldStandard], gold_standard_curation: DataFrame, - v2g: V2G, study_locus_overlap: StudyLocusOverlap, + variant_index: VariantIndex, interactions: DataFrame, ) -> L2GGoldStandard: """Initialise L2GGoldStandard from source dataset. Args: gold_standard_curation (DataFrame): Gold standard curation dataframe, extracted from - v2g (V2G): Variant to gene dataset to bring distance between a variant and a gene's TSS study_locus_overlap (StudyLocusOverlap): Study locus overlap dataset to remove duplicated loci + variant_index (VariantIndex): Dataset to bring distance between a variant and a gene's footprint interactions (DataFrame): Gene-gene interactions dataset to remove negative cases where the gene interacts with a positive gene Returns: @@ -53,8 +56,10 @@ def from_otg_curation( interactions_df = cls.process_gene_interactions(interactions) return ( - OpenTargetsL2GGoldStandard.as_l2g_gold_standard(gold_standard_curation, v2g) - # .filter_unique_associations(study_locus_overlap) + OpenTargetsL2GGoldStandard.as_l2g_gold_standard( + gold_standard_curation, variant_index + ) + .filter_unique_associations(study_locus_overlap) .remove_false_negatives(interactions_df) ) @@ -100,6 +105,38 @@ def process_gene_interactions( "scoring as score", ) + def build_feature_matrix( + self: L2GGoldStandard, + full_feature_matrix: L2GFeatureMatrix, + credible_set: StudyLocus, + ) -> L2GFeatureMatrix: + """Return a feature matrix for study loci in the gold standard. + + Args: + full_feature_matrix (L2GFeatureMatrix): Feature matrix for all study loci to join on + credible_set (StudyLocus): Full credible sets to annotate the feature matrix with variant and study IDs and perform the join + + Returns: + L2GFeatureMatrix: Feature matrix for study loci in the gold standard + """ + from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix + + return L2GFeatureMatrix( + _df=full_feature_matrix._df.join( + credible_set.df.select("studyLocusId", "variantId", "studyId"), + "studyLocusId", + "left", + ) + .join( + f.broadcast(self.df.drop("studyLocusId", "sources")), + on=["studyId", "variantId", "geneId"], + how="inner", + ) + .drop("studyId", "variantId") + .distinct(), + with_gold_standard=True, + ).fill_na() + def filter_unique_associations( self: L2GGoldStandard, study_locus_overlap: StudyLocusOverlap, diff --git a/src/gentropy/dataset/l2g_prediction.py b/src/gentropy/dataset/l2g_prediction.py index e24688da3..64ce964c7 100644 --- a/src/gentropy/dataset/l2g_prediction.py +++ b/src/gentropy/dataset/l2g_prediction.py @@ -6,15 +6,14 @@ from typing import TYPE_CHECKING, Type import pyspark.sql.functions as f -from pyspark.ml.functions import vector_to_array +from pyspark.sql import DataFrame from gentropy.common.schemas import parse_spark_schema -from gentropy.dataset.colocalisation import Colocalisation +from gentropy.common.session import Session from gentropy.dataset.dataset import Dataset from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix from gentropy.dataset.study_index import StudyIndex from gentropy.dataset.study_locus import StudyLocus -from gentropy.dataset.v2g import V2G from gentropy.method.l2g.model import LocusToGeneModel if TYPE_CHECKING: @@ -42,58 +41,139 @@ def get_schema(cls: type[L2GPrediction]) -> StructType: @classmethod def from_credible_set( cls: Type[L2GPrediction], - model_path: str, + session: Session, + credible_set: StudyLocus, + feature_matrix: L2GFeatureMatrix, features_list: list[str], - study_locus: StudyLocus, - study_index: StudyIndex, - v2g: V2G, - coloc: Colocalisation, + model_path: str | None, + hf_token: str | None = None, + download_from_hub: bool = True, ) -> L2GPrediction: """Extract L2G predictions for a set of credible sets derived from GWAS. Args: - model_path (str): Path to the fitted model + session (Session): Session object that contains the Spark session + credible_set (StudyLocus): Dataset containing credible sets from GWAS only + feature_matrix (L2GFeatureMatrix): Dataset containing all credible sets and their annotations features_list (list[str]): List of features to use for the model + model_path (str | None): Path to the model file. It can be either in the filesystem or the name on the Hugging Face Hub (in the form of username/repo_name). + hf_token (str | None): Hugging Face token to download the model from the Hub. Only required if the model is private. + download_from_hub (bool): Whether to download the model from the Hugging Face Hub. Defaults to True. + + Returns: + L2GPrediction: L2G scores for a set of credible sets. + """ + # Load the model + if download_from_hub: + # Model ID defaults to "opentargets/locus_to_gene" and it assumes the name of the classifier is "classifier.skops". + model_id = model_path or "opentargets/locus_to_gene" + l2g_model = LocusToGeneModel.load_from_hub(model_id, hf_token) + elif model_path: + l2g_model = LocusToGeneModel.load_from_disk(model_path) + + # Prepare data + fm = ( + L2GFeatureMatrix( + _df=( + credible_set.df.filter(f.col("studyType") == "gwas") + .select("studyLocusId") + .join(feature_matrix._df, "studyLocusId") + ) + ) + .fill_na() + .select_features(features_list) + ) + + return l2g_model.predict(fm, session) + + def to_disease_target_evidence( + self: L2GPrediction, + study_locus: StudyLocus, + study_index: StudyIndex, + l2g_threshold: float = 0.05, + ) -> DataFrame: + """Convert locus to gene predictions to disease target evidence. + + Args: study_locus (StudyLocus): Study locus dataset study_index (StudyIndex): Study index dataset - v2g (V2G): Variant to gene dataset - coloc (Colocalisation): Colocalisation dataset + l2g_threshold (float): Threshold to consider a gene as a target. Defaults to 0.05. Returns: - L2GPrediction: L2G dataset + DataFrame: Disease target evidence """ - fm = L2GFeatureMatrix.generate_features( - features_list=features_list, - study_locus=study_locus, - study_index=study_index, - variant_gene=v2g, - colocalisation=coloc, - ).fill_na() - - gwas_fm = L2GFeatureMatrix( - _df=( - fm.df.join( - study_locus.filter_by_study_type("gwas", study_index).df, - on="studyLocusId", - ) - ), - _schema=cls.get_schema(), + datasource_id = "gwas_credible_sets" + datatype_id = "genetic_association" + + return ( + self.df.filter(f.col("score") >= l2g_threshold) + .join( + study_locus.df.select("studyLocusId", "studyId"), + on="studyLocusId", + how="inner", + ) + .join( + study_index.df.select("studyId", "diseaseIds"), + on="studyId", + how="inner", + ) + .select( + f.lit(datatype_id).alias("datatypeId"), + f.lit(datasource_id).alias("datasourceId"), + f.col("geneId").alias("targetFromSourceId"), + f.explode(f.col("diseaseIds")).alias("diseaseFromSourceMappedId"), + f.col("score").alias("resourceScore"), + "studyLocusId", + ) + ) + + def add_locus_to_gene_features( + self: L2GPrediction, feature_matrix: L2GFeatureMatrix + ) -> L2GPrediction: + """Add features to the L2G predictions. + + Args: + feature_matrix (L2GFeatureMatrix): Feature matrix dataset + + Returns: + L2GPrediction: L2G predictions with additional features + """ + # Testing if `locusToGeneFeatures` column already exists: + if "locusToGeneFeatures" in self.df.columns: + self.df = self.df.drop("locusToGeneFeatures") + + # Columns identifying a studyLocus/gene pair + prediction_id_columns = ["studyLocusId", "geneId"] + + # L2G matrix columns to build the map: + columns_to_map = [ + column + for column in feature_matrix._df.columns + if column not in prediction_id_columns + ] + + # Aggregating all features into a single map column: + aggregated_features = ( + feature_matrix._df.withColumn( + "locusToGeneFeatures", + f.create_map( + *sum( + [ + (f.lit(colname), f.col(colname)) + for colname in columns_to_map + ], + (), + ) + ), + ) + # from the freshly created map, we filter out the null values + .withColumn( + "locusToGeneFeatures", + f.expr("map_filter(locusToGeneFeatures, (k, v) -> v is not null)"), + ) + .drop(*columns_to_map) ) return L2GPrediction( - # Load and apply fitted model - _df=( - LocusToGeneModel.load_from_disk( - model_path, - features_list=features_list, - ) - .predict(gwas_fm) - # the probability of the positive class is the second element inside the probability array - # - this is selected as the L2G probability - .select( - "studyLocusId", - "geneId", - vector_to_array(f.col("probability"))[1].alias("score"), - ) - ), - _schema=cls.get_schema(), + _df=self.df.join(aggregated_features, on=prediction_id_columns, how="left"), + _schema=self.get_schema(), ) diff --git a/src/gentropy/dataset/pairwise_ld.py b/src/gentropy/dataset/pairwise_ld.py new file mode 100644 index 000000000..b64592094 --- /dev/null +++ b/src/gentropy/dataset/pairwise_ld.py @@ -0,0 +1,106 @@ +"""Pairwise LD dataset.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from math import sqrt +from typing import TYPE_CHECKING + +import numpy as np +from pyspark.sql import functions as f +from pyspark.sql import types as t + +from gentropy.common.schemas import parse_spark_schema +from gentropy.dataset.dataset import Dataset + +if TYPE_CHECKING: + from pyspark.sql.types import StructType + + +@dataclass +class PairwiseLD(Dataset): + """Pairwise variant correlation dataset. + + This class captures logic applied on pairwise linkage data + by validation ensuring data quality. + """ + + dimension: tuple[int, int] = field(init=False) + + def __post_init__(self: PairwiseLD) -> None: + """Validating the dataset upon creation. + + - Besides the schema, a pairwise LD table is expected have rows being a square number. + """ + row_count = self.df.count() + + assert ( + int(sqrt(row_count)) == sqrt(row_count) + ), f"The number of rows in a pairwise LD table has to be square. Got: {row_count}" + + self.dimension = (int(sqrt(row_count)), int(sqrt(row_count))) + self.validate_schema() + + @classmethod + def get_schema(cls: type[PairwiseLD]) -> StructType: + """Provide the schema for the StudyIndex dataset. + + Returns: + StructType: The schema of the StudyIndex dataset. + """ + return parse_spark_schema("pairwise_ld.json") + + def overlap_with_locus(self: PairwiseLD, locus_variants: list[str]) -> PairwiseLD: + """Subset pairwise LD table with locus. + + Args: + locus_variants (list[str]): List of variants found in the locus. + + Returns: + PairwiseLD: _description_ + """ + return PairwiseLD( + _df=( + self.df.filter( + f.col("variantIdI").isin(locus_variants) + & f.col("variantIdJ").isin(locus_variants) + ) + ), + _schema=PairwiseLD.get_schema(), + ) + + def r_to_numpy_matrix(self) -> np.ndarray: + """Convert pairwise LD to a numpy square matrix. + + Returns: + np.ndarray: 2D square matrix with r values. + """ + return np.array( + self.df.select( + f.split("variantIdI", "_")[1].cast(t.IntegerType()).alias("position_i"), + f.split("variantIdJ", "_")[1].cast(t.IntegerType()).alias("position_j"), + "r", + ) + .orderBy(f.col("position_i").asc(), f.col("position_j").asc()) + .select("r") + .collect() + ).reshape(self.dimension) + + def get_variant_list(self) -> list[str]: + """Return a list of unique variants from the dataset. + + Returns: + list[str]: list of variant identifiers sorted by position. + """ + return [ + row["variantId"] + for row in ( + self.df.select( + f.col("variantIdI").alias("variantId"), + f.split(f.col("variantIdI"), "_")[1] + .cast(t.IntegerType()) + .alias("position"), + ) + .orderBy(f.col("position").asc()) + .collect() + ) + ] diff --git a/src/gentropy/dataset/study_index.py b/src/gentropy/dataset/study_index.py index d048157d6..da310f6f1 100644 --- a/src/gentropy/dataset/study_index.py +++ b/src/gentropy/dataset/study_index.py @@ -1,22 +1,64 @@ """Study index dataset.""" + from __future__ import annotations import importlib.resources as pkg_resources import json from dataclasses import dataclass +from enum import Enum from itertools import chain from typing import TYPE_CHECKING from pyspark.sql import functions as f +from pyspark.sql.types import ArrayType, StringType, StructType +from pyspark.sql.window import Window from gentropy.assets import data from gentropy.common.schemas import parse_spark_schema +from gentropy.common.spark_helpers import convert_from_wide_to_long from gentropy.dataset.dataset import Dataset if TYPE_CHECKING: from pyspark.sql import Column, DataFrame from pyspark.sql.types import StructType + from gentropy.dataset.biosample_index import BiosampleIndex + from gentropy.dataset.gene_index import GeneIndex + + +class StudyQualityCheck(Enum): + """Study quality control options listing concerns on the quality of the study. + + Attributes: + UNRESOLVED_TARGET (str): Target/gene identifier could not match to reference - Labelling failing target. + UNRESOLVED_DISEASE (str): Disease identifier could not match to referece or retired identifier - labelling failing disease + UNKNOWN_STUDY_TYPE (str): Indicating the provided type of study is not supported. + UNKNOWN_BIOSAMPLE (str): Flagging if a biosample identifier is not found in the reference. + DUPLICATED_STUDY (str): Flagging if a study identifier is not unique. + SUMSTATS_NOT_AVAILABLE (str): Flagging if harmonized summary statistics are not available or empty. + NO_OT_CURATION (str): Flagging if a study has not been curated by Open Targets. + FAILED_MEAN_BETA_CHECK (str): Flagging if the mean beta QC check value is not within the expected range. + FAILED_PZ_CHECK (str): Flagging if the PZ QC check values are not within the expected range. + FAILED_GC_LAMBDA_CHECK (str): Flagging if the GC lambda value is not within the expected range. + SMALL_NUMBER_OF_SNPS (str): Flagging if the number of SNPs in the study is below the expected threshold. + """ + + UNRESOLVED_TARGET = "Target/gene identifier could not match to reference" + UNRESOLVED_DISEASE = "No valid disease identifier found" + UNKNOWN_STUDY_TYPE = "This type of study is not supported" + UNKNOWN_BIOSAMPLE = "Biosample identifier was not found in the reference" + DUPLICATED_STUDY = "The identifier of this study is not unique" + SUMSTATS_NOT_AVAILABLE = "Harmonized summary statistics are not available or empty" + NO_OT_CURATION = "GWAS Catalog study has not been curated by Open Targets" + FAILED_MEAN_BETA_CHECK = ( + "The mean beta QC check value is not within the expected range" + ) + FAILED_PZ_CHECK = "The PZ QC check values are not within the expected range" + FAILED_GC_LAMBDA_CHECK = "The GC lambda value is not within the expected range" + SMALL_NUMBER_OF_SNPS = ( + "The number of SNPs in the study is below the expected threshold" + ) + @dataclass class StudyIndex(Dataset): @@ -86,6 +128,24 @@ def get_schema(cls: type[StudyIndex]) -> StructType: """ return parse_spark_schema("study_index.json") + @classmethod + def get_QC_column_name(cls: type[StudyIndex]) -> str: + """Return the name of the quality control column. + + Returns: + str: The name of the quality control column. + """ + return "qualityControls" + + @classmethod + def get_QC_mappings(cls: type[StudyIndex]) -> dict[str, str]: + """Quality control flag to QC column category mappings. + + Returns: + dict[str, str]: Mapping between flag name and QC column category value. + """ + return {member.name: member.value for member in StudyQualityCheck} + @classmethod def aggregate_and_map_ancestries( cls: type[StudyIndex], discovery_samples: Column @@ -174,7 +234,7 @@ def is_quality_flagged(self: StudyIndex) -> Column: if "qualityControls" not in self.df.columns: return f.lit(False) else: - return f.size(self.df.qualityControls) != 0 + return f.size(self.df["qualityControls"]) != 0 def has_summarystats(self: StudyIndex) -> Column: """Return a boolean column indicating if a study has harmonized summary statistics. @@ -183,3 +243,507 @@ def has_summarystats(self: StudyIndex) -> Column: Column: True if the study has harmonized summary statistics. """ return self.df.hasSumstats + + def validate_unique_study_id(self: StudyIndex) -> StudyIndex: + """Validating the uniqueness of study identifiers and flagging duplicated studies. + + Returns: + StudyIndex: with flagged duplicated studies. + """ + return StudyIndex( + _df=self.df.withColumn( + "qualityControls", + self.update_quality_flag( + f.col("qualityControls"), + self.flag_duplicates(f.col("studyId")), + StudyQualityCheck.DUPLICATED_STUDY, + ), + ), + _schema=StudyIndex.get_schema(), + ) + + def _normalise_disease( + self: StudyIndex, + source_disease_column_name: str, + disease_column_name: str, + disease_map: DataFrame, + ) -> DataFrame: + """Normalising diseases in the study index. + + Given a reference disease map (containing all potential EFO ids with the corresponding reference disease ids), + this function maps all EFO ids in the study index to the reference disease ids. + + Args: + source_disease_column_name (str): The column name of the disease column to validate. + disease_column_name (str): The resulting disease column name that contains the validated ids. + disease_map (DataFrame): Reference dataframe with diseases + + Returns: + DataFrame: where the newly added diseaseIds column will contain the validated EFO identifiers. + """ + return ( + self.df + # Only validating studies with diseases: + .filter(f.size(f.col(source_disease_column_name)) > 0) + # Explode disease column: + .select( + "studyId", + "studyType", + f.explode_outer(source_disease_column_name).alias("efo"), + ) + # Join disease map: + .join(disease_map, on="efo", how="left") + .groupBy("studyId") + .agg( + f.collect_set(f.col("diseaseId")).alias(disease_column_name), + ) + ) + + def validate_disease(self: StudyIndex, disease_map: DataFrame) -> StudyIndex: + """Validate diseases in the study index dataset. + + Args: + disease_map (DataFrame): a dataframe with two columns (efo, diseaseId). + + Returns: + StudyIndex: where gwas studies are flagged where no valid disease id could be found. + """ + # Because the disease ids are not mandatory fields of the schema, we skip vaildation if these columns are not present: + if ("traitFromSourceMappedIds" not in self.df.columns) or ( + "backgroundTraitFromSourceMappedIds" not in self.df.columns + ): + return self + + # Disease Column names: + foreground_disease_column = "diseaseIds" + background_disease_column = "backgroundDiseaseIds" + + # If diseaseId in schema, we need to drop it: + drop_columns = [ + column + for column in self.df.columns + if column in [foreground_disease_column, background_disease_column] + ] + + if len(drop_columns) > 0: + self.df = self.df.drop(*drop_columns) + + # Normalise disease: + normalised_disease = self._normalise_disease( + "traitFromSourceMappedIds", foreground_disease_column, disease_map + ) + normalised_background_disease = self._normalise_disease( + "backgroundTraitFromSourceMappedIds", background_disease_column, disease_map + ) + + return StudyIndex( + _df=( + self.df.join(normalised_disease, on="studyId", how="left") + .join(normalised_background_disease, on="studyId", how="left") + # Updating disease columns: + .withColumn( + foreground_disease_column, + f.when( + f.col(foreground_disease_column).isNull(), f.array() + ).otherwise(f.col(foreground_disease_column)), + ) + .withColumn( + background_disease_column, + f.when( + f.col(background_disease_column).isNull(), f.array() + ).otherwise(f.col(background_disease_column)), + ) + # Flagging gwas studies where no valid disease is avilable: + .withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + # Flagging all gwas studies with no normalised disease: + (f.size(f.col(foreground_disease_column)) == 0) + & (f.col("studyType") == "gwas"), + StudyQualityCheck.UNRESOLVED_DISEASE, + ), + ) + ), + _schema=StudyIndex.get_schema(), + ) + + def validate_study_type(self: StudyIndex) -> StudyIndex: + """Validating study type and flag unsupported types. + + Returns: + StudyIndex: with flagged studies with unsupported type. + """ + validated_df = ( + self.df + # Flagging unsupported study types: + .withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + f.when( + (f.col("studyType") == "gwas") + | f.col("studyType").endswith("qtl"), + False, + ).otherwise(True), + StudyQualityCheck.UNKNOWN_STUDY_TYPE, + ), + ) + ) + return StudyIndex(_df=validated_df, _schema=StudyIndex.get_schema()) + + def validate_target(self: StudyIndex, target_index: GeneIndex) -> StudyIndex: + """Validating gene identifiers in the study index against the provided target index. + + Args: + target_index (GeneIndex): gene index containing the reference gene identifiers (Ensembl gene identifiers). + + Returns: + StudyIndex: with flagged studies if geneId could not be validated. + """ + gene_set = target_index.df.select("geneId", f.lit(True).alias("isIdFound")) + + # As the geneId is not a mandatory field of study index, we return if the column is not there: + if "geneId" not in self.df.columns: + return self + + validated_df = ( + self.df.join(gene_set, on="geneId", how="left") + .withColumn( + "isIdFound", + f.when( + (f.col("studyType") != "gwas") & f.col("isIdFound").isNull(), + f.lit(False), + ).otherwise(f.lit(True)), + ) + .withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + ~f.col("isIdFound"), + StudyQualityCheck.UNRESOLVED_TARGET, + ), + ) + .drop("isIdFound") + ) + + return StudyIndex(_df=validated_df, _schema=StudyIndex.get_schema()) + + def validate_biosample( + self: StudyIndex, biosample_index: BiosampleIndex + ) -> StudyIndex: + """Validating biosample identifiers in the study index against the provided biosample index. + + Args: + biosample_index (BiosampleIndex): Biosample index containing a reference of biosample identifiers e.g. cell types, tissues, cell lines, etc. + + Returns: + StudyIndex: where non-gwas studies are flagged if biosampleIndex could not be validated. + """ + biosample_set = biosample_index.df.select( + "biosampleId", f.lit(True).alias("isIdFound") + ) + + # If biosampleId in df, we need to drop it: + if "biosampleId" in self.df.columns: + self.df = self.df.drop("biosampleId") + + # As the biosampleFromSourceId is not a mandatory field of study index, we return if the column is not there: + if "biosampleFromSourceId" not in self.df.columns: + return self + + validated_df = ( + self.df.join( + biosample_set, + self.df.biosampleFromSourceId == biosample_set.biosampleId, + how="left", + ) + .withColumn( + "isIdFound", + f.when( + (f.col("studyType") != "gwas") & (f.col("isIdFound").isNull()), + f.lit(False), + ).otherwise(f.lit(True)), + ) + .withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + ~f.col("isIdFound"), + StudyQualityCheck.UNKNOWN_BIOSAMPLE, + ), + ) + .drop("isIdFound") + ) + + return StudyIndex(_df=validated_df, _schema=StudyIndex.get_schema()) + + def annotate_sumstats_qc( + self: StudyIndex, + sumstats_qc: DataFrame, + threshold_mean_beta: float = 0.05, + threshold_mean_diff_pz: float = 0.05, + threshold_se_diff_pz: float = 0.05, + threshold_min_gc_lambda: float = 0.7, + threshold_max_gc_lambda: float = 2.5, + threshold_min_n_variants: int = 2_000_000, + ) -> StudyIndex: + """Annotate summary stats QC information. + + Args: + sumstats_qc (DataFrame): containing summary statistics-based quality controls. + threshold_mean_beta (float): Threshold for mean beta check. Defaults to 0.05. + threshold_mean_diff_pz (float): Threshold for mean diff PZ check. Defaults to 0.05. + threshold_se_diff_pz (float): Threshold for SE diff PZ check. Defaults to 0.05. + threshold_min_gc_lambda (float): Minimum threshold for GC lambda check. Defaults to 0.7. + threshold_max_gc_lambda (float): Maximum threshold for GC lambda check. Defaults to 2.5. + threshold_min_n_variants (int): Minimum number of variants for SuSiE check. Defaults to 2_000_000. + + Returns: + StudyIndex: Updated study index with QC information + """ + # convert all columns in sumstats_qc dataframe in array of structs grouped by studyId + cols = [c for c in sumstats_qc.columns if c != "studyId"] + + studies = self.df + + melted_df = convert_from_wide_to_long( + sumstats_qc, + id_vars=["studyId"], + value_vars=cols, + var_name="QCCheckName", + value_name="QCCheckValue", + ) + + qc_df = ( + melted_df.groupBy("studyId") + .agg( + f.map_from_entries( + f.collect_list( + f.struct(f.col("QCCheckName"), f.col("QCCheckValue")) + ) + ).alias("sumStatQCValues") + ) + .select("studyId", "sumstatQCValues") + ) + + df = ( + studies.drop("sumStatQCValues", "hasSumstats") + .join( + qc_df.withColumn("hasSumstats", f.lit(True)), how="left", on="studyId" + ) + .withColumn("hasSumstats", f.coalesce(f.col("hasSumstats"), f.lit(False))) + .withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + ~f.col("hasSumstats"), + StudyQualityCheck.SUMSTATS_NOT_AVAILABLE, + ), + ) + .withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + ~(f.abs(f.col("sumstatQCValues.mean_beta")) <= threshold_mean_beta), + StudyQualityCheck.FAILED_MEAN_BETA_CHECK, + ), + ) + .withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + ~( + ( + f.abs(f.col("sumstatQCValues.mean_diff_pz")) + <= threshold_mean_diff_pz + ) + & (f.col("sumstatQCValues.se_diff_pz") <= threshold_se_diff_pz) + ), + StudyQualityCheck.FAILED_PZ_CHECK, + ), + ) + .withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + ~( + (f.col("sumstatQCValues.gc_lambda") <= threshold_max_gc_lambda) + & ( + f.col("sumstatQCValues.gc_lambda") + >= threshold_min_gc_lambda + ) + ), + StudyQualityCheck.FAILED_GC_LAMBDA_CHECK, + ), + ) + .withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + (f.col("sumstatQCValues.n_variants") < threshold_min_n_variants), + StudyQualityCheck.SMALL_NUMBER_OF_SNPS, + ), + ) + ) + + # Annotate study index with QC information: + return StudyIndex( + _df=df, + _schema=StudyIndex.get_schema(), + ) + + def deconvolute_studies(self: StudyIndex) -> StudyIndex: + """Deconvolute the study index dataset. + + When ingesting the study index dataset, the same studyId might be ingested from more than one source. + In such cases, the data needs to be merged and the quality control flags need to be combined. + + Returns: + StudyIndex: Deconvoluted study index dataset. + """ + # Windowing by study ID assume random order, but this is OK, because we are not selecting rows by a specific order. + study_id_window = Window.partitionBy("studyId").orderBy(f.rand()) + + # For certain aggregation, the full window is needed to be considered: + full_study_id_window = study_id_window.orderBy("studyId").rangeBetween( + Window.unboundedPreceding, Window.unboundedFollowing + ) + + # Temporary columns to drop at the end: + columns_to_drop = ["keepTopHit", "mostGranular", "rank"] + + return StudyIndex( + _df=( + self.df + # Initialising quality controls column, if not present: + .withColumn( + "qualityControls", + f.when( + f.col("qualityControls").isNull(), + f.array().cast(ArrayType(StringType())), + ).otherwise(f.col("qualityControls")), + ) + # Keeping top hit studies unless the same study is available from a summmary statistics source: + # This value will be set for all rows for the same `studyId`: + .withColumn( + "keepTopHit", + f.when( + f.array_contains( + f.collect_set(f.col("hasSumstats")).over( + full_study_id_window + ), + True, + ), + f.lit(False), + ).otherwise(True), + ) + # For studies without summary statistics, we remove the "Not curated by Open Targets" flag: + .withColumn( + "qualityControls", + f.when( + ~f.col("hasSumstats"), + f.array_remove( + f.col("qualityControls"), + StudyQualityCheck.NO_OT_CURATION.value, + ), + ).otherwise(f.col("qualityControls")), + ) + # If top hits are not kept, we remove the "sumstats not available" flag from all QC lists: + .withColumn( + "qualityControls", + f.when( + ~f.col("keepTopHit"), + f.array_remove( + f.col("qualityControls"), + StudyQualityCheck.SUMSTATS_NOT_AVAILABLE.value, + ), + ).otherwise(f.col("qualityControls")), + ) + # Then propagate quality checks for all sources of the same study: + .withColumn( + "qualityControls", + f.array_distinct( + f.flatten( + f.collect_set("qualityControls").over(full_study_id_window) + ) + ), + ) + # Propagating sumstatQCValues -> map, cannot be flatten: + .withColumn( + "sumstatQCValues", + f.first("sumstatQCValues", ignorenulls=True).over( + full_study_id_window + ), + ) + # Propagating analysisFlags: + .withColumn( + "analysisFlags", + f.flatten( + f.collect_list("analysisFlags").over(full_study_id_window) + ), + ) + # Propagating hasSumstatsFlag - if no flag, leave null: + .withColumn( + "hasSumstats", + f.when( + # There's a true: + f.array_contains( + f.collect_set("hasSumstats").over(full_study_id_window), + True, + ), + f.lit(True), + ).when( + # There's a false: + f.array_contains( + f.collect_set("hasSumstats").over(full_study_id_window), + False, + ), + f.lit(False), + ), + ) + # Propagating disease: when different sets of diseases available for the same study, + # we pick the shortest list, becasuse we assume, that is the most accurate disease assignment: + .withColumn( + "mostGranular", + f.size(f.col("traitFromSourceMappedIds")) + == f.min(f.size(f.col("traitFromSourceMappedIds"))).over( + full_study_id_window + ), + ) + # Remove less granular disease mappings: + .withColumn( + "traitFromSourceMappedIds", + f.when(f.col("mostGranular"), f.col("traitFromSourceMappedIds")), + ) + # Propagate mapped disease: + .withColumn( + "traitFromSourceMappedIds", + f.last(f.col("traitFromSourceMappedIds"), True).over( + full_study_id_window + ), + ) + # Repeating these steps for the `traitFromSource` column: + .withColumn( + "traitFromSource", + f.when(f.col("mostGranular"), f.col("traitFromSource")), + ) + # Propagate disease: + .withColumn( + "traitFromSource", + f.last(f.col("traitFromSource"), True).over(full_study_id_window), + ) + # Distinct study types are joined together into a string. So, if there's ambiguite, the study will be flagged when the study type is validated: + .withColumn( + "studyType", + f.concat_ws( + ",", f.collect_set("studyType").over(full_study_id_window) + ), + ) + # At this point, all studies in one window is expected to be identical. Let's just pick one: + .withColumn("rank", f.row_number().over(study_id_window)) + .filter(f.col("rank") == 1) + .drop(*columns_to_drop) + ), + _schema=StudyIndex.get_schema(), + ) diff --git a/src/gentropy/dataset/study_locus.py b/src/gentropy/dataset/study_locus.py index 1867dfa94..908c093b6 100644 --- a/src/gentropy/dataset/study_locus.py +++ b/src/gentropy/dataset/study_locus.py @@ -1,27 +1,62 @@ """Study locus dataset.""" + from __future__ import annotations from dataclasses import dataclass from enum import Enum from typing import TYPE_CHECKING +import numpy as np import pyspark.sql.functions as f +from pyspark.sql.types import ArrayType, FloatType, StringType +from gentropy.common.genomic_region import GenomicRegion, KnownGenomicRegions from gentropy.common.schemas import parse_spark_schema from gentropy.common.spark_helpers import ( calculate_neglog_pvalue, + create_empty_column_if_not_exists, + get_struct_field_schema, order_array_of_structs_by_field, ) +from gentropy.common.utils import get_logsum +from gentropy.config import WindowBasedClumpingStepConfig from gentropy.dataset.dataset import Dataset +from gentropy.dataset.study_index import StudyQualityCheck from gentropy.dataset.study_locus_overlap import StudyLocusOverlap +from gentropy.dataset.variant_index import VariantIndex from gentropy.method.clump import LDclumping if TYPE_CHECKING: from pyspark.sql import Column, DataFrame from pyspark.sql.types import StructType + from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix from gentropy.dataset.ld_index import LDIndex from gentropy.dataset.study_index import StudyIndex + from gentropy.dataset.summary_statistics import SummaryStatistics + from gentropy.method.l2g.feature_factory import L2GFeatureInputLoader + + +class CredibleSetConfidenceClasses(Enum): + """Confidence assignments for credible sets, based on finemapping method and quality checks. + + List of confidence classes, from the highest to the lowest confidence level. + + Attributes: + FINEMAPPED_IN_SAMPLE_LD (str): SuSiE fine-mapped credible set with in-sample LD + FINEMAPPED_OUT_OF_SAMPLE_LD (str): SuSiE fine-mapped credible set with out-of-sample LD + PICSED_SUMMARY_STATS (str): PICS fine-mapped credible set extracted from summary statistics + PICSED_TOP_HIT (str): PICS fine-mapped credible set based on reported top hit + UNKNOWN (str): Unknown confidence, for credible sets which did not fit any of the above categories + """ + + FINEMAPPED_IN_SAMPLE_LD = "SuSiE fine-mapped credible set with in-sample LD" + FINEMAPPED_OUT_OF_SAMPLE_LD = "SuSiE fine-mapped credible set with out-of-sample LD" + PICSED_SUMMARY_STATS = ( + "PICS fine-mapped credible set extracted from summary statistics" + ) + PICSED_TOP_HIT = "PICS fine-mapped credible set based on reported top hit" + UNKNOWN = "Unknown confidence" class StudyLocusQualityCheck(Enum): @@ -36,9 +71,22 @@ class StudyLocusQualityCheck(Enum): PALINDROMIC_ALLELE_FLAG (str): Alleles are palindromic - cannot harmonize AMBIGUOUS_STUDY (str): Association with ambiguous study UNRESOLVED_LD (str): Variant not found in LD reference - LD_CLUMPED (str): Explained by a more significant variant in high LD (clumped) + LD_CLUMPED (str): Explained by a more significant variant in high LD + WINDOW_CLUMPED (str): Explained by a more significant variant in the same window NO_POPULATION (str): Study does not have population annotation to resolve LD NOT_QUALIFYING_LD_BLOCK (str): LD block does not contain variants at the required R^2 threshold + FLAGGED_STUDY (str): Study has quality control flag(s) + MISSING_STUDY (str): Flagging study loci if the study is not found in the study index as a reference + DUPLICATED_STUDYLOCUS_ID (str): Study-locus identifier is not unique + INVALID_VARIANT_IDENTIFIER (str): Flagging study loci where identifier of any tagging variant was not found in the variant index + TOP_HIT (str): Study locus from curated top hit + IN_MHC (str): Flagging study loci in the MHC region + REDUNDANT_PICS_TOP_HIT (str): Flagging study loci in studies with PICS results from summary statistics + EXPLAINED_BY_SUSIE (str): Study locus in region explained by a SuSiE credible set + ABNORMAL_PIPS (str): Flagging study loci with a sum of PIPs that are not in [0.99,1] + OUT_OF_SAMPLE_LD (str): Study locus finemapped without in-sample LD reference + INVALID_CHROMOSOME (str): Chromosome not in 1:22, X, Y, XY or MT + TOP_HIT_AND_SUMMARY_STATS (str): Curated top hit is flagged because summary statistics are available for study """ SUBSIGNIFICANT_FLAG = "Subsignificant p-value" @@ -49,11 +97,32 @@ class StudyLocusQualityCheck(Enum): PALINDROMIC_ALLELE_FLAG = "Palindrome alleles - cannot harmonize" AMBIGUOUS_STUDY = "Association with ambiguous study" UNRESOLVED_LD = "Variant not found in LD reference" - LD_CLUMPED = "Explained by a more significant variant in high LD (clumped)" + LD_CLUMPED = "Explained by a more significant variant in high LD" + WINDOW_CLUMPED = "Explained by a more significant variant in the same window" NO_POPULATION = "Study does not have population annotation to resolve LD" NOT_QUALIFYING_LD_BLOCK = ( "LD block does not contain variants at the required R^2 threshold" ) + FLAGGED_STUDY = "Study has quality control flag(s)" + MISSING_STUDY = "Study not found in the study index" + DUPLICATED_STUDYLOCUS_ID = "Non-unique study locus identifier" + INVALID_VARIANT_IDENTIFIER = ( + "Some variant identifiers of this locus were not found in variant index" + ) + IN_MHC = "MHC region" + REDUNDANT_PICS_TOP_HIT = ( + "PICS results from summary statistics available for this same study" + ) + TOP_HIT = "Study locus from curated top hit" + EXPLAINED_BY_SUSIE = "Study locus in region explained by a SuSiE credible set" + OUT_OF_SAMPLE_LD = "Study locus finemapped without in-sample LD reference" + ABNORMAL_PIPS = ( + "Study locus with a sum of PIPs that not in the expected range [0.99,1]" + ) + INVALID_CHROMOSOME = "Chromosome not in 1:22, X, Y, XY or MT" + TOP_HIT_AND_SUMMARY_STATS = ( + "Curated top hit is flagged because summary statistics are available for study" + ) class CredibleInterval(Enum): @@ -77,37 +146,385 @@ class StudyLocus(Dataset): This dataset captures associations between study/traits and a genetic loci as provided by finemapping methods. """ + def validate_study(self: StudyLocus, study_index: StudyIndex) -> StudyLocus: + """Flagging study loci if the corresponding study has issues. + + There are two different potential flags: + - flagged study: flagging locus if the study has quality control flags. + - study with summary statistics for top hit: flagging locus if the study has available summary statistics. + - missing study: flagging locus if the study was not found in the reference study index. + + Args: + study_index (StudyIndex): Study index to resolve study types. + + Returns: + StudyLocus: Updated study locus with quality control flags. + """ + # Quality controls is not a mandatory field in the study index schema, so we have to be ready to handle it: + qc_select_expression = ( + f.col("qualityControls") + if "qualityControls" in study_index.df.columns + else f.lit(None).cast(StringType()) + ) + + # The study Id of the study index needs to be kept, because we would not know which study was in the index after the left join: + study_flags = study_index.df.select( + f.col("studyId").alias("study_studyId"), + qc_select_expression.alias("study_qualityControls"), + ) + + return StudyLocus( + _df=( + self.df.join( + study_flags, f.col("studyId") == f.col("study_studyId"), "left" + ) + # Flagging loci with flagged studies - without propagating the actual flags: + .withColumn( + "qualityControls", + StudyLocus.update_quality_flag( + f.col("qualityControls"), + f.size(f.col("study_qualityControls")) > 0, + StudyLocusQualityCheck.FLAGGED_STUDY, + ), + ) + # Flagging top-hits, where the study has available summary statistics: + .withColumn( + "qualityControls", + StudyLocus.update_quality_flag( + f.col("qualityControls"), + # Condition is true, if the study has summary statistics available and the locus is a top hit: + f.array_contains( + f.col("qualityControls"), + StudyLocusQualityCheck.TOP_HIT.value, + ) + & ~f.array_contains( + f.col("study_qualityControls"), + StudyQualityCheck.SUMSTATS_NOT_AVAILABLE.value, + ), + StudyLocusQualityCheck.TOP_HIT_AND_SUMMARY_STATS, + ), + ) + # Flagging loci where no studies were found: + .withColumn( + "qualityControls", + StudyLocus.update_quality_flag( + f.col("qualityControls"), + f.col("study_studyId").isNull(), + StudyLocusQualityCheck.MISSING_STUDY, + ), + ) + .drop("study_studyId", "study_qualityControls") + ), + _schema=self.get_schema(), + ) + + def annotate_study_type(self: StudyLocus, study_index: StudyIndex) -> StudyLocus: + """Gets study type from study index and adds it to study locus. + + Args: + study_index (StudyIndex): Study index to get study type. + + Returns: + StudyLocus: Updated study locus with study type. + """ + return StudyLocus( + _df=( + self.df.drop("studyType").join( + study_index.study_type_lut(), on="studyId", how="left" + ) + ), + _schema=self.get_schema(), + ) + + def validate_chromosome_label(self: StudyLocus) -> StudyLocus: + """Flagging study loci, where chromosome is coded not as 1:22, X, Y, Xy and MT. + + Returns: + StudyLocus: Updated study locus with quality control flags. + """ + # QC column might not be present in the variant index schema, so we have to be ready to handle it: + qc_select_expression = ( + f.col("qualityControls") + if "qualityControls" in self.df.columns + else f.lit(None).cast(ArrayType(StringType())) + ) + valid_chromosomes = [str(i) for i in range(1, 23)] + ["X", "Y", "XY", "MT"] + + return StudyLocus( + _df=( + self.df.withColumn( + "qualityControls", + self.update_quality_flag( + qc_select_expression, + ~f.col("chromosome").isin(valid_chromosomes), + StudyLocusQualityCheck.INVALID_CHROMOSOME, + ), + ) + ), + _schema=self.get_schema(), + ) + + def validate_variant_identifiers( + self: StudyLocus, variant_index: VariantIndex + ) -> StudyLocus: + """Flagging study loci, where tagging variant identifiers are not found in variant index. + + Args: + variant_index (VariantIndex): Variant index to resolve variant identifiers. + + Returns: + StudyLocus: Updated study locus with quality control flags. + """ + # QC column might not be present in the variant index schema, so we have to be ready to handle it: + qc_select_expression = ( + f.col("qualityControls") + if "qualityControls" in self.df.columns + else f.lit(None).cast(ArrayType(StringType())) + ) + + # Find out which study loci have variants not in the variant index: + flag = ( + self.df + # Exploding locus: + .select("studyLocusId", f.explode("locus").alias("locus")) + .select("studyLocusId", "locus.variantId") + # Join with variant index variants: + .join( + variant_index.df.select( + "variantId", f.lit(True).alias("inVariantIndex") + ), + on="variantId", + how="left", + ) + # Flagging variants not in the variant index: + .withColumn("inVariantIndex", f.col("inVariantIndex").isNotNull()) + # Flagging study loci with ANY variants not in the variant index: + .groupBy("studyLocusId") + .agg(f.collect_set("inVariantIndex").alias("inVariantIndex")) + .select( + "studyLocusId", + f.array_contains("inVariantIndex", False).alias("toFlag"), + ) + ) + + return StudyLocus( + _df=( + self.df.join(flag, on="studyLocusId", how="left") + .withColumn( + "qualityControls", + self.update_quality_flag( + qc_select_expression, + f.col("toFlag"), + StudyLocusQualityCheck.INVALID_VARIANT_IDENTIFIER, + ), + ) + .drop("toFlag") + ), + _schema=self.get_schema(), + ) + + def validate_lead_pvalue(self: StudyLocus, pvalue_cutoff: float) -> StudyLocus: + """Flag associations below significant threshold. + + Args: + pvalue_cutoff (float): association p-value cut-off + + Returns: + StudyLocus: Updated study locus with quality control flags. + """ + df = self.df + qc_colname = StudyLocus.get_QC_column_name() + if qc_colname not in self.df.columns: + df = self.df.withColumn( + qc_colname, + create_empty_column_if_not_exists( + qc_colname, + get_struct_field_schema(StudyLocus.get_schema(), qc_colname), + ), + ) + return StudyLocus( + _df=( + df.withColumn( + qc_colname, + # Because this QC might already run on the dataset, the unique set of flags is generated: + f.array_distinct( + self._qc_subsignificant_associations( + f.col("qualityControls"), + f.col("pValueMantissa"), + f.col("pValueExponent"), + pvalue_cutoff, + ) + ), + ) + ), + _schema=self.get_schema(), + ) + + def validate_unique_study_locus_id(self: StudyLocus) -> StudyLocus: + """Validating the uniqueness of study-locus identifiers and flagging duplicated studyloci. + + Returns: + StudyLocus: with flagged duplicated studies. + """ + return StudyLocus( + _df=self.df.withColumn( + "qualityControls", + self.update_quality_flag( + f.col("qualityControls"), + self.flag_duplicates(f.col("studyLocusId")), + StudyLocusQualityCheck.DUPLICATED_STUDYLOCUS_ID, + ), + ), + _schema=StudyLocus.get_schema(), + ) + @staticmethod - def _overlapping_peaks(credset_to_overlap: DataFrame) -> DataFrame: + def _qc_subsignificant_associations( + quality_controls_column: Column, + p_value_mantissa: Column, + p_value_exponent: Column, + pvalue_cutoff: float, + ) -> Column: + """Flag associations below significant threshold. + + Args: + quality_controls_column (Column): QC column + p_value_mantissa (Column): P-value mantissa column + p_value_exponent (Column): P-value exponent column + pvalue_cutoff (float): association p-value cut-off + + Returns: + Column: Updated QC column with flag. + + Examples: + >>> import pyspark.sql.types as t + >>> d = [{'qc': None, 'p_value_mantissa': 1, 'p_value_exponent': -7}, {'qc': None, 'p_value_mantissa': 1, 'p_value_exponent': -8}, {'qc': None, 'p_value_mantissa': 5, 'p_value_exponent': -8}, {'qc': None, 'p_value_mantissa': 1, 'p_value_exponent': -9}] + >>> df = spark.createDataFrame(d, t.StructType([t.StructField('qc', t.ArrayType(t.StringType()), True), t.StructField('p_value_mantissa', t.IntegerType()), t.StructField('p_value_exponent', t.IntegerType())])) + >>> df.withColumn('qc', StudyLocus._qc_subsignificant_associations(f.col("qc"), f.col("p_value_mantissa"), f.col("p_value_exponent"), 5e-8)).show(truncate = False) + +------------------------+----------------+----------------+ + |qc |p_value_mantissa|p_value_exponent| + +------------------------+----------------+----------------+ + |[Subsignificant p-value]|1 |-7 | + |[] |1 |-8 | + |[] |5 |-8 | + |[] |1 |-9 | + +------------------------+----------------+----------------+ + + + """ + return StudyLocus.update_quality_flag( + quality_controls_column, + calculate_neglog_pvalue(p_value_mantissa, p_value_exponent) + < f.lit(-np.log10(pvalue_cutoff)), + StudyLocusQualityCheck.SUBSIGNIFICANT_FLAG, + ) + + def qc_abnormal_pips( + self: StudyLocus, + sum_pips_lower_threshold: float = 0.99, + sum_pips_upper_threshold: float = 1.0001, # Set slightly above 1 to account for floating point errors + ) -> StudyLocus: + """Filter study-locus by sum of posterior inclusion probabilities to ensure that the sum of PIPs is within a given range. + + Args: + sum_pips_lower_threshold (float): Lower threshold for the sum of PIPs. + sum_pips_upper_threshold (float): Upper threshold for the sum of PIPs. + + Returns: + StudyLocus: Filtered study-locus dataset. + """ + # QC column might not be present so we have to be ready to handle it: + qc_select_expression = ( + f.col("qualityControls") + if "qualityControls" in self.df.columns + else f.lit(None).cast(ArrayType(StringType())) + ) + + flag = self.df.withColumn( + "sumPosteriorProbability", + f.aggregate( + f.col("locus"), + f.lit(0.0), + lambda acc, x: acc + x["posteriorProbability"], + ), + ).withColumn( + "pipOutOfRange", + f.when( + (f.col("sumPosteriorProbability") < sum_pips_lower_threshold) + | (f.col("sumPosteriorProbability") > sum_pips_upper_threshold), + True, + ).otherwise(False), + ) + + return StudyLocus( + _df=( + flag + # Flagging loci with failed studies: + .withColumn( + "qualityControls", + self.update_quality_flag( + qc_select_expression, + f.col("pipOutOfRange"), + StudyLocusQualityCheck.ABNORMAL_PIPS, + ), + ).drop("sumPosteriorProbability", "pipOutOfRange") + ), + _schema=self.get_schema(), + ) + + @staticmethod + def _overlapping_peaks( + credset_to_overlap: DataFrame, intra_study_overlap: bool = False + ) -> DataFrame: """Calculate overlapping signals (study-locus) between GWAS-GWAS and GWAS-Molecular trait. Args: credset_to_overlap (DataFrame): DataFrame containing at least `studyLocusId`, `studyType`, `chromosome` and `tagVariantId` columns. + intra_study_overlap (bool): When True, finds intra-study overlaps for credible set deduplication. Default is False. Returns: DataFrame: containing `leftStudyLocusId`, `rightStudyLocusId` and `chromosome` columns. """ # Reduce columns to the minimum to reduce the size of the dataframe credset_to_overlap = credset_to_overlap.select( - "studyLocusId", "studyType", "chromosome", "tagVariantId" + "studyLocusId", + "studyId", + "studyType", + "chromosome", + "region", + "tagVariantId", + ) + # Define join condition - if intra_study_overlap is True, finds overlaps within the same study. Otherwise finds gwas vs everything overlaps for coloc. + join_condition = ( + [ + f.col("left.studyId") == f.col("right.studyId"), + f.col("left.chromosome") == f.col("right.chromosome"), + f.col("left.tagVariantId") == f.col("right.tagVariantId"), + f.col("left.studyLocusId") > f.col("right.studyLocusId"), + f.col("left.region") != f.col("right.region"), + ] + if intra_study_overlap + else [ + f.col("left.chromosome") == f.col("right.chromosome"), + f.col("left.tagVariantId") == f.col("right.tagVariantId"), + (f.col("right.studyType") != "gwas") + | (f.col("left.studyLocusId") > f.col("right.studyLocusId")), + f.col("left.studyType") == f.lit("gwas"), + ] ) + return ( credset_to_overlap.alias("left") - .filter(f.col("studyType") == "gwas") - # Self join with complex condition. Left it's all gwas and right can be gwas or molecular trait + # Self join with complex condition. .join( credset_to_overlap.alias("right"), - on=[ - f.col("left.chromosome") == f.col("right.chromosome"), - f.col("left.tagVariantId") == f.col("right.tagVariantId"), - (f.col("right.studyType") != "gwas") - | (f.col("left.studyLocusId") > f.col("right.studyLocusId")), - ], + on=join_condition, how="inner", ) .select( f.col("left.studyLocusId").alias("leftStudyLocusId"), f.col("right.studyLocusId").alias("rightStudyLocusId"), + f.col("right.studyType").alias("rightStudyType"), f.col("left.chromosome").alias("chromosome"), ) .distinct() @@ -159,11 +576,13 @@ def _align_overlapping_tags( "rightStudyLocusId", "leftStudyLocusId", "tagVariantId", + "rightStudyType", ], how="outer", ).select( "leftStudyLocusId", "rightStudyLocusId", + "rightStudyType", "chromosome", "tagVariantId", f.struct( @@ -176,49 +595,54 @@ def _align_overlapping_tags( ) @staticmethod - def update_quality_flag( - qc: Column, flag_condition: Column, flag_text: StudyLocusQualityCheck - ) -> Column: - """Update the provided quality control list with a new flag if condition is met. + def assign_study_locus_id(uniqueness_defining_columns: list[str]) -> Column: + """Hashes the provided columns to extract a consistent studyLocusId. Args: - qc (Column): Array column with the current list of qc flags. - flag_condition (Column): This is a column of booleans, signing which row should be flagged - flag_text (StudyLocusQualityCheck): Text for the new quality control flag + uniqueness_defining_columns (list[str]): list of columns defining uniqueness Returns: - Column: Array column with the updated list of qc flags. + Column: column with a study locus ID + + Examples: + >>> df = spark.createDataFrame([("GCST000001", "1_1000_A_C", "SuSiE-inf"), ("GCST000002", "1_1000_A_C", "pics")]).toDF("studyId", "variantId", "finemappingMethod") + >>> df.withColumn("study_locus_id", StudyLocus.assign_study_locus_id(["studyId", "variantId", "finemappingMethod"])).show(truncate=False) + +----------+----------+-----------------+--------------------------------+ + |studyId |variantId |finemappingMethod|study_locus_id | + +----------+----------+-----------------+--------------------------------+ + |GCST000001|1_1000_A_C|SuSiE-inf |109804fe1e20c94231a31bafd71b566e| + |GCST000002|1_1000_A_C|pics |de310be4558e0482c9cc359c97d37773| + +----------+----------+-----------------+--------------------------------+ + """ - qc = f.when(qc.isNull(), f.array()).otherwise(qc) - return f.when( - flag_condition, - f.array_union(qc, f.array(f.lit(flag_text.value))), - ).otherwise(qc) + return Dataset.generate_identifier(uniqueness_defining_columns).alias( + "studyLocusId" + ) - @staticmethod - def assign_study_locus_id(study_id_col: Column, variant_id_col: Column) -> Column: - """Hashes a column with a variant ID and a study ID to extract a consistent studyLocusId. + @classmethod + def calculate_credible_set_log10bf(cls: type[StudyLocus], logbfs: Column) -> Column: + """Calculate Bayes factor for the entire credible set. The Bayes factor is calculated as the logsumexp of the logBF values of the variants in the locus. Args: - study_id_col (Column): column name with a study ID - variant_id_col (Column): column name with a variant ID + logbfs (Column): Array column with the logBF values of the variants in the locus. Returns: - Column: column with a study locus ID + Column: log10 Bayes factor for the entire credible set. Examples: - >>> df = spark.createDataFrame([("GCST000001", "1_1000_A_C"), ("GCST000002", "1_1000_A_C")]).toDF("studyId", "variantId") - >>> df.withColumn("study_locus_id", StudyLocus.assign_study_locus_id(f.col("studyId"), f.col("variantId"))).show() - +----------+----------+-------------------+ - | studyId| variantId| study_locus_id| - +----------+----------+-------------------+ - |GCST000001|1_1000_A_C|1553357789130151995| - |GCST000002|1_1000_A_C|-415050894682709184| - +----------+----------+-------------------+ + >>> spark.createDataFrame([([0.2, 0.1, 0.05, 0.0],)]).toDF("logBF").select(f.round(StudyLocus.calculate_credible_set_log10bf(f.col("logBF")), 7).alias("credibleSetlog10BF")).show() + +------------------+ + |credibleSetlog10BF| + +------------------+ + | 0.6412604| + +------------------+ """ - variant_id_col = f.coalesce(variant_id_col, f.rand().cast("string")) - return f.xxhash64(study_id_col, variant_id_col).alias("studyLocusId") + # log10=log/log(10)=log*0.43429448190325176 + logsumexp_udf = f.udf( + lambda x: (get_logsum(x) * 0.43429448190325176), FloatType() + ) + return logsumexp_udf(logbfs).cast("double").alias("credibleSetlog10BF") @classmethod def get_schema(cls: type[StudyLocus]) -> StructType: @@ -229,40 +653,29 @@ def get_schema(cls: type[StudyLocus]) -> StructType: """ return parse_spark_schema("study_locus.json") - def filter_by_study_type( - self: StudyLocus, study_type: str, study_index: StudyIndex - ) -> StudyLocus: - """Creates a new StudyLocus dataset filtered by study type. - - Args: - study_type (str): Study type to filter for. Can be one of `gwas`, `eqtl`, `pqtl`, `eqtl`. - study_index (StudyIndex): Study index to resolve study types. + @classmethod + def get_QC_column_name(cls: type[StudyLocus]) -> str: + """Quality control column. Returns: - StudyLocus: Filtered study-locus dataset. + str: Name of the quality control column. + """ + return "qualityControls" - Raises: - ValueError: If study type is not supported. + @classmethod + def get_QC_mappings(cls: type[StudyLocus]) -> dict[str, str]: + """Quality control flag to QC column category mappings. + + Returns: + dict[str, str]: Mapping between flag name and QC column category value. """ - if study_type not in ["gwas", "eqtl", "pqtl", "sqtl"]: - raise ValueError( - f"Study type {study_type} not supported. Supported types are: gwas, eqtl, pqtl, sqtl." - ) - new_df = ( - self.df.join(study_index.study_type_lut(), on="studyId", how="inner") - .filter(f.col("studyType") == study_type) - .drop("studyType") - ) - return StudyLocus( - _df=new_df, - _schema=self._schema, - ) + return {member.name: member.value for member in StudyLocusQualityCheck} def filter_credible_set( self: StudyLocus, credible_interval: CredibleInterval, ) -> StudyLocus: - """Filter study-locus tag variants based on given credible interval. + """Annotate and filter study-locus tag variants based on given credible interval. Args: credible_interval (CredibleInterval): Credible interval to filter for. @@ -270,34 +683,59 @@ def filter_credible_set( Returns: StudyLocus: Filtered study-locus dataset. """ - self.df = self._df.withColumn( - "locus", + return StudyLocus( + _df=self.annotate_credible_sets().df.withColumn( + "locus", + f.filter( + f.col("locus"), + lambda tag: (tag[credible_interval.value]), + ), + ), + _schema=self._schema, + ) + + @staticmethod + def filter_ld_set(ld_set: Column, r2_threshold: float) -> Column: + """Filter the LD set by a given R2 threshold. + + Args: + ld_set (Column): LD set + r2_threshold (float): R2 threshold to filter the LD set on + + Returns: + Column: Filtered LD index + """ + return f.when( + ld_set.isNotNull(), f.filter( - f.col("locus"), - lambda tag: (tag[credible_interval.value]), + ld_set, + lambda tag: tag["r2Overall"] >= r2_threshold, ), ) - return self - def find_overlaps(self: StudyLocus, study_index: StudyIndex) -> StudyLocusOverlap: + def find_overlaps( + self: StudyLocus, intra_study_overlap: bool = False + ) -> StudyLocusOverlap: """Calculate overlapping study-locus. Find overlapping study-locus that share at least one tagging variant. All GWAS-GWAS and all GWAS-Molecular traits are computed with the Molecular traits always appearing on the right side. Args: - study_index (StudyIndex): Study index to resolve study types. + intra_study_overlap (bool): If True, finds intra-study overlaps for credible set deduplication. Default is False. Returns: StudyLocusOverlap: Pairs of overlapping study-locus with aligned tags. """ loci_to_overlap = ( - self.df.join(study_index.study_type_lut(), on="studyId", how="inner") + self.df.filter(f.col("studyType").isNotNull()) .withColumn("locus", f.explode("locus")) .select( "studyLocusId", + "studyId", "studyType", "chromosome", + "region", f.col("locus.variantId").alias("tagVariantId"), f.col("locus.logBF").alias("logBF"), f.col("locus.posteriorProbability").alias("posteriorProbability"), @@ -309,7 +747,7 @@ def find_overlaps(self: StudyLocus, study_index: StudyIndex) -> StudyLocusOverla ) # overlapping study-locus - peak_overlaps = self._overlapping_peaks(loci_to_overlap) + peak_overlaps = self._overlapping_peaks(loci_to_overlap, intra_study_overlap) # study-locus overlap by aligning overlapping variants return self._align_overlapping_tags(loci_to_overlap, peak_overlaps) @@ -348,6 +786,28 @@ def neglog_pvalue(self: StudyLocus) -> Column: self.df.pValueExponent, ) + def build_feature_matrix( + self: StudyLocus, + features_list: list[str], + features_input_loader: L2GFeatureInputLoader, + ) -> L2GFeatureMatrix: + """Returns the feature matrix for a StudyLocus. + + Args: + features_list (list[str]): List of features to include in the feature matrix. + features_input_loader (L2GFeatureInputLoader): Feature input loader to use. + + Returns: + L2GFeatureMatrix: Feature matrix for this study-locus. + """ + from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix + + return L2GFeatureMatrix.from_features_list( + self, + features_list, + features_input_loader, + ).fill_na() + def annotate_credible_sets(self: StudyLocus) -> StudyLocus: """Annotate study-locus dataset with credible set flags. @@ -403,21 +863,93 @@ def annotate_credible_sets(self: StudyLocus) -> StudyLocus: ) return self + def annotate_locus_statistics( + self: StudyLocus, + summary_statistics: SummaryStatistics, + collect_locus_distance: int, + ) -> StudyLocus: + """Annotates study locus with summary statistics in the specified distance around the position. + + Args: + summary_statistics (SummaryStatistics): Summary statistics to be used for annotation. + collect_locus_distance (int): distance from variant defining window for inclusion of variants in locus. + + Returns: + StudyLocus: Study locus annotated with summary statistics in `locus` column. If no statistics are found, the `locus` column will be empty. + """ + # The clumps will be used several times (persisting) + self.df.persist() + # Renaming columns: + sumstats_renamed = summary_statistics.df.selectExpr( + *[f"{col} as tag_{col}" for col in summary_statistics.df.columns] + ).alias("sumstat") + + locus_df = ( + sumstats_renamed + # Joining the two datasets together: + .join( + f.broadcast( + self.df.alias("clumped").select( + "position", "chromosome", "studyId", "studyLocusId" + ) + ), + on=[ + (f.col("sumstat.tag_studyId") == f.col("clumped.studyId")) + & (f.col("sumstat.tag_chromosome") == f.col("clumped.chromosome")) + & ( + f.col("sumstat.tag_position") + >= (f.col("clumped.position") - collect_locus_distance) + ) + & ( + f.col("sumstat.tag_position") + <= (f.col("clumped.position") + collect_locus_distance) + ) + ], + how="inner", + ) + .withColumn( + "locus", + f.struct( + f.col("tag_variantId").alias("variantId"), + f.col("tag_beta").alias("beta"), + f.col("tag_pValueMantissa").alias("pValueMantissa"), + f.col("tag_pValueExponent").alias("pValueExponent"), + f.col("tag_standardError").alias("standardError"), + ), + ) + .groupBy("studyLocusId") + .agg( + f.collect_list(f.col("locus")).alias("locus"), + ) + ) + + self.df = self.df.drop("locus").join( + locus_df, + on="studyLocusId", + how="left", + ) + + return self + def annotate_ld( - self: StudyLocus, study_index: StudyIndex, ld_index: LDIndex + self: StudyLocus, + study_index: StudyIndex, + ld_index: LDIndex, + r2_threshold: float = 0.0, ) -> StudyLocus: """Annotate LD information to study-locus. Args: study_index (StudyIndex): Study index to resolve ancestries. ld_index (LDIndex): LD index to resolve LD information. + r2_threshold (float): R2 threshold to filter the LD index. Default is 0.0. Returns: StudyLocus: Study locus annotated with ld information from LD index. """ from gentropy.method.ld import LDAnnotator - return LDAnnotator.ld_annotate(self, study_index, ld_index) + return LDAnnotator.ld_annotate(self, study_index, ld_index, r2_threshold) def clump(self: StudyLocus) -> StudyLocus: """Perform LD clumping of the studyLocus. @@ -427,11 +959,12 @@ def clump(self: StudyLocus) -> StudyLocus: Returns: StudyLocus: with empty credible sets for linked variants and QC flag. """ - self.df = ( + clumped_df = ( self.df.withColumn( "is_lead_linked", LDclumping._is_lead_linked( self.df.studyId, + self.df.chromosome, self.df.variantId, self.df.pValueExponent, self.df.pValueMantissa, @@ -452,8 +985,171 @@ def clump(self: StudyLocus) -> StudyLocus: ) .drop("is_lead_linked") ) + return StudyLocus( + _df=clumped_df, + _schema=self.get_schema(), + ) + + def exclude_region( + self: StudyLocus, region: GenomicRegion, exclude_overlap: bool = False + ) -> StudyLocus: + """Exclude a region from the StudyLocus dataset. + + Args: + region (GenomicRegion): genomic region object. + exclude_overlap (bool): If True, excludes StudyLocus windows with any overlap with the region. + + Returns: + StudyLocus: filtered StudyLocus object. + """ + if exclude_overlap: + filter_condition = ~( + (f.col("chromosome") == region.chromosome) + & ( + (f.col("locusStart") <= region.end) + & (f.col("locusEnd") >= region.start) + ) + ) + else: + filter_condition = ~( + (f.col("chromosome") == region.chromosome) + & ( + (f.col("position") >= region.start) + & (f.col("position") <= region.end) + ) + ) + + return StudyLocus( + _df=self.df.filter(filter_condition), + _schema=StudyLocus.get_schema(), + ) + + def qc_MHC_region(self: StudyLocus) -> StudyLocus: + """Adds qualityControl flag when lead overlaps with MHC region. + + Returns: + StudyLocus: including qualityControl flag if in MHC region. + """ + region = GenomicRegion.from_known_genomic_region(KnownGenomicRegions.MHC) + self.df = self.df.withColumn( + "qualityControls", + self.update_quality_flag( + f.col("qualityControls"), + ( + (f.col("chromosome") == region.chromosome) + & ( + (f.col("position") <= region.end) + & (f.col("position") >= region.start) + ) + ), + StudyLocusQualityCheck.IN_MHC, + ), + ) return self + def qc_redundant_top_hits_from_PICS(self: StudyLocus) -> StudyLocus: + """Flag associations from top hits when the study contains other PICS associations from summary statistics. + + This flag can be useful to identify top hits that should be explained by other associations in the study derived from the summary statistics. + + Returns: + StudyLocus: Updated study locus with redundant top hits flagged. + """ + studies_with_pics_sumstats = ( + self.df.filter(f.col("finemappingMethod") == "pics") + # Returns True if the study contains any PICS associations from summary statistics + .withColumn( + "hasPicsSumstats", + ~f.array_contains( + "qualityControls", StudyLocusQualityCheck.TOP_HIT.value + ), + ) + .groupBy("studyId") + .agg(f.max(f.col("hasPicsSumstats")).alias("studiesWithPicsSumstats")) + ) + + return StudyLocus( + _df=self.df.join(studies_with_pics_sumstats, on="studyId", how="left") + .withColumn( + "qualityControls", + self.update_quality_flag( + f.col("qualityControls"), + f.array_contains( + "qualityControls", StudyLocusQualityCheck.TOP_HIT.value + ) + & f.col("studiesWithPicsSumstats"), + StudyLocusQualityCheck.REDUNDANT_PICS_TOP_HIT, + ), + ) + .drop("studiesWithPicsSumstats"), + _schema=StudyLocus.get_schema(), + ) + + def qc_explained_by_SuSiE(self: StudyLocus) -> StudyLocus: + """Flag associations that are explained by SuSiE associations. + + Credible sets overlapping in the same region as a SuSiE credible set are flagged as explained by SuSiE. + + Returns: + StudyLocus: Updated study locus with SuSiE explained flags. + """ + # unique study-regions covered by SuSie credible sets + susie_study_regions = ( + self.filter(f.col("finemappingMethod") == "SuSiE-inf") + .df.select( + "studyId", + "chromosome", + "locusStart", + "locusEnd", + f.lit(True).alias("inSuSiE"), + ) + .distinct() + ) + + # non SuSiE credible sets (studyLocusId) overlapping in any variant with SuSiE locus + redundant_study_locus = ( + self.filter(f.col("finemappingMethod") != "SuSiE-inf") + .df.withColumn("l", f.explode("locus")) + .select( + "studyLocusId", + "studyId", + "chromosome", + f.split(f.col("l.variantId"), "_")[1].alias("tag_position"), + ) + .alias("study_locus") + .join( + susie_study_regions.alias("regions"), + how="inner", + on=[ + (f.col("study_locus.chromosome") == f.col("regions.chromosome")) + & (f.col("study_locus.studyId") == f.col("regions.studyId")) + & (f.col("study_locus.tag_position") >= f.col("regions.locusStart")) + & (f.col("study_locus.tag_position") <= f.col("regions.locusEnd")) + ], + ) + .select("studyLocusId", "inSuSiE") + .distinct() + ) + + return StudyLocus( + _df=( + self.df.join(redundant_study_locus, on="studyLocusId", how="left") + .withColumn( + "qualityControls", + self.update_quality_flag( + f.col("qualityControls"), + # credible set in SuSiE overlapping region + f.col("inSuSiE") + # credible set not based on SuSiE + & (f.col("finemappingMethod") != "SuSiE-inf"), + StudyLocusQualityCheck.EXPLAINED_BY_SUSIE, + ), + ) + .drop("inSuSiE") + ), + _schema=StudyLocus.get_schema(), + ) + def _qc_no_population(self: StudyLocus) -> StudyLocus: """Flag associations where the study doesn't have population information to resolve LD. @@ -473,3 +1169,146 @@ def _qc_no_population(self: StudyLocus) -> StudyLocus: ), ) return self + + def annotate_locus_statistics_boundaries( + self: StudyLocus, + summary_statistics: SummaryStatistics, + ) -> StudyLocus: + """Annotates study locus with summary statistics in the specified boundaries - locusStart and locusEnd. + + Args: + summary_statistics (SummaryStatistics): Summary statistics to be used for annotation. + + Returns: + StudyLocus: Study locus annotated with summary statistics in `locus` column. If no statistics are found, the `locus` column will be empty. + """ + # The clumps will be used several times (persisting) + self.df.persist() + # Renaming columns: + sumstats_renamed = summary_statistics.df.selectExpr( + *[f"{col} as tag_{col}" for col in summary_statistics.df.columns] + ).alias("sumstat") + + locus_df = ( + sumstats_renamed + # Joining the two datasets together: + .join( + f.broadcast( + self.df.alias("clumped").select( + "position", + "chromosome", + "studyId", + "studyLocusId", + "locusStart", + "locusEnd", + ) + ), + on=[ + (f.col("sumstat.tag_studyId") == f.col("clumped.studyId")) + & (f.col("sumstat.tag_chromosome") == f.col("clumped.chromosome")) + & (f.col("sumstat.tag_position") >= (f.col("clumped.locusStart"))) + & (f.col("sumstat.tag_position") <= (f.col("clumped.locusEnd"))) + ], + how="inner", + ) + .withColumn( + "locus", + f.struct( + f.col("tag_variantId").alias("variantId"), + f.col("tag_beta").alias("beta"), + f.col("tag_pValueMantissa").alias("pValueMantissa"), + f.col("tag_pValueExponent").alias("pValueExponent"), + f.col("tag_standardError").alias("standardError"), + ), + ) + .groupBy("studyLocusId") + .agg( + f.collect_list(f.col("locus")).alias("locus"), + ) + ) + + self.df = self.df.drop("locus").join( + locus_df, + on="studyLocusId", + how="left", + ) + + return self + + def window_based_clumping( + self: StudyLocus, + window_size: int = WindowBasedClumpingStepConfig().distance, + ) -> StudyLocus: + """Clump study locus by window size. + + Args: + window_size (int): Window size for clumping. + + Returns: + StudyLocus: Clumped study locus, where clumped associations are flagged. + """ + from gentropy.method.window_based_clumping import WindowBasedClumping + + return WindowBasedClumping.clump(self, window_size) + + def assign_confidence(self: StudyLocus) -> StudyLocus: + """Assign confidence to study locus. + + Returns: + StudyLocus: Study locus with confidence assigned. + """ + # Return self if the required columns are not in the dataframe: + if ( + "qualityControls" not in self.df.columns + or "finemappingMethod" not in self.df.columns + ): + return self + + # Assign confidence based on the presence of quality controls + df = self.df.withColumn( + "confidence", + f.when( + (f.col("finemappingMethod").isin(["SuSiE-inf", "SuSie"])) + & ( + ~f.array_contains( + f.col("qualityControls"), + StudyLocusQualityCheck.OUT_OF_SAMPLE_LD.value, + ) + ), + CredibleSetConfidenceClasses.FINEMAPPED_IN_SAMPLE_LD.value, + ) + .when( + (f.col("finemappingMethod").isin(["SuSiE-inf", "SuSie"])) + & ( + f.array_contains( + f.col("qualityControls"), + StudyLocusQualityCheck.OUT_OF_SAMPLE_LD.value, + ) + ), + CredibleSetConfidenceClasses.FINEMAPPED_OUT_OF_SAMPLE_LD.value, + ) + .when( + (f.col("finemappingMethod") == "pics") + & ( + ~f.array_contains( + f.col("qualityControls"), StudyLocusQualityCheck.TOP_HIT.value + ) + ), + CredibleSetConfidenceClasses.PICSED_SUMMARY_STATS.value, + ) + .when( + (f.col("finemappingMethod") == "pics") + & ( + f.array_contains( + f.col("qualityControls"), StudyLocusQualityCheck.TOP_HIT.value + ) + ), + CredibleSetConfidenceClasses.PICSED_TOP_HIT.value, + ) + .otherwise(CredibleSetConfidenceClasses.UNKNOWN.value), + ) + + return StudyLocus( + _df=df, + _schema=self.get_schema(), + ) diff --git a/src/gentropy/dataset/study_locus_overlap.py b/src/gentropy/dataset/study_locus_overlap.py index 5f839bd9c..d4faea4cc 100644 --- a/src/gentropy/dataset/study_locus_overlap.py +++ b/src/gentropy/dataset/study_locus_overlap.py @@ -1,16 +1,19 @@ """Study locus overlap index dataset.""" + from __future__ import annotations from dataclasses import dataclass from typing import TYPE_CHECKING +import pyspark.sql.functions as f + from gentropy.common.schemas import parse_spark_schema from gentropy.dataset.dataset import Dataset if TYPE_CHECKING: + from pyspark.sql import DataFrame from pyspark.sql.types import StructType - from gentropy.dataset.study_index import StudyIndex from gentropy.dataset.study_locus import StudyLocus @@ -36,18 +39,48 @@ def get_schema(cls: type[StudyLocusOverlap]) -> StructType: @classmethod def from_associations( - cls: type[StudyLocusOverlap], study_locus: StudyLocus, study_index: StudyIndex + cls: type[StudyLocusOverlap], study_locus: StudyLocus ) -> StudyLocusOverlap: """Find the overlapping signals in a particular set of associations (StudyLocus dataset). Args: study_locus (StudyLocus): Study-locus associations to find the overlapping signals - study_index (StudyIndex): Study index to find the overlapping signals Returns: StudyLocusOverlap: Study-locus overlap dataset """ - return study_locus.find_overlaps(study_index) + return study_locus.find_overlaps() + + + def calculate_beta_ratio(self: StudyLocusOverlap) -> DataFrame: + """Calculate the beta ratio for the overlapping signals. + + Returns: + DataFrame: A dataframe containing left and right loci IDs, chromosome + and the average sign of the beta ratio + """ + return ( + # Unpack statistics column: + self.df.select("*", "statistics.*") + .drop("statistics") + # Drop any rows where the beta is null or zero + .filter( + f.col("left_beta").isNotNull() & + f.col("right_beta").isNotNull() & + (f.col("left_beta") != 0) & + (f.col("right_beta") != 0) + ) + # Calculate the beta ratio and get the sign, then calculate the average sign across all variants in the locus + .withColumn( + "betaRatioSign", + f.signum(f.col("left_beta") / f.col("right_beta")) + ) + # Aggregate beta signs: + .groupBy("leftStudyLocusId","rightStudyLocusId","chromosome") + .agg( + f.avg("betaRatioSign").alias("betaRatioSignAverage") + ) + ) def _convert_to_square_matrix(self: StudyLocusOverlap) -> StudyLocusOverlap: """Convert the dataset to a square matrix. @@ -60,7 +93,10 @@ def _convert_to_square_matrix(self: StudyLocusOverlap) -> StudyLocusOverlap: self.df.selectExpr( "leftStudyLocusId as rightStudyLocusId", "rightStudyLocusId as leftStudyLocusId", + "rightStudyType", "tagVariantId", + "chromosome", + "statistics", ) ).distinct(), _schema=self.get_schema(), diff --git a/src/gentropy/dataset/summary_statistics.py b/src/gentropy/dataset/summary_statistics.py index 442672c58..25edbeca7 100644 --- a/src/gentropy/dataset/summary_statistics.py +++ b/src/gentropy/dataset/summary_statistics.py @@ -1,4 +1,5 @@ """Summary satistics dataset.""" + from __future__ import annotations from dataclasses import dataclass @@ -6,10 +7,11 @@ import pyspark.sql.functions as f +from gentropy.common.genomic_region import GenomicRegion from gentropy.common.schemas import parse_spark_schema -from gentropy.common.utils import parse_region, split_pvalue +from gentropy.common.utils import split_pvalue +from gentropy.config import LocusBreakerClumpingConfig, WindowBasedClumpingStepConfig from gentropy.dataset.dataset import Dataset -from gentropy.method.window_based_clumping import WindowBasedClumping if TYPE_CHECKING: from pyspark.sql.types import StructType @@ -57,60 +59,114 @@ def pvalue_filter(self: SummaryStatistics, pvalue: float) -> SummaryStatistics: def window_based_clumping( self: SummaryStatistics, - distance: int = 500_000, - gwas_significance: float = 5e-8, - baseline_significance: float = 0.05, - locus_collect_distance: int | None = None, + distance: int = WindowBasedClumpingStepConfig().distance, + gwas_significance: float = WindowBasedClumpingStepConfig().gwas_significance, ) -> StudyLocus: - """Generate study-locus from summary statistics by distance based clumping + collect locus. + """Generate study-locus from summary statistics using window-based clumping. + + For more info, see [`WindowBasedClumping`][gentropy.method.window_based_clumping.WindowBasedClumping] Args: distance (int): Distance in base pairs to be used for clumping. Defaults to 500_000. gwas_significance (float, optional): GWAS significance threshold. Defaults to 5e-8. - baseline_significance (float, optional): Baseline significance threshold for inclusion in the locus. Defaults to 0.05. - locus_collect_distance (int | None): The distance to collect locus around semi-indices. If not provided, locus is not collected. Returns: - StudyLocus: Clumped study-locus containing variants based on window. + StudyLocus: Clumped study-locus optionally containing variants based on window. + Check WindowBasedClumpingStepConfig object for default values. """ - return ( - WindowBasedClumping.clump_with_locus( - self, - window_length=distance, - p_value_significance=gwas_significance, - p_value_baseline=baseline_significance, - locus_window_length=locus_collect_distance, - ) - if locus_collect_distance - else WindowBasedClumping.clump( - self, - window_length=distance, - p_value_significance=gwas_significance, - ) + from gentropy.method.window_based_clumping import WindowBasedClumping + + return WindowBasedClumping.clump( + # Before clumping, we filter the summary statistics by p-value: + self.pvalue_filter(gwas_significance), + distance=distance, + # After applying the clumping, we filter the clumped loci by the flag: + ).valid_rows(["WINDOW_CLUMPED"]) + + def locus_breaker_clumping( + self: SummaryStatistics, + baseline_pvalue_cutoff: float = LocusBreakerClumpingConfig.lbc_baseline_pvalue, + distance_cutoff: int = LocusBreakerClumpingConfig.lbc_distance_cutoff, + pvalue_cutoff: float = LocusBreakerClumpingConfig.lbc_pvalue_threshold, + flanking_distance: int = LocusBreakerClumpingConfig.lbc_flanking_distance, + ) -> StudyLocus: + """Generate study-locus from summary statistics using locus-breaker clumping method with locus boundaries. + + For more info, see [`locus_breaker`][gentropy.method.locus_breaker_clumping.LocusBreakerClumping] + + Args: + baseline_pvalue_cutoff (float, optional): Baseline significance we consider for the locus. + distance_cutoff (int, optional): Distance in base pairs to be used for clumping. + pvalue_cutoff (float, optional): GWAS significance threshold. + flanking_distance (int, optional): Flank distance in base pairs to be used for clumping. + + Returns: + StudyLocus: Clumped study-locus optionally containing variants based on window. + Check LocusBreakerClumpingConfig object for default values. + """ + from gentropy.method.locus_breaker_clumping import LocusBreakerClumping + + return LocusBreakerClumping.locus_breaker( + self, + baseline_pvalue_cutoff, + distance_cutoff, + pvalue_cutoff, + flanking_distance, ) - def exclude_region(self: SummaryStatistics, region: str) -> SummaryStatistics: + def exclude_region( + self: SummaryStatistics, region: GenomicRegion + ) -> SummaryStatistics: """Exclude a region from the summary stats dataset. Args: - region (str): region given in "chr##:#####-####" format + region (GenomicRegion): Genomic region to be excluded. Returns: SummaryStatistics: filtered summary statistics. """ - (chromosome, start_position, end_position) = parse_region(region) - return SummaryStatistics( _df=( self.df.filter( ~( - (f.col("chromosome") == chromosome) + (f.col("chromosome") == region.chromosome) & ( - (f.col("position") >= start_position) - & (f.col("position") <= end_position) + (f.col("position") >= region.start) + & (f.col("position") <= region.end) ) ) ) ), _schema=SummaryStatistics.get_schema(), ) + + def sanity_filter(self: SummaryStatistics) -> SummaryStatistics: + """The function filters the summary statistics by sanity filters. + + The function filters the summary statistics by the following filters: + - The p-value should be less than 1. + - The pValueMantissa should be greater than 0. + - The beta should not be equal 0. + - The p-value, beta and se should not be NaN. + - The se should be positive. + - The beta and se should not be infinite. + + Returns: + SummaryStatistics: The filtered summary statistics. + """ + gwas_df = self._df + gwas_df = gwas_df.dropna( + subset=["beta", "standardError", "pValueMantissa", "pValueExponent"] + ) + gwas_df = gwas_df.filter((f.col("beta") != 0) & (f.col("standardError") > 0)) + gwas_df = gwas_df.filter( + (f.col("pValueMantissa") * 10 ** f.col("pValueExponent") < 1) + & (f.col("pValueMantissa") > 0) + ) + cols = ["beta", "standardError"] + summary_stats = SummaryStatistics( + _df=gwas_df, + _schema=SummaryStatistics.get_schema(), + ).drop_infinity_values(*cols) + + return summary_stats diff --git a/src/gentropy/dataset/v2g.py b/src/gentropy/dataset/v2g.py deleted file mode 100644 index 04bad2113..000000000 --- a/src/gentropy/dataset/v2g.py +++ /dev/null @@ -1,51 +0,0 @@ -"""V2G dataset.""" -from __future__ import annotations - -from dataclasses import dataclass -from typing import TYPE_CHECKING - -import pyspark.sql.functions as f - -from gentropy.common.schemas import parse_spark_schema -from gentropy.dataset.dataset import Dataset - -if TYPE_CHECKING: - from pyspark.sql.types import StructType - - from gentropy.dataset.gene_index import GeneIndex - - -@dataclass -class V2G(Dataset): - """Variant-to-gene (V2G) evidence dataset. - - A variant-to-gene (V2G) evidence is understood as any piece of evidence that supports the association of a variant with a likely causal gene. The evidence can sometimes be context-specific and refer to specific `biofeatures` (e.g. cell types) - """ - - @classmethod - def get_schema(cls: type[V2G]) -> StructType: - """Provides the schema for the V2G dataset. - - Returns: - StructType: Schema for the V2G dataset - """ - return parse_spark_schema("v2g.json") - - def filter_by_genes(self: V2G, genes: GeneIndex) -> V2G: - """Filter V2G dataset by genes. - - Args: - genes (GeneIndex): Gene index dataset to filter by - - Returns: - V2G: V2G dataset filtered by genes - """ - self.df = self._df.join(genes.df.select("geneId"), on="geneId", how="inner") - return self - - def extract_distance_tss_minimum(self: V2G) -> None: - """Extract minimum distance to TSS.""" - self.df = self._df.filter(f.col("distance")).withColumn( - "distanceTssMinimum", - f.expr("min(distTss) OVER (PARTITION BY studyLocusId)"), - ) diff --git a/src/gentropy/dataset/variant_annotation.py b/src/gentropy/dataset/variant_annotation.py deleted file mode 100644 index 4854a7edd..000000000 --- a/src/gentropy/dataset/variant_annotation.py +++ /dev/null @@ -1,227 +0,0 @@ -"""Variant annotation dataset.""" -from __future__ import annotations - -from dataclasses import dataclass -from typing import TYPE_CHECKING - -import pyspark.sql.functions as f - -from gentropy.common.schemas import parse_spark_schema -from gentropy.common.spark_helpers import ( - get_record_with_maximum_value, - normalise_column, -) -from gentropy.dataset.dataset import Dataset -from gentropy.dataset.v2g import V2G - -if TYPE_CHECKING: - from pyspark.sql import Column, DataFrame - from pyspark.sql.types import StructType - - from gentropy.dataset.gene_index import GeneIndex - - -@dataclass -class VariantAnnotation(Dataset): - """Dataset with variant-level annotations.""" - - @classmethod - def get_schema(cls: type[VariantAnnotation]) -> StructType: - """Provides the schema for the VariantAnnotation dataset. - - Returns: - StructType: Schema for the VariantAnnotation dataset - """ - return parse_spark_schema("variant_annotation.json") - - def max_maf(self: VariantAnnotation) -> Column: - """Maximum minor allele frequency accross all populations. - - Returns: - Column: Maximum minor allele frequency accross all populations. - """ - return f.array_max( - f.transform( - self.df.alleleFrequencies, - lambda af: f.when( - af.alleleFrequency > 0.5, 1 - af.alleleFrequency - ).otherwise(af.alleleFrequency), - ) - ) - - def filter_by_variant_df( - self: VariantAnnotation, df: DataFrame - ) -> VariantAnnotation: - """Filter variant annotation dataset by a variant dataframe. - - Args: - df (DataFrame): A dataframe of variants - - Returns: - VariantAnnotation: A filtered variant annotation dataset - """ - self.df = self._df.join( - f.broadcast(df.select("variantId", "chromosome")), - on=["variantId", "chromosome"], - how="inner", - ) - return self - - def get_transcript_consequence_df( - self: VariantAnnotation, gene_index: GeneIndex | None = None - ) -> DataFrame: - """Dataframe of exploded transcript consequences. - - Optionally the trancript consequences can be reduced to the universe of a gene index. - - Args: - gene_index (GeneIndex | None): A gene index. Defaults to None. - - Returns: - DataFrame: A dataframe exploded by transcript consequences with the columns variantId, chromosome, transcriptConsequence - """ - # exploding the array removes records without VEP annotation - transript_consequences = self.df.withColumn( - "transcriptConsequence", f.explode("vep.transcriptConsequences") - ).select( - "variantId", - "chromosome", - "position", - "transcriptConsequence", - f.col("transcriptConsequence.geneId").alias("geneId"), - ) - if gene_index: - transript_consequences = transript_consequences.join( - f.broadcast(gene_index.df), - on=["chromosome", "geneId"], - ) - return transript_consequences.persist() - - def get_most_severe_vep_v2g( - self: VariantAnnotation, - vep_consequences: DataFrame, - gene_index: GeneIndex, - ) -> V2G: - """Creates a dataset with variant to gene assignments based on VEP's predicted consequence of the transcript. - - Optionally the trancript consequences can be reduced to the universe of a gene index. - - Args: - vep_consequences (DataFrame): A dataframe of VEP consequences - gene_index (GeneIndex): A gene index to filter by. Defaults to None. - - Returns: - V2G: High and medium severity variant to gene assignments - """ - return V2G( - _df=self.get_transcript_consequence_df(gene_index) - .select( - "variantId", - "chromosome", - f.col("transcriptConsequence.geneId").alias("geneId"), - f.explode("transcriptConsequence.consequenceTerms").alias("label"), - f.lit("vep").alias("datatypeId"), - f.lit("variantConsequence").alias("datasourceId"), - ) - .join( - f.broadcast(vep_consequences), - on="label", - how="inner", - ) - .drop("label") - .filter(f.col("score") != 0) - # A variant can have multiple predicted consequences on a transcript, the most severe one is selected - .transform( - lambda df: get_record_with_maximum_value( - df, ["variantId", "geneId"], "score" - ) - ), - _schema=V2G.get_schema(), - ) - - def get_plof_v2g(self: VariantAnnotation, gene_index: GeneIndex) -> V2G: - """Creates a dataset with variant to gene assignments with a flag indicating if the variant is predicted to be a loss-of-function variant by the LOFTEE algorithm. - - Optionally the trancript consequences can be reduced to the universe of a gene index. - - Args: - gene_index (GeneIndex): A gene index to filter by. - - Returns: - V2G: variant to gene assignments from the LOFTEE algorithm - """ - return V2G( - _df=( - self.get_transcript_consequence_df(gene_index) - .filter(f.col("transcriptConsequence.lof").isNotNull()) - .withColumn( - "isHighQualityPlof", - f.when(f.col("transcriptConsequence.lof") == "HC", True).when( - f.col("transcriptConsequence.lof") == "LC", False - ), - ) - .withColumn( - "score", - f.when(f.col("isHighQualityPlof"), 1.0).when( - ~f.col("isHighQualityPlof"), 0 - ), - ) - .select( - "variantId", - "chromosome", - "geneId", - "isHighQualityPlof", - f.col("score"), - f.lit("vep").alias("datatypeId"), - f.lit("loftee").alias("datasourceId"), - ) - ), - _schema=V2G.get_schema(), - ) - - def get_distance_to_tss( - self: VariantAnnotation, - gene_index: GeneIndex, - max_distance: int = 500_000, - ) -> V2G: - """Extracts variant to gene assignments for variants falling within a window of a gene's TSS. - - Args: - gene_index (GeneIndex): A gene index to filter by. - max_distance (int): The maximum distance from the TSS to consider. Defaults to 500_000. - - Returns: - V2G: variant to gene assignments with their distance to the TSS - """ - return V2G( - _df=( - self.df.alias("variant") - .join( - f.broadcast(gene_index.locations_lut()).alias("gene"), - on=[ - f.col("variant.chromosome") == f.col("gene.chromosome"), - f.abs(f.col("variant.position") - f.col("gene.tss")) - <= max_distance, - ], - how="inner", - ) - .withColumn( - "distance", f.abs(f.col("variant.position") - f.col("gene.tss")) - ) - .withColumn( - "inverse_distance", - max_distance - f.col("distance"), - ) - .transform(lambda df: normalise_column(df, "inverse_distance", "score")) - .select( - "variantId", - f.col("variant.chromosome").alias("chromosome"), - "distance", - "geneId", - "score", - f.lit("distance").alias("datatypeId"), - f.lit("canonical_tss").alias("datasourceId"), - ) - ), - _schema=V2G.get_schema(), - ) diff --git a/src/gentropy/dataset/variant_index.py b/src/gentropy/dataset/variant_index.py index 4bafbc288..9e8740aa6 100644 --- a/src/gentropy/dataset/variant_index.py +++ b/src/gentropy/dataset/variant_index.py @@ -1,78 +1,300 @@ -"""Variant index dataset.""" +"""Dataset definition for variant index.""" + from __future__ import annotations -from dataclasses import dataclass +from dataclasses import dataclass, field from typing import TYPE_CHECKING import pyspark.sql.functions as f +import pyspark.sql.types as t from gentropy.common.schemas import parse_spark_schema -from gentropy.common.spark_helpers import nullify_empty_array +from gentropy.common.spark_helpers import ( + get_nested_struct_schema, + rename_all_columns, + safe_array_union, +) from gentropy.dataset.dataset import Dataset -from gentropy.dataset.study_locus import StudyLocus if TYPE_CHECKING: + from pyspark.sql import Column, DataFrame from pyspark.sql.types import StructType - from gentropy.dataset.variant_annotation import VariantAnnotation - @dataclass class VariantIndex(Dataset): - """Variant index dataset. + """Dataset for representing variants and methods applied on them.""" + + id_threshold: int = field(default=300) + + def __post_init__(self: VariantIndex) -> None: + """Forcing the presence of empty arrays even if the schema allows missing values. + + To bring in annotations from other sources, we use the `array_union()` function. However it assumes + both columns have arrays (not just the array schema!). If one of the array is null, the union + is nullified. This needs to be avoided. + """ + # Calling dataset's post init to validate schema: + super().__post_init__() + + # Composing a list of expressions to replace nulls with empty arrays if the schema assumes: + array_columns = { + column.name: f.when(f.col(column.name).isNull(), f.array()).otherwise( + f.col(column.name) + ) + for column in self.df.schema + if "ArrayType" in column.dataType.__str__() + } - Variant index dataset is the result of intersecting the variant annotation dataset with the variants with V2D available information. - """ + # Not returning, but changing the data: + self.df = self.df.withColumns(array_columns).withColumn( + # Hashing long variant identifiers: + "variantId", + self.hash_long_variant_ids( + f.col("variantId"), + f.col("chromosome"), + f.col("position"), + self.id_threshold, + ), + ) @classmethod def get_schema(cls: type[VariantIndex]) -> StructType: - """Provides the schema for the VariantIndex dataset. + """Provides the schema for the variant index dataset. Returns: StructType: Schema for the VariantIndex dataset """ return parse_spark_schema("variant_index.json") - @classmethod - def from_variant_annotation( - cls: type[VariantIndex], - variant_annotation: VariantAnnotation, - study_locus: StudyLocus, - ) -> VariantIndex: - """Initialise VariantIndex from pre-existing variant annotation dataset. + @staticmethod + def hash_long_variant_ids( + variant_id: Column, chromosome: Column, position: Column, threshold: int + ) -> Column: + """Hash long variant identifiers. Args: - variant_annotation (VariantAnnotation): Variant annotation dataset - study_locus (StudyLocus): Study locus dataset with the variants to intersect with the variant annotation dataset + variant_id (Column): Column containing variant identifiers. + chromosome (Column): Chromosome column. + position (Column): position column. + threshold (int): Above this limit, a hash will be generated. Returns: - VariantIndex: Variant index dataset + Column: Hashed variant identifiers for long variants. + + Examples: + >>> ( + ... spark.createDataFrame([('v_short', 'x', 23),('v_looooooong', '23', 23), ('no_chrom', None, None), (None, None, None)], ['variantId', 'chromosome', 'position']) + ... .select('variantId', VariantIndex.hash_long_variant_ids(f.col('variantId'), f.col('chromosome'), f.col('position'), 10).alias('hashedVariantId')) + ... .show(truncate=False) + ... ) + +------------+--------------------------------------------+ + |variantId |hashedVariantId | + +------------+--------------------------------------------+ + |v_short |v_short | + |v_looooooong|OTVAR_23_23_3749d019d645894770c364992ae70a05| + |no_chrom |OTVAR_41acfcd7d4fd523b33600b504914ef25 | + |null |null | + +------------+--------------------------------------------+ + """ - unchanged_cols = [ - "variantId", - "chromosome", - "position", - "referenceAllele", - "alternateAllele", - "chromosomeB37", - "positionB37", - "alleleType", - "alleleFrequencies", - "inSilicoPredictors", - ] - va_slimmed = variant_annotation.filter_by_variant_df( - study_locus.unique_variants_in_locus() + return ( + # If either the position or the chromosome is missing, we hash the identifier: + f.when( + chromosome.isNull() | position.isNull(), + f.concat( + f.lit("OTVAR_"), + f.md5(variant_id).cast("string"), + ), + ) + # If chromosome and position are given, but alleles are too long, create hash: + .when( + f.length(variant_id) >= threshold, + f.concat_ws( + "_", + f.lit("OTVAR"), + chromosome, + position, + f.md5(variant_id).cast("string"), + ), + ) + # Missing and regular variant identifiers are left unchanged: + .otherwise(variant_id) ) - return cls( + + def add_annotation( + self: VariantIndex, annotation_source: VariantIndex + ) -> VariantIndex: + """Import annotation from an other variant index dataset. + + At this point the annotation can be extended with extra cross-references, + in-silico predictions and allele frequencies. + + Args: + annotation_source (VariantIndex): Annotation to add to the dataset + + Returns: + VariantIndex: VariantIndex dataset with the annotation added + """ + # Prefix for renaming columns: + prefix = "annotation_" + + # Generate select expressions that to merge and import columns from annotation: + select_expressions = [] + + # Collect columns by iterating over the variant index schema: + for schema_field in VariantIndex.get_schema(): + column = schema_field.name + + # If an annotation column can be found in both datasets: + if (column in self.df.columns) and (column in annotation_source.df.columns): + # Arrays are merged: + if isinstance(schema_field.dataType, t.ArrayType): + fields_order = None + if isinstance(schema_field.dataType.elementType, t.StructType): + # Extract the schema of the array to get the order of the fields: + array_schema = [ + schema_field + for schema_field in VariantIndex.get_schema().fields + if schema_field.name == column + ][0].dataType + fields_order = get_nested_struct_schema( + array_schema + ).fieldNames() + select_expressions.append( + safe_array_union( + f.col(column), f.col(f"{prefix}{column}"), fields_order + ).alias(column) + ) + # Non-array columns are coalesced: + else: + select_expressions.append( + f.coalesce(f.col(column), f.col(f"{prefix}{column}")).alias( + column + ) + ) + # If the column is only found in the annotation dataset rename it: + elif column in annotation_source.df.columns: + select_expressions.append(f.col(f"{prefix}{column}").alias(column)) + # If the column is only found in the main dataset: + elif column in self.df.columns: + select_expressions.append(f.col(column)) + # VariantIndex columns not found in either dataset are ignored. + + # Join the annotation to the dataset: + return VariantIndex( _df=( - va_slimmed.df.select( - *unchanged_cols, - f.col("vep.mostSevereConsequence").alias("mostSevereConsequence"), - # filters/rsid are arrays that can be empty, in this case we convert them to null - nullify_empty_array(f.col("rsIds")).alias("rsIds"), + f.broadcast(self.df) + .join( + rename_all_columns(annotation_source.df, prefix), + on=[f.col("variantId") == f.col(f"{prefix}variantId")], + how="left", ) - .repartition(400, "chromosome") - .sortWithinPartitions("chromosome", "position") + .select(*select_expressions) + ), + _schema=self.schema, + ) + + def max_maf(self: VariantIndex) -> Column: + """Maximum minor allele frequency accross all populations assuming all variants biallelic. + + Returns: + Column: Maximum minor allele frequency accross all populations. + + Raises: + ValueError: Allele frequencies are not present in the dataset. + """ + if "alleleFrequencies" not in self.df.columns: + raise ValueError("Allele frequencies are not present in the dataset.") + + return f.array_max( + f.transform( + self.df.alleleFrequencies, + lambda af: f.when( + af.alleleFrequency > 0.5, 1 - af.alleleFrequency + ).otherwise(af.alleleFrequency), + ) + ) + + def filter_by_variant(self: VariantIndex, df: DataFrame) -> VariantIndex: + """Filter variant annotation dataset by a variant dataframe. + + Args: + df (DataFrame): A dataframe of variants + + Returns: + VariantIndex: A filtered variant annotation dataset + """ + join_columns = ["variantId", "chromosome"] + + assert all( + col in df.columns for col in join_columns + ), "The variant dataframe must contain the columns 'variantId' and 'chromosome'." + + return VariantIndex( + _df=self._df.join( + f.broadcast(df.select(*join_columns).distinct()), + on=join_columns, + how="inner", ), - _schema=cls.get_schema(), + _schema=self.schema, + ) + + def get_distance_to_gene( + self: VariantIndex, + *, + distance_type: str = "distanceFromTss", + max_distance: int = 500_000, + ) -> DataFrame: + """Extracts variant to gene assignments for variants falling within a window of a gene's TSS or footprint. + + Args: + distance_type (str): The type of distance to use. Can be "distanceFromTss" or "distanceFromFootprint". Defaults to "distanceFromTss". + max_distance (int): The maximum distance to consider. Defaults to 500_000, the default window size for VEP. + + Returns: + DataFrame: A dataframe with the distance between a variant and a gene's TSS or footprint. + + Raises: + ValueError: Invalid distance type. + """ + if distance_type not in {"distanceFromTss", "distanceFromFootprint"}: + raise ValueError( + f"Invalid distance_type: {distance_type}. Must be 'distanceFromTss' or 'distanceFromFootprint'." + ) + df = self.df.select( + "variantId", f.explode("transcriptConsequences").alias("tc") + ).select("variantId", "tc.targetId", f"tc.{distance_type}") + if max_distance == 500_000: + return df + elif max_distance < 500_000: + return df.filter(f"{distance_type} <= {max_distance}") + else: + raise ValueError( + f"max_distance must be less than 500_000. Got {max_distance}." + ) + + def get_loftee(self: VariantIndex) -> DataFrame: + """Returns a dataframe with a flag indicating whether a variant is predicted to cause loss of function in a gene. The source of this information is the LOFTEE algorithm (https://github.com/konradjk/loftee). + + !!! note, "This will return a filtered dataframe with only variants that have been annotated by LOFTEE." + + Returns: + DataFrame: variant to gene assignments from the LOFTEE algorithm + """ + return ( + self.df.select("variantId", f.explode("transcriptConsequences").alias("tc")) + .filter(f.col("tc.lofteePrediction").isNotNull()) + .withColumn( + "isHighQualityPlof", + f.when(f.col("tc.lofteePrediction") == "HC", True).when( + f.col("tc.lofteePrediction") == "LC", False + ), + ) + .select( + "variantId", + f.col("tc.targetId"), + f.col("tc.lofteePrediction"), + "isHighQualityPlof", + ) ) diff --git a/src/gentropy/datasource/biosample_ontologies/__init__.py b/src/gentropy/datasource/biosample_ontologies/__init__.py new file mode 100644 index 000000000..d3fa6b416 --- /dev/null +++ b/src/gentropy/datasource/biosample_ontologies/__init__.py @@ -0,0 +1,3 @@ +"""Biosample index data source.""" + +from __future__ import annotations diff --git a/src/gentropy/datasource/biosample_ontologies/utils.py b/src/gentropy/datasource/biosample_ontologies/utils.py new file mode 100644 index 000000000..2d9bb6bc4 --- /dev/null +++ b/src/gentropy/datasource/biosample_ontologies/utils.py @@ -0,0 +1,142 @@ +"""Utility functions for Biosample ontology processing.""" +import re + +from pyspark.sql import DataFrame, SparkSession +from pyspark.sql import functions as f +from pyspark.sql.types import ArrayType, StringType +from pyspark.sql.window import Window + +from gentropy.dataset.biosample_index import BiosampleIndex + + +def extract_ontology_from_json( + ontology_json : str, + spark : SparkSession +) -> BiosampleIndex: + """Extracts the ontology information from a JSON file. Currently only supports Uberon and Cell Ontology. + + Args: + ontology_json (str): Path to the JSON file containing the ontology information. + spark (SparkSession): Spark session. + + Returns: + BiosampleIndex: Parsed and annotated biosample index table. + """ + + def json_graph_traversal( + df : DataFrame, + node_col : str, + link_col: str, + traversal_type: str + ) -> DataFrame: + """Traverse a graph represented in a DataFrame to find all ancestors or descendants. + + Args: + df (DataFrame): DataFrame containing the graph data. + node_col (str): Column name for the node. + link_col (str): Column name for the link. + traversal_type (str): Type of traversal - "ancestors" or "descendants". + + Returns: + DataFrame: DataFrame with the result column added. + """ + # Collect graph data as a map + graph_map = df.select(node_col, link_col).rdd.collectAsMap() + broadcasted_graph = spark.sparkContext.broadcast(graph_map) + + def get_relationships( + node : str + ) -> list[str]: + """Get all relationships for a given node. + + Args: + node (str): Node ID. + + Returns: + list[str]: List of relationships. + """ + relationships = set() + stack = [node] + while stack: + current = stack.pop() + if current in broadcasted_graph.value: + current_links = broadcasted_graph.value[current] + stack.extend(current_links) + relationships.update(current_links) + return list(relationships) + + # Choose column name based on traversal type + result_col = "ancestors" if traversal_type == "ancestors" else "descendants" + + # Register the UDF based on traversal type + relationship_udf = f.udf(get_relationships, ArrayType(StringType())) + + # Apply the UDF to create the result column + return df.withColumn(result_col, relationship_udf(f.col(node_col))) + + # Load the JSON file + df = spark.read.json(ontology_json, multiLine=True) + + # Exploding the 'graphs' array to make individual records easier to access + df_graphs = df.select(f.explode_outer("graphs").alias("graph")) + + # Exploding the 'nodes' array within each graph + df_nodes = df_graphs.select( + f.col("graph.id").alias("graph_id"), + f.explode_outer("graph.nodes").alias("node")) + + # Exploding the 'edges' array within each graph for relationship data + df_edges = df_graphs.select( + f.col("graph.id").alias("graph_id"), + f.explode_outer("graph.edges").alias("edge") + ).select( + f.col("edge.sub").alias("subject"), + f.col("edge.pred").alias("predicate"), + f.col("edge.obj").alias("object") + ) + + # Remove certain URL prefixes from IDs + urls_to_remove = [ + "http://purl.obolibrary.org/obo/", + "http://www.ebi.ac.uk/efo/" + ] + # Create a regex pattern that matches any of the URLs + escaped_urls_pattern = "|".join([re.escape(url) for url in urls_to_remove]) + + + df_edges = df_edges.withColumn("subject", f.regexp_replace(f.col("subject"), escaped_urls_pattern, "")) + df_edges = df_edges.withColumn("object", f.regexp_replace(f.col("object"), escaped_urls_pattern, "")) + + # Extract the relevant information from the nodes + transformed_df = df_nodes.select( + f.regexp_replace(f.col("node.id"), escaped_urls_pattern, "").alias("biosampleId"), + f.coalesce(f.col("node.lbl"), f.col("node.id")).alias("biosampleName"), + f.col("node.meta.definition.val").alias("description"), + f.collect_set(f.col("node.meta.xrefs.val")).over(Window.partitionBy("node.id")).getItem(0).alias("xrefs"), + f.collect_set(f.col("node.meta.synonyms.val")).over(Window.partitionBy("node.id")).getItem(0).alias("synonyms")) + + + # Extract the relationships from the edges + # Prepare relationship-specific DataFrames + df_parents = df_edges.filter(f.col("predicate") == "is_a").select("subject", "object").withColumnRenamed("object", "parent") + df_children = df_edges.filter(f.col("predicate") == "is_a").select("object", "subject").withColumnRenamed("subject", "child") + + # Aggregate relationships back to nodes + df_parents_grouped = df_parents.groupBy("subject").agg(f.array_distinct(f.collect_list("parent")).alias("parents")) + df_children_grouped = df_children.groupBy("object").agg(f.array_distinct(f.collect_list("child")).alias("children")) + + # Get all ancestors + df_with_ancestors = json_graph_traversal(df_parents_grouped, "subject", "parents", "ancestors") + # Get all descendants + df_with_descendants = json_graph_traversal(df_children_grouped, "object", "children", "descendants") + + # Join the ancestor and descendant DataFrames + df_with_relationships = df_with_ancestors.join(df_with_descendants, df_with_ancestors.subject == df_with_descendants.object, "full_outer").withColumn("biosampleId", f.coalesce(df_with_ancestors.subject, df_with_descendants.object)).drop("subject", "object") + + # Join the original DataFrame with the relationship DataFrame + final_df = transformed_df.join(df_with_relationships, ["biosampleId"], "left") + + return BiosampleIndex( + _df=final_df, + _schema=BiosampleIndex.get_schema() + ) diff --git a/src/gentropy/datasource/ensembl/__init__.py b/src/gentropy/datasource/ensembl/__init__.py new file mode 100644 index 000000000..e20ce9a95 --- /dev/null +++ b/src/gentropy/datasource/ensembl/__init__.py @@ -0,0 +1,3 @@ +"""Ensembl's Variant Effect Predictor datasource.""" + +from __future__ import annotations diff --git a/src/gentropy/datasource/ensembl/api.py b/src/gentropy/datasource/ensembl/api.py new file mode 100644 index 000000000..eb42b3166 --- /dev/null +++ b/src/gentropy/datasource/ensembl/api.py @@ -0,0 +1,95 @@ +"""Methods that interact with Ensembl's API.""" + +from typing import Any + + +def fetch_coordinates_from_rsids( + rsids: list[str], batch_size: int = 200, pause_time: int = 1 +) -> dict[str, list[str]]: + """Batch query the Ensembl API to extract variant coordinates from a list of rsIds. + + Args: + rsids (list[str]): List of rsIDs + batch_size (int): Number of rsIDs to process in each batch. + pause_time (int): Time to pause between batches. + + Returns: + dict[str, list[str]]: Dictionary with rsID as key and list of variant IDs as value. + + Raises: + Exception: If an error occurs while processing the batches. + + Example: + >>> fetch_coordinates_from_rsids(["rs75493593"]) # doctest: +SKIP + {'rs75493593': ['17_7041768_G_C', '17_7041768_G_T']} + """ + + def _ensembl_batch_request(rsids: list[str]) -> dict[str, dict[str, Any]]: + """Access the batch endpoint of Ensembl. + + Args: + rsids (list[str]): List of rsIDs + + Returns: + dict[str, dict[str, Any]]: Dictionary with rsID as key and variant data as value. + """ + url = "https://rest.ensembl.org/variation/human" + headers = {"Content-Type": "application/json", "Accept": "application/json"} + data = json.dumps({"ids": rsids}).encode("utf-8") + + req = request.Request(url, data=data, headers=headers, method="POST") + with request.urlopen(req) as response: + response_data = response.read().decode() + + return json.loads(response_data) + + def _parse_response( + response: dict[str, dict[str, Any]], + ) -> dict[str, list[str]]: + """Parse the response from the Ensembl API. + + Args: + response (dict[str, dict[str, Any]]): Response from the Ensembl API. This is a dictionary where the key is the rsID and the value is the variant data. + + Returns: + dict[str, list[str]]: Dictionary with rsID as key and list of variant IDs as value. + """ + parsed_results = {} + valid_chromosomes = [str(i) for i in range(1, 23)] + ["X", "Y", "MT"] + for rsid in response: + if response[rsid]["mappings"]: + for data in response[rsid]["mappings"]: + if data["seq_region_name"] in valid_chromosomes: + chrom = data["seq_region_name"] + pos = data["start"] + # The first element of allele_string contains the reference allele and the rest are the alternate alleles + ref_allele = data["allele_string"].split("/")[0] + alt_alleles = data["allele_string"].split("/")[1:] + variant_ids = [ + f"{chrom}_{pos}_{ref_allele}_{alt_allele}" + for alt_allele in alt_alleles + ] + parsed_results[rsid] = variant_ids + else: + continue + return parsed_results + + import json + import time + from urllib import request + + all_results = {} + + # Chunk the rsids into batches + for i in range(0, len(rsids), batch_size): + batch = rsids[i : i + batch_size] + try: + variant_data = _ensembl_batch_request(batch) + all_results.update(_parse_response(variant_data)) + + except Exception as e: + raise Exception(f"Error processing batch {i // batch_size + 1}: {e}") from e + + time.sleep(pause_time) + + return all_results diff --git a/src/gentropy/datasource/ensembl/vep_parser.py b/src/gentropy/datasource/ensembl/vep_parser.py new file mode 100644 index 000000000..01c820513 --- /dev/null +++ b/src/gentropy/datasource/ensembl/vep_parser.py @@ -0,0 +1,813 @@ +"""Generating variant index based on Esembl's Variant Effect Predictor output.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from pyspark.sql import SparkSession +from pyspark.sql import functions as f +from pyspark.sql import types as t + +from gentropy.common.schemas import parse_spark_schema +from gentropy.common.spark_helpers import ( + enforce_schema, + get_nested_struct_schema, + map_column_by_dictionary, + order_array_of_structs_by_field, + order_array_of_structs_by_two_fields, +) +from gentropy.dataset.variant_index import VariantIndex + +if TYPE_CHECKING: + from pyspark.sql import Column, DataFrame + +from gentropy.config import VariantIndexConfig + + +class VariantEffectPredictorParser: + """Collection of methods to parse VEP output in json format.""" + + # NOTE: Due to the fact that the comparison of the xrefs is done om the base of rsids + # if the field `colocalised_variants` have multiple rsids, this extracting xrefs will result in + # an array of xref structs, rather then the struct itself. + + DBXREF_SCHEMA = VariantIndex.get_schema()["dbXrefs"].dataType + + # Schema description of the in silico predictor object: + IN_SILICO_PREDICTOR_SCHEMA = get_nested_struct_schema( + VariantIndex.get_schema()["inSilicoPredictors"] + ) + + # Schema for the allele frequency column: + ALLELE_FREQUENCY_SCHEMA = VariantIndex.get_schema()["alleleFrequencies"].dataType + + @staticmethod + def get_schema() -> t.StructType: + """Return the schema of the VEP output. + + Returns: + t.StructType: VEP output schema. + + Examples: + >>> type(VariantEffectPredictorParser.get_schema()) + + """ + return parse_spark_schema("vep_json_output.json") + + @classmethod + def extract_variant_index_from_vep( + cls: type[VariantEffectPredictorParser], + spark: SparkSession, + vep_output_path: str | list[str], + hash_threshold: int, + **kwargs: bool | float | int | str | None, + ) -> VariantIndex: + """Extract variant index from VEP output. + + Args: + spark (SparkSession): Spark session. + vep_output_path (str | list[str]): Path to the VEP output. + hash_threshold (int): Threshold above which variant identifiers will be hashed. + **kwargs (bool | float | int | str | None): Additional arguments to pass to spark.read.json. + + Returns: + VariantIndex: Variant index dataset. + + Raises: + ValueError: Failed reading file or if the dataset is empty. + """ + # To speed things up and simplify the json structure, read data following an expected schema: + vep_schema = cls.get_schema() + + try: + vep_data = spark.read.json(vep_output_path, schema=vep_schema, **kwargs) + except ValueError as e: + raise ValueError(f"Failed reading file: {vep_output_path}.") from e + + if vep_data.isEmpty(): + raise ValueError(f"The dataset is empty: {vep_output_path}") + + # Convert to VariantAnnotation dataset: + return VariantIndex( + _df=VariantEffectPredictorParser.process_vep_output( + vep_data, hash_threshold + ), + _schema=VariantIndex.get_schema(), + id_threshold=hash_threshold, + ) + + @staticmethod + def _extract_ensembl_xrefs(colocated_variants: Column) -> Column: + """Extract rs identifiers and build cross reference to Ensembl's variant page. + + Args: + colocated_variants (Column): Colocated variants field from VEP output. + + Returns: + Column: List of dbXrefs for rs identifiers. + """ + return VariantEffectPredictorParser._generate_dbxrefs( + VariantEffectPredictorParser._colocated_variants_to_rsids( + colocated_variants + ), + "ensembl_variation", + ) + + @enforce_schema(DBXREF_SCHEMA) + @staticmethod + def _generate_dbxrefs(ids: Column, source: str) -> Column: + """Convert a list of variant identifiers to dbXrefs given the source label. + + Identifiers are cast to strings, then Null values are filtered out of the id list. + + Args: + ids (Column): List of variant identifiers. + source (str): Source label for the dbXrefs. + + Returns: + Column: List of dbXrefs. + + Examples: + >>> ( + ... spark.createDataFrame([('rs12',),(None,)], ['test_id']) + ... .select(VariantEffectPredictorParser._generate_dbxrefs(f.array(f.col('test_id')), "ensemblVariation").alias('col')) + ... .show(truncate=False) + ... ) + +--------------------------+ + |col | + +--------------------------+ + |[{rs12, ensemblVariation}]| + |[] | + +--------------------------+ + + >>> ( + ... spark.createDataFrame([('rs12',),(None,)], ['test_id']) + ... .select(VariantEffectPredictorParser._generate_dbxrefs(f.array(f.col('test_id')), "ensemblVariation").alias('col')) + ... .first().col[0].asDict() + ... ) + {'id': 'rs12', 'source': 'ensemblVariation'} + """ + ids = f.filter(ids, lambda id: id.isNotNull()) + xref_column = f.transform( + ids, + lambda id: f.struct( + id.cast(t.StringType()).alias("id"), f.lit(source).alias("source") + ), + ) + + return f.when(xref_column.isNull(), f.array()).otherwise(xref_column) + + @staticmethod + def _colocated_variants_to_rsids(colocated_variants: Column) -> Column: + """Extract rs identifiers from the colocated variants VEP field. + + Args: + colocated_variants (Column): Colocated variants field from VEP output. + + Returns: + Column: List of rs identifiers. + + Examples: + >>> data = [('s1', 'rs1'),('s1', 'rs2'),('s1', 'rs3'),('s2', None),] + >>> ( + ... spark.createDataFrame(data, ['s','v']) + ... .groupBy('s') + ... .agg(f.collect_list(f.struct(f.col('v').alias('id'))).alias('cv')) + ... .select(VariantEffectPredictorParser._colocated_variants_to_rsids(f.col('cv')).alias('rsIds'),) + ... .show(truncate=False) + ... ) + +---------------+ + |rsIds | + +---------------+ + |[rs1, rs2, rs3]| + |[null] | + +---------------+ + + """ + return f.when( + colocated_variants.isNotNull(), + f.transform( + colocated_variants, lambda variant: variant.getItem("id").alias("id") + ), + ).alias("rsIds") + + @staticmethod + def _extract_omim_xrefs(colocated_variants: Column) -> Column: + """Build xdbRefs for OMIM identifiers. + + OMIM identifiers are extracted from the colocated variants field, casted to strings and formatted as dbXrefs. + + Args: + colocated_variants (Column): Colocated variants field from VEP output. + + Returns: + Column: List of dbXrefs for OMIM identifiers. + + Examples: + >>> data = [('234234.32', 'rs1', 's1',),(None, 'rs1', 's1',),] + >>> ( + ... spark.createDataFrame(data, ['id', 'rsid', 's']) + ... .groupBy('s') + ... .agg(f.collect_list(f.struct(f.struct(f.array(f.col('id')).alias('OMIM')).alias('var_synonyms'),f.col('rsid').alias('id'),),).alias('cv'),).select(VariantEffectPredictorParser._extract_omim_xrefs(f.col('cv')).alias('dbXrefs')) + ... .show(truncate=False) + ... ) + +-------------------+ + |dbXrefs | + +-------------------+ + |[{234234#32, omim}]| + +-------------------+ + + """ + variants_w_omim_ref = f.filter( + colocated_variants, + lambda variant: variant.getItem("var_synonyms").getItem("OMIM").isNotNull(), + ) + + omim_var_ids = f.transform( + variants_w_omim_ref, + lambda var: f.transform( + var.getItem("var_synonyms").getItem("OMIM"), + lambda var: f.regexp_replace(var.cast(t.StringType()), r"\.", r"#"), + ), + ) + + return VariantEffectPredictorParser._generate_dbxrefs( + f.flatten(omim_var_ids), "omim" + ) + + @staticmethod + def _extract_clinvar_xrefs(colocated_variants: Column) -> Column: + """Build xdbRefs for VCV ClinVar identifiers. + + ClinVar identifiers are extracted from the colocated variants field. + VCV-identifiers are filtered out to generate cross-references. + + Args: + colocated_variants (Column): Colocated variants field from VEP output. + + Returns: + Column: List of dbXrefs for VCV ClinVar identifiers. + + Examples: + >>> data = [('VCV2323,RCV3423', 'rs1', 's1',),(None, 'rs1', 's1',),] + >>> ( + ... spark.createDataFrame(data, ['id', 'rsid', 's']) + ... .groupBy('s') + ... .agg(f.collect_list(f.struct(f.struct(f.split(f.col('id'),',').alias('ClinVar')).alias('var_synonyms'),f.col('rsid').alias('id'),),).alias('cv'),).select(VariantEffectPredictorParser._extract_clinvar_xrefs(f.col('cv')).alias('dbXrefs')) + ... .show(truncate=False) + ... ) + +--------------------+ + |dbXrefs | + +--------------------+ + |[{VCV2323, clinvar}]| + +--------------------+ + + """ + variants_w_clinvar_ref = f.filter( + colocated_variants, + lambda variant: variant.getItem("var_synonyms") + .getItem("ClinVar") + .isNotNull(), + ) + + clin_var_ids = f.transform( + variants_w_clinvar_ref, + lambda var: f.filter( + var.getItem("var_synonyms").getItem("ClinVar"), + lambda x: x.startswith("VCV"), + ), + ) + + return VariantEffectPredictorParser._generate_dbxrefs( + f.flatten(clin_var_ids), "clinvar" + ) + + @staticmethod + def _get_most_severe_transcript( + transcript_column_name: str, score_field_name: str + ) -> Column: + """Return transcript with the highest in silico predicted score. + + This method assumes the higher the score, the more severe the consequence of the variant is. + Hence, by selecting the transcript with the highest score, we are selecting the most severe consequence. + + Args: + transcript_column_name (str): Name of the column containing the list of transcripts. + score_field_name (str): Name of the field containing the severity score. + + Returns: + Column: Most severe transcript. + + Examples: + >>> data = [("v1", 0.2, 'transcript1'),("v1", None, 'transcript2'),("v1", 0.6, 'transcript3'),("v1", 0.4, 'transcript4'),] + >>> ( + ... spark.createDataFrame(data, ['v', 'score', 'transcriptId']) + ... .groupBy('v') + ... .agg(f.collect_list(f.struct(f.col('score'),f.col('transcriptId'))).alias('transcripts')) + ... .select(VariantEffectPredictorParser._get_most_severe_transcript('transcripts', 'score').alias('most_severe_transcript')) + ... .show(truncate=False) + ... ) + +----------------------+ + |most_severe_transcript| + +----------------------+ + |{0.6, transcript3} | + +----------------------+ + + """ + assert isinstance( + transcript_column_name, str + ), "transcript_column_name must be a string and not a column." + # Order transcripts by severity score: + ordered_transcripts = order_array_of_structs_by_field( + transcript_column_name, score_field_name + ) + + # Drop transcripts with no severity score and return the most severe one: + return f.filter( + ordered_transcripts, + lambda transcript: transcript.getItem(score_field_name).isNotNull(), + )[0] + + @enforce_schema(IN_SILICO_PREDICTOR_SCHEMA) + @staticmethod + def _get_max_alpha_missense(transcripts: Column) -> Column: + """Return the most severe alpha missense prediction from all transcripts. + + This function assumes one variant can fall onto only one gene with alpha-sense prediction available on the canonical transcript. + + Args: + transcripts (Column): List of transcripts. + + Returns: + Column: Most severe alpha missense prediction. + + Examples: + >>> data = [('v1', 0.4, 'assessment 1'), ('v1', None, None), ('v1', None, None),('v2', None, None),] + >>> ( + ... spark.createDataFrame(data, ['v', 'a', 'b']) + ... .groupBy('v') + ... .agg(f.collect_list(f.struct(f.struct( + ... f.col('a').alias('am_pathogenicity'), + ... f.col('b').alias('am_class')).alias('alphamissense'), + ... f.lit('gene1').alias('gene_id'))).alias('transcripts') + ... ) + ... .select(VariantEffectPredictorParser._get_max_alpha_missense(f.col('transcripts')).alias('am')) + ... .show(truncate=False) + ... ) + +----------------------------------------------------+ + |am | + +----------------------------------------------------+ + |{max alpha missense, assessment 1, 0.4, null, gene1}| + |{max alpha missense, null, null, null, gene1} | + +----------------------------------------------------+ + + """ + return f.transform( + # Extract transcripts with alpha missense values: + f.filter( + transcripts, + lambda transcript: transcript.getItem("alphamissense").isNotNull(), + ), + # Extract alpha missense values: + lambda transcript: f.struct( + transcript.getItem("alphamissense") + .getItem("am_pathogenicity") + .cast(t.FloatType()) + .alias("score"), + transcript.getItem("alphamissense") + .getItem("am_class") + .alias("assessment"), + f.lit("max alpha missense").alias("method"), + transcript.getItem("gene_id").alias("targetId"), + ), + )[0] + + @enforce_schema(IN_SILICO_PREDICTOR_SCHEMA) + @staticmethod + def _vep_in_silico_prediction_extractor( + transcript_column_name: str, + method_name: str, + score_column_name: str | None = None, + assessment_column_name: str | None = None, + assessment_flag_column_name: str | None = None, + ) -> Column: + """Extract in silico prediction from VEP output. + + Args: + transcript_column_name (str): Name of the column containing the list of transcripts. + method_name (str): Name of the in silico predictor. + score_column_name (str | None): Name of the column containing the score. + assessment_column_name (str | None): Name of the column containing the assessment. + assessment_flag_column_name (str | None): Name of the column containing the assessment flag. + + Returns: + Column: In silico predictor. + """ + # Get highest score: + most_severe_transcript: Column = ( + # Getting the most severe transcript: + VariantEffectPredictorParser._get_most_severe_transcript( + transcript_column_name, score_column_name + ) + if score_column_name is not None + # If we don't have score, just pick one of the transcript where assessment is available: + else f.filter( + f.col(transcript_column_name), + lambda transcript: transcript.getItem( + assessment_column_name + ).isNotNull(), + ) + ) + + return f.when( + most_severe_transcript.isNotNull(), + f.struct( + # Adding method name: + f.lit(method_name).cast(t.StringType()).alias("method"), + # Adding assessment: + f.lit(None).cast(t.StringType()).alias("assessment") + if assessment_column_name is None + else most_severe_transcript.getField(assessment_column_name).alias( + "assessment" + ), + # Adding score: + f.lit(None).cast(t.FloatType()).alias("score") + if score_column_name is None + else most_severe_transcript.getField(score_column_name) + .cast(t.FloatType()) + .alias("score"), + # Adding assessment flag: + f.lit(None).cast(t.StringType()).alias("assessmentFlag") + if assessment_flag_column_name is None + else most_severe_transcript.getField(assessment_flag_column_name) + .cast(t.FloatType()) + .alias("assessmentFlag"), + # Adding target id if present: + most_severe_transcript.getItem("gene_id").alias("targetId"), + ), + ) + + @staticmethod + def _parser_amino_acid_change(amino_acids: Column, protein_end: Column) -> Column: + """Convert VEP amino acid change information to one letter code aa substitution code. + + The logic assumes that the amino acid change is given in the format "from/to" and the protein end is given also. + If any of the information is missing, the amino acid change will be set to None. + + Args: + amino_acids (Column): Amino acid change information. + protein_end (Column): Protein end information. + + Returns: + Column: Amino acid change in one letter code. + + Examples: + >>> data = [('A/B', 1),('A/B', None),(None, 1),] + >>> ( + ... spark.createDataFrame(data, ['amino_acids', 'protein_end']) + ... .select(VariantEffectPredictorParser._parser_amino_acid_change(f.col('amino_acids'), f.col('protein_end')).alias('amino_acid_change')) + ... .show() + ... ) + +-----------------+ + |amino_acid_change| + +-----------------+ + | A1B| + | null| + | null| + +-----------------+ + + """ + return f.when( + amino_acids.isNotNull() & protein_end.isNotNull(), + f.concat( + f.split(amino_acids, r"\/")[0], + protein_end, + f.split(amino_acids, r"\/")[1], + ), + ).otherwise(f.lit(None)) + + @staticmethod + def _collect_uniprot_accessions(trembl: Column, swissprot: Column) -> Column: + """Flatten arrays containing Uniprot accessions. + + Args: + trembl (Column): List of TrEMBL protein accessions. + swissprot (Column): List of SwissProt protein accessions. + + Returns: + Column: List of unique Uniprot accessions extracted from swissprot and trembl arrays, splitted from version numbers. + + Examples: + >>> data = [('v1', ["sp_1"], ["tr_1"]), ('v1', None, None), ('v1', None, ["tr_2"]),] + >>> ( + ... spark.createDataFrame(data, ['v', 'sp', 'tr']) + ... .select(VariantEffectPredictorParser._collect_uniprot_accessions(f.col('sp'), f.col('tr')).alias('proteinIds')) + ... .show() + ... ) + +------------+ + | proteinIds| + +------------+ + |[sp_1, tr_1]| + | []| + | [tr_2]| + +------------+ + + """ + # Dropping Null values and flattening the arrays: + return f.filter( + f.array_distinct( + f.transform( + f.flatten( + f.filter( + f.array(trembl, swissprot), + lambda x: x.isNotNull(), + ) + ), + lambda x: f.split(x, r"\.")[0], + ) + ), + lambda x: x.isNotNull(), + ) + + @staticmethod + def _parse_variant_location_id(vep_input_field: Column) -> list[Column]: + r"""Parse variant identifier, chromosome, position, reference allele and alternate allele from VEP input field. + + Args: + vep_input_field (Column): Column containing variant vcf string used as VEP input. + + Returns: + list[Column]: List of columns containing chromosome, position, reference allele and alternate allele. + """ + variant_fields = f.split(vep_input_field, r"\t") + return [ + f.concat_ws( + "_", + f.array( + variant_fields[0], + variant_fields[1], + variant_fields[3], + variant_fields[4], + ), + ).alias("variantId"), + variant_fields[0].cast(t.StringType()).alias("chromosome"), + variant_fields[1].cast(t.IntegerType()).alias("position"), + variant_fields[3].cast(t.StringType()).alias("referenceAllele"), + variant_fields[4].cast(t.StringType()).alias("alternateAllele"), + ] + + @classmethod + def process_vep_output( + cls, vep_output: DataFrame, hash_threshold: int = 100 + ) -> DataFrame: + """Process and format a VEP output in JSON format. + + Args: + vep_output (DataFrame): raw VEP output, read as spark DataFrame. + hash_threshold (int): threshold above which variant identifiers will be hashed. + + Returns: + DataFrame: processed data in the right shape. + """ + # Consequence to sequence ontology map: + sequence_ontology_map = { + item["label"]: item["id"] + for item in VariantIndexConfig.consequence_to_pathogenicity_score + } + # Sequence ontology to score map: + label_to_score_map = { + item["label"]: item["score"] + for item in VariantIndexConfig.consequence_to_pathogenicity_score + } + # Processing VEP output: + return ( + vep_output + # Dropping non-canonical transcript consequences: # TODO: parametrize this. + .withColumn( + "transcript_consequences", + f.filter( + f.col("transcript_consequences"), + lambda consequence: consequence.getItem("canonical") == 1, + ), + ) + .select( + # Parse id and chr:pos:alt:ref: + *cls._parse_variant_location_id(f.col("input")), + # Extracting corss_references from colocated variants: + cls._extract_ensembl_xrefs(f.col("colocated_variants")).alias( + "ensembl_xrefs" + ), + cls._extract_omim_xrefs(f.col("colocated_variants")).alias( + "omim_xrefs" + ), + cls._extract_clinvar_xrefs(f.col("colocated_variants")).alias( + "clinvar_xrefs" + ), + # Extracting in silico predictors + f.when( + # The following in-silico predictors are only available for variants with transcript consequences: + f.col("transcript_consequences").isNotNull(), + f.filter( + f.array( + # Extract CADD scores: + cls._vep_in_silico_prediction_extractor( + transcript_column_name="transcript_consequences", + method_name="phred scaled CADD", + score_column_name="cadd_phred", + ), + # Extract polyphen scores: + cls._vep_in_silico_prediction_extractor( + transcript_column_name="transcript_consequences", + method_name="polyphen", + score_column_name="polyphen_score", + assessment_column_name="polyphen_prediction", + ), + # Extract sift scores: + cls._vep_in_silico_prediction_extractor( + transcript_column_name="transcript_consequences", + method_name="sift", + score_column_name="sift_score", + assessment_column_name="sift_prediction", + ), + # Extract loftee scores: + cls._vep_in_silico_prediction_extractor( + method_name="loftee", + transcript_column_name="transcript_consequences", + score_column_name="lof", + assessment_column_name="lof", + assessment_flag_column_name="lof_filter", + ), + # Extract max alpha missense: + cls._get_max_alpha_missense( + f.col("transcript_consequences") + ), + ), + lambda predictor: predictor.isNotNull(), + ), + ) + .otherwise( + # Extract CADD scores from intergenic object: + f.array( + cls._vep_in_silico_prediction_extractor( + transcript_column_name="intergenic_consequences", + method_name="phred scaled CADD", + score_column_name="cadd_phred", + ), + ) + ) + .alias("inSilicoPredictors"), + # Convert consequence to SO: + map_column_by_dictionary( + f.col("most_severe_consequence"), sequence_ontology_map + ).alias("mostSevereConsequenceId"), + # Extract HGVS identifier: + f.when( + f.size("transcript_consequences") > 0, + f.col("transcript_consequences").getItem(0).getItem("hgvsg"), + ) + .when( + f.size("intergenic_consequences") > 0, + f.col("intergenic_consequences").getItem(0).getItem("hgvsg"), + ) + .otherwise(f.lit(None)) + .alias("hgvsId"), + # Collect transcript consequence: + f.when( + f.col("transcript_consequences").isNotNull(), + f.transform( + f.col("transcript_consequences"), + lambda transcript: f.struct( + # Convert consequence terms to SO identifier: + f.transform( + transcript.consequence_terms, + lambda y: map_column_by_dictionary( + y, sequence_ontology_map + ), + ).alias("variantFunctionalConsequenceIds"), + # Convert consequence terms to consequence score: + f.array_max( + f.transform( + transcript.consequence_terms, + lambda term: map_column_by_dictionary( + term, label_to_score_map + ), + ) + ) + .cast(t.FloatType()) + .alias("consequenceScore"), + # Format amino acid change: + cls._parser_amino_acid_change( + transcript.amino_acids, transcript.protein_end + ).alias("aminoAcidChange"), + # Extract and clean uniprot identifiers: + cls._collect_uniprot_accessions( + transcript.swissprot, + transcript.trembl, + ).alias("uniprotAccessions"), + # Add canonical flag: + f.when(transcript.canonical == 1, f.lit(True)) + .otherwise(f.lit(False)) + .alias("isEnsemblCanonical"), + # Extract footprint distance: + transcript.codons.alias("codons"), + f.when(transcript.distance.isNotNull(), transcript.distance) + .otherwise(f.lit(0)) + .cast(t.LongType()) + .alias("distanceFromFootprint"), + # Extract distance from the transcription start site: + transcript.tssdistance.cast(t.LongType()).alias( + "distanceFromTss" + ), + # Extracting APPRIS isoform annotation for this transcript: + transcript.appris.alias("appris"), + # Extracting MANE select transcript: + transcript.mane_select.alias("maneSelect"), + transcript.gene_id.alias("targetId"), + transcript.impact.alias("impact"), + transcript.lof.cast(t.StringType()).alias( + "lofteePrediction" + ), + transcript.lof.cast(t.FloatType()).alias("siftPrediction"), + transcript.lof.cast(t.FloatType()).alias( + "polyphenPrediction" + ), + transcript.transcript_id.alias("transcriptId"), + ), + ), + ).alias("transcriptConsequences"), + # Extracting rsids: + cls._colocated_variants_to_rsids(f.col("colocated_variants")).alias( + "rsIds" + ), + # Adding empty array for allele frequencies - now this piece of data is not coming form the VEP data: + f.array().cast(cls.ALLELE_FREQUENCY_SCHEMA).alias("alleleFrequencies"), + ) + # Dropping transcripts where the consequence score or the distance is null: + .withColumn( + "transcriptConsequences", + f.filter( + f.col("transcriptConsequences"), + lambda x: x.getItem("consequenceScore").isNotNull() + & x.getItem("distanceFromFootprint").isNotNull(), + ), + ) + # Sort transcript consequences by consequence score and distance from footprint and add index: + .withColumn( + "transcriptConsequences", + f.when( + f.col("transcriptConsequences").isNotNull(), + f.transform( + order_array_of_structs_by_two_fields( + "transcriptConsequences", + "consequenceScore", + "distanceFromFootprint", + ), + lambda x, i: x.withField("transcriptIndex", i + f.lit(1)), + ), + ), + ) + # Adding protvar xref for missense variants: # TODO: making and extendable list of consequences + .withColumn( + "protvar_xrefs", + f.when( + f.size( + f.filter( + f.col("transcriptConsequences"), + lambda x: f.array_contains( + x.variantFunctionalConsequenceIds, "SO_0001583" + ), + ) + ) + > 0, + cls._generate_dbxrefs(f.array(f.col("variantId")), "protvar"), + ), + ) + .withColumn( + "dbXrefs", + f.flatten( + f.filter( + f.array( + f.col("ensembl_xrefs"), + f.col("omim_xrefs"), + f.col("clinvar_xrefs"), + f.col("protvar_xrefs"), + ), + lambda x: x.isNotNull(), + ) + ), + ) + # If the variantId is too long, hash it: + .withColumn( + "variantId", + VariantIndex.hash_long_variant_ids( + f.col("variantId"), + f.col("chromosome"), + f.col("position"), + hash_threshold, + ), + ) + # Dropping intermediate xref columns: + .drop(*["ensembl_xrefs", "omim_xrefs", "clinvar_xrefs", "protvar_xrefs"]) + # Drooping rows with null position: + .filter(f.col("position").isNotNull()) + ) diff --git a/src/gentropy/datasource/eqtl_catalogue/finemapping.py b/src/gentropy/datasource/eqtl_catalogue/finemapping.py new file mode 100644 index 000000000..ea46359df --- /dev/null +++ b/src/gentropy/datasource/eqtl_catalogue/finemapping.py @@ -0,0 +1,323 @@ +"""Process SuSIE finemapping results from eQTL Catalogue.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING + +import pyspark.sql.functions as f +from pyspark.sql import Column, DataFrame, Window +from pyspark.sql.types import ( + DoubleType, + IntegerType, + StringType, + StructField, + StructType, +) + +from gentropy.common.session import Session +from gentropy.common.utils import parse_pvalue +from gentropy.dataset.study_locus import StudyLocus +from gentropy.datasource.eqtl_catalogue.study_index import EqtlCatalogueStudyIndex + +if TYPE_CHECKING: + from pyspark.sql import DataFrame + + +@dataclass +class EqtlCatalogueFinemapping: + """SuSIE finemapping dataset for eQTL Catalogue. + + Credible sets from SuSIE are extracted and transformed into StudyLocus objects: + - A study ID is defined as a triad between: the publication, the tissue, and the measured trait (e.g. Braineac2_substantia_nigra_ENSG00000248275) + - Each row in the `credible_sets.tsv.gz` files is represented by molecular_trait_id/variant/rsid trios relevant for a given tissue. Each have their own finemapping statistics + - log Bayes Factors are available for all variants in the `lbf_variable.txt` files + """ + + raw_credible_set_schema: StructType = StructType( + [ + StructField("molecular_trait_id", StringType(), True), + StructField("gene_id", StringType(), True), + StructField("cs_id", StringType(), True), + StructField("variant", StringType(), True), + StructField("rsid", StringType(), True), + StructField("cs_size", IntegerType(), True), + StructField("pip", DoubleType(), True), + StructField("pvalue", DoubleType(), True), + StructField("beta", DoubleType(), True), + StructField("se", DoubleType(), True), + StructField("z", DoubleType(), True), + StructField("cs_min_r2", DoubleType(), True), + StructField("region", StringType(), True), + ] + ) + raw_lbf_schema: StructType = StructType( + [ + StructField("molecular_trait_id", StringType(), True), + StructField("region", StringType(), True), + StructField("variant", StringType(), True), + StructField("chromosome", StringType(), True), + StructField("position", IntegerType(), True), + StructField("lbf_variable1", DoubleType(), True), + StructField("lbf_variable2", DoubleType(), True), + StructField("lbf_variable3", DoubleType(), True), + StructField("lbf_variable4", DoubleType(), True), + StructField("lbf_variable5", DoubleType(), True), + StructField("lbf_variable6", DoubleType(), True), + StructField("lbf_variable7", DoubleType(), True), + StructField("lbf_variable8", DoubleType(), True), + StructField("lbf_variable9", DoubleType(), True), + StructField("lbf_variable10", DoubleType(), True), + ] + ) + + @classmethod + def _extract_credible_set_index( + cls: type[EqtlCatalogueFinemapping], cs_id: Column + ) -> Column: + """Extract the credible set index from the cs_id. + + Args: + cs_id (Column): column with the credible set id as defined in the eQTL Catalogue. + + Returns: + Column: The credible set index. + + Examples: + >>> spark.createDataFrame([("QTD000046_L1",)], ["cs_id"]).select(EqtlCatalogueFinemapping._extract_credible_set_index(f.col("cs_id"))).show() + +----------------+ + |credibleSetIndex| + +----------------+ + | 1| + +----------------+ + + """ + return f.split(cs_id, "_L")[1].cast(IntegerType()).alias("credibleSetIndex") + + @classmethod + def _extract_dataset_id_from_file_path( + cls: type[EqtlCatalogueFinemapping], file_path: Column + ) -> Column: + """Extract the dataset_id from the file_path. The dataset_id follows the pattern QTD{6}. + + Args: + file_path (Column): A column containing the file path. + + Returns: + Column: The dataset_id. + + Examples: + >>> spark.createDataFrame([("QTD000046.credible_sets.tsv",)], ["filename"]).select(EqtlCatalogueFinemapping._extract_dataset_id_from_file_path(f.col("filename"))).show() + +----------+ + |dataset_id| + +----------+ + | QTD000046| + +----------+ + + """ + return f.regexp_extract(file_path, r"QTD\d{6}", 0).alias("dataset_id") + + @classmethod + def parse_susie_results( + cls: type[EqtlCatalogueFinemapping], + credible_sets: DataFrame, + lbf: DataFrame, + studies_metadata: DataFrame, + ) -> DataFrame: + """Parse the SuSIE results into a DataFrame containing the finemapping statistics and metadata about the studies. + + Args: + credible_sets (DataFrame): DataFrame containing raw statistics of all variants in the credible sets. + lbf (DataFrame): DataFrame containing the raw log Bayes Factors for all variants. + studies_metadata (DataFrame): DataFrame containing the study metadata. + + Returns: + DataFrame: Processed SuSIE results to contain metadata about the studies and the finemapping statistics. + """ + ss_ftp_path_template = "https://ftp.ebi.ac.uk/pub/databases/spot/eQTL/sumstats" + return ( + lbf.join( + credible_sets.join(f.broadcast(studies_metadata), on="dataset_id"), + on=["molecular_trait_id", "region", "variant", "dataset_id"], + how="inner", + ) + .withColumn( + "logBF", + f.when(f.col("credibleSetIndex") == 1, f.col("lbf_variable1")) + .when(f.col("credibleSetIndex") == 2, f.col("lbf_variable2")) + .when(f.col("credibleSetIndex") == 3, f.col("lbf_variable3")) + .when(f.col("credibleSetIndex") == 4, f.col("lbf_variable4")) + .when(f.col("credibleSetIndex") == 5, f.col("lbf_variable5")) + .when(f.col("credibleSetIndex") == 6, f.col("lbf_variable6")) + .when(f.col("credibleSetIndex") == 7, f.col("lbf_variable7")) + .when(f.col("credibleSetIndex") == 8, f.col("lbf_variable8")) + .when(f.col("credibleSetIndex") == 9, f.col("lbf_variable9")) + .when(f.col("credibleSetIndex") == 10, f.col("lbf_variable10")), + ) + .select( + f.regexp_replace(f.col("variant"), r"chr", "").alias("variantId"), + f.col("region"), + f.col("chromosome"), + f.col("position"), + f.col("pip").alias("posteriorProbability"), + *parse_pvalue(f.col("pvalue")), + f.col("sample_size").alias("nSamples"), + f.col("beta"), + f.col("se").alias("standardError"), + f.col("credibleSetIndex"), + f.col("logBF"), + f.lit("SuSie").alias("finemappingMethod"), + # Study metadata + f.col("molecular_trait_id").alias("traitFromSource"), + f.col("gene_id").alias("geneId"), + f.col("dataset_id"), + f.concat_ws( + "_", + f.col("study_label"), + f.col("quant_method"), + f.col("sample_group"), + f.col("molecular_trait_id"), + ).alias("studyId"), + f.col("tissue_id").alias("biosampleFromSourceId"), + EqtlCatalogueStudyIndex._identify_study_type( + f.col("quant_method"), f.col("tissue_id") + ).alias("studyType"), + f.col("study_label").alias("projectId"), + f.concat_ws( + "/", + f.lit(ss_ftp_path_template), + f.col("study_id"), + f.col("dataset_id"), + ).alias("summarystatsLocation"), + f.lit(True).alias("hasSumstats"), + f.col("molecular_trait_id"), + f.col("pmid").alias("pubmedId"), + f.col("condition_label").alias("condition"), + ) + ) + + @classmethod + def from_susie_results( + cls: type[EqtlCatalogueFinemapping], processed_finemapping_df: DataFrame + ) -> StudyLocus: + """Create a StudyLocus object from the processed SuSIE results. + + Args: + processed_finemapping_df (DataFrame): DataFrame containing the processed SuSIE results. + + Returns: + StudyLocus: eQTL Catalogue credible sets. + """ + lead_w = Window.partitionBy( + "dataset_id", "molecular_trait_id", "region", "credibleSetIndex" + ) + study_locus_cols = [ + field.name + for field in StudyLocus.get_schema().fields + if field.name in processed_finemapping_df.columns + ] + ["locus"] + return StudyLocus( + _df=( + processed_finemapping_df.withColumn( + "isLead", + f.row_number().over(lead_w.orderBy(f.desc("posteriorProbability"))) + == f.lit(1), + ) + .withColumn( + # Collecting all variants that constitute the credible set brings as many variants as the credible set size + "locus", + f.when( + f.col("isLead"), + f.collect_list( + f.struct( + "variantId", + "posteriorProbability", + "pValueMantissa", + "pValueExponent", + "logBF", + "beta", + "standardError", + ) + ).over(lead_w), + ), + ) + .filter(f.col("isLead")) + .drop("isLead") + .select( + *study_locus_cols, + StudyLocus.assign_study_locus_id( + ["studyId", "variantId", "finemappingMethod"] + ), + StudyLocus.calculate_credible_set_log10bf( + f.col("locus.logBF") + ).alias("credibleSetlog10BF"), + ) + ), + _schema=StudyLocus.get_schema(), + ).annotate_credible_sets() + + @classmethod + def read_credible_set_from_source( + cls: type[EqtlCatalogueFinemapping], + session: Session, + credible_set_path: str | list[str], + ) -> DataFrame: + """Load raw credible sets from eQTL Catalogue. + + Args: + session (Session): Spark session. + credible_set_path (str | list[str]): Path to raw table(s) containing finemapping results for any variant belonging to a credible set. + + Returns: + DataFrame: Credible sets DataFrame. + """ + return ( + session.spark.read.csv( + credible_set_path, + sep="\t", + header=True, + schema=cls.raw_credible_set_schema, + ) + .withColumns( + { + # Adding dataset id based on the input file name: + "dataset_id": cls._extract_dataset_id_from_file_path( + f.input_file_name() + ), + # Parsing credible set index from the cs_id: + "credibleSetIndex": cls._extract_credible_set_index(f.col("cs_id")), + } + ) + # Remove duplicates caused by explosion of single variants to multiple rsid-s: + .drop("rsid") + .distinct() + ) + + @classmethod + def read_lbf_from_source( + cls: type[EqtlCatalogueFinemapping], + session: Session, + lbf_path: str | list[str], + ) -> DataFrame: + """Load raw log Bayes Factors from eQTL Catalogue. + + Args: + session (Session): Spark session. + lbf_path (str | list[str]): Path to raw table(s) containing Log Bayes Factors for each variant. + + Returns: + DataFrame: Log Bayes Factors DataFrame. + """ + return ( + session.spark.read.csv( + lbf_path, + sep="\t", + header=True, + schema=cls.raw_lbf_schema, + ) + .withColumn( + "dataset_id", + cls._extract_dataset_id_from_file_path(f.input_file_name()), + ) + .distinct() + ) diff --git a/src/gentropy/datasource/eqtl_catalogue/study_index.py b/src/gentropy/datasource/eqtl_catalogue/study_index.py index bcfc5e50f..d284eb781 100644 --- a/src/gentropy/datasource/eqtl_catalogue/study_index.py +++ b/src/gentropy/datasource/eqtl_catalogue/study_index.py @@ -1,10 +1,15 @@ """Study Index for eQTL Catalogue data source.""" + from __future__ import annotations -from typing import TYPE_CHECKING, Type +from itertools import chain +from typing import TYPE_CHECKING +import pandas as pd import pyspark.sql.functions as f +from pyspark.sql.types import IntegerType, StringType, StructField, StructType +from gentropy.common.session import Session from gentropy.dataset.study_index import StudyIndex if TYPE_CHECKING: @@ -13,167 +18,140 @@ class EqtlCatalogueStudyIndex: - """Study index dataset from eQTL Catalogue.""" - - study_config = { - "GTEx_V8": { - "nSamples": 838, - "initialSampleSize": "838 (281 females and 557 males)", - "discoverySamples": [ - {"sampleSize": 715, "ancestry": "European American"}, - {"sampleSize": 103, "ancestry": "African American"}, - {"sampleSize": 12, "ancestry": "Asian American"}, - {"sampleSize": 16, "ancestry": "Hispanic or Latino"}, - ], - "ldPopulationStructure": [ - {"ldPopulation": "nfe", "relativeSampleSize": 0.85}, - {"ldPopulation": "afr", "relativeSampleSize": 0.12}, - {"ldPopulation": "eas", "relativeSampleSize": 0.01}, - {"ldPopulation": "amr", "relativeSampleSize": 0.02}, - ], - "pubmedId": "32913098", - "publicationTitle": "The GTEx Consortium atlas of genetic regulatory effects across human tissues", - "publicationFirstAuthor": "GTEx Consortium", - "publicationDate": "2020-09-11", - "publicationJournal": "Science", - }, + """Study index dataset from eQTL Catalogue. + + We extract study level metadata from eQTL Catalogue's fine mapping results. All available studies can be found [here](https://www.ebi.ac.uk/eqtl/Studies/). + + One study from the eQTL Catalogue clusters together all the molecular QTLs (mQTLs) that were found: + + - in the same publication (e.g. Alasoo_2018) + - in the same cell type or tissue (e.g. monocytes) + - and for the same measured molecular trait (e.g. ENSG00000141510) + + """ + + raw_studies_metadata_schema: StructType = StructType( + [ + StructField("study_id", StringType(), True), + StructField("dataset_id", StringType(), True), + StructField("study_label", StringType(), True), + StructField("sample_group", StringType(), True), + StructField("tissue_id", StringType(), True), + StructField("tissue_label", StringType(), True), + StructField("condition_label", StringType(), True), + StructField("sample_size", IntegerType(), True), + StructField("quant_method", StringType(), True), + StructField("pmid", StringType(), True), + ] + ) + raw_studies_metadata_path = "https://raw.githubusercontent.com/eQTL-Catalogue/eQTL-Catalogue-resources/092e01a9601feb404f1c88f86311b43b907a88f6/data_tables/dataset_metadata_upcoming.tsv" + method_to_study_type_mapping = { + "ge": "eqtl", + "exon": "eqtl", + "tx": "eqtl", + "microarray": "eqtl", + "leafcutter": "sqtl", + "aptamer": "pqtl", + "txrev": "tuqtl", } @classmethod - def get_study_attribute( - cls: Type[EqtlCatalogueStudyIndex], attribute_key: str + def _identify_study_type( + cls: type[EqtlCatalogueStudyIndex], + quantification_method_col: Column, + biosample_col: Column, ) -> Column: - """Returns a Column expression that dynamically assigns the attribute based on the study. + """Identify the study type based on the method to quantify the trait and the biosample where the trait was measured. + + The quantification method identifies the type of molecular QTLs that were found. + The biosample identifies the biosample where the trait was measured, distinguishing between bulk and single cell. Args: - attribute_key (str): The attribute key to assign. + quantification_method_col (Column): column with the label of the method to quantify the trait. Available methods are [here](https://www.ebi.ac.uk/eqtl/Methods/) + biosample_col (Column): column with the label of the biosample where the trait was measured. Returns: - Column: The dynamically assigned attribute. - - Raises: - ValueError: If the attribute key is not known for the study. + Column: The study type. + + Examples: + >>> df = spark.createDataFrame([("ge", "CL_1"), ("leafcutter", "UBERON_2"), ("tx", "EFO_3")], ["quant_method", "tissue_id"]) + >>> df.withColumn("study_type", EqtlCatalogueStudyIndex._identify_study_type(f.col("quant_method"), f.col("tissue_id"))).show() + +------------+---------+----------+ + |quant_method|tissue_id|study_type| + +------------+---------+----------+ + | ge| CL_1| sceqtl| + | leafcutter| UBERON_2| sqtl| + | tx| EFO_3| eqtl| + +------------+---------+----------+ + """ - study_column = f.col("study") - for study, config in cls.study_config.items(): - attribute_value = config.get(attribute_key) - if attribute_value is None: - raise ValueError( - f"Unknown attribute key {attribute_key} for study {study}" - ) - # Convert list of dicts to array of structs - if isinstance(attribute_value, list) and isinstance( - attribute_value[0], dict - ): - struct_fields = [ - f.struct(*[f.lit(value).alias(key) for key, value in item.items()]) - for item in attribute_value - ] - attribute_value = f.array(struct_fields) - # Convert dict to struct - elif isinstance(attribute_value, dict): - attribute_value = f.struct( - *[f.lit(value).alias(key) for key, value in attribute_value.items()] - ) - return f.when(study_column == study, attribute_value).alias(attribute_key) + qtl_type_mapping = f.create_map( + *[f.lit(x) for x in chain(*cls.method_to_study_type_mapping.items())] + )[quantification_method_col] + return f.when( + biosample_col.startswith("CL"), f.concat(f.lit("sc"), qtl_type_mapping) + ).otherwise(qtl_type_mapping) @classmethod - def _all_attributes(cls: Type[EqtlCatalogueStudyIndex]) -> list[Column]: - """A helper function to return all study index attribute expressions. + def get_studies_of_interest( + cls: type[EqtlCatalogueStudyIndex], + studies_metadata: DataFrame, + ) -> list[str]: + """Filter studies of interest from the raw studies metadata. + + Args: + studies_metadata (DataFrame): raw studies metadata filtered with studies of interest. Returns: - list[Column]: A list of all study index attribute expressions. + list[str]: QTD IDs defining the studies of interest for ingestion. """ - study_column = f.col("study") - - static_study_attributes = [ - study_column.alias("projectId"), - f.concat(study_column, f.lit("_"), f.col("qtl_group")).alias("studyId"), - f.col("ftp_path").alias("summarystatsLocation"), - f.lit(True).alias("hasSumstats"), - f.lit("eqtl").alias("studyType"), - ] - tissue_attributes = [ - # Human readable tissue label, example: "Adipose - Subcutaneous". - f.col("tissue_label").alias("traitFromSource"), - # Ontology identifier for the tissue, for example: "UBERON:0001157". - f.array( - f.regexp_replace( - f.regexp_replace( - f.col("tissue_ontology_id"), - "UBER_", - "UBERON_", - ), - "_", - ":", - ) - ).alias("traitFromSourceMappedIds"), - ] - dynamic_study_attributes = [ - cls.get_study_attribute("nSamples"), - cls.get_study_attribute("initialSampleSize"), - cls.get_study_attribute("discoverySamples"), - cls.get_study_attribute("ldPopulationStructure"), - cls.get_study_attribute("pubmedId"), - cls.get_study_attribute("publicationTitle"), - cls.get_study_attribute("publicationFirstAuthor"), - cls.get_study_attribute("publicationDate"), - cls.get_study_attribute("publicationJournal"), - ] - return static_study_attributes + tissue_attributes + dynamic_study_attributes + return ( + studies_metadata.select("dataset_id") + .distinct() + .toPandas()["dataset_id"] + .tolist() + ) @classmethod - def add_gene_to_study_id( + def from_susie_results( cls: type[EqtlCatalogueStudyIndex], - study_index_df: DataFrame, - summary_stats_df: DataFrame, + processed_finemapping_df: DataFrame, ) -> StudyIndex: - """Update the studyId to include gene information from summary statistics. A geneId column is also added. - - While the original list contains one entry per tissue, what we consider as a single study is one mini-GWAS for - an expression of a _particular gene_ in a particular study. At this stage we have a study index with partial - study IDs like "PROJECT_QTLGROUP", and a summary statistics object with full study IDs like - "PROJECT_QTLGROUP_GENEID", so we need to perform a merge and explosion to obtain our final study index. + """Ingest study level metadata from eQTL Catalogue. Args: - study_index_df (DataFrame): preliminary study index for eQTL Catalogue studies. - summary_stats_df (DataFrame): summary statistics dataframe for eQTL Catalogue data. + processed_finemapping_df (DataFrame): processed fine mapping results with study metadata. Returns: - StudyIndex: final study index for eQTL Catalogue studies. + StudyIndex: eQTL Catalogue study index dataset derived from the selected SuSIE results. """ - partial_to_full_study_id = summary_stats_df.select( - f.col("studyId").alias("fullStudyId"), # PROJECT_QTLGROUP_GENEID - f.regexp_extract(f.col("studyId"), r"^(.*)_ENSG\d+", 1).alias( - "studyId" - ), # PROJECT_QTLGROUP - ).distinct() - study_index_df = ( - partial_to_full_study_id.join( - f.broadcast(study_index_df), "studyId", "inner" - ) - # Change studyId to fullStudyId - .drop("studyId") - .withColumnRenamed("fullStudyId", "studyId") - # Add geneId column - .withColumn("geneId", f.regexp_extract(f.col("studyId"), r"([^_]+)$", 1)) + study_index_cols = [ + field.name + for field in StudyIndex.get_schema().fields + if field.name in processed_finemapping_df.columns + ] + return StudyIndex( + _df=processed_finemapping_df.select(study_index_cols).distinct(), + _schema=StudyIndex.get_schema(), ) - return StudyIndex(_df=study_index_df, _schema=StudyIndex.get_schema()) @classmethod - def from_source( + def read_studies_from_source( cls: type[EqtlCatalogueStudyIndex], - eqtl_studies: DataFrame, - ) -> StudyIndex: - """Ingest study level metadata from eQTL Catalogue. + session: Session, + mqtl_quantification_methods_blacklist: list[str], + ) -> DataFrame: + """Read raw studies metadata from eQTL Catalogue. Args: - eqtl_studies (DataFrame): ingested but unprocessed eQTL Catalogue studies. + session (Session): Spark session. + mqtl_quantification_methods_blacklist (list[str]): Molecular trait quantification methods that we don't want to ingest. Available options in https://github.com/eQTL-Catalogue/eQTL-Catalogue-resources/blob/master/data_tables/dataset_metadata.tsv Returns: - StudyIndex: preliminary processed study index for eQTL Catalogue studies. + DataFrame: raw studies metadata. """ - return StudyIndex( - _df=eqtl_studies.select(*cls._all_attributes()), - _schema=StudyIndex.get_schema(), - ) + pd.DataFrame.iteritems = pd.DataFrame.items + return session.spark.createDataFrame( + pd.read_csv(cls.raw_studies_metadata_path, sep="\t"), + schema=cls.raw_studies_metadata_schema, + ).filter(~(f.col("quant_method").isin(mqtl_quantification_methods_blacklist))) diff --git a/src/gentropy/datasource/finngen/finemapping.py b/src/gentropy/datasource/finngen/finemapping.py index 8fd12b0ba..36ab97e80 100644 --- a/src/gentropy/datasource/finngen/finemapping.py +++ b/src/gentropy/datasource/finngen/finemapping.py @@ -5,6 +5,7 @@ from dataclasses import dataclass +import hail as hl import pyspark.sql.functions as f import pyspark.sql.types as t from pyspark.sql import SparkSession, Window @@ -21,7 +22,7 @@ class FinnGenFinemapping: Credible sets from SuSIE are extracted and transformed into StudyLocus objects: - - Study ID in the special format (e.g. FINNGEN_R10_*) + - Study ID in the special format (e.g. FINNGEN_R11*) - Credible set specific finemapping statistics (e.g. LogBayesFactors, Alphas/Posterior) - Additional credible set level BayesFactor filtering is applied (LBF > 2) - StudyLocusId is annotated for each credible set. @@ -109,39 +110,194 @@ class FinnGenFinemapping: ] ) + raw_hail_shema: hl.tstruct = hl.tstruct( + trait=hl.tstr, + region=hl.tstr, + v=hl.tstr, + rsid=hl.tstr, + chromosome=hl.tstr, + position=hl.tstr, + allele1=hl.tstr, + allele2=hl.tstr, + maf=hl.tstr, + beta=hl.tstr, + se=hl.tstr, + p=hl.tstr, + mean=hl.tstr, + sd=hl.tstr, + prob=hl.tstr, + cs=hl.tstr, + cs_specific_prob=hl.tfloat64, + low_purity=hl.tstr, + lead_r2=hl.tstr, + mean_99=hl.tstr, + sd_99=hl.tstr, + prob_99=hl.tstr, + cs_99=hl.tstr, + cs_specific_prob_99=hl.tstr, + low_purity_99=hl.tstr, + lead_r2_99=hl.tstr, + alpha1=hl.tfloat64, + alpha2=hl.tfloat64, + alpha3=hl.tfloat64, + alpha4=hl.tfloat64, + alpha5=hl.tfloat64, + alpha6=hl.tfloat64, + alpha7=hl.tfloat64, + alpha8=hl.tfloat64, + alpha9=hl.tfloat64, + alpha10=hl.tfloat64, + mean1=hl.tstr, + mean2=hl.tstr, + mean3=hl.tstr, + mean4=hl.tstr, + mean5=hl.tstr, + mean6=hl.tstr, + mean7=hl.tstr, + mean8=hl.tstr, + mean9=hl.tstr, + mean10=hl.tstr, + sd1=hl.tstr, + sd2=hl.tstr, + sd3=hl.tstr, + sd4=hl.tstr, + sd5=hl.tstr, + sd6=hl.tstr, + sd7=hl.tstr, + sd8=hl.tstr, + sd9=hl.tstr, + sd10=hl.tstr, + lbf_variable1=hl.tfloat64, + lbf_variable2=hl.tfloat64, + lbf_variable3=hl.tfloat64, + lbf_variable4=hl.tfloat64, + lbf_variable5=hl.tfloat64, + lbf_variable6=hl.tfloat64, + lbf_variable7=hl.tfloat64, + lbf_variable8=hl.tfloat64, + lbf_variable9=hl.tfloat64, + lbf_variable10=hl.tfloat64, + ) + + summary_hail_schema: hl.tstruct = hl.tstruct( + trait=hl.tstr, + region=hl.tstr, + cs=hl.tstr, + cs_log10bf=hl.tfloat64, + ) + + @staticmethod + def _infer_block_gzip_compression(paths: str | list[str]) -> bool: + """Naively infer compression type based on the file extension. + + Args: + paths (str | list[str]): File path(s). + + Returns: + bool: True if block gzipped, False otherwise. + """ + if isinstance(paths, str): + return paths.endswith(".bgz") + return all(path.endswith(".bgz") for path in paths) + @classmethod def from_finngen_susie_finemapping( cls: type[FinnGenFinemapping], spark: SparkSession, - finngen_finemapping_df: (str | list[str]), - finngen_finemapping_summaries: (str | list[str]), + finngen_susie_finemapping_snp_files: (str | list[str]), + finngen_susie_finemapping_cs_summary_files: (str | list[str]), finngen_release_prefix: str, credset_lbf_threshold: float = 0.8685889638065036, ) -> StudyLocus: """Process the SuSIE finemapping output for FinnGen studies. + The finngen_susie_finemapping_snp_files are files that contain variant summaries with credible set information with following shema: + - trait: phenotype + - region: region for which the fine-mapping was run. + - v, rsid: variant ids + - chromosome + - position + - allele1 + - allele2 + - maf: minor allele frequency + - beta: original marginal beta + - se: original se + - p: original p + - mean: posterior mean beta after fine-mapping + - sd: posterior standard deviation after fine-mapping. + - prob: posterior inclusion probability + - cs: credible set index within region + - lead_r2: r2 value to a lead variant (the one with maximum PIP) in a credible set + - alphax: posterior inclusion probability for the x-th single effect (x := 1..L where L is the number of single effects (causal variants) specified; default: L = 10). + - lbfx: log-Bayes Factor for each variable and single effect (i.e credible set). + - meanx: posterior mean for each variable and single effect (i.e credible set). + - sdx: posterior sd of mean for each variable and single effect (i.e credible set). + As for r11 finngen release these files are ingested from `https://console.cloud.google.com/storage/browser/finngen-public-data-r11/finemap/full/susie/` by + - *.snp.bgz + - *.snp.bgz.tbi + Each file contains index (.tbi) file that is required to read the block gzipped compressed snp file. These files needs to be + downloaded, transfromed from block gzipped to plain gzipped and then uploaded to the storage bucket, before they can be read by spark or read by hail directly as import table. + + The finngen_susie_finemapping_cs_summary_files are files that Contains credible set summaries from SuSiE fine-mapping for all genome-wide significant regions with following schema: + - trait: phenotype + - region: region for which the fine-mapping was run. + - cs: running number for independent credible sets in a region + - cs_log10bf: Log10 bayes factor of comparing the solution of this model (cs independent credible sets) to cs -1 credible sets + - cs_avg_r2: Average correlation R2 between variants in the credible set + - cs_min_r2: minimum r2 between variants in the credible set + - low_purity: boolean (TRUE,FALSE) indicator if the CS is low purity (low min r2) + - cs_size: how many snps does this credible set contain + - good_cs: boolean (TRUE,FALSE) indicator if this CS is considered reliable. IF this is FALSE then top variant reported for the CS will be chosen based on minimum p-value in the credible set, otherwise top variant is chosen by maximum PIP + - cs_id: + - v: top variant (chr:pos:ref:alt) + - p: top variant p-value + - beta: top variant beta + - sd: top variant standard deviation + - prob: overall PIP of the variant in the region + - cs_specific_prob: PIP of the variant in the current credible set (this and previous are typically almost identical) + - 0..n: configured annotation columns. Typical default most_severe,gene_most_severe giving consequence and gene of top variant + These files needs to be downloaded from the `https://console.cloud.google.com/storage/browser/finngen-public-data-r11/finemap/summary/` by *.cred.summary.tsv pattern, + Args: - spark (SparkSession): Spark session object. - finngen_finemapping_df (str | list[str]): SuSIE finemapping output filename(s). - finngen_finemapping_summaries (str | list[str]): filename of SuSIE finemapping summaries. - finngen_release_prefix (str): FinnGen study prefix. + spark (SparkSession): SparkSession object. + finngen_susie_finemapping_snp_files (str | list[str]): SuSIE finemapping output filename(s). + finngen_susie_finemapping_cs_summary_files (str | list[str]): filename of SuSIE finemapping credible set summaries. + finngen_release_prefix (str): Finngen project release prefix. Should look like FINNGEN_R*. credset_lbf_threshold (float, optional): Filter out credible sets below, Default 0.8685889638065036 == np.log10(np.exp(2)), this is threshold from publication. Returns: StudyLocus: Processed SuSIE finemapping output in StudyLocus format. """ + # NOTE: hail allows for importing block gzipped files, spark does not without external libraries. + # check https://github.com/projectglow/glow/blob/36bf6121fbc4ccc33a13b028deb87b63faeba7a9/core/src/main/scala/io/projectglow/vcf/VCFFileFormat.scala#L274 + # how it could be implemented with spark. + bgzip_compressed_snps = cls._infer_block_gzip_compression( + finngen_susie_finemapping_snp_files + ) + + # NOTE: fallback to spark read if not block gzipped file in the input + if bgzip_compressed_snps: + snps_df = hl.import_table( + finngen_susie_finemapping_snp_files, + delimiter="\t", + types=cls.raw_hail_shema, + ).to_spark() + else: + snps_df = ( + spark.read.schema(cls.raw_schema) + .option("delimiter", "\t") + .option("compression", "gzip") + .csv(finngen_susie_finemapping_snp_files, header=True) + ) + processed_finngen_finemapping_df = ( - spark.read.schema(cls.raw_schema) - .option("delimiter", "\t") - .option("compression", "gzip") - .csv(finngen_finemapping_df, header=True) # Drop rows which don't have proper position. - .filter(f.col("position").cast(t.IntegerType()).isNotNull()) + snps_df.filter(f.col("position").cast(t.IntegerType()).isNotNull()) # Drop non credible set SNPs: .filter(f.col("cs").cast(t.IntegerType()) > 0) .select( # Add study idenfitier. - f.concat(f.lit(finngen_release_prefix), f.col("trait")) + f.concat_ws("_", f.lit(finngen_release_prefix), f.col("trait")) .cast(t.StringType()) .alias("studyId"), f.col("region"), @@ -156,8 +312,7 @@ def from_finngen_susie_finemapping( f.col("allele2").cast(t.StringType()).alias("alt"), # Parse p-value into mantissa and exponent. *parse_pvalue(f.col("p")), - # Add beta, standard error, and allele frequency information. - f.col("beta").cast("double"), + # Add standard error, and allele frequency information. f.col("se").cast("double").alias("standardError"), f.col("maf").cast("float").alias("effectAlleleFrequencyFromSource"), f.lit("SuSie").cast("string").alias("finemappingMethod"), @@ -169,6 +324,10 @@ def from_finngen_susie_finemapping( f.col(f"lbf_variable{i}").cast(t.DoubleType()).alias(f"lbf_{i}") for i in range(1, 11) ], + *[ + f.col(f"mean{i}").cast(t.DoubleType()).alias(f"beta_{i}") + for i in range(1, 11) + ], ) .withColumn( "posteriorProbability", @@ -220,16 +379,58 @@ def from_finngen_susie_finemapping( "lbf_9", "lbf_10", ) + .withColumn( + "beta", + f.when(f.col("credibleSetIndex") == 1, f.col("beta_1")) + .when(f.col("credibleSetIndex") == 2, f.col("beta_2")) + .when(f.col("credibleSetIndex") == 3, f.col("beta_3")) + .when(f.col("credibleSetIndex") == 4, f.col("beta_4")) + .when(f.col("credibleSetIndex") == 5, f.col("beta_5")) + .when(f.col("credibleSetIndex") == 6, f.col("beta_6")) + .when(f.col("credibleSetIndex") == 7, f.col("beta_7")) + .when(f.col("credibleSetIndex") == 8, f.col("beta_8")) + .when(f.col("credibleSetIndex") == 9, f.col("beta_9")) + .when(f.col("credibleSetIndex") == 10, f.col("beta_10")), + ) + .drop( + "beta_1", + "beta_2", + "beta_3", + "beta_4", + "beta_5", + "beta_6", + "beta_7", + "beta_8", + "beta_9", + "beta_10", + ) + ) + + bgzip_compressed_cs_summaries = cls._infer_block_gzip_compression( + finngen_susie_finemapping_cs_summary_files ) + # NOTE: fallback to spark read if not block gzipped file in the input + # in case we want to use the raw files from the + # https://console.cloud.google.com/storage/browser/finngen-public-data-r11/finemap/full/susie/*.cred.gz + if bgzip_compressed_cs_summaries: + cs_summary_df = hl.import_table( + finngen_susie_finemapping_cs_summary_files, + delimiter="\t", + types=cls.summary_hail_schema, + ).to_spark() + else: + cs_summary_df = ( + spark.read.schema(cls.summary_schema) + .option("delimiter", "\t") + .csv(finngen_susie_finemapping_cs_summary_files, header=True) + ) + # drop credible sets where logbf > 2. Except when there's only one credible set in region: # 0.8685889638065036 corresponds to np.log10(np.exp(2)), to match the orginal threshold in publication. finngen_finemapping_summaries_df = ( # Read credible set level lbf, it is output as a different file which is not ideal. - spark.read.schema(cls.summary_schema) - .option("delimiter", "\t") - .csv(finngen_finemapping_summaries, header=True) - .select( + cs_summary_df.select( f.col("region"), f.col("trait"), f.col("cs").cast("integer").alias("credibleSetIndex"), @@ -240,7 +441,8 @@ def from_finngen_susie_finemapping( | (f.col("credibleSetIndex") == 1) ) .withColumn( - "studyId", f.concat(f.lit(finngen_release_prefix), f.col("trait")) + "studyId", + f.concat_ws("_", f.lit(finngen_release_prefix), f.col("trait")), ) ) @@ -253,10 +455,7 @@ def from_finngen_susie_finemapping( toploci_df = get_top_ranked_in_window( processed_finngen_finemapping_df, Window.partitionBy("studyId", "region", "credibleSetIndex").orderBy( - *[ - f.col("pValueExponent").asc(), - f.col("pValueMantissa").asc(), - ] + f.desc("posteriorProbability") ), ).select( "variantId", @@ -304,9 +503,19 @@ def from_finngen_susie_finemapping( on=["studyId", "region", "credibleSetIndex"], how="inner", ) + .withColumns( + { + "locusStart": f.split(f.split("region", ":")[1], "-")[0].cast( + "int" + ), + "locusEnd": f.split(f.split("region", ":")[1], "-")[1].cast("int"), + } + ) ).withColumn( "studyLocusId", - StudyLocus.assign_study_locus_id(f.col("studyId"), f.col("variantId")), + StudyLocus.assign_study_locus_id( + ["studyId", "variantId", "finemappingMethod"] + ), ) return StudyLocus( diff --git a/src/gentropy/datasource/finngen/study_index.py b/src/gentropy/datasource/finngen/study_index.py index 6745a2542..dc0389aa1 100644 --- a/src/gentropy/datasource/finngen/study_index.py +++ b/src/gentropy/datasource/finngen/study_index.py @@ -1,54 +1,160 @@ -"""Study Index for Finngen data source.""" +"""Study Index for FinnGen data source.""" + from __future__ import annotations +import re +from typing import TypedDict from urllib.request import urlopen import pyspark.sql.functions as f -from pyspark.sql import SparkSession +from pyspark.sql import DataFrame, SparkSession from gentropy.dataset.study_index import StudyIndex +class FinngenPrefixMatch(TypedDict): + """Class to store the output of the validate_release_prefix.""" + + prefix: str + release: str + + class FinnGenStudyIndex: """Study index dataset from FinnGen. The following information is aggregated/extracted: - - Study ID in the special format (e.g. FINNGEN_R10_*) + - Study ID in the special format (e.g. FINNGEN_R11_*) - Trait name (for example, Amoebiasis) - Number of cases and controls - Link to the summary statistics location + - EFO mapping from curated EFO mapping file Some fields are also populated as constants, such as study type and the initial sample size. """ - finngen_phenotype_table_url: str = "https://r10.finngen.fi/api/phenos" - finngen_release_prefix: str = "FINNGEN_R10" - finngen_summary_stats_url_prefix: str = ( - "gs://finngen-public-data-r10/summary_stats/finngen_R10_" - ) - finngen_summary_stats_url_suffix: str = ".gz" + @staticmethod + def validate_release_prefix(release_prefix: str) -> FinngenPrefixMatch: + """Validate release prefix passed to finngen StudyIndex. + + Args: + release_prefix (str): Finngen release prefix, should be a string like FINNGEN_R*. + + Returns: + FinngenPrefixMatch: Object containing valid prefix and release strings. + + Raises: + ValueError: when incorrect release prefix is provided. + + This method ensures that the trailing underscore is removed from prefix. + """ + pattern = re.compile(r"FINNGEN_(?PR\d+){1}_?") + pattern_match = pattern.match(release_prefix) + if not pattern_match: + raise ValueError( + f"Invalid FinnGen release prefix: {release_prefix}, use the format FINNGEN_R*" + ) + release = pattern_match.group("release").upper() + if release_prefix.endswith("_"): + release_prefix = release_prefix[:-1] + return FinngenPrefixMatch(prefix=release_prefix, release=release) + + @staticmethod + def read_efo_curation(session: SparkSession, url: str) -> DataFrame: + """Read efo curation from provided url. + + Args: + session (SparkSession): Session to use when reading the mapping file. + url (str): Url to the mapping file. The file provided should be a tsv file. + + Returns: + DataFrame: DataFrame with EFO mappings. + + Example of the file can be found in https://raw.githubusercontent.com/opentargets/curation/refs/heads/master/mappings/disease/manual_string.tsv. + """ + csv_data = urlopen(url).readlines() + csv_rows = [row.decode("utf8") for row in csv_data] + rdd = session.sparkContext.parallelize(csv_rows) + # NOTE: type annotations for spark.read.csv miss the fact that the first param can be [RDD[str]] + efo_curation_mapping_df = session.read.csv(rdd, header=True, sep="\t") + return efo_curation_mapping_df + + @staticmethod + def join_efo_mapping( + study_index: StudyIndex, + efo_curation_mapping: DataFrame, + finngen_release: str, + ) -> StudyIndex: + """Add EFO mapping to the Finngen study index table. + + This function performs inner join on table of EFO mappings to the study index table by trait name. + All studies without EFO traits are dropped. The EFO mappings are then aggregated into lists per + studyId. + + NOTE: preserve all studyId entries even if they don't have EFO mappings. + This is to avoid discrepancies between `study_index` and `credible_set` `studyId` column. + The rows with missing EFO mappings will be dropped in the study_index validation step. + + Args: + study_index (StudyIndex): Study index table. + efo_curation_mapping (DataFrame): Dataframe with EFO mappings. + finngen_release (str): FinnGen release. + + Returns: + StudyIndex: Study index table with added EFO mappings. + """ + efo_mappings = ( + efo_curation_mapping.withColumn("STUDY", f.upper(f.col("STUDY"))) + .filter(f.col("STUDY").contains("FINNGEN")) + .filter(f.upper(f.col("STUDY")).contains(finngen_release)) + .select( + f.regexp_replace(f.col("SEMANTIC_TAG"), r"^.*/", "").alias( + "traitFromSourceMappedId" + ), + f.col("PROPERTY_VALUE").alias("traitFromSource"), + ) + ) + + si_df = study_index.df.join( + efo_mappings, on="traitFromSource", how="left_outer" + ) + common_cols = [c for c in si_df.columns if c != "traitFromSourceMappedId"] + si_df = si_df.groupby(common_cols).agg( + f.collect_list("traitFromSourceMappedId").alias("traitFromSourceMappedIds") + ) + return StudyIndex(_df=si_df, _schema=StudyIndex.get_schema()) @classmethod def from_source( cls: type[FinnGenStudyIndex], spark: SparkSession, + finngen_phenotype_table_url: str, + finngen_release_prefix: str, + finngen_summary_stats_url_prefix: str, + finngen_summary_stats_url_suffix: str, + sample_size: int, ) -> StudyIndex: """This function ingests study level metadata from FinnGen. Args: spark (SparkSession): Spark session object. + finngen_phenotype_table_url (str): URL to the FinnGen phenotype table. + finngen_release_prefix (str): FinnGen release prefix. + finngen_summary_stats_url_prefix (str): FinnGen summary stats URL prefix. + finngen_summary_stats_url_suffix (str): FinnGen summary stats URL suffix. + sample_size (int): Number of individuals participated in sample collection. Returns: StudyIndex: Parsed and annotated FinnGen study table. """ - json_data = urlopen(cls.finngen_phenotype_table_url).read().decode("utf-8") + json_data = urlopen(finngen_phenotype_table_url).read().decode("utf-8") rdd = spark.sparkContext.parallelize([json_data]) raw_df = spark.read.json(rdd) + return StudyIndex( _df=raw_df.select( f.concat( - f.lit(f"{cls.finngen_release_prefix}_"), f.col("phenocode") + f.concat_ws("_", f.lit(finngen_release_prefix), f.col("phenocode")) ).alias("studyId"), f.col("phenostring").alias("traitFromSource"), f.col("num_cases").cast("integer").alias("nCases"), @@ -56,24 +162,24 @@ def from_source( (f.col("num_cases") + f.col("num_controls")) .cast("integer") .alias("nSamples"), - f.lit(cls.finngen_release_prefix).alias("projectId"), + f.lit(finngen_release_prefix).alias("projectId"), f.lit("gwas").alias("studyType"), f.lit(True).alias("hasSumstats"), - f.lit("377,277 (210,870 females and 166,407 males)").alias( + f.lit("453,733 (254,618 females and 199,115 males)").alias( "initialSampleSize" ), f.array( f.struct( - f.lit(377277).cast("integer").alias("sampleSize"), + f.lit(sample_size).cast("integer").alias("sampleSize"), f.lit("Finnish").alias("ancestry"), ) ).alias("discoverySamples"), # Cohort label is consistent with GWAS Catalog curation. f.array(f.lit("FinnGen")).alias("cohorts"), f.concat( - f.lit(cls.finngen_summary_stats_url_prefix), + f.lit(finngen_summary_stats_url_prefix), f.col("phenocode"), - f.lit(cls.finngen_summary_stats_url_suffix), + f.lit(finngen_summary_stats_url_suffix), ).alias("summarystatsLocation"), ).withColumn( "ldPopulationStructure", diff --git a/src/gentropy/datasource/finngen/summary_stats.py b/src/gentropy/datasource/finngen/summary_stats.py index 0d77f7d5c..08403bed5 100644 --- a/src/gentropy/datasource/finngen/summary_stats.py +++ b/src/gentropy/datasource/finngen/summary_stats.py @@ -50,7 +50,6 @@ def from_source( Returns: SummaryStatistics: Processed summary statistics dataset """ - study_id = raw_file.split("/")[-1].split(".")[0].upper() processed_summary_stats_df = ( spark.read.schema(cls.raw_schema) .option("delimiter", "\t") @@ -59,7 +58,11 @@ def from_source( .filter(f.col("pos").cast(t.IntegerType()).isNotNull()) .select( # From the full path, extracts just the filename, and converts to upper case to get the study ID. - f.lit(study_id).alias("studyId"), + f.upper( + f.regexp_extract( + f.input_file_name(), r"([^/]+)(\.tsv\.gz|\.gz|\.tsv)", 1 + ) + ).alias("studyId"), # Add variant information. f.concat_ws( "_", diff --git a/src/gentropy/datasource/finngen_ukb_meta/__init__.py b/src/gentropy/datasource/finngen_ukb_meta/__init__.py new file mode 100644 index 000000000..8d6cfd752 --- /dev/null +++ b/src/gentropy/datasource/finngen_ukb_meta/__init__.py @@ -0,0 +1,3 @@ +"""FinnGen UKB meta-analysis data source.""" + +from __future__ import annotations diff --git a/src/gentropy/datasource/finngen_ukb_meta/study_index.py b/src/gentropy/datasource/finngen_ukb_meta/study_index.py new file mode 100644 index 000000000..eefb2da7c --- /dev/null +++ b/src/gentropy/datasource/finngen_ukb_meta/study_index.py @@ -0,0 +1,88 @@ +"""Study Index for Finngen data source.""" + +from __future__ import annotations + +from urllib.request import urlopen + +import pyspark.sql.functions as f +from pyspark.sql import SparkSession + +from gentropy.config import FinngenStudiesConfig +from gentropy.dataset.study_index import StudyIndex +from gentropy.datasource.finngen.study_index import FinnGenStudyIndex + + +class FinngenUkbMetaStudyIndex(StudyIndex): + """Study index dataset from FinnGen UKB meta-analysis.""" + + @classmethod + def from_source( + cls: type[FinngenUkbMetaStudyIndex], + spark: SparkSession, + raw_study_index_path_from_tsv: str, + efo_curation_mapping_url: str = FinngenStudiesConfig().efo_curation_mapping_url, + ) -> StudyIndex: + """This function ingests study level metadata from FinnGen UKB meta-analysis. + + Args: + spark (SparkSession): Spark session object. + raw_study_index_path_from_tsv (str): Raw study index path. + efo_curation_mapping_url (str): URL to the EFO curation mapping file. + + Returns: + StudyIndex: Parsed and annotated FinnGen UKB meta-analysis study table. + """ + # Read the raw study index and process. + study_index_df = spark.read.csv( + raw_study_index_path_from_tsv, sep="\t", header=True + ).select( + f.lit("gwas").alias("studyType"), + f.lit("FINNGEN_R11_UKB_META").alias("projectId"), + f.col("_gentropy_study_id").alias("studyId"), + f.col("name").alias("traitFromSource"), + f.lit(True).alias("hasSumstats"), + f.col("_gentropy_summary_stats_link").alias("summarystatsLocation"), + ( + f.col("fg_n_cases") + + f.col("ukbb_n_cases") + + f.col("fg_n_controls") + + f.col("ukbb_n_controls") + ) + .cast("integer") + .alias("nSamples"), + f.array( + f.struct( + (f.col("fg_n_cases") + f.col("fg_n_controls")) + .cast("integer") + .alias("sampleSize"), + f.lit("Finnish").alias("ancestry"), + ), + f.struct( + (f.col("ukbb_n_cases") + f.col("ukbb_n_controls")) + .cast("integer") + .alias("sampleSize"), + f.lit("European").alias("ancestry"), + ), + ).alias("discoverySamples"), + ) + # Add population structure. + study_index_df = study_index_df.withColumn( + "ldPopulationStructure", + cls.aggregate_and_map_ancestries(f.col("discoverySamples")), + ) + # Create study index. + study_index = StudyIndex( + _df=study_index_df, + _schema=StudyIndex.get_schema(), + ) + # Add EFO mappings. + csv_data = urlopen(efo_curation_mapping_url).readlines() + csv_rows = [row.decode("utf8") for row in csv_data] + rdd = spark.sparkContext.parallelize(csv_rows) + efo_curation_mapping = spark.read.csv(rdd, header=True, sep="\t") + study_index = FinnGenStudyIndex.join_efo_mapping( + study_index, + efo_curation_mapping, + finngen_release="R11", + ) + return study_index diff --git a/src/gentropy/datasource/finngen_ukb_meta/summary_stats.py b/src/gentropy/datasource/finngen_ukb_meta/summary_stats.py new file mode 100644 index 000000000..b77d57966 --- /dev/null +++ b/src/gentropy/datasource/finngen_ukb_meta/summary_stats.py @@ -0,0 +1,67 @@ +"""Summary statistics ingestion for FinnGen UKB meta-analysis.""" + +from __future__ import annotations + +from dataclasses import dataclass + +import pyspark.sql.functions as f +from pyspark.sql import SparkSession + +from gentropy.common.harmonise import harmonise_summary_stats +from gentropy.dataset.summary_statistics import SummaryStatistics + + +@dataclass +class FinngenUkbMetaSummaryStats: + """Summary statistics dataset for FinnGen UKB meta-analysis.""" + + @classmethod + def from_source( + cls: type[FinngenUkbMetaSummaryStats], + spark: SparkSession, + raw_summary_stats_path: str, + tmp_variant_annotation_path: str, + chromosome: str, + study_index_path: str, + ) -> SummaryStatistics: + """Ingest and harmonise all summary stats for FinnGen UKB meta-analysis data. + + Args: + spark (SparkSession): Spark session object. + raw_summary_stats_path (str): Input raw summary stats path. + tmp_variant_annotation_path (str): Input variant annotation dataset path. + chromosome (str): Which chromosome to process. + study_index_path (str): The path to study index, which is necessary in some cases to populate the sample size column. + + Returns: + SummaryStatistics: Processed summary statistics dataset for a given chromosome. + """ + # Run the harmonisation steps. + df = harmonise_summary_stats( + spark, + raw_summary_stats_path, + tmp_variant_annotation_path, + chromosome, + colname_position="POS", + colname_allele0="REF", + colname_allele1="ALT", + colname_a1freq=None, + colname_info=None, + colname_beta="all_inv_var_meta_beta", + colname_se="all_inv_var_meta_sebeta", + colname_mlog10p="all_inv_var_meta_mlogp", + colname_n=None, + ) + + # Populate the sample size column from the study index. + study_index = spark.read.parquet(study_index_path).select( + "studyId", + f.col("nSamples").cast("integer").alias("sampleSize") + ) + df = df.join(study_index, on=["studyId"], how="inner") + + # Create the summary statistics object. + return SummaryStatistics( + _df=df, + _schema=SummaryStatistics.get_schema(), + ) diff --git a/src/gentropy/datasource/gnomad/ld.py b/src/gentropy/datasource/gnomad/ld.py index 1d2d6c18f..e53410d69 100644 --- a/src/gentropy/datasource/gnomad/ld.py +++ b/src/gentropy/datasource/gnomad/ld.py @@ -1,54 +1,56 @@ """Step to import filtered version of a LD matrix (block matrix).""" + from __future__ import annotations import sys -from dataclasses import dataclass, field from functools import reduce from typing import TYPE_CHECKING import hail as hl +import numpy as np import pyspark.sql.functions as f from hail.linalg import BlockMatrix from pyspark.sql import Window from gentropy.common.spark_helpers import get_top_ranked_in_window, get_value_from_row -from gentropy.common.utils import _liftover_loci, convert_gnomad_position_to_ensembl +from gentropy.common.types import LD_Population +from gentropy.common.utils import _liftover_loci +from gentropy.config import LDIndexConfig from gentropy.dataset.ld_index import LDIndex if TYPE_CHECKING: - from pyspark.sql import DataFrame + from pyspark.sql import DataFrame, Row -@dataclass class GnomADLDMatrix: - """Toolset ot interact with GnomAD LD dataset (version: r2.1.1). - - Datasets are accessed in Hail's native format, as provided by the [GnomAD consortium](https://gnomad.broadinstitute.org/downloads/#v2-linkage-disequilibrium). - - Attributes: - ld_matrix_template (str): Template for the LD matrix path. Defaults to "gs://gcp-public-data--gnomad/release/2.1.1/ld/gnomad.genomes.r2.1.1.{POP}.common.adj.ld.bm". - ld_index_raw_template (str): Template for the LD index path. Defaults to "gs://gcp-public-data--gnomad/release/2.1.1/ld/gnomad.genomes.r2.1.1.{POP}.common.ld.variant_indices.ht". - grch37_to_grch38_chain_path (str): Path to the chain file used to lift over the coordinates. Defaults to "gs://hail-common/references/grch37_to_grch38.over.chain.gz". - ld_populations (list[str]): List of populations to use to build the LDIndex. Defaults to ["afr", "amr", "asj", "eas", "fin", "nfe", "nwe", "seu"]. - """ - - ld_matrix_template: str = "gs://gcp-public-data--gnomad/release/2.1.1/ld/gnomad.genomes.r2.1.1.{POP}.common.adj.ld.bm" - ld_index_raw_template: str = "gs://gcp-public-data--gnomad/release/2.1.1/ld/gnomad.genomes.r2.1.1.{POP}.common.ld.variant_indices.ht" - grch37_to_grch38_chain_path: str = ( - "gs://hail-common/references/grch37_to_grch38.over.chain.gz" - ) - ld_populations: list[str] = field( - default_factory=lambda: [ - "afr", # African-American - "amr", # American Admixed/Latino - "asj", # Ashkenazi Jewish - "eas", # East Asian - "fin", # Finnish - "nfe", # Non-Finnish European - "nwe", # Northwestern European - "seu", # Southeastern European - ] - ) + """Toolset ot interact with GnomAD LD dataset (version: r2.1.1).""" + + def __init__( + self, + ld_matrix_template: str = LDIndexConfig().ld_matrix_template, + ld_index_raw_template: str = LDIndexConfig().ld_index_raw_template, + grch37_to_grch38_chain_path: str = LDIndexConfig().grch37_to_grch38_chain_path, + ld_populations: list[LD_Population | str] = LDIndexConfig().ld_populations, + liftover_ht_path: str = LDIndexConfig().liftover_ht_path, + ): + """Initialize. + + Datasets are accessed in Hail's native format, as provided by the [GnomAD consortium](https://gnomad.broadinstitute.org/downloads/#v2-linkage-disequilibrium). + + Args: + ld_matrix_template (str): Template for the LD matrix path. + ld_index_raw_template (str): Template for the LD index path. + grch37_to_grch38_chain_path (str): Path to the chain file used to lift over the coordinates. + ld_populations (list[LD_Population | str]): List of populations to use to build the LDIndex. + liftover_ht_path (str): Path to the liftover ht file. + + Default values are set in LDIndexConfig. + """ + self.ld_matrix_template = ld_matrix_template + self.ld_index_raw_template = ld_index_raw_template + self.grch37_to_grch38_chain_path = grch37_to_grch38_chain_path + self.ld_populations = ld_populations + self.liftover_ht_path = liftover_ht_path @staticmethod def _aggregate_ld_index_across_populations( @@ -175,24 +177,15 @@ def _process_variant_indices( ld_index_38.to_spark() # Filter out variants where the liftover failed .filter(f.col("`locus_GRCh38.position`").isNotNull()) - .withColumn( - "chromosome", f.regexp_replace("`locus_GRCh38.contig`", "chr", "") - ) - .withColumn( - "position", - convert_gnomad_position_to_ensembl( - f.col("`locus_GRCh38.position`"), - f.col("`alleles`").getItem(0), - f.col("`alleles`").getItem(1), - ), - ) .select( - "chromosome", - "position", + f.regexp_replace("`locus_GRCh38.contig`", "chr", "").alias( + "chromosome" + ), + f.col("`locus_GRCh38.position`").alias("position"), f.concat_ws( "_", - f.col("chromosome"), - f.col("position"), + f.regexp_replace("`locus_GRCh38.contig`", "chr", ""), + f.col("`locus_GRCh38.position`"), f.col("`alleles`").getItem(0), f.col("`alleles`").getItem(1), ).alias("variantId"), @@ -218,9 +211,9 @@ def _resolve_variant_indices( DataFrame: Dataframe with variant IDs instead of `i` and `j` indices """ ld_index_i = ld_index.selectExpr( - "idx as i", "variantId as variantId_i", "chromosome" + "idx as i", "variantId as variantIdI", "chromosome" ) - ld_index_j = ld_index.selectExpr("idx as j", "variantId as variantId_j") + ld_index_j = ld_index.selectExpr("idx as j", "variantId as variantIdJ") return ( ld_matrix.join(ld_index_i, on="i", how="inner") .join(ld_index_j, on="j", how="inner") @@ -238,35 +231,35 @@ def _transpose_ld_matrix(ld_matrix: DataFrame) -> DataFrame: DataFrame: Square LD matrix without diagonal duplicates Examples: - >>> df = spark.createDataFrame( - ... [ - ... (1, 1, 1.0, "1", "AFR"), - ... (1, 2, 0.5, "1", "AFR"), - ... (2, 2, 1.0, "1", "AFR"), - ... ], - ... ["variantId_i", "variantId_j", "r", "chromosome", "population"], - ... ) - >>> GnomADLDMatrix._transpose_ld_matrix(df).show() - +-----------+-----------+---+----------+----------+ - |variantId_i|variantId_j| r|chromosome|population| - +-----------+-----------+---+----------+----------+ - | 1| 2|0.5| 1| AFR| - | 1| 1|1.0| 1| AFR| - | 2| 1|0.5| 1| AFR| - | 2| 2|1.0| 1| AFR| - +-----------+-----------+---+----------+----------+ - + >>> df = spark.createDataFrame( + ... [ + ... (1, 1, 1.0, "1", "AFR"), + ... (1, 2, 0.5, "1", "AFR"), + ... (2, 2, 1.0, "1", "AFR"), + ... ], + ... ["variantIdI", "variantIdJ", "r", "chromosome", "population"], + ... ) + >>> GnomADLDMatrix._transpose_ld_matrix(df).show() + +----------+----------+---+----------+----------+ + |variantIdI|variantIdJ| r|chromosome|population| + +----------+----------+---+----------+----------+ + | 1| 2|0.5| 1| AFR| + | 1| 1|1.0| 1| AFR| + | 2| 1|0.5| 1| AFR| + | 2| 2|1.0| 1| AFR| + +----------+----------+---+----------+----------+ + """ ld_matrix_transposed = ld_matrix.selectExpr( - "variantId_i as variantId_j", - "variantId_j as variantId_i", + "variantIdI as variantIdJ", + "variantIdJ as variantIdI", "r", "chromosome", "population", ) - return ld_matrix.filter( - f.col("variantId_i") != f.col("variantId_j") - ).unionByName(ld_matrix_transposed) + return ld_matrix.filter(f.col("variantIdI") != f.col("variantIdJ")).unionByName( + ld_matrix_transposed + ) def as_ld_index( self: GnomADLDMatrix, @@ -307,8 +300,8 @@ def as_ld_index( GnomADLDMatrix._transpose_ld_matrix( reduce(lambda df1, df2: df1.unionByName(df2), ld_indices_unaggregated) ) - .withColumnRenamed("variantId_i", "variantId") - .withColumnRenamed("variantId_j", "tagVariantId") + .withColumnRenamed("variantIdI", "variantId") + .withColumnRenamed("variantIdJ", "tagVariantId") ) return LDIndex( _df=self._aggregate_ld_index_across_populations(ld_index_unaggregated), @@ -345,7 +338,6 @@ def get_ld_variants( & (f.col("position") <= end) ) .select("chromosome", "position", "variantId", "idx") - .persist() ) if ld_index_df.limit(1).count() == 0: @@ -395,7 +387,7 @@ def _extract_square_matrix( .join( ld_index_df.select( f.col("idx").alias("idx_i"), - f.col("variantId").alias("variantId_i"), + f.col("variantId").alias("variantIdI"), ), on="idx_i", how="inner", @@ -403,12 +395,12 @@ def _extract_square_matrix( .join( ld_index_df.select( f.col("idx").alias("idx_j"), - f.col("variantId").alias("variantId_j"), + f.col("variantId").alias("variantIdJ"), ), on="idx_j", how="inner", ) - .select("variantId_i", "variantId_j", "r") + .select("variantIdI", "variantIdJ", "r") ) def get_ld_matrix_slice( @@ -448,3 +440,115 @@ def get_ld_matrix_slice( .alias("r"), ) ) + + def get_locus_index( + self: GnomADLDMatrix, + study_locus_row: Row, + radius: int = 500_000, + major_population: str = "nfe", + ) -> DataFrame: + """Extract hail matrix index from StudyLocus rows. + + Args: + study_locus_row (Row): Study-locus row + radius (int): Locus radius to extract from gnomad matrix + major_population (str): Major population to extract from gnomad matrix, default is "nfe" + + Returns: + DataFrame: Returns the index of the gnomad matrix for the locus + + """ + chromosome = str("chr" + study_locus_row["chromosome"]) + start = study_locus_row["position"] - radius + end = study_locus_row["position"] + radius + + liftover_ht = hl.read_table(self.liftover_ht_path) + liftover_ht = ( + liftover_ht.filter( + (liftover_ht.locus.contig == chromosome) + & (liftover_ht.locus.position >= start) + & (liftover_ht.locus.position <= end) + ) + .key_by() + .select("locus", "alleles", "original_locus") + .key_by("original_locus", "alleles") + .naive_coalesce(20) + ) + + hail_index = hl.read_table( + self.ld_index_raw_template.format(POP=major_population) + ) + + joined_index = ( + liftover_ht.join(hail_index, how="inner").order_by("idx").to_spark() + ) + + return joined_index + + @staticmethod + def get_numpy_matrix( + locus_index: DataFrame, + gnomad_ancestry: str = "nfe", + ) -> np.ndarray: + """Extract the LD block matrix for a locus. + + Args: + locus_index (DataFrame): hail matrix variant index table + gnomad_ancestry (str): GnomAD major ancestry label eg. `nfe` + + Returns: + np.ndarray: LD block matrix for the locus + """ + idx = [row["idx"] for row in locus_index.select("idx").collect()] + + half_matrix = ( + BlockMatrix.read( + GnomADLDMatrix().ld_matrix_template.format(POP=gnomad_ancestry) + ) + .filter(idx, idx) + .to_numpy() + ) + + return (half_matrix + half_matrix.T) - np.diag(np.diag(half_matrix)) + + def get_locus_index_boundaries( + self: GnomADLDMatrix, + study_locus_row: Row, + major_population: str = "nfe", + ) -> DataFrame: + """Extract hail matrix index from StudyLocus rows. + + Args: + study_locus_row (Row): Study-locus row + major_population (str): Major population to extract from gnomad matrix, default is "nfe" + + Returns: + DataFrame: Returns the index of the gnomad matrix for the locus + + """ + chromosome = str("chr" + study_locus_row["chromosome"]) + start = int(study_locus_row["locusStart"]) + end = int(study_locus_row["locusEnd"]) + + liftover_ht = hl.read_table(self.liftover_ht_path) + liftover_ht = ( + liftover_ht.filter( + (liftover_ht.locus.contig == chromosome) + & (liftover_ht.locus.position >= start) + & (liftover_ht.locus.position <= end) + ) + .key_by() + .select("locus", "alleles", "original_locus") + .key_by("original_locus", "alleles") + .naive_coalesce(20) + ) + + hail_index = hl.read_table( + self.ld_index_raw_template.format(POP=major_population) + ) + + joined_index = ( + liftover_ht.join(hail_index, how="inner").order_by("idx").to_spark() + ) + + return joined_index diff --git a/src/gentropy/datasource/gnomad/variants.py b/src/gentropy/datasource/gnomad/variants.py index 58f5f8093..0575261c2 100644 --- a/src/gentropy/datasource/gnomad/variants.py +++ b/src/gentropy/datasource/gnomad/variants.py @@ -1,68 +1,46 @@ """Import gnomAD variants dataset.""" + from __future__ import annotations -from dataclasses import dataclass, field from typing import TYPE_CHECKING import hail as hl +import pyspark.sql.functions as f +import pyspark.sql.types as t -from gentropy.dataset.variant_annotation import VariantAnnotation +from gentropy.common.types import VariantPopulation +from gentropy.config import GnomadVariantConfig, VariantIndexConfig +from gentropy.dataset.variant_index import VariantIndex if TYPE_CHECKING: - from hail.expr.expressions import Int32Expression, StringExpression + pass -@dataclass class GnomADVariants: - """GnomAD variants included in the GnomAD genomes dataset. - - Attributes: - gnomad_genomes (str): Path to gnomAD genomes hail table. Defaults to gnomAD's 4.0 release. - chain_hail_38_37 (str): Path to GRCh38 to GRCh37 chain file. Defaults to Hail's chain file. - populations (list[str]): List of populations to include. Defaults to all populations. - """ - - gnomad_genomes: str = "gs://gcp-public-data--gnomad/release/4.0/ht/genomes/gnomad.genomes.v4.0.sites.ht/" - chain_hail_38_37: str = "gs://hail-common/references/grch38_to_grch37.over.chain.gz" - populations: list[str] = field( - default_factory=lambda: [ - "afr", # African-American - "amr", # American Admixed/Latino - "ami", # Amish ancestry - "asj", # Ashkenazi Jewish - "eas", # East Asian - "fin", # Finnish - "nfe", # Non-Finnish European - "mid", # Middle Eastern - "sas", # South Asian - "remaining", # Other - ] - ) - - @staticmethod - def _convert_gnomad_position_to_ensembl_hail( - position: Int32Expression, - reference: StringExpression, - alternate: StringExpression, - ) -> Int32Expression: - """Convert GnomAD variant position to Ensembl variant position in hail table. - - For indels (the reference or alternate allele is longer than 1), then adding 1 to the position, for SNPs, the position is unchanged. - More info about the problem: https://www.biostars.org/p/84686/ + """GnomAD variants included in the GnomAD genomes dataset.""" + + def __init__( + self, + gnomad_genomes_path: str = GnomadVariantConfig().gnomad_genomes_path, + gnomad_variant_populations: list[ + VariantPopulation | str + ] = GnomadVariantConfig().gnomad_variant_populations, + hash_threshold: int = VariantIndexConfig().hash_threshold, + ): + """Initialize. Args: - position (Int32Expression): Position of the variant in the GnomAD genome. - reference (StringExpression): The reference allele. - alternate (StringExpression): The alternate allele + gnomad_genomes_path (str): Path to gnomAD genomes hail table. + gnomad_variant_populations (list[VariantPopulation | str]): List of populations to include. + hash_threshold (int): longer variant ids will be hashed. - Returns: - Int32Expression: The position of the variant according to Ensembl genome. + All defaults are stored in GnomadVariantConfig. """ - return hl.if_else( - (reference.length() > 1) | (alternate.length() > 1), position + 1, position - ) + self.gnomad_genomes_path = gnomad_genomes_path + self.gnomad_variant_populations = gnomad_variant_populations + self.lenght_threshold = hash_threshold - def as_variant_annotation(self: GnomADVariants) -> VariantAnnotation: + def as_variant_index(self: GnomADVariants) -> VariantIndex: """Generate variant annotation dataset from gnomAD. Some relevant modifications to the original dataset are: @@ -72,28 +50,23 @@ def as_variant_annotation(self: GnomADVariants) -> VariantAnnotation: 3. Field names are converted to camel case to follow the convention. Returns: - VariantAnnotation: Variant annotation dataset + VariantIndex: GnomaAD variants dataset. """ # Load variants dataset ht = hl.read_table( - self.gnomad_genomes, + self.gnomad_genomes_path, _load_refs=False, ) - # Liftover - grch37 = hl.get_reference("GRCh37") - grch38 = hl.get_reference("GRCh38") - grch38.add_liftover(self.chain_hail_38_37, grch37) - # Drop non biallelic variants ht = ht.filter(ht.alleles.length() == 2) - # Liftover - ht = ht.annotate(locus_GRCh37=hl.liftover(ht.locus, "GRCh37")) + # Select relevant fields and nested records to create class - return VariantAnnotation( + return VariantIndex( _df=( ht.select( - gnomadVariantId=hl.str("-").join( + # Extract mandatory fields: + variantId=hl.str("_").join( [ ht.locus.contig.replace("chr", ""), hl.str(ht.locus.position), @@ -102,68 +75,80 @@ def as_variant_annotation(self: GnomADVariants) -> VariantAnnotation: ] ), chromosome=ht.locus.contig.replace("chr", ""), - position=GnomADVariants._convert_gnomad_position_to_ensembl_hail( - ht.locus.position, ht.alleles[0], ht.alleles[1] - ), - variantId=hl.str("_").join( - [ - ht.locus.contig.replace("chr", ""), - hl.str( - GnomADVariants._convert_gnomad_position_to_ensembl_hail( - ht.locus.position, ht.alleles[0], ht.alleles[1] - ) - ), - ht.alleles[0], - ht.alleles[1], - ] - ), - chromosomeB37=ht.locus_GRCh37.contig.replace("chr", ""), - positionB37=ht.locus_GRCh37.position, + position=ht.locus.position, referenceAllele=ht.alleles[0], alternateAllele=ht.alleles[1], - rsIds=ht.rsid, - alleleType=ht.allele_info.allele_type, + # Extract allele frequencies from populations of interest: alleleFrequencies=hl.set( - [f"{pop}_adj" for pop in self.populations] + [f"{pop}_adj" for pop in self.gnomad_variant_populations] ).map( lambda p: hl.struct( populationName=p, alleleFrequency=ht.freq[ht.globals.freq_index_dict[p]].AF, ) ), - vep=hl.struct( - mostSevereConsequence=ht.vep.most_severe_consequence, - transcriptConsequences=hl.map( - lambda x: hl.struct( - aminoAcids=x.amino_acids, - consequenceTerms=x.consequence_terms, - geneId=x.gene_id, - lof=x.lof, + # Extract in silico predictors: + inSilicoPredictors=hl.array( + [ + hl.struct( + method=hl.str("spliceai"), + assessment=hl.missing(hl.tstr), + score=hl.expr.functions.float32( + ht.in_silico_predictors.spliceai_ds_max + ), + assessmentFlag=hl.missing(hl.tstr), + targetId=hl.missing(hl.tstr), ), - # Only keeping canonical transcripts - ht.vep.transcript_consequences.filter( - lambda x: (x.canonical == 1) - & (x.gene_symbol_source == "HGNC") + hl.struct( + method=hl.str("pangolin"), + assessment=hl.missing(hl.tstr), + score=hl.expr.functions.float32( + ht.in_silico_predictors.pangolin_largest_ds + ), + assessmentFlag=hl.missing(hl.tstr), + targetId=hl.missing(hl.tstr), ), - ), + ] ), - inSilicoPredictors=hl.struct( - cadd=hl.struct( - phred=ht.in_silico_predictors.cadd.phred, - raw=ht.in_silico_predictors.cadd.raw_score, - ), - revelMax=ht.in_silico_predictors.revel_max, - spliceaiDsMax=ht.in_silico_predictors.spliceai_ds_max, - pangolinLargestDs=ht.in_silico_predictors.pangolin_largest_ds, - phylop=ht.in_silico_predictors.phylop, - siftMax=ht.in_silico_predictors.sift_max, - polyphenMax=ht.in_silico_predictors.polyphen_max, + # Extract cross references to GnomAD: + dbXrefs=hl.array( + [ + hl.struct( + id=hl.str("-").join( + [ + ht.locus.contig.replace("chr", ""), + hl.str(ht.locus.position), + ht.alleles[0], + ht.alleles[1], + ] + ), + source=hl.str("gnomad"), + ) + ] ), ) .key_by("chromosome", "position") .drop("locus", "alleles") .select_globals() .to_spark(flatten=False) + .withColumns( + { + # Once The parsing is done, we have to drop objects with no score from inSilicoPredictors: + "inSilicoPredictors": f.filter( + f.col("inSilicoPredictors"), + lambda predictor: predictor["score"].isNotNull(), + ), + # Generate a variantId that is hashed for long variant ids: + "variantId": VariantIndex.hash_long_variant_ids( + f.col("variantId"), + f.col("chromosome"), + f.col("position"), + self.lenght_threshold, + ), + # We are not capturing the most severe consequence from GnomAD, but this column needed for the schema: + "mostSevereConsequenceId": f.lit(None).cast(t.StringType()), + } + ) ), - _schema=VariantAnnotation.get_schema(), + _schema=VariantIndex.get_schema(), ) diff --git a/src/gentropy/datasource/gwas_catalog/associations.py b/src/gentropy/datasource/gwas_catalog/associations.py index 5ff499f2c..31238e425 100644 --- a/src/gentropy/datasource/gwas_catalog/associations.py +++ b/src/gentropy/datasource/gwas_catalog/associations.py @@ -1,4 +1,5 @@ """Study Locus for GWAS Catalog data source.""" + from __future__ import annotations import importlib.resources as pkg_resources @@ -7,30 +8,63 @@ from itertools import chain from typing import TYPE_CHECKING -import numpy as np import pyspark.sql.functions as f -from pyspark.sql.types import DoubleType, IntegerType, LongType +from pyspark.sql.types import DoubleType, FloatType, IntegerType, StringType from pyspark.sql.window import Window from gentropy.assets import data from gentropy.common.spark_helpers import ( - calculate_neglog_pvalue, get_record_with_maximum_value, - pvalue_to_zscore, + get_standard_error_from_confidence_interval, ) -from gentropy.common.utils import parse_efos +from gentropy.common.utils import convert_odds_ratio_to_beta, parse_efos +from gentropy.config import WindowBasedClumpingStepConfig from gentropy.dataset.study_locus import StudyLocus, StudyLocusQualityCheck if TYPE_CHECKING: from pyspark.sql import Column, DataFrame - from gentropy.dataset.variant_annotation import VariantAnnotation + from gentropy.dataset.variant_index import VariantIndex @dataclass class GWASCatalogCuratedAssociationsParser: """GWAS Catalog curated associations parser.""" + @staticmethod + def convert_gnomad_position_to_ensembl( + position: Column, reference: Column, alternate: Column + ) -> Column: + """Convert GnomAD variant position to Ensembl variant position. + + For indels (the reference or alternate allele is longer than 1), then adding 1 to the position, for SNPs, + the position is unchanged. More info about the problem: https://www.biostars.org/p/84686/ + + Args: + position (Column): Position of the variant in GnomAD's coordinates system. + reference (Column): The reference allele in GnomAD's coordinates system. + alternate (Column): The alternate allele in GnomAD's coordinates system. + + Returns: + Column: The position of the variant in the Ensembl genome. + + Examples: + >>> d = [(1, "A", "C"), (2, "AA", "C"), (3, "A", "AA")] + >>> df = spark.createDataFrame(d).toDF("position", "reference", "alternate") + >>> df.withColumn("new_position", GWASCatalogCuratedAssociationsParser.convert_gnomad_position_to_ensembl(f.col("position"), f.col("reference"), f.col("alternate"))).show() + +--------+---------+---------+------------+ + |position|reference|alternate|new_position| + +--------+---------+---------+------------+ + | 1| A| C| 1| + | 2| AA| C| 3| + | 3| A| AA| 4| + +--------+---------+---------+------------+ + + """ + return f.when( + (f.length(reference) > 1) | (f.length(alternate) > 1), position + 1 + ).otherwise(position) + @staticmethod def _parse_pvalue(pvalue: Column) -> tuple[Column, Column]: """Parse p-value column. @@ -106,8 +140,8 @@ def _normalise_pvaluetext(p_value_text: Column) -> Column: ) @staticmethod - def _normalise_risk_allele(risk_allele: Column) -> Column: - """Normalised risk allele column to a standardised format. + def _extract_risk_allele(risk_allele: Column) -> Column: + """Extract risk allele from provided "STRONGEST SNP-RISK ALLELE" input column. If multiple risk alleles are present, the first one is returned. @@ -121,7 +155,7 @@ def _normalise_risk_allele(risk_allele: Column) -> Column: >>> import pyspark.sql.types as t >>> d = [("rs1234-A-G"), ("rs1234-A"), ("rs1234-A; rs1235-G")] >>> df = spark.createDataFrame(d, t.StringType()) - >>> df.withColumn('normalised', GWASCatalogCuratedAssociationsParser._normalise_risk_allele(f.col('value'))).show() + >>> df.withColumn('normalised', GWASCatalogCuratedAssociationsParser._extract_risk_allele(f.col('value'))).show() +------------------+----------+ | value|normalised| +------------------+----------+ @@ -130,7 +164,6 @@ def _normalise_risk_allele(risk_allele: Column) -> Column: |rs1234-A; rs1235-G| A| +------------------+----------+ - """ # GWAS Catalog to risk allele mapping return f.split(f.split(risk_allele, "; ").getItem(0), "-").getItem(1) @@ -161,14 +194,14 @@ def _collect_rsids( return f.array_distinct(f.array(snp_id, snp_id_current, risk_allele)) @staticmethod - def _map_to_variant_annotation_variants( - gwas_associations: DataFrame, variant_annotation: VariantAnnotation + def _map_variants_to_gnomad_variants( + gwas_associations: DataFrame, variant_index: VariantIndex ) -> DataFrame: """Add variant metadata in associations. Args: - gwas_associations (DataFrame): raw GWAS Catalog associations - variant_annotation (VariantAnnotation): variant annotation dataset + gwas_associations (DataFrame): raw GWAS Catalog associations. + variant_index (VariantIndex): GnomaAD variants dataset with allele frequencies. Returns: DataFrame: GWAS Catalog associations data including `variantId`, `referenceAllele`, @@ -176,35 +209,41 @@ def _map_to_variant_annotation_variants( """ # Subset of GWAS Catalog associations required for resolving variant IDs: gwas_associations_subset = gwas_associations.select( - "studyLocusId", + "rowId", f.col("CHR_ID").alias("chromosome"), - f.col("CHR_POS").cast(IntegerType()).alias("position"), + # The positions from GWAS Catalog are from ensembl that causes discrepancy for indels: + f.col("CHR_POS").cast(IntegerType()).alias("ensemblPosition"), # List of all SNPs associated with the variant GWASCatalogCuratedAssociationsParser._collect_rsids( f.split(f.col("SNPS"), "; ").getItem(0), f.col("SNP_ID_CURRENT"), f.split(f.col("STRONGEST SNP-RISK ALLELE"), "; ").getItem(0), ).alias("rsIdsGwasCatalog"), - GWASCatalogCuratedAssociationsParser._normalise_risk_allele( + GWASCatalogCuratedAssociationsParser._extract_risk_allele( f.col("STRONGEST SNP-RISK ALLELE") ).alias("riskAllele"), ) # Subset of variant annotation required for GWAS Catalog annotations: - va_subset = variant_annotation.df.select( + va_subset = variant_index.df.select( "variantId", "chromosome", + # Calculate the position in Ensembl coordinates for indels: + GWASCatalogCuratedAssociationsParser.convert_gnomad_position_to_ensembl( + f.col("position"), + f.col("referenceAllele"), + f.col("alternateAllele"), + ).alias("ensemblPosition"), + # Keeping GnomAD position: "position", f.col("rsIds").alias("rsIdsGnomad"), "referenceAllele", "alternateAllele", "alleleFrequencies", - variant_annotation.max_maf().alias("maxMaf"), + variant_index.max_maf().alias("maxMaf"), ).join( - f.broadcast( - gwas_associations_subset.select("chromosome", "position").distinct() - ), - on=["chromosome", "position"], + gwas_associations_subset.select("chromosome", "ensemblPosition").distinct(), + on=["chromosome", "ensemblPosition"], how="inner", ) @@ -212,14 +251,14 @@ def _map_to_variant_annotation_variants( # based on rsIds or allele concordance) filtered_associations = ( gwas_associations_subset.join( - f.broadcast(va_subset), - on=["chromosome", "position"], + va_subset, + on=["chromosome", "ensemblPosition"], how="left", ) .withColumn( "rsIdFilter", GWASCatalogCuratedAssociationsParser._flag_mappings_to_retain( - f.col("studyLocusId"), + f.col("rowId"), GWASCatalogCuratedAssociationsParser._compare_rsids( f.col("rsIdsGnomad"), f.col("rsIdsGwasCatalog") ), @@ -228,7 +267,7 @@ def _map_to_variant_annotation_variants( .withColumn( "concordanceFilter", GWASCatalogCuratedAssociationsParser._flag_mappings_to_retain( - f.col("studyLocusId"), + f.col("rowId"), GWASCatalogCuratedAssociationsParser._check_concordance( f.col("riskAllele"), f.col("referenceAllele"), @@ -246,11 +285,11 @@ def _map_to_variant_annotation_variants( ) ) - # Keep only highest maxMaf variant per studyLocusId + # Keep only highest maxMaf variant per rowId fully_mapped_associations = get_record_with_maximum_value( - filtered_associations, grouping_col="studyLocusId", sorting_col="maxMaf" + filtered_associations, grouping_col="rowId", sorting_col="maxMaf" ).select( - "studyLocusId", + "rowId", "variantId", "referenceAllele", "alternateAllele", @@ -259,7 +298,7 @@ def _map_to_variant_annotation_variants( ) return gwas_associations.join( - fully_mapped_associations, on="studyLocusId", how="left" + fully_mapped_associations, on="rowId", how="left" ) @staticmethod @@ -528,175 +567,107 @@ def _are_alleles_palindromic( @staticmethod def _harmonise_beta( - risk_allele: Column, - reference_allele: Column, - alternate_allele: Column, effect_size: Column, confidence_interval: Column, + flipping_needed: Column, ) -> Column: - """A function to extract the beta value from the effect size and confidence interval. + """A function to extract the beta value from the effect size and confidence interval and harmonises for the alternate allele. If the confidence interval contains the word "increase" or "decrease" it indicates, we are dealing with betas. - If it's "increase" and the effect size needs to be harmonized, then multiply the effect size by -1 + If it's "increase" and the effect size needs to be harmonized, then multiply the effect size by -1. + The sign of the effect size is flipped if the confidence interval contains "decrease". + + eg. if the reported value is 0.5, and the confidence interval tells "decrease"? -> beta is -0.5 Args: - risk_allele (Column): Risk allele column - reference_allele (Column): Reference allele column - alternate_allele (Column): Alternate allele column - effect_size (Column): GWAS Catalog effect size column - confidence_interval (Column): GWAS Catalog confidence interval column + effect_size (Column): GWAS Catalog effect size column. + confidence_interval (Column): GWAS Catalog confidence interval column to know the direction of the effect. + flipping_needed (Column): Boolean flag indicating if effect needs to be flipped based on the alleles. Returns: Column: A column containing the beta value. + + Examples: + >>> d = [ + ... # positive effect -no flipping: + ... (0.5, 'increase', False), + ... # Positive effect - flip: + ... (0.5, 'decrease', False), + ... # Positive effect - flip: + ... (0.5, 'decrease', True), + ... # Negative effect - no flip: + ... (0.5, 'increase', True), + ... # Negative effect - flip: + ... (0.5, 'decrease', False), + ... ] + >>> ( + ... spark.createDataFrame(d, ['effect', 'ci_text', 'flip']) + ... .select("effect", "ci_text", 'flip', GWASCatalogCuratedAssociationsParser._harmonise_beta(f.col("effect"), f.col("ci_text"), f.lit(False)).alias("beta")) + ... .show() + ... ) + +------+--------+-----+----+ + |effect| ci_text| flip|beta| + +------+--------+-----+----+ + | 0.5|increase|false| 0.5| + | 0.5|decrease|false|-0.5| + | 0.5|decrease| true|-0.5| + | 0.5|increase| true| 0.5| + | 0.5|decrease|false|-0.5| + +------+--------+-----+----+ + """ return ( f.when( - GWASCatalogCuratedAssociationsParser._are_alleles_palindromic( - reference_allele, alternate_allele - ), - None, - ) - .when( - ( - GWASCatalogCuratedAssociationsParser._effect_needs_harmonisation( - risk_allele, reference_allele - ) - & confidence_interval.contains("increase") - ) - | ( - ~GWASCatalogCuratedAssociationsParser._effect_needs_harmonisation( - risk_allele, reference_allele - ) - & confidence_interval.contains("decrease") - ), + (flipping_needed & confidence_interval.contains("increase")) + | (~flipping_needed & confidence_interval.contains("decrease")), -effect_size, ) .otherwise(effect_size) .cast(DoubleType()) ) - @staticmethod - def _harmonise_beta_ci( - risk_allele: Column, - reference_allele: Column, - alternate_allele: Column, - effect_size: Column, - confidence_interval: Column, - p_value: Column, - direction: str, - ) -> Column: - """Calculating confidence intervals for beta values. - - Args: - risk_allele (Column): Risk allele column - reference_allele (Column): Reference allele column - alternate_allele (Column): Alternate allele column - effect_size (Column): GWAS Catalog effect size column - confidence_interval (Column): GWAS Catalog confidence interval column - p_value (Column): GWAS Catalog p-value column - direction (str): This is the direction of the confidence interval. It can be either "upper" or "lower". - - Returns: - Column: The upper and lower bounds of the confidence interval for the beta coefficient. - """ - zscore_95 = f.lit(1.96) - beta = GWASCatalogCuratedAssociationsParser._harmonise_beta( - risk_allele, - reference_allele, - alternate_allele, - effect_size, - confidence_interval, - ) - zscore = pvalue_to_zscore(p_value) - return ( - f.when(f.lit(direction) == "upper", beta + f.abs(zscore_95 * beta) / zscore) - .when(f.lit(direction) == "lower", beta - f.abs(zscore_95 * beta) / zscore) - .otherwise(None) - ) - @staticmethod def _harmonise_odds_ratio( - risk_allele: Column, - reference_allele: Column, - alternate_allele: Column, effect_size: Column, - confidence_interval: Column, + flipping_needed: Column, ) -> Column: - """Harmonizing odds ratio. + """Odds ratio is either propagated as is, or flipped if indicated, meaning returning a reciprocal value. Args: - risk_allele (Column): Risk allele column - reference_allele (Column): Reference allele column - alternate_allele (Column): Alternate allele column - effect_size (Column): GWAS Catalog effect size column - confidence_interval (Column): GWAS Catalog confidence interval column + effect_size (Column): containing effect size, + flipping_needed (Column): Boolean flag indicating if effect needs to be flipped Returns: Column: A column with the odds ratio, or 1/odds_ratio if harmonization required. + + Examples: + >>> d = [(0.5, False), (0.5, True), (0.0, False), (0.0, True)] + >>> ( + ... spark.createDataFrame(d, ['effect', 'flip']) + ... .select("effect", "flip", GWASCatalogCuratedAssociationsParser._harmonise_odds_ratio(f.col("effect"), f.col("flip")).alias("odds_ratio")) + ... .show() + ... ) + +------+-----+----------+ + |effect| flip|odds_ratio| + +------+-----+----------+ + | 0.5|false| 0.5| + | 0.5| true| 2.0| + | 0.0|false| 0.0| + | 0.0| true| null| + +------+-----+----------+ + """ return ( - f.when( - GWASCatalogCuratedAssociationsParser._are_alleles_palindromic( - reference_allele, alternate_allele - ), - None, - ) + # We are not flipping zero effect size: + f.when((effect_size.cast(DoubleType()) == 0) & flipping_needed, f.lit(None)) .when( - ( - GWASCatalogCuratedAssociationsParser._effect_needs_harmonisation( - risk_allele, reference_allele - ) - & ~confidence_interval.rlike("|".join(["decrease", "increase"])) - ), + flipping_needed, 1 / effect_size, ) .otherwise(effect_size) .cast(DoubleType()) ) - @staticmethod - def _harmonise_odds_ratio_ci( - risk_allele: Column, - reference_allele: Column, - alternate_allele: Column, - effect_size: Column, - confidence_interval: Column, - p_value: Column, - direction: str, - ) -> Column: - """Calculating confidence intervals for beta values. - - Args: - risk_allele (Column): Risk allele column - reference_allele (Column): Reference allele column - alternate_allele (Column): Alternate allele column - effect_size (Column): GWAS Catalog effect size column - confidence_interval (Column): GWAS Catalog confidence interval column - p_value (Column): GWAS Catalog p-value column - direction (str): This is the direction of the confidence interval. It can be either "upper" or "lower". - - Returns: - Column: The upper and lower bounds of the 95% confidence interval for the odds ratio. - """ - zscore_95 = f.lit(1.96) - odds_ratio = GWASCatalogCuratedAssociationsParser._harmonise_odds_ratio( - risk_allele, - reference_allele, - alternate_allele, - effect_size, - confidence_interval, - ) - odds_ratio_estimate = f.log(odds_ratio) - zscore = pvalue_to_zscore(p_value) - odds_ratio_se = odds_ratio_estimate / zscore - return f.when( - f.lit(direction) == "upper", - f.exp(odds_ratio_estimate + f.abs(zscore_95 * odds_ratio_se)), - ).when( - f.lit(direction) == "lower", - f.exp(odds_ratio_estimate - f.abs(zscore_95 * odds_ratio_se)), - ) - @staticmethod def _concatenate_substudy_description( association_trait: Column, pvalue_text: Column, mapped_trait_uri: Column @@ -774,7 +745,7 @@ def _qc_all( qc = GWASCatalogCuratedAssociationsParser._qc_variant_interactions( qc, strongest_snp_risk_allele ) - qc = GWASCatalogCuratedAssociationsParser._qc_subsignificant_associations( + qc = StudyLocus._qc_subsignificant_associations( qc, p_value_mantissa, p_value_exponent, p_value_cutoff ) qc = GWASCatalogCuratedAssociationsParser._qc_genomic_location( @@ -810,47 +781,6 @@ def _qc_variant_interactions( StudyLocusQualityCheck.COMPOSITE_FLAG, ) - @staticmethod - def _qc_subsignificant_associations( - qc: Column, - p_value_mantissa: Column, - p_value_exponent: Column, - pvalue_cutoff: float, - ) -> Column: - """Flag associations below significant threshold. - - Args: - qc (Column): QC column - p_value_mantissa (Column): P-value mantissa column - p_value_exponent (Column): P-value exponent column - pvalue_cutoff (float): association p-value cut-off - - Returns: - Column: Updated QC column with flag. - - Examples: - >>> import pyspark.sql.types as t - >>> d = [{'qc': None, 'p_value_mantissa': 1, 'p_value_exponent': -7}, {'qc': None, 'p_value_mantissa': 1, 'p_value_exponent': -8}, {'qc': None, 'p_value_mantissa': 5, 'p_value_exponent': -8}, {'qc': None, 'p_value_mantissa': 1, 'p_value_exponent': -9}] - >>> df = spark.createDataFrame(d, t.StructType([t.StructField('qc', t.ArrayType(t.StringType()), True), t.StructField('p_value_mantissa', t.IntegerType()), t.StructField('p_value_exponent', t.IntegerType())])) - >>> df.withColumn('qc', GWASCatalogCuratedAssociationsParser._qc_subsignificant_associations(f.col("qc"), f.col("p_value_mantissa"), f.col("p_value_exponent"), 5e-8)).show(truncate = False) - +------------------------+----------------+----------------+ - |qc |p_value_mantissa|p_value_exponent| - +------------------------+----------------+----------------+ - |[Subsignificant p-value]|1 |-7 | - |[] |1 |-8 | - |[] |5 |-8 | - |[] |1 |-9 | - +------------------------+----------------+----------------+ - - - """ - return StudyLocus.update_quality_flag( - qc, - calculate_neglog_pvalue(p_value_mantissa, p_value_exponent) - < f.lit(-np.log10(pvalue_cutoff)), - StudyLocusQualityCheck.SUBSIGNIFICANT_FLAG, - ) - @staticmethod def _qc_genomic_location( qc: Column, chromosome: Column, position: Column @@ -987,12 +917,178 @@ def _qc_palindromic_alleles( StudyLocusQualityCheck.PALINDROMIC_ALLELE_FLAG, ) + @staticmethod + def _get_effect_type(ci_text: Column) -> Column: + """Extracts the effect type from the 95% CI text. + + The GWAS Catalog confidence interval column contains text that can be used to infer the effect type. + If the text contains "increase" or "decrease", the effect type is beta, otherwise it is odds ratio. + Null columns return null as the effect type. + + Args: + ci_text (Column): Column containing the 95% CI text. + + Returns: + Column: A column containing the effect type. + + Examples: + >>> data = [{"ci_text": "95% CI: [0.1-0.2]"}, {"ci_text": "95% CI: [0.1-0.2] increase"}, {"ci_text": "95% CI: [0.1-0.2] decrease"}, {"ci_text": None}] + >>> spark.createDataFrame(data).select('ci_text', GWASCatalogCuratedAssociationsParser._get_effect_type(f.col('ci_text')).alias('effect_type')).show(truncate=False) + +--------------------------+-----------+ + |ci_text |effect_type| + +--------------------------+-----------+ + |95% CI: [0.1-0.2] |odds_ratio | + |95% CI: [0.1-0.2] increase|beta | + |95% CI: [0.1-0.2] decrease|beta | + |null |null | + +--------------------------+-----------+ + + + """ + return f.when( + f.lower(ci_text).contains("increase") + | f.lower(ci_text).contains("decrease"), + f.lit("beta"), + ).when(ci_text.isNotNull(), f.lit("odds_ratio")) + + @staticmethod + def harmonise_association_effect_to_beta( + df: DataFrame, + ) -> DataFrame: + """Harmonise effect to beta value. + + The harmonisation process has a number of steps: + - Extracting the reported effect allele. + - Flagging palindromic alleles. + - Flagging associations where the effect direction needs to be flipped. + - Flagging the effect type. + - Getting the standard error from the confidence interval text. + - Harmonising both beta and odds ratio. + - Converting the odds ratio to beta. + + Args: + df (DataFrame): DataFrame with the following columns: + + Returns: + DataFrame: DataFrame with the following columns: + + Raises: + ValueError: If any of the required columns are missing. + + Examples: + >>> data = [ + ... # Flagged as palindromic: + ... ('rs123-T', 'A', 'T', '0.1', '[0.08-0.12] unit increase'), + ... # Not palindromic, beta needs to be flipped: + ... ('rs123-C', 'G', 'T', '0.1', '[0.08-0.12] unit increase'), + ... # Beta is not flipped: + ... ('rs123-T', 'C', 'T', '0.1', '[0.08-0.12] unit increase'), + ... # odds ratio: + ... ('rs123-T', 'C', 'T', '0.1', '[0.08-0.12]'), + ... # odds ratio flipped: + ... ('rs123-C', 'G', 'T', '0.1', '[0.08-0.12]'), + ... ] + >>> schema = ["STRONGEST SNP-RISK ALLELE", "referenceAllele", "alternateAllele", "OR or BETA", "95% CI (TEXT)"] + >>> df = spark.createDataFrame(data, schema) + >>> GWASCatalogCuratedAssociationsParser.harmonise_association_effect_to_beta(df).show() + +-------------------------+---------------+---------------+----------+--------------------+-------------------+--------------------+ + |STRONGEST SNP-RISK ALLELE|referenceAllele|alternateAllele|OR or BETA| 95% CI (TEXT)| beta| standardError| + +-------------------------+---------------+---------------+----------+--------------------+-------------------+--------------------+ + | rs123-T| A| T| 0.1|[0.08-0.12] unit ...| null| null| + | rs123-C| G| T| 0.1|[0.08-0.12] unit ...| -0.1|0.010204081404574064| + | rs123-T| C| T| 0.1|[0.08-0.12] unit ...| 0.1|0.010204081404574064| + | rs123-T| C| T| 0.1| [0.08-0.12]|-2.3025850929940455| null| + | rs123-C| G| T| 0.1| [0.08-0.12]| 2.302585092994046| null| + +-------------------------+---------------+---------------+----------+--------------------+-------------------+--------------------+ + + """ + # Testing if all columns are in the dataframe: + required_columns = [ + "STRONGEST SNP-RISK ALLELE", + "referenceAllele", + "alternateAllele", + "OR or BETA", + "95% CI (TEXT)", + ] + + for column in required_columns: + if column not in df.columns: + raise ValueError( + f"Column {column} is required for harmonising effect to beta value." + ) + + return ( + df.withColumn( + "reportedRiskAllele", + GWASCatalogCuratedAssociationsParser._extract_risk_allele( + f.col("STRONGEST SNP-RISK ALLELE") + ), + ) + .withColumns( + { + # Flag palindromic alleles: + "isAllelePalindromic": GWASCatalogCuratedAssociationsParser._are_alleles_palindromic( + f.col("referenceAllele"), f.col("alternateAllele") + ), + # Flag associations, where the effect direction needs to be flipped: + "needsFlipping": GWASCatalogCuratedAssociationsParser._effect_needs_harmonisation( + f.col("reportedRiskAllele"), f.col("referenceAllele") + ), + # Flag effect type: + "effectType": GWASCatalogCuratedAssociationsParser._get_effect_type( + f.col("95% CI (TEXT)") + ), + # Get standard error from confidence interval text: + "standardError": get_standard_error_from_confidence_interval( + f.regexp_extract( + "95% CI (TEXT)", r"\[(\d+\.*\d*)-\d+\.*\d*\]", 1 + ).cast(FloatType()), + f.regexp_extract( + "95% CI (TEXT)", r"\[\d+\.*\d*-(\d+\.*\d*)\]", 1 + ).cast(FloatType()), + ), + } + ) + # Harmonise both beta and odds ratio: + .withColumns( + { # Normalise beta value of the association: + "effect_beta": f.when( + (f.col("effectType") == "beta") + & (~f.col("isAllelePalindromic")), + GWASCatalogCuratedAssociationsParser._harmonise_beta( + f.col("OR or BETA"), + f.col("95% CI (TEXT)"), + f.col("needsFlipping"), + ), + ), + # Normalise odds ratio of the association: + "effect_odds_ratio": f.when( + (f.col("effectType") == "odds_ratio") + & (~f.col("isAllelePalindromic")), + GWASCatalogCuratedAssociationsParser._harmonise_odds_ratio( + f.col("OR or BETA"), + f.col("needsFlipping"), + ), + ), + }, + ) + .select( + *df.columns, + # Harmonise OR effect to beta: + *convert_odds_ratio_to_beta( + f.col("effect_beta"), + f.col("effect_odds_ratio"), + f.col("standardError"), + ), + ) + ) + @classmethod def from_source( cls: type[GWASCatalogCuratedAssociationsParser], gwas_associations: DataFrame, - variant_annotation: VariantAnnotation, - pvalue_threshold: float = 5e-8, + gnomad_variants: VariantIndex, + pvalue_threshold: float = WindowBasedClumpingStepConfig.gwas_significance, ) -> StudyLocusGWASCatalog: """Read GWASCatalog associations. @@ -1000,38 +1096,52 @@ def from_source( applies some pre-defined filters on the data: Args: - gwas_associations (DataFrame): GWAS Catalog raw associations dataset - variant_annotation (VariantAnnotation): Variant annotation dataset - pvalue_threshold (float): P-value threshold for flagging associations + gwas_associations (DataFrame): GWAS Catalog raw associations dataset. + gnomad_variants (VariantIndex): Variant dataset from GnomAD, with allele frequencies. + pvalue_threshold (float): P-value threshold for flagging associations. Returns: StudyLocusGWASCatalog: GWASCatalogAssociations dataset + + pvalue_threshold is keeped in sync with the WindowBasedClumpingStep gwas_significance. """ return StudyLocusGWASCatalog( _df=gwas_associations.withColumn( - "studyLocusId", f.monotonically_increasing_id().cast(LongType()) + # temporary column + "rowId", f.monotonically_increasing_id().cast(StringType()) ) .transform( # Map/harmonise variants to variant annotation dataset: # This function adds columns: variantId, referenceAllele, alternateAllele, chromosome, position - lambda df: GWASCatalogCuratedAssociationsParser._map_to_variant_annotation_variants( - df, variant_annotation + lambda df: GWASCatalogCuratedAssociationsParser._map_variants_to_gnomad_variants( + df, gnomad_variants ) ) - .withColumn( + .withColumns( # Perform all quality control checks: - "qualityControls", - GWASCatalogCuratedAssociationsParser._qc_all( - f.array().alias("qualityControls"), - f.col("CHR_ID"), - f.col("CHR_POS").cast(IntegerType()), - f.col("referenceAllele"), - f.col("alternateAllele"), - f.col("STRONGEST SNP-RISK ALLELE"), - *GWASCatalogCuratedAssociationsParser._parse_pvalue( - f.col("P-VALUE") - ), - pvalue_threshold, + { + "qualityControls": GWASCatalogCuratedAssociationsParser._qc_all( + f.array().alias("qualityControls"), + f.col("CHR_ID"), + f.col("CHR_POS").cast(IntegerType()), + f.col("referenceAllele"), + f.col("alternateAllele"), + f.col("STRONGEST SNP-RISK ALLELE"), + *GWASCatalogCuratedAssociationsParser._parse_pvalue( + f.col("P-VALUE") + ), + pvalue_threshold, + ) + } + ) + # Harmonising effect to beta value and flip effect if needed: + .transform(cls.harmonise_association_effect_to_beta) + .withColumnRenamed("STUDY ACCESSION", "studyId") + # Adding study-locus id: + .withColumn( + "studyLocusId", + StudyLocus.assign_study_locus_id( + ["studyId", "variantId"] ), ) .select( @@ -1041,17 +1151,7 @@ def from_source( # Mapped genomic location of the variant (; separated list) "chromosome", "position", - f.col("STUDY ACCESSION").alias("studyId"), - # beta value of the association - GWASCatalogCuratedAssociationsParser._harmonise_beta( - GWASCatalogCuratedAssociationsParser._normalise_risk_allele( - f.col("STRONGEST SNP-RISK ALLELE") - ), - f.col("referenceAllele"), - f.col("alternateAllele"), - f.col("OR or BETA"), - f.col("95% CI (TEXT)"), - ).alias("beta"), + "studyId", # p-value of the association, string: split into exponent and mantissa. *GWASCatalogCuratedAssociationsParser._parse_pvalue(f.col("P-VALUE")), # Capturing phenotype granularity at the association level @@ -1062,6 +1162,8 @@ def from_source( ).alias("subStudyDescription"), # Quality controls (array of strings) "qualityControls", + "beta", + "standardError", ), _schema=StudyLocusGWASCatalog.get_schema(), ) @@ -1093,7 +1195,7 @@ def update_study_id( .drop("subStudyDescription", "updatedStudyId") ).withColumn( "studyLocusId", - StudyLocus.assign_study_locus_id(f.col("studyId"), f.col("variantId")), + StudyLocus.assign_study_locus_id(["studyId", "variantId"]), ) return self @@ -1117,6 +1219,24 @@ def qc_ambiguous_study(self: StudyLocusGWASCatalog) -> StudyLocusGWASCatalog: ) return self + def qc_flag_all_tophits(self: StudyLocusGWASCatalog) -> StudyLocusGWASCatalog: + """Flag all associations as top hits. + + Returns: + StudyLocusGWASCatalog: Updated study locus. + """ + return StudyLocusGWASCatalog( + _df=self._df.withColumn( + "qualityControls", + StudyLocus.update_quality_flag( + f.col("qualityControls"), + f.lit(True), + StudyLocusQualityCheck.TOP_HIT, + ), + ), + _schema=StudyLocusGWASCatalog.get_schema(), + ) + def apply_inclusion_list( self: StudyLocusGWASCatalog, inclusion_list: DataFrame ) -> StudyLocusGWASCatalog: diff --git a/src/gentropy/datasource/gwas_catalog/study_index.py b/src/gentropy/datasource/gwas_catalog/study_index.py index cb6d3338a..c01d6d263 100644 --- a/src/gentropy/datasource/gwas_catalog/study_index.py +++ b/src/gentropy/datasource/gwas_catalog/study_index.py @@ -1,4 +1,5 @@ """Study Index for GWAS Catalog data source.""" + from __future__ import annotations from dataclasses import dataclass @@ -6,61 +7,15 @@ import pyspark.sql.functions as f import pyspark.sql.types as t -from pyspark import SparkFiles -from gentropy.common.session import Session from gentropy.common.spark_helpers import column2camel_case from gentropy.common.utils import parse_efos -from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_index import StudyIndex, StudyQualityCheck if TYPE_CHECKING: from pyspark.sql import Column, DataFrame -def read_curation_table( - curation_path: str | None, session: Session -) -> DataFrame | None: - """Read curation table if path or URL is given. - - Curation itself is fully optional everything should work without it. - - Args: - curation_path (str | None): Optionally given path the curation tsv. - session (Session): session object - - Returns: - DataFrame | None: if curation was provided, - """ - # If no curation path provided, we are returning none: - if curation_path is None: - return None - # Read curation from the web: - elif curation_path.startswith("http"): - # Registering file: - session.spark.sparkContext.addFile(curation_path) - - # Reading file: - curation_df = session.spark.read.csv( - SparkFiles.get(curation_path.split("/")[-1]), sep="\t", header=True - ) - # Read curation from file: - else: - curation_df = session.spark.read.csv(curation_path, sep="\t", header=True) - return curation_df.select( - "studyId", - "studyType", - f.when(f.col("analysisFlag").isNotNull(), f.split(f.col("analysisFlag"), r"\|")) - .otherwise(f.array()) - .alias("analysisFlags"), - f.when( - f.col("qualityControl").isNotNull(), f.split(f.col("qualityControl"), r"\|") - ) - .otherwise(f.array()) - .alias("qualityControls"), - f.col("isCurated").cast(t.BooleanType()), - ) - - @dataclass class StudyIndexGWASCatalogParser: """GWAS Catalog study index parser. @@ -264,14 +219,14 @@ def parse_cohorts(raw_cohort: Column) -> Column: |parsedCohorts | +--------------------------------------+ |[BioME, CaPS, Estonia, FHS, UKB, GERA]| - |[null] | + |null | +--------------------------------------+ """ return f.when( - (raw_cohort.isNull()) | (raw_cohort == ""), - f.array(f.lit(None).cast(t.StringType())), - ).otherwise(f.array_distinct(f.split(raw_cohort, r"\|"))) + (raw_cohort.isNotNull()) & (raw_cohort != ""), + f.array_distinct(f.split(raw_cohort, r"\|")), + ) @classmethod def _parse_study_table( @@ -305,6 +260,7 @@ def _parse_study_table( parse_efos(f.col("MAPPED BACKGROUND TRAIT URI")).alias( "backgroundTraitFromSourceMappedIds" ), + cls.parse_cohorts(f.col("COHORT")).alias("cohorts"), ), _schema=StudyIndexGWASCatalog.get_schema(), ) @@ -314,14 +270,12 @@ def from_source( cls: type[StudyIndexGWASCatalogParser], catalog_studies: DataFrame, ancestry_file: DataFrame, - sumstats_lut: DataFrame, ) -> StudyIndexGWASCatalog: """Ingests study level metadata from the GWAS Catalog. Args: catalog_studies (DataFrame): GWAS Catalog raw study table ancestry_file (DataFrame): GWAS Catalog ancestry table. - sumstats_lut (DataFrame): GWAS Catalog summary statistics list. Returns: StudyIndexGWASCatalog: Parsed and annotated GWAS Catalog study table. @@ -330,7 +284,6 @@ def from_source( return ( cls._parse_study_table(catalog_studies) .annotate_ancestries(ancestry_file) - .annotate_sumstats_info(sumstats_lut) .annotate_discovery_sample_sizes() ) @@ -401,7 +354,13 @@ def annotate_from_study_curation( if curation_table is None: return self - columns = self.df.columns + studies = self.df + + if "qualityControls" not in studies.columns: + studies = studies.withColumn("qualityControls", f.array()) + + if "analysisFlags" not in studies.columns: + studies = studies.withColumn("analysisFlags", f.array()) # Adding prefix to columns in the curation table: curation_table = curation_table.select( @@ -413,46 +372,34 @@ def annotate_from_study_curation( ] ) - # Create expression how to update/create quality controls dataset: - qualityControls_expression = ( - f.col("curation_qualityControls") - if "qualityControls" not in columns - else f.when( - f.col("curation_qualityControls").isNotNull(), - f.array_union( - f.col("qualityControls"), f.array(f.col("curation_qualityControls")) - ), - ).otherwise(f.col("qualityControls")) - ) - - # Create expression how to update/create analysis flag: - analysis_expression = ( - f.col("curation_analysisFlags") - if "analysisFlags" not in columns - else f.when( - f.col("curation_analysisFlags").isNotNull(), - f.array_union( - f.col("analysisFlags"), f.array(f.col("curation_analysisFlags")) - ), - ).otherwise(f.col("analysisFlags")) - ) - - # Updating columns list. We might or might not list columns twice, but that doesn't matter, unique set will generated: - columns = list(set(columns + ["qualityControls", "analysisFlags"])) - # Based on the curation table, columns needs to be updated: curated_df = ( - self.df.join(curation_table, on="studyId", how="left") + studies.join( + curation_table.withColumn("isCurated", f.lit(True)), + on="studyId", + how="left", + ) + .withColumn("isCurated", f.coalesce(f.col("isCurated"), f.lit(False))) # Updating study type: .withColumn( "studyType", f.coalesce(f.col("curation_studyType"), f.col("studyType")) ) - # Updating quality controls: - .withColumn("qualityControls", qualityControls_expression) # Updating study annotation flags: - .withColumn("analysisFlags", analysis_expression) + .withColumn( + "analysisFlags", + f.array_union(f.col("analysisFlags"), f.col("curation_analysisFlags")), + ) + .withColumn("analysisFlags", f.coalesce(f.col("analysisFlags"), f.array())) + .withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + ~f.col("isCurated"), + StudyQualityCheck.NO_OT_CURATION, + ), + ) # Dropping columns coming from the curation table: - .select(*columns) + .select(*studies.columns) ) return StudyIndexGWASCatalog( _df=curated_df, _schema=StudyIndexGWASCatalog.get_schema() @@ -548,14 +495,6 @@ def annotate_ancestries( ) # studyId has not been split yet ) - # Parsing cohort information: - cohorts = ancestry_lut.select( - f.col("STUDY ACCESSION").alias("studyId"), - GWASCatalogStudyIndexParser.parse_cohorts(f.col("COHORT(S)")).alias( - "cohorts" - ), - ).distinct() - # Get a high resolution dataset on experimental stage: ancestry_stages = ( ancestry.groupBy("studyId") @@ -644,50 +583,7 @@ def annotate_ancestries( ).select( "studyId", "discoverySamples", "ldPopulationStructure", "replicationSamples" ) - self.df = self.df.join(parsed_ancestry_lut, on="studyId", how="left").join( - cohorts, on="studyId", how="left" - ) - return self - - def annotate_sumstats_info( - self: StudyIndexGWASCatalog, sumstats_lut: DataFrame - ) -> StudyIndexGWASCatalog: - """Annotate summary stat locations. - - Args: - sumstats_lut (DataFrame): listing GWAS Catalog summary stats paths - - Returns: - StudyIndexGWASCatalog: including `summarystatsLocation` and `hasSumstats` columns - - Raises: - ValueError: if the sumstats_lut table doesn't have the right columns - """ - gwas_sumstats_base_uri = ( - "ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/" - ) - - if "_c0" not in sumstats_lut.columns: - raise ValueError( - f'Sumstats look-up table needs to have `_c0` column. However it has: {",".join(sumstats_lut.columns)}' - ) - - parsed_sumstats_lut = sumstats_lut.withColumn( - "summarystatsLocation", - f.concat( - f.lit(gwas_sumstats_base_uri), - f.regexp_replace(f.col("_c0"), r"^\.\/", ""), - ), - ).select( - self._parse_gwas_catalog_study_id("summarystatsLocation").alias("studyId"), - "summarystatsLocation", - f.lit(True).alias("hasSumstats"), - ) - self.df = ( - self.df.drop("hasSumstats") - .join(parsed_sumstats_lut, on="studyId", how="left") - .withColumn("hasSumstats", f.coalesce(f.col("hasSumstats"), f.lit(False))) - ) + self.df = self.df.join(parsed_ancestry_lut, on="studyId", how="left") return self def annotate_discovery_sample_sizes( @@ -751,6 +647,22 @@ def apply_inclusion_list( _schema=StudyIndexGWASCatalog.get_schema(), ) + def add_no_sumstats_flag(self: StudyIndexGWASCatalog) -> StudyIndexGWASCatalog: + """Add a flag to the study index if no summary statistics are available. + + Returns: + StudyIndexGWASCatalog: Updated study index. + """ + self.df = self.df.withColumn( + "qualityControls", + StudyIndex.update_quality_flag( + f.col("qualityControls"), + ~f.col("hasSumstats"), + StudyQualityCheck.SUMSTATS_NOT_AVAILABLE, + ), + ) + return self + @staticmethod def _parse_gwas_catalog_study_id(sumstats_path_column: str) -> Column: """Extract GWAS Catalog study accession from the summary statistics path. diff --git a/src/gentropy/datasource/gwas_catalog/study_index_ot_curation.py b/src/gentropy/datasource/gwas_catalog/study_index_ot_curation.py new file mode 100644 index 000000000..8d75e4824 --- /dev/null +++ b/src/gentropy/datasource/gwas_catalog/study_index_ot_curation.py @@ -0,0 +1,90 @@ +"""Study Index for GWAS Catalog data source.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING + +import pyspark.sql.functions as f +import pyspark.sql.types as t +from pyspark import SparkFiles + +from gentropy.common.session import Session + +if TYPE_CHECKING: + from pyspark.sql import DataFrame + + +@dataclass +class StudyIndexGWASCatalogOTCuration: + """Study Index Curation for GWAS Catalog data source. + + This class is responsible for parsing additional curation for the GWAS Catalog studies. + """ + + @staticmethod + def _parser(df: DataFrame) -> DataFrame: + """Parse the curation table. + + Args: + df (DataFrame): DataFrame with the curation table. + + Returns: + DataFrame: DataFrame with the parsed curation table. + """ + if "qualityControl" not in df.columns: + # Add the 'qualityControl' column with null values + df = df.withColumn("qualityControl", f.lit(None).cast("string")) + return df.select( + "studyId", + "studyType", + f.when( + f.col("analysisFlag").isNotNull(), f.split(f.col("analysisFlag"), r"\|") + ) + .otherwise(f.array()) + .alias("analysisFlags"), + f.when( + f.col("qualityControl").isNotNull(), + f.split(f.col("qualityControl"), r"\|"), + ) + .otherwise(f.array()) + .alias("qualityControls"), + f.col("isCurated").cast(t.BooleanType()), + ) + + @classmethod + def from_csv( + cls: type[StudyIndexGWASCatalogOTCuration], session: Session, curation_path: str + ) -> DataFrame: + """Read curation table from csv. + + Args: + session (Session): Session object. + curation_path (str): Path to the curation table. + + Returns: + DataFrame: DataFrame with the curation table. + """ + return cls._parser(session.spark.read.csv(curation_path, sep="\t", header=True)) + + @classmethod + def from_url( + cls: type[StudyIndexGWASCatalogOTCuration], session: Session, curation_url: str + ) -> DataFrame: + """Read curation table from URL. + + Args: + session (Session): Session object. + curation_url (str): URL to the curation table. + + Returns: + DataFrame: DataFrame with the curation table. + """ + # Registering file: + session.spark.sparkContext.addFile(curation_url) + + return cls._parser( + session.spark.read.csv( + SparkFiles.get(curation_url.split("/")[-1]), sep="\t", header=True + ) + ) diff --git a/src/gentropy/datasource/gwas_catalog/study_splitter.py b/src/gentropy/datasource/gwas_catalog/study_splitter.py index 882a9893a..0b11e797a 100644 --- a/src/gentropy/datasource/gwas_catalog/study_splitter.py +++ b/src/gentropy/datasource/gwas_catalog/study_splitter.py @@ -132,5 +132,7 @@ def split( st_ass.select( "updatedStudyId", "studyId", "subStudyDescription" ).distinct() - ).qc_ambiguous_study(), + ) + .qc_ambiguous_study() + .qc_flag_all_tophits(), ) diff --git a/src/gentropy/datasource/intervals/thurman.py b/src/gentropy/datasource/intervals/thurman.py index 88ed5b6ad..a8113e5a6 100644 --- a/src/gentropy/datasource/intervals/thurman.py +++ b/src/gentropy/datasource/intervals/thurman.py @@ -41,7 +41,7 @@ def read(spark: SparkSession, path: str) -> DataFrame: t.StructField("score", t.FloatType(), False), ] ) - return spark.read.csv(path, sep="\t", header=True, schema=thurman_schema) + return spark.read.csv(path, sep="\t", header=False, schema=thurman_schema) @classmethod def parse( diff --git a/src/gentropy/datasource/open_targets/l2g_gold_standard.py b/src/gentropy/datasource/open_targets/l2g_gold_standard.py index 97b04320c..21edcc201 100644 --- a/src/gentropy/datasource/open_targets/l2g_gold_standard.py +++ b/src/gentropy/datasource/open_targets/l2g_gold_standard.py @@ -1,4 +1,5 @@ """Parser for OTPlatform locus to gene gold standards curation.""" + from __future__ import annotations from typing import Type @@ -8,7 +9,7 @@ from gentropy.dataset.l2g_gold_standard import L2GGoldStandard from gentropy.dataset.study_locus import StudyLocus -from gentropy.dataset.v2g import V2G +from gentropy.dataset.variant_index import VariantIndex class OpenTargetsL2GGoldStandard: @@ -51,7 +52,7 @@ def parse_positive_curation( ) .withColumn( "studyLocusId", - StudyLocus.assign_study_locus_id(f.col("studyId"), f.col("variantId")), + StudyLocus.assign_study_locus_id(["studyId", "variantId"]), ) .groupBy("studyLocusId", "studyId", "variantId", "geneId") .agg(f.collect_set("source").alias("sources")) @@ -59,7 +60,9 @@ def parse_positive_curation( @classmethod def expand_gold_standard_with_negatives( - cls: Type[OpenTargetsL2GGoldStandard], positive_set: DataFrame, v2g: V2G + cls: Type[OpenTargetsL2GGoldStandard], + positive_set: DataFrame, + variant_index: VariantIndex, ) -> DataFrame: """Create full set of positive and negative evidence of locus to gene associations. @@ -67,7 +70,7 @@ def expand_gold_standard_with_negatives( Args: positive_set (DataFrame): Positive set from curation - v2g (V2G): Variant to gene dataset to bring distance between a variant and a gene's TSS + variant_index (VariantIndex): Variant index to get distance to gene Returns: DataFrame: Full set of positive and negative evidence of locus to gene associations @@ -75,9 +78,13 @@ def expand_gold_standard_with_negatives( return ( positive_set.withColumnRenamed("geneId", "curated_geneId") .join( - v2g.df.selectExpr( - "variantId", "geneId as non_curated_geneId", "distance" - ).filter(f.col("distance") <= cls.LOCUS_TO_GENE_WINDOW), + variant_index.get_distance_to_gene() + .selectExpr( + "variantId", + "targetId as non_curated_geneId", + "distanceFromTss", + ) + .filter(f.col("distanceFromTss") <= cls.LOCUS_TO_GENE_WINDOW), on="variantId", how="left", ) @@ -85,7 +92,7 @@ def expand_gold_standard_with_negatives( "goldStandardSet", f.when( (f.col("curated_geneId") == f.col("non_curated_geneId")) - # to keep the positives that are outside the v2g dataset + # to keep the positives that are not part of the variant index | (f.col("non_curated_geneId").isNull()), f.lit(L2GGoldStandard.GS_POSITIVE_LABEL), ).otherwise(L2GGoldStandard.GS_NEGATIVE_LABEL), @@ -97,27 +104,27 @@ def expand_gold_standard_with_negatives( f.col("curated_geneId"), ).otherwise(f.col("non_curated_geneId")), ) - .drop("distance", "curated_geneId", "non_curated_geneId") + .drop("distanceFromTss", "curated_geneId", "non_curated_geneId") ) @classmethod def as_l2g_gold_standard( cls: type[OpenTargetsL2GGoldStandard], gold_standard_curation: DataFrame, - v2g: V2G, + variant_index: VariantIndex, ) -> L2GGoldStandard: """Initialise L2GGoldStandard from source dataset. Args: gold_standard_curation (DataFrame): Gold standard curation dataframe, extracted from https://github.com/opentargets/genetics-gold-standards - v2g (V2G): Variant to gene dataset to bring distance between a variant and a gene's TSS + variant_index (VariantIndex): Dataset to bring distance between a variant and a gene's footprint Returns: L2GGoldStandard: L2G Gold Standard dataset. False negatives have not yet been removed. """ return L2GGoldStandard( _df=cls.parse_positive_curation(gold_standard_curation).transform( - cls.expand_gold_standard_with_negatives, v2g + cls.expand_gold_standard_with_negatives, variant_index ), _schema=L2GGoldStandard.get_schema(), ) diff --git a/src/gentropy/datasource/open_targets/variants.py b/src/gentropy/datasource/open_targets/variants.py new file mode 100644 index 000000000..5b6822ae6 --- /dev/null +++ b/src/gentropy/datasource/open_targets/variants.py @@ -0,0 +1,136 @@ +"""Process OT dataset with variant information.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pyspark.sql.functions as f + +from gentropy.common.spark_helpers import ( + create_empty_column_if_not_exists, + safe_array_union, +) +from gentropy.dataset.study_locus import StudyLocus +from gentropy.datasource.ensembl.api import fetch_coordinates_from_rsids + +if TYPE_CHECKING: + from pyspark.sql import DataFrame + + from gentropy.common.session import Session + + +class OpenTargetsVariant: + """Process OT dataset with variant information.""" + + @classmethod + def map_rsids_to_variant_ids( + cls: type[OpenTargetsVariant], + session: Session, + variant_df: DataFrame, + ) -> DataFrame: + """Map rsIDs to variant IDs where variantId is null. + + Args: + session (Session): Spark session. + variant_df (DataFrame): DataFrame with variant information. + + Returns: + DataFrame: DataFrame with mapped variant IDs. + """ + if rsids_to_map := ( + variant_df.filter( + (f.col("variantId").isNull()) & (f.col("variantRsId").isNotNull()) + ) + .select("variantRsId") + .distinct() + .toPandas()["variantRsId"] + .to_list() + ): + rsid_to_variantids = fetch_coordinates_from_rsids(rsids_to_map) + mapping_df = session.spark.createDataFrame( + rsid_to_variantids.items(), schema=["variantRsId", "mappedVariantIds"] + ).select( + "variantRsId", f.explode("mappedVariantIds").alias("mappedVariantId") + ) + + variant_df = ( + variant_df.join(mapping_df, "variantRsId", "left") + .withColumn( + "variantId", + f.coalesce(f.col("variantId"), f.col("mappedVariantId")), + ) + .drop("mappedVariantId") + ) + + return variant_df + + @classmethod + def as_vcf_df( + cls: type[OpenTargetsVariant], + session: Session, + variant_df: DataFrame, + ) -> DataFrame: + """Convert OT dataset to VCF format. VCF format is widely used and is compatible with most variant annotation tools, including VEP. + + Args: + session (Session): Spark session. + variant_df (DataFrame): DataFrame with variant information. + + Returns: + DataFrame: DataFrame with variant information in VCF format. + """ + # Add necessary cols if not present and apply rsID mappings + mandatory_cols = ["variantId", "variantRsId", "locus"] + missing_cols = [col for col in mandatory_cols if col not in variant_df.columns] + for col in missing_cols: + if col == "locus": + variant_df = variant_df.withColumn( + col, + create_empty_column_if_not_exists( + col, + StudyLocus.get_schema()["locus"].dataType, + ), + ) + else: + variant_df = variant_df.withColumn( + col, create_empty_column_if_not_exists(col) + ) + return ( + variant_df.filter(f.col("variantId").isNotNull()) + .withColumn( + # Combine variant IDs from variantId and locus.variantId + "variantId", + f.explode( + safe_array_union( + f.array(f.col("variantId")), + f.col("locus.variantId"), + ) + ), + ) + .select( + f.coalesce(f.split(f.col("variantId"), "_")[0], f.lit(".")).alias( + "#CHROM" + ), + f.coalesce(f.split(f.col("variantId"), "_")[1], f.lit(".")) + .cast("int") + .alias("POS"), + f.coalesce(f.col("variantRsId"), f.lit(".")).alias("ID"), + f.coalesce(f.split(f.col("variantId"), "_")[2], f.lit(".")).alias( + "REF" + ), + f.coalesce(f.split(f.col("variantId"), "_")[3], f.lit(".")).alias( + "ALT" + ), + f.lit(".").alias("QUAL"), + f.lit(".").alias("FILTER"), + f.lit(".").alias("INFO"), + ) + .distinct() + .filter( + (f.col("#CHROM") != ".") + & (f.col("POS").isNotNull()) + & (f.col("REF").rlike("^[GCTA.]*$")) + & (f.col("ALT").rlike("^[GCTA.]*$")) + ) + .orderBy(f.col("#CHROM").asc(), f.col("POS").asc()) + ) diff --git a/src/gentropy/datasource/pan_ukbb_ld/__init__.py b/src/gentropy/datasource/pan_ukbb_ld/__init__.py new file mode 100644 index 000000000..dc57b8db8 --- /dev/null +++ b/src/gentropy/datasource/pan_ukbb_ld/__init__.py @@ -0,0 +1,3 @@ +"""Pan UKBB Data Source.""" + +from __future__ import annotations diff --git a/src/gentropy/datasource/pan_ukbb_ld/ld.py b/src/gentropy/datasource/pan_ukbb_ld/ld.py new file mode 100644 index 000000000..1926ddc9c --- /dev/null +++ b/src/gentropy/datasource/pan_ukbb_ld/ld.py @@ -0,0 +1,214 @@ +"""Step to import filtered version of a LD matrix (block matrix).""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import hail as hl +import numpy as np +import pyspark.sql.functions as f +from hail.linalg import BlockMatrix +from pyspark.sql.window import Window + +from gentropy.common.session import Session +from gentropy.config import PanUKBBConfig + +if TYPE_CHECKING: + from pyspark.sql import DataFrame, Row + + +class PanUKBBLDMatrix: + """Toolset to work with Pan UKBB LD matrices.""" + + def __init__( + self, + pan_ukbb_ht_path: str = PanUKBBConfig().pan_ukbb_ht_path, + pan_ukbb_bm_path: str = PanUKBBConfig().pan_ukbb_bm_path, + ld_populations: list[str] = PanUKBBConfig().pan_ukbb_pops, + ukbb_annotation_path: str = PanUKBBConfig().ukbb_annotation_path, + ): + """Initialize. + + Datasets are in hail native format. + + Args: + pan_ukbb_ht_path (str): Path to hail table, source: gs://ukb-diverse-pops-public/ld_release + pan_ukbb_bm_path (str): Path to hail block matrix + ld_populations (list[str]): List of populations + ukbb_annotation_path (str): Path to pan-ukbb variant LD index with alleles flipped to match the order in OT variant annotation + Default values are set in PanUKBBConfig. + """ + self.pan_ukbb_ht_path = pan_ukbb_ht_path + self.pan_ukbb_bm_path = pan_ukbb_bm_path + self.ld_populations = ld_populations + self.ukbb_annotation_output_path = ukbb_annotation_path + + def align_ld_index_alleles( + self, + variant_annotation: DataFrame, + population: str, + hail_table_path: str = PanUKBBConfig.pan_ukbb_ht_path, + hail_table_output: str = PanUKBBConfig.ukbb_annotation_path, + ) -> None: + """Align Pan-UKBB variant LD index alleles with the Open Targets variant annotation. + + Args: + variant_annotation (DataFrame): Open Targets variant annotation DataFrame + population (str): Population label + hail_table_path (str): Path to hail table with Pan-UKBB variant LD index + hail_table_output (str): Path to output the aligned Pan-UKBB variant LD index with alleles in the correct order + """ + ht = hl.read_table(hail_table_path.format(POP=population)) + ht = ( + ht.to_spark() + .select( + "`locus.contig`", + "`locus.position`", + "`alleles`", + "`idx`", + ) + .withColumns( + { + "chromosome": f.split("`locus.contig`", "chr")[1], + "position": f.col("`locus.position`"), + "referenceAllele": f.element_at("`alleles`", 1), + "alternateAllele": f.element_at("`alleles`", 2), + } + ) + .drop("locus.contig", "locus.position", "alleles") + .dropDuplicates( + ["chromosome", "position", "referenceAllele", "alternateAllele"] + ) + ) + ht_va = ( + ht.alias("ukbb") + .join( + variant_annotation.select( + "chromosome", + "position", + f.col("referenceAllele").alias("va_ref"), + f.col("alternateAllele").alias("va_alt"), + ).dropDuplicates(["chromosome", "position", "va_ref", "va_alt"]), + on=["chromosome", "position"], + how="left", + ) + .filter( + ( + (f.col("referenceAllele") == f.col("va_ref")) + & (f.col("alternateAllele") == f.col("va_alt")) + ) + | ( + (f.col("referenceAllele") == f.col("va_alt")) + & (f.col("alternateAllele") == f.col("va_ref")) + ) + | (f.col("va_ref").isNull() | f.col("va_alt").isNull()) + ) + .withColumns( + { + "alleleOrder": f.when( + (f.col("referenceAllele") == f.col("va_alt")) + & (f.col("alternateAllele") == f.col("va_ref")), + -1, + ).otherwise(1), + "new_referenceAllele": f.when( + (f.col("referenceAllele") == f.col("va_alt")) + & (f.col("alternateAllele") == f.col("va_ref")), + f.col("va_ref"), + ).otherwise(f.col("referenceAllele")), + "new_alternateAllele": f.when( + (f.col("alternateAllele") == f.col("va_ref")) + & (f.col("referenceAllele") == f.col("va_alt")), + f.col("va_alt"), + ).otherwise(f.col("alternateAllele")), + } + ) + .select( + f.concat_ws( + "_", + "chromosome", + "position", + "new_referenceAllele", + "new_alternateAllele", + ).alias("variantId"), + "chromosome", + "position", + f.col("new_referenceAllele").alias("referenceAllele"), + f.col("new_alternateAllele").alias("alternateAllele"), + "alleleOrder", + "idx", + ) + ) + window_spec = Window.partitionBy("idx").orderBy(f.col("alleleOrder").desc()) + ht_va = ( + ht_va.withColumn("rank", f.rank().over(window_spec)) + .filter(f.col("rank") == 1) + .drop("rank") + ) + ht_va.write.mode("overwrite").parquet(hail_table_output.format(POP=population)) + + def get_numpy_matrix( + self: PanUKBBLDMatrix, + locus_index: DataFrame, + ancestry: str, + ) -> np.ndarray: + """Extract the LD block matrix for a locus. + + Args: + locus_index (DataFrame): hail matrix variant index table + ancestry (str): Ancestry label + + Returns: + np.ndarray: LD block matrix for the locus + """ + idx = [row["idx"] for row in locus_index.select("idx").collect()] + + half_matrix = ( + BlockMatrix.read(self.pan_ukbb_bm_path.format(POP=ancestry)) + .filter(idx, idx) + .to_numpy() + ) + + alleleOrder = [ + row["alleleOrder"] for row in locus_index.select("alleleOrder").collect() + ] + outer_allele_order = np.outer(alleleOrder, alleleOrder) + np.fill_diagonal(outer_allele_order, 1) + + ld_matrix = (half_matrix + half_matrix.T) - np.diag(np.diag(half_matrix)) + ld_matrix = ld_matrix * outer_allele_order + np.fill_diagonal(ld_matrix, 1) + + return ld_matrix + + def get_locus_index_boundaries( + self, + session: Session, + study_locus_row: Row, + ancestry: str = "EUR", + ) -> DataFrame: + """Extract hail matrix index from StudyLocus rows. + + Args: + session (Session): Session object + study_locus_row (Row): Study-locus row + ancestry (str): Major population, default is "EUR" + + Returns: + DataFrame: Returns the index of the pan-ukbb matrix for the locus + + """ + chromosome = str(study_locus_row["chromosome"]) + start = int(study_locus_row["locusStart"]) + end = int(study_locus_row["locusEnd"]) + + index_file = session.spark.read.parquet( + self.ukbb_annotation_output_path.format(POP=ancestry) + ) + + index_file = index_file.filter( + (f.col("chromosome") == chromosome) + & (f.col("position") >= start) + & (f.col("position") <= end) + ).sort("idx") + + return index_file diff --git a/src/gentropy/datasource/ukb_ppp_eur/__init__.py b/src/gentropy/datasource/ukb_ppp_eur/__init__.py new file mode 100644 index 000000000..784877a26 --- /dev/null +++ b/src/gentropy/datasource/ukb_ppp_eur/__init__.py @@ -0,0 +1,3 @@ +"""UKB PPP (EUR) data source.""" + +from __future__ import annotations diff --git a/src/gentropy/datasource/ukb_ppp_eur/study_index.py b/src/gentropy/datasource/ukb_ppp_eur/study_index.py new file mode 100644 index 000000000..890630e54 --- /dev/null +++ b/src/gentropy/datasource/ukb_ppp_eur/study_index.py @@ -0,0 +1,75 @@ +"""Study Index for Finngen data source.""" + +from __future__ import annotations + +import pyspark.sql.functions as f +from pyspark.sql import SparkSession + +from gentropy.dataset.study_index import StudyIndex + + +class UkbPppEurStudyIndex(StudyIndex): + """Study index dataset from UKB PPP (EUR).""" + + @classmethod + def from_source( + cls: type[UkbPppEurStudyIndex], + spark: SparkSession, + raw_study_index_path_from_tsv: str, + raw_summary_stats_path: str, + ) -> StudyIndex: + """This function ingests study level metadata from UKB PPP (EUR). + + Args: + spark (SparkSession): Spark session object. + raw_study_index_path_from_tsv (str): Raw study index path. + raw_summary_stats_path (str): Raw summary stats path. + + Returns: + StudyIndex: Parsed and annotated UKB PPP (EUR) study table. + """ + # In order to populate the nSamples column, we need to peek inside the summary stats dataframe. + num_of_samples = ( + spark.read.parquet(raw_summary_stats_path) + .filter(f.col("chromosome") == "22") + .groupBy("studyId") + .agg(f.first("N").cast("integer").alias("nSamples")) + .select("*") + ) + # Now we can read the raw study index and complete the processing. + study_index_df = ( + spark.read.csv(raw_study_index_path_from_tsv, sep="\t", header=True) + .select( + f.lit("pqtl").alias("studyType"), + f.lit("UKB_PPP_EUR").alias("projectId"), + f.col("_gentropy_study_id").alias("studyId"), + f.col("UKBPPP_ProteinID").alias("traitFromSource"), + f.lit("UBERON_0001969").alias("biosampleFromSourceId"), + f.col("ensembl_id").alias("geneId"), + f.lit(True).alias("hasSumstats"), + f.col("_gentropy_summary_stats_link").alias("summarystatsLocation"), + ) + .join(num_of_samples, "studyId", "inner") + ) + # Add population structure. + study_index_df = ( + study_index_df.withColumn( + "discoverySamples", + f.array( + f.struct( + f.col("nSamples").cast("integer").alias("sampleSize"), + f.lit("European").alias("ancestry"), + ) + ), + ) + .withColumn( + "ldPopulationStructure", + cls.aggregate_and_map_ancestries(f.col("discoverySamples")), + ) + .withColumn("biosampleFromSourceId", f.lit("UBERON_0001969")) + ) + + return StudyIndex( + _df=study_index_df, + _schema=StudyIndex.get_schema(), + ) diff --git a/src/gentropy/datasource/ukb_ppp_eur/summary_stats.py b/src/gentropy/datasource/ukb_ppp_eur/summary_stats.py new file mode 100644 index 000000000..5ded9c891 --- /dev/null +++ b/src/gentropy/datasource/ukb_ppp_eur/summary_stats.py @@ -0,0 +1,58 @@ +"""Summary statistics ingestion for UKB PPP (EUR).""" + +from __future__ import annotations + +from dataclasses import dataclass + +from pyspark.sql import SparkSession + +from gentropy.common.harmonise import harmonise_summary_stats +from gentropy.dataset.summary_statistics import SummaryStatistics + + +@dataclass +class UkbPppEurSummaryStats: + """Summary statistics dataset for UKB PPP (EUR).""" + + @classmethod + def from_source( + cls: type[UkbPppEurSummaryStats], + spark: SparkSession, + raw_summary_stats_path: str, + tmp_variant_annotation_path: str, + chromosome: str, + study_index_path: str, + ) -> SummaryStatistics: + """Ingest and harmonise all summary stats for UKB PPP (EUR) data. + + Args: + spark (SparkSession): Spark session object. + raw_summary_stats_path (str): Input raw summary stats path. + tmp_variant_annotation_path (str): Input variant annotation dataset path. + chromosome (str): Which chromosome to process. + study_index_path (str): The path to study index, which is necessary in some cases to populate the sample size column. + + Returns: + SummaryStatistics: Processed summary statistics dataset for a given chromosome. + """ + df = harmonise_summary_stats( + spark, + raw_summary_stats_path, + tmp_variant_annotation_path, + chromosome, + colname_position="GENPOS", + colname_allele0="ALLELE0", + colname_allele1="ALLELE1", + colname_a1freq="A1FREQ", + colname_info="INFO", + colname_beta="BETA", + colname_se="SE", + colname_mlog10p="LOG10P", + colname_n="N", + ) + + # Create the summary statistics object. + return SummaryStatistics( + _df=df, + _schema=SummaryStatistics.get_schema(), + ) diff --git a/src/gentropy/datasource/ukbiobank/__init__.py b/src/gentropy/datasource/ukbiobank/__init__.py index 544779b18..910603703 100644 --- a/src/gentropy/datasource/ukbiobank/__init__.py +++ b/src/gentropy/datasource/ukbiobank/__init__.py @@ -1,3 +1,3 @@ -"""GWAS Catalog Data Source.""" +"""UK biobank.""" from __future__ import annotations diff --git a/src/gentropy/eqtl_catalogue.py b/src/gentropy/eqtl_catalogue.py index 22c8880f0..3ad61ddea 100644 --- a/src/gentropy/eqtl_catalogue.py +++ b/src/gentropy/eqtl_catalogue.py @@ -1,62 +1,79 @@ -"""Step to run eQTL Catalogue study table ingestion.""" +"""Step to run eQTL Catalogue credible set and study index ingestion.""" from __future__ import annotations from gentropy.common.session import Session +from gentropy.config import EqtlCatalogueConfig +from gentropy.datasource.eqtl_catalogue.finemapping import EqtlCatalogueFinemapping from gentropy.datasource.eqtl_catalogue.study_index import EqtlCatalogueStudyIndex -from gentropy.datasource.eqtl_catalogue.summary_stats import EqtlCatalogueSummaryStats class EqtlCatalogueStep: - """eQTL Catalogue ingestion step.""" + """eQTL Catalogue ingestion step. + + From SuSIE fine mapping results (available at [their FTP](https://ftp.ebi.ac.uk/pub/databases/spot/eQTL/susie/) ), we extract credible sets and study index datasets from gene expression QTL studies. + """ def __init__( self, session: Session, + mqtl_quantification_methods_blacklist: list[str], eqtl_catalogue_paths_imported: str, eqtl_catalogue_study_index_out: str, - eqtl_catalogue_summary_stats_out: str, + eqtl_catalogue_credible_sets_out: str, + eqtl_lead_pvalue_threshold: float = EqtlCatalogueConfig().eqtl_lead_pvalue_threshold, ) -> None: """Run eQTL Catalogue ingestion step. Args: session (Session): Session object. - eqtl_catalogue_paths_imported (str): Input eQTL Catalogue study index path. + mqtl_quantification_methods_blacklist (list[str]): Molecular trait quantification methods that we don't want to ingest. Available options in https://github.com/eQTL-Catalogue/eQTL-Catalogue-resources/blob/master/data_tables/dataset_metadata.tsv + eqtl_catalogue_paths_imported (str): Input eQTL Catalogue fine mapping results path. eqtl_catalogue_study_index_out (str): Output eQTL Catalogue study index path. - eqtl_catalogue_summary_stats_out (str): Output eQTL Catalogue summary stats path. + eqtl_catalogue_credible_sets_out (str): Output eQTL Catalogue credible sets path. + eqtl_lead_pvalue_threshold (float, optional): Lead p-value threshold. Defaults to EqtlCatalogueConfig().eqtl_lead_pvalue_threshold. """ - # Fetch study index. - df = session.spark.read.option("delimiter", "\t").csv( - eqtl_catalogue_paths_imported, header=True + # Extract + studies_metadata = EqtlCatalogueStudyIndex.read_studies_from_source( + session, list(mqtl_quantification_methods_blacklist) + ) + + # Load raw data only for the studies we are interested in ingestion. This makes the proces much lighter. + studies_to_ingest = EqtlCatalogueStudyIndex.get_studies_of_interest( + studies_metadata ) - # Process partial study index. At this point, it is not complete because we don't have the gene IDs, which we - # will only get once the summary stats are ingested. - study_index_df = EqtlCatalogueStudyIndex.from_source(df).df - - # Fetch summary stats. - input_filenames = [row.summarystatsLocation for row in study_index_df.collect()] - summary_stats_df = ( - session.spark.read.option("delimiter", "\t") - .csv(input_filenames, header=True) - .repartition(1280) + credible_sets_df = EqtlCatalogueFinemapping.read_credible_set_from_source( + session, + credible_set_path=[ + f"{eqtl_catalogue_paths_imported}/{qtd_id}.credible_sets.tsv" + for qtd_id in studies_to_ingest + ], ) - # Process summary stats. - summary_stats_df = EqtlCatalogueSummaryStats.from_source(summary_stats_df).df - - # Add geneId column to the study index. - study_index_df = EqtlCatalogueStudyIndex.add_gene_to_study_id( - study_index_df, - summary_stats_df, - ).df - - # Write study index. - study_index_df.write.mode(session.write_mode).parquet( - eqtl_catalogue_study_index_out + lbf_df = EqtlCatalogueFinemapping.read_lbf_from_source( + session, + lbf_path=[ + f"{eqtl_catalogue_paths_imported}/{qtd_id}.lbf_variable.txt" + for qtd_id in studies_to_ingest + ], + ) + + # Transform + processed_susie_df = EqtlCatalogueFinemapping.parse_susie_results( + credible_sets_df, lbf_df, studies_metadata ) - # Write summary stats. + + ( + EqtlCatalogueStudyIndex.from_susie_results(processed_susie_df) + # Writing the output: + .df.write.mode(session.write_mode) + .parquet(eqtl_catalogue_study_index_out) + ) + ( - summary_stats_df.sortWithinPartitions("position") - .write.partitionBy("chromosome") - .mode(session.write_mode) - .parquet(eqtl_catalogue_summary_stats_out) + EqtlCatalogueFinemapping.from_susie_results(processed_susie_df) + # Flagging sub-significnat loci: + .validate_lead_pvalue(pvalue_cutoff=eqtl_lead_pvalue_threshold) + # Writing the output: + .df.write.mode(session.write_mode) + .parquet(eqtl_catalogue_credible_sets_out) ) diff --git a/src/gentropy/finemapping_simulations.py b/src/gentropy/finemapping_simulations.py new file mode 100644 index 000000000..92b30566c --- /dev/null +++ b/src/gentropy/finemapping_simulations.py @@ -0,0 +1,267 @@ +"""Step/stash of functions to run a simulations to benchmark finemapping.""" + +from typing import Any + +import numpy as np +import pandas as pd +from pyspark.sql import DataFrame +from pyspark.sql.functions import array_contains, col, when +from pyspark.sql.types import DoubleType, StringType, StructField, StructType +from scipy.stats import chi2 + +from gentropy.common.session import Session +from gentropy.susie_finemapper import SusieFineMapperStep + + +class FineMappingSimulations: + """The module describes functions for running fine-mapping simulations and benchmarking.""" + + @staticmethod + def ProvideSummary(cred_sets: DataFrame, n_causal: int) -> dict[str, Any]: + """Provides summary for the simulation results. + + Args: + cred_sets (DataFrame): DataFrame containing the credible sets. + n_causal (int): Number of causal SNPs. + + Returns: + dict[str, Any]: Dictionary containing the summary. + """ + return { + "successful_runs": cred_sets["studyId"].nunique(), + "number_of_cs": len(cred_sets["is_in_X"]), + "expected_results": n_causal * cred_sets["studyId"].nunique(), + "false_positives": (len(cred_sets["is_in_X"]) - sum(cred_sets["is_in_X"])) + / len(cred_sets["is_in_X"]), + "accuracy": sum(cred_sets["is_in_X"]) / len(cred_sets["is_in_X"]), + "accuracy_lead": sum(cred_sets["is_in_lead"]) + / len(cred_sets["is_in_lead"]), + "sensitivity": sum(cred_sets["is_in_X"]) + / (n_causal * cred_sets["studyId"].nunique()), + } + + @staticmethod + def SimulationLoop( + n_iter: int, + ld_index: DataFrame, + n_causal: int, + ld_matrix_for_sim: np.ndarray, + session: Session, + he2_reggen: float, + sample_size: int, + noise: bool = False, + scale_noise: float = 1, + run_carma: bool = False, + run_sumstat_imputation: bool = False, + prop_of_snps_to_noise: float = 0.1, + ) -> DataFrame: + """Run a simulation cycle. + + Args: + n_iter (int): Number of iterations. + ld_index (DataFrame): DataFrame containing the LD index. + n_causal (int): Number of causal SNPs. + ld_matrix_for_sim (np.ndarray): LD matrix. + session (Session): Session object. + he2_reggen (float): Heritability explained by the combined effect of the region and gene. + sample_size (int): Sample size. + noise (bool, optional): Add noise to the simulation. Defaults to False. + scale_noise (float, optional): Scale of the noise. Defaults to 1. + run_carma (bool, optional): Run CARMA. Defaults to False. + run_sumstat_imputation (bool, optional): Run summary statistics imputation. Defaults to False. + prop_of_snps_to_noise (float, optional): Proportion of SNPs to add noise to. Defaults to 0.1. + + Returns: + DataFrame: DataFrame containing the credible sets. + """ + pd.DataFrame.iteritems = pd.DataFrame.items + + ld_index_pd = ld_index.toPandas() + counter = 1 + cred_sets = None + iteration = 0 + column_list = [ + "credibleSetIndex", + "studyLocusId", + "studyId", + "region", + "exploded_locus", + "variantId", + "chromosome", + "position", + "credibleSetlog10BF", + "purityMeanR2", + "purityMinR2", + "zScore", + "pValueMantissa", + "pValueExponent", + "is_in_X", + "is_in_lead", + ] + for iteration in range(n_iter): + x_cycle = FineMappingSimulations.SimSumStatFromLD( + n_causal=n_causal, + he2_reggen=he2_reggen, + n=sample_size, + U=ld_matrix_for_sim, + noise=noise, + scale_noise=scale_noise, + ) + + if sum(x_cycle["P"] <= 5e-8) > 0: + df = pd.DataFrame( + {"z": x_cycle["Z"], "variantId": ld_index_pd["variantId"]} + ) + schema = StructType( + [ + StructField("z", DoubleType(), True), + StructField("variantId", StringType(), True), + ] + ) + df_spark = session.spark.createDataFrame(df, schema=schema) + + j = "" + for ii in ld_index_pd["variantId"][x_cycle["indexes"]].tolist(): + j = j + str(ii) + "," + + CS_sim = SusieFineMapperStep.susie_finemapper_from_prepared_dataframes( + GWAS_df=df_spark, + ld_index=ld_index, + gnomad_ld=ld_matrix_for_sim, + L=10, + session=session, + studyId="sim" + str(iteration), + region=j, + susie_est_tausq=False, + run_carma=run_carma, + run_sumstat_imputation=run_sumstat_imputation, + carma_time_limit=600, + imputed_r2_threshold=0.9, + ld_score_threshold=5, + sum_pips=0.99, + lead_pval_threshold=1, + purity_mean_r2_threshold=0, + purity_min_r2_threshold=0, + cs_lbf_thr=2, + ld_min_r2=0.9, + locusStart=1, + locusEnd=2, + ) + + if CS_sim is not None: + cs_sl = CS_sim["study_locus"] + cred_set = cs_sl.df + + X = ld_index_pd["variantId"][x_cycle["indexes"]].tolist() + + cred_set = cred_set.withColumn( + "exploded_locus", col("locus.variantId") + ) + # Create a condition for each element in X + conditions = [array_contains(col("exploded_locus"), x) for x in X] + # Combine the conditions using the | operator + combined_condition = conditions[0] + for condition in conditions[1:]: + combined_condition = combined_condition | condition + # Create a new column that is True if any condition is True and False otherwise + cred_set = cred_set.withColumn("is_in_X", combined_condition) + + cred_set = cred_set.withColumn( + "is_in_lead", when(col("variantId").isin(X), 1).otherwise(0) + ) + + cred_set = cred_set.toPandas() + cred_set = cred_set[column_list] + + if counter == 1: + cred_sets = cred_set + else: + # cred_sets = cred_sets.unionByName(cred_set) + cred_sets = pd.concat([cred_sets, cred_set], axis=0) + # cred_sets=cred_sets.merge(cred_set) + counter = counter + 1 + + return cred_sets + + @staticmethod + def SimSumStatFromLD( + n_causal: int, + he2_reggen: float, + U: np.ndarray, + n: int, + noise: bool = False, + scale_noise: float = 1, + ) -> dict[str, Any]: + """Simulates summary statistics (vector of Z-scores) using numbr of causla SNPs and LD matrix as input. + + Args: + n_causal (int): number of causal snps. + he2_reggen (float): Heritability explained by the combined effect of the region and gene. + U (np.ndarray): LD. + n (int): Sample size. + noise (bool, optional): Add noise to the simulation. Defaults to False. + scale_noise (float, optional): Scale of the noise. Defaults to 1. + + Returns: + dict[str, Any]: Dictionary containing the simulated summary statistics. + """ + # Calculate the total number of SNPs in analysis + M = U.shape[0] + + # Calculate heritability explained by one causal SNP + Tau = n * he2_reggen / n_causal + + # Simulate the causal status of SNPs + indexes = np.random.choice(np.arange(M), size=n_causal, replace=False) + cc = np.repeat(0, M) + cc[indexes] = 1 + + # Simulate joint z-statistics + jz = np.zeros(M) + x = np.random.normal(loc=0, scale=1, size=n_causal) + jz[cc == 1] = x * np.sqrt(Tau) + + # Simulate GWAS z-statistics + muz = U @ jz + GWASz = np.random.multivariate_normal(mean=muz, cov=U, size=1) + + GWASz = GWASz.flatten() + + if noise: + # M1 = int(M * prop_of_snps_to_noise) + # indexes_causal = indexes[ + # np.random.choice(np.arange(len(indexes)), size=1, replace=False) + # ] + # indexes_noise = np.random.choice(np.arange(M), size=M1, replace=False) + # combined = np.concatenate((indexes_causal, indexes_noise)) + # unique_elements = np.unique(combined) + # GWASz[unique_elements] = GWASz[unique_elements] + np.random.normal( + # loc=0, scale=scale_noise, size=len(unique_elements) + # ) + indexes_causal = indexes[ + np.random.choice(np.arange(len(indexes)), size=1, replace=False) + ] + x_tmp = U[indexes_causal, :] + x_tmp = np.abs(x_tmp) + x_tmp = x_tmp.flatten() + x_tmp[indexes_causal] = 0 + ind_tmp = np.where(x_tmp > 0.5) + ind_tmp = ind_tmp[0] + if len(ind_tmp) >= 2: + indexes_noise = ind_tmp[ + np.random.choice(np.arange(len(ind_tmp)), size=2, replace=False) + ] + else: + indexes_noise = np.random.choice(M, size=2, replace=False) + GWASz[indexes_noise] = GWASz[indexes_noise] + np.random.normal( + loc=0, scale=scale_noise, size=len(indexes_noise) + ) + + GWASp = chi2.sf(GWASz**2, df=1) # convert Z to Pval + + return { + "Z": GWASz.flatten(), + "P": GWASp.flatten(), + "Tau": Tau, + "indexes": indexes, + } diff --git a/src/gentropy/finngen_finemapping_ingestion.py b/src/gentropy/finngen_finemapping_ingestion.py index d70316b5e..c093bdb3d 100644 --- a/src/gentropy/finngen_finemapping_ingestion.py +++ b/src/gentropy/finngen_finemapping_ingestion.py @@ -6,7 +6,9 @@ from dataclasses import dataclass from gentropy.common.session import Session +from gentropy.config import FinngenFinemappingConfig from gentropy.datasource.finngen.finemapping import FinnGenFinemapping +from gentropy.datasource.finngen.study_index import FinnGenStudyIndex @dataclass @@ -16,30 +18,38 @@ class FinnGenFinemappingIngestionStep(FinnGenFinemapping): def __init__( self, session: Session, - finngen_finemapping_results_path: str, - finngen_finemapping_summaries_path: str, - finngen_release_prefix: str, finngen_finemapping_out: str, + finngen_susie_finemapping_snp_files: str = FinngenFinemappingConfig().finngen_susie_finemapping_snp_files, + finngen_susie_finemapping_cs_summary_files: str = FinngenFinemappingConfig().finngen_susie_finemapping_cs_summary_files, + finngen_finemapping_lead_pvalue_threshold: float = FinngenFinemappingConfig().finngen_finemapping_lead_pvalue_threshold, + finngen_release_prefix: str = FinngenFinemappingConfig().finngen_release_prefix, ) -> None: """Run FinnGen finemapping ingestion step. Args: session (Session): Session object. - finngen_finemapping_results_path (str): Path to the FinnGen SuSIE finemapping results. - finngen_finemapping_summaries_path (str): FinnGen SuSIE summaries for CS filters(LBF>2). - finngen_release_prefix (str): Release prefix for FinnGen. finngen_finemapping_out (str): Output path for the finemapping results in StudyLocus format. + finngen_susie_finemapping_snp_files(str): Path to the FinnGen SuSIE finemapping results. + finngen_susie_finemapping_cs_summary_files (str): FinnGen SuSIE summaries for CS filters(LBF>2). + finngen_finemapping_lead_pvalue_threshold (float): Lead p-value threshold. + finngen_release_prefix (str): Finngen project release prefix. Should look like FINNGEN_R*. """ # Read finemapping outputs from the input paths. - - finngen_finemapping_df = FinnGenFinemapping.from_finngen_susie_finemapping( - spark=session.spark, - finngen_finemapping_df=finngen_finemapping_results_path, - finngen_finemapping_summaries=finngen_finemapping_summaries_path, - finngen_release_prefix=finngen_release_prefix, - ) - - # Write the output. - finngen_finemapping_df.df.write.mode(session.write_mode).parquet( - finngen_finemapping_out + finngen_release_prefix = FinnGenStudyIndex.validate_release_prefix( + finngen_release_prefix + )["prefix"] + ( + FinnGenFinemapping.from_finngen_susie_finemapping( + spark=session.spark, + finngen_susie_finemapping_snp_files=finngen_susie_finemapping_snp_files, + finngen_susie_finemapping_cs_summary_files=finngen_susie_finemapping_cs_summary_files, + finngen_release_prefix=finngen_release_prefix, + ) + # Flagging sub-significnat loci: + .validate_lead_pvalue( + pvalue_cutoff=finngen_finemapping_lead_pvalue_threshold + ) + # Writing the output: + .df.write.mode(session.write_mode) + .parquet(finngen_finemapping_out) ) diff --git a/src/gentropy/finngen_studies.py b/src/gentropy/finngen_studies.py index c1869b736..d7fd558ef 100644 --- a/src/gentropy/finngen_studies.py +++ b/src/gentropy/finngen_studies.py @@ -3,20 +3,57 @@ from __future__ import annotations from gentropy.common.session import Session +from gentropy.config import FinngenStudiesConfig from gentropy.datasource.finngen.study_index import FinnGenStudyIndex class FinnGenStudiesStep: """FinnGen study index generation step.""" - def __init__(self, session: Session, finngen_study_index_out: str) -> None: + def __init__( + self, + session: Session, + finngen_study_index_out: str, + finngen_phenotype_table_url: str = FinngenStudiesConfig().finngen_phenotype_table_url, + finngen_release_prefix: str = FinngenStudiesConfig().finngen_release_prefix, + finngen_summary_stats_url_prefix: str = FinngenStudiesConfig().finngen_summary_stats_url_prefix, + finngen_summary_stats_url_suffix: str = FinngenStudiesConfig().finngen_summary_stats_url_suffix, + efo_curation_mapping_url: str = FinngenStudiesConfig().efo_curation_mapping_url, + sample_size: int = FinngenStudiesConfig().sample_size, + ) -> None: """Run FinnGen study index generation step. Args: session (Session): Session object. finngen_study_index_out (str): Output FinnGen study index path. + finngen_phenotype_table_url (str): URL to the FinnGen phenotype table. + finngen_release_prefix (str): FinnGen release prefix. + finngen_summary_stats_url_prefix (str): FinnGen summary stats URL prefix. + finngen_summary_stats_url_suffix (str): FinnGen summary stats URL suffix. + efo_curation_mapping_url (str): URL to the EFO curation mapping file + sample_size (int): Number of individuals that participated in sample collection, derived from finngen release metadata. """ - # Fetch study index. - FinnGenStudyIndex.from_source(session.spark).df.write.mode( - session.write_mode - ).parquet(finngen_study_index_out) + _match = FinnGenStudyIndex.validate_release_prefix(finngen_release_prefix) + release_prefix = _match["prefix"] + release = _match["release"] + + efo_curation_df = FinnGenStudyIndex.read_efo_curation( + session.spark, + efo_curation_mapping_url, + ) + study_index = FinnGenStudyIndex.from_source( + session.spark, + finngen_phenotype_table_url, + release_prefix, + finngen_summary_stats_url_prefix, + finngen_summary_stats_url_suffix, + sample_size, + ) + study_index_with_efo = FinnGenStudyIndex.join_efo_mapping( + study_index, + efo_curation_df, + release, + ) + study_index_with_efo.df.write.mode(session.write_mode).parquet( + finngen_study_index_out + ) diff --git a/src/gentropy/finngen_ukb_meta.py b/src/gentropy/finngen_ukb_meta.py new file mode 100644 index 000000000..eafd2a659 --- /dev/null +++ b/src/gentropy/finngen_ukb_meta.py @@ -0,0 +1,49 @@ +"""Step to run FinnGen UKB meta-analysis data ingestion.""" + +from __future__ import annotations + +from gentropy.common.per_chromosome import ( + prepare_va, + process_summary_stats_per_chromosome, +) +from gentropy.common.session import Session +from gentropy.datasource.finngen_ukb_meta.study_index import FinngenUkbMetaStudyIndex +from gentropy.datasource.finngen_ukb_meta.summary_stats import ( + FinngenUkbMetaSummaryStats, +) + + +class FinngenUkbMetaIngestionStep: + """FinnGen UKB meta-analysis data ingestion and harmonisation.""" + + def __init__( + self, session: Session, raw_study_index_path_from_tsv: str, raw_summary_stats_path: str, variant_annotation_path: str, tmp_variant_annotation_path: str, study_index_output_path: str, summary_stats_output_path: str + ) -> None: + """Data ingestion and harmonisation step for FinnGen UKB meta-analysis. + + Args: + session (Session): Session object. + raw_study_index_path_from_tsv (str): Input raw study index path. + raw_summary_stats_path (str): Input raw summary stats path. + variant_annotation_path (str): Input variant annotation dataset path. + tmp_variant_annotation_path (str): Temporary output path for variant annotation dataset. + study_index_output_path (str): Study index output path. + summary_stats_output_path (str): Summary stats output path. + """ + session.logger.info("Pre-compute the direct and flipped variant annotation dataset.") + prepare_va(session, variant_annotation_path, tmp_variant_annotation_path) + + session.logger.info("Process study index.") + ( + FinngenUkbMetaStudyIndex.from_source( + spark=session.spark, + raw_study_index_path_from_tsv=raw_study_index_path_from_tsv, + ) + .df + .write + .mode(session.write_mode) + .parquet(study_index_output_path) + ) + + session.logger.info("Process and harmonise summary stats.") + process_summary_stats_per_chromosome(session, FinngenUkbMetaSummaryStats, raw_summary_stats_path, tmp_variant_annotation_path, summary_stats_output_path, study_index_output_path) diff --git a/src/gentropy/gnomad_ingestion.py b/src/gentropy/gnomad_ingestion.py new file mode 100644 index 000000000..8d2cd92f9 --- /dev/null +++ b/src/gentropy/gnomad_ingestion.py @@ -0,0 +1,119 @@ +"""Step to dump a filtered version of a LD matrix (block matrix) and GnomAD variants.""" + +from __future__ import annotations + +from gentropy.common.session import Session +from gentropy.common.types import LD_Population, VariantPopulation +from gentropy.common.version_engine import VersionEngine +from gentropy.config import GnomadVariantConfig, LDIndexConfig +from gentropy.datasource.gnomad.ld import GnomADLDMatrix +from gentropy.datasource.gnomad.variants import GnomADVariants + + +class LDIndexStep: + """LD index step. + + !!! warning "This step is resource intensive" + + Suggested params: high memory machine, 5TB of boot disk, no SSDs. + + """ + + def __init__( + self, + session: Session, + ld_index_out: str, + min_r2: float = LDIndexConfig().min_r2, + ld_matrix_template: str = LDIndexConfig().ld_matrix_template, + ld_index_raw_template: str = LDIndexConfig().ld_index_raw_template, + ld_populations: list[LD_Population | str] = LDIndexConfig().ld_populations, + liftover_ht_path: str = LDIndexConfig().liftover_ht_path, + grch37_to_grch38_chain_path: str = LDIndexConfig().grch37_to_grch38_chain_path, + use_version_from_input: bool = LDIndexConfig().use_version_from_input, + ) -> None: + """Run step. + + Args: + session (Session): Session object. + ld_index_out (str): Output LD index path. (required) + min_r2 (float): Minimum r2 to consider when considering variants within a window. + ld_matrix_template (str): Input path to the gnomAD ld file with placeholder for population + ld_index_raw_template (str): Input path to the raw gnomAD LD indices file with placeholder for population string + ld_populations (list[LD_Population | str]): Population names derived from the ld file paths + liftover_ht_path (str): Path to the liftover ht file + grch37_to_grch38_chain_path (str): Path to the chain file used to lift over the coordinates. + use_version_from_input (bool): Append version derived from input ld_matrix_template to the output ld_index_out. Defaults to False. + + In case use_version_from_input is set to True, + data source version inferred from ld_matrix_temolate is appended as the last path segment to the output path. + Default values are provided in LDIndexConfig. + """ + if use_version_from_input: + # amend data source version to output path + ld_index_out = VersionEngine("gnomad").amend_version( + ld_matrix_template, ld_index_out + ) + ( + GnomADLDMatrix( + ld_matrix_template=ld_matrix_template, + ld_index_raw_template=ld_index_raw_template, + grch37_to_grch38_chain_path=grch37_to_grch38_chain_path, + ld_populations=ld_populations, + liftover_ht_path=liftover_ht_path, + ) + .as_ld_index(min_r2) + .df.write.partitionBy("chromosome") + .mode(session.write_mode) + .parquet(ld_index_out) + ) + session.logger.info(ld_index_out) + + +class GnomadVariantIndexStep: + """A step to generate variant index dataset from gnomad data. + + Variant annotation step produces a dataset of the type `VariantIndex` derived from gnomADs `gnomad.genomes.vX.X.X.sites.ht` Hail's table. + This dataset is used to validate variants and as a source of annotation. + """ + + def __init__( + self, + session: Session, + variant_annotation_path: str = GnomadVariantConfig().variant_annotation_path, + gnomad_genomes_path: str = GnomadVariantConfig().gnomad_genomes_path, + gnomad_variant_populations: list[ + VariantPopulation | str + ] = GnomadVariantConfig().gnomad_variant_populations, + use_version_from_input: bool = GnomadVariantConfig().use_version_from_input, + ) -> None: + """Run Variant Annotation step. + + Args: + session (Session): Session object. + variant_annotation_path (str): Path to resulting dataset. + gnomad_genomes_path (str): Path to gnomAD genomes hail table, e.g. `gs://gcp-public-data--gnomad/release/4.0/ht/genomes/gnomad.genomes.v4.0.sites.ht/`. + gnomad_variant_populations (list[VariantPopulation | str]): List of populations to include. + use_version_from_input (bool): Append version derived from input gnomad_genomes_path to the output variant_annotation_path. Defaults to False. + + In case use_version_from_input is set to True, + data source version inferred from gnomad_genomes_path is appended as the last path segment to the output path. + All defaults are stored in the GnomadVariantConfig. + """ + # amend data source version to output path + if use_version_from_input: + variant_annotation_path = VersionEngine("gnomad").amend_version( + gnomad_genomes_path, variant_annotation_path + ) + + # Parse variant info from source. + ( + GnomADVariants( + gnomad_genomes_path=gnomad_genomes_path, + gnomad_variant_populations=gnomad_variant_populations, + ) + # Convert data to variant index: + .as_variant_index() + # Write file: + .df.write.mode(session.write_mode) + .parquet(variant_annotation_path) + ) diff --git a/src/gentropy/gwas_catalog_ingestion.py b/src/gentropy/gwas_catalog_ingestion.py deleted file mode 100644 index 6930a2df9..000000000 --- a/src/gentropy/gwas_catalog_ingestion.py +++ /dev/null @@ -1,86 +0,0 @@ -"""Step to process GWAS Catalog associations and study table.""" -from __future__ import annotations - -from gentropy.common.session import Session -from gentropy.dataset.variant_annotation import VariantAnnotation -from gentropy.datasource.gwas_catalog.associations import ( - GWASCatalogCuratedAssociationsParser, -) -from gentropy.datasource.gwas_catalog.study_index import ( - StudyIndexGWASCatalogParser, - read_curation_table, -) -from gentropy.datasource.gwas_catalog.study_splitter import GWASCatalogStudySplitter - - -class GWASCatalogIngestionStep: - """GWAS Catalog ingestion step to extract GWASCatalog Study and StudyLocus tables. - - !!! note This step currently only processes the GWAS Catalog curated list of top hits. - """ - - def __init__( - self, - session: Session, - catalog_study_files: list[str], - catalog_ancestry_files: list[str], - catalog_sumstats_lut: str, - catalog_associations_file: str, - variant_annotation_path: str, - catalog_studies_out: str, - catalog_associations_out: str, - gwas_catalog_study_curation_file: str | None = None, - inclusion_list_path: str | None = None, - ) -> None: - """Run step. - - Args: - session (Session): Session object. - catalog_study_files (list[str]): List of raw GWAS catalog studies file. - catalog_ancestry_files (list[str]): List of raw ancestry annotations files from GWAS Catalog. - catalog_sumstats_lut (str): GWAS Catalog summary statistics lookup table. - catalog_associations_file (str): Raw GWAS catalog associations file. - variant_annotation_path (str): Input variant annotation path. - catalog_studies_out (str): Output GWAS catalog studies path. - catalog_associations_out (str): Output GWAS catalog associations path. - gwas_catalog_study_curation_file (str | None): file of the curation table. Optional. - inclusion_list_path (str | None): optional inclusion list (parquet) - """ - # Extract - va = VariantAnnotation.from_parquet(session, variant_annotation_path) - catalog_studies = session.spark.read.csv( - list(catalog_study_files), sep="\t", header=True - ) - ancestry_lut = session.spark.read.csv( - list(catalog_ancestry_files), sep="\t", header=True - ) - sumstats_lut = session.spark.read.csv( - catalog_sumstats_lut, sep="\t", header=False - ) - catalog_associations = session.spark.read.csv( - catalog_associations_file, sep="\t", header=True - ).persist() - gwas_catalog_study_curation = read_curation_table( - gwas_catalog_study_curation_file, session - ) - - # Transform - study_index, study_locus = GWASCatalogStudySplitter.split( - StudyIndexGWASCatalogParser.from_source( - catalog_studies, ancestry_lut, sumstats_lut - ).annotate_from_study_curation(gwas_catalog_study_curation), - GWASCatalogCuratedAssociationsParser.from_source(catalog_associations, va), - ) - - # if inclusion list is provided apply filter: - if inclusion_list_path: - inclusion_list = session.spark.read.parquet( - inclusion_list_path, sep="\t", header=True - ) - - study_index = study_index.apply_inclusion_list(inclusion_list) - study_locus = study_locus.apply_inclusion_list(inclusion_list) - - # Load - study_index.df.write.mode(session.write_mode).parquet(catalog_studies_out) - study_locus.df.write.mode(session.write_mode).parquet(catalog_associations_out) diff --git a/src/gentropy/gwas_catalog_study_curation.py b/src/gentropy/gwas_catalog_study_curation.py index 7329a1679..530e03ea6 100644 --- a/src/gentropy/gwas_catalog_study_curation.py +++ b/src/gentropy/gwas_catalog_study_curation.py @@ -1,10 +1,13 @@ """Step to update GWAS Catalog study curation file based on newly released GWAS Catalog dataset.""" + from __future__ import annotations from gentropy.common.session import Session from gentropy.datasource.gwas_catalog.study_index import ( StudyIndexGWASCatalogParser, - read_curation_table, +) +from gentropy.datasource.gwas_catalog.study_index_ot_curation import ( + StudyIndexGWASCatalogOTCuration, ) @@ -16,7 +19,6 @@ def __init__( session: Session, catalog_study_files: list[str], catalog_ancestry_files: list[str], - catalog_sumstats_lut: str, gwas_catalog_study_curation_out: str, gwas_catalog_study_curation_file: str | None, ) -> None: @@ -26,9 +28,11 @@ def __init__( session (Session): Session object. catalog_study_files (list[str]): List of raw GWAS catalog studies file. catalog_ancestry_files (list[str]): List of raw ancestry annotations files from GWAS Catalog. - catalog_sumstats_lut (str): GWAS Catalog summary statistics lookup table. gwas_catalog_study_curation_out (str): Path for the updated curation table. gwas_catalog_study_curation_file (str | None): Path to the original curation table. Optinal + + Raises: + ValueError: If the curation file is provided but not a CSV file or URL. """ catalog_studies = session.spark.read.csv( list(catalog_study_files), sep="\t", header=True @@ -36,18 +40,24 @@ def __init__( ancestry_lut = session.spark.read.csv( list(catalog_ancestry_files), sep="\t", header=True ) - sumstats_lut = session.spark.read.csv( - catalog_sumstats_lut, sep="\t", header=False - ) - gwas_catalog_study_curation = read_curation_table( - gwas_catalog_study_curation_file, session - ) + + if gwas_catalog_study_curation_file: + if gwas_catalog_study_curation_file.endswith(".csv"): + gwas_catalog_study_curation = StudyIndexGWASCatalogOTCuration.from_csv( + session, gwas_catalog_study_curation_file + ) + elif gwas_catalog_study_curation_file.startswith("http"): + gwas_catalog_study_curation = StudyIndexGWASCatalogOTCuration.from_url( + session, gwas_catalog_study_curation_file + ) + else: + raise ValueError( + "Only CSV files or URLs are accepted as curation file." + ) # Process GWAS Catalog studies and get list of studies for curation: ( - StudyIndexGWASCatalogParser.from_source( - catalog_studies, ancestry_lut, sumstats_lut - ) + StudyIndexGWASCatalogParser.from_source(catalog_studies, ancestry_lut) # Adding existing curation: .annotate_from_study_curation(gwas_catalog_study_curation) # Extract new studies for curation: diff --git a/src/gentropy/gwas_catalog_study_inclusion.py b/src/gentropy/gwas_catalog_study_inclusion.py deleted file mode 100644 index 872177601..000000000 --- a/src/gentropy/gwas_catalog_study_inclusion.py +++ /dev/null @@ -1,187 +0,0 @@ -"""Step to generate an GWAS Catalog study identifier inclusion and exclusion list.""" -from __future__ import annotations - -from typing import TYPE_CHECKING - -from pyspark.sql import functions as f - -from gentropy.common.session import Session -from gentropy.dataset.variant_annotation import VariantAnnotation -from gentropy.datasource.gwas_catalog.associations import ( - GWASCatalogCuratedAssociationsParser, -) -from gentropy.datasource.gwas_catalog.study_index import ( - StudyIndexGWASCatalog, - StudyIndexGWASCatalogParser, - read_curation_table, -) -from gentropy.datasource.gwas_catalog.study_splitter import GWASCatalogStudySplitter - -if TYPE_CHECKING: - from pyspark.sql import Column, DataFrame - - -class GWASCatalogStudyInclusionGenerator: - """GWAS Catalog study eligibility for ingestion based on curation and the provided criteria.""" - - @staticmethod - def flag_eligible_studies( - study_index: StudyIndexGWASCatalog, criteria: str - ) -> DataFrame: - """Apply filter on GWAS Catalog studies based on the provided criteria. - - Args: - study_index (StudyIndexGWASCatalog): complete study index to be filtered based on the provided filter set - criteria (str): name of the filter set to be applied. - - Raises: - ValueError: if the provided filter set is not in the accepted values. - - Returns: - DataFrame: filtered dataframe containing only eligible studies. - """ - filters: dict[str, Column] = { - # Filters applied on studies for ingesting curated associations: - "curation": (study_index.is_gwas() & study_index.has_mapped_trait()), - # Filters applied on studies for ingesting summary statistics: - "summary_stats": ( - study_index.is_gwas() - & study_index.has_mapped_trait() - & (~study_index.is_quality_flagged()) - & study_index.has_summarystats() - ), - } - - if criteria not in filters: - raise ValueError( - f'Wrong value as filter set ({criteria}). Accepted: {",".join(filters.keys())}' - ) - - # Applying the relevant filter to the study: - return study_index.df.select( - "studyId", - "studyType", - "traitFromSource", - "traitFromSourceMappedIds", - "qualityControls", - "hasSumstats", - filters[criteria].alias("isEligible"), - ) - - @staticmethod - def process_harmonised_list(studies: list[str], session: Session) -> DataFrame: - """Generate spark dataframe from the provided list. - - Args: - studies (list[str]): list of path pointing to harmonised summary statistics. - session (Session): session - - Returns: - DataFrame: column name is consistent with original implementatin - """ - return session.spark.createDataFrame([{"_c0": path} for path in studies]) - - @staticmethod - def get_gwas_catalog_study_index( - session: Session, - variant_annotation_path: str, - catalog_study_files: list[str], - catalog_ancestry_files: list[str], - harmonised_study_file: str, - catalog_associations_file: str, - gwas_catalog_study_curation_file: str, - ) -> StudyIndexGWASCatalog: - """Return GWAS Catalog study index. - - Args: - session (Session): Session object. - variant_annotation_path (str): Input variant annotation path. - catalog_study_files (list[str]): List of raw GWAS catalog studies file. - catalog_ancestry_files (list[str]): List of raw ancestry annotations files from GWAS Catalog. - harmonised_study_file (str): GWAS Catalog summary statistics lookup table. - catalog_associations_file (str): Raw GWAS catalog associations file. - gwas_catalog_study_curation_file (str): file of the curation table. Optional. - - Returns: - StudyIndexGWASCatalog: Completely processed and fully annotated study index. - """ - # Extract - va = VariantAnnotation.from_parquet(session, variant_annotation_path) - catalog_studies = session.spark.read.csv( - list(catalog_study_files), sep="\t", header=True - ) - ancestry_lut = session.spark.read.csv( - list(catalog_ancestry_files), sep="\t", header=True - ) - sumstats_lut = session.spark.read.csv( - harmonised_study_file, sep="\t", header=False - ) - catalog_associations = session.spark.read.csv( - catalog_associations_file, sep="\t", header=True - ).persist() - gwas_catalog_study_curation = read_curation_table( - gwas_catalog_study_curation_file, session - ) - - # Transform - study_index, _ = GWASCatalogStudySplitter.split( - StudyIndexGWASCatalogParser.from_source( - catalog_studies, - ancestry_lut, - sumstats_lut, - ).annotate_from_study_curation(gwas_catalog_study_curation), - GWASCatalogCuratedAssociationsParser.from_source(catalog_associations, va), - ) - - return study_index - - def __init__( - self, - session: Session, - catalog_study_files: list[str], - catalog_ancestry_files: list[str], - catalog_associations_file: str, - gwas_catalog_study_curation_file: str, - variant_annotation_path: str, - harmonised_study_file: str, - criteria: str, - inclusion_list_path: str, - exclusion_list_path: str, - ) -> None: - """Run step. - - Args: - session (Session): Session object. - catalog_study_files (list[str]): List of raw GWAS catalog studies file. - catalog_ancestry_files (list[str]): List of raw ancestry annotations files from GWAS Catalog. - catalog_associations_file (str): Raw GWAS catalog associations file. - gwas_catalog_study_curation_file (str): file of the curation table. Optional. - variant_annotation_path (str): Input variant annotation path. - harmonised_study_file (str): GWAS Catalog summary statistics lookup table. - criteria (str): name of the filter set to be applied. - inclusion_list_path (str): Output path for the inclusion list. - exclusion_list_path (str): Output path for the exclusion list. - """ - # Create study index: - study_index = self.get_gwas_catalog_study_index( - session, - variant_annotation_path, - catalog_study_files, - catalog_ancestry_files, - harmonised_study_file, - catalog_associations_file, - gwas_catalog_study_curation_file, - ) - - # Get study indices for inclusion: - flagged_studies = self.flag_eligible_studies(study_index, criteria) - - # Output inclusion list: - eligible = ( - flagged_studies.filter(f.col("isEligible")).select("studyId").persist() - ) - eligible.write.mode(session.write_mode).parquet(inclusion_list_path) - - # Output exclusion list: - excluded = flagged_studies.filter(~f.col("isEligible")).persist() - excluded.write.mode(session.write_mode).parquet(exclusion_list_path) diff --git a/src/gentropy/gwas_catalog_study_index.py b/src/gentropy/gwas_catalog_study_index.py new file mode 100644 index 000000000..6c19b6909 --- /dev/null +++ b/src/gentropy/gwas_catalog_study_index.py @@ -0,0 +1,99 @@ +"""Step to generate an GWAS Catalog study identifier inclusion and exclusion list.""" + +from __future__ import annotations + +from pyspark.sql.types import DoubleType, LongType, StringType, StructField, StructType + +from gentropy.common.session import Session +from gentropy.datasource.gwas_catalog.study_index import StudyIndexGWASCatalogParser +from gentropy.datasource.gwas_catalog.study_index_ot_curation import ( + StudyIndexGWASCatalogOTCuration, +) + + +class GWASCatalogStudyIndexGenerationStep: + """GWAS Catalog study index generation. + + This step generates a study index from the GWAS Catalog studies and ancestry files. It can also add additional curation information and summary statistics QC information when available. + + ''' warning + This step does not generate study index for gwas catalog top hits. + + This step provides several optional arguments to add additional information to the study index: + + - gwas_catalog_study_curation_file: csv file or URL containing the curation table. If provided it annotates the study index with the additional curation information performed by the Open Targets team. + - sumstats_qc_path: Path to the summary statistics QC table. If provided it annotates the study index with the summary statistics QC information in the `sumStatQCValues` columns (e.g. `n_variants`, `n_variants_sig` etc.). + """ + + def __init__( + self, + session: Session, + catalog_study_files: list[str], + catalog_ancestry_files: list[str], + study_index_path: str, + gwas_catalog_study_curation_file: str | None = None, + sumstats_qc_path: str | None = None, + ) -> None: + """Run step. + + Args: + session (Session): Session objecct. + catalog_study_files (list[str]): List of raw GWAS catalog studies file. + catalog_ancestry_files (list[str]): List of raw ancestry annotations files from GWAS Catalog. + study_index_path (str): Output GWAS catalog studies path. + gwas_catalog_study_curation_file (str | None): csv file or URL containing the curation table. Optional. + sumstats_qc_path (str | None): Path to the summary statistics QC table. Optional. + + Raises: + ValueError: If the curation file is provided but not a CSV file or URL. + """ + # Core Study Index Generation: + study_index = StudyIndexGWASCatalogParser.from_source( + session.spark.read.csv(list(catalog_study_files), sep="\t", header=True), + session.spark.read.csv(list(catalog_ancestry_files), sep="\t", header=True), + ) + + # Annotate with curation if provided: + if gwas_catalog_study_curation_file: + if gwas_catalog_study_curation_file.endswith( + ".tsv" + ) | gwas_catalog_study_curation_file.endswith(".tsv"): + gwas_catalog_study_curation = StudyIndexGWASCatalogOTCuration.from_csv( + session, gwas_catalog_study_curation_file + ) + elif gwas_catalog_study_curation_file.startswith("http"): + gwas_catalog_study_curation = StudyIndexGWASCatalogOTCuration.from_url( + session, gwas_catalog_study_curation_file + ) + else: + raise ValueError( + "Only CSV/TSV files or URLs are accepted as curation file." + ) + study_index = study_index.annotate_from_study_curation( + gwas_catalog_study_curation + ) + + # Annotate with sumstats QC if provided: + if sumstats_qc_path: + schema = StructType( + [ + StructField("studyId", StringType(), True), + StructField("mean_beta", DoubleType(), True), + StructField("mean_diff_pz", DoubleType(), True), + StructField("se_diff_pz", DoubleType(), True), + StructField("gc_lambda", DoubleType(), True), + StructField("n_variants", LongType(), True), + StructField("n_variants_sig", LongType(), True), + ] + ) + sumstats_qc = session.spark.read.schema(schema).parquet( + sumstats_qc_path, recursiveFileLookup=True + ) + study_index_with_qc = study_index.annotate_sumstats_qc(sumstats_qc) + + # Write the study + study_index_with_qc.df.write.mode(session.write_mode).parquet( + study_index_path + ) + else: + study_index.df.write.mode(session.write_mode).parquet(study_index_path) diff --git a/src/gentropy/gwas_catalog_sumstat_preprocess.py b/src/gentropy/gwas_catalog_sumstat_preprocess.py index 6ac5e2bf8..2a4a4ce99 100644 --- a/src/gentropy/gwas_catalog_sumstat_preprocess.py +++ b/src/gentropy/gwas_catalog_sumstat_preprocess.py @@ -8,7 +8,10 @@ class GWASCatalogSumstatsPreprocessStep: - """Step to preprocess GWAS Catalog harmonised summary stats.""" + """Step to preprocess GWAS Catalog harmonised summary stats. + + It additionally performs sanity filter of GWAS before saving it. + """ def __init__( self, session: Session, raw_sumstats_path: str, out_sumstats_path: str @@ -23,5 +26,5 @@ def __init__( # Processing dataset: GWASCatalogSummaryStatistics.from_gwas_harmonized_summary_stats( session.spark, raw_sumstats_path - ).df.write.mode(session.write_mode).parquet(out_sumstats_path) + ).sanity_filter().df.write.mode(session.write_mode).parquet(out_sumstats_path) session.logger.info("Processing dataset successfully completed.") diff --git a/src/gentropy/gwas_catalog_top_hits.py b/src/gentropy/gwas_catalog_top_hits.py new file mode 100644 index 000000000..2295900e8 --- /dev/null +++ b/src/gentropy/gwas_catalog_top_hits.py @@ -0,0 +1,76 @@ +"""Step to process GWAS Catalog associations and study table.""" + +from __future__ import annotations + +from gentropy.common.session import Session +from gentropy.config import WindowBasedClumpingStepConfig +from gentropy.dataset.variant_index import VariantIndex +from gentropy.datasource.gwas_catalog.associations import ( + GWASCatalogCuratedAssociationsParser, +) +from gentropy.datasource.gwas_catalog.study_index import ( + StudyIndexGWASCatalogParser, +) +from gentropy.datasource.gwas_catalog.study_splitter import GWASCatalogStudySplitter + + +class GWASCatalogTopHitIngestionStep: + """GWAS Catalog ingestion step to extract GWASCatalog top hits.""" + + def __init__( + self, + session: Session, + catalog_study_files: list[str], + catalog_ancestry_files: list[str], + catalog_associations_file: str, + variant_annotation_path: str, + catalog_studies_out: str, + catalog_associations_out: str, + distance: int = WindowBasedClumpingStepConfig().distance, + ) -> None: + """Run step. + + Args: + session (Session): Session object. + catalog_study_files (list[str]): List of raw GWAS catalog studies file. + catalog_ancestry_files (list[str]): List of raw ancestry annotations files from GWAS Catalog. + catalog_associations_file (str): Raw GWAS catalog associations file. + variant_annotation_path (str): Path to GnomAD variants. + catalog_studies_out (str): Output GWAS catalog studies path. + catalog_associations_out (str): Output GWAS catalog associations path. + distance (int): Distance, within which tagging variants are collected around the semi-index. + """ + # Extract + gnomad_variants = VariantIndex.from_parquet(session, variant_annotation_path) + catalog_studies = session.spark.read.csv( + list(catalog_study_files), sep="\t", header=True + ) + ancestry_lut = session.spark.read.csv( + list(catalog_ancestry_files), sep="\t", header=True + ) + catalog_associations = session.spark.read.csv( + catalog_associations_file, sep="\t", header=True + ).persist() + + # Transform + study_index, study_locus = GWASCatalogStudySplitter.split( + StudyIndexGWASCatalogParser.from_source(catalog_studies, ancestry_lut), + GWASCatalogCuratedAssociationsParser.from_source( + catalog_associations, gnomad_variants + ), + ) + # Load + ( + study_index + # Flag all studies without sumstats + .add_no_sumstats_flag() + # Save dataset: + .df.write.mode(session.write_mode) + .parquet(catalog_studies_out) + ) + + ( + study_locus.window_based_clumping(distance) + .df.write.mode(session.write_mode) + .parquet(catalog_associations_out) + ) diff --git a/src/gentropy/l2g.py b/src/gentropy/l2g.py index d00a91596..b585166c1 100644 --- a/src/gentropy/l2g.py +++ b/src/gentropy/l2g.py @@ -1,176 +1,385 @@ """Step to run Locus to Gene either for inference or for training.""" + from __future__ import annotations from typing import Any import pyspark.sql.functions as f -import sklearn -from xgboost.spark import SparkXGBClassifier +from sklearn.ensemble import GradientBoostingClassifier +from wandb import login as wandb_login from gentropy.common.session import Session +from gentropy.common.spark_helpers import calculate_harmonic_sum +from gentropy.common.utils import access_gcp_secret from gentropy.dataset.colocalisation import Colocalisation +from gentropy.dataset.gene_index import GeneIndex from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix from gentropy.dataset.l2g_gold_standard import L2GGoldStandard from gentropy.dataset.l2g_prediction import L2GPrediction from gentropy.dataset.study_index import StudyIndex from gentropy.dataset.study_locus import StudyLocus -from gentropy.dataset.v2g import V2G +from gentropy.dataset.variant_index import VariantIndex +from gentropy.method.l2g.feature_factory import L2GFeatureInputLoader from gentropy.method.l2g.model import LocusToGeneModel from gentropy.method.l2g.trainer import LocusToGeneTrainer +class LocusToGeneFeatureMatrixStep: + """Annotate credible set with functional genomics features.""" + + def __init__( + self, + session: Session, + *, + features_list: list[str], + credible_set_path: str, + variant_index_path: str | None = None, + colocalisation_path: str | None = None, + study_index_path: str | None = None, + gene_index_path: str | None = None, + feature_matrix_path: str, + ) -> None: + """Initialise the step and run the logic based on mode. + + Args: + session (Session): Session object that contains the Spark session + features_list (list[str]): List of features to use for the model + credible_set_path (str): Path to the credible set dataset necessary to build the feature matrix + variant_index_path (str | None): Path to the variant index dataset + colocalisation_path (str | None): Path to the colocalisation dataset + study_index_path (str | None): Path to the study index dataset + gene_index_path (str | None): Path to the gene index dataset + feature_matrix_path (str): Path to the L2G feature matrix output dataset + """ + credible_set = StudyLocus.from_parquet( + session, credible_set_path, recursiveFileLookup=True + ) + studies = ( + StudyIndex.from_parquet(session, study_index_path, recursiveFileLookup=True) + if study_index_path + else None + ) + variant_index = ( + VariantIndex.from_parquet(session, variant_index_path) + if variant_index_path + else None + ) + coloc = ( + Colocalisation.from_parquet( + session, colocalisation_path, recursiveFileLookup=True + ) + if colocalisation_path + else None + ) + gene_index = ( + GeneIndex.from_parquet(session, gene_index_path, recursiveFileLookup=True) + if gene_index_path + else None + ) + features_input_loader = L2GFeatureInputLoader( + variant_index=variant_index, + colocalisation=coloc, + study_index=studies, + study_locus=credible_set, + gene_index=gene_index, + ) + + fm = credible_set.filter(f.col("studyType") == "gwas").build_feature_matrix( + features_list, features_input_loader + ) + fm._df.write.mode(session.write_mode).parquet(feature_matrix_path) + + class LocusToGeneStep: """Locus to gene step.""" def __init__( self, session: Session, + hyperparameters: dict[str, Any], + *, run_mode: str, - model_path: str, - predictions_path: str, - credible_set_path: str, - variant_gene_path: str, - colocalisation_path: str, - study_index_path: str, - gold_standard_curation_path: str, - gene_interactions_path: str, features_list: list[str], - hyperparameters: dict[str, Any], - wandb_run_name: str | None = None, - perform_cross_validation: bool = False, + download_from_hub: bool, + wandb_run_name: str, + credible_set_path: str, + feature_matrix_path: str, + model_path: str | None = None, + gold_standard_curation_path: str | None = None, + variant_index_path: str | None = None, + gene_interactions_path: str | None = None, + predictions_path: str | None = None, + hf_hub_repo_id: str | None, ) -> None: - """Run step. + """Initialise the step and run the logic based on mode. Args: - session (Session): Session object. - run_mode (str): One of "train" or "predict". - model_path (str): Path to save the model. - predictions_path (str): Path to save the predictions. - credible_set_path (str): Path to credible set Parquet files. - variant_gene_path (str): Path to variant to gene Parquet files. - colocalisation_path (str): Path to colocalisation Parquet files. - study_index_path (str): Path to study index Parquet files. - gold_standard_curation_path (str): Path to gold standard curation JSON files. - gene_interactions_path (str): Path to gene interactions Parquet files. - features_list (list[str]): List of features to use. - hyperparameters (dict[str, Any]): Hyperparameters for the model. - wandb_run_name (str | None): Name of the run to be tracked on W&B. - perform_cross_validation (bool): Whether to perform cross validation. + session (Session): Session object that contains the Spark session + hyperparameters (dict[str, Any]): Hyperparameters for the model + run_mode (str): Run mode, either 'train' or 'predict' + features_list (list[str]): List of features to use for the model + download_from_hub (bool): Whether to download the model from Hugging Face Hub + wandb_run_name (str): Name of the run to track model training in Weights and Biases + credible_set_path (str): Path to the credible set dataset necessary to build the feature matrix + feature_matrix_path (str): Path to the L2G feature matrix input dataset + model_path (str | None): Path to the model. It can be either in the filesystem or the name on the Hugging Face Hub (in the form of username/repo_name). + gold_standard_curation_path (str | None): Path to the gold standard curation file + variant_index_path (str | None): Path to the variant index + gene_interactions_path (str | None): Path to the gene interactions dataset + predictions_path (str | None): Path to the L2G predictions output dataset + hf_hub_repo_id (str | None): Hugging Face Hub repository ID. If provided, the model will be uploaded to Hugging Face. Raises: - ValueError: if run_mode is not one of "train" or "predict". + ValueError: If run_mode is not 'train' or 'predict' """ - print("Sci-kit learn version: ", sklearn.__version__) # noqa: T201 if run_mode not in ["train", "predict"]: raise ValueError( f"run_mode must be one of 'train' or 'predict', got {run_mode}" ) + + self.session = session + self.run_mode = run_mode + self.model_path = model_path + self.predictions_path = predictions_path + self.features_list = list(features_list) + self.hyperparameters = dict(hyperparameters) + self.wandb_run_name = wandb_run_name + self.hf_hub_repo_id = hf_hub_repo_id + self.download_from_hub = download_from_hub + # Load common inputs - credible_set = StudyLocus.from_parquet( + self.credible_set = StudyLocus.from_parquet( session, credible_set_path, recursiveFileLookup=True ) - studies = StudyIndex.from_parquet( - session, study_index_path, recursiveFileLookup=True + self.feature_matrix = L2GFeatureMatrix( + _df=session.load_data(feature_matrix_path), features_list=self.features_list + ) + self.variant_index = ( + VariantIndex.from_parquet(session, variant_index_path) + if variant_index_path + else None ) - v2g = V2G.from_parquet(session, variant_gene_path) - coloc = Colocalisation.from_parquet(session, colocalisation_path) if run_mode == "predict": - if not model_path or not predictions_path: - raise ValueError( - "model_path and predictions_path must be set for predict mode." - ) - predictions = L2GPrediction.from_credible_set( - model_path, list(features_list), credible_set, studies, v2g, coloc + self.run_predict() + elif run_mode == "train": + self.gs_curation = ( + self.session.spark.read.json(gold_standard_curation_path) + if gold_standard_curation_path + else None ) - predictions.df.write.mode(session.write_mode).parquet(predictions_path) - session.logger.info(predictions_path) - elif ( - run_mode == "train" - and gold_standard_curation_path - and gene_interactions_path + self.interactions = ( + self.session.spark.read.parquet(gene_interactions_path) + if gene_interactions_path + else None + ) + self.run_train() + + def run_predict(self) -> None: + """Run the prediction step. + + Raises: + ValueError: If predictions_path is not provided for prediction mode + """ + if not self.predictions_path: + raise ValueError("predictions_path must be provided for prediction mode") + predictions = L2GPrediction.from_credible_set( + self.session, + self.credible_set, + self.feature_matrix, + self.features_list, + model_path=self.model_path, + hf_token=access_gcp_secret("hfhub-key", "open-targets-genetics-dev"), + download_from_hub=self.download_from_hub, + ) + predictions.add_locus_to_gene_features(self.feature_matrix).df.write.mode( + self.session.write_mode + ).parquet(self.predictions_path) + self.session.logger.info("L2G predictions saved successfully.") + + def run_train(self) -> None: + """Run the training step.""" + if ( + self.gs_curation + and self.interactions + and self.wandb_run_name + and self.model_path ): - # Process gold standard and L2G features - gs_curation = session.spark.read.json(gold_standard_curation_path).persist() - interactions = session.spark.read.parquet(gene_interactions_path) + wandb_key = access_gcp_secret("wandb-key", "open-targets-genetics-dev") + + # Instantiate classifier and train model + l2g_model = LocusToGeneModel( + model=GradientBoostingClassifier(random_state=42), + hyperparameters=self.hyperparameters, + ) + wandb_login(key=wandb_key) + trained_model = LocusToGeneTrainer( + model=l2g_model, + feature_matrix=self._annotate_gold_standards_w_feature_matrix(), + ).train(self.wandb_run_name) + if trained_model.training_data and trained_model.model and self.model_path: + trained_model.save(self.model_path) + if self.hf_hub_repo_id: + hf_hub_token = access_gcp_secret( + "hfhub-key", "open-targets-genetics-dev" + ) + trained_model.export_to_hugging_face_hub( + # we upload the model in the filesystem + self.model_path.split("/")[-1], + hf_hub_token, + data=trained_model.training_data._df.drop( + "goldStandardSet", "geneId" + ).toPandas(), + repo_id=self.hf_hub_repo_id, + commit_message="chore: update model", + ) + + def _annotate_gold_standards_w_feature_matrix(self) -> L2GFeatureMatrix: + """Generate the feature matrix of annotated gold standards. + + Returns: + L2GFeatureMatrix: Feature matrix with gold standards annotated with features. + + Raises: + ValueError: Not all training dependencies are defined + """ + if self.gs_curation and self.interactions and self.variant_index: study_locus_overlap = StudyLocus( - # We just extract overlaps of associations in the gold standard. This parsing is a duplication of the one in the gold standard curation, - # but we need to do it here to be able to parse gold standards later - _df=credible_set.df.join( - f.broadcast( - gs_curation.select( - StudyLocus.assign_study_locus_id( - f.col("association_info.otg_id"), # studyId - f.concat_ws( # variantId - "_", - f.col("sentinel_variant.locus_GRCh38.chromosome"), - f.col("sentinel_variant.locus_GRCh38.position"), - f.col("sentinel_variant.alleles.reference"), - f.col("sentinel_variant.alleles.alternative"), - ), - ).alias("studyLocusId"), - ) + _df=self.credible_set.df.join( + self.gs_curation.select( + f.concat_ws( + "_", + f.col("sentinel_variant.locus_GRCh38.chromosome"), + f.col("sentinel_variant.locus_GRCh38.position"), + f.col("sentinel_variant.alleles.reference"), + f.col("sentinel_variant.alleles.alternative"), + ).alias("variantId"), + f.col("association_info.otg_id").alias("studyId"), ), - "studyLocusId", - "inner", + on=[ + "studyId", + "variantId", + ], + how="inner", ), _schema=StudyLocus.get_schema(), - ).find_overlaps(studies) + ).find_overlaps() gold_standards = L2GGoldStandard.from_otg_curation( - gold_standard_curation=gs_curation, - v2g=v2g, + gold_standard_curation=self.gs_curation, + variant_index=self.variant_index, study_locus_overlap=study_locus_overlap, - interactions=interactions, + interactions=self.interactions, ) - fm = L2GFeatureMatrix.generate_features( - features_list=features_list, - study_locus=credible_set, - study_index=studies, - variant_gene=v2g, - colocalisation=coloc, + return ( + gold_standards.build_feature_matrix( + self.feature_matrix, self.credible_set + ) + .select_features(self.features_list) + .persist() ) + raise ValueError("Dependencies for train mode not set.") - # Join and fill null values with 0 - data = L2GFeatureMatrix( - _df=fm.df.join( - f.broadcast( - gold_standards.df.drop("variantId", "studyId", "sources") - ), - on=["studyLocusId", "geneId"], - how="inner", - ), - _schema=L2GFeatureMatrix.get_schema(), - ).fill_na() - - # Instantiate classifier - estimator = SparkXGBClassifier( - eval_metric="logloss", - features_col="features", - label_col="label", - max_depth=5, + +class LocusToGeneEvidenceStep: + """Locus to gene evidence step.""" + + def __init__( + self, + session: Session, + locus_to_gene_predictions_path: str, + credible_set_path: str, + study_index_path: str, + evidence_output_path: str, + locus_to_gene_threshold: float, + ) -> None: + """Initialise the step and generate disease/target evidence. + + Args: + session (Session): Session object that contains the Spark session + locus_to_gene_predictions_path (str): Path to the L2G predictions dataset + credible_set_path (str): Path to the credible set dataset + study_index_path (str): Path to the study index dataset + evidence_output_path (str): Path to the L2G evidence output dataset + locus_to_gene_threshold (float, optional): Threshold to consider a gene as a target. Defaults to 0.05. + """ + # Reading the predictions + locus_to_gene_prediction = L2GPrediction.from_parquet( + session, locus_to_gene_predictions_path + ) + # Reading the credible set + credible_sets = StudyLocus.from_parquet(session, credible_set_path) + + # Reading the study index + study_index = StudyIndex.from_parquet(session, study_index_path) + + # Generate evidence and save file: + ( + locus_to_gene_prediction.to_disease_target_evidence( + credible_sets, study_index, locus_to_gene_threshold ) - l2g_model = LocusToGeneModel( - features_list=list(features_list), estimator=estimator + .write.mode(session.write_mode) + .json(evidence_output_path) + ) + + +class LocusToGeneAssociationsStep: + """Locus to gene associations step.""" + + def __init__( + self, + session: Session, + evidence_input_path: str, + disease_index_path: str, + direct_associations_output_path: str, + indirect_associations_output_path: str, + ) -> None: + """Create direct and indirect association datasets. + + Args: + session (Session): Session object that contains the Spark session + evidence_input_path (str): Path to the L2G evidence input dataset + disease_index_path (str): Path to disease index file + direct_associations_output_path (str): Path to the direct associations output dataset + indirect_associations_output_path (str): Path to the indirect associations output dataset + """ + # Read in the disease index + disease_index = session.spark.read.parquet(disease_index_path).select( + f.col("id").alias("diseaseId"), + f.explode("ancestors").alias("ancestorDiseaseId"), + ) + + # Read in the L2G evidence + disease_target_evidence = session.spark.read.json(evidence_input_path).select( + f.col("targetFromSourceId").alias("targetId"), + f.col("diseaseFromSourceMappedId").alias("diseaseId"), + f.col("resourceScore"), + ) + + # Generate direct assocations and save file + ( + disease_target_evidence.groupBy("targetId", "diseaseId") + .agg(f.collect_set("resourceScore").alias("scores")) + .select( + "targetId", + "diseaseId", + calculate_harmonic_sum(f.col("scores")).alias("harmonicSum"), ) - if perform_cross_validation: - # Perform cross validation to extract what are the best hyperparameters - cv_folds = hyperparameters.get("cross_validation_folds", 5) - LocusToGeneTrainer.cross_validate( - l2g_model=l2g_model, - data=data, - num_folds=cv_folds, - ) - else: - # Train model - LocusToGeneTrainer.train( - data=data, - l2g_model=l2g_model, - features_list=list(features_list), - model_path=model_path, - evaluate=True, - wandb_run_name=wandb_run_name, - **hyperparameters, - ) - session.logger.info(model_path) + .write.mode(session.write_mode) + .parquet(direct_associations_output_path) + ) + + # Generate indirect assocations and save file + ( + disease_target_evidence.join(disease_index, on="diseaseId", how="inner") + .groupBy("targetId", "ancestorDiseaseId") + .agg(f.collect_set("resourceScore").alias("scores")) + .select( + "targetId", + "ancestorDiseaseId", + calculate_harmonic_sum(f.col("scores")).alias("harmonicSum"), + ) + .write.mode(session.write_mode) + .parquet(indirect_associations_output_path) + ) diff --git a/src/gentropy/ld_index.py b/src/gentropy/ld_index.py deleted file mode 100644 index cb260977d..000000000 --- a/src/gentropy/ld_index.py +++ /dev/null @@ -1,34 +0,0 @@ -"""Step to dump a filtered version of a LD matrix (block matrix) as Parquet files.""" -from __future__ import annotations - -import hail as hl - -from gentropy.common.session import Session -from gentropy.datasource.gnomad.ld import GnomADLDMatrix - - -class LDIndexStep: - """LD index step. - - !!! warning "This step is resource intensive" - - Suggested params: high memory machine, 5TB of boot disk, no SSDs. - """ - - def __init__(self, session: Session, min_r2: float, ld_index_out: str) -> None: - """Run step. - - Args: - session (Session): Session object. - min_r2 (float): Minimum r2 to consider when considering variants within a window. - ld_index_out (str): Output LD index path. - """ - hl.init(sc=session.spark.sparkContext, log="/dev/null") - ( - GnomADLDMatrix() - .as_ld_index(min_r2) - .df.write.partitionBy("chromosome") - .mode(session.write_mode) - .parquet(ld_index_out) - ) - session.logger.info(ld_index_out) diff --git a/src/gentropy/locus_breaker_clumping.py b/src/gentropy/locus_breaker_clumping.py new file mode 100644 index 000000000..fde722354 --- /dev/null +++ b/src/gentropy/locus_breaker_clumping.py @@ -0,0 +1,78 @@ +"""Step to apply linkage based clumping on study-locus dataset.""" + +from __future__ import annotations + +from gentropy.common.genomic_region import GenomicRegion, KnownGenomicRegions +from gentropy.common.session import Session +from gentropy.dataset.summary_statistics import SummaryStatistics +from gentropy.method.locus_breaker_clumping import LocusBreakerClumping + + +class LocusBreakerClumpingStep: + """Step to perform locus-breaker clumping on a study.""" + + def __init__( + self, + session: Session, + summary_statistics_input_path: str, + clumped_study_locus_output_path: str, + lbc_baseline_pvalue: float, + lbc_distance_cutoff: int, + lbc_pvalue_threshold: float, + lbc_flanking_distance: int, + large_loci_size: int, + wbc_clump_distance: int, + wbc_pvalue_threshold: float, + collect_locus: bool = False, + remove_mhc: bool = True, + ) -> None: + """Run locus-breaker clumping step. + + This step will perform locus-breaker clumping on the full set of summary statistics. + StudyLocus larger than the large_loci_size, by distance, will be further clumped with window-based + clumping. + + Args: + session (Session): Session object. + summary_statistics_input_path (str): Path to the input study locus. + clumped_study_locus_output_path (str): path of the resulting, clumped study-locus dataset. + lbc_baseline_pvalue (float): Baseline p-value for locus breaker clumping. + lbc_distance_cutoff (int): Distance cutoff for locus breaker clumping. + lbc_pvalue_threshold (float): P-value threshold for locus breaker clumping. + lbc_flanking_distance (int): Flanking distance for locus breaker clumping. + large_loci_size (int): Threshold distance to define large loci for window-based clumping. + wbc_clump_distance (int): Clump distance for window breaker clumping. + wbc_pvalue_threshold (float): P-value threshold for window breaker clumping. + collect_locus (bool, optional): Whether to collect locus. Defaults to False. + remove_mhc (bool, optional): If true will use exclude_region() to remove the MHC region. + """ + sum_stats = SummaryStatistics.from_parquet( + session, + summary_statistics_input_path, + ) + lbc = sum_stats.locus_breaker_clumping( + lbc_baseline_pvalue, + lbc_distance_cutoff, + lbc_pvalue_threshold, + lbc_flanking_distance, + ) + wbc = sum_stats.window_based_clumping(wbc_clump_distance, wbc_pvalue_threshold) + + clumped_result = LocusBreakerClumping.process_locus_breaker_output( + lbc, + wbc, + large_loci_size, + ) + if remove_mhc: + clumped_result = clumped_result.exclude_region( + GenomicRegion.from_known_genomic_region(KnownGenomicRegions.MHC), + exclude_overlap=True, + ) + + if collect_locus: + clumped_result = clumped_result.annotate_locus_statistics_boundaries( + sum_stats + ) + clumped_result.df.write.partitionBy("studyLocusId").mode( + session.write_mode + ).parquet(clumped_study_locus_output_path) diff --git a/src/gentropy/method/carma.py b/src/gentropy/method/carma.py index 85722d24d..b46f23a56 100644 --- a/src/gentropy/method/carma.py +++ b/src/gentropy/method/carma.py @@ -1,6 +1,9 @@ """CARMA outlier detection method.""" + from __future__ import annotations +import concurrent.futures +import warnings from itertools import combinations from math import floor, lgamma from typing import Any @@ -14,6 +17,42 @@ class CARMA: """Implementation of CARMA outlier detection method.""" + @staticmethod + def time_limited_CARMA_spike_slab_noEM( + z: np.ndarray, + ld: np.ndarray, + sec_threshold: float = 600, + tau: float = 0.04, + ) -> dict[str, Any]: + """The wrapper for the CARMA_spike_slab_noEM function that runs the function in a separate thread and terminates it if it takes too long. + + Args: + z (np.ndarray): Numeric vector representing z-scores. + ld (np.ndarray): Numeric matrix representing the linkage disequilibrium (LD) matrix. + sec_threshold (float): The time threshold in seconds. + tau (float): Tuning parameter controlling the level of shrinkage of the LD matrix + + Returns: + dict[str, Any]: A dictionary containing the following results: + - PIPs: A numeric vector of posterior inclusion probabilities (PIPs) for all SNPs or None. + - B_list: A dataframe containing the marginal likelihoods and the corresponding model space or None. + - Outliers: A list of outlier SNPs or None. + """ + # Ignore pandas future warnings + warnings.simplefilter(action="ignore", category=FutureWarning) + try: + # Execute CARMA.CARMA_spike_slab_noEM with a timeout + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor: + future = executor.submit( + CARMA.CARMA_spike_slab_noEM, z=z, ld=ld, tau=tau + ) + result = future.result(timeout=sec_threshold) + except concurrent.futures.TimeoutError: + # If execution exceeds the timeout, return None + result = {"PIPs": None, "B_list": None, "Outliers": None} + + return result + @staticmethod def CARMA_spike_slab_noEM( z: np.ndarray, @@ -39,7 +78,7 @@ def CARMA_spike_slab_noEM( all_inner_iter (int): The number of inner iterations in each CARMA iteration. epsilon_threshold (float): Threshold for convergence in CARMA iterations. num_causal (int): Maximal number of causal variants to be selected in the final model. - tau (float): Tuning parameter controlling the degree of sparsity in the Spike-and-Slab prior. + tau (float): Tuning parameter controlling the level of shrinkage of the LD matrix. outlier_switch (bool): Whether to consider outlier detection in the analysis. outlier_BF_index (float): Bayes Factor threshold for identifying outliers. @@ -105,7 +144,6 @@ def CARMA_spike_slab_noEM( "Outliers": all_C_list["conditional_S_list"], } - return results_list @staticmethod @@ -573,7 +611,7 @@ def _MCS_modified( # noqa: C901 num_causal (int): Maximal number of causal variants to be selected in the final model. outlier_switch (bool): Whether to consider outlier detection in the analysis. input_conditional_S_list (list[int] | None): The conditional set. Defaults to None. - tau (float): Tuning parameter controlling the degree of sparsity in the Spike-and-Slab prior. + tau (float): Tuning parameter controlling the level of shrinkage of the LD matrix. epsilon (float): Threshold for convergence in CARMA iterations. inner_all_iter (int): The number of inner iterations in each CARMA iteration. outlier_BF_index (float | None): Bayes Factor threshold for identifying outliers. Defaults to None. @@ -826,9 +864,19 @@ def _MCS_modified( # noqa: C901 sec_sample = np.random.choice( range(0, 3), 1, p=np.exp(aa) / np.sum(np.exp(aa)) ) - S = set_gamma[sec_sample[0]][ - int(set_star["gamma_set_index"][sec_sample[0]]) - ].tolist() + if set_gamma[sec_sample[0]] is not None: + S = set_gamma[sec_sample[0]][ + int(set_star["gamma_set_index"][sec_sample[0]]) + ].tolist() + else: + sec_sample = np.random.choice( + range(1, 3), + 1, + p=np.exp(aa)[[1, 2]] / np.sum(np.exp(aa)[[1, 2]]), + ) + S = set_gamma[sec_sample[0]][ + int(set_star["gamma_set_index"][sec_sample[0]]) + ].tolist() for item in conditional_S: if item not in S: diff --git a/src/gentropy/method/clump.py b/src/gentropy/method/clump.py index d239c062c..442d9d611 100644 --- a/src/gentropy/method/clump.py +++ b/src/gentropy/method/clump.py @@ -1,6 +1,5 @@ """Clumps GWAS significant variants to generate a studyLocus dataset of independent variants.""" - from __future__ import annotations from typing import TYPE_CHECKING @@ -20,6 +19,7 @@ class LDclumping: @staticmethod def _is_lead_linked( study_id: Column, + chromosome: Column, variant_id: Column, p_value_exponent: Column, p_value_mantissa: Column, @@ -29,6 +29,7 @@ def _is_lead_linked( Args: study_id (Column): studyId + chromosome (Column): chromosome variant_id (Column): Lead variant id p_value_exponent (Column): p-value exponent p_value_mantissa (Column): p-value mantissa @@ -37,31 +38,31 @@ def _is_lead_linked( Returns: Column: Boolean in which True indicates that the lead is linked to another tag in the same dataset. """ - leads_in_study = f.collect_set(variant_id).over(Window.partitionBy(study_id)) - tags_in_studylocus = f.array_union( - # Get all tag variants from the credible set per studyLocusId - f.transform(ld_set, lambda x: x.tagVariantId), - # And append the lead variant so that the intersection is the same for all studyLocusIds in a study - f.array(variant_id), - ) - intersect_lead_tags = f.array_sort( - f.array_intersect(leads_in_study, tags_in_studylocus) + # Partitoning data by study and chromosome - this is the scope for looking for linked loci. + # Within the partition, we order the data by increasing p-value, and we collect the more significant lead variants in the window. + windowspec = ( + Window.partitionBy(study_id, chromosome) + .orderBy(p_value_exponent.asc(), p_value_mantissa.asc()) + .rowsBetween(Window.unboundedPreceding, Window.currentRow) ) - return ( - # If the lead is in the credible set, we rank the peaks by p-value - f.when( - f.size(intersect_lead_tags) > 0, - f.row_number().over( - Window.partitionBy(study_id, intersect_lead_tags).orderBy( - p_value_exponent, p_value_mantissa - ) - ) - > 1, + more_significant_leads = f.collect_set(variant_id).over(windowspec) + + # Collect all variants from the ld_set + adding the lead variant to the list to make sure that the lead is always in the list. + tags_in_studylocus = f.array_distinct( + f.array_union( + f.array(variant_id), + f.transform(ld_set, lambda x: x.getField("tagVariantId")), ) - # If the intersection is empty (lead is not in the credible set or cred set is empty), the association is not linked - .otherwise(f.lit(False)) ) + # If more than one tags of the ld_set can be found in the list of the more significant leads, the lead is linked. + # Study loci without variantId is considered as not linked. + # Also leads that were not found in the LD index is also considered as not linked. + return f.when( + variant_id.isNotNull(), + f.size(f.array_intersect(more_significant_leads, tags_in_studylocus)) > 1, + ).otherwise(f.lit(False)) + @classmethod def clump(cls: type[LDclumping], associations: StudyLocus) -> StudyLocus: """Perform clumping on studyLocus dataset. diff --git a/src/gentropy/method/colocalisation.py b/src/gentropy/method/colocalisation.py index fd56398f0..c20f4909c 100644 --- a/src/gentropy/method/colocalisation.py +++ b/src/gentropy/method/colocalisation.py @@ -2,29 +2,109 @@ from __future__ import annotations -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Protocol import numpy as np import pyspark.ml.functions as fml import pyspark.sql.functions as f +import pyspark.sql.types as t from pyspark.ml.linalg import DenseVector, Vectors, VectorUDT from pyspark.sql.types import DoubleType +from gentropy.common.utils import get_logsum from gentropy.dataset.colocalisation import Colocalisation if TYPE_CHECKING: + from typing import Any + from numpy.typing import NDArray from pyspark.sql import Column from gentropy.dataset.study_locus_overlap import StudyLocusOverlap -class ECaviar: +def get_tag_variant_source(statistics: Column) -> Column: + """Get the source of the tag variant for a locus-overlap row. + + Args: + statistics (Column): statistics column + + Returns: + Column: source of the tag variant + + Examples: + >>> data = [('a', 'b'),(None, 'b'),('a', None),] + >>> ( + ... spark.createDataFrame(data, ['a', 'b']) + ... .select( + ... 'a', 'b', + ... get_tag_variant_source( + ... f.struct( + ... f.col('a').alias('left_posteriorProbability'), + ... f.col('b').alias('right_posteriorProbability'), + ... ) + ... ).alias('source') + ... ) + ... .show() + ... ) + +----+----+------+ + | a| b|source| + +----+----+------+ + | a| b| both| + |null| b| right| + | a|null| left| + +----+----+------+ + + """ + return ( + # Both posterior probabilities are not null: + f.when( + statistics.left_posteriorProbability.isNotNull() + & statistics.right_posteriorProbability.isNotNull(), + f.lit("both"), + ) + # Only the left posterior probability is not null: + .when(statistics.left_posteriorProbability.isNotNull(), f.lit("left")) + # It must be right only: + .otherwise(f.lit("right")) + ) + + +class ColocalisationMethodInterface(Protocol): + """Colocalisation method interface.""" + + METHOD_NAME: str + METHOD_METRIC: str + + @classmethod + def colocalise( + cls, overlapping_signals: StudyLocusOverlap, **kwargs: Any + ) -> Colocalisation: + """Method to generate the colocalisation. + + Args: + overlapping_signals (StudyLocusOverlap): Overlapping study loci. + **kwargs (Any): Additional keyword arguments to the colocalise method. + + + Returns: + Colocalisation: loci colocalisation + + Raises: + NotImplementedError: Implement in derivative classes. + """ + raise NotImplementedError("Implement in derivative classes.") + + +class ECaviar(ColocalisationMethodInterface): """ECaviar-based colocalisation analysis. It extends [CAVIAR](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5142122/#bib18)Β framework to explicitly estimate the posterior probability that the same variant is causal in 2 studies while accounting for the uncertainty of LD. eCAVIAR computes the colocalization posterior probability (**CLPP**) by utilizing the marginal posterior probabilities. This framework allows for **multiple variants to be causal** in a single locus. """ + METHOD_NAME: str = "eCAVIAR" + METHOD_METRIC: str = "clpp" + @staticmethod def _get_clpp(left_pp: Column, right_pp: Column) -> Column: """Calculate the colocalisation posterior probability (CLPP). @@ -56,37 +136,60 @@ def _get_clpp(left_pp: Column, right_pp: Column) -> Column: @classmethod def colocalise( - cls: type[ECaviar], overlapping_signals: StudyLocusOverlap + cls: type[ECaviar], + overlapping_signals: StudyLocusOverlap, + **kwargs: Any, ) -> Colocalisation: """Calculate bayesian colocalisation based on overlapping signals. Args: overlapping_signals (StudyLocusOverlap): overlapping signals. + **kwargs (Any): Additional parameters passed to the colocalise method. Returns: Colocalisation: colocalisation results based on eCAVIAR. """ return Colocalisation( _df=( - overlapping_signals.df.withColumn( - "clpp", - ECaviar._get_clpp( - f.col("statistics.left_posteriorProbability"), - f.col("statistics.right_posteriorProbability"), - ), + overlapping_signals.df.withColumns( + { + "clpp": ECaviar._get_clpp( + f.col("statistics.left_posteriorProbability"), + f.col("statistics.right_posteriorProbability"), + ), + "tagVariantSource": get_tag_variant_source(f.col("statistics")), + } + ) + .groupBy( + "leftStudyLocusId", + "rightStudyLocusId", + "rightStudyType", + "chromosome", ) - .groupBy("leftStudyLocusId", "rightStudyLocusId", "chromosome") .agg( - f.count("*").alias("numberColocalisingVariants"), + # Count the number of tag variants that can be found in both loci: + f.size( + f.filter( + f.collect_list(f.col("tagVariantSource")), + lambda x: x == "both", + ) + ) + .cast(t.LongType()) + .alias("numberColocalisingVariants"), f.sum(f.col("clpp")).alias("clpp"), ) - .withColumn("colocalisationMethod", f.lit("eCAVIAR")) + .withColumn("colocalisationMethod", f.lit(cls.METHOD_NAME)) + .join( + overlapping_signals.calculate_beta_ratio(), + on=["leftStudyLocusId", "rightStudyLocusId","chromosome"], + how="left" + ) ), _schema=Colocalisation.get_schema(), ) -class Coloc: +class Coloc(ColocalisationMethodInterface): """Calculate bayesian colocalisation based on overlapping signals from credible sets. Based on the [R COLOC package](https://github.com/chr1swallace/coloc/blob/main/R/claudia.R), which uses the Bayes factors from the credible set to estimate the posterior probability of colocalisation. This method makes the simplifying assumption that **only one single causal variant** exists for any given trait in any genomic region. @@ -103,29 +206,13 @@ class Coloc: Coloc requires the availability of Bayes factors (BF) for each variant in the credible set (`logBF` column). + Attributes: + PSEUDOCOUNT (float): Pseudocount to avoid log(0). Defaults to 1e-10. """ - @staticmethod - def _get_logsum(log_bf: NDArray[np.float64]) -> float: - """Calculates logsum of vector. - - This function calculates the log of the sum of the exponentiated - logs taking out the max, i.e. insuring that the sum is not Inf - - Args: - log_bf (NDArray[np.float64]): log bayes factor - - Returns: - float: logsum - - Example: - >>> l = [0.2, 0.1, 0.05, 0] - >>> round(Coloc._get_logsum(l), 6) - 1.476557 - """ - themax = np.max(log_bf) - result = themax + np.log(np.sum(np.exp(log_bf - themax))) - return float(result) + METHOD_NAME: str = "COLOC" + METHOD_METRIC: str = "h4" + PSEUDOCOUNT: float = 1e-10 @staticmethod def _get_posteriors(all_bfs: NDArray[np.float64]) -> DenseVector: @@ -142,7 +229,7 @@ def _get_posteriors(all_bfs: NDArray[np.float64]) -> DenseVector: >>> Coloc._get_posteriors(l) DenseVector([0.279, 0.2524, 0.2401, 0.2284]) """ - diff = all_bfs - Coloc._get_logsum(all_bfs) + diff = all_bfs - get_logsum(all_bfs) bfs_posteriors = np.exp(diff) return Vectors.dense(bfs_posteriors) @@ -150,44 +237,74 @@ def _get_posteriors(all_bfs: NDArray[np.float64]) -> DenseVector: def colocalise( cls: type[Coloc], overlapping_signals: StudyLocusOverlap, - priorc1: float = 1e-4, - priorc2: float = 1e-4, - priorc12: float = 1e-5, + **kwargs: float, ) -> Colocalisation: """Calculate bayesian colocalisation based on overlapping signals. Args: overlapping_signals (StudyLocusOverlap): overlapping peaks + **kwargs (float): Additional parameters passed to the colocalise method. + + Keyword Args: priorc1 (float): Prior on variant being causal for trait 1. Defaults to 1e-4. priorc2 (float): Prior on variant being causal for trait 2. Defaults to 1e-4. priorc12 (float): Prior on variant being causal for traits 1 and 2. Defaults to 1e-5. Returns: Colocalisation: Colocalisation results + + Raises: + TypeError: When passed incorrect prior argument types. """ + # Ensure priors are always present, even if not passed + priorc1 = kwargs.get("priorc1") or 1e-4 + priorc2 = kwargs.get("priorc2") or 1e-4 + priorc12 = kwargs.get("priorc12") or 1e-5 + priors = [priorc1, priorc2, priorc12] + if any(not isinstance(prior, float) for prior in priors): + raise TypeError( + "Passed incorrect type(s) for prior parameters. got %s", + {type(p): p for p in priors}, + ) + # register udfs - logsum = f.udf(Coloc._get_logsum, DoubleType()) + logsum = f.udf(get_logsum, DoubleType()) posteriors = f.udf(Coloc._get_posteriors, VectorUDT()) return Colocalisation( _df=( - overlapping_signals.df + overlapping_signals.df.withColumn( + "tagVariantSource", get_tag_variant_source(f.col("statistics")) + ) + .select("*", "statistics.*") # Before summing log_BF columns nulls need to be filled with 0: - .fillna(0, subset=["statistics.left_logBF", "statistics.right_logBF"]) + .fillna(0, subset=["left_logBF", "right_logBF"]) # Sum of log_BFs for each pair of signals .withColumn( "sum_log_bf", - f.col("statistics.left_logBF") + f.col("statistics.right_logBF"), + f.col("left_logBF") + f.col("right_logBF"), ) # Group by overlapping peak and generating dense vectors of log_BF: - .groupBy("chromosome", "leftStudyLocusId", "rightStudyLocusId") + .groupBy( + "chromosome", + "leftStudyLocusId", + "rightStudyLocusId", + "rightStudyType", + ) .agg( - f.count("*").alias("numberColocalisingVariants"), - fml.array_to_vector( - f.collect_list(f.col("statistics.left_logBF")) - ).alias("left_logBF"), - fml.array_to_vector( - f.collect_list(f.col("statistics.right_logBF")) - ).alias("right_logBF"), + f.size( + f.filter( + f.collect_list(f.col("tagVariantSource")), + lambda x: x == "both", + ) + ) + .cast(t.LongType()) + .alias("numberColocalisingVariants"), + fml.array_to_vector(f.collect_list(f.col("left_logBF"))).alias( + "left_logBF" + ), + fml.array_to_vector(f.collect_list(f.col("right_logBF"))).alias( + "right_logBF" + ), fml.array_to_vector(f.collect_list(f.col("sum_log_bf"))).alias( "sum_log_bf" ), @@ -209,12 +326,12 @@ def colocalise( .withColumn("lH2bf", f.log(f.col("priorc2")) + f.col("logsum2")) # h3 .withColumn("sumlogsum", f.col("logsum1") + f.col("logsum2")) - # exclude null H3/H4s: due to sumlogsum == logsum12 - .filter(f.col("sumlogsum") != f.col("logsum12")) .withColumn("max", f.greatest("sumlogsum", "logsum12")) .withColumn( "logdiff", - ( + f.when( + f.col("sumlogsum") == f.col("logsum12"), Coloc.PSEUDOCOUNT + ).otherwise( f.col("max") + f.log( f.exp(f.col("sumlogsum") - f.col("max")) @@ -256,20 +373,22 @@ def colocalise( .withColumn("h2", f.col("posteriors").getItem(2)) .withColumn("h3", f.col("posteriors").getItem(3)) .withColumn("h4", f.col("posteriors").getItem(4)) - .withColumn("h4h3", f.col("h4") / f.col("h3")) - .withColumn("log2h4h3", f.log2(f.col("h4h3"))) # clean up .drop( "posteriors", "allBF", - "h4h3", "lH0bf", "lH1bf", "lH2bf", "lH3bf", "lH4bf", ) - .withColumn("colocalisationMethod", f.lit("COLOC")) + .withColumn("colocalisationMethod", f.lit(cls.METHOD_NAME)) + .join( + overlapping_signals.calculate_beta_ratio(), + on=["leftStudyLocusId", "rightStudyLocusId","chromosome"], + how="left" + ) ), _schema=Colocalisation.get_schema(), ) diff --git a/src/gentropy/method/l2g/evaluator.py b/src/gentropy/method/l2g/evaluator.py deleted file mode 100644 index f41b1d45e..000000000 --- a/src/gentropy/method/l2g/evaluator.py +++ /dev/null @@ -1,204 +0,0 @@ -"""Module that integrates Spark ML Evaluators with W&B for experiment tracking.""" -from __future__ import annotations - -import itertools -from typing import TYPE_CHECKING, Any, Dict - -from pyspark import keyword_only -from pyspark.ml.evaluation import ( - BinaryClassificationEvaluator, - Evaluator, - MulticlassClassificationEvaluator, -) -from pyspark.ml.param import Param, Params, TypeConverters -from wandb.sdk.wandb_run import Run - -if TYPE_CHECKING: - from pyspark.sql import DataFrame - - -class WandbEvaluator(Evaluator): - """Wrapper for pyspark Evaluators. It is expected that the user will provide an Evaluators, and this wrapper will log metrics from said evaluator to W&B.""" - - spark_ml_evaluator: Param[Evaluator] = Param( - Params._dummy(), "spark_ml_evaluator", "evaluator from pyspark.ml.evaluation" - ) - - wandb_run: Param[Run] = Param( - Params._dummy(), - "wandb_run", - "wandb run. Expects an already initialized run. You should set this, or wandb_run_kwargs, NOT BOTH", - ) - - wandb_run_kwargs: Param[Any] = Param( - Params._dummy(), - "wandb_run_kwargs", - "kwargs to be passed to wandb.init. You should set this, or wandb_runId, NOT BOTH. Setting this is useful when using with WandbCrossValdidator", - ) - - wandb_runId: Param[str] = Param( # noqa: N815 - Params._dummy(), - "wandb_runId", - "wandb run id. if not providing an intialized run to wandb_run, a run with id wandb_runId will be resumed", - ) - - wandb_project_name: Param[str] = Param( - Params._dummy(), - "wandb_project_name", - "name of W&B project", - typeConverter=TypeConverters.toString, - ) - - label_values: Param[list[str]] = Param( - Params._dummy(), - "label_values", - "for classification and multiclass classification, this is a list of values the label can assume\nIf provided Multiclass or Multilabel evaluator without label_values, we'll figure it out from dataset passed through to evaluate.", - ) - - _input_kwargs: Dict[str, Any] - - @keyword_only - def __init__( - self: WandbEvaluator, - label_values: list[str] | None = None, - **kwargs: BinaryClassificationEvaluator - | MulticlassClassificationEvaluator - | Run, - ) -> None: - """Initialize a WandbEvaluator. - - Args: - label_values (list[str] | None): List of label values. - **kwargs (BinaryClassificationEvaluator | MulticlassClassificationEvaluator | Run): Keyword arguments. - """ - if label_values is None: - label_values = [] - super(Evaluator, self).__init__() - - self.metrics = { - MulticlassClassificationEvaluator: [ - "f1", - "accuracy", - "weightedPrecision", - "weightedRecall", - "weightedTruePositiveRate", - "weightedFalsePositiveRate", - "weightedFMeasure", - "truePositiveRateByLabel", - "falsePositiveRateByLabel", - "precisionByLabel", - "recallByLabel", - "fMeasureByLabel", - "logLoss", - "hammingLoss", - ], - BinaryClassificationEvaluator: ["areaUnderROC", "areaUnderPR"], - } - - self._setDefault(label_values=[]) - kwargs = self._input_kwargs - self._set(**kwargs) - - def setspark_ml_evaluator(self: WandbEvaluator, value: Evaluator) -> None: - """Set the spark_ml_evaluator parameter. - - Args: - value (Evaluator): Spark ML evaluator. - """ - self._set(spark_ml_evaluator=value) - - def setlabel_values(self: WandbEvaluator, value: list[str]) -> None: - """Set the label_values parameter. - - Args: - value (list[str]): List of label values. - """ - self._set(label_values=value) - - def getspark_ml_evaluator(self: WandbEvaluator) -> Evaluator: - """Get the spark_ml_evaluator parameter. - - Returns: - Evaluator: Spark ML evaluator. - """ - return self.getOrDefault(self.spark_ml_evaluator) - - def getwandb_run(self: WandbEvaluator) -> Run: - """Get the wandb_run parameter. - - Returns: - Run: Wandb run object. - """ - return self.getOrDefault(self.wandb_run) - - def getwandb_project_name(self: WandbEvaluator) -> Any: - """Get the wandb_project_name parameter. - - Returns: - Any: Name of the W&B project. - """ - return self.getOrDefault(self.wandb_project_name) - - def getlabel_values(self: WandbEvaluator) -> list[str]: - """Get the label_values parameter. - - Returns: - list[str]: List of label values. - """ - return self.getOrDefault(self.label_values) - - def _evaluate(self: WandbEvaluator, dataset: DataFrame) -> float: - """Evaluate the model on the given dataset. - - Args: - dataset (DataFrame): Dataset to evaluate the model on. - - Returns: - float: Metric value. - """ - dataset.persist() - metric_values: list[tuple[str, Any]] = [] - label_values = self.getlabel_values() - spark_ml_evaluator: BinaryClassificationEvaluator | MulticlassClassificationEvaluator = ( - self.getspark_ml_evaluator() # type: ignore[assignment, unused-ignore] - ) - run = self.getwandb_run() - evaluator_type = type(spark_ml_evaluator) - for metric in self.metrics[evaluator_type]: - if "ByLabel" in metric and label_values == []: - print( # noqa: T201 - "no label_values for the target have been provided and will be determined by the dataset. This could take some time" - ) - label_values = [ - r[spark_ml_evaluator.getLabelCol()] - for r in dataset.select(spark_ml_evaluator.getLabelCol()) - .distinct() - .collect() - ] - if isinstance(label_values[0], list): - merged = list(itertools.chain(*label_values)) - label_values = list(dict.fromkeys(merged).keys()) - self.setlabel_values(label_values) - for label in label_values: - out = spark_ml_evaluator.evaluate( - dataset, - { - spark_ml_evaluator.metricLabel: label, # type: ignore[assignment, unused-ignore] - spark_ml_evaluator.metricName: metric, - }, - ) - metric_values.append((f"{metric}:{label}", out)) - out = spark_ml_evaluator.evaluate( - dataset, {spark_ml_evaluator.metricName: metric} - ) - metric_values.append((f"{metric}", out)) - run.log(dict(metric_values)) - config = [ - (f"{k.parent.split('_')[0]}.{k.name}", v) - for k, v in spark_ml_evaluator.extractParamMap().items() - if "metric" not in k.name - ] - run.config.update(dict(config)) - return_metric = spark_ml_evaluator.evaluate(dataset) - dataset.unpersist() - return return_metric diff --git a/src/gentropy/method/l2g/feature_factory.py b/src/gentropy/method/l2g/feature_factory.py index a3dba6688..fe792058a 100644 --- a/src/gentropy/method/l2g/feature_factory.py +++ b/src/gentropy/method/l2g/feature_factory.py @@ -1,392 +1,196 @@ -"""Collection of methods that extract features from the gentropy datasets to be fed in L2G.""" -from __future__ import annotations - -from functools import reduce -from typing import TYPE_CHECKING +# isort: skip_file +"""Factory that computes features based on an input list.""" -import pyspark.sql.functions as f +from __future__ import annotations -from gentropy.common.spark_helpers import ( - convert_from_wide_to_long, - get_record_with_maximum_value, +from typing import Any, Iterator, Mapping + +from gentropy.dataset.l2g_features.colocalisation import ( + EQtlColocClppMaximumFeature, + EQtlColocClppMaximumNeighbourhoodFeature, + EQtlColocH4MaximumFeature, + EQtlColocH4MaximumNeighbourhoodFeature, + PQtlColocClppMaximumFeature, + PQtlColocClppMaximumNeighbourhoodFeature, + PQtlColocH4MaximumFeature, + PQtlColocH4MaximumNeighbourhoodFeature, + SQtlColocClppMaximumFeature, + SQtlColocClppMaximumNeighbourhoodFeature, + SQtlColocH4MaximumFeature, + SQtlColocH4MaximumNeighbourhoodFeature, +) +from gentropy.dataset.l2g_features.distance import ( + DistanceFootprintMeanFeature, + DistanceFootprintMeanNeighbourhoodFeature, + DistanceSentinelFootprintFeature, + DistanceSentinelFootprintNeighbourhoodFeature, + DistanceSentinelTssFeature, + DistanceSentinelTssNeighbourhoodFeature, + DistanceTssMeanFeature, + DistanceTssMeanNeighbourhoodFeature, ) -from gentropy.dataset.l2g_feature import L2GFeature -from gentropy.dataset.study_locus import CredibleInterval, StudyLocus +from gentropy.dataset.l2g_features.l2g_feature import L2GFeature +from gentropy.dataset.l2g_features.other import ( + CredibleSetConfidenceFeature, + GeneCountFeature, + ProteinGeneCountFeature, + ProteinCodingFeature, +) +from gentropy.dataset.l2g_features.vep import ( + VepMaximumFeature, + VepMaximumNeighbourhoodFeature, + VepMeanFeature, + VepMeanNeighbourhoodFeature, +) +from gentropy.dataset.l2g_gold_standard import L2GGoldStandard +from gentropy.dataset.study_locus import StudyLocus -if TYPE_CHECKING: - from pyspark.sql import Column, DataFrame - from gentropy.dataset.colocalisation import Colocalisation - from gentropy.dataset.study_index import StudyIndex - from gentropy.dataset.v2g import V2G +class L2GFeatureInputLoader: + """Loads all input datasets required for the L2GFeature dataset.""" + def __init__( + self, + **kwargs: Any, + ) -> None: + """Initializes L2GFeatureInputLoader with the provided inputs and returns loaded dependencies as a dictionary. -class ColocalisationFactory: - """Feature extraction in colocalisation.""" + Args: + **kwargs (Any): keyword arguments with the name of the dependency and the dependency itself. + """ + self.input_dependencies = {k: v for k, v in kwargs.items() if v is not None} - @staticmethod - def _get_max_coloc_per_study_locus( - study_locus: StudyLocus, - studies: StudyIndex, - colocalisation: Colocalisation, - colocalisation_method: str, - ) -> L2GFeature: - """Get the maximum colocalisation posterior probability for each pair of overlapping study-locus per type of colocalisation method and QTL type. + def get_dependency_by_type( + self, dependency_type: list[Any] | Any + ) -> dict[str, Any]: + """Returns the dependency that matches the provided type. Args: - study_locus (StudyLocus): Study locus dataset - studies (StudyIndex): Study index dataset - colocalisation (Colocalisation): Colocalisation dataset - colocalisation_method (str): Colocalisation method to extract the max from + dependency_type (list[Any] | Any): type(s) of the dependency to return. Returns: - L2GFeature: Stores the features with the max coloc probabilities for each pair of study-locus - - Raises: - ValueError: If the colocalisation method is not supported + dict[str, Any]: dictionary of dependenci(es) that match the provided type(s). """ - if colocalisation_method not in ["COLOC", "eCAVIAR"]: - raise ValueError( - f"Colocalisation method {colocalisation_method} not supported" - ) - if colocalisation_method == "COLOC": - coloc_score_col_name = "log2h4h3" - coloc_feature_col_template = "ColocLlrMaximum" + if not isinstance(dependency_type, list): + dependency_type = [dependency_type] + return { + k: v + for k, v in self.input_dependencies.items() + if isinstance(v, tuple(dependency_type)) + } - elif colocalisation_method == "eCAVIAR": - coloc_score_col_name = "clpp" - coloc_feature_col_template = "ColocClppMaximum" + def __iter__(self) -> Iterator[tuple[str, Any]]: + """Make the class iterable, returning an iterator over key-value pairs. - colocalising_study_locus = ( - study_locus.df.select("studyLocusId", "studyId") - # annotate studyLoci with overlapping IDs on the left - to just keep GWAS associations - .join( - colocalisation.df.selectExpr( - "leftStudyLocusId as studyLocusId", - "rightStudyLocusId", - "colocalisationMethod", - f"{coloc_score_col_name} as coloc_score", - ), - on="studyLocusId", - how="inner", - ) - # bring study metadata to just keep QTL studies on the right - .join( - study_locus.df.selectExpr( - "studyLocusId as rightStudyLocusId", "studyId as right_studyId" - ), - on="rightStudyLocusId", - how="inner", - ) - .join( - f.broadcast( - studies.df.selectExpr( - "studyId as right_studyId", - "studyType as right_studyType", - "geneId", - ) - ), - on="right_studyId", - how="inner", - ) - .filter( - (f.col("colocalisationMethod") == colocalisation_method) - & (f.col("right_studyType") != "gwas") - ) - .select("studyLocusId", "right_studyType", "geneId", "coloc_score") - ) - - # Max PP calculation per studyLocus AND type of QTL - local_max = get_record_with_maximum_value( - colocalising_study_locus, - ["studyLocusId", "right_studyType", "geneId"], - "coloc_score", - ).persist() - - intercept = 0.0001 - neighbourhood_max = ( - ( - local_max.selectExpr( - "studyLocusId", "coloc_score as coloc_local_max", "geneId" - ) - .join( - # Add maximum in the neighborhood - get_record_with_maximum_value( - colocalising_study_locus.withColumnRenamed( - "coloc_score", "coloc_neighborhood_max" - ), - ["studyLocusId", "right_studyType"], - "coloc_neighborhood_max", - ).drop("geneId"), - on="studyLocusId", - ) - .withColumn( - f"{coloc_feature_col_template}Neighborhood", - f.log10( - f.abs( - f.col("coloc_local_max") - - f.col("coloc_neighborhood_max") - + f.lit(intercept) - ) - ), - ) - ) - .drop("coloc_neighborhood_max", "coloc_local_max") - .persist() - ) - - # Split feature per molQTL - local_dfs = [] - nbh_dfs = [] - qtl_types: list[str] = ( - colocalising_study_locus.select("right_studyType") - .distinct() - .toPandas()["right_studyType"] - .tolist() - ) or ["eqtl", "pqtl", "sqtl"] - for qtl_type in qtl_types: - filtered_local_max = ( - local_max.filter(f.col("right_studyType") == qtl_type) - .withColumnRenamed( - "coloc_score", - f"{qtl_type}{coloc_feature_col_template}", - ) - .drop("right_studyType") - ) - local_dfs.append(filtered_local_max) - - filtered_neighbourhood_max = ( - neighbourhood_max.filter(f.col("right_studyType") == qtl_type) - .withColumnRenamed( - f"{coloc_feature_col_template}Neighborhood", - f"{qtl_type}{coloc_feature_col_template}Neighborhood", - ) - .drop("right_studyType") - ) - nbh_dfs.append(filtered_neighbourhood_max) - - wide_dfs = reduce( - lambda x, y: x.unionByName(y, allowMissingColumns=True), - local_dfs + nbh_dfs, - ) - - return L2GFeature( - _df=convert_from_wide_to_long( - wide_dfs.groupBy("studyLocusId", "geneId").agg( - *( - f.first(f.col(c), ignorenulls=True).alias(c) - for c in wide_dfs.columns - if c - not in [ - "studyLocusId", - "geneId", - ] - ) - ), - id_vars=("studyLocusId", "geneId"), - var_name="featureName", - value_name="featureValue", - ), - _schema=L2GFeature.get_schema(), - ) - - @staticmethod - def _get_coloc_features( - study_locus: StudyLocus, studies: StudyIndex, colocalisation: Colocalisation - ) -> L2GFeature: - """Calls _get_max_coloc_per_study_locus for both methods and concatenates the results. + Returns: + Iterator[tuple[str, Any]]: iterator over the dictionary's key-value pairs. + """ + return iter(self.input_dependencies.items()) - !!! note "Colocalisation features are only available for the eCAVIAR results for now." + def __repr__(self) -> str: + """Return a string representation of the input dependencies. - Args: - study_locus (StudyLocus): Study locus dataset - studies (StudyIndex): Study index dataset - colocalisation (Colocalisation): Colocalisation dataset + Useful for understanding the loader content without having to print the object attribute. Returns: - L2GFeature: Stores the features with the max coloc probabilities for each pair of study-locus + str: string representation of the input dependencies. """ - coloc_clpp = ColocalisationFactory._get_max_coloc_per_study_locus( - study_locus, - studies, - colocalisation, - "eCAVIAR", - ) + return repr(self.input_dependencies) + + +class FeatureFactory: + """Factory class for creating features.""" + + feature_mapper: Mapping[str, type[L2GFeature]] = { + "distanceSentinelTss": DistanceSentinelTssFeature, + "distanceSentinelTssNeighbourhood": DistanceSentinelTssNeighbourhoodFeature, + "distanceSentinelFootprint": DistanceSentinelFootprintFeature, + "distanceSentinelFootprintNeighbourhood": DistanceSentinelFootprintNeighbourhoodFeature, + "distanceTssMean": DistanceTssMeanFeature, + "distanceTssMeanNeighbourhood": DistanceTssMeanNeighbourhoodFeature, + "distanceFootprintMean": DistanceFootprintMeanFeature, + "distanceFootprintMeanNeighbourhood": DistanceFootprintMeanNeighbourhoodFeature, + "eQtlColocClppMaximum": EQtlColocClppMaximumFeature, + "eQtlColocClppMaximumNeighbourhood": EQtlColocClppMaximumNeighbourhoodFeature, + "pQtlColocClppMaximum": PQtlColocClppMaximumFeature, + "pQtlColocClppMaximumNeighbourhood": PQtlColocClppMaximumNeighbourhoodFeature, + "sQtlColocClppMaximum": SQtlColocClppMaximumFeature, + "sQtlColocClppMaximumNeighbourhood": SQtlColocClppMaximumNeighbourhoodFeature, + "eQtlColocH4Maximum": EQtlColocH4MaximumFeature, + "eQtlColocH4MaximumNeighbourhood": EQtlColocH4MaximumNeighbourhoodFeature, + "pQtlColocH4Maximum": PQtlColocH4MaximumFeature, + "pQtlColocH4MaximumNeighbourhood": PQtlColocH4MaximumNeighbourhoodFeature, + "sQtlColocH4Maximum": SQtlColocH4MaximumFeature, + "sQtlColocH4MaximumNeighbourhood": SQtlColocH4MaximumNeighbourhoodFeature, + "vepMean": VepMeanFeature, + "vepMeanNeighbourhood": VepMeanNeighbourhoodFeature, + "vepMaximum": VepMaximumFeature, + "vepMaximumNeighbourhood": VepMaximumNeighbourhoodFeature, + "geneCount500kb": GeneCountFeature, + "proteinGeneCount500kb": ProteinGeneCountFeature, + "isProteinCoding": ProteinCodingFeature, + "credibleSetConfidence": CredibleSetConfidenceFeature, + } + + def __init__( + self: FeatureFactory, + study_loci_to_annotate: StudyLocus | L2GGoldStandard, + features_list: list[str], + ) -> None: + """Initializes the factory. - return L2GFeature( - _df=coloc_clpp.df, - _schema=L2GFeature.get_schema(), - ) - - -class StudyLocusFactory(StudyLocus): - """Feature extraction in study locus.""" + Args: + study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation + features_list (list[str]): list of features to compute. + """ + self.study_loci_to_annotate = study_loci_to_annotate + self.features_list = features_list - @staticmethod - def _get_tss_distance_features( - study_locus: StudyLocus, distances: V2G - ) -> L2GFeature: - """Joins StudyLocus with the V2G to extract the minimum distance to a gene TSS of all variants in a StudyLocus credible set. + def generate_features( + self: FeatureFactory, + features_input_loader: L2GFeatureInputLoader, + ) -> list[L2GFeature]: + """Generates a feature matrix by reading an object with instructions on how to create the features. Args: - study_locus (StudyLocus): Study locus dataset - distances (V2G): Dataframe containing the distances of all variants to all genes TSS within a region + features_input_loader (L2GFeatureInputLoader): object with required features dependencies. Returns: - L2GFeature: Stores the features with the minimum distance among all variants in the credible set and a gene TSS. + list[L2GFeature]: list of computed features. + Raises: + ValueError: If feature not found. """ - wide_df = ( - study_locus.filter_credible_set(CredibleInterval.IS95) - .df.select( - "studyLocusId", - "variantId", - f.explode("locus.variantId").alias("tagVariantId"), - ) - .join( - distances.df.selectExpr( - "variantId as tagVariantId", "geneId", "distance" - ), - on="tagVariantId", - how="inner", - ) - .groupBy("studyLocusId", "geneId") - .agg( - f.min("distance").alias("distanceTssMinimum"), - f.mean("distance").alias("distanceTssMean"), - ) - ) - - return L2GFeature( - _df=convert_from_wide_to_long( - wide_df, - id_vars=("studyLocusId", "geneId"), - var_name="featureName", - value_name="featureValue", - ), - _schema=L2GFeature.get_schema(), - ) - - @staticmethod - def _get_vep_features( - credible_set: StudyLocus, - v2g: V2G, + computed_features = [] + for feature in self.features_list: + if feature in self.feature_mapper: + computed_features.append( + self.compute_feature(feature, features_input_loader) + ) + else: + raise ValueError(f"Feature {feature} not found.") + return computed_features + + def compute_feature( + self: FeatureFactory, + feature_name: str, + features_input_loader: L2GFeatureInputLoader, ) -> L2GFeature: - """Get the maximum VEP score for all variants in a locus's 95% credible set. - - This informs about functional impact of the variants in the locus. For more information on variant consequences, see: https://www.ensembl.org/info/genome/variation/prediction/predicted_data.html - Two metrics: max VEP score per study locus and gene, and max VEP score per study locus. - + """Instantiates feature class. Args: - credible_set (StudyLocus): Study locus dataset with the associations to be annotated - v2g (V2G): V2G dataset with the variant/gene relationships and their consequences + feature_name (str): name of the feature + features_input_loader (L2GFeatureInputLoader): Object that contais features input. Returns: - L2GFeature: Stores the features with the max VEP score. + L2GFeature: instantiated feature object """ - - def _aggregate_vep_feature( - df: DataFrame, - aggregation_expr: Column, - aggregation_cols: list[str], - feature_name: str, - ) -> DataFrame: - """Extracts the maximum or average VEP score after grouping by the given columns. Different aggregations return different predictive annotations. - - If the group_cols include "geneId", the maximum/mean VEP score per gene is returned. - Otherwise, the maximum/mean VEP score for all genes in the neighborhood of the locus is returned. - - Args: - df (DataFrame): DataFrame with the VEP scores for each variant in a studyLocus - aggregation_expr (Column): Aggregation expression to apply - aggregation_cols (list[str]): Columns to group by - feature_name (str): Name of the feature to be returned - - Returns: - DataFrame: DataFrame with the maximum VEP score per locus or per locus/gene - """ - if "geneId" in aggregation_cols: - return df.groupBy(aggregation_cols).agg( - aggregation_expr.alias(feature_name) - ) - return ( - df.groupBy(aggregation_cols) - .agg( - aggregation_expr.alias(feature_name), - f.collect_set("geneId").alias("geneId"), - ) - .withColumn("geneId", f.explode("geneId")) - ) - - credible_set_w_variant_consequences = ( - credible_set.filter_credible_set(CredibleInterval.IS95) - .df.withColumn("variantInLocusId", f.explode(f.col("locus.variantId"))) - .withColumn( - "variantInLocusPosteriorProbability", - f.explode(f.col("locus.posteriorProbability")), - ) - .join( - # Join with V2G to get variant consequences - v2g.df.filter( - f.col("datasourceId") == "variantConsequence" - ).withColumnRenamed("variantId", "variantInLocusId"), - on="variantInLocusId", - ) - .withColumn( - "weightedScore", - f.col("score") * f.col("variantInLocusPosteriorProbability"), - ) - .select( - "studyLocusId", - "variantId", - "studyId", - "geneId", - "score", - "weightedScore", - ) - .distinct() - .persist() - ) - - return L2GFeature( - _df=convert_from_wide_to_long( - reduce( - lambda x, y: x.unionByName(y, allowMissingColumns=True), - [ - # Calculate overall max VEP score for all genes in the vicinity - credible_set_w_variant_consequences.transform( - _aggregate_vep_feature, - f.max("score"), - ["studyLocusId"], - "vepMaximumNeighborhood", - ), - # Calculate overall max VEP score per gene - credible_set_w_variant_consequences.transform( - _aggregate_vep_feature, - f.max("score"), - ["studyLocusId", "geneId"], - "vepMaximum", - ), - # Calculate mean VEP score for all genes in the vicinity - credible_set_w_variant_consequences.transform( - _aggregate_vep_feature, - f.mean("weightedScore"), - ["studyLocusId"], - "vepMeanNeighborhood", - ), - # Calculate mean VEP score per gene - credible_set_w_variant_consequences.transform( - _aggregate_vep_feature, - f.mean("weightedScore"), - ["studyLocusId", "geneId"], - "vepMean", - ), - ], - ), - id_vars=("studyLocusId", "geneId"), - var_name="featureName", - value_name="featureValue", - ).filter(f.col("featureValue").isNotNull()), - _schema=L2GFeature.get_schema(), + # Extract feature class and dependency type + feature_cls = self.feature_mapper[feature_name] + feature_dependency_type = feature_cls.feature_dependency_type + return feature_cls.compute( + study_loci_to_annotate=self.study_loci_to_annotate, + feature_dependency=features_input_loader.get_dependency_by_type( + feature_dependency_type + ), ) diff --git a/src/gentropy/method/l2g/model.py b/src/gentropy/method/l2g/model.py index 5df189d33..e35e255a2 100644 --- a/src/gentropy/method/l2g/model.py +++ b/src/gentropy/method/l2g/model.py @@ -2,304 +2,261 @@ from __future__ import annotations -from dataclasses import dataclass +import json +from dataclasses import dataclass, field +from pathlib import Path from typing import TYPE_CHECKING, Any, Type -from pyspark.ml import Pipeline, PipelineModel -from pyspark.ml.evaluation import ( - BinaryClassificationEvaluator, - MulticlassClassificationEvaluator, -) -from pyspark.ml.feature import StringIndexer, VectorAssembler -from pyspark.ml.tuning import ParamGridBuilder -from wandb.data_types import Table -from wandb.sdk import init as wandb_init -from wandb.wandb_run import Run -from xgboost.spark.core import SparkXGBClassifierModel - -from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix -from gentropy.method.l2g.evaluator import WandbEvaluator +import pandas as pd +import skops.io as sio +from pandas import DataFrame as pd_dataframe +from pandas import to_numeric as pd_to_numeric +from sklearn.ensemble import GradientBoostingClassifier +from skops import hub_utils + +from gentropy.common.session import Session +from gentropy.common.utils import copy_to_gcs if TYPE_CHECKING: - from pyspark.ml import Transformer - from pyspark.sql import DataFrame + from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix + from gentropy.dataset.l2g_prediction import L2GPrediction @dataclass class LocusToGeneModel: """Wrapper for the Locus to Gene classifier.""" - features_list: list[str] - estimator: Any = None - pipeline: Pipeline = Pipeline(stages=[]) - model: PipelineModel | None = None - wandb_l2g_project_name: str = "otg_l2g" + model: Any = GradientBoostingClassifier(random_state=42) + hyperparameters: dict[str, Any] | None = None + training_data: L2GFeatureMatrix | None = None + label_encoder: dict[str, int] = field( + default_factory=lambda: { + "negative": 0, + "positive": 1, + } + ) def __post_init__(self: LocusToGeneModel) -> None: - """Post init that adds the model to the ML pipeline.""" - label_indexer = StringIndexer( - inputCol="goldStandardSet", outputCol="label", handleInvalid="keep" - ) - vector_assembler = LocusToGeneModel.features_vector_assembler( - self.features_list - ) - - self.pipeline = Pipeline( - stages=[ - label_indexer, - vector_assembler, - ] - ) + """Post-initialisation to fit the estimator with the provided params.""" + if self.hyperparameters: + self.model.set_params(**self.hyperparameters_dict) - def save(self: LocusToGeneModel, path: str) -> None: - """Saves fitted pipeline model to disk. + @classmethod + def load_from_disk( + cls: Type[LocusToGeneModel], path: str | Path + ) -> LocusToGeneModel: + """Load a fitted model from disk. Args: - path (str): Path to save the model to + path (str | Path): Path to the model + + Returns: + LocusToGeneModel: L2G model loaded from disk Raises: ValueError: If the model has not been fitted yet """ - if self.model is None: + loaded_model = sio.load(path, trusted=sio.get_untrusted_types(file=path)) + if not loaded_model._is_fitted(): raise ValueError("Model has not been fitted yet.") - self.model.write().overwrite().save(path) + return cls(model=loaded_model) - @property - def classifier(self: LocusToGeneModel) -> Any: - """Return the model. - - Returns: - Any: An estimator object from Spark ML - """ - return self.estimator - - @staticmethod - def features_vector_assembler(features_cols: list[str]) -> VectorAssembler: - """Spark transformer to assemble the feature columns into a vector. + @classmethod + def load_from_hub( + cls: Type[LocusToGeneModel], + model_id: str, + hf_token: str | None = None, + model_name: str = "classifier.skops", + ) -> LocusToGeneModel: + """Load a model from the Hugging Face Hub. This will download the model from the hub and load it from disk. Args: - features_cols (list[str]): List of feature columns to assemble + model_id (str): Model ID on the Hugging Face Hub + hf_token (str | None): Hugging Face Hub token to download the model (only required if private) + model_name (str): Name of the persisted model to load. Defaults to "classifier.skops". Returns: - VectorAssembler: Spark transformer to assemble the feature columns into a vector - - Examples: - >>> from pyspark.ml.feature import VectorAssembler - >>> df = spark.createDataFrame([(5.2, 3.5)], schema="feature_1 FLOAT, feature_2 FLOAT") - >>> assembler = LocusToGeneModel.features_vector_assembler(["feature_1", "feature_2"]) - >>> assembler.transform(df).show() - +---------+---------+--------------------+ - |feature_1|feature_2| features| - +---------+---------+--------------------+ - | 5.2| 3.5|[5.19999980926513...| - +---------+---------+--------------------+ - + LocusToGeneModel: L2G model loaded from the Hugging Face Hub """ - return ( - VectorAssembler(handleInvalid="error") - .setInputCols(features_cols) - .setOutputCol("features") - ) + local_path = Path(model_id) + hub_utils.download(repo_id=model_id, dst=local_path, token=hf_token) + return cls.load_from_disk(Path(local_path) / model_name) - def log_to_wandb( - self: LocusToGeneModel, - results: DataFrame, - training_data: L2GFeatureMatrix, - evaluators: list[ - BinaryClassificationEvaluator | MulticlassClassificationEvaluator - ], - wandb_run: Run, - ) -> None: - """Log evaluation results and feature importance to W&B. + @property + def hyperparameters_dict(self) -> dict[str, Any]: + """Return hyperparameters as a dictionary. - Args: - results (DataFrame): Dataframe containing the predictions - training_data (L2GFeatureMatrix): Training data used for the model. If provided, the table and the number of positive and negative labels will be logged to W&B - evaluators (list[BinaryClassificationEvaluator | MulticlassClassificationEvaluator]): List of Spark ML evaluators to use for evaluation - wandb_run (Run): W&B run to log the results to + Returns: + dict[str, Any]: Hyperparameters + + Raises: + ValueError: If hyperparameters have not been set """ - ## Track evaluation metrics - for evaluator in evaluators: - wandb_evaluator = WandbEvaluator( - spark_ml_evaluator=evaluator, wandb_run=wandb_run - ) - wandb_evaluator.evaluate(results) - ## Track feature importance - wandb_run.log({"importances": self.get_feature_importance()}) - ## Track training set - training_table = Table(dataframe=training_data.df.toPandas()) - wandb_run.log({"trainingSet": training_table}) - # Count number of positive and negative labels - gs_counts_dict = { - "goldStandard" + row["goldStandardSet"].capitalize(): row["count"] - for row in training_data.df.groupBy("goldStandardSet").count().collect() - } - wandb_run.log(gs_counts_dict) - # Missingness rates - wandb_run.log( - {"missingnessRates": training_data.calculate_feature_missingness_rate()} - ) + if not self.hyperparameters: + raise ValueError("Hyperparameters have not been set.") + elif isinstance(self.hyperparameters, dict): + return self.hyperparameters + return self.hyperparameters.default_factory() - @classmethod - def load_from_disk( - cls: Type[LocusToGeneModel], path: str, features_list: list[str] - ) -> LocusToGeneModel: - """Load a fitted pipeline model from disk. + def predict( + self: LocusToGeneModel, + feature_matrix: L2GFeatureMatrix, + session: Session, + ) -> L2GPrediction: + """Apply the model to a given feature matrix dataframe. The feature matrix needs to be preprocessed first. Args: - path (str): Path to the model - features_list (list[str]): List of features used for the model + feature_matrix (L2GFeatureMatrix): Feature matrix to apply the model to. + session (Session): Session object to convert data to Spark Returns: - LocusToGeneModel: L2G model loaded from disk + L2GPrediction: Dataset containing credible sets and their L2G scores """ - return cls(model=PipelineModel.load(path), features_list=features_list) + from gentropy.dataset.l2g_prediction import L2GPrediction + + pd_dataframe.iteritems = pd_dataframe.items + + feature_matrix_pdf = feature_matrix._df.toPandas() + # L2G score is the probability the classifier assigns to the positive class (the second element in the probability array) + feature_matrix_pdf["score"] = self.model.predict_proba( + # We drop the fixed columns to only pass the feature values to the classifier + feature_matrix_pdf.drop(feature_matrix.fixed_cols, axis=1) + .apply(pd_to_numeric) + .values + )[:, 1] + output_cols = [field.name for field in L2GPrediction.get_schema().fields] + return L2GPrediction( + _df=session.spark.createDataFrame(feature_matrix_pdf.filter(output_cols)), + _schema=L2GPrediction.get_schema(), + ) - @classifier.setter # type: ignore - def classifier(self: LocusToGeneModel, new_estimator: Any) -> None: - """Set the model. + def save(self: LocusToGeneModel, path: str) -> None: + """Saves fitted model to disk using the skops persistence format. Args: - new_estimator (Any): An estimator object from Spark ML - """ - self.estimator = new_estimator - - def get_param_grid(self: LocusToGeneModel) -> list[Any]: - """Return the parameter grid for the model. + path (str): Path to save the persisted model. Should end with .skops - Returns: - list[Any]: List of parameter maps to use for cross validation + Raises: + ValueError: If the model has not been fitted yet or if the path does not end with .skops """ - return ( - ParamGridBuilder() - .addGrid(self.estimator.max_depth, [3, 5, 7]) - .addGrid(self.estimator.learning_rate, [0.01, 0.1, 1.0]) - .build() - ) + if self.model is None: + raise ValueError("Model has not been fitted yet.") + if not path.endswith(".skops"): + raise ValueError("Path should end with .skops") + if path.startswith("gs://"): + local_path = path.split("/")[-1] + sio.dump(self.model, local_path) + copy_to_gcs(local_path, path) + else: + sio.dump(self.model, path) - def add_pipeline_stage( - self: LocusToGeneModel, transformer: Transformer - ) -> LocusToGeneModel: - """Adds a stage to the L2G pipeline. + @staticmethod + def load_feature_matrix_from_wandb(wandb_run_name: str) -> pd.DataFrame: + """Loads dataset of feature matrix used during a wandb run. Args: - transformer (Transformer): Spark transformer to add to the pipeline + wandb_run_name (str): Name of the wandb run to load the feature matrix from Returns: - LocusToGeneModel: L2G model with the new transformer - - Examples: - >>> from pyspark.ml.regression import LinearRegression - >>> estimator = LinearRegression() - >>> test_model = LocusToGeneModel(features_list=["a", "b"]) - >>> print(len(test_model.pipeline.getStages())) - 2 - >>> print(len(test_model.add_pipeline_stage(estimator).pipeline.getStages())) - 3 + pd.DataFrame: Feature matrix used during the wandb run """ - pipeline_stages = self.pipeline.getStages() - new_stages = pipeline_stages + [transformer] - self.pipeline = Pipeline(stages=new_stages) - return self + with open(wandb_run_name) as f: + raw_data = json.load(f) - def evaluate( + data = raw_data["data"] + columns = raw_data["columns"] + return pd.DataFrame(data, columns=columns) + + def _create_hugging_face_model_card( self: LocusToGeneModel, - results: DataFrame, - hyperparameters: dict[str, Any], - wandb_run_name: str | None, - training_data: L2GFeatureMatrix | None = None, + local_repo: str, ) -> None: - """Perform evaluation of the model predictions for the test set and track the results with W&B. + """Create a model card to document the model in the hub. The model card is saved in the local repo before pushing it to the hub. Args: - results (DataFrame): Dataframe containing the predictions - hyperparameters (dict[str, Any]): Hyperparameters used for the model - wandb_run_name (str | None): Descriptive name for the run to be tracked with W&B - training_data (L2GFeatureMatrix | None): Training data used for the model. If provided, the ratio of positive to negative labels will be logged to W&B + local_repo (str): Path to the folder where the README file will be saved to be pushed to the Hugging Face Hub """ - binary_evaluator = BinaryClassificationEvaluator( - rawPredictionCol="rawPrediction", labelCol="label" - ) - multi_evaluator = MulticlassClassificationEvaluator( - labelCol="label", predictionCol="prediction" - ) - - if wandb_run_name and training_data: - run = wandb_init( - project=self.wandb_l2g_project_name, - config=hyperparameters, - name=wandb_run_name, - ) - if isinstance(run, Run): - self.log_to_wandb( - results, training_data, [binary_evaluator, multi_evaluator], run - ) - run.finish() + from skops import card - @property - def feature_name_map(self: LocusToGeneModel) -> dict[str, str]: - """Return a dictionary mapping encoded feature names to the original names. + # Define card metadata + description = """The locus-to-gene (L2G) model derives features to prioritise likely causal genes at each GWAS locus based on genetic and functional genomics features. The main categories of predictive features are: - Returns: - dict[str, str]: Feature name map of the model + - Distance: (from credible set variants to gene) + - Molecular QTL Colocalization + - Chromatin Interaction: (e.g., promoter-capture Hi-C) + - Variant Pathogenicity: (from VEP) - Raises: - ValueError: If the model has not been fitted yet + More information at: https://opentargets.github.io/gentropy/python_api/methods/l2g/_l2g/ """ - if not self.model: - raise ValueError("Model not fitted yet. `fit()` has to be called first.") - elif isinstance(self.model.stages[1], VectorAssembler): - feature_names = self.model.stages[1].getInputCols() - return {f"f{i}": feature_name for i, feature_name in enumerate(feature_names)} - - def get_feature_importance(self: LocusToGeneModel) -> dict[str, float]: - """Return dictionary with relative importances of every feature in the model. Feature names are encoded and have to be mapped back to their original names. + how_to = """To use the model, you can load it using the `LocusToGeneModel.load_from_hub` method. This will return a `LocusToGeneModel` object that can be used to make predictions on a feature matrix. + The model can then be used to make predictions using the `predict` method. - Returns: - dict[str, float]: Dictionary mapping feature names to their importance - - Raises: - ValueError: If the model has not been fitted yet or is not an XGBoost model + More information can be found at: https://opentargets.github.io/gentropy/python_api/methods/l2g/model/ """ - if not self.model or not isinstance( - self.model.stages[-1], SparkXGBClassifierModel - ): - raise ValueError( - f"Model type {type(self.model)} not supported for feature importance." - ) - importance_map = self.model.stages[-1].get_feature_importances() - return {self.feature_name_map[k]: v for k, v in importance_map.items()} - - def fit( - self: LocusToGeneModel, - feature_matrix: L2GFeatureMatrix, - ) -> LocusToGeneModel: - """Fit the pipeline to the feature matrix dataframe. - - Args: - feature_matrix (L2GFeatureMatrix): Feature matrix dataframe to fit the model to - - Returns: - LocusToGeneModel: Fitted model - """ - self.model = self.pipeline.fit(feature_matrix.df) - return self + model_card = card.Card( + self.model, + metadata=card.metadata_from_config(Path(local_repo)), + ) + model_card.add( + **{ + "Model description": description, + "Model description/Training Procedure": "Gradient Boosting Classifier", + "How to Get Started with the Model": how_to, + "Model Card Authors": "Open Targets", + "License": "MIT", + "Citation": "https://doi.org/10.1038/s41588-021-00945-5", + } + ) + model_card.delete("Model description/Training Procedure/Model Plot") + model_card.delete("Model description/Evaluation Results") + model_card.delete("Model Card Authors") + model_card.delete("Model Card Contact") + model_card.save(Path(local_repo) / "README.md") - def predict( + def export_to_hugging_face_hub( self: LocusToGeneModel, - feature_matrix: L2GFeatureMatrix, - ) -> DataFrame: - """Apply the model to a given feature matrix dataframe. The feature matrix needs to be preprocessed first. + model_path: str, + hf_hub_token: str, + data: pd_dataframe, + commit_message: str, + repo_id: str = "opentargets/locus_to_gene", + local_repo: str = "locus_to_gene", + ) -> None: + """Share the model on Hugging Face Hub. Args: - feature_matrix (L2GFeatureMatrix): Feature matrix dataframe to apply the model to - - Returns: - DataFrame: Dataframe with predictions + model_path (str): The path to the L2G model file. + hf_hub_token (str): Hugging Face Hub token + data (pd_dataframe): Data used to train the model. This is used to have an example input for the model and to store the column order. + commit_message (str): Commit message for the push + repo_id (str): The Hugging Face Hub repo id where the model will be stored. + local_repo (str): Path to the folder where the contents of the model repo + the documentation are located. This is used to push the model to the Hugging Face Hub. Raises: - ValueError: If the model has not been fitted yet + RuntimeError: If the push to the Hugging Face Hub fails """ - if not self.model: - raise ValueError("Model not fitted yet. `fit()` has to be called first.") - return self.model.transform(feature_matrix.df) + from sklearn import __version__ as sklearn_version + + try: + hub_utils.init( + model=model_path, + requirements=[f"scikit-learn={sklearn_version}"], + dst=local_repo, + task="tabular-classification", + data=data, + ) + self._create_hugging_face_model_card(local_repo) + hub_utils.push( + repo_id=repo_id, + source=local_repo, + token=hf_hub_token, + commit_message=commit_message, + create_remote=True, + ) + except Exception as e: + # remove the local repo if the push fails + if Path(local_repo).exists(): + for p in Path(local_repo).glob("*"): + p.unlink() + Path(local_repo).rmdir() + raise RuntimeError from e diff --git a/src/gentropy/method/l2g/trainer.py b/src/gentropy/method/l2g/trainer.py index 8d2b3d7aa..fe56b3f42 100644 --- a/src/gentropy/method/l2g/trainer.py +++ b/src/gentropy/method/l2g/trainer.py @@ -2,111 +2,293 @@ from __future__ import annotations +import os from dataclasses import dataclass -from typing import Any, Optional +from functools import partial +from typing import TYPE_CHECKING, Any -from pyspark.ml.evaluation import MulticlassClassificationEvaluator -from pyspark.ml.tuning import CrossValidator +import matplotlib.pyplot as plt +import pandas as pd +import shap +from sklearn.metrics import ( + accuracy_score, + f1_score, + precision_score, + recall_score, + roc_auc_score, +) +from sklearn.model_selection import train_test_split +from wandb.data_types import Image, Table +from wandb.errors.term import termlog as wandb_termlog +from wandb.sdk.wandb_init import init as wandb_init +from wandb.sdk.wandb_sweep import sweep as wandb_sweep +from wandb.sklearn import plot_classifier +from wandb.wandb_agent import agent as wandb_agent from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix from gentropy.method.l2g.model import LocusToGeneModel +if TYPE_CHECKING: + from matplotlib.axes._axes import Axes + from shap._explanation import Explanation + from wandb.sdk.wandb_run import Run + @dataclass class LocusToGeneTrainer: """Modelling of what is the most likely causal gene associated with a given locus.""" - _model: LocusToGeneModel - train_set: L2GFeatureMatrix + model: LocusToGeneModel + feature_matrix: L2GFeatureMatrix - @classmethod - def train( - cls: type[LocusToGeneTrainer], - data: L2GFeatureMatrix, - l2g_model: LocusToGeneModel, - features_list: list[str], - evaluate: bool, - wandb_run_name: str | None = None, - model_path: str | None = None, - **hyperparams: dict[str, Any], - ) -> LocusToGeneModel: - """Train the Locus to Gene model. + # Initialise vars + features_list: list[str] | None = None + label_col: str = "goldStandardSet" + x_train: pd.DataFrame | None = None + y_train: pd.Series | None = None + x_test: pd.DataFrame | None = None + y_test: pd.Series | None = None + run: Run | None = None + wandb_l2g_project_name: str = "gentropy-locus-to-gene" - Args: - data (L2GFeatureMatrix): Feature matrix containing the data - l2g_model (LocusToGeneModel): Model to fit to the data on - features_list (list[str]): List of features to use for the model - evaluate (bool): Whether to evaluate the model on a test set - wandb_run_name (str | None): Descriptive name for the run to be tracked with W&B - model_path (str | None): Path to save the model to - **hyperparams (dict[str, Any]): Hyperparameters to use for the model + def __post_init__(self) -> None: + """Set default features_list to feature_matrix's features_list if not provided.""" + self.features_list = ( + self.feature_matrix.features_list + if self.features_list is None + else self.features_list + ) + + def fit( + self: LocusToGeneTrainer, + ) -> LocusToGeneModel: + """Fit the pipeline to the feature matrix dataframe. Returns: - LocusToGeneModel: Trained model + LocusToGeneModel: Fitted model + + Raises: + ValueError: Train data not set, nothing to fit. """ - train, test = data.select_features(features_list).train_test_split(fraction=0.8) + if self.x_train is not None and self.y_train is not None: + assert ( + not self.x_train.empty and not self.y_train.empty + ), "Train data not set, nothing to fit." + fitted_model = self.model.model.fit(X=self.x_train.values, y=self.y_train) + self.model = LocusToGeneModel( + model=fitted_model, + hyperparameters=fitted_model.get_params(), + training_data=self.feature_matrix, + ) + return self.model + raise ValueError("Train data not set, nothing to fit.") - model = l2g_model.add_pipeline_stage(l2g_model.estimator).fit(train) + def _get_shap_explanation( + self: LocusToGeneTrainer, + model: LocusToGeneModel, + ) -> Explanation: + """Get the SHAP values for the given model and data. We pass the full X matrix (without the labels) to interpret their shap values. - if evaluate: - l2g_model.evaluate( - results=model.predict(test), - hyperparameters=hyperparams, - wandb_run_name=wandb_run_name, - training_data=train, + Args: + model (LocusToGeneModel): Model to explain. + + Returns: + Explanation: SHAP values for the given model and data. + + Raises: + ValueError: Train data not set, cannot get SHAP values. + """ + if self.x_train is not None and self.x_test is not None: + training_data = pd.concat([self.x_train, self.x_test], ignore_index=True) + explainer = shap.TreeExplainer( + model.model, + data=training_data, + feature_perturbation="interventional", ) - if model_path: - l2g_model.save(model_path) - return l2g_model - - @classmethod - def cross_validate( - cls: type[LocusToGeneTrainer], - l2g_model: LocusToGeneModel, - data: L2GFeatureMatrix, - num_folds: int, - param_grid: Optional[list] = None, # type: ignore - ) -> LocusToGeneModel: - """Perform k-fold cross validation on the model. + return explainer(training_data) + raise ValueError("Train data not set.") - By providing a model with a parameter grid, this method will perform k-fold cross validation on the model for each - combination of parameters and return the best model. + def log_plot_image_to_wandb( + self: LocusToGeneTrainer, title: str, plot: Axes + ) -> None: + """Accepts a plot object, and saves the fig to PNG to then log it in W&B. Args: - l2g_model (LocusToGeneModel): Model to fit to the data on - data (L2GFeatureMatrix): Data to perform cross validation on - num_folds (int): Number of folds to use for cross validation - param_grid (Optional[list]): List of parameter maps to use for cross validation + title (str): Title of the plot. + plot (Axes): Shap plot to log. - Returns: - LocusToGeneModel: Trained model fitted with the best hyperparameters + Raises: + ValueError: Run not set, cannot log to W&B. + """ + if self.run is None: + raise ValueError("Run not set, cannot log to W&B.") + if not plot: + # Scatter plot returns none, so we need to handle this case + plt.savefig("tmp.png", bbox_inches="tight") + else: + plot.figure.savefig("tmp.png", bbox_inches="tight") + self.run.log({title: Image("tmp.png")}) + plt.close() + os.remove("tmp.png") + + def log_to_wandb( + self: LocusToGeneTrainer, + wandb_run_name: str, + ) -> None: + """Log evaluation results and feature importance to W&B to compare between different L2G runs. + + Dashboard is available at https://wandb.ai/open-targets/gentropy-locus-to-gene?nw=nwuseropentargets + Credentials to access W&B are available at the OT central login sheet. + + Args: + wandb_run_name (str): Name of the W&B run Raises: - ValueError: Parameter grid is empty. Cannot perform cross-validation. - ValueError: Unable to retrieve the best model. + ValueError: If dependencies are not available. """ - evaluator = MulticlassClassificationEvaluator() - params_grid = param_grid or l2g_model.get_param_grid() - if not param_grid: - raise ValueError( - "Parameter grid is empty. Cannot perform cross-validation." + if ( + self.x_train is not None + and self.x_test is not None + and self.y_train is not None + and self.y_test is not None + and self.features_list is not None + ): + assert ( + not self.x_train.empty and not self.y_train.empty + ), "Train data not set, nothing to evaluate." + fitted_classifier = self.model.model + y_predicted = fitted_classifier.predict(self.x_test.values) + y_probas = fitted_classifier.predict_proba(self.x_test.values) + self.run = wandb_init( + project=self.wandb_l2g_project_name, + name=wandb_run_name, + config=fitted_classifier.get_params(), + ) + # Track classification plots + plot_classifier( + self.model.model, + self.x_train.values, + self.x_test.values, + self.y_train, + self.y_test, + y_predicted, + y_probas, + labels=list(self.model.label_encoder.values()), + model_name="L2G-classifier", + feature_names=self.features_list, + is_binary=True, ) - cv = CrossValidator( - numFolds=num_folds, - estimator=l2g_model.estimator, - estimatorParamMaps=params_grid, - evaluator=evaluator, - parallelism=2, - collectSubModels=False, - seed=42, + # Track evaluation metrics + self.run.log( + { + "areaUnderROC": roc_auc_score( + self.y_test, y_probas[:, 1], average="weighted" + ) + } + ) + self.run.log({"accuracy": accuracy_score(self.y_test, y_predicted)}) + self.run.log( + { + "weightedPrecision": precision_score( + self.y_test, y_predicted, average="weighted" + ) + } + ) + self.run.log( + { + "weightedRecall": recall_score( + self.y_test, y_predicted, average="weighted" + ) + } + ) + self.run.log({"f1": f1_score(self.y_test, y_predicted, average="weighted")}) + # Track gold standards and their features + self.run.log( + {"featureMatrix": Table(dataframe=self.feature_matrix._df.toPandas())} + ) + # Log feature missingness + self.run.log( + { + "missingnessRates": self.feature_matrix.calculate_feature_missingness_rate() + } + ) + # Plot marginal contribution of each feature + explanation = self._get_shap_explanation(self.model) + self.log_plot_image_to_wandb( + "Feature Contribution", + shap.plots.bar( + explanation, max_display=len(self.x_train.columns), show=False + ), + ) + self.log_plot_image_to_wandb( + "Beeswarm Plot", + shap.plots.beeswarm( + explanation, max_display=len(self.x_train.columns), show=False + ), + ) + # Plot correlation between feature values and their importance + for feature in self.features_list: + self.log_plot_image_to_wandb( + f"Effect of {feature} on the predictions", + shap.plots.scatter( + explanation[:, feature], + show=False, + ), + ) + wandb_termlog("Logged Shapley contributions.") + self.run.finish() + else: + raise ValueError("Something went wrong, couldn't log to W&B.") + + def train( + self: LocusToGeneTrainer, + wandb_run_name: str, + ) -> LocusToGeneModel: + """Train the Locus to Gene model. + + Args: + wandb_run_name (str): Name of the W&B run. Unless this is provided, the model will not be logged to W&B. + + Returns: + LocusToGeneModel: Fitted model + """ + data_df = self.feature_matrix._df.drop("geneId", "studyLocusId").toPandas() + + # Encode labels in `goldStandardSet` to a numeric value + data_df[self.label_col] = data_df[self.label_col].map(self.model.label_encoder) + + # Ensure all columns are numeric and split + data_df = data_df.apply(pd.to_numeric) + X = data_df[self.features_list].copy() + y = data_df[self.label_col].copy() + self.x_train, self.x_test, self.y_train, self.y_test = train_test_split( + X, y, test_size=0.2, random_state=42 ) - l2g_model.add_pipeline_stage(cv) # type: ignore[assignment, unused-ignore] + # Train + model = self.fit() - # Integrate the best model from the last stage of the pipeline - if (full_pipeline_model := l2g_model.fit(data).model) is None or not hasattr( - full_pipeline_model, "stages" - ): - raise ValueError("Unable to retrieve the best model.") - l2g_model.model = full_pipeline_model.stages[-1].bestModel # type: ignore[assignment, unused-ignore] - return l2g_model + # Evaluate + self.log_to_wandb( + wandb_run_name=wandb_run_name, + ) + + return model + + def hyperparameter_tuning( + self: LocusToGeneTrainer, wandb_run_name: str, parameter_grid: dict[str, Any] + ) -> None: + """Perform hyperparameter tuning on the model with W&B Sweeps. Metrics for every combination of hyperparameters will be logged to W&B for comparison. + + Args: + wandb_run_name (str): Name of the W&B run + parameter_grid (dict[str, Any]): Dictionary containing the hyperparameters to sweep over. The keys are the hyperparameter names, and the values are dictionaries containing the values to sweep over. + """ + sweep_config = { + "method": "grid", + "metric": {"name": "roc", "goal": "maximize"}, + "parameters": parameter_grid, + } + sweep_id = wandb_sweep(sweep_config, project=self.wandb_l2g_project_name) + + wandb_agent(sweep_id, partial(self.train, wandb_run_name=wandb_run_name)) diff --git a/src/gentropy/method/ld.py b/src/gentropy/method/ld.py index f0eab7c4b..64d47451d 100644 --- a/src/gentropy/method/ld.py +++ b/src/gentropy/method/ld.py @@ -1,10 +1,12 @@ """Performing linkage disequilibrium (LD) operations.""" + from __future__ import annotations from typing import TYPE_CHECKING from pyspark.sql import functions as f +from gentropy.common.spark_helpers import order_array_of_structs_by_field from gentropy.dataset.study_locus import StudyLocus, StudyLocusQualityCheck if TYPE_CHECKING: @@ -18,60 +20,64 @@ class LDAnnotator: """Class to annotate linkage disequilibrium (LD) operations from GnomAD.""" @staticmethod - def _calculate_weighted_r_overall(ld_set: Column) -> Column: - """Aggregation of weighted R information using ancestry proportions. + def _get_major_population(ordered_populations: Column) -> Column: + """Get major population based on an ldPopulationStructure array ordered by relativeSampleSize. + + If there is a tie for the major population, nfe is selected if it is one of the major populations. + The first population in the array is selected if there is no tie for the major population, or there is a tie but nfe is not one of the major populations. Args: - ld_set (Column): LD set + ordered_populations (Column): ldPopulationStructure array ordered by relativeSampleSize Returns: - Column: LD set with added 'r2Overall' field + Column: major population """ - return f.transform( - ld_set, - lambda x: f.struct( - x["tagVariantId"].alias("tagVariantId"), - # r2Overall is the accumulated sum of each r2 relative to the population size - f.aggregate( - x["rValues"], - f.lit(0.0), - lambda acc, y: acc - + f.coalesce( - f.pow(y["r"], 2) * y["relativeSampleSize"], f.lit(0.0) - ), # we use coalesce to avoid problems when r/relativeSampleSize is null - ).alias("r2Overall"), - ), + major_population_size = ordered_populations["relativeSampleSize"][0] + major_populations = f.filter( + ordered_populations, + lambda x: x["relativeSampleSize"] == major_population_size + ) + # Check if nfe (Non-Finnish European) is one of the major populations + has_nfe = f.filter( + major_populations, + lambda x: x["ldPopulation"] == "nfe" + ) + return f.when( + (f.size(major_populations) > 1) & (f.size(has_nfe) == 1), + f.lit("nfe") + ).otherwise( + ordered_populations["ldPopulation"][0] ) @staticmethod - def _add_population_size(ld_set: Column, study_populations: Column) -> Column: - """Add population size to each rValues entry in the ldSet. + def _calculate_r2_major(ld_set: Column, major_population: Column) -> Column: + """Calculate R2 using R of the major population in the study. Args: ld_set (Column): LD set - study_populations (Column): Study populations + major_population (Column): Major population of the study Returns: - Column: LD set with added 'relativeSampleSize' field + Column: LD set with added 'r2Overall' field """ - # Create a population to relativeSampleSize map from the struct - populations_map = f.map_from_arrays( - study_populations["ldPopulation"], - study_populations["relativeSampleSize"], - ) - return f.transform( + ld_set_with_major_pop = f.transform( ld_set, lambda x: f.struct( x["tagVariantId"].alias("tagVariantId"), - f.transform( + f.filter( x["rValues"], - lambda y: f.struct( - y["population"].alias("population"), - y["r"].alias("r"), - populations_map[y["population"]].alias("relativeSampleSize"), - ), - ).alias("rValues"), - ), + lambda y: y["population"] == major_population + ).alias("rValues") + ) + ) + return f.transform( + ld_set_with_major_pop, + lambda x: f.struct( + x["tagVariantId"].alias("tagVariantId"), + f.coalesce( + f.pow(x["rValues"]["r"][0], 2), f.lit(0.0) + ).alias("r2Overall") + ) ) @staticmethod @@ -120,21 +126,26 @@ def ld_annotate( associations: StudyLocus, studies: StudyIndex, ld_index: LDIndex, + r2_threshold: float = 0.5, ) -> StudyLocus: """Annotate linkage disequilibrium (LD) information to a set of studyLocus. This function: - 1. Annotates study locus with population structure information from the study index + 1. Annotates study locus with population structure information ordered by relativeSampleSize from the study index 2. Joins the LD index to the StudyLocus - 3. Adds the population size of the study to each rValues entry in the ldSet - 4. Calculates the overall R weighted by the ancestry proportions in every given study. + 3. Gets the major population from the population structure + 4. Calculates R2 by using the R of the major ancestry 5. Flags associations with variants that are not found in the LD reference 6. Rescues lead variant when no LD information is available but lead variant is available + !!! note + Because the LD index has a pre-set threshold of R2 = 0.5, this is the minimum threshold for the LD information to be included in the ldSet. + Args: associations (StudyLocus): Dataset to be LD annotated studies (StudyIndex): Dataset with study information ld_index (LDIndex): Dataset with LD information for every variant present in LD matrix + r2_threshold (float): R2 threshold to filter the LD set on. Default is 0.5. Returns: StudyLocus: including additional column with LD information. @@ -144,9 +155,14 @@ def ld_annotate( associations.df # Drop ldSet column if already available .select(*[col for col in associations.df.columns if col != "ldSet"]) - # Annotate study locus with population structure from study index + # Annotate study locus with population structure ordered by relativeSampleSize from study index .join( - studies.df.select("studyId", "ldPopulationStructure"), + studies.df.select( + "studyId", + order_array_of_structs_by_field( + "ldPopulationStructure", "relativeSampleSize" + ).alias("ldPopulationStructure") + ), on="studyId", how="left", ) @@ -156,25 +172,33 @@ def ld_annotate( on=["variantId", "chromosome"], how="left", ) - # Add population size to each rValues entry in the ldSet if population structure available: + # Get major population from population structure if population structure available .withColumn( - "ldSet", + "majorPopulation", f.when( f.col("ldPopulationStructure").isNotNull(), - cls._add_population_size( - f.col("ldSet"), f.col("ldPopulationStructure") - ), - ), + cls._get_major_population( + f.col("ldPopulationStructure") + ) + ) ) - # Aggregate weighted R information using ancestry proportions + # Calculate R2 using R of the major population .withColumn( "ldSet", f.when( f.col("ldPopulationStructure").isNotNull(), - cls._calculate_weighted_r_overall(f.col("ldSet")), - ), + cls._calculate_r2_major( + f.col("ldSet"), f.col("majorPopulation") + ) + ) + ) + .drop("ldPopulationStructure", "majorPopulation") + # Filter the LD set by the R2 threshold and set to null if no LD information passes the threshold + .withColumn( + "ldSet", + StudyLocus.filter_ld_set(f.col("ldSet"), r2_threshold), ) - .drop("ldPopulationStructure") + .withColumn("ldSet", f.when(f.size("ldSet") > 0, f.col("ldSet"))) # QC: Flag associations with variants that are not found in the LD reference .withColumn( "qualityControls", diff --git a/src/gentropy/method/ld_matrix_interface.py b/src/gentropy/method/ld_matrix_interface.py new file mode 100644 index 000000000..d0051101b --- /dev/null +++ b/src/gentropy/method/ld_matrix_interface.py @@ -0,0 +1,101 @@ +"""Step to import filtered version of a LD matrix (block matrix).""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import pyspark.sql.functions as f + +from gentropy.common.session import Session +from gentropy.datasource.gnomad.ld import GnomADLDMatrix +from gentropy.datasource.pan_ukbb_ld.ld import PanUKBBLDMatrix + +if TYPE_CHECKING: + from pyspark.sql import DataFrame, Row + + +class LDMatrixInterface: + """Toolset to interact with LD matrices.""" + + ancestry_map = { + "nfe": "EUR", + "csa": "CSA", + "afr": "AFR", + } + + @staticmethod + def get_locus_index_boundaries( + session: Session, + study_locus_row: Row, + ancestry: str = "nfe", + ) -> DataFrame: + """Extract hail matrix index from StudyLocus rows. + + Args: + session (Session): Session object + study_locus_row (Row): Study-locus row + ancestry (str): Major population to extract from gnomad matrix, default is "nfe" + + Returns: + DataFrame: Returns the index of the gnomad matrix for the locus + + """ + if ancestry in ("nfe", "csa", "afr"): + joined_index = PanUKBBLDMatrix().get_locus_index_boundaries( + session=session, + study_locus_row=study_locus_row, + ancestry=LDMatrixInterface.ancestry_map.get(ancestry, ancestry), + ) + else: + joined_index = ( + GnomADLDMatrix() + .get_locus_index_boundaries( + study_locus_row=study_locus_row, + major_population=ancestry, + ) + .withColumn( + "variantId", + f.concat( + f.regexp_replace(f.col("`locus.contig`"), "chr", ""), + f.lit("_"), + f.col("`locus.position`"), + f.lit("_"), + f.col("alleles").getItem(0), + f.lit("_"), + f.col("alleles").getItem(1), + ).cast("string"), + ) + ) + + return joined_index + + @staticmethod + def get_numpy_matrix( + locus_index: DataFrame, + ancestry: str = "nfe", + ) -> np.ndarray: + """Extract the LD block matrix for a locus. + + Args: + locus_index (DataFrame): hail matrix variant index table + ancestry (str): major ancestry label eg. `nfe` + + Returns: + np.ndarray: LD block matrix for the locus + """ + if ancestry in ( + "afr", + "csa", + "nfe", + ): + block_matrix = PanUKBBLDMatrix().get_numpy_matrix( + locus_index=locus_index, + ancestry=LDMatrixInterface.ancestry_map.get(ancestry, ancestry), + ) + else: + block_matrix = GnomADLDMatrix.get_numpy_matrix( + locus_index=locus_index, gnomad_ancestry=ancestry + ) + + return block_matrix diff --git a/src/gentropy/method/locus_breaker_clumping.py b/src/gentropy/method/locus_breaker_clumping.py new file mode 100644 index 000000000..3ab1e605a --- /dev/null +++ b/src/gentropy/method/locus_breaker_clumping.py @@ -0,0 +1,167 @@ +"""Locus-breaker clumping method.""" + +from __future__ import annotations + +import sys + +import numpy as np +import pyspark.sql.functions as f +import pyspark.sql.types as t +from pyspark.sql.window import Window + +from gentropy.common.spark_helpers import calculate_neglog_pvalue +from gentropy.dataset.study_locus import StudyLocus +from gentropy.dataset.summary_statistics import SummaryStatistics + + +class LocusBreakerClumping: + """Locus-breaker clumping method.""" + + @staticmethod + def locus_breaker( + summary_statistics: SummaryStatistics, + baseline_pvalue_cutoff: float, + distance_cutoff: int, + pvalue_cutoff: float, + flanking_distance: int, + ) -> StudyLocus: + """Identify GWAS associated loci based on the provided p-value and distance cutoff. + + - The GWAS associated loci identified by this method have a varying width, and are separated by a distance greater than the provided distance cutoff. + - The distance is only calculted between single point associations that reach the baseline p-value cutoff. + - As the width of the selected genomic region dynamically depends on the loci, the resulting StudyLocus object will contain the locus start and end position. + - To ensure completeness, the locus is extended by a flanking distance in both ends. + + Args: + summary_statistics (SummaryStatistics): Input summary statistics dataset. + baseline_pvalue_cutoff (float): baseline significance we consider for the locus. + distance_cutoff (int): minimum distance that separates two loci. + pvalue_cutoff (float): the minimum significance the locus should have. + flanking_distance (int): the distance to extend the locus in both directions. + + Returns: + StudyLocus: clumped study loci with locus start and end positions + lead variant from the locus. + """ + # Extract columns from the summary statistics: + columns_sumstats_columns = summary_statistics.df.columns + # Convert pvalue_cutoff to neglog scale: + neglog_pv_cutoff = -np.log10(pvalue_cutoff) + + # First window to calculate the distance between consecutive positions: + w1 = Window.partitionBy("studyId", "chromosome").orderBy("position") + + # Second window to calculate the locus start and end: + w2 = ( + Window.partitionBy("studyId", "chromosome", "locusStart") + .orderBy("position") + .rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing) + ) + + # Third window to rank the variants within the locus based on neglog p-value to find top loci: + w3 = Window.partitionBy( + "studyId", "chromosome", "locusStart", "locusEnd" + ).orderBy(f.col("negLogPValue").desc()) + + return StudyLocus( + _df=( + # Applying the baseline p-value cutoff: + summary_statistics.pvalue_filter(baseline_pvalue_cutoff) + # Calculating the neglog p-value for easier sorting: + .df.withColumn( + "negLogPValue", + calculate_neglog_pvalue( + f.col("pValueMantissa"), f.col("pValueExponent") + ), + ) + # Calculating the distance between consecutive positions, then identifying the locus start and end: + .withColumn("next_position", f.lag(f.col("position")).over(w1)) + .withColumn("distance", f.col("position") - f.col("next_position")) + .withColumn( + "locusStart", + f.when( + (f.col("distance") > distance_cutoff) + | f.col("distance").isNull(), + f.col("position"), + ), + ) + .withColumn( + "locusStart", + f.when( + f.last(f.col("locusStart") - flanking_distance, True).over( + w1.rowsBetween(-sys.maxsize, 0) + ) + > 0, + f.last(f.col("locusStart") - flanking_distance, True).over( + w1.rowsBetween(-sys.maxsize, 0) + ), + ).otherwise(f.lit(0)), + ) + .withColumn( + "locusEnd", f.max(f.col("position") + flanking_distance).over(w2) + ) + .withColumn("rank", f.rank().over(w3)) + .filter( + (f.col("rank") == 1) & (f.col("negLogPValue") > neglog_pv_cutoff) + ) + .select( + *columns_sumstats_columns, + # To make sure that the type of locusStart and locusEnd follows schema of StudyLocus: + f.col("locusStart").cast(t.IntegerType()).alias("locusStart"), + f.col("locusEnd").cast(t.IntegerType()).alias("locusEnd"), + f.lit(None) + .cast(t.ArrayType(t.StringType())) + .alias("qualityControls"), + StudyLocus.assign_study_locus_id(["studyId", "variantId"]), + ) + ), + _schema=StudyLocus.get_schema(), + ) + + @staticmethod + def process_locus_breaker_output( + lbc: StudyLocus, + wbc: StudyLocus, + large_loci_size: int, + ) -> StudyLocus: + """Process the locus breaker method result, and run window-based clumping on large loci. + + Args: + lbc (StudyLocus): StudyLocus object from locus-breaker clumping. + wbc (StudyLocus): StudyLocus object from window-based clumping. + large_loci_size (int): the size to define large loci which should be broken with wbc. + + Returns: + StudyLocus: clumped study loci with large loci broken by window-based clumping. + """ + large_loci_size = int(large_loci_size) + small_loci = lbc.filter( + (f.col("locusEnd") - f.col("locusStart")) <= large_loci_size + ) + large_loci = lbc.filter( + (f.col("locusEnd") - f.col("locusStart")) > large_loci_size + ) + large_loci_wbc = StudyLocus( + wbc.df.alias("wbc") + .join( + large_loci.df.alias("ll"), + (f.col("wbc.studyId") == f.col("ll.studyId")) + & (f.col("wbc.chromosome") == f.col("ll.chromosome")) + & ( + f.col("wbc.position").between( + f.col("ll.locusStart"), f.col("ll.locusEnd") + ) + ), + "semi", + ) + .withColumns( + { + "locusStart": f.col("position") - large_loci_size // 2, + "locusEnd": f.col("position") + large_loci_size // 2, + } + ), + StudyLocus.get_schema(), + ) + return StudyLocus( + large_loci_wbc.df.unionByName(small_loci.df), + StudyLocus.get_schema(), + ) diff --git a/src/gentropy/method/pics.py b/src/gentropy/method/pics.py index e5ed5f2c6..918850527 100644 --- a/src/gentropy/method/pics.py +++ b/src/gentropy/method/pics.py @@ -17,6 +17,18 @@ class PICS: """Probabilistic Identification of Causal SNPs (PICS), an algorithm estimating the probability that an individual variant is causal considering the haplotype structure and observed pattern of association at the genetic locus.""" + # The fields for the picsed locus + ldSet tagVariantId is renamed to variantId: + PICSED_LOCUS_SCHEMA = t.ArrayType( + t.StructType( + [ + t.StructField("variantId", t.StringType(), True), + t.StructField("r2Overall", t.DoubleType(), True), + t.StructField("posteriorProbability", t.DoubleType(), True), + t.StructField("standardError", t.DoubleType(), True), + ] + ) + ) + @staticmethod def _pics_relative_posterior_probability( neglog_p: float, pics_snp_mu: float, pics_snp_std: float @@ -148,6 +160,10 @@ def _finemap( # If PICS cannot be calculated, we drop the variant from the credible set continue + # Chaing chema: + if "tagVariantId" in tag_dict: + tag_dict["variantId"] = tag_dict.pop("tagVariantId") + pics_snp_mu = PICS._pics_mu(lead_neglog_p, tag_dict["r2Overall"]) pics_snp_std = PICS._pics_standard_deviation( lead_neglog_p, tag_dict["r2Overall"], k @@ -195,36 +211,24 @@ def finemap( Returns: StudyLocus: Study locus with PICS results """ - # Register UDF by defining the structure of the output locus array of structs - # it also renames tagVariantId to variantId - - picsed_ldset_schema = t.ArrayType( - t.StructType( - [ - t.StructField("tagVariantId", t.StringType(), True), - t.StructField("r2Overall", t.DoubleType(), True), - t.StructField("posteriorProbability", t.DoubleType(), True), - t.StructField("standardError", t.DoubleType(), True), - ] - ) - ) - picsed_study_locus_schema = t.ArrayType( - t.StructType( - [ - t.StructField("variantId", t.StringType(), True), - t.StructField("r2Overall", t.DoubleType(), True), - t.StructField("posteriorProbability", t.DoubleType(), True), - t.StructField("standardError", t.DoubleType(), True), - ] - ) - ) - _finemap_udf = f.udf( - lambda locus, neglog_p: PICS._finemap(locus, neglog_p, k), - picsed_ldset_schema, + # Finemapping method is an optional column: + finemapping_method_expression = ( + f.lit("pics") + if "finemappingMethod" not in associations.df.columns + else f.coalesce(f.col("finemappingMethod"), f.lit("pics")) ) + + # Flagging expression for loci that do not qualify for PICS: non_picsable_expr = ( f.size(f.filter(f.col("ldSet"), lambda x: x.r2Overall >= 0.5)) == 0 ) + + # Registering the UDF to be used in the pipeline: + finemap_udf = f.udf( + lambda ld_set, neglog_p: cls._finemap(ld_set, neglog_p, k), + cls.PICSED_LOCUS_SCHEMA, + ) + return StudyLocus( _df=( associations.df @@ -237,11 +241,31 @@ def finemap( "locus", f.when( f.col("ldSet").isNotNull(), - _finemap_udf(f.col("ldSet"), f.col("neglog_pvalue")).cast( - picsed_study_locus_schema + finemap_udf(f.col("ldSet"), f.col("neglog_pvalue")), + ), + ) + # Updating single point statistics in the locus object for the lead variant: + .withColumn( + "locus", + f.transform( + f.col("locus"), + lambda tag: f.when( + f.col("variantId") == tag["variantId"], + tag.withField("pValueMantissa", f.col("pValueMantissa")) + .withField("pValueExponent", f.col("pValueExponent")) + .withField("beta", f.col("beta")), + ).otherwise( + tag.withField( + "pValueMantissa", f.lit(None).cast(t.FloatType()) + ) + .withField( + "pValueExponent", f.lit(None).cast(t.IntegerType()) + ) + .withField("beta", f.lit(None).cast(t.DoubleType())) ), ), ) + # Flagging loci that do not qualify for PICS: .withColumn( "qualityControls", StudyLocus.update_quality_flag( @@ -252,7 +276,13 @@ def finemap( ) .withColumn( "finemappingMethod", - f.coalesce(f.col("finemappingMethod"), f.lit("pics")), + finemapping_method_expression, + ) + .withColumn( + "studyLocusId", + StudyLocus.assign_study_locus_id( + ["studyId", "variantId", "finemappingMethod"] + ), ) .drop("neglog_pvalue") ), diff --git a/src/gentropy/method/sumstat_imputation.py b/src/gentropy/method/sumstat_imputation.py new file mode 100644 index 000000000..b53295560 --- /dev/null +++ b/src/gentropy/method/sumstat_imputation.py @@ -0,0 +1,172 @@ +"""RAISS summary statstics imputation model.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +import scipy.linalg + + +class SummaryStatisticsImputation: + """Implementation of RAISS summary statstics imputation model.""" + + @staticmethod + def raiss_model( + z_scores_known: np.ndarray, + ld_matrix_known: np.ndarray, + ld_matrix_known_missing: np.ndarray, + lamb: float = 0.01, + rtol: float = 0.01, + ) -> dict[str, Any]: + """Compute the imputation of the z-score using the RAISS model. + + Args: + z_scores_known (np.ndarray): the vector of known Z scores + ld_matrix_known (np.ndarray) : the matrix of known LD correlations + ld_matrix_known_missing (np.ndarray): LD matrix of known SNPs with other unknown SNPs in large matrix (similar to `ld[unknowns, :][:,known]`) + lamb (float): size of the small value added to the diagonal of the covariance matrix before inversion. Defaults to 0.01. + rtol (float): threshold to filter eigenvectos by its eigenvalue. It makes an inversion biased but much more numerically robust. Default to 0.01. + + Returns: + dict[str, Any]: + - var (np.ndarray): variance of the imputed SNPs + - mu (np.ndarray): the estimation of the zscore of the imputed SNPs + - ld_score (np.ndarray): the linkage disequilibrium score of the imputed SNPs + - condition_number (np.ndarray): the condition number of the correlation matrix + - correct_inversion (np.ndarray): a boolean array indicating if the inversion was successful + - imputation_r2 (np.ndarray): the R2 of the imputation + """ + sig_t_inv = SummaryStatisticsImputation._invert_sig_t( + ld_matrix_known, lamb, rtol + ) + if sig_t_inv is None: + return { + "var": None, + "mu": None, + "ld_score": None, + "condition_number": None, + "correct_inversion": None, + "imputation_r2": None, + } + else: + condition_number = np.array( + [np.linalg.cond(ld_matrix_known)] * ld_matrix_known_missing.shape[0] + ) + correct_inversion = np.array( + [ + SummaryStatisticsImputation._check_inversion( + ld_matrix_known, sig_t_inv + ) + ] + * ld_matrix_known_missing.shape[0] + ) + + var, ld_score = SummaryStatisticsImputation._compute_var( + ld_matrix_known_missing, sig_t_inv, lamb + ) + + mu = SummaryStatisticsImputation._compute_mu( + ld_matrix_known_missing, sig_t_inv, z_scores_known + ) + var_norm = SummaryStatisticsImputation._var_in_boundaries(var, lamb) + + R2 = (1 + lamb) - var_norm + + mu = mu / np.sqrt(R2) + return { + "var": var, + "mu": mu, + "ld_score": ld_score, + "condition_number": condition_number, + "correct_inversion": correct_inversion, + "imputation_r2": 1 - var, + } + + @staticmethod + def _compute_mu( + sig_i_t: np.ndarray, sig_t_inv: np.ndarray, zt: np.ndarray + ) -> np.ndarray: + """Compute the estimation of z-score from neighborring snp. + + Args: + sig_i_t (np.ndarray) : correlation matrix with line corresponding to unknown Snp (snp to impute) and column to known SNPs + sig_t_inv (np.ndarray): inverse of the correlation matrix of known matrix + zt (np.ndarray): Zscores of known snp + Returns: + np.ndarray: a vector of length i containing the estimate of zscore + + """ + return np.dot(sig_i_t, np.dot(sig_t_inv, zt)) + + @staticmethod + def _compute_var( + sig_i_t: np.ndarray, sig_t_inv: np.ndarray, lamb: float + ) -> tuple[np.ndarray, np.ndarray]: + """Compute the expected variance of the imputed SNPs. + + Args: + sig_i_t (np.ndarray) : correlation matrix with line corresponding to unknown Snp (snp to impute) and column to known SNPs + sig_t_inv (np.ndarray): inverse of the correlation matrix of known matrix + lamb (float): regularization term added to matrix + + Returns: + tuple[np.ndarray, np.ndarray]: a tuple containing the variance and the ld score + """ + var = (1 + lamb) - np.einsum( + "ij,jk,ki->i", sig_i_t, sig_t_inv, sig_i_t.transpose() + ) + ld_score = (sig_i_t**2).sum(1) + + return var, ld_score + + @staticmethod + def _check_inversion(sig_t: np.ndarray, sig_t_inv: np.ndarray) -> bool: + """Check if the inversion is correct. + + Args: + sig_t (np.ndarray): the correlation matrix + sig_t_inv (np.ndarray): the inverse of the correlation matrix + Returns: + bool: True if the inversion is correct, False otherwise + """ + return np.allclose(sig_t, np.dot(sig_t, np.dot(sig_t_inv, sig_t))) + + @staticmethod + def _var_in_boundaries(var: np.ndarray, lamb: float) -> np.ndarray: + """Forces the variance to be in the 0 to 1+lambda boundary. Theoritically we shouldn't have to do that. + + Args: + var (np.ndarray): the variance of the imputed SNPs + lamb (float): regularization term added to the diagonal of the sig_t matrix + + Returns: + np.ndarray: the variance of the imputed SNPs + """ + id_neg = np.where(var < 0) + var[id_neg] = 0 + id_inf = np.where(var > (0.99999 + lamb)) + var[id_inf] = 1 + + return var + + @staticmethod + def _invert_sig_t(sig_t: np.ndarray, lamb: float, rtol: float) -> np.ndarray: + """Invert the correlation matrix. If the provided regularization values are not enough to stabilize the inversion process for the given matrix, the function calls itself recursively, increasing lamb and rtol by 10%. + + Args: + sig_t (np.ndarray): the correlation matrix + lamb (float): regularization term added to the diagonal of the sig_t matrix + rtol (float): threshold to filter eigenvector with a eigenvalue under rtol make inversion biased but much more numerically robust + + Returns: + np.ndarray: the inverse of the correlation matrix + """ + try: + np.fill_diagonal(sig_t, (1 + lamb)) + sig_t_inv = scipy.linalg.pinv(sig_t, rtol=rtol, atol=0) + return sig_t_inv + except np.linalg.LinAlgError: + return SummaryStatisticsImputation._invert_sig_t( + sig_t, lamb * 1.1, rtol * 1.1 + ) diff --git a/src/gentropy/method/sumstat_quality_controls.py b/src/gentropy/method/sumstat_quality_controls.py new file mode 100644 index 000000000..eadfc58d6 --- /dev/null +++ b/src/gentropy/method/sumstat_quality_controls.py @@ -0,0 +1,263 @@ +"""Summary statistics qulity control methods.""" + +from __future__ import annotations + +import numpy as np +import pyspark.sql.functions as f +import pyspark.sql.types as t +import scipy as sc +from pyspark.sql import DataFrame +from pyspark.sql.functions import expr, log10, row_number +from pyspark.sql.window import Window +from scipy.stats import chi2 + +from gentropy.dataset.summary_statistics import SummaryStatistics + + +class SummaryStatisticsQC: + """Summary statistics QC methods. + + This module contains methods for quality control of GWAS summary statistics. + The list of methods includes: + + - sumstat_qc_beta_check: This is the mean beta check. The mean beta should be close to 0. + + - sumstat_qc_pz_check: This is the PZ check. It runs a linear regression between reported p-values and p-values inferred from z-scores. + + - sumstat_n_eff_check: This is the effective sample size check. It estimates the ratio between the effective sample size and the expected one and checks its distribution. + + - gc_lambda_check: This is the genomic control lambda check. + + - number_of_snps: This function calculates the number of SNPs and the number of SNPs with a p-value less than 5e-8. + """ + + @staticmethod + def sumstat_qc_beta_check( + gwas_for_qc: SummaryStatistics, + ) -> DataFrame: + """The mean beta check for QC of GWAS summary statstics. + + Args: + gwas_for_qc (SummaryStatistics): The instance of the SummaryStatistics class. + + Returns: + DataFrame: PySpark DataFrame with the mean beta for each study. + """ + gwas_df = gwas_for_qc._df + qc_c = gwas_df.groupBy("studyId").agg( + f.mean("beta").alias("mean_beta"), + ) + return qc_c + + @staticmethod + def _calculate_logpval(z2: float) -> float: + """Calculate negative log10-pval from Z-score. + + Args: + z2 (float): Z-score squared. + + Returns: + float: log10-pval. + + Examples: + >>> SummaryStatisticsQC._calculate_logpval(1.0) + 0.49851554582799334 + """ + logpval = -np.log10(sc.stats.chi2.sf((z2), 1)) + return float(logpval) + + @staticmethod + def sumstat_qc_pz_check( + gwas_for_qc: SummaryStatistics, + ) -> DataFrame: + """The PZ check for QC of GWAS summary statstics. It runs linear regression between reported p-values and p-values infered from z-scores. + + Args: + gwas_for_qc (SummaryStatistics): The instance of the SummaryStatistics class. + + Returns: + DataFrame: PySpark DataFrame with the results of the linear regression for each study. + """ + gwas_df = gwas_for_qc._df + + calculate_logpval_udf = f.udf( + SummaryStatisticsQC._calculate_logpval, t.DoubleType() + ) + + qc_c = ( + gwas_df.withColumn("Z2", (f.col("beta") / f.col("standardError")) ** 2) + .filter(f.col("Z2") <= 100) + .withColumn("new_logpval", calculate_logpval_udf(f.col("Z2"))) + .withColumn("log_mantissa", log10("pValueMantissa")) + .withColumn( + "diffpval", + -f.col("log_mantissa") - f.col("pValueExponent") - f.col("new_logpval"), + ) + .groupBy("studyId") + .agg( + f.mean("diffpval").alias("mean_diff_pz"), + f.stddev("diffpval").alias("se_diff_pz"), + ) + .select("studyId", "mean_diff_pz", "se_diff_pz") + ) + + return qc_c + + @staticmethod + def sumstat_n_eff_check( + gwas_for_qc: SummaryStatistics, + n_total: int = 100_000, + limit: int = 10_000_000, + min_count: int = 100, + ) -> DataFrame: + """The effective sample size check for QC of GWAS summary statstics. + + It estiamtes the ratio between effective sample size and the expected one and checks it's distribution. + It is possible to conduct only if the effective allele frequency is provided in the study. + The median rartio is always close to 1, but standard error could be inflated. + + Args: + gwas_for_qc (SummaryStatistics): The instance of the SummaryStatistics class. + n_total (int): The reported sample size of the study. The QC metrics is robust toward the sample size. + limit (int): The limit for the number of variants to be used for the estimation. + min_count (int): The minimum number of variants to be used for the estimation. + + Returns: + DataFrame: PySpark DataFrame with the effective sample size ratio for each study. + """ + gwas_df = gwas_for_qc._df + + gwas_df = gwas_df.dropna(subset=["effectAlleleFrequencyFromSource"]) + + counts_df = gwas_df.groupBy("studyId").count() + + # Join the original DataFrame with the counts DataFrame + df_with_counts = gwas_df.join(counts_df, on="studyId") + + # Filter the DataFrame to keep only the groups with count greater than or equal to min_count + filtered_df = df_with_counts.filter(f.col("count") >= min_count).drop("count") + + window = Window.partitionBy("studyId").orderBy("studyId") + gwas_df = ( + filtered_df.withColumn("row_num", row_number().over(window)) + .filter(f.col("row_num") <= limit) + .drop("row_num") + ) + + gwas_df = gwas_df.withColumn( + "var_af", + 2 + * ( + f.col("effectAlleleFrequencyFromSource") + * (1 - f.col("effectAlleleFrequencyFromSource")) + ), + ).withColumn( + "pheno_var", + ((f.col("standardError") ** 2) * n_total * f.col("var_af")) + + ((f.col("beta") ** 2) * f.col("var_af")), + ) + + window = Window.partitionBy("studyId").orderBy("studyId") + + # Calculate the median of 'pheno_var' for each 'studyId' and add it as a new column + gwas_df = gwas_df.withColumn( + "pheno_median", expr("percentile_approx(pheno_var, 0.5)").over(window) + ) + + gwas_df = gwas_df.withColumn( + "N_hat_ratio", + ( + (f.col("pheno_median") - ((f.col("beta") ** 2) * f.col("var_af"))) + / ((f.col("standardError") ** 2) * f.col("var_af") * n_total) + ), + ) + + qc_c = ( + gwas_df.groupBy("studyId") + .agg( + f.stddev("N_hat_ratio").alias("se_N"), + ) + .select("studyId", "se_N") + ) + + return qc_c + + @staticmethod + def gc_lambda_check( + gwas_for_qc: SummaryStatistics, + ) -> DataFrame: + """The genomic control lambda check for QC of GWAS summary statstics. + + Args: + gwas_for_qc (SummaryStatistics): The instance of the SummaryStatistics class. + + Returns: + DataFrame: PySpark DataFrame with the genomic control lambda for each study. + """ + gwas_df = gwas_for_qc._df + + qc_c = ( + gwas_df.select("studyId", "beta", "standardError") + .withColumn("Z2", (f.col("beta") / f.col("standardError")) ** 2) + .groupBy("studyId") + .agg(f.expr("percentile_approx(Z2, 0.5)").alias("gc_lambda")) + .withColumn("gc_lambda", f.col("gc_lambda") / chi2.ppf(0.5, df=1)) + .select("studyId", "gc_lambda") + ) + + return qc_c + + @staticmethod + def number_of_snps( + gwas_for_qc: SummaryStatistics, pval_threshold: float = 5e-8 + ) -> DataFrame: + """The function caluates number of SNPs and number of SNPs with p-value less than 5e-8. + + Args: + gwas_for_qc (SummaryStatistics): The instance of the SummaryStatistics class. + pval_threshold (float): The threshold for the p-value. + + Returns: + DataFrame: PySpark DataFrame with the number of SNPs and number of SNPs with p-value less than threshold. + """ + gwas_df = gwas_for_qc._df + + snp_counts = gwas_df.groupBy("studyId").agg( + f.count("*").alias("n_variants"), + f.sum( + ( + f.log10(f.col("pValueMantissa")) + f.col("pValueExponent") + <= np.log10(pval_threshold) + ).cast("int") + ).alias("n_variants_sig"), + ) + + return snp_counts + + @staticmethod + def get_quality_control_metrics( + gwas: SummaryStatistics, + pval_threshold: float = 1e-8, + ) -> DataFrame: + """The function calculates the quality control metrics for the summary statistics. + + Args: + gwas (SummaryStatistics): The instance of the SummaryStatistics class. + pval_threshold (float): The threshold for the p-value. + + Returns: + DataFrame: PySpark DataFrame with the quality control metrics for the summary statistics. + """ + qc1 = SummaryStatisticsQC.sumstat_qc_beta_check(gwas_for_qc=gwas) + qc2 = SummaryStatisticsQC.sumstat_qc_pz_check(gwas_for_qc=gwas) + qc4 = SummaryStatisticsQC.gc_lambda_check(gwas_for_qc=gwas) + qc5 = SummaryStatisticsQC.number_of_snps( + gwas_for_qc=gwas, pval_threshold=pval_threshold + ) + df = ( + qc1.join(qc2, on="studyId", how="outer") + .join(qc4, on="studyId", how="outer") + .join(qc5, on="studyId", how="outer") + ) + + return df diff --git a/src/gentropy/method/susie_inf.py b/src/gentropy/method/susie_inf.py index d493285a0..e8a4a57b1 100644 --- a/src/gentropy/method/susie_inf.py +++ b/src/gentropy/method/susie_inf.py @@ -6,11 +6,17 @@ from typing import Any import numpy as np +import pyspark.sql.functions as f import scipy.linalg import scipy.special +from pyspark.sql.window import Window from scipy.optimize import minimize, minimize_scalar from scipy.special import logsumexp +from gentropy.dataset.ld_index import LDIndex +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_locus import StudyLocus, StudyLocusQualityCheck + @dataclass class SUSIE_inf: @@ -18,6 +24,9 @@ class SUSIE_inf: Note: code copied from fine-mapping-inf package as a placeholder https://github.com/FinucaneLab/fine-mapping-inf + + Raises: + RuntimeError: if missing LD or if unsupported variance estimation """ @staticmethod @@ -34,7 +43,7 @@ def susie_inf( # noqa: C901 ssq_range: tuple[float, float] = (0, 1), pi0: np.ndarray | None = None, est_sigmasq: bool = True, - est_tausq: bool = True, + est_tausq: bool = False, sigmasq: float = 1, tausq: float = 0, sigmasq_range: tuple[float, float] | None = None, @@ -84,8 +93,7 @@ def susie_inf( # noqa: C901 lbf -- length-p array of log-Bayes-factors for each CS Raises: - RuntimeError: if missing LD - RuntimeError: if unsupported variance estimation method + RuntimeError: if missing LD or if unsupported variance estimation method """ p = len(z) # Precompute V,D^2 in the SVD X=UDV', and V'X'y and y'y @@ -399,7 +407,7 @@ def g(x: float) -> float: def cred_inf( PIP: np.ndarray, n: int = 100_000, - coverage: float = 0.9, + coverage: float = 0.99, purity: float = 0.5, LD: np.ndarray | None = None, V: np.ndarray | None = None, @@ -423,6 +431,7 @@ def cred_inf( Raises: RuntimeError: if missing inputs for purity filtering + ValueError: if either LD or V, Dsq are None """ if (V is None or Dsq is None or n is None) and LD is None: raise RuntimeError("Missing inputs for purity filtering") @@ -457,3 +466,65 @@ def cred_inf( ) ) return cred + + @staticmethod + def credible_set_qc( + cred_sets: StudyLocus, + p_value_threshold: float = 1e-5, + purity_min_r2: float = 0.01, + clump: bool = False, + ld_index: LDIndex | None = None, + study_index: StudyIndex | None = None, + ld_min_r2: float | None = 0.8, + ) -> StudyLocus: + """Filter credible sets by lead P-value and min-R2 purity, and performs LD clumping. + + In case of duplicated loci, the filtering retains the loci wth the highest credibleSetlog10BF. + + + Args: + cred_sets (StudyLocus): StudyLocus object with credible sets to filter/clump + p_value_threshold (float): p-value threshold for filtering credible sets, default is 1e-5 + purity_min_r2 (float): min-R2 purity threshold for filtering credible sets, default is 0.01 + clump (bool): Whether to clump the credible sets by LD, default is False + ld_index (LDIndex | None): LDIndex object + study_index (StudyIndex | None): StudyIndex object + ld_min_r2 (float | None): LD R2 threshold for clumping, default is 0.8 + + Returns: + StudyLocus: Credible sets which pass filters and LD clumping. + """ + cred_sets.df = ( + cred_sets.df.withColumn( + "pValue", f.col("pValueMantissa") * f.pow(10, f.col("pValueExponent")) + ) + .filter(f.col("pValue") <= p_value_threshold) + .filter(f.col("purityMinR2") >= purity_min_r2) + .drop("pValue") + .withColumn( + "rn", + f.row_number().over( + Window.partitionBy("studyLocusId").orderBy( + f.desc("credibleSetLog10BF") + ) + ), + ) + .filter(f.col("rn") == 1) + .drop("rn") + ) + if clump: + assert study_index, "Running in clump mode, which requires study_index." + assert ld_index, "Running in clump mode, which requires ld_index." + assert ld_min_r2, "Running in clump mode, which requires ld_min_r2 value." + cred_sets = ( + cred_sets.annotate_ld(study_index, ld_index, ld_min_r2) + .clump() + .filter( + ~f.array_contains( + f.col("qualityControls"), + StudyLocusQualityCheck.LD_CLUMPED.value, + ) + ) + ) + + return cred_sets diff --git a/src/gentropy/method/window_based_clumping.py b/src/gentropy/method/window_based_clumping.py index a2ae12419..3ab15d42f 100644 --- a/src/gentropy/method/window_based_clumping.py +++ b/src/gentropy/method/window_based_clumping.py @@ -11,7 +11,8 @@ from pyspark.ml.linalg import DenseVector, VectorUDT from pyspark.sql.window import Window -from gentropy.dataset.study_locus import StudyLocus +from gentropy.config import WindowBasedClumpingStepConfig +from gentropy.dataset.study_locus import StudyLocus, StudyLocusQualityCheck if TYPE_CHECKING: from numpy.typing import NDArray @@ -151,23 +152,40 @@ def _prune_peak(position: NDArray[np.float64], window_size: int) -> DenseVector: return DenseVector(is_lead) - @classmethod + @staticmethod def clump( - cls: type[WindowBasedClumping], - summary_stats: SummaryStatistics, - window_length: int, - p_value_significance: float = 5e-8, + unclumped_associations: SummaryStatistics | StudyLocus, + distance: int = WindowBasedClumpingStepConfig().distance, ) -> StudyLocus: - """Clump summary statistics by distance. + """Clump single point associations from summary statistics or study locus dataset based on window. Args: - summary_stats (SummaryStatistics): summary statistics to clump - window_length (int): window length in basepair - p_value_significance (float): only more significant variants are considered + unclumped_associations (SummaryStatistics | StudyLocus): Input dataset to be used for clumping. Assumes that the input dataset is already filtered for significant variants. + distance (int): Distance in base pairs to be used for clumping. Defaults to 500_000. Returns: - StudyLocus: clumped summary statistics + StudyLocus: clumped associations, where the clumped variants are flagged. """ + # Quality check expression that flags variants that are not considered lead variant: + qc_check = f.col("semiIndices")[f.col("pvRank") - 1] <= 0 + + # The quality control expression will depend on the input dataset, as the column might be already present: + qc_expression = ( + # When the column is already present and the condition is met, the value is appended to the array, otherwise keep as is: + f.when( + qc_check, + f.array_union( + f.col("qualityControls"), + f.array(f.lit(StudyLocusQualityCheck.WINDOW_CLUMPED.value)), + ), + ).otherwise(f.col("qualityControls")) + if "qualityControls" in unclumped_associations.df.columns + # If column is not there yet, initialize it with the flag value, or an empty array: + else f.when( + qc_check, f.array(f.lit(StudyLocusQualityCheck.WINDOW_CLUMPED.value)) + ).otherwise(f.array().cast(t.ArrayType(t.StringType()))) + ) + # Create window for locus clusters # - variants where the distance between subsequent variants is below the defined threshold. # - Variants are sorted by descending significance @@ -177,18 +195,15 @@ def clump( return StudyLocus( _df=( - summary_stats - # Dropping snps below significance - all subsequent steps are done on significant variants: - .pvalue_filter(p_value_significance) - .df - # Clustering summary variants for efficient windowing (complexity reduction): + unclumped_associations.df + # Clustering variants for efficient windowing (complexity reduction): .withColumn( "cluster_id", WindowBasedClumping._cluster_peaks( f.col("studyId"), f.col("chromosome"), f.col("position"), - window_length, + distance, ), ) # Within each cluster variants are ranked by significance: @@ -205,7 +220,7 @@ def clump( ), ).otherwise(f.array()), ) - # Get semi indices only ONCE per cluster: + # Collect top loci per cluster: .withColumn( "semiIndices", f.when( @@ -213,7 +228,7 @@ def clump( fml.vector_to_array( f.udf(WindowBasedClumping._prune_peak, VectorUDT())( fml.array_to_vector(f.col("collectedPositions")), - f.lit(window_length), + f.lit(distance), ) ), ), @@ -228,108 +243,16 @@ def clump( ), ).otherwise(f.col("semiIndices")), ) - # Keeping semi indices only: - .filter(f.col("semiIndices")[f.col("pvRank") - 1] > 0) - .drop("pvRank", "collectedPositions", "semiIndices", "cluster_id") # Adding study-locus id: .withColumn( "studyLocusId", StudyLocus.assign_study_locus_id( - f.col("studyId"), f.col("variantId") + ["studyId", "variantId"] ), ) # Initialize QC column as array of strings: - .withColumn( - "qualityControls", f.array().cast(t.ArrayType(t.StringType())) - ) + .withColumn("qualityControls", qc_expression) + .drop("pvRank", "collectedPositions", "semiIndices", "cluster_id") ), _schema=StudyLocus.get_schema(), ) - - @classmethod - def clump_with_locus( - cls: type[WindowBasedClumping], - summary_stats: SummaryStatistics, - window_length: int, - p_value_significance: float = 5e-8, - p_value_baseline: float = 0.05, - locus_window_length: int | None = None, - ) -> StudyLocus: - """Clump significant associations while collecting locus around them. - - Args: - summary_stats (SummaryStatistics): Input summary statistics dataset - window_length (int): Window size in bp, used for distance based clumping. - p_value_significance (float): GWAS significance threshold used to filter peaks. Defaults to 5e-8. - p_value_baseline (float): Least significant threshold. Below this, all snps are dropped. Defaults to 0.05. - locus_window_length (int | None): The distance for collecting locus around the semi indices. Defaults to None. - - Returns: - StudyLocus: StudyLocus after clumping with information about the `locus` - """ - # If no locus window provided, using the same value: - if locus_window_length is None: - locus_window_length = window_length - - # Run distance based clumping on the summary stats: - clumped_dataframe = WindowBasedClumping.clump( - summary_stats, - window_length=window_length, - p_value_significance=p_value_significance, - ).df.alias("clumped") - - # Get list of columns from clumped dataset for further propagation: - clumped_columns = clumped_dataframe.columns - - # Dropping variants not meeting the baseline criteria: - sumstats_baseline = summary_stats.pvalue_filter(p_value_baseline).df - - # Renaming columns: - sumstats_baseline_renamed = sumstats_baseline.selectExpr( - *[f"{col} as tag_{col}" for col in sumstats_baseline.columns] - ).alias("sumstat") - - study_locus_df = ( - sumstats_baseline_renamed - # Joining the two datasets together: - .join( - f.broadcast(clumped_dataframe), - on=[ - (f.col("sumstat.tag_studyId") == f.col("clumped.studyId")) - & (f.col("sumstat.tag_chromosome") == f.col("clumped.chromosome")) - & ( - f.col("sumstat.tag_position") - >= (f.col("clumped.position") - locus_window_length) - ) - & ( - f.col("sumstat.tag_position") - <= (f.col("clumped.position") + locus_window_length) - ) - ], - how="right", - ) - .withColumn( - "locus", - f.struct( - f.col("tag_variantId").alias("variantId"), - f.col("tag_beta").alias("beta"), - f.col("tag_pValueMantissa").alias("pValueMantissa"), - f.col("tag_pValueExponent").alias("pValueExponent"), - f.col("tag_standardError").alias("standardError"), - ), - ) - .groupby("studyLocusId") - .agg( - *[ - f.first(col).alias(col) - for col in clumped_columns - if col != "studyLocusId" - ], - f.collect_list(f.col("locus")).alias("locus"), - ) - ) - - return StudyLocus( - _df=study_locus_df, - _schema=StudyLocus.get_schema(), - ) diff --git a/src/gentropy/pics.py b/src/gentropy/pics.py index c2ed9bf66..f96f54997 100644 --- a/src/gentropy/pics.py +++ b/src/gentropy/pics.py @@ -3,7 +3,8 @@ from __future__ import annotations from gentropy.common.session import Session -from gentropy.dataset.study_locus import StudyLocus +from gentropy.config import WindowBasedClumpingStepConfig +from gentropy.dataset.study_locus import CredibleInterval, StudyLocus from gentropy.method.pics import PICS @@ -28,6 +29,14 @@ def __init__( session, study_locus_ld_annotated_in ) # PICS - picsed_sl = PICS.finemap(study_locus_ld_annotated).annotate_credible_sets() - # Write - picsed_sl.df.write.mode(session.write_mode).parquet(picsed_study_locus_out) + ( + PICS.finemap(study_locus_ld_annotated) + .filter_credible_set(credible_interval=CredibleInterval.IS99) + # Flagging sub-significnat loci: + .validate_lead_pvalue( + pvalue_cutoff=WindowBasedClumpingStepConfig().gwas_significance + ) + # Writing the output: + .df.write.mode(session.write_mode) + .parquet(picsed_study_locus_out) + ) diff --git a/src/gentropy/study_locus_validation.py b/src/gentropy/study_locus_validation.py new file mode 100644 index 000000000..1d1d128b6 --- /dev/null +++ b/src/gentropy/study_locus_validation.py @@ -0,0 +1,64 @@ +"""Step to validate study locus dataset against study index.""" + +from __future__ import annotations + +from gentropy.common.session import Session +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_locus import CredibleInterval, StudyLocus + + +class StudyLocusValidationStep: + """Study index validation step. + + This step reads and outputs a study index dataset with flagged studies + when target of disease validation fails. + """ + + def __init__( + self, + session: Session, + study_index_path: str, + study_locus_path: list[str], + valid_study_locus_path: str, + invalid_study_locus_path: str, + invalid_qc_reasons: list[str] = [], + ) -> None: + """Initialize step. + + Args: + session (Session): Session object. + study_index_path (str): Path to study index file. + study_locus_path (list[str]): Path to study locus dataset. + valid_study_locus_path (str): Path to write the valid records. + invalid_study_locus_path (str): Path to write the output file. + invalid_qc_reasons (list[str]): List of invalid quality check reason names from `StudyLocusQualityCheck` (e.g. ['SUBSIGNIFICANT_FLAG']). + """ + # Reading datasets: + study_index = StudyIndex.from_parquet(session, study_index_path) + + # Running validation then writing output: + study_locus_with_qc = ( + StudyLocus.from_parquet(session, list(study_locus_path)) + # Add flag for MHC region + .qc_MHC_region() + .validate_study(study_index) # Flagging studies not in study index + .annotate_study_type(study_index) # Add study type to study locus + .qc_redundant_top_hits_from_PICS() # Flagging top hits from studies with PICS summary statistics + .qc_explained_by_SuSiE() # Flagging credible sets in regions explained by SuSiE + # Flagging credible sets with PIP > 1 or PIP < 0.99 + .qc_abnormal_pips( + sum_pips_lower_threshold=0.99, sum_pips_upper_threshold=1.0001 + ) + # Annotates credible intervals and filter to only keep 99% credible sets + .filter_credible_set(credible_interval=CredibleInterval.IS99) + # Annotate credible set confidence: + .assign_confidence() + ).persist() # we will need this for 2 types of outputs + + study_locus_with_qc.valid_rows(invalid_qc_reasons, invalid=True).df.write.mode( + session.write_mode + ).parquet(invalid_study_locus_path) + + study_locus_with_qc.valid_rows(invalid_qc_reasons).df.write.mode( + session.write_mode + ).parquet(valid_study_locus_path) diff --git a/src/gentropy/study_validation.py b/src/gentropy/study_validation.py new file mode 100644 index 000000000..08f601f1e --- /dev/null +++ b/src/gentropy/study_validation.py @@ -0,0 +1,80 @@ +"""Step to validate study index against disease and target index.""" + +from __future__ import annotations + +from pyspark.sql import functions as f + +from gentropy.common.session import Session +from gentropy.dataset.biosample_index import BiosampleIndex +from gentropy.dataset.gene_index import GeneIndex +from gentropy.dataset.study_index import StudyIndex + + +class StudyValidationStep: + """Study index validation step. + + This step reads and outputs a study index dataset with flagged studies + when target of disease validation fails. + """ + + def __init__( + self, + session: Session, + study_index_path: list[str], + target_index_path: str, + disease_index_path: str, + biosample_index_path: str, + valid_study_index_path: str, + invalid_study_index_path: str, + invalid_qc_reasons: list[str] = [], + ) -> None: + """Initialize step. + + Args: + session (Session): Session object. + study_index_path (list[str]): Path to study index file. + target_index_path (str): Path to target index file. + disease_index_path (str): Path to disease index file. + biosample_index_path (str): Path to biosample index file. + valid_study_index_path (str): Path to write the valid records. + invalid_study_index_path (str): Path to write the output file. + invalid_qc_reasons (list[str]): List of invalid quality check reason names from `StudyQualityCheck` (e.g. ['DUPLICATED_STUDY']). + """ + # Reading datasets: + target_index = GeneIndex.from_parquet(session, target_index_path) + biosample_index = BiosampleIndex.from_parquet(session, biosample_index_path) + # Reading disease index and pre-process. + # This logic does not belong anywhere, but gentorpy has no disease dataset yet. + disease_index = ( + session.spark.read.parquet(disease_index_path) + .select( + f.col("id").alias("diseaseId"), + f.explode_outer( + f.when( + f.col("obsoleteTerms").isNotNull(), + f.array_union(f.array("id"), f.col("obsoleteTerms")), + ) + ).alias("efo"), + ) + .withColumn("efo", f.coalesce(f.col("efo"), f.col("diseaseId"))) + ) + study_index = StudyIndex.from_parquet(session, list(study_index_path)) + + # Running validation: + study_index_with_qc = ( + study_index.deconvolute_studies() # Deconvolute studies where the same study is ingested from multiple sources + .validate_study_type() # Flagging non-supported study types + .validate_target(target_index) # Flagging QTL studies with invalid targets + .validate_disease(disease_index) # Flagging invalid EFOs + .validate_biosample( + biosample_index + ) # Flagging QTL studies with invalid biosamples + ).persist() # we will need this for 2 types of outputs + + study_index_with_qc.valid_rows(invalid_qc_reasons, invalid=True).df.write.mode( + session.write_mode + ).parquet(invalid_study_index_path) + + study_index_with_qc.valid_rows(invalid_qc_reasons).df.write.mode( + session.write_mode + ).parquet(valid_study_index_path) diff --git a/src/gentropy/sumstat_qc_step.py b/src/gentropy/sumstat_qc_step.py new file mode 100644 index 000000000..49ddc3c4a --- /dev/null +++ b/src/gentropy/sumstat_qc_step.py @@ -0,0 +1,37 @@ +"""Step to calculate quality control metrics on the provided GWAS study.""" + +from __future__ import annotations + +from gentropy.common.session import Session +from gentropy.dataset.summary_statistics import SummaryStatistics +from gentropy.method.sumstat_quality_controls import SummaryStatisticsQC + + +class SummaryStatisticsQCStep: + """Step to run GWAS QC.""" + + def __init__( + self, + session: Session, + gwas_path: str, + output_path: str, + pval_threshold: float = 1e-8, + ) -> None: + """Calculating quality control metrics on the provided GWAS study. + + Args: + session (Session): Spark session + gwas_path (str): Path to the GWAS summary statistics. + output_path (str): Output path for the QC results. + pval_threshold (float): P-value threshold for the QC. Default is 1e-8. + + """ + gwas = SummaryStatistics.from_parquet(session, path=gwas_path) + + ( + SummaryStatisticsQC.get_quality_control_metrics( + gwas=gwas, pval_threshold=pval_threshold + ) + .write.mode(session.write_mode) + .parquet(output_path) + ) diff --git a/src/gentropy/susie_finemapper.py b/src/gentropy/susie_finemapper.py new file mode 100644 index 000000000..03a8730ef --- /dev/null +++ b/src/gentropy/susie_finemapper.py @@ -0,0 +1,1127 @@ +"""Step to run a finemapping using.""" + +from __future__ import annotations + +import logging +import time +from typing import Any + +import numpy as np +import pandas as pd +import pyspark.sql.functions as f +import scipy as sc +from pyspark.sql import DataFrame, Row, Window +from pyspark.sql.functions import desc, row_number +from pyspark.sql.types import ( + DoubleType, + IntegerType, + StringType, + StructField, + StructType, +) + +from gentropy.common.session import Session +from gentropy.common.spark_helpers import ( + neglog_pvalue_to_mantissa_and_exponent, + order_array_of_structs_by_field, +) +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_locus import StudyLocus, StudyLocusQualityCheck +from gentropy.method.carma import CARMA +from gentropy.method.ld import LDAnnotator +from gentropy.method.ld_matrix_interface import LDMatrixInterface +from gentropy.method.sumstat_imputation import SummaryStatisticsImputation +from gentropy.method.susie_inf import SUSIE_inf + + +class SusieFineMapperStep: + """SuSie finemaping. It has generic methods to run SuSie fine mapping for a study locus. + + This class/step is the temporary solution of the fine-mapping warpper for the development purposes. + In the future this step will be refactored and moved to the methods module. + """ + + def __init__( + self, + session: Session, + study_index_path: str, + study_locus_manifest_path: str, + study_locus_index: int, + max_causal_snps: int = 10, + lead_pval_threshold: float = 1e-5, + purity_mean_r2_threshold: float = 0, + purity_min_r2_threshold: float = 0.25, + cs_lbf_thr: float = 2, + sum_pips: float = 0.99, + susie_est_tausq: bool = False, + run_carma: bool = False, + run_sumstat_imputation: bool = False, + carma_time_limit: int = 600, + carma_tau: float = 0.15, + imputed_r2_threshold: float = 0.9, + ld_score_threshold: float = 5, + ld_min_r2: float = 0.8, + ) -> None: + """Run fine-mapping on a studyLocusId from a collected studyLocus table. + + Method require a `study_locus_manifest_path` file that will contain ["study_locus_input", "study_locus_output", "log_file"]. `log_file` + is optional parameter to the manifest. In case it does not exist, the logs from the finemapper are saved under the same directory + as the `study_locus_output` with `.log` suffix. + Each execution of the method will only evaluate a single row from the `study_locus_manifest` that is inferred from the `study_locus_index` + variable. + + Args: + session (Session): Spark session + study_index_path (str): path to the study index + study_locus_manifest_path (str): Path to the CSV manifest containing all study locus input and output locations. Should contain two columns: study_locus_input and study_locus_output + study_locus_index (int): Index (0-based) of the locus in the manifest to process in this call + max_causal_snps (int): Maximum number of causal variants in locus, default is 10 + lead_pval_threshold (float): p-value threshold for the lead variant from CS, default is 1e-5 + purity_mean_r2_threshold (float): threshold for purity mean r2 qc metrics for filtering credible sets, default is 0 + purity_min_r2_threshold (float): threshold for purity min r2 qc metrics for filtering credible sets, default is 0.25 + cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2 + sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set) + susie_est_tausq (bool): estimate tau squared, default is False + run_carma (bool): run CARMA, default is False + run_sumstat_imputation (bool): run summary statistics imputation, default is False + carma_time_limit (int): CARMA time limit, default is 600 seconds + carma_tau (float): CARMA tau, shrinkage parameter + imputed_r2_threshold (float): imputed R2 threshold, default is 0.9 + ld_score_threshold (float): LD score threshold ofr imputation, default is 5 + ld_min_r2 (float): Threshold to filter CS by leads in high LD, default is 0.8 + """ + # Read locus manifest. + study_locus_manifest = pd.read_csv(study_locus_manifest_path) + row = study_locus_manifest.loc[study_locus_index] + study_locus_input = row["study_locus_input"] + study_locus_output = row["study_locus_output"] + log_output = study_locus_output + ".log" + if "log_output" in study_locus_manifest.columns: + log_output = row["log_output"] + ".log" + + # Read studyLocus + study_locus = ( + StudyLocus.from_parquet(session, study_locus_input) + .df.withColumn( + "studyLocusId", + StudyLocus.assign_study_locus_id( + ["studyId", "variantId", "finemappingMethod"] + ), + ) + .collect()[0] + ) + study_index = StudyIndex.from_parquet(session, study_index_path) + # Run fine-mapping + + result_logging = self.susie_finemapper_one_sl_row_gathered_boundaries( + session=session, + study_locus_row=study_locus, + study_index=study_index, + max_causal_snps=max_causal_snps, + purity_mean_r2_threshold=purity_mean_r2_threshold, + purity_min_r2_threshold=purity_min_r2_threshold, + cs_lbf_thr=cs_lbf_thr, + sum_pips=sum_pips, + lead_pval_threshold=lead_pval_threshold, + susie_est_tausq=susie_est_tausq, + run_carma=run_carma, + run_sumstat_imputation=run_sumstat_imputation, + carma_tau=carma_tau, + carma_time_limit=carma_time_limit, + imputed_r2_threshold=imputed_r2_threshold, + ld_score_threshold=ld_score_threshold, + ld_min_r2=ld_min_r2, + log_output=log_output, + ) + + if result_logging is not None: + if result_logging["study_locus"] is not None: + # Write result + df = result_logging["study_locus"].df + df = df.withColumn("qualityControls", f.lit(None)) + df = df.withColumn( + "qualityControls", + StudyLocus.update_quality_flag( + f.col("qualityControls"), + f.lit(True), + StudyLocusQualityCheck.OUT_OF_SAMPLE_LD, + ), + ) + df.write.mode(session.write_mode).parquet(study_locus_output) + if result_logging["log"] is not None: + # Write log + result_logging["log"].to_csv(log_output, index=False, sep="\t") + + @staticmethod + def _empty_log_mg(studyId: str, region: str, error_mg: str, path_out: str) -> None: + """Create an empty log DataFrame with error message. + + Args: + studyId (str): study ID + region (str): region + error_mg (str): error message + path_out (str): output path + """ + pd.DataFrame( + { + "studyId": studyId, + "region": region, + "N_gwas_before_dedupl": 0, + "N_gwas": 0, + "N_ld": 0, + "N_overlap": 0, + "N_outliers": 0, + "N_imputed": 0, + "N_final_to_fm": 0, + "elapsed_time": 0, + "number_of_CS": 0, + "error": error_mg, + }, + index=[0], + ).to_csv(path_out, index=False, sep="\t") + + @staticmethod + def susie_inf_to_studylocus( # noqa: C901 + susie_output: dict[str, Any], + session: Session, + studyId: str, + region: str, + variant_index: DataFrame, + ld_matrix: np.ndarray, + locusStart: int, + locusEnd: int, + cs_lbf_thr: float = 2, + sum_pips: float = 0.99, + lead_pval_threshold: float = 1, + purity_mean_r2_threshold: float = 0, + purity_min_r2_threshold: float = 0, + ld_min_r2: float = 0.9, + ) -> StudyLocus | None: + """Convert SuSiE-inf output to StudyLocus DataFrame. + + Args: + susie_output (dict[str, Any]): SuSiE-inf output dictionary + session (Session): Spark session + studyId (str): study ID + region (str): region + variant_index (DataFrame): DataFrame with variant information + ld_matrix (np.ndarray): LD matrix used for fine-mapping + locusStart (int): locus start + locusEnd (int): locus end + cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2 + sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set) + lead_pval_threshold (float): p-value threshold for the lead variant from CS + purity_mean_r2_threshold (float): thrshold for purity mean r2 qc metrics for filtering credible sets + purity_min_r2_threshold (float): thrshold for purity min r2 qc metrics for filtering credible sets + ld_min_r2 (float): Threshold to fillter CS by leads in high LD, default is 0.9 + + Returns: + StudyLocus | None: StudyLocus object with fine-mapped credible sets + """ + # PLEASE DO NOT REMOVE THIS LINE + pd.DataFrame.iteritems = pd.DataFrame.items + + variants = np.array( + [row["variantId"] for row in variant_index.select("variantId").collect()] + ).reshape(-1, 1) + + PIPs = susie_output["PIP"] + lbfs = susie_output["lbf_variable"] + mu = susie_output["mu"] + susie_result = np.hstack((variants, PIPs, lbfs, mu)) + + L_snps = PIPs.shape[1] + + # Extracting credible sets + order_creds = list(enumerate(susie_output["lbf"])) + order_creds.sort(key=lambda x: x[1], reverse=True) + + counter = 0 + for i, cs_lbf_value in order_creds: + if counter > 0 and cs_lbf_value < cs_lbf_thr: + counter += 1 + continue + counter += 1 + sorted_arr = susie_result[ + susie_result[:, i + 1].astype(float).argsort()[::-1] + ] + cumsum_arr = np.cumsum(sorted_arr[:, i + 1].astype(float)) + filter_row = np.argmax(cumsum_arr >= sum_pips) + if filter_row == 0 and cumsum_arr[0] < sum_pips: + filter_row = len(cumsum_arr) + filter_row += 1 + filtered_arr = sorted_arr[:filter_row] + cred_set = filtered_arr[:, [0, i + 1, i + L_snps + 1, i + 2 * L_snps + 1]] + win = Window.rowsBetween( + Window.unboundedPreceding, Window.unboundedFollowing + ) + + cred_set = ( + session.spark.createDataFrame( + cred_set.tolist(), + ["variantId", "posteriorProbability", "logBF", "beta"], + ) + .join( + variant_index.select( + "variantId", + "chromosome", + "position", + ), + "variantId", + ) + .sort(f.desc("posteriorProbability")) + .withColumn( + "locus", + f.collect_list( + f.struct( + f.col("variantId").cast("string").alias("variantId"), + f.col("posteriorProbability") + .cast("double") + .alias("posteriorProbability"), + f.col("logBF").cast("double").alias("logBF"), + f.col("beta").cast("double").alias("beta"), + ) + ).over(win), + ) + .limit(1) + .withColumns( + { + "studyId": f.lit(studyId), + "region": f.lit(region), + "credibleSetIndex": f.lit(counter), + "credibleSetlog10BF": f.lit(cs_lbf_value * 0.4342944819), + "finemappingMethod": f.lit("SuSiE-inf"), + } + ) + .withColumn( + "studyLocusId", + StudyLocus.assign_study_locus_id( + ["studyId", "variantId", "finemappingMethod"] + ), + ) + .select( + "studyLocusId", + "studyId", + "region", + "credibleSetIndex", + "locus", + "variantId", + "chromosome", + "position", + "finemappingMethod", + "credibleSetlog10BF", + ) + ) + if counter == 1: + cred_sets = cred_set + else: + cred_sets = cred_sets.unionByName(cred_set) + + # Calulating purity + variant_index_df = variant_index.toPandas() + cred_sets_variantId = cred_sets.select("locus.variantId").toPandas() + + lead_variantId_list = ( + cred_sets.select("variantId").toPandas()["variantId"].tolist() + ) + cred_set_index = ( + cred_sets.select("credibleSetIndex").toPandas()["credibleSetIndex"].tolist() + ) + vlist_series = pd.Series(lead_variantId_list) + ind = vlist_series.map(variant_index_df.set_index("variantId").index.get_loc) + z_values = variant_index_df.iloc[ind]["z"].tolist() + z_values_array = np.array(z_values) + pval = sc.stats.chi2.sf((z_values_array**2), 1) + + # sometimes pval is 0, we need to avoid it + pval[pval < 1e-322] = 1e-322 + + neglogpval = -np.log10(pval) + neglogpval = neglogpval.tolist() + + list_purity_mean_r2 = [] + list_purity_min_r2 = [] + for _, row in cred_sets_variantId.iterrows(): + row = row.iloc[0] + vlist_series = pd.Series(row) + ind = vlist_series.map( + variant_index_df.set_index("variantId").index.get_loc + ) + # print(variant_index_df.iloc[ind,0]==vlist) + squared_matrix = ld_matrix[ind, :][:, ind] ** 2 + purity_mean_r2 = np.mean(squared_matrix) + purity_min_r2 = np.min(squared_matrix) + list_purity_mean_r2.append(purity_mean_r2) + list_purity_min_r2.append(purity_min_r2) + + cred_sets = cred_sets.drop("pValueMantissa", "pValueExponent") + + df = pd.DataFrame( + { + "credibleSetIndex": cred_set_index, + "purityMeanR2": list_purity_mean_r2, + "purityMinR2": list_purity_min_r2, + "zScore": z_values, + "neglogpval": neglogpval, + } + ) + schema = StructType( + [ + StructField("credibleSetIndex", IntegerType(), True), + StructField("purityMeanR2", DoubleType(), True), + StructField("purityMinR2", DoubleType(), True), + StructField("zScore", DoubleType(), True), + StructField("neglogpval", DoubleType(), True), + ] + ) + + df_spark = session.spark.createDataFrame(df, schema=schema) + + cred_sets = cred_sets.join(df_spark, on="credibleSetIndex") + + mantissa, exponent = neglog_pvalue_to_mantissa_and_exponent( + cred_sets.neglogpval + ) + cred_sets = cred_sets.withColumn("pValueMantissa", mantissa) + cred_sets = cred_sets.withColumn("pValueExponent", exponent) + cred_sets = cred_sets.withColumn( + "pValueMantissa", f.col("pValueMantissa").cast("float") + ) + + # Filter by lead p-value, credible set logBF, purity mean r2 and purity min r2 + cred_sets = cred_sets.filter( + (f.col("neglogpval") >= -np.log10(lead_pval_threshold)) + & (f.col("credibleSetlog10BF") >= cs_lbf_thr * 0.4342944819) + & (~f.isnan(f.col("credibleSetlog10BF"))) + & (f.col("purityMinR2") >= purity_min_r2_threshold) + & (f.col("purityMeanR2") >= purity_mean_r2_threshold) + ) + + if cred_sets.count() == 0: + return None + + # Remove duplicated by lead variant + if cred_sets.count() > 1: + window = Window.partitionBy("variantId").orderBy("credibleSetIndex") + cred_sets = cred_sets.withColumn("rank", row_number().over(window)) + cred_sets = cred_sets.filter(cred_sets["rank"] == 1).drop("rank") + cred_sets = cred_sets.orderBy("credibleSetIndex") + + # Remove CSs with high LD between leads + if cred_sets.count() > 1: + cred_sets = cred_sets.orderBy(desc("neglogpval")) + lead_variantId_list = ( + cred_sets.select("variantId").toPandas()["variantId"].tolist() + ) + vlist_series = pd.Series(lead_variantId_list) + ind = vlist_series.map( + variant_index_df.set_index("variantId").index.get_loc + ) + ld_leads = ld_matrix[ind, :][:, ind] + ld_leads = ld_leads**2 + ld_leads = ld_leads - np.tril(ld_leads) + np.fill_diagonal(ld_leads, -1) + + lead_variantId_list_to_delete: list[str] = [] + for idx in range(len(lead_variantId_list)): + vId = lead_variantId_list[idx] + if vId in lead_variantId_list_to_delete: + continue + high_ld_indices = np.where(ld_leads[idx, :] >= ld_min_r2)[0] + if len(high_ld_indices) > 0: + lead_variantId_list_to_delete = ( + lead_variantId_list_to_delete + + list(np.array(lead_variantId_list)[high_ld_indices]) + ) + if len(lead_variantId_list_to_delete) > 0: + for vId in lead_variantId_list_to_delete: + cred_sets = cred_sets.filter(f.col("variantId") != vId) + + cred_sets = cred_sets.drop("neglogpval") + cred_sets = cred_sets.withColumn("locusStart", f.lit(locusStart)) + cred_sets = cred_sets.withColumn("locusEnd", f.lit(locusEnd)) + + cred_sets = cred_sets.drop("beta").withColumn( + "beta", + f.expr(""" + filter(locus, x -> x.variantId = variantId)[0].beta + """), + ) + + return StudyLocus( + _df=cred_sets, + _schema=StudyLocus.get_schema(), + ) + + @staticmethod + def susie_finemapper_from_prepared_dataframes( + GWAS_df: DataFrame, + ld_index: DataFrame, + gnomad_ld: np.ndarray, + L: int, + session: Session, + studyId: str, + region: str, + locusStart: int, + locusEnd: int, + susie_est_tausq: bool = False, + run_carma: bool = False, + run_sumstat_imputation: bool = False, + carma_time_limit: int = 600, + carma_tau: float = 0.04, + imputed_r2_threshold: float = 0.8, + ld_score_threshold: float = 4, + sum_pips: float = 0.99, + lead_pval_threshold: float = 1e-5, + purity_mean_r2_threshold: float = 0, + purity_min_r2_threshold: float = 0.25, + cs_lbf_thr: float = 2, + ld_min_r2: float = 0.9, + N_total: int = 100_000, + ) -> dict[str, Any] | None: + """Susie fine-mapper function that uses LD, z-scores, variant info and other options for Fine-Mapping. + + Args: + GWAS_df (DataFrame): GWAS DataFrame with mandotary columns: z, variantId + ld_index (DataFrame): LD index DataFrame + gnomad_ld (np.ndarray): GnomAD LD matrix + L (int): number of causal variants + session (Session): Spark session + studyId (str): study ID + region (str): region + locusStart (int): locus start + locusEnd (int): locus end + susie_est_tausq (bool): estimate tau squared, default is False + run_carma (bool): run CARMA, default is False + run_sumstat_imputation (bool): run summary statistics imputation, default is False + carma_time_limit (int): CARMA time limit, default is 600 seconds + carma_tau (float): CARMA tau, shrinkage parameter + imputed_r2_threshold (float): imputed R2 threshold, default is 0.8 + ld_score_threshold (float): LD score threshold ofr imputation, default is 4 + sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set) + lead_pval_threshold (float): p-value threshold for the lead variant from CS, default is 1e-5 + purity_mean_r2_threshold (float): thrshold for purity mean r2 qc metrics for filtering credible sets + purity_min_r2_threshold (float): thrshold for purity min r2 qc metrics for filtering credible sets + cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2 + ld_min_r2 (float): Threshold to fillter CS by leads in high LD, default is 0.9 + N_total (int): total number of samples, default is 100_000 + + Returns: + dict[str, Any] | None: dictionary with study locus, number of GWAS variants, number of LD variants, number of variants after merge, number of outliers, number of imputed variants, number of variants to fine-map + """ + # PLEASE DO NOT REMOVE THIS LINE + pd.DataFrame.iteritems = pd.DataFrame.items + + start_time = time.time() + GWAS_df = GWAS_df.toPandas() + N_gwas_before_dedupl = len(GWAS_df) + + GWAS_df = GWAS_df.drop_duplicates(subset="variantId", keep=False) + GWAS_df = GWAS_df.reset_index() + + ld_index = ld_index.toPandas() + ld_index = ld_index.reset_index() + + N_gwas = len(GWAS_df) + N_ld = len(ld_index) + + # Filtering out the variants that are not in the LD matrix, we don't need them + df_columns = ["variantId", "z"] + GWAS_df = GWAS_df.merge(ld_index, on="variantId", how="inner") + GWAS_df = GWAS_df[df_columns].reset_index() + N_after_merge = len(GWAS_df) + + merged_df = GWAS_df.merge( + ld_index, left_on="variantId", right_on="variantId", how="inner" + ) + indices = merged_df["index_y"].values + + ld_to_fm = gnomad_ld[indices][:, indices] + z_to_fm = GWAS_df["z"].values + + if run_carma: + carma_output = CARMA.time_limited_CARMA_spike_slab_noEM( + z=z_to_fm, ld=ld_to_fm, sec_threshold=carma_time_limit, tau=carma_tau + ) + if carma_output["Outliers"] != [] and carma_output["Outliers"] is not None: + GWAS_df.drop(carma_output["Outliers"], inplace=True) + GWAS_df = GWAS_df.reset_index() + ld_index = ld_index.reset_index() + merged_df = GWAS_df.merge( + ld_index, left_on="variantId", right_on="variantId", how="inner" + ) + indices = merged_df["index_y"].values + + ld_to_fm = gnomad_ld[indices][:, indices] + z_to_fm = GWAS_df["z"].values + N_outliers = len(carma_output["Outliers"]) + else: + N_outliers = 0 + else: + N_outliers = 0 + + if run_sumstat_imputation: + known = indices + unknown = [ + index for index in list(range(len(gnomad_ld))) if index not in known + ] + sig_t = gnomad_ld[known, :][:, known] + sig_i_t = gnomad_ld[unknown, :][:, known] + zt = z_to_fm + + sumstat_imp_res = SummaryStatisticsImputation.raiss_model( + z_scores_known=zt, + ld_matrix_known=sig_t, + ld_matrix_known_missing=sig_i_t, + lamb=0.01, + rtol=0.01, + ) + + bool_index = (sumstat_imp_res["imputation_r2"] >= imputed_r2_threshold) * ( + sumstat_imp_res["ld_score"] >= ld_score_threshold + ) + if sum(bool_index) >= 1: + indices = np.where(bool_index)[0] + index_to_add = [unknown[i] for i in indices] + index_to_fm = np.concatenate((known, index_to_add)) + + ld_to_fm = gnomad_ld[index_to_fm][:, index_to_fm] + + snp_info_to_add = pd.DataFrame( + { + "variantId": ld_index.iloc[index_to_add, :]["variantId"], + "z": sumstat_imp_res["mu"][indices], + } + ) + GWAS_df = pd.concat([GWAS_df, snp_info_to_add], ignore_index=True) + z_to_fm = GWAS_df["z"].values + + N_imputed = len(indices) + else: + N_imputed = 0 + else: + N_imputed = 0 + + susie_output = SUSIE_inf.susie_inf( + z=z_to_fm, LD=ld_to_fm, L=L, est_tausq=susie_est_tausq, n=N_total + ) + + schema = StructType( + [ + StructField("variantId", StringType(), True), + StructField("z", DoubleType(), True), + ] + ) + variant_index = ( + session.spark.createDataFrame( + GWAS_df[["variantId", "z"]], + schema=schema, + ) + .withColumn( + "chromosome", f.split(f.col("variantId"), "_")[0].cast("string") + ) + .withColumn("position", f.split(f.col("variantId"), "_")[1].cast("int")) + ) + + study_locus = SusieFineMapperStep.susie_inf_to_studylocus( + susie_output=susie_output, + session=session, + studyId=studyId, + region=region, + variant_index=variant_index, + sum_pips=sum_pips, + ld_matrix=ld_to_fm, + lead_pval_threshold=lead_pval_threshold, + purity_mean_r2_threshold=purity_mean_r2_threshold, + purity_min_r2_threshold=purity_min_r2_threshold, + cs_lbf_thr=cs_lbf_thr, + ld_min_r2=ld_min_r2, + locusStart=locusStart, + locusEnd=locusEnd, + ) + + end_time = time.time() + + if study_locus is not None: + log_df = pd.DataFrame( + { + "studyId": studyId, + "region": region, + "N_gwas_before_dedupl": N_gwas_before_dedupl, + "N_gwas": N_gwas, + "N_ld": N_ld, + "N_overlap": N_after_merge, + "N_outliers": N_outliers, + "N_imputed": N_imputed, + "N_final_to_fm": len(ld_to_fm), + "elapsed_time": end_time - start_time, + "number_of_CS": study_locus.df.count(), + "error": "", + }, + index=[0], + ) + else: + log_df = pd.DataFrame( + { + "studyId": studyId, + "region": region, + "N_gwas_before_dedupl": N_gwas_before_dedupl, + "N_gwas": N_gwas, + "N_ld": N_ld, + "N_overlap": N_after_merge, + "N_outliers": N_outliers, + "N_imputed": N_imputed, + "N_final_to_fm": len(ld_to_fm), + "elapsed_time": end_time - start_time, + "number_of_CS": 0, + "error": "", + }, + index=[0], + ) + + return { + "study_locus": study_locus, + "log": log_df, + "LD": ld_to_fm, + "GWAS_df": GWAS_df, + } + + @staticmethod + def susie_finemapper_one_sl_row_gathered_boundaries( # noqa: C901 + session: Session, + study_locus_row: Row, + study_index: StudyIndex, + max_causal_snps: int = 10, + susie_est_tausq: bool = False, + run_carma: bool = False, + run_sumstat_imputation: bool = False, + carma_time_limit: int = 600, + carma_tau: float = 0.04, + imputed_r2_threshold: float = 0.9, + ld_score_threshold: float = 5, + sum_pips: float = 0.99, + lead_pval_threshold: float = 1e-5, + purity_mean_r2_threshold: float = 0, + purity_min_r2_threshold: float = 0.25, + cs_lbf_thr: float = 2, + ld_min_r2: float = 0.9, + log_output: str = "", + ) -> dict[str, Any] | None: + """Susie fine-mapper function that uses study-locus row with collected locus, chromosome and position as inputs. + + Args: + session (Session): Spark session + study_locus_row (Row): StudyLocus row with collected locus + study_index (StudyIndex): StudyIndex object + max_causal_snps (int): maximum number of causal variants + susie_est_tausq (bool): estimate tau squared, default is False + run_carma (bool): run CARMA, default is False + run_sumstat_imputation (bool): run summary statistics imputation, default is False + carma_time_limit (int): CARMA time limit, default is 600 seconds + carma_tau (float): CARMA tau, shrinkage parameter + imputed_r2_threshold (float): imputed R2 threshold, default is 0.8 + ld_score_threshold (float): LD score threshold ofr imputation, default is 4 + sum_pips (float): the expected sum of posterior probabilities in the locus, default is 0.99 (99% credible set) + lead_pval_threshold (float): p-value threshold for the lead variant from CS, default is 1e-5 + purity_mean_r2_threshold (float): thrshold for purity mean r2 qc metrics for filtering credible sets + purity_min_r2_threshold (float): thrshold for purity min r2 qc metrics for filtering credible sets + cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2 + ld_min_r2 (float): Threshold to fillter CS by leads in high LD, default is 0.9 + log_output (str): path to the log output + + Returns: + dict[str, Any] | None: dictionary with study locus, number of GWAS variants, number of LD variants, number of variants after merge, number of outliers, number of imputed variants, number of variants to fine-map, or None + """ + # PLEASE DO NOT REMOVE THIS LINE + pd.DataFrame.iteritems = pd.DataFrame.items + + chromosome = study_locus_row["chromosome"] + studyId = study_locus_row["studyId"] + locusStart = study_locus_row["locusStart"] + locusEnd = study_locus_row["locusEnd"] + + study_index_df = study_index._df + study_index_df = study_index_df.filter(f.col("studyId") == studyId) + + # Desision tree - study index + if study_index_df.count() == 0: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region="", + error_mg="No study index found for the studyId", + path_out=log_output, + ) + logging.warning("No study index found for the studyId") + return None + + major_population = ( + study_index_df.select( + "studyId", + order_array_of_structs_by_field( + "ldPopulationStructure", "relativeSampleSize" + ).alias("ldPopulationStructure"), + ) + .withColumn( + "majorPopulation", + f.when( + f.col("ldPopulationStructure").isNotNull(), + LDAnnotator._get_major_population(f.col("ldPopulationStructure")), + ), + ) + .collect()[0]["majorPopulation"] + ) + + # This is a temporary solution + if major_population == "eas": + major_population = "csa" + + N_total = int(study_index_df.select("nSamples").collect()[0]["nSamples"]) + if N_total is None: + N_total = 100_000 + + locusStart = max(locusStart, 0) + + region = chromosome + ":" + str(int(locusStart)) + "-" + str(int(locusEnd)) + + # Desision tree - studyType + if study_index_df.select("studyType").collect()[0]["studyType"] not in [ + "gwas", + "pqtl", + ]: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="Study type is not GWAS or non gwas catalog pqtl", + path_out=log_output, + ) + logging.warning("Study type is not GWAS or non gwas catalog pqtl") + return None + + # Desision tree - ancestry + if major_population not in ["nfe", "csa", "afr"]: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="Major ancestry is not nfe, csa or afr", + path_out=log_output, + ) + logging.warning("Major ancestry is not nfe, csa or afr") + return None + + # Desision tree - hasSumstats + if not study_index_df.select("hasSumstats").collect()[0]["hasSumstats"]: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="No sumstats found for the studyId", + path_out=log_output, + ) + logging.warning("No sumstats found for the studyId") + return None + + # Desision tree - qulityControls + keys_reasons = [ + "SMALL_NUMBER_OF_SNPS", + "FAILED_GC_LAMBDA_CHECK", + "FAILED_PZ_CHECK", + "FAILED_MEAN_BETA_CHECK", + "NO_OT_CURATION", + "SUMSTATS_NOT_AVAILABLE", + ] + + qc_mappings_dict = StudyIndex.get_QC_mappings() + invalid_reasons = [ + qc_mappings_dict[key] for key in keys_reasons if key in qc_mappings_dict + ] + + x_boolean = ( + study_index_df.withColumn( + "FailedQC", + f.arrays_overlap( + f.col("qualityControls"), + f.array([f.lit(reason) for reason in invalid_reasons]), + ), + ) + .select("FailedQC") + .collect()[0]["FailedQC"] + ) + if x_boolean: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="Quality control check failed for this study", + path_out=log_output, + ) + logging.warning("Quality control check failed for this study") + return None + + # Desision tree - analysisFlags + study_index_df = study_index_df.drop("FailedQC") + invalid_reasons = [ + "Multivariate analysis", + "ExWAS", + "Non-additive model", + "GxG", + "GxE", + "Case-case study", + ] + x_boolean = ( + study_index_df.withColumn( + "FailedQC", + f.arrays_overlap( + f.col("analysisFlags"), + f.array([f.lit(reason) for reason in invalid_reasons]), + ), + ) + .select("FailedQC") + .collect()[0]["FailedQC"] + ) + if x_boolean: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="Analysis Flags check failed for this study", + path_out=log_output, + ) + logging.warning("Analysis Flags check failed for this study") + return None + + schema = StudyLocus.get_schema() + gwas_df = session.spark.createDataFrame([study_locus_row], schema=schema) + exploded_df = gwas_df.select(f.explode("locus").alias("locus")) + + result_df = exploded_df.select( + "locus.variantId", "locus.beta", "locus.standardError" + ) + gwas_df = ( + result_df.withColumn("z", f.col("beta") / f.col("standardError")) + .withColumn( + "chromosome", f.split(f.col("variantId"), "_")[0].cast("string") + ) + .withColumn("position", f.split(f.col("variantId"), "_")[1].cast("int")) + .filter(f.col("chromosome") == chromosome) + .filter(f.col("position") >= int(locusStart)) + .filter(f.col("position") <= int(locusEnd)) + .filter(f.col("z").isNotNull()) + ) + + # Remove ALL duplicated variants from GWAS DataFrame - we don't know which is correct + variant_counts = gwas_df.groupBy("variantId").count() + unique_variants = variant_counts.filter(f.col("count") == 1) + gwas_df = gwas_df.join(unique_variants, on="variantId", how="left_semi") + + ld_index = LDMatrixInterface.get_locus_index_boundaries( + study_locus_row=study_locus_row, ancestry=major_population, session=session + ) + + # Remove ALL duplicated variants from ld_index DataFrame - we don't know which is correct + variant_counts = ld_index.groupBy("variantId").count() + unique_variants = variant_counts.filter(f.col("count") == 1) + ld_index = ld_index.join(unique_variants, on="variantId", how="left_semi").sort( + "idx" + ) + if "alleleOrder" not in ld_index.columns: + ld_index = ld_index.withColumn("alleleOrder", f.lit(1)) + + if not run_sumstat_imputation: + # Filtering out the variants that are not in the LD matrix, we don't need them + gwas_index = gwas_df.join( + ld_index.select("variantId", "idx", "alleleOrder"), on="variantId" + ).sort("idx") + gwas_df = gwas_index.select( + "variantId", + "z", + "chromosome", + "position", + "beta", + "StandardError", + ) + gwas_index = gwas_index.drop( + "z", "chromosome", "position", "beta", "StandardError" + ) + if gwas_index.rdd.isEmpty(): + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="No overlapping variants in the LD Index", + path_out=log_output, + ) + logging.warning("No overlapping variants in the LD Index") + return None + gnomad_ld = LDMatrixInterface.get_numpy_matrix( + gwas_index, ancestry=major_population + ) + + # Module to remove NANs from the LD matrix + if sum(sum(np.isnan(gnomad_ld))) > 0: + gwas_index = gwas_index.toPandas() + + # First round of filtering out the variants with NANs + nan_count = 1 - (sum(np.isnan(gnomad_ld)) / len(gnomad_ld)) + indices = np.where(nan_count >= 0.98) + indices = indices[0] + gnomad_ld = gnomad_ld[indices][:, indices] + + gwas_index = gwas_index.iloc[indices, :] + + if len(gwas_index) == 0: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="No overlapping variants in the LD Index", + path_out=log_output, + ) + logging.warning("No overlapping variants in the LD Index") + return None + + # Second round of filtering out the variants with NANs + nan_count = sum(np.isnan(gnomad_ld)) + indices = np.where(nan_count == 0) + indices = indices[0] + + gnomad_ld = gnomad_ld[indices][:, indices] + gwas_index = gwas_index.iloc[indices, :] + + if len(gwas_index) == 0: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="No overlapping variants in the LD Index", + path_out=log_output, + ) + logging.warning("No overlapping variants in the LD Index") + return None + + gwas_index = session.spark.createDataFrame(gwas_index) + + else: + gwas_index = gwas_df.join( + ld_index.select("variantId", "idx", "alleleOrder"), on="variantId" + ).sort("idx") + if gwas_index.rdd.isEmpty(): + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="No overlapping variants in the LD Index", + path_out=log_output, + ) + logging.warning("No overlapping variants in the LD Index") + return None + gwas_index = ld_index + gnomad_ld = LDMatrixInterface.get_numpy_matrix( + gwas_index, ancestry=major_population + ) + + # Module to remove NANs from the LD matrix + if sum(sum(np.isnan(gnomad_ld))) > 0: + gwas_index = gwas_index.toPandas() + + # First round of filtering out the variants with NANs + nan_count = 1 - (sum(np.isnan(gnomad_ld)) / len(gnomad_ld)) + indices = np.where(nan_count >= 0.98) + indices = indices[0] + gnomad_ld = gnomad_ld[indices][:, indices] + + gwas_index = gwas_index.iloc[indices, :] + + if len(gwas_index) == 0: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="No overlapping variants in the LD Index", + path_out=log_output, + ) + logging.warning("No overlapping variants in the LD Index") + return None + + # Second round of filtering out the variants with NANs + nan_count = sum(np.isnan(gnomad_ld)) + indices = np.where(nan_count == 0) + indices = indices[0] + + gnomad_ld = gnomad_ld[indices][:, indices] + gwas_index = gwas_index.iloc[indices, :] + + if len(gwas_index) == 0: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="No overlapping variants in the LD Index", + path_out=log_output, + ) + logging.warning("No overlapping variants in the LD Index") + return None + + gwas_index = session.spark.createDataFrame(gwas_index) + + # sanity filters on LD matrix + np.fill_diagonal(gnomad_ld, 1) + gnomad_ld[gnomad_ld > 1] = 1 + gnomad_ld[gnomad_ld < -1] = -1 + upper_triangle = np.triu(gnomad_ld) + gnomad_ld = ( + upper_triangle + upper_triangle.T - np.diag(upper_triangle.diagonal()) + ) + np.fill_diagonal(gnomad_ld, 1) + + # Desision tree - number of variants + if gwas_index.count() < 100: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="Less than 100 variants after joining GWAS and LD index", + path_out=log_output, + ) + logging.warning("Less than 100 variants after joining GWAS and LD index") + return None + elif gwas_index.count() >= 15_000: + if log_output != "": + SusieFineMapperStep._empty_log_mg( + studyId=studyId, + region=region, + error_mg="More than 15000 variants after joining GWAS and LD index", + path_out=log_output, + ) + logging.warning("More than 15000 variants after joining GWAS and LD index") + return None + + out = SusieFineMapperStep.susie_finemapper_from_prepared_dataframes( + GWAS_df=gwas_df, + ld_index=gwas_index, + gnomad_ld=gnomad_ld, + L=max_causal_snps, + session=session, + studyId=studyId, + region=region, + locusStart=int(locusStart), + locusEnd=int(locusEnd), + susie_est_tausq=susie_est_tausq, + run_carma=run_carma, + run_sumstat_imputation=run_sumstat_imputation, + carma_time_limit=carma_time_limit, + carma_tau=carma_tau, + imputed_r2_threshold=imputed_r2_threshold, + ld_score_threshold=ld_score_threshold, + sum_pips=sum_pips, + lead_pval_threshold=lead_pval_threshold, + purity_mean_r2_threshold=purity_mean_r2_threshold, + purity_min_r2_threshold=purity_min_r2_threshold, + cs_lbf_thr=cs_lbf_thr, + ld_min_r2=ld_min_r2, + N_total=N_total, + ) + + return out diff --git a/src/gentropy/ukb_ppp_eur_sumstat_preprocess.py b/src/gentropy/ukb_ppp_eur_sumstat_preprocess.py new file mode 100644 index 000000000..8f0f3eef2 --- /dev/null +++ b/src/gentropy/ukb_ppp_eur_sumstat_preprocess.py @@ -0,0 +1,62 @@ +"""Step to run UKB PPP (EUR) data ingestion.""" + +from __future__ import annotations + +from gentropy.common.per_chromosome import ( + prepare_va, + process_summary_stats_per_chromosome, +) +from gentropy.common.session import Session +from gentropy.datasource.ukb_ppp_eur.study_index import UkbPppEurStudyIndex +from gentropy.datasource.ukb_ppp_eur.summary_stats import UkbPppEurSummaryStats + + +class UkbPppEurStep: + """UKB PPP (EUR) data ingestion and harmonisation.""" + + def __init__( + self, + session: Session, + raw_study_index_path_from_tsv: str, + raw_summary_stats_path: str, + variant_annotation_path: str, + tmp_variant_annotation_path: str, + study_index_output_path: str, + summary_stats_output_path: str, + ) -> None: + """Run UKB PPP (EUR) data ingestion and harmonisation step. + + Args: + session (Session): Session object. + raw_study_index_path_from_tsv (str): Input raw study index path. + raw_summary_stats_path (str): Input raw summary stats path. + variant_annotation_path (str): Input variant annotation dataset path. + tmp_variant_annotation_path (str): Temporary output path for variant annotation dataset. + study_index_output_path (str): Study index output path. + summary_stats_output_path (str): Summary stats output path. + """ + session.logger.info( + "Pre-compute the direct and flipped variant annotation dataset." + ) + prepare_va(session, variant_annotation_path, tmp_variant_annotation_path) + + session.logger.info("Process study index.") + ( + UkbPppEurStudyIndex.from_source( + spark=session.spark, + raw_study_index_path_from_tsv=raw_study_index_path_from_tsv, + raw_summary_stats_path=raw_summary_stats_path, + ) + .df.write.mode("overwrite") + .parquet(study_index_output_path) + ) + + session.logger.info("Process and harmonise summary stats.") + process_summary_stats_per_chromosome( + session, + UkbPppEurSummaryStats, + raw_summary_stats_path, + tmp_variant_annotation_path, + summary_stats_output_path, + study_index_output_path, + ) diff --git a/src/gentropy/v2g.py b/src/gentropy/v2g.py deleted file mode 100644 index e98348a01..000000000 --- a/src/gentropy/v2g.py +++ /dev/null @@ -1,129 +0,0 @@ -"""Step to generate variant annotation dataset.""" -from __future__ import annotations - -from functools import reduce - -import pyspark.sql.functions as f - -from gentropy.common.Liftover import LiftOverSpark -from gentropy.common.session import Session -from gentropy.dataset.gene_index import GeneIndex -from gentropy.dataset.intervals import Intervals -from gentropy.dataset.v2g import V2G -from gentropy.dataset.variant_annotation import VariantAnnotation -from gentropy.dataset.variant_index import VariantIndex - - -class V2GStep: - """Variant-to-gene (V2G) step. - - This step aims to generate a dataset that contains multiple pieces of evidence supporting the functional association of specific variants with genes. Some of the evidence types include: - - 1. Chromatin interaction experiments, e.g. Promoter Capture Hi-C (PCHi-C). - 2. In silico functional predictions, e.g. Variant Effect Predictor (VEP) from Ensembl. - 3. Distance between the variant and each gene's canonical transcription start site (TSS). - - Attributes: - session (Session): Session object. - variant_index_path (str): Input variant index path. - variant_annotation_path (str): Input variant annotation path. - gene_index_path (str): Input gene index path. - vep_consequences_path (str): Input VEP consequences path. - liftover_chain_file_path (str): Path to GRCh37 to GRCh38 chain file. - liftover_max_length_difference: Maximum length difference for liftover. - max_distance (int): Maximum distance to consider. - approved_biotypes (list[str]): List of approved biotypes. - intervals (dict): Dictionary of interval sources. - v2g_path (str): Output V2G path. - """ - - def __init__( - self, - session: Session, - variant_index_path: str, - variant_annotation_path: str, - gene_index_path: str, - vep_consequences_path: str, - liftover_chain_file_path: str, - approved_biotypes: list[str], - interval_sources: dict[str, str], - v2g_path: str, - max_distance: int = 500_000, - liftover_max_length_difference: int = 100, - ) -> None: - """Run Variant-to-gene (V2G) step. - - Args: - session (Session): Session object. - variant_index_path (str): Input variant index path. - variant_annotation_path (str): Input variant annotation path. - gene_index_path (str): Input gene index path. - vep_consequences_path (str): Input VEP consequences path. - liftover_chain_file_path (str): Path to GRCh37 to GRCh38 chain file. - approved_biotypes (list[str]): List of approved biotypes. - interval_sources (dict[str, str]): Dictionary of interval sources. - v2g_path (str): Output V2G path. - max_distance (int): Maximum distance to consider. - liftover_max_length_difference (int): Maximum length difference for liftover. - """ - # Read - gene_index = GeneIndex.from_parquet(session, gene_index_path) - vi = VariantIndex.from_parquet(session, variant_index_path).persist() - va = VariantAnnotation.from_parquet(session, variant_annotation_path) - vep_consequences = session.spark.read.csv( - vep_consequences_path, sep="\t", header=True - ).select( - f.element_at(f.split("Accession", r"/"), -1).alias( - "variantFunctionalConsequenceId" - ), - f.col("Term").alias("label"), - f.col("v2g_score").cast("double").alias("score"), - ) - - # Transform - lift = LiftOverSpark( - # lift over variants to hg38 - liftover_chain_file_path, - liftover_max_length_difference, - ) - gene_index_filtered = gene_index.filter_by_biotypes( - # Filter gene index by approved biotypes to define V2G gene universe - list(approved_biotypes) - ) - va_slimmed = va.filter_by_variant_df( - # Variant annotation reduced to the variant index to define V2G variant universe - vi.df - ).persist() - intervals = Intervals( - _df=reduce( - lambda x, y: x.unionByName(y, allowMissingColumns=True), - # create interval instances by parsing each source - [ - Intervals.from_source( - session.spark, source_name, source_path, gene_index, lift - ).df - for source_name, source_path in interval_sources.items() - ], - ), - _schema=Intervals.get_schema(), - ) - v2g_datasets = [ - va_slimmed.get_distance_to_tss(gene_index_filtered, max_distance), - va_slimmed.get_most_severe_vep_v2g(vep_consequences, gene_index_filtered), - va_slimmed.get_plof_v2g(gene_index_filtered), - intervals.v2g(vi), - ] - v2g = V2G( - _df=reduce( - lambda x, y: x.unionByName(y, allowMissingColumns=True), - [dataset.df for dataset in v2g_datasets], - ).repartition("chromosome"), - _schema=V2G.get_schema(), - ) - - # Load - ( - v2g.df.write.partitionBy("chromosome") - .mode(session.write_mode) - .parquet(v2g_path) - ) diff --git a/src/gentropy/variant_annotation.py b/src/gentropy/variant_annotation.py deleted file mode 100644 index ff35c1915..000000000 --- a/src/gentropy/variant_annotation.py +++ /dev/null @@ -1,32 +0,0 @@ -"""Step to generate variant annotation dataset.""" -from __future__ import annotations - -import hail as hl - -from gentropy.common.session import Session -from gentropy.datasource.gnomad.variants import GnomADVariants - - -class VariantAnnotationStep: - """Variant annotation step. - - Variant annotation step produces a dataset of the type `VariantAnnotation` derived from gnomADs `gnomad.genomes.vX.X.X.sites.ht` Hail's table. This dataset is used to validate variants and as a source of annotation. - """ - - def __init__(self, session: Session, variant_annotation_path: str) -> None: - """Run Variant Annotation step. - - Args: - session (Session): Session object. - variant_annotation_path (str): Variant annotation dataset path. - """ - # Initialise hail session. - hl.init(sc=session.spark.sparkContext, log="/dev/null") - # Run variant annotation. - variant_annotation = GnomADVariants().as_variant_annotation() - # Write data partitioned by chromosome and position. - ( - variant_annotation.df.write.mode(session.write_mode).parquet( - variant_annotation_path - ) - ) diff --git a/src/gentropy/variant_index.py b/src/gentropy/variant_index.py index bdf838ce2..9eac684b2 100644 --- a/src/gentropy/variant_index.py +++ b/src/gentropy/variant_index.py @@ -1,44 +1,131 @@ """Step to generate variant index dataset.""" + from __future__ import annotations +import math +from functools import reduce + +from pyspark.sql import functions as f + from gentropy.common.session import Session -from gentropy.dataset.study_locus import StudyLocus -from gentropy.dataset.variant_annotation import VariantAnnotation from gentropy.dataset.variant_index import VariantIndex +from gentropy.datasource.ensembl.vep_parser import VariantEffectPredictorParser +from gentropy.datasource.open_targets.variants import OpenTargetsVariant class VariantIndexStep: - """Run variant index step to only variants in study-locus sets. + """Generate variant index based on a VEP output in json format. - Using a `VariantAnnotation` dataset as a reference, this step creates and writes a dataset of the type `VariantIndex` that includes only variants that have disease-association data with a reduced set of annotations. + The variant index is a dataset that contains variant annotations extracted from VEP output. It is expected that all variants in the VEP output are present in the variant index. + There's an option to provide extra variant annotations to be added to the variant index eg. allele frequencies from GnomAD. """ def __init__( self: VariantIndexStep, session: Session, - variant_annotation_path: str, - credible_set_path: str, + vep_output_json_path: str, variant_index_path: str, + hash_threshold: int, + gnomad_variant_annotations_path: str | None = None, ) -> None: """Run VariantIndex step. Args: session (Session): Session object. - variant_annotation_path (str): Variant annotation dataset path. - credible_set_path (str): Credible set dataset path. - variant_index_path (str): Variant index dataset path. + vep_output_json_path (str): Variant effect predictor output path (in json format). + variant_index_path (str): Variant index dataset path to save resulting data. + hash_threshold (int): Hash threshold for variant identifier length. + gnomad_variant_annotations_path (str | None): Path to extra variant annotation dataset. """ - # Extract - va = VariantAnnotation.from_parquet(session, variant_annotation_path) - credible_set = StudyLocus.from_parquet( - session, credible_set_path, recursiveFileLookup=True + # Extract variant annotations from VEP output: + variant_index = VariantEffectPredictorParser.extract_variant_index_from_vep( + session.spark, vep_output_json_path, hash_threshold ) - # Transform - vi = VariantIndex.from_variant_annotation(va, credible_set) + # Process variant annotations if provided: + if gnomad_variant_annotations_path: + # Read variant annotations from parquet: + annotations = VariantIndex.from_parquet( + session=session, + path=gnomad_variant_annotations_path, + recursiveFileLookup=True, + id_threshold=hash_threshold, + ) + + # Update file with extra annotations: + variant_index = variant_index.add_annotation(annotations) ( - vi.df.write.partitionBy("chromosome") - .mode(session.write_mode) + variant_index.df.repartitionByRange("chromosome", "position") + .sortWithinPartitions("chromosome", "position") + .write.mode(session.write_mode) .parquet(variant_index_path) ) + + +class ConvertToVcfStep: + """Convert dataset with variant annotation to VCF step. + + This step converts in-house data source formats to VCF like format. + + NOTE! Due to the csv DataSourceWriter limitations we can not save the column name + `#CHROM` as in vcf file. The column is replaced with `CHROM`. + """ + + def __init__( + self, + session: Session, + source_paths: list[str], + source_formats: list[str], + output_path: str, + partition_size: int, + ) -> None: + """Initialize step. + + Args: + session (Session): Session object. + source_paths (list[str]): Input dataset path. + source_formats (list[str]): Format of the input dataset. + output_path (str): Output VCF file path. + partition_size (int): Approximate number of variants in each output partition. + """ + assert len(source_formats) == len( + source_paths + ), "Must provide format for each source path." + + # Load + raw_variants = [ + session.load_data(p, f) + for p, f in zip(source_paths, source_formats, strict=True) + ] + + # Extract + processed_variants = [ + OpenTargetsVariant.as_vcf_df(session, df) for df in raw_variants + ] + + # Merge + merged_variants = reduce( + lambda x, y: x.unionByName(y), processed_variants + ).drop_duplicates(["#CHROM", "POS", "REF", "ALT"]) + + variant_count = merged_variants.count() + n_partitions = int(math.ceil(variant_count / partition_size)) + partitioned_variants = ( + merged_variants.repartitionByRange( + n_partitions, f.col("#CHROM"), f.col("POS") + ) + .sortWithinPartitions(f.col("#CHROM").asc(), f.col("POS").asc()) + # Due to the large number of partitions ensure we do not lose the partitions before saving them + .persist() + # FIXME the #CHROM column is saved as "#CHROM" by pyspark which fails under VEP, + # The native solution would be to implement the datasource with proper writer + # see https://docs.databricks.com/en/pyspark/datasources.html. + # Proposed solution will require adding # at the start of the first line of + # vcf before processing it in orchestration. + .withColumnRenamed("#CHROM", "CHROM") + ) + # Write + partitioned_variants.write.mode(session.write_mode).option("sep", "\t").option( + "quote", "" + ).option("quoteAll", False).option("header", True).csv(output_path) diff --git a/src/gentropy/window_based_clumping.py b/src/gentropy/window_based_clumping.py index fcc680ef7..e605c4564 100644 --- a/src/gentropy/window_based_clumping.py +++ b/src/gentropy/window_based_clumping.py @@ -1,7 +1,9 @@ """Step to run window based clumping on summary statistics datasts.""" + from __future__ import annotations from gentropy.common.session import Session +from gentropy.config import WindowBasedClumpingStepConfig from gentropy.dataset.summary_statistics import SummaryStatistics @@ -13,8 +15,12 @@ def __init__( session: Session, summary_statistics_input_path: str, study_locus_output_path: str, - inclusion_list_path: str | None = None, - locus_collect_distance: int | None = None, + distance: int = WindowBasedClumpingStepConfig().distance, + gwas_significance: float = WindowBasedClumpingStepConfig().gwas_significance, + collect_locus: bool = WindowBasedClumpingStepConfig().collect_locus, + collect_locus_distance: int = WindowBasedClumpingStepConfig().collect_locus_distance, + inclusion_list_path: str + | None = WindowBasedClumpingStepConfig().inclusion_list_path, ) -> None: """Run window-based clumping step. @@ -22,8 +28,13 @@ def __init__( session (Session): Session object. summary_statistics_input_path (str): Path to the harmonized summary statistics dataset. study_locus_output_path (str): Output path for the resulting study locus dataset. + distance (int): Distance, within which tagging variants are collected around the semi-index. Optional. + gwas_significance (float): GWAS significance threshold. Defaults to 5e-8. + collect_locus (bool): Whether to collect locus around semi-indices. Optional. + collect_locus_distance (int): Distance, within which tagging variants are collected around the semi-index. Optional. inclusion_list_path (str | None): Path to the inclusion list (list of white-listed study identifier). Optional. - locus_collect_distance (int | None): Distance, within which tagging variants are collected around the semi-index. Optional. + + Check WindowBasedClumpingStepConfig object for default values. """ # If inclusion list path is provided, only these studies will be read: if inclusion_list_path: @@ -35,16 +46,22 @@ def __init__( # If no inclusion list is provided, read all summary stats in folder: study_ids_to_ingest = [summary_statistics_input_path] - ( - SummaryStatistics.from_parquet( - session, - study_ids_to_ingest, - recursiveFileLookup=True, - ) - .coalesce(4000) - # Applying window based clumping: - .window_based_clumping(locus_collect_distance=locus_collect_distance) - # Save resulting study locus dataset: - .df.write.mode(session.write_mode) - .parquet(study_locus_output_path) + ss = SummaryStatistics.from_parquet( + session, + study_ids_to_ingest, + recursiveFileLookup=True, + ) + + # Clumping: + study_locus = ss.window_based_clumping( + distance=distance, gwas_significance=gwas_significance ) + + # Optional locus collection: + if collect_locus: + # Collecting locus around semi-indices: + study_locus = study_locus.annotate_locus_statistics( + ss, collect_locus_distance=collect_locus_distance + ) + + study_locus.df.write.mode(session.write_mode).parquet(study_locus_output_path) diff --git a/src/vep/Dockerfile b/src/vep/Dockerfile new file mode 100644 index 000000000..ade55e3da --- /dev/null +++ b/src/vep/Dockerfile @@ -0,0 +1,31 @@ +FROM ensemblorg/ensembl-vep:release_111.0 + +USER root + +RUN apt-get update && apt-get -y install \ + wget \ + libncurses5-dev \ + libncursesw5-dev \ + libbz2-dev \ + liblzma-dev \ + sqlite3 \ + libsqlite3-dev \ + cpanminus \ + git \ + && rm -rf /var/lib/apt/lists/* + +RUN cpanm DBD::SQLite + +RUN wget --progress=dot:giga https://github.com/samtools/samtools/releases/download/1.7/samtools-1.7.tar.bz2 && \ + tar xjvf samtools-1.7.tar.bz2 && \ + cd samtools-1.7 && \ + make && \ + make install + +RUN wget --progress=dot:giga https://personal.broadinstitute.org/konradk/loftee_data/GRCh38/loftee.sql.gz --directory-prefix=/opt/vep/ && \ + gunzip /opt/vep/loftee.sql.gz + +# Make sure the mounting points exist: +RUN mkdir -p /mnt/disks/share/cache && \ + mkdir -p /mnt/disks/share/input && \ + mkdir -p /mnt/disks/share/output diff --git a/tests/airflow/test_dag.py b/tests/airflow/test_dag.py deleted file mode 100644 index 8676d70f4..000000000 --- a/tests/airflow/test_dag.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Check for airflow import errors. Inspiration from https://garystafford.medium.com/devops-for-dataops-building-a-ci-cd-pipeline-for-apache-airflow-dags-975e4a622f83.""" -from __future__ import annotations - -import pytest -from airflow.models import DagBag - - -@pytest.fixture(params=["./src/airflow/dags"]) -def dag_bag(request: pytest.FixtureRequest) -> DagBag: - """Return a DAG bag for testing.""" - return DagBag(dag_folder=request.param, include_examples=False) - - -def test_no_import_errors(dag_bag: DagBag) -> None: - """Test for import errors.""" - assert ( - not dag_bag.import_errors - ), f"DAG import failures. Errors: {dag_bag.import_errors}" - - -def test_requires_tags(dag_bag: DagBag) -> None: - """Tags should be defined for each DAG.""" - for _, dag in dag_bag.dags.items(): - assert dag.tags - - -def test_owner_len_greater_than_five(dag_bag: DagBag) -> None: - """Owner should be defined for each DAG and be longer than 5 characters.""" - for _, dag in dag_bag.dags.items(): - assert len(dag.owner) > 5 - - -def test_desc_len_greater_than_fifteen(dag_bag: DagBag) -> None: - """Description should be defined for each DAG and be longer than 30 characters.""" - for _, dag in dag_bag.dags.items(): - if isinstance(dag.description, str): - assert len(dag.description) > 30 - - -def test_owner_not_airflow(dag_bag: DagBag) -> None: - """Owner should not be 'airflow'.""" - for _, dag in dag_bag.dags.items(): - assert str.lower(dag.owner) != "airflow" - - -def test_three_or_less_retries(dag_bag: DagBag) -> None: - """Retries should be 3 or less.""" - for _, dag in dag_bag.dags.items(): - assert dag.default_args["retries"] <= 3 diff --git a/tests/gentropy/common/test_schema_methods.py b/tests/gentropy/common/test_schema_methods.py new file mode 100644 index 000000000..8ed1342b5 --- /dev/null +++ b/tests/gentropy/common/test_schema_methods.py @@ -0,0 +1,303 @@ +"""Tests methods dealing with schema comparison.""" + +from __future__ import annotations + +from collections import defaultdict + +from pyspark.sql.types import ( + ArrayType, + IntegerType, + StringType, + StructField, + StructType, +) + +from gentropy.common.schemas import ( + compare_array_schemas, + compare_struct_schemas, +) + + +class TestSchemaComparisonMethods: + """Class for testing schema comparison methods.""" + + STRUCT_FIELD_STRING = StructField("a", StringType(), True) + STRUCT_FIELD_STRING_MANDATORY = StructField("a", StringType(), False) + STRUCT_FIELD_INTEGER = StructField("b", IntegerType(), True) + STRUCT_FIELD_WRONGTYPE = StructField("a", IntegerType(), True) + + def test_struct_validation_return_type(self: TestSchemaComparisonMethods) -> None: + """Test successful validation of StructType.""" + observed = StructType([self.STRUCT_FIELD_STRING, self.STRUCT_FIELD_INTEGER]) + expected = StructType([self.STRUCT_FIELD_STRING, self.STRUCT_FIELD_INTEGER]) + + discrepancy = compare_struct_schemas(observed, expected) + assert isinstance(discrepancy, defaultdict) + + def test_struct_validation_success(self: TestSchemaComparisonMethods) -> None: + """Test successful validation of StructType.""" + observed = StructType([self.STRUCT_FIELD_STRING, self.STRUCT_FIELD_INTEGER]) + expected = StructType([self.STRUCT_FIELD_STRING, self.STRUCT_FIELD_INTEGER]) + + discrepancy = compare_struct_schemas(observed, expected) + assert not discrepancy + + def test_struct_validation_non_matching_type( + self: TestSchemaComparisonMethods, + ) -> None: + """Test unsuccessful validation of StructType.""" + observed = StructType([self.STRUCT_FIELD_STRING]) + expected = StructType([self.STRUCT_FIELD_WRONGTYPE]) + + discrepancy = compare_struct_schemas(observed, expected) + + # Test there's a discrepancy: + assert discrepancy + + # Test that the discrepancy is in the field name: + assert "columns_with_non_matching_type" in discrepancy + + def test_struct_validation_missing_mandatory( + self: TestSchemaComparisonMethods, + ) -> None: + """Test unsuccessful validation of StructType.""" + observed = StructType([self.STRUCT_FIELD_INTEGER]) + expected = StructType( + [self.STRUCT_FIELD_STRING_MANDATORY, self.STRUCT_FIELD_INTEGER] + ) + + discrepancy = compare_struct_schemas(observed, expected) + + # Test there's a discrepancy: + assert discrepancy + + # Test that the discrepancy is in the field name: + assert "missing_mandatory_columns" in discrepancy + + # Test that the right column is flagged as missing: + assert ( + self.STRUCT_FIELD_STRING_MANDATORY.name + in discrepancy["missing_mandatory_columns"] + ) + + def test_struct_validation_unexpected_column( + self: TestSchemaComparisonMethods, + ) -> None: + """Test unsuccessful validation of StructType.""" + observed = StructType( + [self.STRUCT_FIELD_STRING_MANDATORY, self.STRUCT_FIELD_INTEGER] + ) + expected = StructType([self.STRUCT_FIELD_STRING_MANDATORY]) + + discrepancy = compare_struct_schemas(observed, expected) + + # Test there's a discrepancy: + assert discrepancy + + # Test that the discrepancy is in the field name: + assert "unexpected_columns" in discrepancy + + # Test that the right column is flagged as unexpected: + assert self.STRUCT_FIELD_INTEGER.name in discrepancy["unexpected_columns"] + + def test_struct_validation_duplicated_columns( + self: TestSchemaComparisonMethods, + ) -> None: + """Test unsuccessful validation of StructType.""" + observed = StructType( + [ + self.STRUCT_FIELD_STRING, + self.STRUCT_FIELD_STRING, + self.STRUCT_FIELD_INTEGER, + ] + ) + expected = StructType([self.STRUCT_FIELD_STRING, self.STRUCT_FIELD_INTEGER]) + + discrepancy = compare_struct_schemas(observed, expected) + + # Test there's a discrepancy: + assert discrepancy + + # Test that the discrepancy is in the field name: + assert "duplicated_columns" in discrepancy + + # Test that the right column is flagged as duplicated: + assert self.STRUCT_FIELD_STRING.name in discrepancy["duplicated_columns"] + + def test_struct_validation_success_nested_struct( + self: TestSchemaComparisonMethods, + ) -> None: + """Test successful validation of nested StructType.""" + nested_struct = StructType( + [self.STRUCT_FIELD_STRING, self.STRUCT_FIELD_INTEGER] + ) + + observed = StructType([StructField("c", nested_struct)]) + expected = StructType([StructField("c", nested_struct)]) + + discrepancy = compare_struct_schemas(observed, expected) + assert not discrepancy + + def test_struct_validation_non_matching_type_nested_struct( + self: TestSchemaComparisonMethods, + ) -> None: + """Test unsuccessful validation of nested StructType.""" + nested_struct = StructType([self.STRUCT_FIELD_STRING]) + + observed = StructType([StructField("c", nested_struct)]) + expected = StructType( + [StructField("c", StructType([self.STRUCT_FIELD_WRONGTYPE]))] + ) + + discrepancy = compare_struct_schemas(observed, expected) + + # Test there's a discrepancy: + assert discrepancy + + # Test that the discrepancy is in the field name: + assert "columns_with_non_matching_type" in discrepancy + + def test_array_validation_success(self: TestSchemaComparisonMethods) -> None: + """Test successful validation of ArrayType.""" + observed = ArrayType(StringType()) + expected = ArrayType(StringType()) + + discrepancy = compare_array_schemas(observed, expected) + assert not discrepancy + + def test_array_validation_non_matching_type( + self: TestSchemaComparisonMethods, + ) -> None: + """Test unsuccessful validation of ArrayType.""" + observed = ArrayType(StringType()) + expected = ArrayType(IntegerType()) + + discrepancy = compare_array_schemas(observed, expected) + + # Test there's a discrepancy: + assert discrepancy + + # Test that the discrepancy is in the field name: + assert "columns_with_non_matching_type" in discrepancy + + def test_array_validation_nested_array(self: TestSchemaComparisonMethods) -> None: + """Test successful validation of nested ArrayType.""" + nested_array = ArrayType(StringType()) + + observed = ArrayType(nested_array) + expected = ArrayType(nested_array) + + discrepancy = compare_array_schemas(observed, expected) + assert not discrepancy + + def test_array_validation_non_matching_type_nested_array( + self: TestSchemaComparisonMethods, + ) -> None: + """Test unsuccessful validation of nested ArrayType.""" + observed = ArrayType(ArrayType(StringType())) + expected = ArrayType(ArrayType(IntegerType())) + + discrepancy = compare_array_schemas(observed, expected) + + # Test there's a discrepancy: + assert discrepancy + + # Test that the discrepancy is in the field name: + assert "columns_with_non_matching_type" in discrepancy + + def test_struct_validation_success_nested_with_array( + self: TestSchemaComparisonMethods, + ) -> None: + """Test successful validation of nested StructType with ArrayType.""" + nested_array = StructField("a", ArrayType(StringType()), True) + nested_struct = StructType([self.STRUCT_FIELD_STRING, nested_array]) + + observed = StructType([StructField("c", nested_struct, True)]) + expected = StructType([StructField("c", nested_struct, True)]) + + discrepancy = compare_struct_schemas(observed, expected) + assert not discrepancy + + def test_struct_validation_non_matching_type_nested_with_array( + self: TestSchemaComparisonMethods, + ) -> None: + """Test unsuccessful validation of nested StructType with ArrayType.""" + nested_array = StructField("a", ArrayType(StringType()), True) + nested_array_wrong_type = StructField("a", ArrayType(IntegerType()), True) + nested_struct = StructType([self.STRUCT_FIELD_STRING, nested_array]) + nested_struct_wrong_type = StructType( + [self.STRUCT_FIELD_STRING, nested_array_wrong_type] + ) + observed = StructType([StructField("c", nested_struct, True)]) + expected = StructType([StructField("c", nested_struct_wrong_type, True)]) + + discrepancy = compare_struct_schemas(observed, expected) + + # Test there's a discrepancy: + assert discrepancy + + # Test that the discrepancy is in the field name: + assert "columns_with_non_matching_type" in discrepancy + + def test_struct_validation_failing_with_multiple_reasons( + self: TestSchemaComparisonMethods, + ) -> None: + """Test unsuccessful validation of StructType with multiple issues.""" + observed = StructType( + [ + StructField( + "a", + ArrayType( + ArrayType( + StructType( + [ + StructField("a", IntegerType(), False), + StructField("c", StringType(), True), + StructField("c", StringType(), True), + ] + ), + False, + ), + False, + ), + False, + ), + ] + ) + + expected = StructType( + [ + StructField( + "a", + ArrayType( + ArrayType( + StructType( + [ + StructField("b", IntegerType(), False), + StructField("c", StringType(), True), + StructField("d", StringType(), True), + ] + ), + False, + ), + False, + ), + False, + ), + ] + ) + + discrepancy = compare_struct_schemas(observed, expected) + + # Test there's a discrepancy: + assert discrepancy + + # Test if the returned list of discrepancies is correct: + assert discrepancy == defaultdict( + list, + { + "duplicated_columns": ["a[][].c"], + "missing_mandatory_columns": ["a[][].b"], + "unexpected_columns": ["a[][].a"], + }, + ) diff --git a/tests/gentropy/common/test_session.py b/tests/gentropy/common/test_session.py index b7d0156d2..6afc640ab 100644 --- a/tests/gentropy/common/test_session.py +++ b/tests/gentropy/common/test_session.py @@ -1,4 +1,5 @@ -"""Tests GWAS Catalog study splitter.""" +"""Tests Gentropy session.""" + from __future__ import annotations from typing import TYPE_CHECKING diff --git a/tests/gentropy/common/test_spark_helpers.py b/tests/gentropy/common/test_spark_helpers.py new file mode 100644 index 000000000..bd872ae90 --- /dev/null +++ b/tests/gentropy/common/test_spark_helpers.py @@ -0,0 +1,155 @@ +"""Tests spark-helper functions.""" + +from __future__ import annotations + +import pytest +from pyspark.sql import Column, SparkSession +from pyspark.sql import functions as f +from pyspark.sql import types as t + +from gentropy.common.spark_helpers import ( + enforce_schema, + order_array_of_structs_by_field, +) + + +def test_order_array_of_structs_by_field(spark: SparkSession) -> None: + """Test order_array_of_structs_by_field.""" + data = [ + # Values are the same: + ("a", 12), + ("a", 12), + # First value bigger: + ("b", 12), + ("b", 1), + # Second value bigger: + ("c", 1), + ("c", 12), + # First value is null: + ("d", None), + ("d", 12), + # Second value is null: + ("e", 12), + ("e", None), + # Both values are null: + ("f", None), + ("f", None), + ] + + processed_data = ( + spark.createDataFrame(data, ["group", "value"]) + .groupBy("group") + .agg( + f.collect_list(f.struct(f.col("value").alias("value"))).alias("values"), + f.max(f.col("value")).alias("max_value"), + ) + .withColumn("sorted_values", order_array_of_structs_by_field("values", "value")) + .withColumn("sorted_max", f.col("sorted_values")[0].getField("value")) + .select("max_value", "sorted_max") + .collect() + ) + + for row in processed_data: + assert row["max_value"] == row["sorted_max"] + + +class TestEnforceSchema: + """Test enforce schema.""" + + EXPECTED_SCHEMA = t.StructType( + [ + t.StructField("field1", t.StringType(), True), + t.StructField("field2", t.StringType(), True), + t.StructField("field3", t.StringType(), True), + t.StructField("field4", t.FloatType(), True), + ] + ) + + @staticmethod + @enforce_schema(expected_schema=EXPECTED_SCHEMA) + def good_schema_test() -> Column: + """Create a struct with the expected schema.""" + return f.struct( + f.lit("test1").alias("field1"), + f.lit("test2").alias("field2"), + f.lit("test3").alias("field3"), + f.lit(2.0).alias("field4"), + ) + + @staticmethod + @enforce_schema(expected_schema=EXPECTED_SCHEMA) + def missing_column_test() -> Column: + """Create a struct with a missing column.""" + return f.struct( + f.lit("test1").alias("field1"), + f.lit("test3").alias("field3"), + ) + + @staticmethod + @enforce_schema(expected_schema=EXPECTED_SCHEMA) + def wrong_order_test() -> Column: + """Create a struct with the wrong order.""" + return f.struct( + f.lit("test2").alias("field2"), + f.lit("test1").alias("field1"), + ) + + @staticmethod + @enforce_schema(expected_schema=EXPECTED_SCHEMA) + def extra_column_test() -> Column: + """Create a struct with an extra column.""" + return f.struct( + f.lit("test2").alias("field2"), + f.lit("test1").alias("field1"), + f.lit("test5").alias("field5"), + f.lit(12.1).alias("field6"), + ) + + @staticmethod + @enforce_schema(expected_schema=EXPECTED_SCHEMA) + def wrong_type_test_1() -> Column: + """Create a struct with the wrong type.""" + return f.struct( + f.lit("test2").alias("field2"), + f.lit("test1").alias("field1"), + f.lit(5).cast(t.IntegerType()).alias("field3"), + ) + + @staticmethod + @enforce_schema(expected_schema=EXPECTED_SCHEMA) + def wrong_type_test_2() -> Column: + """Create a struct with the wrong type.""" + return f.struct( + f.lit("test2").alias("field2"), + f.lit("test1").alias("field1"), + f.lit("test").alias("field4"), + ) + + @pytest.fixture(autouse=True) + def _setup(self: TestEnforceSchema, spark: SparkSession) -> None: + """Setup fixture.""" + self.test_dataset = ( + spark.createDataFrame( + [("a",)], + ["label"], + ) + .withColumn("struct_1", self.good_schema_test()) + .withColumn("struct_2", self.missing_column_test()) + .withColumn("struct_3", self.wrong_order_test()) + .withColumn("struct_4", self.extra_column_test()) + .withColumn("struct_5", self.wrong_type_test_1()) + .withColumn("struct_6", self.wrong_type_test_2()) + ) + + def test_schema_consistency(self: TestEnforceSchema) -> None: + """Test enforce schema consistency.""" + # Looping through all the struct column and test if the schema is consistent + for column in [ + "struct_1", + "struct_2", + "struct_3", + "struct_4", + "struct_5", + "struct_6", + ]: + assert self.test_dataset.schema[column].dataType == self.EXPECTED_SCHEMA diff --git a/tests/gentropy/common/test_version_engine.py b/tests/gentropy/common/test_version_engine.py new file mode 100644 index 000000000..2ee2e12ce --- /dev/null +++ b/tests/gentropy/common/test_version_engine.py @@ -0,0 +1,115 @@ +"""Tests version engine class.""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + +from gentropy.common.version_engine import GnomADVersionSeeker, VersionEngine + + +@pytest.mark.parametrize( + ["text", "version"], + [ + pytest.param( + "gcp-public-data--gnomad/release/2.1.1/vcf/genomes/gnomad.genomes.r2.1.1.sites.7.vcf", + "2.1.1", + id="GnomAD v2.1.1", + ), + pytest.param( + "/gcp-public-data--gnomad/release/3.0/vcf/genomes/gnomad.genomes.r3.0.sites.chr6.vcf", + "3.0", + id="GnomAD v3.0", + ), + pytest.param( + "gs://gcp-public-data--gnomad/release/3.1.1/vcf/genomes/gnomad.genomes.v3.1.1.sites.chr1.vcf", + "3.1.1", + id="GnomAD v3.1.1", + ), + pytest.param( + "gs://gcp-public-data--gnomad/release/3.1.2/vcf/genomes/gnomad.genomes.v3.1.2.sites.chrY.vcf", + "3.1.2", + id="GnomAD v3.1.2", + ), + pytest.param( + "gsa://gcp-public-data--gnomad/release/4.0/vcf/genomes/gnomad.genomes.v4.0.sites.chrY.vcf", + "4.0", + id="GnomAD v4.0", + ), + pytest.param( + "gs://gcp-public-data--gnomad/release/4.1/vcf/genomes/gnomad.genomes.v4.1.sites.chr18.vcf", + "4.1", + id="GnomAD v4.1", + ), + pytest.param( + "/some/path/to/the/version/r20.111.44", + "20.111.44", + id="Extreme version number", + ), + ], +) +def test_extracting_version_with_gnomad_seeker(text: str, version: str) -> None: + """Test gnomad version extraction with GnomADVersionSeeker.""" + version_seeker = GnomADVersionSeeker().seek_version + assert version_seeker(text) == version + + +def test_not_registered_datasource_raises_error() -> None: + """Test that unknown datasource raises error.""" + with pytest.raises(ValueError) as e: + VersionEngine("ClinVar").seek("some/path/to/the/version/v20.111.44") # type: ignore + assert e.value.args[0].startswith("Invalid datasource ClinVar") + + +def test_extracting_version_when_no_version_is_found() -> None: + """Test that unknown datasource raises error.""" + with pytest.raises(ValueError) as e: + VersionEngine("ClinVar").seek("some/path/without/version") # type: ignore + assert e.value.args[0].startswith( + "Can not find version in some/path/without/version" + ) + + +def test_non_string_path_raises_error() -> None: + """Test that non-string path raises error.""" + with pytest.raises(TypeError) as e: + VersionEngine("gnomad").seek(123) # type: ignore + assert e.value.args[0].startswith("Can not infer version from 123") + + +@pytest.mark.parametrize( + ["text", "version"], + [ + pytest.param(Path("some/file/path/v3.1.1"), "3.1.1", id="Path object"), + pytest.param("s3://some/file/path/v3.1.1", "3.1.1", id="S3 protocol"), + pytest.param("gs://some/file/path/v3.1.1", "3.1.1", id="GS protocol"), + ], +) +def test_extracting_version_with_version_engine(text: str | Path, version: str) -> None: + """Check if concrete data types and file protocols does not return an error while passed to VersionEngine.""" + assert VersionEngine("gnomad").seek(text) == version + + +@pytest.mark.parametrize( + ["input_path", "output_path", "expected_output"], + [ + pytest.param( + "input/v20.111.44", "output", "output/20.111.44", id="Append version" + ), + pytest.param( + "input/1.0.0", + "output/1.0.0", + "output/1.0.0", + id="Do not append version, already present", + ), + pytest.param( + Path("input/1.0.0"), Path("output/"), "output/1.0.0", id="Path objects" + ), + ], +) +def test_appending_version_to_path( + input_path: Path | str, output_path: Path | str, expected_output: str +) -> None: + """Test that the version is ammended at the end of the output path.""" + VersionEngine("gnomad").amend_version(input_path, output_path) == expected_output diff --git a/tests/gentropy/conftest.py b/tests/gentropy/conftest.py index f3dc197f9..10298205d 100644 --- a/tests/gentropy/conftest.py +++ b/tests/gentropy/conftest.py @@ -1,4 +1,5 @@ """Unit test configuration.""" + from __future__ import annotations from pathlib import Path @@ -8,8 +9,11 @@ import numpy as np import pandas as pd import pytest +from pyspark.sql import DataFrame, SparkSession + from gentropy.common.Liftover import LiftOverSpark from gentropy.common.session import Session +from gentropy.dataset.biosample_index import BiosampleIndex from gentropy.dataset.colocalisation import Colocalisation from gentropy.dataset.gene_index import GeneIndex from gentropy.dataset.intervals import Intervals @@ -21,13 +25,11 @@ from gentropy.dataset.study_locus import StudyLocus from gentropy.dataset.study_locus_overlap import StudyLocusOverlap from gentropy.dataset.summary_statistics import SummaryStatistics -from gentropy.dataset.v2g import V2G -from gentropy.dataset.variant_annotation import VariantAnnotation from gentropy.dataset.variant_index import VariantIndex +from gentropy.datasource.eqtl_catalogue.finemapping import EqtlCatalogueFinemapping +from gentropy.datasource.eqtl_catalogue.study_index import EqtlCatalogueStudyIndex from gentropy.datasource.gwas_catalog.associations import StudyLocusGWASCatalog from gentropy.datasource.gwas_catalog.study_index import StudyIndexGWASCatalog -from pyspark.sql import DataFrame, SparkSession - from utils.spark import get_spark_testing_conf @@ -79,8 +81,12 @@ def mock_colocalisation(spark: SparkSession) -> Colocalisation: .withColumnSpec("h2", percentNulls=0.1) .withColumnSpec("h3", percentNulls=0.1) .withColumnSpec("h4", percentNulls=0.1) - .withColumnSpec("log2h4h3", percentNulls=0.1) .withColumnSpec("clpp", percentNulls=0.1) + .withColumnSpec( + "colocalisationMethod", + percentNulls=0.0, + values=["COLOC", "eCAVIAR"], + ) ) return Colocalisation(_df=data_spec.build(), _schema=coloc_schema) @@ -181,13 +187,13 @@ def mock_study_locus_data(spark: SparkSession) -> DataFrame: ) .withSchema(sl_schema) .withColumnSpec("chromosome", percentNulls=0.1) - .withColumnSpec("position", percentNulls=0.1) + .withColumnSpec("position", minValue=100, percentNulls=0.1) .withColumnSpec("beta", percentNulls=0.1) .withColumnSpec("effectAlleleFrequencyFromSource", percentNulls=0.1) .withColumnSpec("standardError", percentNulls=0.1) .withColumnSpec("subStudyDescription", percentNulls=0.1) .withColumnSpec("pValueMantissa", minValue=1, percentNulls=0.1) - .withColumnSpec("pValueExponent", minValue=1, percentNulls=0.1) + .withColumnSpec("pValueExponent", minValue=-10, percentNulls=0.1) .withColumnSpec( "qualityControls", expr="array(cast(rand() as string))", @@ -243,70 +249,6 @@ def mock_intervals(spark: SparkSession) -> Intervals: return Intervals(_df=data_spec.build(), _schema=interval_schema) -@pytest.fixture() -def mock_v2g(spark: SparkSession) -> V2G: - """Mock v2g dataset.""" - v2g_schema = V2G.get_schema() - - data_spec = ( - dg.DataGenerator( - spark, - rows=400, - partitions=4, - randomSeedMethod="hash_fieldname", - ) - .withSchema(v2g_schema) - .withColumnSpec("distance", percentNulls=0.1) - .withColumnSpec("resourceScore", percentNulls=0.1) - .withColumnSpec("score", percentNulls=0.1) - .withColumnSpec("pmid", percentNulls=0.1) - .withColumnSpec("biofeature", percentNulls=0.1) - .withColumnSpec("variantFunctionalConsequenceId", percentNulls=0.1) - .withColumnSpec("isHighQualityPlof", percentNulls=0.1) - ) - - return V2G(_df=data_spec.build(), _schema=v2g_schema) - - -@pytest.fixture() -def mock_variant_annotation(spark: SparkSession) -> VariantAnnotation: - """Mock variant annotation.""" - va_schema = VariantAnnotation.get_schema() - - data_spec = ( - dg.DataGenerator( - spark, - rows=400, - partitions=4, - randomSeedMethod="hash_fieldname", - ) - .withSchema(va_schema) - .withColumnSpec("alleleType", percentNulls=0.1) - .withColumnSpec("chromosomeB37", percentNulls=0.1) - .withColumnSpec("positionB37", percentNulls=0.1) - # Nested column handling workaround - # https://github.com/databrickslabs/dbldatagen/issues/135 - # It's a workaround for nested column handling in dbldatagen. - .withColumnSpec( - "alleleFrequencies", - expr='array(named_struct("alleleFrequency", rand(), "populationName", cast(rand() as string)))', - percentNulls=0.1, - ) - .withColumnSpec("rsIds", expr="array(cast(rand() AS string))", percentNulls=0.1) - .withColumnSpec( - "vep", - expr='named_struct("mostSevereConsequence", cast(rand() as string), "transcriptConsequences", array(named_struct("aminoAcids", cast(rand() as string), "consequenceTerms", array(cast(rand() as string)), "geneId", cast(rand() as string), "lof", cast(rand() as string))))', - percentNulls=0.1, - ) - .withColumnSpec( - "inSilicoPredictors", - expr='named_struct("cadd", named_struct("phred", cast(rand() as float), "raw_score", cast(rand() as float)), "revelMax", cast(rand() as double), "spliceaiDsMax", cast(rand() as float), "pangolinLargestDs", cast(rand() as double), "phylop", cast(rand() as double), "polyphenMax", cast(rand() as double), "siftMax", cast(rand() as double))', - percentNulls=0.1, - ) - ) - return VariantAnnotation(_df=data_spec.build(), _schema=va_schema) - - @pytest.fixture() def mock_variant_index(spark: SparkSession) -> VariantIndex: """Mock variant index.""" @@ -320,23 +262,70 @@ def mock_variant_index(spark: SparkSession) -> VariantIndex: randomSeedMethod="hash_fieldname", ) .withSchema(vi_schema) - .withColumnSpec("chromosomeB37", percentNulls=0.1) - .withColumnSpec("positionB37", percentNulls=0.1) - .withColumnSpec("mostSevereConsequence", percentNulls=0.1) + .withColumnSpec("mostSevereConsequenceId", percentNulls=0.1) # Nested column handling workaround # https://github.com/databrickslabs/dbldatagen/issues/135 # It's a workaround for nested column handling in dbldatagen. + .withColumnSpec( + "inSilicoPredictors", + expr=""" + array( + named_struct( + "method", cast(rand() as string), + "assessment", cast(rand() as string), + "score", rand(), + "assessmentFlag", cast(rand() as string), + "targetId", cast(rand() as string) + ) + ) + """, + percentNulls=0.1, + ) .withColumnSpec( "alleleFrequencies", expr='array(named_struct("alleleFrequency", rand(), "populationName", cast(rand() as string)))', percentNulls=0.1, ) + .withColumnSpec("rsIds", expr="array(cast(rand() AS string))", percentNulls=0.1) .withColumnSpec( - "inSilicoPredictors", - expr='named_struct("cadd", named_struct("phred", cast(rand() as float), "raw_score", cast(rand() as float)), "revelMax", cast(rand() as double), "spliceaiDsMax", cast(rand() as float), "pangolinLargestDs", cast(rand() as double), "phylop", cast(rand() as double), "polyphenMax", cast(rand() as double), "siftMax", cast(rand() as double))', + "transcriptConsequences", + expr=""" + array( + named_struct( + "variantFunctionalConsequenceIds", array(cast(rand() as string)), + "aminoAcidChange", cast(rand() as string), + "uniprotAccessions", array(cast(rand() as string)), + "isEnsemblCanonical", cast(rand() as boolean), + "codons", cast(rand() as string), + "distanceFromTss", cast(rand() as long), + "distanceFromFootprint", cast(rand() as long), + "appris", cast(rand() as string), + "maneSelect", cast(rand() as string), + "targetId", cast(rand() as string), + "impact", cast(rand() as string), + "lofteePrediction", cast(rand() as string), + "siftPrediction", rand(), + "polyphenPrediction", rand(), + "consequenceScore", cast(rand() as float), + "transcriptIndex", cast(rand() as integer), + "transcriptId", cast(rand() as string) + ) + ) + """, + percentNulls=0.1, + ) + .withColumnSpec( + "dbXrefs", + expr=""" + array( + named_struct( + "id", cast(rand() as string), + "source", cast(rand() as string) + ) + ) + """, percentNulls=0.1, ) - .withColumnSpec("rsIds", expr="array(cast(rand() AS string))", percentNulls=0.1) ) return VariantIndex(_df=data_spec.build(), _schema=vi_schema) @@ -370,9 +359,9 @@ def mock_summary_statistics_data(spark: SparkSession) -> DataFrame: # Allowing missingness: .withColumnSpec("standardError", percentNulls=0.1) # Making sure p-values are below 1: - ).build() + ) - return data_spec + return data_spec.build() @pytest.fixture() @@ -411,7 +400,7 @@ def mock_ld_index(spark: SparkSession) -> LDIndex: def sample_gwas_catalog_studies(spark: SparkSession) -> DataFrame: """Sample GWAS Catalog studies.""" return spark.read.csv( - "tests/gentropy/data_samples/gwas_catalog_studies_sample-r2022-11-29.tsv", + "tests/gentropy/data_samples/gwas_catalog_studies.tsv", sep="\t", header=True, ) @@ -421,7 +410,7 @@ def sample_gwas_catalog_studies(spark: SparkSession) -> DataFrame: def sample_gwas_catalog_ancestries_lut(spark: SparkSession) -> DataFrame: """Sample GWAS ancestries sample data.""" return spark.read.csv( - "tests/gentropy/data_samples/gwas_catalog_ancestries_sample_v1.0.3-r2022-11-29.tsv", + "tests/gentropy/data_samples/gwas_catalog_ancestries.tsv", sep="\t", header=True, ) @@ -441,7 +430,7 @@ def sample_gwas_catalog_harmonised_sumstats_list(spark: SparkSession) -> DataFra def sample_gwas_catalog_associations(spark: SparkSession) -> DataFrame: """Sample GWAS raw associations sample data.""" return spark.read.csv( - "tests/gentropy/data_samples/gwas_catalog_associations_sample_e107_r2022-11-29.tsv", + "tests/gentropy/data_samples/gwas_catalog_associations.tsv", sep="\t", header=True, ) @@ -470,28 +459,30 @@ def sample_finngen_studies(spark: SparkSession) -> DataFrame: @pytest.fixture() -def sample_eqtl_catalogue_studies(spark: SparkSession) -> DataFrame: - """Sample eQTL Catalogue studies.""" - # For reference, the sample file was generated with the following command: - # curl https://raw.githubusercontent.com/eQTL-Catalogue/eQTL-Catalogue-resources/master/tabix/tabix_ftp_paths_imported.tsv | head -n11 > tests/gentropy/data_samples/eqtl_catalogue_studies_sample.tsv - with open( - "tests/gentropy/data_samples/eqtl_catalogue_studies_sample.tsv" - ) as eqtl_catalogue: - tsv = eqtl_catalogue.read() - rdd = spark.sparkContext.parallelize([tsv]) - return spark.read.csv(rdd, sep="\t", header=True) +def sample_eqtl_catalogue_finemapping_credible_sets(session: Session) -> DataFrame: + """Sample raw eQTL Catalogue credible sets outputted by SuSIE.""" + return EqtlCatalogueFinemapping.read_credible_set_from_source( + session, + credible_set_path=["tests/gentropy/data_samples/QTD000584.credible_sets.tsv"], + ) + + +@pytest.fixture() +def sample_eqtl_catalogue_finemapping_lbf(session: Session) -> DataFrame: + """Sample raw eQTL Catalogue table with logBayesFactors outputted by SuSIE.""" + return EqtlCatalogueFinemapping.read_lbf_from_source( + session, + lbf_path=["tests/gentropy/data_samples/QTD000584.lbf_variable.txt"], + ) @pytest.fixture() -def sample_eqtl_catalogue_summary_stats(spark: SparkSession) -> DataFrame: - """Sample eQTL Catalogue summary stats.""" - # For reference, the sample file was generated with the following commands: - # mkdir -p tests/gentropy/data_samples/imported/GTEx_V8/ge - # curl ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Adipose_Subcutaneous.tsv.gz | gzip -cd | head -n11 | gzip -c > tests/gentropy/data_samples/imported/GTEx_V8/ge/Adipose_Subcutaneous.tsv.gz - # It's important for the test file to be named in exactly this way, because eQTL Catalogue study ID is populated based on input file name. +def sample_eqtl_catalogue_studies_metadata(spark: SparkSession) -> DataFrame: + """Sample raw eQTL Catalogue table with metadata about the QTD000584 study.""" return spark.read.option("delimiter", "\t").csv( - "tests/gentropy/data_samples/imported/GTEx_V8/ge/Adipose_Subcutaneous.tsv.gz", + "tests/gentropy/data_samples/sample_eqtl_catalogue_studies.tsv", header=True, + schema=EqtlCatalogueStudyIndex.raw_studies_metadata_schema, ) @@ -507,6 +498,15 @@ def sample_ukbiobank_studies(spark: SparkSession) -> DataFrame: ) +@pytest.fixture() +def study_locus_sample_for_colocalisation(spark: SparkSession) -> DataFrame: + """Sample study locus data for colocalisation.""" + return StudyLocus( + _df=spark.read.parquet("tests/gentropy/data_samples/coloc_test.parquet"), + _schema=StudyLocus.get_schema(), + ) + + @pytest.fixture() def sample_target_index(spark: SparkSession) -> DataFrame: """Sample target index sample data.""" @@ -540,6 +540,35 @@ def mock_gene_index(spark: SparkSession) -> GeneIndex: return GeneIndex(_df=data_spec.build(), _schema=gi_schema) +@pytest.fixture() +def mock_biosample_index(spark: SparkSession) -> BiosampleIndex: + """Mock biosample index dataset.""" + bi_schema = BiosampleIndex.get_schema() + + # Makes arrays of varying length with random integers between 1 and 100 + array_expression = "transform(sequence(1, 1 + floor(rand() * 9)), x -> cast((rand() * 100) as int))" + + data_spec = ( + dg.DataGenerator( + spark, + rows=400, + partitions=4, + randomSeedMethod="hash_fieldname", + ) + .withSchema(bi_schema) + .withColumnSpec("biosampleName", percentNulls=0.1) + .withColumnSpec("description", percentNulls=0.1) + .withColumnSpec("xrefs", expr=array_expression, percentNulls=0.1) + .withColumnSpec("synonyms", expr=array_expression, percentNulls=0.1) + .withColumnSpec("parents", expr=array_expression, percentNulls=0.1) + .withColumnSpec("ancestors", expr=array_expression, percentNulls=0.1) + .withColumnSpec("descendants", expr=array_expression, percentNulls=0.1) + .withColumnSpec("children", expr=array_expression, percentNulls=0.1) + ) + + return BiosampleIndex(_df=data_spec.build(), _schema=bi_schema) + + @pytest.fixture() def liftover_chain_37_to_38(spark: SparkSession) -> LiftOverSpark: """Sample liftover chain file.""" @@ -565,37 +594,17 @@ def sample_otp_interactions(spark: SparkSession) -> DataFrame: @pytest.fixture() def mock_l2g_feature_matrix(spark: SparkSession) -> L2GFeatureMatrix: """Mock l2g feature matrix dataset.""" - schema = L2GFeatureMatrix.get_schema() - - data_spec = ( - dg.DataGenerator( - spark, - rows=50, - partitions=4, - randomSeedMethod="hash_fieldname", - ) - .withSchema(schema) - .withColumnSpec("distanceTssMean", percentNulls=0.1) - .withColumnSpec("distanceTssMinimum", percentNulls=0.1) - .withColumnSpec("eqtlColocClppMaximum", percentNulls=0.1) - .withColumnSpec("eqtlColocClppMaximumNeighborhood", percentNulls=0.1) - .withColumnSpec("eqtlColocLlrMaximum", percentNulls=0.1) - .withColumnSpec("eqtlColocLlrMaximumNeighborhood", percentNulls=0.1) - .withColumnSpec("pqtlColocClppMaximum", percentNulls=0.1) - .withColumnSpec("pqtlColocClppMaximumNeighborhood", percentNulls=0.1) - .withColumnSpec("pqtlColocLlrMaximum", percentNulls=0.1) - .withColumnSpec("pqtlColocLlrMaximumNeighborhood", percentNulls=0.1) - .withColumnSpec("sqtlColocClppMaximum", percentNulls=0.1) - .withColumnSpec("sqtlColocClppMaximumNeighborhood", percentNulls=0.1) - .withColumnSpec("sqtlColocLlrMaximum", percentNulls=0.1) - .withColumnSpec("sqtlColocLlrMaximumNeighborhood", percentNulls=0.1) - .withColumnSpec( - "goldStandardSet", percentNulls=0.0, values=["positive", "negative"] - ) + return L2GFeatureMatrix( + _df=spark.createDataFrame( + [ + ("1", "gene1", 100.0, None, True), + ("2", "gene2", 1000.0, 0.0, False), + ], + "studyLocusId STRING, geneId STRING, distanceTssMean FLOAT, distanceSentinelTssMinimum FLOAT, goldStandardSet BOOLEAN", + ), + with_gold_standard=True, ) - return L2GFeatureMatrix(_df=data_spec.build(), _schema=schema) - @pytest.fixture() def mock_l2g_gold_standard(spark: SparkSession) -> L2GGoldStandard: diff --git a/tests/gentropy/data_samples/QTD000584.credible_sets.tsv b/tests/gentropy/data_samples/QTD000584.credible_sets.tsv new file mode 100644 index 000000000..2c1cc8843 --- /dev/null +++ b/tests/gentropy/data_samples/QTD000584.credible_sets.tsv @@ -0,0 +1,201 @@ +molecular_trait_id gene_id cs_id variant rsid cs_size pip pvalue beta se z cs_min_r2 region +RBP7.14208.3.3..1 ENSG00000162444 RBP7.14208.3.3..1_L1 chr1_10000458_T_C rs3003377 22 0.0170703689724805 1.33564e-10 0.139135 0.0215922 6.45120520807609 0.649351082174683 chr1:8997206-10997206 +RBP7.14208.3.3..1 ENSG00000162444 RBP7.14208.3.3..1_L1 chr1_10003040_A_G rs3003378 22 0.0346598350987255 5.69374e-11 -0.140285 0.0213409 -6.58178370994891 0.649351082174683 chr1:8997206-10997206 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1004625_A_G rs2799056 14 0.0475804849731317 2.17772e-16 0.155149 0.018795 8.27268578736212 0.617640026801365 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1005429_C_CA rs58913475 14 0.0821809105318325 1.22244e-16 0.156317 0.018777 8.34295752984943 0.617640026801365 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1005429_C_CA rs397795410 14 0.0821809105318325 1.22244e-16 0.156317 0.018777 8.34295752984943 0.617640026801365 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1005904_C_T rs3128116 14 0.082073967987591 1.22412e-16 0.15631 0.0187765 8.34279413645199 0.617640026801365 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1005954_G_A rs57683598 14 0.0829345886044969 1.20956e-16 0.156326 0.0187752 8.34423990954914 0.617640026801365 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1006159_C_T rs9778087 14 0.0287755307773455 3.71195e-16 0.154747 0.0188957 8.20709279382202 0.617640026801365 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1007746_CTTTTTTTTTTTTTT_C rs1196345893 14 0.169853803516783 5.20268e-17 0.176669 0.020963 8.44875667909097 0.617640026801365 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1008088_T_C rs3121567 17 0.0408954841106202 5.10581e-17 0.436084 0.0517305 8.44446424543245 0.830135313092393 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1008307_G_C rs2465140 14 0.113047689891171 8.91487e-17 0.156234 0.0186815 8.3812176673309 0.617640026801365 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L1 chr1_1008307_G_C rs2465140 6 0.0340123675250653 6.68955e-36 0.240866 0.0190257 12.6742943381175 0.960143135725474 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1009184_T_C rs3128117 14 0.114792998892356 8.78673e-17 0.156301 0.0186856 8.38296665698105 0.617640026801365 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L1 chr1_1009184_T_C rs3128117 6 0.0368499383801147 5.5694e-36 0.241193 0.0190289 12.689400366898 0.960143135725474 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1009716_C_T rs3135457 17 0.0392096517327741 5.41887e-18 0.443041 0.0509582 8.70818986165733 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1009731_C_T rs13303172 17 0.0491133974154425 2.89124e-18 0.455466 0.0519529 8.78131609329298 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1010094_C_T rs3121566 17 0.0498970509479227 1.25258e-18 0.449307 0.0506956 8.87789801891556 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1010232_C_T rs3121565 17 0.0632524792227336 1.28581e-18 0.434519 0.0490436 8.87518117824451 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1010481_ATTAT_A rs150242145 17 0.0497030361393638 1.52877e-18 0.431754 0.0488405 8.8551869891939 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1010481_ATTAT_A rs4039717 17 0.0497030361393638 1.52877e-18 0.431754 0.0488405 8.8551869891939 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1010481_ATTAT_A rs35187710 17 0.0497030361393638 1.52877e-18 0.431754 0.0488405 8.8551869891939 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1010747_GT_G rs34487852 17 0.0555195289512095 1.48341e-18 0.43187 0.0488346 8.85865222353006 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1010755_T_G rs9723307 17 0.0555195289512095 1.48341e-18 0.43187 0.0488346 8.85865222353006 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1011654_G_A rs2465126 17 0.0396092590819197 9.26558e-19 0.434713 0.0488596 8.91210923928129 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1013041_A_AAAC rs113047134 17 0.0502336150812414 1.21932e-18 0.439008 0.0495164 8.88138113776474 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1013312_G_A rs2341365 17 0.0670537982566278 9.19098e-19 0.429695 0.0482907 8.91296598275893 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1013490_C_G rs4615788 17 0.0596684312812006 1.30541e-18 0.434291 0.0490274 8.87346286235471 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1013541_T_C rs15842 17 0.0580138201506556 1.34141e-18 0.434388 0.0490555 8.87034155283964 0.830135313092393 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1013855_G_A rs2465124 17 0.0591361376280749 1.32867e-18 0.433762 0.0489788 8.87144043891645 0.830135313092393 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1014228_G_A rs1921 14 0.12433309239898 8.57119e-17 0.15341 0.0183335 8.38560743650197 0.617640026801365 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L1 chr1_1014228_G_A rs1921 6 0.819317788540627 1.62353e-36 0.238448 0.0186635 12.7903962671107 0.960143135725474 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1014545_C_T rs2799070 17 0.0932055530070119 6.4825e-18 0.414546 0.0477952 8.68848253629389 0.830135313092393 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1014863_A_C rs1891906 14 0.0193128429509125 5.74201e-16 0.152247 0.0187132 8.15329964672768 0.617640026801365 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L1 chr1_1014863_A_C rs1891906 6 0.0253214818240058 1.12696e-35 0.240358 0.0190503 12.6312379431961 0.960143135725474 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1015336_A_T rs2799069 17 0.0705760271470166 1.24902e-18 0.447782 0.0505217 8.87870564010476 0.830135313092393 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1015925_A_ATT rs575235313 14 0.0290657333363032 3.71821e-16 0.152991 0.0186817 8.2068585448562 0.617640026801365 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1015925_A_ATT rs369296755 14 0.0290657333363032 3.71821e-16 0.152991 0.0186817 8.2068585448562 0.617640026801365 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1015925_A_ATT rs59612205 14 0.0290657333363032 3.71821e-16 0.152991 0.0186817 8.2068585448562 0.617640026801365 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L1 chr1_1015925_A_ATT rs575235313 6 0.0224165593925054 1.03516e-35 0.240113 0.0190203 12.6381934222441 0.960143135725474 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L1 chr1_1015925_A_ATT rs369296755 6 0.0224165593925054 1.03516e-35 0.240113 0.0190203 12.6381934222441 0.960143135725474 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L1 chr1_1015925_A_ATT rs59612205 6 0.0224165593925054 1.03516e-35 0.240113 0.0190203 12.6381934222441 0.960143135725474 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L2 chr1_1015950_G_A rs9697717 17 0.0572897456684484 5.86568e-18 0.418525 0.0481893 8.70025128920918 0.830135313092393 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1016623_G_A rs3128118 14 0.0249715969486798 4.40368e-16 0.152839 0.0187107 8.1860030389925 0.617640026801365 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1017114_A_AT rs59239970 14 0.0258900605545787 4.21998e-16 0.152767 0.0186899 8.19127935909139 0.617640026801365 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1017114_A_AT rs397687829 14 0.0258900605545787 4.21998e-16 0.152767 0.0186899 8.19127935909139 0.617640026801365 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L1 chr1_1017114_A_AT rs59239970 6 0.0248308249271214 9.24949e-36 0.240377 0.0190272 12.6474850406766 0.960143135725474 chr1:1138-2001138 +ISG15.14151.4.3..1 ENSG00000187608 ISG15.14151.4.3..1_L1 chr1_1017114_A_AT rs397687829 6 0.0248308249271214 9.24949e-36 0.240377 0.0190272 12.6474850406766 0.960143135725474 chr1:1138-2001138 +ISG15.14148.2.3..1 ENSG00000187608 ISG15.14148.2.3..1_L1 chr1_1022518_G_T rs2799064 14 0.0319029278184674 2.66215e-16 0.155806 0.0189309 8.25406439760094 0.617640026801365 chr1:1138-2001138 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L8 chr1_103440589_TA_T rs1327439088 5 0.418485920864639 1.08665e-07 0.632842 0.118879 5.34468451936006 0.710818444681624 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L7 chr1_103534368_A_G rs36133328 1 0.999623621396104 0.282208 0.0493205 0.0458561 1.07174598226751 1 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L1 chr1_103537231_T_TA rs553487095 4 0.770652473010321 6.33883e-62 -1.04771 0.0617702 -16.992451463208 0.88938645908643 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L1 chr1_103579772_G_A rs78811372 4 0.0518885686269311 2.90639e-62 -1.00991 0.0593671 -17.0425983327703 0.88938645908643 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L1 chr1_103581934_T_C rs113630435 4 0.0693676553148963 1.90336e-62 -1.01685 0.0596806 -17.069616976694 0.88938645908643 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L1 chr1_103581970_C_G rs79456674 4 0.0693676553148963 1.90336e-62 -1.01685 0.0596806 -17.069616976694 0.88938645908643 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L10 chr1_103590182_C_A rs35945519 2 0.369662271975257 8.86659e-07 0.575069 0.116773 4.92945612380697 0.72482441906489 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L10 chr1_103605780_A_C rs535256526 2 0.628073732155603 4.75362e-07 0.640926 0.127014 5.05119658925642 0.72482441906489 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L2 chr1_103615613_C_T rs142439658 2 0.717277857674304 4.86208e-19 0.862569 0.0961591 8.9907709256386 0.918383081007505 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L3 chr1_103629402_A_G rs137872678 1 0.999999999693002 7.98208e-25 0.376931 0.0363406 10.3860823285899 1 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L2 chr1_103668785_C_T rs531730279 2 0.24978154476556 6.56656e-19 0.892757 0.0999028 8.95721672444223 0.918383081007505 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L8 chr1_103763515_C_T rs112518149 5 0.158205957223133 1.82525e-07 0.617843 0.118191 5.24944586203691 0.710818444681624 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L8 chr1_103780382_A_C rs74486377 5 0.0631024593524357 1.66298e-07 0.555245 0.105865 5.26583426302732 0.710818444681624 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L8 chr1_103784694_G_A rs111937937 5 0.299969225009035 6.44072e-08 0.607864 0.112182 5.44035433710252 0.710818444681624 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L5 chr1_103791530_T_C rs12718032 2 0.498456726418405 7.80792e-12 -0.888432 0.129377 -6.88371950851072 1 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L5 chr1_103791531_C_T rs12718033 2 0.498456726418405 7.80792e-12 -0.888432 0.129377 -6.88371950851072 1 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L8 chr1_103868893_C_T rs112582891 5 0.0108660096782747 1.25217e-06 0.573224 0.118039 4.87740414910442 0.710818444681624 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L6 chr1_103869827_C_T rs77395797 4 0.136398531014545 1.88188e-16 -0.903813 0.109254 -8.29678045059477 0.765529570853573 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L6 chr1_103896606_AAC_A rs149362978 4 0.394459586630998 4.4686e-17 -0.936885 0.110929 -8.4704863307765 0.765529570853573 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L6 chr1_103941863_A_C rs78691055 4 0.381808971655203 1.6995e-16 -0.887289 0.107096 -8.30886315642385 0.765529570853573 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L6 chr1_104066239_T_A rs79264627 4 0.087290671754178 1.52577e-15 -0.854343 0.106604 -8.03746208287604 0.765529570853573 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L4 chr1_104140351_A_C rs140988946 2 0.499735709754288 1.27153e-12 -0.937449 0.131568 -7.13696932535266 1 chr1:102655290-104655290 +AMY1A.7918.114.3..1 ENSG00000237763 AMY1A.7918.114.3..1_L4 chr1_104142525_T_C rs144738643 2 0.499735709754288 1.27153e-12 -0.937449 0.131568 -7.13696932535266 1 chr1:102655290-104655290 +NTNG1.5637.81.3..1 ENSG00000162631 NTNG1.5637.81.3..1_L1 chr1_107135646_G_C rs115668827 1 0.999999999152377 7.63332e-99 1.08093 0.0494955 21.8723371836279 1 chr1:106140007-108140007 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L3 chr1_109589235_A_G rs115113608 4 0.124950624508756 0.0177043 -0.15027 0.0633265 -2.37593150661648 0.736625376789901 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L3 chr1_109612653_G_A rs41313290 4 0.11989060447971 0.0184856 -0.148156 0.0628601 -2.35988376221321 0.736625376789901 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L3 chr1_109618135_A_G rs143373095 4 0.0380482611599771 0.0122033 -0.183543 0.0731946 -2.51102566998723 0.736625376789901 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L3 chr1_109666423_CA_C rs200217278 4 0.68348010200753 0.0196244 -0.18258 0.0782063 -2.33862128021981 0.736625376789901 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L2 chr1_109669912_T_C rs687643 6 0.146931800078394 1.45153e-15 -0.142702 0.0177923 -8.03384911518914 0.994717403110067 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L2 chr1_109670378_T_A rs1149178 6 0.168288212623384 1.24632e-15 -0.143086 0.0177978 -8.052901044418 0.994717403110067 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L2 chr1_109672545_G_C rs676828 6 0.171447243842212 1.2321e-15 -0.143085 0.0177945 -8.05434906241406 0.994717403110067 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L2 chr1_109672546_T_A rs655323 6 0.171447243842212 1.2321e-15 -0.143085 0.0177945 -8.05434906241406 0.994717403110067 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L2 chr1_109672556_A_G rs655315 6 0.176956554895134 1.18157e-15 -0.143187 0.0177957 -8.0595874634879 0.994717403110067 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L2 chr1_109674419_C_G rs428434 6 0.132857170363337 1.74238e-15 -0.142778 0.0178527 -8.01110529958856 0.994717403110067 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L1 chr1_109675302_G_A rs611951 1 0.999790629660438 7.20622e-22 -0.246472 0.0254637 -9.69359103959016 1 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L4 chr1_109687431_T_C rs185411429 7 0.0391957610979906 0.000188762 0.259963 0.0695489 3.74246573009135 0.77221111610039 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L4 chr1_109703659_C_A rs114807258 7 0.138495056822159 8.88168e-05 0.271657 0.0692269 3.92882510953298 0.77221111610039 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L4 chr1_109705752_G_A rs4119680 7 0.00392261918335524 0.000972837 0.190925 0.0578343 3.30563451682445 0.77221111610039 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L4 chr1_109705872_A_G rs4111793 7 0.00392261918335524 0.000972837 0.190925 0.0578343 3.30563451682445 0.77221111610039 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L4 chr1_109706742_T_G rs10857797 7 0.00394765617720327 0.000965776 0.191005 0.0578226 3.30768231999452 0.77221111610039 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L4 chr1_109707875_G_A rs112697537 7 0.226519414991752 5.50397e-05 0.241719 0.0598547 4.04352621452757 0.77221111610039 chr1:108687814-110687814 +GSTM1.7239.9.3..1 ENSG00000134184 GSTM1.7239.9.3..1_L4 chr1_109711080_C_T rs114184542 7 0.535991863096291 3.82274e-05 0.253832 0.0615564 4.12854314327639 0.77221111610039 chr1:108687814-110687814 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111218425_C_CT rs71096398 12 0.0140423605605697 8.11237e-12 -0.187906 0.0273858 -6.87465242188135 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111218425_C_CT rs398103176 12 0.0140423605605697 8.11237e-12 -0.187906 0.0273858 -6.87465242188135 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111222999_C_T rs7541319 12 0.012183707666001 9.5671e-12 0.188284 0.027537 6.85067991238155 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111223001_C_T rs7541320 12 0.012183707666001 9.5671e-12 0.188284 0.027537 6.85067991238155 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111223049_C_A rs79561756 12 0.0180624375586194 6.21533e-12 0.190394 0.0275935 6.9129520675186 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111226195_G_A rs112495041 12 0.0569065732801756 1.8385e-12 0.196544 0.0277861 7.08664061321594 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111227161_A_AT rs1405449799 12 0.0471145548781091 2.14633e-12 0.202848 0.0287662 7.06515582921552 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111229637_A_C rs6537684 12 0.212196762028722 4.88456e-13 0.192206 0.0264824 7.2710930985834 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111230685_T_C rs75230690 12 0.214417276190273 4.84114e-13 0.190763 0.0262792 7.27250335093835 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111231515_C_G rs2182115 12 0.105244955003551 1.03237e-12 0.186235 0.0260313 7.16751235705965 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111234055_G_C rs112260552 12 0.0956202047980552 1.14336e-12 0.185076 0.0259209 7.15332881048668 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111234063_G_T rs112597784 12 0.0956202047980552 1.14336e-12 0.185076 0.0259209 7.15332881048668 0.768788206750205 chr1:110200771-112200771 +CHI3L2.9383.24.3..1 ENSG00000064886 CHI3L2.9383.24.3..1_L1 chr1_111235703_C_T rs11556868 12 0.0858826844912999 1.27931e-12 0.183342 0.0257346 7.13758000707761 0.768788206750205 chr1:110200771-112200771 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113925634_T_A rs2476598 14 0.0337327235534424 3.88914e-09 0.13644 0.0231064 5.91832957550532 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113926881_A_G rs2147079 14 0.0372064009911297 3.55253e-09 0.136645 0.0230822 5.93340344771317 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113928567_C_T rs1000528 14 0.0493352275266787 2.58002e-09 0.136864 0.0229142 5.98659760343015 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113929596_A_G rs1217441 14 0.045677537570453 2.79766e-09 0.136482 0.0229016 5.97322704690054 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113936808_A_G rs7545038 14 0.0490337269493919 2.60726e-09 0.135722 0.0227297 5.98506993969208 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113938196_G_A rs6658209 14 0.0491235729591243 2.60086e-09 0.135727 0.0227289 5.98547504828744 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113938757_A_G rs7551215 14 0.0489021837909578 2.61454e-09 0.135702 0.0227281 5.98460867697915 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113938927_A_AT rs60152674 14 0.288773995100406 2.98325e-10 0.139953 0.0221473 6.33316344212625 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113947147_A_C rs4381184 14 0.0670684954378246 1.86331e-09 0.137142 0.0227572 6.04025344482872 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113969681_A_G rs7554371 14 0.036201612926157 3.84263e-09 0.13879 0.0234964 5.92120797884107 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113970866_G_T rs7522987 14 0.107140790515865 1.06156e-09 0.138267 0.0226013 6.13204505007826 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113978911_T_TA rs28365907 14 0.0604574665409252 2.12504e-09 0.134455 0.0223913 6.01911896722037 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113979061_A_AAAC rs3841011 14 0.0500160199149901 2.59801e-09 0.133925 0.0224265 5.98605424918997 0.776861365904863 chr1:112979391-114979391 +OLFML3.8660.5.3..1 ENSG00000116774 OLFML3.8660.5.3..1_L1 chr1_113979261_C_G rs6702833 14 0.0503931225390332 2.58312e-09 0.133848 0.02241 5.98698014955762 0.776861365904863 chr1:112979391-114979391 +CD58.10938.13.3..1 ENSG00000116815 CD58.10938.13.3..1_L1 chr1_116517087_A_G rs60612523 8 0.0475309309988685 1.44588e-06 0.462598 0.0958263 4.83398207403841 0.483641872973346 chr1:115571039-117571039 +CD58.10938.13.3..1 ENSG00000116815 CD58.10938.13.3..1_L1 chr1_116536107_A_G rs35768283 8 0.0850172382512443 7.72843e-07 0.467779 0.0944689 4.95843797092849 0.483641872973346 chr1:115571039-117571039 +CD58.10938.13.3..1 ENSG00000116815 CD58.10938.13.3..1_L1 chr1_116537006_A_G rs61789227 8 0.0896182831808431 7.25971e-07 0.472739 0.095235 4.97061069700849 0.483641872973346 chr1:115571039-117571039 +CD58.10938.13.3..1 ENSG00000116815 CD58.10938.13.3..1_L1 chr1_116537166_G_C rs61789228 8 0.15459839986684 4.02323e-07 0.489369 0.0963674 5.08505784950955 0.483641872973346 chr1:115571039-117571039 +CD58.10938.13.3..1 ENSG00000116815 CD58.10938.13.3..1_L1 chr1_116542790_A_G rs61789230 8 0.0953989793595664 6.78616e-07 0.472429 0.0949205 4.98381705849787 0.483641872973346 chr1:115571039-117571039 +CD58.10938.13.3..1 ENSG00000116815 CD58.10938.13.3..1_L1 chr1_116544968_G_A rs61789231 8 0.0960369373263085 6.73437e-07 0.472791 0.0949647 4.98530739399096 0.483641872973346 chr1:115571039-117571039 +CD58.10938.13.3..1 ENSG00000116815 CD58.10938.13.3..1_L1 chr1_116551018_T_C rs61789232 8 0.0944902273640924 6.85365e-07 0.472377 0.094947 4.98187575193265 0.483641872973346 chr1:115571039-117571039 +CD58.10938.13.3..1 ENSG00000116815 CD58.10938.13.3..1_L1 chr1_116584795_C_T rs41301291 8 0.323529981719682 1.78331e-07 0.389214 0.0743932 5.23694904568754 0.483641872973346 chr1:115571039-117571039 +PTGFRN.12727.7.3..1 ENSG00000134247 PTGFRN.12727.7.3..1_L1 chr1_116947639_G_T rs4233450 1 1 2.05602e-251 0.996044 0.0268832 37.112422729511 1 chr1:115909916-117909916 +PTGFRN.12727.7.3..1 ENSG00000134247 PTGFRN.12727.7.3..1_L4 chr1_116950430_CT_C rs5777294 1 0.999999896923805 1.49413e-14 -0.230896 0.029896 -7.74331858390703 1 chr1:115909916-117909916 +PTGFRN.12727.7.3..1 ENSG00000134247 PTGFRN.12727.7.3..1_L2 chr1_116953853_C_CT rs531278323 1 0.99914874906682 8.96156e-41 -0.7726 0.0569945 -13.5720346162712 1 chr1:115909916-117909916 +PTGFRN.12727.7.3..1 ENSG00000134247 PTGFRN.12727.7.3..1_L3 chr1_116964780_T_C rs4131408 1 0.999999991354537 1.49928e-06 0.121934 0.0252965 4.82836758942392 1 chr1:115909916-117909916 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L3 chr1_117315906_C_T rs867990055 6 0.411416697474189 3.16669e-16 -0.661814 0.0806202 -8.22192939624096 0.862857216242153 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L3 chr1_117342995_G_A rs115842852 6 0.111339038895784 3.4635e-17 -0.690533 0.0814675 -8.49097213310816 0.862857216242153 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117367337_G_GGAA rs762272197 145 0.00582517797248594 4.96014e-18 0.308426 0.035433 8.71622250083696 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117372502_A_T rs11587759 145 0.0032418248899162 1.74898e-17 0.292183 0.0341451 8.56990827272488 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117372791_A_AT rs139509107 145 0.00346487155868735 1.62658e-17 0.290973 0.0339698 8.57847033612913 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117376113_C_T rs10923274 145 0.00346487155868735 1.62658e-17 0.290973 0.0339698 8.57847033612913 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117376392_A_G rs10923275 145 0.00346487155868735 1.62658e-17 0.290973 0.0339698 8.57847033612913 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117377406_T_C rs10801957 145 0.00346487155868735 1.62658e-17 0.290973 0.0339698 8.57847033612913 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117377685_T_TTG rs150115826 145 0.00346487155868735 1.62658e-17 0.290973 0.0339698 8.57847033612913 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117377685_T_TTG rs66692703 145 0.00346487155868735 1.62658e-17 0.290973 0.0339698 8.57847033612913 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117377689_G_T rs77807296 145 0.00346487155868735 1.62658e-17 0.290973 0.0339698 8.57847033612913 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117379379_T_C rs10923278 145 0.00346487155868735 1.62658e-17 0.290973 0.0339698 8.57847033612913 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117381450_T_C rs61809515 145 0.00337869890474929 1.68938e-17 0.290864 0.0339747 8.5739953104312 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117382621_C_T rs4466644 145 0.00494876042950465 9.35165e-18 0.289286 0.0335186 8.64353396958903 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117382683_A_G rs143694657 145 0.0045989093527693 9.8882e-18 0.290932 0.0337348 8.63698200016798 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117383251_AT_A rs140454348 145 0.00469507297668226 1.00976e-17 0.289073 0.0335288 8.63451866319521 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117383513_G_A rs11577335 145 0.00482805778172257 9.70829e-18 0.289192 0.0335247 8.6391329235063 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117383719_G_A rs78922094 145 0.0045989093527693 9.8882e-18 0.290932 0.0337348 8.63698200016798 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117386177_G_A rs17037223 145 0.00420332453499861 1.25095e-17 0.289251 0.0336475 8.60943083403802 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117388172_AAG_A rs145841958 145 0.00462542560547441 9.81299e-18 0.290932 0.0337313 8.63787375468341 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117388320_A_G rs17037231 145 0.00486618147990769 9.60142e-18 0.289199 0.0335205 8.64042686468959 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117388469_A_G rs61809517 145 0.00486618147990769 9.60142e-18 0.289199 0.0335205 8.64042686468959 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117389722_T_G rs6662808 145 0.00496777662591308 9.28271e-18 0.289389 0.0335272 8.64438097408257 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117390964_G_A rs12127141 145 0.00462542560547441 9.81299e-18 0.290932 0.0337313 8.63787375468341 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117390987_G_A rs10923281 145 0.00486618147990769 9.60142e-18 0.289199 0.0335205 8.64042686468959 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117394741_C_T rs61808995 145 0.0049180343646329 8.87759e-18 0.290963 0.0336892 8.64959312126768 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117396856_G_T rs61808999 145 0.00497455489107657 8.7351e-18 0.290933 0.0336783 8.65148010037419 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117397066_AC_A rs137887215 145 0.00506904403119912 8.98083e-18 0.28899 0.033466 8.64822528825749 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117397948_G_A rs61809002 145 0.00506904403119912 8.98083e-18 0.28899 0.033466 8.64822528825749 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117398905_G_A rs11583061 145 0.00537241932910204 7.74494e-18 0.290688 0.0335953 8.66565015835898 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117400240_G_A rs12131109 145 0.00526664585733616 7.96184e-18 0.290757 0.0336158 8.66241397178223 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117401571_G_C rs17037253 145 0.00530614979824895 8.34733e-18 0.287966 0.0333144 8.65685819867821 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117403552_C_G rs61809004 145 0.00559289535671625 7.74852e-18 0.287933 0.0332772 8.66554603174975 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117403971_A_G rs10923284 145 0.00555361816450295 7.38537e-18 0.290605 0.0335642 8.67118317858973 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117404338_G_A rs10754344 145 0.00570456635025374 7.52854e-18 0.287988 0.0332706 8.66891636175415 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117404623_T_C rs4460623 145 0.0055730773677457 7.83881e-18 0.285863 0.033043 8.66410690773968 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117404886_C_G rs10923285 145 0.00560352877715464 7.73514e-18 0.287916 0.0332744 8.66574672027815 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117407111_G_GA rs140717342 145 0.00352672502195639 1.39766e-17 0.296032 0.0344886 8.59644136438616 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117408302_TA_T rs1342460042 145 0.0120352881604295 1.19735e-17 0.287192 0.0333879 8.61460962436664 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117409007_T_G rs7547211 145 0.00570456635025374 7.52854e-18 0.287988 0.0332706 8.66891636175415 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117410623_GA_G rs544835333 145 0.00556771872764772 7.36399e-18 0.290596 0.0335618 8.67152053983828 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117411268_A_G rs2359247 145 0.00560352877715464 7.73514e-18 0.287916 0.0332744 8.66574672027815 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117414017_T_C rs7546280 145 0.00560352877715464 7.73514e-18 0.287916 0.0332744 8.66574672027815 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117415063_C_A rs61805762 145 0.00556771872764772 7.36399e-18 0.290596 0.0335618 8.67152053983828 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117415799_A_G rs61805763 145 0.00560352877715464 7.73514e-18 0.287916 0.0332744 8.66574672027815 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117416122_AT_A rs140783195 145 0.00556845764048652 7.36269e-18 0.290596 0.0335618 8.67154112836966 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117418588_T_C rs10923289 145 0.00570534096812014 7.52127e-18 0.287993 0.0332707 8.66902918380216 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117418762_C_G rs12116979 145 0.00560428756468934 7.72768e-18 0.287921 0.0332745 8.66585956261713 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117423885_CT_C rs568455134 145 0.00668098758586155 5.91187e-18 0.289065 0.0332867 8.69714037798592 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117423945_C_T rs12117443 145 0.004648148017805 1.16946e-17 0.288911 0.0335771 8.61736137367636 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117425126_G_A rs74496340 145 0.00560428756468934 7.72768e-18 0.287921 0.0332745 8.66585956261713 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L1 chr1_117427158_AT_A rs35505705 1 0.953988049682964 1.03087e-22 -0.265522 0.0268696 -9.90286663026821 1 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L1 chr1_117427158_AT_A rs397981454 1 0.953988049682964 1.03087e-22 -0.265522 0.0268696 -9.90286663026821 1 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117428091_A_ATC rs1218648156 145 0.00363318147979164 1.39357e-17 0.287771 0.0335247 8.59679354806247 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117428091_A_ATC rs1491298022 145 0.00363318147979164 1.39357e-17 0.287771 0.0335247 8.59679354806247 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117428091_A_ATC rs765944373 145 0.00363318147979164 1.39357e-17 0.287771 0.0335247 8.59679354806247 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117428091_A_ATC rs763805236 145 0.00363318147979164 1.39357e-17 0.287771 0.0335247 8.59679354806247 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117428725_A_G rs12140576 145 0.00555624928260612 7.36476e-18 0.290746 0.0335792 8.67150893052922 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117428784_CTTTTTT_C rs780515731 145 0.00405100662029789 1.46695e-17 0.300194 0.0349966 8.59067578887828 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L2 chr1_117429446_C_A rs541770553 3 0.303218120492753 3.17651e-11 -0.631867 0.0948611 -6.67183526765225 0.969414739960008 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117430198_CGGGCGGGGGGCT_C rs1326038533 145 0.0869199101985334 2.05697e-19 0.351434 0.0387605 9.0820290747647 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117430230_C_T rs540552506 145 0.0992282846092515 2.34587e-19 0.341169 0.0376894 9.06655537110236 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117430984_G_T rs61805784 145 0.00587051811904449 5.54143e-18 0.295072 0.0339491 8.70471525040322 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117431559_G_A rs11576543 145 0.0059327683485686 5.4582e-18 0.292465 0.0336423 8.70642946518756 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117432712_C_T rs10923291 145 0.0059327683485686 5.4582e-18 0.292465 0.0336423 8.70642946518756 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117433011_G_C rs7533053 145 0.0059327683485686 5.4582e-18 0.292465 0.0336423 8.70642946518756 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117438133_G_A rs1316326 145 0.00587051811904449 5.54143e-18 0.295072 0.0339491 8.70471525040322 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117438323_C_CT rs141467989 145 0.00587051811904449 5.54143e-18 0.295072 0.0339491 8.70471525040322 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117438352_G_GT rs149598021 145 0.00594085514620113 5.58358e-18 0.292345 0.0336387 8.70378434768917 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117438402_A_G rs12145435 145 0.00587051811904449 5.54143e-18 0.295072 0.0339491 8.70471525040322 0.892607416891782 chr1:116367449-118367449 +MAN1A2.9077.10.3..1 ENSG00000198162 MAN1A2.9077.10.3..1_L4 chr1_117438591_A_C rs10923295 145 0.00593614790797936 5.45267e-18 0.292468 0.0336421 8.70654733442469 0.892607416891782 chr1:116367449-118367449 diff --git a/tests/gentropy/data_samples/QTD000584.lbf_variable.txt b/tests/gentropy/data_samples/QTD000584.lbf_variable.txt new file mode 100644 index 000000000..2e5f080b7 --- /dev/null +++ b/tests/gentropy/data_samples/QTD000584.lbf_variable.txt @@ -0,0 +1,93 @@ +molecular_trait_id region variant chromosome position lbf_variable1 lbf_variable2 lbf_variable3 lbf_variable4 lbf_variable5 lbf_variable6 lbf_variable7 lbf_variable8 lbf_variable9 lbf_variable10 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1004625_A_G 1 1004625 31.6611029091783 -0.202959075620786 -0.202130730314703 -0.20961965656411 -0.220935552186467 -0.231637998079872 -0.238725148211663 -0.24082771183179 -0.237793637394013 -0.230147752064775 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1005429_C_CA 1 1005429 32.221487936929 -0.195965362215776 -0.195159905703761 -0.202442615288331 -0.213450908310367 -0.223867149857143 -0.230767346101171 -0.232814731479065 -0.229859524607447 -0.222413891866702 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1005429_C_CA 1 1005429 32.221487936929 -0.195965362215776 -0.195159905703761 -0.202442615288331 -0.213450908310367 -0.223867149857143 -0.230767346101171 -0.232814731479065 -0.229859524607447 -0.222413891866702 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1005904_C_T 1 1005904 32.2201611534391 -0.195991762843284 -0.195186220371416 -0.202469703004364 -0.213479148809414 -0.223896462401234 -0.230797358533138 -0.232844950282998 -0.229889448070568 -0.222443065660339 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1005954_G_A 1 1005954 32.2307888818185 -0.19578105619256 -0.19497623026446 -0.202253251284341 -0.213253021383546 -0.223661291151955 -0.230556257657266 -0.232602096369102 -0.229649101642647 -0.222209070783182 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1006159_C_T 1 1006159 31.136467292701 -0.209990149528141 -0.209138719976353 -0.216835315041259 -0.228461477008492 -0.239453018312253 -0.246729173853613 -0.248887512499901 -0.245773726161243 -0.237925750736744 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1007746_CTTTTTTTTTTTTTT_C 1 1007746 32.9559850984322 -0.038316648883864 -0.0380238494960996 -0.0406893282630594 -0.0448305028253735 -0.04887519730207 -0.051621545891178 -0.0524445636406954 -0.0512453110009252 -0.0482663766774842 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1008088_T_C 1 1008088 1.37412874657265 0.327702121948042 0.326632473445201 0.336381513669191 0.350847368073308 0.364259715641688 0.373012957292964 0.375606404726796 0.37190257498043 0.362433775328293 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1008307_G_C 1 1008307 32.5456111578158 -0.194700204398019 -0.193898250606131 -0.201149852637882 -0.212113006036389 -0.222488569395536 -0.229362885636536 -0.231402755433439 -0.228458430823835 -0.22104090567139 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1008307_G_C 1 1008307 32.5456111578158 -0.194700204398019 -0.193898250606131 -0.201149852637882 -0.212113006036389 -0.222488569395536 -0.229362885636536 -0.231402755433439 -0.228458430823835 -0.22104090567139 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1009184_T_C 1 1009184 32.5611388775887 -0.194290897321716 -0.193490230314105 -0.200730245427149 -0.211676195244074 -0.222035837874369 -0.228899799643638 -0.230936619539856 -0.227996653323558 -0.220590227413268 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1009184_T_C 1 1009184 32.5611388775887 -0.194290897321716 -0.193490230314105 -0.200730245427149 -0.211676195244074 -0.222035837874369 -0.228899799643638 -0.230936619539856 -0.227996653323558 -0.220590227413268 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1009716_C_T 1 1009716 2.71104079064173 0.494816635254447 0.493192703993635 0.50794034053314 0.529835423625354 0.550140638470176 0.563389361772642 0.567308216212787 0.561697932338878 0.547375550942794 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1009731_C_T 1 1009731 2.82150405521786 0.540342129323484 0.538562825668397 0.554719332306166 0.57871213290032 0.600969016749107 0.615494215722806 0.619790737162891 0.61363890191046 0.597937184159163 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1010094_C_T 1 1010094 2.24493920482519 0.411968335337582 0.41061718608088 0.422907017935993 0.441149233643318 0.458066284407125 0.469106147268475 0.472374152232941 0.467700270944111 0.455760977707615 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1010232_C_T 1 1010232 1.91202916188143 0.359803709417789 0.358629772331635 0.36932105260348 0.385180619164498 0.399879856468536 0.409469520222412 0.41230946464606 0.408251380242343 0.397876921424855 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1010481_ATTAT_A 1 1010481 1.77173807821859 0.32367057938181 0.322617627724349 0.332215602605901 0.34644932972347 0.359638760397212 0.368242813192753 0.370791808350112 0.367151750023736 0.357841775774211 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1010481_ATTAT_A 1 1010481 1.77173807821859 0.32367057938181 0.322617627724349 0.332215602605901 0.34644932972347 0.359638760397212 0.368242813192753 0.370791808350112 0.367151750023736 0.357841775774211 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1010481_ATTAT_A 1 1010481 1.77173807821859 0.32367057938181 0.322617627724349 0.332215602605901 0.34644932972347 0.359638760397212 0.368242813192753 0.370791808350112 0.367151750023736 0.357841775774211 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1010747_GT_G 1 1010747 1.75810715157238 0.324689439194826 0.323633006588353 0.33326218961169 0.347542511100306 0.360775365411779 0.369407817534277 0.371965166683577 0.368313013217345 0.358972409164632 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1010755_T_G 1 1010755 1.75810715157238 0.324689439194826 0.323633006588353 0.33326218961169 0.347542511100306 0.360775365411779 0.369407817534277 0.371965166683577 0.368313013217345 0.358972409164632 +ISG15.14148.2.3..1 chr1:1138-2001138 chr1_1011654_G_A 1 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chr1:1138-2001138 chr1_1014228_G_A 1 1014228 68.87357824629 -1.44285040965178 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +ISG15.14151.4.3..1 chr1:1138-2001138 chr1_1014545_C_T 1 1014545 -1.01063070074985 26.0676629268844 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +ISG15.14151.4.3..1 chr1:1138-2001138 chr1_1014863_A_C 1 1014863 65.3967593369157 -1.45885206221497 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +ISG15.14151.4.3..1 chr1:1138-2001138 chr1_1014863_A_C 1 1014863 65.3967593369157 -1.45885206221497 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +ISG15.14151.4.3..1 chr1:1138-2001138 chr1_1015336_A_T 1 1015336 -0.739349906651994 25.7895461533593 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +ISG15.14151.4.3..1 chr1:1138-2001138 chr1_1015925_A_ATT 1 1015925 65.2749061618345 -1.41461869181183 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +ISG15.14151.4.3..1 chr1:1138-2001138 chr1_1015925_A_ATT 1 1015925 65.2749061618345 -1.41461869181183 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +ISG15.14151.4.3..1 chr1:1138-2001138 chr1_1015925_A_ATT 1 1015925 65.2749061618345 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In some species, non-germ cells known as Sertoli cells also play a role in spermatogenesis.", + "xrefs": [ + "PMID:29462262", + "https://orcid.org/0000-0001-5208-3432" + ] + }, + "subsets": ["http://purl.obolibrary.org/obo/cl#cellxgene_subset"], + "xrefs": [ + { + "val": "FMA:72290" + }, + { + "val": "MA:0002765" + }, + { + "val": "VHOG:0001531" + }, + { + "val": "ncithesaurus:Spermatogenic_Cell" + } + ], + "basicPropertyValues": [ + { + "pred": "http://www.w3.org/2000/01/rdf-schema#seeAlso", + "val": "https://github.com/obophenotype/cell-ontology/issues/574" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000019", + "lbl": "sperm", + "type": "CLASS", + "meta": { + "definition": { + "val": "A mature male germ cell that develops from a spermatid.", + "xrefs": ["GOC:tfm", "MESH:D013094"] + }, + "subsets": ["http://purl.obolibrary.org/obo/cl#cellxgene_subset"], + "synonyms": [ + { + "pred": "hasExactSynonym", + "val": "sperm cell" + }, + { + "pred": "hasExactSynonym", + "val": "spermatozoid" + }, + { + "pred": "hasExactSynonym", + "val": "spermatozoon" + } + ], + "xrefs": [ + { + "val": "BTO:0001277" + }, + { + "val": "BTO:0002046" + }, + { + "val": "CALOHA:TS-0949" + }, + { + "val": "FBbt:00004954" + }, + { + "val": "FMA:67338" + }, + { + "val": "MA:0002765" + }, + { + "val": "MAT:0000131" + }, + { + "val": "MeSH:D012661" + }, + { + "val": "NCIt:C12602" + }, + { + "val": "NCIt:C13277" + }, + { + "val": "SAEL:93" + }, + { + "val": "WBbt:0005321" + }, + { + "val": "WBbt:0006798" + }, + { + "val": "ZFA:0009006" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000023", + "lbl": "oocyte", + "type": "CLASS", + "meta": { + "definition": { + "val": "A female germ cell that has entered meiosis.", + "xrefs": ["GOC:tfm", "ISBN:0721662544"] + }, + "subsets": ["http://purl.obolibrary.org/obo/cl#cellxgene_subset"], + "synonyms": [ + { + "pred": "hasRelatedSynonym", + "val": "oogonium" + } + ], + "xrefs": [ + { + "val": "BTO:0000964" + }, + { + "val": "CALOHA:TS-0711" + }, + { + "val": "FBbt:00004886" + }, + { + "val": "FMA:18644" + }, + { + "val": "MESH:D009865" + }, + { + "val": "NCIt:C12598" + }, + { + "val": "SNOMEDCT:86082002" + }, + { + "val": "WBbt:0006797" + }, + { + "val": "ZFA:0001109" + }, + { + "val": "ZFA:0009008" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000025", + "lbl": "egg cell", + "type": "CLASS", + "meta": { + "definition": { + "val": "A female gamete where meiosis has progressed to metaphase II and is able to participate in fertilization.", + "xrefs": ["GOC:tfm", "ISBN:0721662544"] + }, + "synonyms": [ + { + "pred": "hasExactSynonym", + "val": "mature oocyte" + }, + { + "pred": "hasExactSynonym", + "val": "ovum" + } + ], + "xrefs": [ + { + "val": "BTO:0000369" + }, + { + "val": "BTO:0003801" + }, + { + "val": "CALOHA:TS-2191" + }, + { + "val": "FBbt:00057012" + }, + { + "val": "FMA:67343" + }, + { + "val": "MAT:0000213" + }, + { + "val": "MESH:D010063" + }, + { + "val": "MeSH:D010063" + }, + { + "val": "PO:0020094" + }, + { + "val": "SNOMEDCT:73153001" + }, + { + "val": "ZFA:0001570" + } + ], + "basicPropertyValues": [ + { + "pred": "http://xmlns.com/foaf/0.1/depiction", + "val": "https://www.swissbiopics.org/api/image/Egg_cell.svg" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000031", + "lbl": "neuroblast (sensu Vertebrata)", + "type": "CLASS", + "meta": { + "definition": { + "val": "A cell that will develop into a neuron often after a migration phase.", + "xrefs": ["GOC:NV", "http://en.wikipedia.org/wiki/Neuroblast"] + }, + "subsets": ["http://purl.obolibrary.org/obo/cl#cellxgene_subset"], + "synonyms": [ + { + "pred": "hasExactSynonym", + "val": "neuroblast" + } + ], + "xrefs": [ + { + "val": "BTO:0000930" + }, + { + "val": "FMA:70563" + }, + { + "val": "ZFA:0009011" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000034", + "lbl": "stem cell", + "type": "CLASS", + "meta": { + "definition": { + "val": "A relatively undifferentiated cell that retains the ability to divide and proliferate throughout life to provide progenitor cells that can differentiate into specialized cells.", + "xrefs": ["GOC:tfm", "MESH:D013234"] + }, + "comments": [ + "This term applies to metazoan. For plant stem cells, consider using PO:0004011 β€˜initial cell’ or its parent PO:0004010 β€˜meristematic cell’." + ], + "subsets": [ + "http://purl.obolibrary.org/obo/cl#cellxgene_subset", + "http://purl.obolibrary.org/obo/cl#general_cell_types_upper_slim", + "http://purl.obolibrary.org/obo/uberon/core#human_reference_atlas" + ], + "synonyms": [ + { + "pred": "hasExactSynonym", + "val": "animal stem cell" + } + ], + "xrefs": [ + { + "val": "CALOHA:TS-2086" + }, + { + "val": "FMA:63368" + }, + { + "val": "NCIt:C12662" + }, + { + "val": "SNOMEDCT:419758009" + }, + { + "val": "ZFA:0005957" + } + ], + "basicPropertyValues": [ + { + "pred": "http://purl.obolibrary.org/obo/RO_0002175", + "val": "http://purl.obolibrary.org/obo/NCBITaxon_9606" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000037", + "lbl": "hematopoietic stem cell", + "type": "CLASS", + "meta": { + "definition": { + "val": "A stem cell from which all cells of the lymphoid and myeloid lineages develop, including blood cells and cells of the immune system. Hematopoietic stem cells lack cell markers of effector cells (lin-negative). Lin-negative is defined by lacking one or more of the following cell surface markers: CD2, CD3 epsilon, CD4, CD5 ,CD8 alpha chain, CD11b, CD14, CD19, CD20, CD56, ly6G, ter119.", + "xrefs": [ + "GOC:add", + "GOC:dsd", + "GOC:tfm", + "PMID:19022770", + "http://en.wikipedia.org/wiki/Hematopoietic_stem_cell" + ] + }, + "comments": [ + "Markers differ between species, and two sets of markers have been described for mice. HSCs are reportedly CD34-positive, CD45-positive, CD48-negative, CD150-positive, CD133-positive, and CD244-negative." + ], + "subsets": [ + "http://purl.obolibrary.org/obo/cl#blood_and_immune_upper_slim", + "http://purl.obolibrary.org/obo/cl#cellxgene_subset", + "http://purl.obolibrary.org/obo/uberon/core#human_reference_atlas" + ], + "synonyms": [ + { + "pred": "hasExactSynonym", + "val": "blood forming stem cell" + }, + { + "pred": "hasExactSynonym", + "val": "hemopoietic stem cell" + }, + { + "synonymType": "http://purl.obolibrary.org/obo/OMO_0003000", + "pred": "hasRelatedSynonym", + "val": "HSC" + }, + { + "pred": "hasRelatedSynonym", + "val": "colony forming unit hematopoietic" + } + ], + "xrefs": [ + { + "val": "BTO:0000725" + }, + { + "val": "CALOHA:TS-0448" + }, + { + "val": "FMA:86475" + }, + { + "val": "MESH:D006412" + }, + { + "val": "NCIt:C12551" + }, + { + "val": "SNOMEDCT:418318001" + }, + { + "val": "VHOG:0001485" + }, + { + "val": "ZFA:0009014" + } + ], + "basicPropertyValues": [ + { + "pred": "http://purl.obolibrary.org/obo/RO_0002175", + "val": "http://purl.obolibrary.org/obo/NCBITaxon_9606" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000038", + "lbl": "erythroid progenitor cell", + "type": "CLASS", + "meta": { + "definition": { + "val": "A progenitor cell committed to the erythroid lineage.", + "xrefs": ["GOC:add", "ISBN:0721601464"] + }, + "subsets": [ + "http://purl.obolibrary.org/obo/cl#blood_and_immune_upper_slim", + "http://purl.obolibrary.org/obo/cl#cellxgene_subset" + ], + "synonyms": [ + { + "synonymType": "http://purl.obolibrary.org/obo/OMO_0003000", + "pred": "hasRelatedSynonym", + "val": "BFU-E" + }, + { + "synonymType": "http://purl.obolibrary.org/obo/OMO_0003000", + "pred": "hasRelatedSynonym", + "val": "CFU-E" + }, + { + "pred": "hasRelatedSynonym", + "val": "blast forming unit erythroid" + }, + { + "pred": "hasRelatedSynonym", + "val": "burst forming unit erythroid" + }, + { + "pred": "hasRelatedSynonym", + "val": "colony forming unit erythroid" + }, + { + "pred": "hasRelatedSynonym", + "val": "erythroid stem cell" + } + ], + "xrefs": [ + { + "val": "BTO:0004911" + }, + { + "val": "NCIt:C12526" + }, + { + "val": "ZFA:0009015" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000040", + "lbl": "monoblast", + "type": "CLASS", + "meta": { + "definition": { + "val": "A myeloid progenitor cell committed to the monocyte lineage. This cell is CD11b-positive, has basophilic cytoplasm, euchromatin, and the presence of a nucleolus.", + "xrefs": [ + "GOC:add", + "PMID:1104740", + "http://en.wikipedia.org/wiki/Monoblast", + "http://www.copewithcytokines.de" + ] + }, + "comments": [ + "Morphology: mononuclear cell, diameter 12-20 _M, non-granular, N/C ratio 3/1 - 4/1; markers: CD11b (shared with many other myeloid cells); location: Adult: bone marrow; fetal: liver, Yolk Sac; role or process: hematopoiesis, monocyte development; lineage: hematopoietic, myeloid." + ], + "synonyms": [ + { + "synonymType": "http://purl.obolibrary.org/obo/OMO_0003000", + "pred": "hasRelatedSynonym", + "val": "CFU-M" + }, + { + "pred": "hasRelatedSynonym", + "val": "colony forming unit macrophage" + }, + { + "pred": "hasRelatedSynonym", + "val": "colony forming unit monocyte" + }, + { + "pred": "hasRelatedSynonym", + "val": "monocyte stem cell" + } + ], + "xrefs": [ + { + "val": "CALOHA:TS-1195" + }, + { + "val": "FMA:83553" + }, + { + "val": "NCIt:C13014" + }, + { + "val": "SNOMEDCT:53945006" + }, + { + "val": "ZFA:0009017" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000041", + "lbl": "mature eosinophil", + "type": "CLASS", + "meta": { + "definition": { + "val": "A fully differentiated eosinophil, a granular leukocyte with a nucleus that usually has two lobes connected by one or more slender threads, and cytoplasm containing coarse, round granules that are uniform in size and which can be stained by the dye eosin. Cells are also differentiated from other granulocytes by a small nuclear-to-cytoplasm ratio (1:3). This cell type is CD49d-positive.", + "xrefs": [ + "GOC:add", + "GOC:dsd", + "GOC:tfm", + "ISBN:0721601464", + "http://www.cap.org" + ] + }, + "comments": [ + "Eosinophils are CD125-positive (IL-5R), GM-CSFR-positive, IL-3R-positive, VLA4-positive. They can also express MHC Class I & II, CD4, CD9, CD11a, CD11b, CD11c, CD13, CD15, CD16, CD17, CD18, CD24, CD25,CD28, CD29, CD32, CD33, CD35, CD37, CD39, CD43, CD44, CD45, CD45RB, CD45RO, CD46, CD47, CD48, CD49d, CD49f, CD50, CD52, CD53, CD54, CD55, CD58, CD59, CD62L, CD63, CD65, CD66, CD69, CD71, CD76, CD80, CD81, CD82, CD86, CD87, CD88, CD89, CD92, CD95, CD97, CD98, CD99, CD100, CD101, CD116, CD117, CD119, CD120, CD123, CD124, CD125, CD131, CD137, CD139, CD148, CD149, CD151, CD153, CD156, CD162, CD161, CD162, CD165, CD174, CD182, CD183, CD191, CD192, CD193, CD196, CD213, IL9R, ad integrin, beta-7 integrin, FceRI, IL13Ra1, TGFbR, PAFR, LTB4R, C3aR, CystLT1R, CystLT2R, fMLPR, CRTH2 (PGD2 receptor), histamine 4R, IDO, KYN, PAR-2, Siglec-8, Siglec-10, LIR1, LIR2, LIR3, LIR7, TLR7, TLR8, and VLA-4. Eosinophils can also secrete CXCL1, eotaxin-1, GM-CSF, IL-2, IL-3, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-13, IL-16, IL-18, IFN-gamma, LTC4, MIP-1alpha, NGF, PAF, RANTES, substance P, TGF-alpha, TGF-beta, TNF-alpha, and VIP." + ], + "subsets": [ + "http://purl.obolibrary.org/obo/uberon/core#human_reference_atlas" + ], + "synonyms": [ + { + "pred": "hasBroadSynonym", + "val": "polymorphonuclear leucocyte" + }, + { + "pred": "hasBroadSynonym", + "val": "polymorphonuclear leukocyte" + }, + { + "pred": "hasExactSynonym", + "val": "mature eosinocyte" + }, + { + "pred": "hasExactSynonym", + "val": "mature eosinophil leucocyte" + }, + { + "pred": "hasExactSynonym", + "val": "mature eosinophil leukocyte" + } + ], + "basicPropertyValues": [ + { + "pred": "http://purl.obolibrary.org/obo/IAO_0000116", + "val": "The status of eosinophils as true professional antigen presenting cells is unclear, despite their ability to present exogenous peptides and peptides processed from exogenous proteins (in certain studies) via MHC Class II and activate T cells. Per the equivalence axioms, 'eosinophil' is reasoned to be a subclass of 'professional antigen presenting cell', though the role of eosinophils as such in the body may be limited." + }, + { + "pred": "http://purl.obolibrary.org/obo/RO_0002175", + "val": "http://purl.obolibrary.org/obo/NCBITaxon_9606" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000042", + "lbl": "neutrophilic myeloblast", + "type": "CLASS", + "meta": { + "definition": { + "val": "A myeloblast committed to the neutrophil lineage. This cell type is GATA-1 positive, C/EBPa-positive, AML-1-positive, c-myb-positive and has low expression of PU.1 transcription factor.", + "xrefs": [ + "GOC:add", + "ISBN:0721601464", + "PMID:12560239", + "PMID:15514007" + ] + }, + "comments": [ + "These cells are CD11b-negative, CD15-negative, CD16-negative, CD35-negative, CD49d-positive, CD68-positive, lactotransferrin-negative, and fMLP receptor-negative. They are found in the Band 3 fraction." + ], + "synonyms": [ + { + "pred": "hasRelatedSynonym", + "val": "neutrophilic granuloblast" + } + ], + "xrefs": [ + { + "val": "ZFA:0009018" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000043", + "lbl": "mature basophil", + "type": "CLASS", + "meta": { + "definition": { + "val": "A fully differentiated basophil, a granular leukocyte with an irregularly shaped, pale-staining nucleus that is partially constricted into two lobes, and with cytoplasm that contains coarse granules of variable size. Basophils contain vasoactive amines such as histamine and serotonin, which are released on appropriate stimulation.", + "xrefs": [ + "GOC:add", + "GOC:dsd", + "GOC:tfm", + "ISBN:0721601464", + "PMID:18466030", + "PMID:19231594", + "PMID:20837449", + "http://www.cap.org" + ] + }, + "comments": [ + "Mature basophils are also capable of producing IL-3, IL-5, IL-6, IL-8, IL-13, IL-25, CCL22, tslp, vegf, and LTC4." + ], + "synonyms": [ + { + "pred": "hasBroadSynonym", + "val": "polymorphonuclear leucocyte" + }, + { + "pred": "hasBroadSynonym", + "val": "polymorphonuclear leukocyte" + }, + { + "pred": "hasExactSynonym", + "val": "mature basophil leucocyte" + }, + { + "pred": "hasExactSynonym", + "val": "mature basophil leukocyte" + } + ], + "xrefs": [ + { + "val": "BTO:0001026" + }, + { + "val": "CALOHA:TS-0688" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000047", + "lbl": "neural stem cell", + "type": "CLASS", + "meta": { + "definition": { + "val": "An undifferentiated neural cell that originates from the neuroectoderm and has the capacity both to perpetually self-renew without differentiating and to generate multiple central nervous system neuronal and glial cell types.", + "xrefs": ["PMID:30639325"] + }, + "subsets": ["http://purl.obolibrary.org/obo/cl#cellxgene_subset"], + "synonyms": [ + { + "pred": "hasExactSynonym", + "val": "NSC" + }, + { + "pred": "hasExactSynonym", + "val": "neuronal stem cell", + "xrefs": ["PMID:16305818"] + } + ], + "xrefs": [ + { + "val": "BTO:0002881" + }, + { + "val": "CALOHA:TS-2360" + }, + { + "val": "FMA:86684" + }, + { + "val": "NCIt:C12985" + }, + { + "val": "ZFA:0009019" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000048", + "lbl": "multi fate stem cell", + "type": "CLASS", + "meta": { + "definition": { + "val": "A stem cell that can give rise to multiple lineages of cells.", + "xrefs": ["GOC:add"] + }, + "synonyms": [ + { + "pred": "hasExactSynonym", + "val": "multi-fate stem cell" + }, + { + "pred": "hasExactSynonym", + "val": "multifate stem cell" + }, + { + "pred": "hasExactSynonym", + "val": "multipotent cell" + }, + { + "pred": "hasExactSynonym", + "val": "multipotent stem cell" + } + ], + "xrefs": [ + { + "val": "FMA:84789" + }, + { + "val": "ZFA:0009020" + } + ] + } + }, + { + "id": "http://purl.obolibrary.org/obo/CL_0000049", + "lbl": "common myeloid progenitor", + "type": "CLASS", + "meta": { + "definition": { + "val": "A progenitor cell committed to myeloid lineage, including the megakaryocyte and erythroid lineages.", + "xrefs": ["GOC:add", "ISBN:0878932437", "MESH:D023461"] + }, + "comments": [ + "This cell type is intended to be compatible with any vertebrate common myeloid progenitor. For mammalian CMP known to be CD34-positive, please use the term 'common myeloid progenitor, CD34-positive' (CL_0001059)." + ], + "subsets": [ + "http://purl.obolibrary.org/obo/cl#cellxgene_subset", + "http://purl.obolibrary.org/obo/uberon/core#human_reference_atlas" + ], + "synonyms": [ + { + "pred": "hasExactSynonym", + "val": "common myeloid precursor" + }, + { + "synonymType": "http://purl.obolibrary.org/obo/OMO_0003000", + "pred": "hasRelatedSynonym", + "val": "CFU-GEMM", + "xrefs": ["ISBN:0878932437"] + }, + { + "synonymType": "http://purl.obolibrary.org/obo/OMO_0003000", + "pred": "hasRelatedSynonym", + "val": "CFU-S", + "xrefs": ["ISBN:0878932437"] + }, + { + "synonymType": "http://purl.obolibrary.org/obo/OMO_0003000", + "pred": "hasRelatedSynonym", + "val": "CMP", + "xrefs": ["ISBN:0878932437"] + }, + { + "pred": "hasRelatedSynonym", + "val": "colony forming unit granulocyte, erythrocyte, macrophage, and megakaryocyte", + "xrefs": ["ISBN:0878932437"] + }, + { + "pred": 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cingulate cortex Brain - Anterior cingulate cortex (BA24) naive ge ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Brain_Anterior_cingulate_cortex_BA24.tsv.gz -GTEx_V8 Brain_Caudate_basal_ganglia UBER_0001873 caudate nucleus Brain - Caudate (basal ganglia) naive ge ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Brain_Caudate_basal_ganglia.tsv.gz -GTEx_V8 Brain_Cerebellar_Hemisphere UBER_0002037 cerebellum Brain - Cerebellar Hemisphere naive ge ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Brain_Cerebellar_Hemisphere.tsv.gz diff --git a/tests/gentropy/data_samples/finngen_R9_AB1_EBV.SUSIE.snp b/tests/gentropy/data_samples/finngen_R9_AB1_EBV.SUSIE.snp new file mode 100644 index 000000000..052399388 --- /dev/null +++ b/tests/gentropy/data_samples/finngen_R9_AB1_EBV.SUSIE.snp @@ -0,0 +1,90 @@ +trait region v rsid chromosome position allele1 allele2 maf beta se p mean sd prob cs cs_specific_prob low_purity lead_r2 mean_99 sd_99 prob_99 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0.000108945579309375 0.000109043388481492 0.00010909516101071 0.000109088003495295 0.000109013906271378 1.7706840523719e-09 0.000105696396775546 4.49708995093949e-05 4.50550210557385e-05 4.5173821756935e-05 4.52927095848752e-05 4.53860323694479e-05 4.54355104110357e-05 4.5428780052821e-05 4.53582133617496e-05 4.23059122786113e-05 0.0102300083056357 0.00513146611005017 0.0051381716493634 0.00514763567977716 0.0051570999330498 0.00516452473746057 0.00516846065280203 0.00516792812105516 0.00516231993799184 0.336333289791154 1.28525610863321 0.90674179140902 0.907595813479623 0.908799794774941 0.910002132328979 0.910944034613751 0.911442419834467 0.91137369825334 0.910660619838302 +AB1_EBV chr6:1412516-4412516 6:2912215:A:C chr6_2912215_A_C chr6 2912215 A C 0.000455488 0.61666 0.755233 0.414205 0.000112004643456391 0.0113904316010689 0.000333457603954423 -1 NA NA NA 0.000112004643456391 0.0113904316010689 0.000333457603954423 -1 NA NA NA 3.24538856818094e-10 1.7385629796308e-05 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0.000388321169953576 -1 NA NA NA -7.30572554286086e-05 0.00577508239019442 0.000388321169953576 -1 NA NA NA 6.85520808868472e-10 2.2868798622248e-05 4.57069624728122e-05 4.57020544071268e-05 4.56950926405767e-05 4.56880889393967e-05 4.56825623082732e-05 4.56796131516313e-05 4.56799871004238e-05 4.56841289950174e-05 -3.16876086277397e-10 -7.94803449252908e-06 -8.11205163551567e-06 -8.11973214656119e-06 -8.130556928672e-06 -8.14136422670553e-06 -8.14983138284861e-06 -8.15431809887712e-06 -8.15371694660398e-06 -8.14733269420916e-06 1.46564883619148e-05 0.00220059709444493 0.00188328457825256 0.001884574271071 0.00188639223184881 0.00188820763332014 0.00188963024641197 0.00189038432858177 0.00189028374688367 0.00188921187334649 -0.805538306419943 -0.445015513483716 0.0412518323613069 0.041184142630815 0.0410880529833104 0.040991275198154 0.0409148284635545 0.0408739783371201 0.0408790532144785 0.0409361160965389 +AB1_EBV chr6:1412516-4412516 6:2916373:AG:A chr6_2916373_AG_A chr6 2916373 AG 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1.25974414810072e-06 1.25841584583106e-06 3.35268268988337e-05 0.00031533030047395 0.000229694983463847 0.000229904152209295 0.000230198240225988 0.000230491662996154 0.00023072132284593 0.000230842536310908 0.000230825180163132 0.000230650554226152 4.08032480416118 -0.00644690209912024 0.2529267939592 0.253135137015608 0.253424346235863 0.253710721570567 0.253932958830646 0.254047984566061 0.254026965973055 0.253852000911881 +AB1_EBV chr6:1412516-4412516 6:2917284:G:A chr6_2917284_G_A chr6 2917284 G A 0.000185896 2.17343 1.33618 0.103821 0.000613554969808555 0.0347133916275768 0.000548510584581052 -1 NA NA NA 0.000613554969808555 0.0347133916275768 0.000548510584581052 -1 NA NA NA 8.4773204979451e-10 4.37818021334355e-05 6.30578137241499e-05 6.30722778698699e-05 6.30926174150932e-05 6.31128835806283e-05 6.3128733630544e-05 6.31371227967198e-05 6.31360029299488e-05 6.31240802836088e-05 1.79370456202886e-09 8.949699191577e-05 6.52179650543132e-05 6.53014400084777e-05 6.54191687976645e-05 6.55368291330057e-05 6.56291037498186e-05 6.56780492507771e-05 6.567155176552e-05 6.56020764286465e-05 7.26163392126802e-05 0.0158929736566894 0.0108758088609039 0.0108860145849345 0.010900406902664 0.0109147885991464 0.0109260658449788 0.0109320473330933 0.0109312538622067 0.01092276412053 -0.593152553870173 0.20442926523208 0.363053178106279 0.36332222809547 0.363700907419618 0.364078573063729 0.364374204594942 0.364530795146633 0.364509946503933 0.364287483000271 +AB1_EBV chr6:1412516-4412516 6:2917286:G:A chr6_2917286_G_A chr6 2917286 G A 0.00245976 0.211607 0.306676 0.490192 3.95662131838167e-05 0.00440843832881892 0.000321126231617619 -1 NA NA NA 3.95662131838167e-05 0.00440843832881892 0.000321126231617619 -1 NA NA NA 2.95218797816824e-10 1.62372798357836e-05 3.81578001835832e-05 3.8145998770834e-05 3.81293338499226e-05 3.81126398975393e-05 3.80995090421955e-05 3.80925049378021e-05 3.80933601060031e-05 3.81031383111853e-05 6.0571039331081e-11 3.76880877656163e-06 4.46368651768963e-06 4.4668710239593e-06 4.47136000792566e-06 4.4758343419471e-06 4.47932955082859e-06 4.48116730343495e-06 4.48088951852954e-06 4.47820557190098e-06 6.28796818169363e-06 0.0014951406154105 0.00146376129947197 0.00146447844632928 0.00146548908090669 0.00146649716193881 0.00146728583268409 0.00146770237436583 0.00146764374925418 0.00146704496098117 -1.64800039177406 -0.787479166502865 -0.139268607899495 -0.139538237731458 -0.139918953887551 -0.140300369876369 -0.140600431855955 -0.140760576327229 -0.140741238231277 -0.14051818561178 +AB1_EBV chr6:1412516-4412516 6:2917318:G:A chr6_2917318_G_A chr6 2917318 G A 0.058009 -0.131377 0.0635492 0.0387035 -7.99509123567877e-06 0.00090682772847784 0.000319083731836756 -1 NA NA NA -7.99509123567877e-06 0.00090682772847784 0.000319083731836756 -1 NA NA NA 1.72502156829369e-09 1.62653333570556e-05 3.78964433213557e-05 3.78853134403692e-05 3.78695978162394e-05 3.7853849576543e-05 3.78414495824049e-05 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3.8498544760935e-05 3.84882018246528e-05 3.84736412993685e-05 3.84590928995726e-05 3.8447696457833e-05 3.84416897867099e-05 3.84425830315233e-05 3.84513011942451e-05 -6.77317053600951e-12 8.35503496407967e-07 9.88349177806006e-07 9.89186716492214e-07 9.90372368094158e-07 9.91559209796093e-07 9.92492417400822e-07 9.92991882622889e-07 9.92935723200553e-07 9.92246925136894e-07 1.11796892423137e-06 0.000324638038118916 0.000316223270509299 0.000316393706294589 0.000316634430626803 0.000316875070328934 0.000317063943593495 0.000317164552651642 0.00031715224106143 0.000317011488610749 -1.79324513204221 -0.772818538324604 -0.130378363475097 -0.130607360246403 -0.130929491861886 -0.131251198503196 -0.131503043786358 -0.131635576107759 -0.131615451449086 -0.131422298142715 +AB1_EBV chr6:1412516-4412516 6:2917734:C:G chr6_2917734_C_G chr6 2917734 C G 0.00245994 0.211399 0.306657 0.490594 3.9524191170592e-05 0.00440673010071969 0.000321045681277887 -1 NA NA NA 3.9524191170592e-05 0.00440673010071969 0.000321045681277887 -1 NA NA NA 2.95091129039371e-10 1.62296994164451e-05 3.81486982592076e-05 3.81368904147737e-05 3.81202164335897e-05 3.81035134521812e-05 3.80903755433228e-05 3.80833677277856e-05 3.80842234498353e-05 3.80940070589232e-05 6.04839396679464e-11 3.76407889292354e-06 4.45904038663407e-06 4.46222044321167e-06 4.46670315578475e-06 4.47117123112336e-06 4.47466154078533e-06 4.47649670406267e-06 4.47621928120657e-06 4.47353905092034e-06 6.28435419419683e-06 0.00149427406313861 0.00146323196856338 0.00146394847452663 0.00146495820496947 0.00146596538275929 0.00146675334579134 0.00146716951316541 0.00146711094031488 0.00146651268904095 -1.64843294008008 -0.787946128299184 -0.13950717013481 -0.139777042428051 -0.140158100645596 -0.140539858357503 -0.140840188064173 -0.141000474072818 -0.140981116047954 -0.140757860005393 +AB1_EBV chr6:1412516-4412516 6:2917881:C:A chr6_2917881_C_A chr6 2917881 C A 0.00116755 0.370974 0.425921 0.383759 3.53736518553954e-05 0.00545300435877033 0.000294806223373634 -1 NA NA NA 3.53736518553954e-05 0.00545300435877033 0.000294806223373634 -1 NA NA NA 3.40527758013535e-10 1.37669358643734e-05 3.51818012726602e-05 3.51682166763565e-05 3.51490289628559e-05 3.51298263665128e-05 3.51147499283518e-05 3.51067488721605e-05 3.51078174978935e-05 3.51191886372793e-05 1.23616403503735e-10 3.09563226436544e-06 4.02551039150873e-06 4.02818215912796e-06 4.03194076257432e-06 4.03568836199183e-06 4.03862275252425e-06 4.04018062501683e-06 4.03998251792172e-06 4.03778840396079e-06 1.02563779922254e-05 0.00170919184224034 0.00182823169391561 0.00182896711066571 0.00183000229100878 0.00183103458991319 0.0018318426997936 0.00183227111349744 0.00183221507839199 0.00183160844974472 -1.50522051711543 -0.952519223989972 -0.220469866027951 -0.22081636908141 -0.221305864754931 -0.221795829164183 -0.222180559793347 -0.222384730765843 -0.222357403332269 -0.222067168807617 +AB1_EBV chr6:1412516-4412516 6:2917970:G:A chr6_2917970_G_A chr6 2917970 G A 0.062377 -0.00141246 0.0610677 0.981547 1.27354036804579e-05 0.00105882371419752 0.00037421893669809 -1 NA NA NA 1.27354036804579e-05 0.00105882371419752 0.00037421893669809 -1 NA NA NA 2.35118899459539e-10 2.16104077010826e-05 4.41025022891234e-05 4.40971333598514e-05 4.40895829848684e-05 4.40820023943699e-05 4.40760067470158e-05 4.40727569685085e-05 4.40730346409258e-05 4.40773046136013e-05 -7.95686349561399e-13 1.37289483938082e-06 1.41555781180205e-06 1.41693236704312e-06 1.41887466937707e-06 1.42081459579131e-06 1.42233290866819e-06 1.42313298367922e-06 1.42301415005418e-06 1.42185015034832e-06 9.26739669098075e-07 0.000398779291250974 0.000345973236210372 0.000346207904069368 0.000346539213308086 0.000346870131954123 0.00034712928751056 0.000347266191582495 0.000347246721450257 0.000347049466693444 -1.87562581937529 -0.50161395275396 0.00551771645585486 0.00543566869900669 0.00532068394824137 0.00520523735131473 0.00511374182841173 0.00506371743170675 0.0050669063026163 0.00513018064624138 +AB1_EBV chr6:1412516-4412516 6:2917981:C:T chr6_2917981_C_T chr6 2917981 C T 0.0106991 -0.179925 0.144395 0.212743 -1.46565807176174e-05 0.00193638244665908 0.000304691406547875 -1 NA NA NA -1.46565807176174e-05 0.00193638244665908 0.000304691406547875 -1 NA NA NA 4.83699583433394e-10 1.47995421524578e-05 3.62857206053768e-05 3.62729810870845e-05 3.62550074572633e-05 3.62370233763348e-05 3.6222902803972e-05 3.62154049104344e-05 3.62163926891588e-05 3.62270121787056e-05 -8.29883274842750e-11 -1.36433698015565e-06 -1.65744443378711e-06 -1.6586210465356e-06 -1.66028304758396e-06 -1.66194249736079e-06 -1.66324242346387e-06 -1.66393134904925e-06 -1.66383901756174e-06 -1.66285693379194e-06 4.90772202982209e-06 0.000633019966572649 0.000646002662206916 0.000646290010074167 0.000646695172156275 0.00064709951297063 0.000647416170251992 0.000647583981357366 0.000647561655030434 0.000647323077262763 -1.15425314209433 -0.880192744763693 -0.189574513510048 -0.189885967007717 -0.190325348593937 -0.190765011444355 -0.191110234953281 -0.191293539992217 -0.191269376694 -0.191009801374269 +AB1_EBV chr6:1412516-4412516 6:2918284:C:A chr6_2918284_C_A chr6 2918284 C A 0.000833908 -0.556691 0.523047 0.287183 -7.44030003889521e-05 0.00777112881458192 0.00032953541860814 -1 NA NA NA -7.44030003889521e-05 0.00777112881458192 0.00032953541860814 -1 NA NA NA 4.06846382975239e-10 1.69954040133585e-05 3.91104286633995e-05 3.90998019339285e-05 3.90847734770854e-05 3.90697107981359e-05 3.90578611687294e-05 3.90515458445335e-05 3.90523375456124e-05 3.90612055195211e-05 -2.20524022350319e-10 -7.18967998108677e-06 -8.37893111831845e-06 -8.38551510128346e-06 -8.39478151687654e-06 -8.40401980457779e-06 -8.41124403165043e-06 -8.41505667879456e-06 -8.41451703711257e-06 -8.40903459522916e-06 1.51066998856523e-05 0.00268035395968752 0.0025741739876467 0.00257554281723802 0.00257747058093055 0.00257939380192304 0.00258089918016679 0.00258169547515111 0.00258158646111776 0.00258044903598945 -1.32728148392639 -0.741846034061399 -0.114609656474713 -0.114841707112959 -0.115169891099532 -0.115498845938077 -0.1157576615097 -0.115895656525368 -0.115878494926097 -0.115685046477238 diff --git a/tests/gentropy/data_samples/finngen_R9_AB1_EBV.SUSIE.snp.bgz b/tests/gentropy/data_samples/finngen_R9_AB1_EBV.SUSIE.snp.bgz new file mode 100644 index 000000000..2f298d48b Binary files /dev/null and b/tests/gentropy/data_samples/finngen_R9_AB1_EBV.SUSIE.snp.bgz differ diff --git a/tests/gentropy/data_samples/finngen_credset_summary_sample.tsv.bgz b/tests/gentropy/data_samples/finngen_credset_summary_sample.tsv.bgz new file mode 100644 index 000000000..148f0f22f Binary files /dev/null and b/tests/gentropy/data_samples/finngen_credset_summary_sample.tsv.bgz differ diff --git a/tests/gentropy/data_samples/finucane_PIPs.npy b/tests/gentropy/data_samples/finucane_PIPs.npy deleted file mode 100644 index 2a93a5c71..000000000 Binary files a/tests/gentropy/data_samples/finucane_PIPs.npy and /dev/null differ diff --git a/tests/gentropy/data_samples/gwas_catalog_ancestries.tsv b/tests/gentropy/data_samples/gwas_catalog_ancestries.tsv new file mode 100644 index 000000000..48423eeee --- /dev/null +++ b/tests/gentropy/data_samples/gwas_catalog_ancestries.tsv @@ -0,0 +1,20 @@ +STUDY ACCESSION PUBMED ID FIRST AUTHOR DATE INITIAL SAMPLE DESCRIPTION REPLICATION SAMPLE DESCRIPTION STAGE NUMBER OF INDIVIDUALS BROAD ANCESTRAL CATEGORY COUNTRY OF ORIGIN COUNTRY OF RECRUITMENT ADDITIONAL ANCESTRY DESCRIPTION ANCESTRY DESCRIPTOR FOUNDER/GENETICALLY ISOLATED POPULATION NUMBER OF CASES NUMBER OF CONTROLS SAMPLE DESCRIPTION +GCST004795 28763065 Xia K 2017-08-01 295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs. NA initial 205 Asian unspecified, African American or Afro-Caribbean, Native American, Other admixed ancestry NR U.S. +GCST004795 28763065 Xia K 2017-08-01 295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs. NA initial 356 European NR U.S. +GCST004796 28763065 Xia K 2017-08-01 295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs. NA initial 356 European NR U.S. +GCST004796 28763065 Xia K 2017-08-01 295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs. NA initial 205 Asian unspecified, African American or Afro-Caribbean, Native American, Other admixed ancestry NR U.S. +GCST004797 28763065 Xia K 2017-08-01 295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs. NA initial 205 Asian unspecified, African American or Afro-Caribbean, Native American, Other admixed ancestry NR U.S. +GCST004797 28763065 Xia K 2017-08-01 295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs. NA initial 356 European NR U.S. +GCST004794 28763065 Xia K 2017-08-01 295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs. NA initial 356 European NR U.S. +GCST004794 28763065 Xia K 2017-08-01 295 European, African American, Asian, Native American and other admixed ancestry infants, 17 European, African American, Asian, Native American and other admixed ancestry sibling pairs, 116 European, African American, Asian, Native American and other admixed ancestry twin pairs. NA initial 205 Asian unspecified, African American or Afro-Caribbean, Native American, Other admixed ancestry NR U.S. +GCST005522 23459209 Faraco J 2013-01-01 1,886 European ancestry cases, 10,421 European ancestry controls NA initial 12307 European NR Canada, U.S., Australia, Austria, France, Germany, Netherlands, Switzerland, Argentina, Israel, Turkey, Czech Republic, Poland, Slovakia, Denmark, Finland, Norway, U.K., Italy, Portugal, Spain +GCST004692 27455348 van Rheenen W 2016-07-25 12,577 European ancestry cases, 23,475 European ancestry controls 2,579 European ancestry cases, 2,767 European ancestry controls initial 36052 European NR U.S., Belgium, France, Germany, Netherlands, Switzerland, Finland, Republic of Ireland, Sweden, U.K., Italy, Portugal, Spain +GCST004692 27455348 van Rheenen W 2016-07-25 12,577 European ancestry cases, 23,475 European ancestry controls 2,579 European ancestry cases, 2,767 European ancestry controls replication 5346 European NR Australia, Belgium, France, Germany, Netherlands, Turkey, Republic of Ireland, Italy +GCST005134 28800628 Salem JE 2017-08-11 448 European ancestry individuals, 47 North African ancestry individuals 431 European ancestry individuals, 64 North African ancestry individuals replication 64 Greater Middle Eastern (Middle Eastern, North African or Persian) NR France +GCST005134 28800628 Salem JE 2017-08-11 448 European ancestry individuals, 47 North African ancestry individuals 431 European ancestry individuals, 64 North African ancestry individuals replication 431 European NR France +GCST005134 28800628 Salem JE 2017-08-11 448 European ancestry individuals, 47 North African ancestry individuals 431 European ancestry individuals, 64 North African ancestry individuals initial 448 European NR France +GCST005134 28800628 Salem JE 2017-08-11 448 European ancestry individuals, 47 North African ancestry individuals 431 European ancestry individuals, 64 North African ancestry individuals initial 47 Greater Middle Eastern (Middle Eastern, North African or Persian) NR France +GCST005135 28800628 Salem JE 2017-08-11 448 European ancestry individuals, 47 North African ancestry individuals 431 European ancestry individuals, 64 North African ancestry individuals initial 448 European NR France +GCST005135 28800628 Salem JE 2017-08-11 448 European ancestry individuals, 47 North African ancestry individuals 431 European ancestry individuals, 64 North African ancestry individuals initial 47 Greater Middle Eastern (Middle Eastern, North African or Persian) NR France +GCST005135 28800628 Salem JE 2017-08-11 448 European ancestry individuals, 47 North African ancestry individuals 431 European ancestry individuals, 64 North African ancestry individuals replication 431 European NR France +GCST005135 28800628 Salem JE 2017-08-11 448 European ancestry individuals, 47 North African ancestry individuals 431 European ancestry individuals, 64 North African ancestry individuals replication 64 Greater Middle Eastern (Middle Eastern, North African or Persian) NR France diff --git a/tests/gentropy/data_samples/gwas_catalog_ancestries_sample_v1.0.3-r2022-11-29.tsv b/tests/gentropy/data_samples/gwas_catalog_ancestries_sample_v1.0.3-r2022-11-29.tsv deleted file mode 100644 index 92d87d92b..000000000 --- a/tests/gentropy/data_samples/gwas_catalog_ancestries_sample_v1.0.3-r2022-11-29.tsv +++ /dev/null @@ -1,20 +0,0 @@ -STUDY ACCESSION PUBMED ID FIRST AUTHOR DATE INITIAL SAMPLE DESCRIPTION REPLICATION SAMPLE DESCRIPTION STAGE NUMBER OF INDIVIDUALS BROAD ANCESTRAL CATEGORY COUNTRY OF ORIGIN COUNTRY OF RECRUITMENT ADDITIONAL ANCESTRY DESCRIPTION ANCESTRY DESCRIPTOR FOUNDER/GENETICALLY ISOLATED POPULATION NUMBER OF CASES NUMBER OF CONTROLS SAMPLE DESCRIPTION COHORT(S) COHORT-SPECIFIC REFERENCE -GCST008644 26546613 Gutierrez-Achury J 2016-01-01 371 South Asian ancestry celiac disease cases, 3,138 European ancestry celiac disease cases, 4,418 European ancestry rheumatoid arthritis cases, 509 South Asian ancestry celiac disease controls, 2,473 European ancestry celiac disease controls, 3,300 European ancestry rheumatoid arthritis controls, 8,872 celiac disease cases, 9,401 rheumatoid arthritis cases, 4,845 celiac disease controls, 9,627 rheumatoid arthritis controls NA initial 32475 NR NR U.S., Netherlands, U.K. -GCST008644 26546613 Gutierrez-Achury J 2016-01-01 371 South Asian ancestry celiac disease cases, 3,138 European ancestry celiac disease cases, 4,418 European ancestry rheumatoid arthritis cases, 509 South Asian ancestry celiac disease controls, 2,473 European ancestry celiac disease controls, 3,300 European ancestry rheumatoid arthritis controls, 8,872 celiac disease cases, 9,401 rheumatoid arthritis cases, 4,845 celiac disease controls, 9,627 rheumatoid arthritis controls NA initial 13329 European NR Sweden, Poland, Italy, Spain -GCST008644 26546613 Gutierrez-Achury J 2016-01-01 371 South Asian ancestry celiac disease cases, 3,138 European ancestry celiac disease cases, 4,418 European ancestry rheumatoid arthritis cases, 509 South Asian ancestry celiac disease controls, 2,473 European ancestry celiac disease controls, 3,300 European ancestry rheumatoid arthritis controls, 8,872 celiac disease cases, 9,401 rheumatoid arthritis cases, 4,845 celiac disease controls, 9,627 rheumatoid arthritis controls NA initial 880 South Asian NR India -GCST004026 27911795 Schumann G 2016-11-28 up to 70,460 European ancestry drinker individuals up to 35,438 European ancestry drinker individuals replication 35438 European NR Finland, Sweden, Italy, Netherlands, U.K., Austria, France, Republic of Ireland -GCST004026 27911795 Schumann G 2016-11-28 up to 70,460 European ancestry drinker individuals up to 35,438 European ancestry drinker individuals initial 70460 European NR Finland, U.S., Australia, Iceland, Netherlands, Germany, U.K., Switzerland, Estonia, NR -GCST004027 27911795 Schumann G 2016-11-28 up to 74,711 European ancestry heavy and light/non-drinker individuals up to 31,021 European ancestry heavy and light/non-drinker individuals initial 74711 European NR Finland, U.S., Australia, Iceland, Netherlands, Germany, U.K., Switzerland, Estonia, NR, France -GCST004027 27911795 Schumann G 2016-11-28 up to 74,711 European ancestry heavy and light/non-drinker individuals up to 31,021 European ancestry heavy and light/non-drinker individuals replication 31021 European NR Finland, U.S., Italy, Netherlands, U.K., Austria, Republic of Ireland -GCST004281 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 23 South Asian NR U.S. -GCST004281 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 329 Hispanic or Latin American NR U.S. -GCST004281 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 21 African American or Afro-Caribbean NR U.S. -GCST004281 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 273 European NR U.S. -GCST004281 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 22 Other NR U.S. -GCST004281 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 122 Asian unspecified NR U.S. -GCST004284 28235828 Traglia M 2017-04-03 764 fetuses NA initial 764 European, South Asian, Asian unspecified, African American or Afro-Caribbean, Hispanic or Latin American, Other NR U.S. -GCST004285 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 329 Hispanic or Latin American NR U.S. -GCST004285 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 273 European NR U.S. -GCST004285 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 23 South Asian NR U.S. -GCST004285 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 21 African American or Afro-Caribbean NR U.S. -GCST004285 28235828 Traglia M 2017-04-03 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA initial 22 Other NR U.S. diff --git a/tests/gentropy/data_samples/gwas_catalog_associations_sample_e107_r2022-11-29.tsv b/tests/gentropy/data_samples/gwas_catalog_associations.tsv similarity index 100% rename from tests/gentropy/data_samples/gwas_catalog_associations_sample_e107_r2022-11-29.tsv rename to tests/gentropy/data_samples/gwas_catalog_associations.tsv diff --git a/tests/gentropy/data_samples/gwas_catalog_studies.tsv b/tests/gentropy/data_samples/gwas_catalog_studies.tsv new file mode 100644 index 000000000..aca9ed666 --- /dev/null +++ b/tests/gentropy/data_samples/gwas_catalog_studies.tsv @@ -0,0 +1,20 @@ +DATE ADDED TO CATALOG PUBMED ID FIRST AUTHOR DATE JOURNAL LINK STUDY DISEASE/TRAIT INITIAL SAMPLE SIZE REPLICATION SAMPLE SIZE PLATFORM [SNPS PASSING QC] ASSOCIATION COUNT MAPPED_TRAIT MAPPED_TRAIT_URI STUDY ACCESSION GENOTYPING TECHNOLOGY SUBMISSION DATE STATISTICAL MODEL BACKGROUND TRAIT MAPPED BACKGROUND TRAIT MAPPED BACKGROUND TRAIT URI COHORT FULL SUMMARY STATISTICS SUMMARY STATS LOCATION +2017-09-11 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Insomnia complaints 12,863 European ancestry male cases, 19,521 European ancestry female cases, 40,776 European ancestry male controls, 39,846 European ancestry female controls 1,983 Icelandic ancestry male cases, 1,791 Icelandic ancestry female cases, 2,064 Icelandic ancestry male controls, 1,727 Icelandic ancestry female controls Affymetrix [at least 12428592] (imputed) 2 insomnia http://www.ebi.ac.uk/efo/EFO_0004698 GCST004695 Genome-wide genotyping array yes http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004695 +2017-09-11 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Sleep duration 112,411 European ancestry male individuals NA Affymetrix [at least 12428592] (imputed) 3 sleep duration http://www.ebi.ac.uk/efo/EFO_0005271 GCST004694 Genome-wide genotyping array no NA +2017-09-11 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Insomnia complaints (sex interaction) 12,863 European ancestry male cases, 19,521 European ancestry female cases, 40,776 European ancestry male controls, 39,846 European ancestry female controls NA Affymetrix [at least 12428592] (imputed) 0 insomnia http://www.ebi.ac.uk/efo/EFO_0004698 GCST004700 Genome-wide genotyping array no NA +2017-09-11 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Insomnia complaints (continuous) 12,863 European ancestry male cases, 19,521 European ancestry female cases, 40,776 European ancestry male controls, 39,846 European ancestry female controls NA Affymetrix [at least 12428592] (imputed) 1 insomnia http://www.ebi.ac.uk/efo/EFO_0004698 GCST004701 Genome-wide genotyping array no NA +2017-09-11 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Insomnia complaints (dichotomous) 32,384 European ancestry cases, 27,128 European ancestry controls NA Affymetrix [at least 12428592] (imputed) 1 insomnia http://www.ebi.ac.uk/efo/EFO_0004698 GCST004702 Genome-wide genotyping array no NA +2017-09-11 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Chronotype 101,185 European ancestry individuals NA Affymetrix [at least 12428592] (imputed) 9 circadian rhythm http://www.ebi.ac.uk/efo/EFO_0004354 GCST004696 Genome-wide genotyping array no NA +2017-09-11 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Ease of getting up in the morning 112,866 European ancestry individuals NA Affymetrix [at least 12428592] (imputed) 5 chronotype measurement http://www.ebi.ac.uk/efo/EFO_0008328 GCST004697 Genome-wide genotyping array no NA +2017-09-11 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Daytime nap 113,054 European ancestry cases and controls NA Affymetrix [at least 12428592] (imputed) 4 daytime rest measurement http://www.ebi.ac.uk/efo/EFO_0007828 GCST004693 Genome-wide genotyping array no NA +2017-09-11 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Snoring 105,377 European ancestry cases and controls NA Affymetrix [at least 12428592] (imputed) 1 snoring measurement http://www.ebi.ac.uk/efo/EFO_0008341 GCST004698 Genome-wide genotyping array no NA +2017-09-11 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Daytime sleepiness 112,717 European ancestry cases and controls NA Affymetrix [at least 12428592] (imputed) 6 excessive daytime sleepiness measurement http://www.ebi.ac.uk/efo/EFO_0007875 GCST004699 Genome-wide genotyping array no NA +2018-11-02 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Insomnia complaints 19,521 European ancestry female cases, 39,846 European ancestry female controls 1,983 Icelandic ancestry male cases, 1,791 Icelandic ancestry female cases, 2,064 Icelandic ancestry male controls, 1,727 Icelandic ancestry female controls Affymetrix [at least 12428592] (imputed) 1 insomnia http://www.ebi.ac.uk/efo/EFO_0004698 GCST006487 Genome-wide genotyping array yes http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST006001-GCST007000/GCST006487 +2018-11-02 28604731 Hammerschlag AR 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604731 Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Insomnia complaints 12,863 European ancestry male cases, 40,776 European ancestry male controls 1,983 Icelandic ancestry male cases, 1,791 Icelandic ancestry female cases, 2,064 Icelandic ancestry male controls, 1,727 Icelandic ancestry female controls Affymetrix [at least 12428592] (imputed) 2 insomnia http://www.ebi.ac.uk/efo/EFO_0004698 GCST006488 Genome-wide genotyping array yes http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST006001-GCST007000/GCST006488 +2017-09-14 28610988 Kerr KF 2017-06-10 Heart Rhythm www.ncbi.nlm.nih.gov/pubmed/28610988 Genome-wide association study of heart rate and its variability in Hispanic/Latino cohorts. Heart rate 13,184 Hispanic/Latino individuals 7,073 European ancestry individuals, 4,771 African American individuals Affymetrix, Illumina [16967914] (imputed) 2 heart rate http://www.ebi.ac.uk/efo/EFO_0004326 GCST004715 Genome-wide genotyping array no NA +2017-09-14 28610988 Kerr KF 2017-06-10 Heart Rhythm www.ncbi.nlm.nih.gov/pubmed/28610988 Genome-wide association study of heart rate and its variability in Hispanic/Latino cohorts. Heart rate variability traits (RMSSD) 13,767 Hispanic/Latino individuals 4,730 European ancestry individuals, 2,908 African American individuals Affymetrix, Illumina [17209892] (imputed) 2 heart rate variability measurement http://www.ebi.ac.uk/efo/EFO_0008003 GCST004716 Genome-wide genotyping array no NA +2017-09-14 28610988 Kerr KF 2017-06-10 Heart Rhythm www.ncbi.nlm.nih.gov/pubmed/28610988 Genome-wide association study of heart rate and its variability in Hispanic/Latino cohorts. Heart rate variability traits (SDNN) 13,184 Hispanic/Latino individuals 7,073 European ancestry individuals, 2,908 African American individuals Affymetrix, Illumina [17209740] (imputed) 3 heart rate variability measurement http://www.ebi.ac.uk/efo/EFO_0008003 GCST004714 Genome-wide genotyping array no NA +2017-09-18 28604730 McKay JD 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604730 Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Lung cancer 29,266 European ancestry cases, 56,450 European ancestry controls NA Illumina [10439017] (imputed) 135 lung carcinoma http://www.ebi.ac.uk/efo/EFO_0001071 GCST004748 Genome-wide genotyping array yes http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004748 +2017-09-18 28604730 McKay JD 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604730 Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Lung adenocarcinoma 11,273 European ancestry cases, 55,483 European ancestry controls NA Illumina [10439017] (imputed) 79 lung adenocarcinoma http://www.ebi.ac.uk/efo/EFO_0000571 GCST004744 Genome-wide genotyping array yes http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004744 +2017-09-18 28604730 McKay JD 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604730 Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Squamous cell lung carcinoma 7,426 European ancestry cases, 55,627 European ancestry controls NA Illumina [10439017] (imputed) 101 squamous cell lung carcinoma http://www.ebi.ac.uk/efo/EFO_0000708 GCST004750 Genome-wide genotyping array yes http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004750 +2017-09-18 28604730 McKay JD 2017-06-12 Nat Genet www.ncbi.nlm.nih.gov/pubmed/28604730 Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Small cell lung carcinoma 2,664 European ancestry cases, 21,444 European ancestry controls NA Illumina [10439017] (imputed) 50 small cell lung carcinoma http://www.ebi.ac.uk/efo/EFO_0000702 GCST004746 Genome-wide genotyping array yes http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004746 diff --git a/tests/gentropy/data_samples/gwas_catalog_studies_sample-r2022-11-29.tsv b/tests/gentropy/data_samples/gwas_catalog_studies_sample-r2022-11-29.tsv deleted file mode 100644 index 7db8f5302..000000000 --- a/tests/gentropy/data_samples/gwas_catalog_studies_sample-r2022-11-29.tsv +++ /dev/null @@ -1,20 +0,0 @@ -DATE ADDED TO CATALOG PUBMED ID FIRST AUTHOR DATE JOURNAL LINK STUDY DISEASE/TRAIT INITIAL SAMPLE SIZE REPLICATION SAMPLE SIZE PLATFORM [SNPS PASSING QC] ASSOCIATION COUNT MAPPED_TRAIT MAPPED_TRAIT_URI STUDY ACCESSION GENOTYPING TECHNOLOGY SUMMARY STATS LOCATION SUBMISSION DATE STATISTICAL MODEL BACKGROUND TRAIT MAPPED BACKGROUND TRAIT MAPPED BACKGROUND TRAIT URI -2019-09-11 26546613 Gutierrez-Achury J 2016-01-01 Hum Mol Genet www.ncbi.nlm.nih.gov/pubmed/26546613 Functional implications of disease-specific variants in loci jointly associated with coeliac disease and rheumatoid arthritis. Celiac disease and Rheumatoid arthritis 371 South Asian ancestry celiac disease cases, 3,138 European ancestry celiac disease cases, 4,418 European ancestry rheumatoid arthritis cases, 509 South Asian ancestry celiac disease controls, 2,473 European ancestry celiac disease controls, 3,300 European ancestry rheumatoid arthritis controls, 8,872 celiac disease cases, 9,401 rheumatoid arthritis cases, 4,845 celiac disease controls, 9,627 rheumatoid arthritis controls NA Illumina [109572] 24 rheumatoid arthritis, celiac disease http://www.ebi.ac.uk/efo/EFO_0000685, http://www.ebi.ac.uk/efo/EFO_0001060 GCST008644 Targeted genotyping array [Immunochip] -2017-05-12 27911795 Schumann G 2016-11-28 Proc Natl Acad Sci U S A www.ncbi.nlm.nih.gov/pubmed/27911795 KLB is associated with alcohol drinking, and its gene product Ξ²-Klotho is necessary for FGF21 regulation of alcohol preference. Alcohol consumption up to 70,460 European ancestry drinker individuals up to 35,438 European ancestry drinker individuals Affymetrix, Illumina, Perlegen [at least 316407] (imputed) 3 alcohol consumption measurement http://www.ebi.ac.uk/efo/EFO_0007878 GCST004026 Genome-wide genotyping array -2017-05-12 27911795 Schumann G 2016-11-28 Proc Natl Acad Sci U S A www.ncbi.nlm.nih.gov/pubmed/27911795 KLB is associated with alcohol drinking, and its gene product Ξ²-Klotho is necessary for FGF21 regulation of alcohol preference. Alcohol consumption (heavy vs. light/non-drinkers) up to 74,711 European ancestry heavy and light/non-drinker individuals up to 31,021 European ancestry heavy and light/non-drinker individuals Affymetrix, Illumina, Perlegen [at least 316407] (imputed) 2 alcohol consumption measurement http://www.ebi.ac.uk/efo/EFO_0007878 GCST004027 Genome-wide genotyping array -2017-07-03 28235828 Traglia M 2017-04-03 G3 (Bethesda) www.ncbi.nlm.nih.gov/pubmed/28235828 Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants. Midgestational circulating levels of organochlorine pesticides 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA Affymetrix [629686] 2 gestational serum measurement, organochlorine pesticide measurement http://www.ebi.ac.uk/efo/EFO_0007964, http://www.ebi.ac.uk/efo/EFO_0007960 GCST004281 Genome-wide genotyping array -2017-07-03 28235828 Traglia M 2017-04-03 G3 (Bethesda) www.ncbi.nlm.nih.gov/pubmed/28235828 Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants. Midgestational circulating levels of PCBs (fetal genetic effect) 764 fetuses NA Affymetrix [622716] 11 polychlorinated biphenyls measurement, gestational serum measurement, fetal genotype effect measurement http://www.ebi.ac.uk/efo/EFO_0007042, http://www.ebi.ac.uk/efo/EFO_0007964, http://www.ebi.ac.uk/efo/EFO_0007959 GCST004284 Genome-wide genotyping array -2017-07-03 28235828 Traglia M 2017-04-03 G3 (Bethesda) www.ncbi.nlm.nih.gov/pubmed/28235828 Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants. Midgestational circulating levels of PBDEs 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA Affymetrix [629686] 9 polybrominated biphenyl measurement, gestational serum measurement, polybrominated diphenyl ether measurement http://www.ebi.ac.uk/efo/EFO_0007961, http://www.ebi.ac.uk/efo/EFO_0007964, http://www.ebi.ac.uk/efo/EFO_0007962 GCST004285 Genome-wide genotyping array -2017-07-03 28235828 Traglia M 2017-04-03 G3 (Bethesda) www.ncbi.nlm.nih.gov/pubmed/28235828 Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants. Midgestational circulating levels of PBDEs (fetal genetic effect) 764 fetuses NA Affymetrix [629686] 11 polybrominated biphenyl measurement, gestational serum measurement, fetal genotype effect measurement, polybrominated diphenyl ether measurement http://www.ebi.ac.uk/efo/EFO_0007961, http://www.ebi.ac.uk/efo/EFO_0007964, http://www.ebi.ac.uk/efo/EFO_0007959, http://www.ebi.ac.uk/efo/EFO_0007962 GCST004286 Genome-wide genotyping array -2017-07-03 28235828 Traglia M 2017-04-03 G3 (Bethesda) www.ncbi.nlm.nih.gov/pubmed/28235828 Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants. Midgestational circulating levels of organochlorine pesticides (fetal genetic effect) 764 fetuses NA Affymetrix [629686] 0 gestational serum measurement, fetal genotype effect measurement, organochlorine pesticide measurement http://www.ebi.ac.uk/efo/EFO_0007964, http://www.ebi.ac.uk/efo/EFO_0007959, http://www.ebi.ac.uk/efo/EFO_0007960 GCST004282 Genome-wide genotyping array -2017-07-03 28235828 Traglia M 2017-04-03 G3 (Bethesda) www.ncbi.nlm.nih.gov/pubmed/28235828 Independent Maternal and Fetal Genetic Effects on Midgestational Circulating Levels of Environmental Pollutants. Midgestational circulating levels of PCBs 329 Hispanic mothers, 273 European ancestry mothers, 122 Asian ancestry mothers, 23 South Asian ancestry mothers, 21 African American mothers, 22 other ancestry mothers NA Affymetrix [629686] 25 polychlorinated biphenyls measurement, gestational serum measurement http://www.ebi.ac.uk/efo/EFO_0007042, http://www.ebi.ac.uk/efo/EFO_0007964 GCST004283 Genome-wide genotyping array -2016-09-12 26325155 Brehm JM 2015-09-01 Am J Respir Crit Care Med www.ncbi.nlm.nih.gov/pubmed/26325155 A Genome-Wide Association Study of Post-bronchodilator Lung Function in Children with Asthma. Post-bronchodilator lung function in asthma (FEV1) 447 Puerto Rican ancestry cases 568 European, black or Hispanic cases, 2,414 Hispanic cases Illumina [NR] 0 pulmonary function measurement, forced expiratory volume, response to bronchodilator http://www.ebi.ac.uk/efo/EFO_0003892, http://www.ebi.ac.uk/efo/EFO_0004314, http://purl.obolibrary.org/obo/GO_0097366 GCST003110 Genome-wide genotyping array asthma http://purl.obolibrary.org/obo/MONDO_0004979 -2016-09-12 26325155 Brehm JM 2015-09-01 Am J Respir Crit Care Med www.ncbi.nlm.nih.gov/pubmed/26325155 A Genome-Wide Association Study of Post-bronchodilator Lung Function in Children with Asthma. Post-bronchodilator lung function in asthma (FEV1/FVC) 447 Puerto Rican ancestry cases 568 European, black or Hispanic cases, 2,414 Hispanic cases Illumina [NR] 0 pulmonary function measurement, response to bronchodilator, FEV/FEC ratio http://www.ebi.ac.uk/efo/EFO_0003892, http://purl.obolibrary.org/obo/GO_0097366, http://www.ebi.ac.uk/efo/EFO_0004713 GCST003107 Genome-wide genotyping array asthma http://purl.obolibrary.org/obo/MONDO_0004979 -2016-07-27 26237429 Aminkeng F 2015-08-03 Nat Genet www.ncbi.nlm.nih.gov/pubmed/26237429 A coding variant in RARG confers susceptibility to anthracycline-induced cardiotoxicity in childhood cancer. Anthracycline-induced cardiotoxicity in childhood cancer 32 European ancestry cases with cardiotoxicity, 248 European ancestry cases without cardiotoxicity 22 European ancestry cases with cardiotoxicity, 74 European ancestry cases without cardiotoxicity, 2 African cases with cardiotoxicity, 9 African cases without cardiotoxicity, 5 Hispanic cases with cardiotoxicity, 18 Hispanic cases without cardiotoxicity, 8 East Asian cases with cardiotoxicity, 23 East Asian cases without cardiotoxicity, 4 Aboriginal Canadian cases with cardiotoxicity, 11 Aboriginal Canadian cases without cardiotoxicity Illumina [657694] 2 cardiotoxicity, response to anthracycline-based chemotherapy http://www.ebi.ac.uk/efo/EFO_1001482, http://www.ebi.ac.uk/efo/EFO_0005257 GCST003062 Genome-wide genotyping array childhood cancer http://www.ebi.ac.uk/efo/EFO_1000654 -2017-02-25 27387956 Murk W 2016-07-07 BMC Genet www.ncbi.nlm.nih.gov/pubmed/27387956 Genome-wide search identifies a gene-gene interaction between 20p13 and 2q14 in asthma. Asthma (SNP x SNP interaction) 802 European ancestry cases, 823 European ancestry controls 754 European ancestry cases, 57 European and unknown ancestry cases, 2,573 cases, 880 European ancestry controls, 68 European and unknown ancestry controls, 2,145 controls Illumina [301547] 0 asthma http://purl.obolibrary.org/obo/MONDO_0004979 GCST003682 Genome-wide genotyping array -2016-12-09 27008869 Chen MM 2016-03-23 Hum Mol Genet www.ncbi.nlm.nih.gov/pubmed/27008869 GWAS meta-analysis of 16 852 women identifies new susceptibility locus for endometrial cancer. Endometrial cancer 4,907 European ancestry cases, 11,945 European ancestry controls NA Illumina [9486271] (imputed) 4 endometrial carcinoma http://www.ebi.ac.uk/efo/EFO_1001512 GCST003436 Genome-wide genotyping array -2016-12-01 27016271 Kornilov SA 2016-03-25 Pediatrics www.ncbi.nlm.nih.gov/pubmed/27016271 Genome-Wide Association and Exome Sequencing Study of Language Disorder in an Isolated Population. Developmental language disorder 149 isolated population cases, 210 isolated population controls NA Illumina [223580] 4 specific language impairment http://www.ebi.ac.uk/efo/EFO_1001510 GCST003396 Genome-wide genotyping array -2016-12-01 27016271 Kornilov SA 2016-03-25 Pediatrics www.ncbi.nlm.nih.gov/pubmed/27016271 Genome-Wide Association and Exome Sequencing Study of Language Disorder in an Isolated Population. Developmental language disorder (linguistic errors) 149 isolated population cases, 210 isolated population controls NA Illumina [223580] 9 linguistic error measurement, specific language impairment http://www.ebi.ac.uk/efo/EFO_0007798, http://www.ebi.ac.uk/efo/EFO_1001510 GCST003397 Genome-wide genotyping array -2016-12-01 27016271 Kornilov SA 2016-03-25 Pediatrics www.ncbi.nlm.nih.gov/pubmed/27016271 Genome-Wide Association and Exome Sequencing Study of Language Disorder in an Isolated Population. Developmental language disorder (syntactic complexity) 149 isolated population cases, 210 isolated population controls NA Illumina [223580] 3 syntactic complexity measurement, specific language impairment http://www.ebi.ac.uk/efo/EFO_0007799, http://www.ebi.ac.uk/efo/EFO_1001510 GCST003398 Genome-wide genotyping array -2016-12-13 27005419 Borne Y 2016-03-22 Hum Mol Genet www.ncbi.nlm.nih.gov/pubmed/27005419 Genome wide association study identifies two loci associated with cadmium in erythrocytes among never-smokers. Erythrocyte cadmium concentration 2,704 ever-smoker individuals, 1,728 never-smoker individuals NA Illumina [658884] 5 erythrocyte cadmium measurement http://www.ebi.ac.uk/efo/EFO_0007807 GCST003449 Genome-wide genotyping array -2016-12-13 27005419 Borne Y 2016-03-22 Hum Mol Genet www.ncbi.nlm.nih.gov/pubmed/27005419 Genome wide association study identifies two loci associated with cadmium in erythrocytes among never-smokers. 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submitter","studyId":"RCV002083386","releaseDate":"2022-04-08","targetFromSourceId":"ENSG00000072778","variantFunctionalConsequenceId":"SO_0002169","variantId":"17_7224135_T_C","variantRsId":"rs745620433","cohortPhenotypes":["VLCAD deficiency","Very long chain acyl-CoA dehydrogenase deficiency"],"diseaseFromSource":"Very long chain acyl-CoA dehydrogenase deficiency","diseaseFromSourceId":"C3887523","diseaseFromSourceMappedId":"MONDO_0008723","variantHgvsId":"NC_000017.11:g.7224135T>C"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV002203969","releaseDate":"2022-04-12","targetFromSourceId":"ENSG00000125741","variantFunctionalConsequenceId":"SO_0001819","variantId":"19_45584735_C_T","variantRsId":"rs1131692018","cohortPhenotypes":["3-Methylglutaconic aciduria type 3","3-alpha methylglutaconic aciduria type III","3-methylglutaconic aciduria type III","Costeff optic atrophy syndrome","Iraqi Jewish optic atrophy plus","MGA type III","OPA3, AUTOSOMAL RECESSIVE","OPA3-Related 3-Methylglutaconic Aciduria","OPTIC ATROPHY 3, AUTOSOMAL DOMINANT","OPTIC ATROPHY 3, AUTOSOMAL RECESSIVE","Optic atrophy 3","Optic atrophy and cataract, autosomal dominant","Optic atrophy infantile with chorea and spastic paraplegia","Optic atrophy, cataract, and neurologic disorder"],"diseaseFromSource":"Optic atrophy 3","diseaseFromSourceId":"C1833809","diseaseFromSourceMappedId":"Orphanet_67036","variantHgvsId":"NC_000019.10:g.45584735C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV002676812","releaseDate":"2023-02-07","targetFromSourceId":"ENSG00000125826","variantFunctionalConsequenceId":"SO_0001583","variantId":"20_419394_C_A","variantRsId":null,"cohortPhenotypes":["POLYGLUCOSAN BODY MYOPATHY WITHOUT IMMUNODEFICIENCY","Polyglucosan body myopathy 1 with or without immunodeficiency","Polyglucosan body myopathy type 1"],"diseaseFromSource":"Polyglucosan body myopathy type 1","diseaseFromSourceId":"C4014605","diseaseFromSourceMappedId":"Orphanet_397937","variantHgvsId":"NC_000020.11:g.419394C>A"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV002680839","releaseDate":"2023-02-07","targetFromSourceId":"ENSG00000160789","variantFunctionalConsequenceId":"SO_0001583","variantId":"1_156137732_C_T","variantRsId":null,"cohortPhenotypes":["Charcot-Marie-Tooth disease type 2","Charcot-Marie-Tooth, Type 2"],"diseaseFromSource":"Charcot-Marie-Tooth disease type 2","diseaseFromSourceId":"C0270914","diseaseFromSourceMappedId":"MONDO_0018993","variantHgvsId":"NC_000001.11:g.156137732C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["pathogenic"],"confidence":"criteria provided, single submitter","studyId":"RCV002847567","releaseDate":"2023-02-07","targetFromSourceId":"ENSG00000075891","variantFunctionalConsequenceId":"SO_0001589","variantId":"10_100750706_C_CG","variantRsId":null,"cohortPhenotypes":["CAKUT WITH OR WITHOUT OCULAR ABNORMALITIES","CONGENITAL ANOMALIES OF THE KIDNEY AND URINARY TRACT WITH OR WITHOUT OCULAR ABNORMALITIES","Coloboma of optic nerve with renal disease","Focal segmental glomerulosclerosis 7","Optic coloboma, vesicoureteral reflux, and renal anomalies","Optic nerve coloboma with renal disease","PAPILLORENAL SYNDROME WITH MILD OCULAR ABNORMALITIES","Papillorenal syndrome","RENAL-COLOBOMA SYNDROME WITH MACULAR ABNORMALITIES","Renal coloboma syndrome"],"diseaseFromSource":"Focal segmental glomerulosclerosis 7","diseaseFromSourceId":"C4014925","diseaseFromSourceMappedId":"MONDO_0014451","variantHgvsId":"NC_000010.11:g.100750708dup"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["benign"],"confidence":"criteria provided, single submitter","studyId":"RCV003621924","releaseDate":"2024-02-20","targetFromSourceId":"ENSG00000008056","variantFunctionalConsequenceId":"SO_0001819","variantId":"X_47619585_G_C","variantRsId":null,"cohortPhenotypes":["Epilepsy, X-linked 1, with variable learning disabilities and behavior disorders","Epilepsy, X-linked, with variable learning disabilities and behavior disorders","X-linked epilepsy-learning disabilities-behavior disorders syndrome"],"diseaseFromSource":"Epilepsy, X-linked 1, with variable learning disabilities and behavior disorders","diseaseFromSourceId":"C5774177","diseaseFromSourceMappedId":"Orphanet_85294","variantHgvsId":"NC_000023.11:g.47619585G>C"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV003636240","releaseDate":"2024-02-20","targetFromSourceId":"ENSG00000143669","variantFunctionalConsequenceId":"SO_0001819","variantId":"1_235744114_A_G","variantRsId":null,"cohortPhenotypes":["Chediak-Higashi Syndrome","ChΓ©diak-Higashi syndrome"],"diseaseFromSource":"ChΓ©diak-Higashi syndrome","diseaseFromSourceId":"C0007965","diseaseFromSourceMappedId":"Orphanet_167","variantHgvsId":"NC_000001.11:g.235744114A>G"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely pathogenic"],"confidence":"criteria provided, single submitter","studyId":"RCV003735165","releaseDate":"2024-02-20","targetFromSourceId":"ENSG00000196569","variantFunctionalConsequenceId":"SO_0001575","variantId":"6_129453132_G_C","variantRsId":null,"cohortPhenotypes":["LAMA2-related muscular dystrophy","Laminin alpha 2-related dystrophy"],"diseaseFromSource":"LAMA2-related muscular dystrophy","diseaseFromSourceId":"C5679788","diseaseFromSourceMappedId":"MONDO_0100228","variantHgvsId":"NC_000006.12:g.129453132G>C"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV000797080","releaseDate":"2019-08-14","targetFromSourceId":"ENSG00000179348","variantFunctionalConsequenceId":"SO_0001583","variantId":"3_128485795_C_A","variantRsId":"rs1576748419","cohortPhenotypes":["COMBINED IMMUNODEFICIENCY WITH SUSCEPTIBILITY TO MYCOBACTERIAL, VIRAL, AND FUNGAL INFECTIONS","Deafness-lymphedema-leukemia syndrome","Dendritic cell, monocyte, B lymphocyte, and natural killer lymphocyte deficiency","Emberger syndrome","GATA2 DEFICIENCY","IMMUNODEFICIENCY 21","Lymphedema, primary, with myelodysplasia","MONOCYTOPENIA AND MYCOBACTERIAL INFECTION SYNDROME","MONOCYTOPENIA WITH SUSCEPTIBILITY TO MYCOBACTERIAL, FUNGAL, AND PAPILLOMAVIRUS INFECTIONS AND MYELODYSPLASIA","Monocytopenia with susceptibility to infections"],"diseaseFromSource":"Monocytopenia with susceptibility to infections","diseaseFromSourceId":"C3280030","diseaseFromSourceMappedId":"MONDO_0013607","variantHgvsId":"NC_000003.12:g.128485795C>A"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV001500249","releaseDate":"2021-06-08","targetFromSourceId":"ENSG00000159082","variantFunctionalConsequenceId":"SO_0001819","variantId":"21_32694306_C_T","variantRsId":"rs780954414","cohortPhenotypes":["Developmental and epileptic encephalopathy, 53","Early-onset Parkinson disease 20","Epileptic encephalopathy, early infantile, 53"],"diseaseFromSource":"Developmental and epileptic encephalopathy, 53","diseaseFromSourceId":"C4479313","diseaseFromSourceMappedId":"Orphanet_1934","variantHgvsId":"NC_000021.9:g.32694306C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV002392200","releaseDate":"2022-11-29","targetFromSourceId":"ENSG00000184058","variantFunctionalConsequenceId":"SO_0001819","variantId":"22_19766420_C_T","variantRsId":null,"cohortPhenotypes":["Cardiovascular phenotype"],"diseaseFromSource":"Cardiovascular phenotype","diseaseFromSourceId":"CN230736","diseaseFromSourceMappedId":"HP_0001626","variantHgvsId":"NC_000022.11:g.19766420C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV003774575","releaseDate":"2024-02-28","targetFromSourceId":"ENSG00000128591","variantFunctionalConsequenceId":"SO_0001583","variantId":"7_128841305_G_A","variantRsId":null,"cohortPhenotypes":["Cardiomyopathy, familial hypertrophic, 26","Dilated Cardiomyopathy, Dominant","Distal myopathy with posterior leg and anterior hand involvement","FILAMINOPATHY, AUTOSOMAL DOMINANT","Filaminopathy (type)","Hypertrophic cardiomyopathy 26","Myofibrillar myopathy 5","Myofibrillar myopathy, filamin C-related","Myopathy, distal, 4","WILLIAMS DISTAL MYOPATHY"],"diseaseFromSource":"Hypertrophic cardiomyopathy 26","diseaseFromSourceId":"C4310749","diseaseFromSourceMappedId":"EFO_0000538","variantHgvsId":"NC_000007.14:g.128841305G>A"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV003779225","releaseDate":"2024-02-28","targetFromSourceId":"ENSG00000151929","variantFunctionalConsequenceId":"SO_0001821","variantId":"10_119670132_G_GGCA","variantRsId":null,"cohortPhenotypes":["Dilated cardiomyopathy 1HH","Myofibrillar myopathy 6","Myofibrillar myopathy, BAG3-related"],"diseaseFromSource":"Dilated cardiomyopathy 1HH","diseaseFromSourceId":"C3151293","diseaseFromSourceMappedId":"MONDO_0013479","variantHgvsId":"NC_000010.11:g.119670135_119670137dup"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV001490387","releaseDate":"2021-06-08","targetFromSourceId":"ENSG00000134853","variantFunctionalConsequenceId":"SO_0001627","variantId":"4_54287551_T_C","variantRsId":"rs2110341918","cohortPhenotypes":["Gastrointestinal Stromal Sarcoma","Gastrointestinal stroma tumor","Gastrointestinal stromal tumor","Gastrointestinal stromal tumor, somatic"],"diseaseFromSource":"Gastrointestinal stromal tumor","diseaseFromSourceId":"C0238198","diseaseFromSourceMappedId":"MONDO_0011719","variantHgvsId":"NC_000004.12:g.54287551T>C"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV003041098","releaseDate":"2023-02-07","targetFromSourceId":"ENSG00000007314","variantFunctionalConsequenceId":"SO_0001583","variantId":"17_63948745_A_T","variantRsId":null,"cohortPhenotypes":["Adynamia episodica hereditaria with or without myotonia","Familial hyperkalemic periodic paralysis","Gamstorp disease","Gamstorp episodic adynamy","Hyperkalemic periodic paralysis"],"diseaseFromSource":"Hyperkalemic periodic paralysis","diseaseFromSourceId":"C0238357","diseaseFromSourceMappedId":"MONDO_0008224","variantHgvsId":"NC_000017.11:g.63948745A>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV003966094","releaseDate":"2024-03-16","targetFromSourceId":"ENSG00000164818","variantFunctionalConsequenceId":"SO_0001819","variantId":"7_756970_C_G","variantRsId":"rs571248637","cohortPhenotypes":["DNAAF5-related condition","DNAAF5-related disorder"],"diseaseFromSource":"DNAAF5-related disorder","diseaseFromSourceId":null,"diseaseFromSourceMappedId":null,"variantHgvsId":"NC_000007.14:g.756970C>G"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV003921540","releaseDate":"2024-03-16","targetFromSourceId":"ENSG00000134982","variantFunctionalConsequenceId":"SO_0001623","variantId":"5_112738418_A_G","variantRsId":null,"cohortPhenotypes":["APC-related condition","APC-related disorder"],"diseaseFromSource":"APC-related disorder","diseaseFromSourceId":null,"diseaseFromSourceMappedId":null,"variantHgvsId":"NC_000005.10:g.112738418A>G"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV001340631","releaseDate":"2021-03-22","targetFromSourceId":"ENSG00000139618","variantFunctionalConsequenceId":"SO_0001583","variantId":"13_32340936_T_C","variantRsId":"rs876660049","cohortPhenotypes":["Breast and ovarian cancer","Hereditary breast and ovarian cancer","Hereditary breast and ovarian cancer syndrome","Hereditary breast and ovarian cancer syndrome (HBOC)","Hereditary breast ovarian cancer syndrome"],"diseaseFromSource":"Hereditary breast ovarian cancer syndrome","diseaseFromSourceId":"C0677776","diseaseFromSourceMappedId":"MONDO_0003582","variantHgvsId":"NC_000013.11:g.32340936T>C"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["conflicting interpretations of pathogenicity"],"confidence":"no assertion criteria provided","studyId":"RCV000056173","releaseDate":"2013-10-01","targetFromSourceId":"ENSG00000196218","variantFunctionalConsequenceId":"SO_0001583","variantId":"19_38585013_C_T","variantRsId":"rs118192153","cohortPhenotypes":["Central core disease","Central core disease of muscle","Central core myopathy","Muscle core disease","Muscular central core disease","Myopathy, central fibrillar","Shy-Magee syndrome"],"diseaseFromSource":"Central core myopathy","diseaseFromSourceId":"C0751951","diseaseFromSourceMappedId":"EFO_1000855","variantHgvsId":"NC_000019.10:g.38585013C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV002249476","releaseDate":"2022-05-28","targetFromSourceId":"ENSG00000228253","variantFunctionalConsequenceId":"SO_0001631","variantId":"MT_7462_C_T","variantRsId":"rs1569484151","cohortPhenotypes":["COX deficiency","Complex 4 mitochondrial respiratory chain deficiency","Complex IV deficiency","Deficiency of mitochondrial respiratory chain complex4","Mitochondrial complex IV deficiency","Mitochondrial complex IV deficiency, nuclear type 1"],"diseaseFromSource":"Mitochondrial complex IV deficiency, nuclear type 1","diseaseFromSourceId":"C5435656","diseaseFromSourceMappedId":"MONDO_0859160","variantHgvsId":"NC_012920.1:m.7462C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV003093847","releaseDate":"2023-02-07","targetFromSourceId":"ENSG00000130294","variantFunctionalConsequenceId":"SO_0001630","variantId":"2_240767028_G_C","variantRsId":"rs748724769","cohortPhenotypes":["Hereditary sensory and autonomic neuropathy type IIC","Hereditary spastic paraplegia 30","Intellectual disability, autosomal dominant 9","Mental retardation, autosomal dominant 9","NESCAV SYNDROME","Neuropathy, hereditary sensory, type 2C","SPASTIC PARAPLEGIA 30, AUTOSOMAL DOMINANT","Spastic paraplegia 30, autosomal recessive"],"diseaseFromSource":"Neuropathy, hereditary sensory, type 2C","diseaseFromSourceId":"C3280168","diseaseFromSourceMappedId":"Orphanet_970","variantHgvsId":"NC_000002.12:g.240767028G>C"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV002016427","releaseDate":"2022-03-28","targetFromSourceId":"ENSG00000159884","variantFunctionalConsequenceId":"SO_0001631","variantId":"9_35657840_C_G","variantRsId":"rs1287720442","cohortPhenotypes":["Anauxetic dysplasia","SPONDYLOMETAEPIPHYSEAL DYSPLASIA, ANAUXETIC TYPE"],"diseaseFromSource":"Anauxetic dysplasia","diseaseFromSourceId":"C1846796","diseaseFromSourceMappedId":"MONDO_0011773","variantHgvsId":"NC_000009.12:g.35657840C>G"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV002625025","releaseDate":"2023-02-07","targetFromSourceId":"ENSG00000054983","variantFunctionalConsequenceId":"SO_0001583","variantId":"14_87976417_C_A","variantRsId":null,"cohortPhenotypes":["Galactocerebrosidase deficiency","Galactosylceramide beta-galactosidase deficiency","Globoid cell leukoencephalopathy","Krabbe leukodystrophy","Leukodystrophy, Globoid Cell"],"diseaseFromSource":"Galactosylceramide beta-galactosidase deficiency","diseaseFromSourceId":"C0023521","diseaseFromSourceMappedId":"MONDO_0009499","variantHgvsId":"NC_000014.9:g.87976417C>A"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV003516889","releaseDate":"2024-02-14","targetFromSourceId":"ENSG00000046604","variantFunctionalConsequenceId":"SO_0001583","variantId":"18_31520935_C_A","variantRsId":null,"cohortPhenotypes":["ARRHYTHMOGENIC RIGHT VENTRICULAR DYSPLASIA, FAMILIAL, 10","Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy10","Arrhythmogenic right ventricular cardiomyopathy, type 10","Arrhythmogenic right ventricular dysplasia 10","Arrhythmogenic right ventricular dysplasia/cardiomyopathy, type 10"],"diseaseFromSource":"Arrhythmogenic right ventricular dysplasia 10","diseaseFromSourceId":"C1857777","diseaseFromSourceMappedId":"Orphanet_247","variantHgvsId":"NC_000018.10:g.31520935C>A"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV003583433","releaseDate":"2024-02-14","targetFromSourceId":"ENSG00000163932","variantFunctionalConsequenceId":"SO_0001583","variantId":"3_53186260_A_G","variantRsId":null,"cohortPhenotypes":["Autoimmune lymphoproliferative syndrome, type III","Autoimmune lymphoproliferative syndrome, type III caused by mutation in PRKCD"],"diseaseFromSource":"Autoimmune lymphoproliferative syndrome, type III caused by mutation in PRKCD","diseaseFromSourceId":"C3809928","diseaseFromSourceMappedId":"Orphanet_3261","variantHgvsId":"NC_000003.12:g.53186260A>G"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV001419044","releaseDate":"2021-05-16","targetFromSourceId":"ENSG00000171759","variantFunctionalConsequenceId":"SO_0001819","variantId":"12_102912824_A_C","variantRsId":"rs773425620","cohortPhenotypes":["Folling disease","Oligophrenia phenylpyruvica","Phenylketonuria","Phenylketonurias"],"diseaseFromSource":"Phenylketonuria","diseaseFromSourceId":"C0031485","diseaseFromSourceMappedId":"MONDO_0009861","variantHgvsId":"NC_000012.12:g.102912824A>C"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV001861075","releaseDate":"2022-03-28","targetFromSourceId":"ENSG00000138622","variantFunctionalConsequenceId":"SO_0001583","variantId":"15_73324179_T_G","variantRsId":"rs1161776375","cohortPhenotypes":["Brugada syndrome 8"],"diseaseFromSource":"Brugada syndrome 8","diseaseFromSourceId":"C2751083","diseaseFromSourceMappedId":"MONDO_0013148","variantHgvsId":"NC_000015.10:g.73324179T>G"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV002138447","releaseDate":"2022-04-08","targetFromSourceId":"ENSG00000162065","variantFunctionalConsequenceId":"SO_0001627","variantId":"16_2499947_G_A","variantRsId":"rs1173044946","cohortPhenotypes":["Autosomal dominant nonsyndromic hearing loss 65","Caused by mutation in the TBC1 domain family, member 24","Deafness, autosomal dominant 65","Developmental and epileptic encephalopathy, 1","Epileptic encephalopathy, early infantile, 1","INFANTILE SPASM SYNDROME, X-LINKED 1","OHTAHARA SYNDROME, X-LINKED","Tonic spasms with clustering, arrest of psychomotor development and hypsarrhythmia on EEG","West's syndrome","X-Linked Infantile Spasm Syndrome","X-linked infantile spasms"],"diseaseFromSource":"Developmental and epileptic encephalopathy, 1","diseaseFromSourceId":"C3463992","diseaseFromSourceMappedId":"Orphanet_364063","variantHgvsId":"NC_000016.10:g.2499947G>A"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV002933690","releaseDate":"2023-02-07","targetFromSourceId":"ENSG00000165280","variantFunctionalConsequenceId":"SO_0001819","variantId":"9_35063060_A_C","variantRsId":null,"cohortPhenotypes":["Amyotrophic lateral sclerosis 14, with or without frontotemporal dementia","Frontotemporal dementia and/or amyotrophic lateral sclerosis 6","Inclusion body myopathy with Paget disease of bone and frontotemporal dementia","Inclusion body myopathy with early-onset Paget disease and frontotemporal dementia","VCP-Related Amyotrophic Lateral Sclerosis","VCP-Related Amyotrophic Lateral Sclerosis/Frontotemporal Dementia"],"diseaseFromSource":"Inclusion body myopathy with Paget disease of bone and frontotemporal dementia","diseaseFromSourceId":"C1833662","diseaseFromSourceMappedId":"Orphanet_52430","variantHgvsId":"NC_000009.12:g.35063060A>C"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV003804888","releaseDate":"2024-02-28","targetFromSourceId":"ENSG00000178209","variantFunctionalConsequenceId":"SO_0001819","variantId":"8_143920449_G_A","variantRsId":null,"cohortPhenotypes":["Autosomal recessive limb-girdle muscular dystrophy type 2Q","EPIDERMOLYSIS BULLOSA SIMPLEX 5A, OGNA TYPE","EPIDERMOLYSIS BULLOSA SIMPLEX AND LIMB-GIRDLE MUSCULAR DYSTROPHY","Epidermolysa bullosa simplex and limb girdle muscular dystrophy","Epidermolysis bullosa simplex 5B, with muscular dystrophy","Epidermolysis bullosa simplex 5C, with pyloric atresia","Epidermolysis bullosa simplex with muscular dystrophy","Epidermolysis bullosa simplex with nail dystrophy","Epidermolysis bullosa simplex with pyloric atresia","Epidermolysis bullosa simplex, Ogna type","Limb-girdle muscular dystrophy, type 2Q","MUSCULAR DYSTROPHY, LIMB-GIRDLE, AUTOSOMAL RECESSIVE 17","PLEC1-Related Epidermolysis Bullosa with Pyloric Atresia","Pidermolysis bullosa simplex 5A, Ogna type"],"diseaseFromSource":"Epidermolysis bullosa simplex 5B, with muscular dystrophy","diseaseFromSourceId":"C2931072","diseaseFromSourceMappedId":"Orphanet_257","variantHgvsId":"NC_000008.11:g.143920449G>A"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV003805362","releaseDate":"2024-02-28","targetFromSourceId":"ENSG00000101310","variantFunctionalConsequenceId":"SO_0002169","variantId":"20_18515639_C_T","variantRsId":null,"cohortPhenotypes":["CDA 2","Congenital dyserythropoietic anemia, type II","Cowden syndrome 7","Dyserythropoietic anemia, congenital type 2","HEMPAS anemia","Hereditary Erythroblastic Multinuclearity with Positive Acidified-Serum test'"],"diseaseFromSource":"Cowden syndrome 7","diseaseFromSourceId":"C4225179","diseaseFromSourceMappedId":"MONDO_0014802","variantHgvsId":"NC_000020.11:g.18515639C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely pathogenic"],"confidence":"criteria provided, single submitter","studyId":"RCV003806863","releaseDate":"2024-02-28","targetFromSourceId":"ENSG00000134569","variantFunctionalConsequenceId":"SO_0001574","variantId":"11_46883977_C_T","variantRsId":null,"cohortPhenotypes":["Cenani syndactylism","Cenani-Lenz syndactyly syndrome","Congenital myasthenic syndrome 17","SYNDACTYLY, TYPE VII","Sclerosteosis 2","Syndactyly Cenani Lenz type","Syndactyly type 7"],"diseaseFromSource":"Sclerosteosis 2","diseaseFromSourceId":"C3280402","diseaseFromSourceMappedId":"MONDO_0013679","variantHgvsId":"NC_000011.10:g.46883977C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV003850101","releaseDate":"2024-02-28","targetFromSourceId":"ENSG00000054983","variantFunctionalConsequenceId":"SO_0002169","variantId":"14_87988221_T_C","variantRsId":null,"cohortPhenotypes":["Galactocerebrosidase deficiency","Galactosylceramide beta-galactosidase deficiency","Globoid cell leukoencephalopathy","Krabbe leukodystrophy","Leukodystrophy, Globoid Cell"],"diseaseFromSource":"Galactosylceramide beta-galactosidase deficiency","diseaseFromSourceId":"C0023521","diseaseFromSourceMappedId":"MONDO_0009499","variantHgvsId":"NC_000014.9:g.87988221T>C"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV001944434","releaseDate":"2022-03-28","targetFromSourceId":"ENSG00000178209","variantFunctionalConsequenceId":"SO_0001583","variantId":"8_143917391_C_T","variantRsId":"rs541897170","cohortPhenotypes":["Autosomal recessive limb-girdle muscular dystrophy type 2Q","EPIDERMOLYSIS BULLOSA SIMPLEX 5A, OGNA TYPE","EPIDERMOLYSIS BULLOSA SIMPLEX AND LIMB-GIRDLE MUSCULAR DYSTROPHY","Epidermolysa bullosa simplex and limb girdle muscular dystrophy","Epidermolysis bullosa simplex 5B, with muscular dystrophy","Epidermolysis bullosa simplex 5C, with pyloric atresia","Epidermolysis bullosa simplex with muscular dystrophy","Epidermolysis bullosa simplex with nail dystrophy","Epidermolysis bullosa simplex with pyloric atresia","Epidermolysis bullosa simplex, Ogna type","Limb-girdle muscular dystrophy, type 2Q","MUSCULAR DYSTROPHY, LIMB-GIRDLE, AUTOSOMAL RECESSIVE 17","PLEC1-Related Epidermolysis Bullosa with Pyloric Atresia","Pidermolysis bullosa simplex 5A, Ogna type"],"diseaseFromSource":"Autosomal recessive limb-girdle muscular dystrophy type 2Q","diseaseFromSourceId":"C3150989","diseaseFromSourceMappedId":"Orphanet_254361","variantHgvsId":"NC_000008.11:g.143917391C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV000547369","releaseDate":"2017-12-26","targetFromSourceId":"ENSG00000008056","variantFunctionalConsequenceId":"SO_0001819","variantId":"X_47574166_C_T","variantRsId":"rs967215240","cohortPhenotypes":["Epilepsy, X-linked 1, with variable learning disabilities and behavior disorders","Epilepsy, X-linked, with variable learning disabilities and behavior disorders","X-linked epilepsy-learning disabilities-behavior disorders syndrome"],"diseaseFromSource":"Epilepsy, X-linked 1, with variable learning disabilities and behavior disorders","diseaseFromSourceId":"C5774177","diseaseFromSourceMappedId":"MONDO_0010339","variantHgvsId":"NC_000023.11:g.47574166C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["uncertain significance"],"confidence":"criteria provided, single submitter","studyId":"RCV002912729","releaseDate":"2023-02-07","targetFromSourceId":"ENSG00000173040","variantFunctionalConsequenceId":"SO_0001583","variantId":"4_5640686_T_C","variantRsId":null,"cohortPhenotypes":["Acrofacial dysostosis of Weyers","Chondroectodermal dysplasia","Curry-Hall syndrome","Ellis-van Creveld syndrome","Mesoectodermal dysplasia","WEYERS ACRODENTAL DYSOSTOSIS"],"diseaseFromSource":"Ellis-van Creveld syndrome","diseaseFromSourceId":"C0013903","diseaseFromSourceMappedId":"Orphanet_289","variantHgvsId":"NC_000004.12:g.5640686T>C"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely benign"],"confidence":"criteria provided, single submitter","studyId":"RCV002933106","releaseDate":"2023-02-07","targetFromSourceId":"ENSG00000187535","variantFunctionalConsequenceId":"SO_0001819","variantId":"16_1523551_C_T","variantRsId":null,"cohortPhenotypes":["Conorenal syndrome","Renal dysplasia, retinal pigmentary dystrophy, cerebellar ataxia and skeletal dysplasia","SHORT-RIB THORACIC DYSPLASIA 9 WITH OR WITHOUT POLYDACTYLY","SHORT-RIB THORACIC DYSPLASIA 9 WITHOUT POLYDACTYLY","Saldino-Mainzer syndrome"],"diseaseFromSource":"Saldino-Mainzer syndrome","diseaseFromSourceId":"C1849437","diseaseFromSourceMappedId":"Orphanet_140969","variantHgvsId":"NC_000016.10:g.1523551C>T"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["pathogenic"],"confidence":"criteria provided, single submitter","studyId":"RCV003398441","releaseDate":"2023-11-20","targetFromSourceId":"ENSG00000123191","variantFunctionalConsequenceId":"SO_0001583","variantId":"13_51958333_C_A","variantRsId":"rs28942074","cohortPhenotypes":["ATP7B-related condition","ATP7B-related disorder"],"diseaseFromSource":"ATP7B-related disorder","diseaseFromSourceId":null,"diseaseFromSourceMappedId":null,"variantHgvsId":"NC_000013.11:g.51958333C>A"} +{"alleleOrigins":["germline"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["likely pathogenic","pathogenic"],"confidence":"criteria provided, multiple submitters, no conflicts","studyId":"RCV000116005","releaseDate":"2014-05-17","targetFromSourceId":"ENSG00000183765","variantFunctionalConsequenceId":"SO_0001587","variantId":"22_28687974_G_A","variantRsId":"rs200432447","cohortPhenotypes":["Cancer predisposition","Hereditary Cancer Syndrome","Hereditary cancer-predisposing syndrome","Hereditary neoplastic syndrome","Neoplastic Syndromes, Hereditary","Tumor predisposition"],"diseaseFromSource":"Hereditary cancer-predisposing syndrome","diseaseFromSourceId":"C0027672","diseaseFromSourceMappedId":"MONDO_0015356","variantHgvsId":"NC_000022.11:g.28687974G>A"} +{"alleleOrigins":["de novo"],"datasourceId":"eva","datatypeId":"genetic_association","clinicalSignificances":["pathogenic"],"confidence":"criteria provided, single submitter","studyId":"RCV000855501","releaseDate":"2019-11-08","targetFromSourceId":"ENSG00000152217","variantFunctionalConsequenceId":"SO_0001583","variantId":"18_44951952_T_C","variantRsId":"rs267607038","cohortPhenotypes":["Arthrogryposis multiplex congenita","Congenital arthromyodysplasia","Congenital multiple arthrogryposis","Fetal akinesia deformation sequence 1","Fetal akinesia sequence","Fibrous ankylosis of multiple joints","Guerin-Stern syndrome","GuΓ©rin-Stern syndrome","Lethal Pena-Shokeir 1 syndrome","Myodystrophia fetalis deformans","Otto syndrome","Pena Shokeir syndrome, type 1","Pena-Shokeir syndrome type I","Rocher-Sheldon syndrome","Rossi syndrome"],"diseaseFromSource":"Fetal akinesia deformation sequence 1","diseaseFromSourceId":"C1276035","diseaseFromSourceMappedId":"Orphanet_994","variantHgvsId":"NC_000018.10:g.44951952T>C"} diff --git a/tests/gentropy/data_samples/variant_sources/pharmacogenomics-test.jsonl b/tests/gentropy/data_samples/variant_sources/pharmacogenomics-test.jsonl new file mode 100644 index 000000000..b5cbb5dca --- /dev/null +++ b/tests/gentropy/data_samples/variant_sources/pharmacogenomics-test.jsonl @@ -0,0 +1,46 @@ +{"genotypeId":"7_87531302_A_A,C","variantId":"7_87531302_A_C","genotypeAnnotationText":"Patients with the AC genotype may have an increased risk of biopsy-proven acute rejection at 12 month post-transplant when treated with cyclosporine and mycophenolate mofetil as compared to patients with the CC genotype. Other genetic and clinical factors may also influence a patient's response to mycophenolate mofetil.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"cyclosporine"}],"evidenceLevel":"3","genotype":"AC","literature":["18444945"],"pgxCategory":"efficacy","studyId":"982040619","targetFromSourceId":"ENSG00000085563","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs2032582","phenotypeText":"increased risk of biopsy-proven acute rejection at 12 month post-transplant"} +{"genotypeId":"3_41237949_A_G,G","variantId":"3_41237949_A_G","genotypeAnnotationText":"Patients with the GG genotype and multiple myeloma may have a decreased response to cyclophosphamide, dexamethasone, and thalidomide as compared to patients with the AA genotypes. However, they may also be at decreased risk for neutropenia when treated with lenalidomide. Other genetic and clinical factors may also influence a patient's response to cyclophosphamide, dexamethasone, and thalidomide, and risk of neutropenia when treated with lenalidomide.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"cyclophosphamide"}],"evidenceLevel":"3","genotype":"GG","literature":["26521987","26521987"],"pgxCategory":"toxicity","studyId":"1447953328","targetFromSourceId":"ENSG00000168036","variantFunctionalConsequenceId":"SO_0001627","variantRsId":"rs4135385","phenotypeText":null} +{"genotypeId":"18_63312127_C_T,T","variantId":"18_63312127_C_T","genotypeAnnotationText":"Patients with the TT genotype and ovarian cancer may have an increased risk of neurotoxicity when treated with carboplatin in combination with either docetaxel or paclitaxel, as compared to patients with the CC or CT genotype. Other genetic and clinical factors may also influence risk of neurotoxicity.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"docetaxel"}],"evidenceLevel":"3","genotype":"TT","literature":["23963862"],"pgxCategory":"toxicity","studyId":"1183631554","targetFromSourceId":"ENSG00000171791","variantFunctionalConsequenceId":"SO_0001627","variantRsId":"rs2849380","phenotypeText":"increased risk of neurotoxicity"} +{"genotypeId":"20_34927985_A_T,T","variantId":"20_34927985_A_T","genotypeAnnotationText":"No patients with the TT genotype were available for analysis, but patients with the AT genotype and non-small-cell lung cancer may have shorter overall survival times when treated with platinum agents in combination with gemcitabine or taxanes, as compared to patients with the AA genotype. Other genetic and clinical factors may also influence overall survival times in non-small-cell lung cancer patients.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"carboplatin"}],"evidenceLevel":"3","genotype":"TT","literature":["21636554"],"pgxCategory":"efficacy","studyId":"1447960379","targetFromSourceId":"ENSG00000100983","variantFunctionalConsequenceId":"SO_0001632","variantRsId":"rs17309872","phenotypeText":"shorter overall survival times"} +{"genotypeId":"9_84948455_T_C,T","variantId":"9_84948455_T_C","genotypeAnnotationText":"Patients with the CT genotype and heroin addiction may require a higher dose of methadone when undergoing methadone maintenance treatment as compared to patients with the TT genotype. Other genetic and clinical factors may also influence methadone dose required for effective treatment.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"methadone"}],"evidenceLevel":"3","genotype":"CT","literature":["23651024"],"pgxCategory":"dosage","studyId":"982032396","targetFromSourceId":"ENSG00000148053","variantFunctionalConsequenceId":"SO_0001630","variantRsId":"rs2289658","phenotypeText":"require a higher dose of methadone"} +{"genotypeId":"21_36135203_G_A,A","variantId":"21_36135203_G_A","genotypeAnnotationText":"Patients with the AA genotype and breast cancer who are treated with doxorubicin: 1) may have decreased metabolism of doxorubicin 2) may have greater tumor reduction 3) may have increased severity of neutropenia as compared to patients with the GG genotype. Other genetic and clinical factors may also influence a patient's response to doxorubicin treatment and risk of toxicity.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"doxorubicin"}],"evidenceLevel":"3","genotype":"AA","literature":["18551042","18551042","18551042","18551042"],"pgxCategory":"toxicity","studyId":"652882846","targetFromSourceId":"ENSG00000159231","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs8133052","phenotypeText":"greater tumor reduction"} +{"genotypeId":"14_47546779_A_A,C","variantId":"14_47546779_A_C","genotypeAnnotationText":"Patients with the AC genotype and major depression who are treated with fluvoxamine, milnacipran or paroxetine may have an increased risk of sexual dysfunction as compared to patients with the CC genotype or may have a decreased, but not absent, risk of sexual dysfunction as compared to patients with the AA genotype. Other genetic and clinical factors may also affect patients' response to fluvoxamine, milnacipran or paroxetine.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"paroxetine"}],"evidenceLevel":"3","genotype":"AC","literature":["22445761"],"pgxCategory":"toxicity","studyId":"1444700688","targetFromSourceId":"ENSG00000139915","variantFunctionalConsequenceId":"SO_0001627","variantRsId":"rs1160351","phenotypeText":"increased risk of sexual dysfunction"} +{"genotypeId":"15_78590583_G_A,A","variantId":"15_78590583_G_A","genotypeAnnotationText":"Patients with the AA genotype who are in chronic pain and receive opioid medications for treatment may be at increased risk for addiction as compared to patients with the GG genotype. Other genetic and clinical factors may also influence risk of opiate addiction.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"Opium alkaloids and derivatives"}],"evidenceLevel":"3","genotype":"AA","literature":["20725741"],"pgxCategory":"toxicity","studyId":"1448101149","targetFromSourceId":"ENSG00000169684","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs16969968","phenotypeText":"increased risk for addiction"} +{"genotypeId":"11_113475530_G_G,GG","variantId":"11_113475530_G_GG","genotypeAnnotationText":"Patients with the G/del genotype and Schizophrenia who are treated with antipsychotics 1) may have decreased response 2) may have increased time until response, compared to patients with the GG genotype. Please note that there is contradictory evidence from studies that report no association with these alleles and response to antipsychotics. Other genetic and clinical factors may also influence a patient's response to antipsychotics.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"antipsychotics"}],"evidenceLevel":"3","genotype":"G/del","literature":["18926547","16513877","15830237","9858029","9858029","9918131","17105675","15694263","11505224","20194480","28673279"],"pgxCategory":"efficacy","studyId":"655388510","targetFromSourceId":"ENSG00000149295","variantFunctionalConsequenceId":"SO_0001631","variantRsId":"rs1799732","phenotypeText":"increased time until response"} +{"genotypeId":"15_78601997_G_A,A","variantId":"15_78601997_G_A","genotypeAnnotationText":"Patients with the AA genotype may be less likely to remain abstinent from smoking when treated with placebo as compared to patients with the GG genotype. Other genetic and clinical factors may also affect a patient's success at smoking cessation.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"Drugs used in nicotine dependence"}],"evidenceLevel":"3","genotype":"AA","literature":["23249876"],"pgxCategory":"other","studyId":"1450929137","targetFromSourceId":"ENSG00000080644","variantFunctionalConsequenceId":"SO_0001819","variantRsId":"rs1051730","phenotypeText":"less likely to remain abstinent from smoking"} +{"genotypeId":"1_46405089_C_A,C","variantId":"1_46405089_C_A","genotypeAnnotationText":"Patients with the AC genotype and Psychotic Disorders who are treated with aripiprazole, clozapine, haloperidol, olanzapine, quetiapine or risperidone may have an increased likelihood of weight gain of more than 7% of baseline body weight as compared to patients with the CC genotype. However, this is contradicted in one study with risperidone. Other genetic and clinical factors may also influence a patient's risk for treatment-induced weight gain.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"haloperidol"}],"evidenceLevel":"3","genotype":"AC","literature":["20631561","23799528"],"pgxCategory":"toxicity","studyId":"1043880750","targetFromSourceId":"ENSG00000117480","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs324420","phenotypeText":"increased likelihood of weight gain of more than 7% of baseline body weight"} +{"genotypeId":"4_88174909_G_A,G","variantId":"4_88174909_G_A","genotypeAnnotationText":"Patients with the AG genotype may have increased tumor response rate and increased risk of grade 3-4 nonhematological toxicity when treated with fluorouracil, irinotecan and leucovorin as compared to patients with the GG genotype or may have decreased tumor response rate and decreased risk of grade 3-4 nonhematological toxicity when treated with fluorouracil, irinotecan and leucovorin as compared to patients with the AA genotype. Other genetic and clinical factors may also influence a patient's response to fluorouracil, irinotecan and leucovorin.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"leucovorin"}],"evidenceLevel":"3","genotype":"AG","literature":["24018773","24018773"],"pgxCategory":"efficacy","studyId":"1444700860","targetFromSourceId":"ENSG00000118777","variantFunctionalConsequenceId":"SO_0001627","variantRsId":"rs7699188","phenotypeText":"increased tumor response rate"} +{"genotypeId":"X_154536002_C_T,T","variantId":"X_154536002_C_T","genotypeAnnotationText":"Patients with the TT genotype with Malaria who are treated with artesunate, chlorproguanil and dapsone may have an increased risk of hemolysis and severe/unsafe hemoglobin decreases as compared to patients with the CC genotype. Other genetic and clinical factors may also influence a patient's response to artesunate, chlorproguanil and dapsone.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"artesunate"}],"evidenceLevel":"4","genotype":"TT","literature":["19112496","19690618","19690618"],"pgxCategory":"toxicity","studyId":"981352141","targetFromSourceId":"ENSG00000160211","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs1050828","phenotypeText":"increased risk of hemolysis and severe/unsafe hemoglobin decreases"} +{"genotypeId":"17_18328782_G_A,A","variantId":"17_18328782_G_A","genotypeAnnotationText":"Pediatric patients with the AA genotype and with Precursor Cell Lymphoblastic Leukemia-Lymphoma who are treated with methotrexate may have increased catalytic activity of TYMS as compared to pediatric patients with the AG and GG genotype. Patients with the AA genotype and with Precursor Cell Lymphoblastic Leukemia-Lymphoma who are treated with methotrexate may have increased likelihood of Toxic liver disease as compared to patients with the AG genotype. However, this association is contradicted in other studies. Other genetic and clinical factors may also influence a patient's response to methotrexate.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"methotrexate"}],"evidenceLevel":"3","genotype":"AA","literature":["22838948","18368069","15797993"],"pgxCategory":"toxicity","studyId":"981238370","targetFromSourceId":"ENSG00000176974","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs1979277","phenotypeText":"increased catalytic activity of TYMS"} +{"genotypeId":"4_88131171_G_T,T","variantId":"4_88131171_G_T","genotypeAnnotationText":"Patients with the TT genotype and HIV infection who are treated with efavirenz may have an increased risk of abnormal dreams as compared to patients with the GG genotype. Other genetic and clinical factors may also influence a patient's risk of efavirenz-induced side effects.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"efavirenz"}],"evidenceLevel":"3","genotype":"TT","literature":["23859571"],"pgxCategory":"toxicity","studyId":"1183614716","targetFromSourceId":"ENSG00000118777","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs2231142","phenotypeText":"increased risk of abnormal dreams"} +{"genotypeId":"19_45351661_T_G,G","variantId":"19_45351661_T_G","genotypeAnnotationText":"Patients with the GG genotype and Colorectal Neoplasms who are treated with fluorouracil and leucovorin or fluorouracil, leucovorin and oxaliplatin may have 1) an increased risk of Drug Toxicity 2) an increased risk of early relapse and 3) decreased progression free survival as compared to patients with the TT genotype. Other genetic and clinical factors may also influence a patient's response to fluorouracil, leucovorin and oxaliplatin.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"fluorouracil"}],"evidenceLevel":"3","genotype":"GG","literature":["20385995","18267032","20078613"],"pgxCategory":"efficacy","studyId":"981237959","targetFromSourceId":"ENSG00000104884","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs13181","phenotypeText":"increased risk of Drug Toxicity"} +{"genotypeId":"6_35639794_T_C,C","variantId":"6_35639794_T_C","genotypeAnnotationText":"Patients with the CC genotype may 1) have decreased response to antidepressants 2) have decreased, but not absent, risk for suicide ideation with paroxetine, venlafaxine, clomipramine, lithium, liothyronine or nefazodone as compared to patients with the CT or TT genotype. However, contradictory findings regarding an association of the opposite allele or no association with response have been reported. Other genetic and clinical factors may also influence a patient's response to antidepressants.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"antidepressants"}],"evidenceLevel":"3","genotype":"CC","literature":["20709156","17467808","23733030","23733030","18597649","21449676","21449676","19676097","15565110"],"pgxCategory":"efficacy","studyId":"655384568","targetFromSourceId":"ENSG00000096060","variantFunctionalConsequenceId":"SO_0001627","variantRsId":"rs1360780","phenotypeText":"decreased response to antidepressants"} +{"genotypeId":"5_112865397_A_A,G","variantId":"5_112865397_A_G","genotypeAnnotationText":"Patients with the AG genotype and major depressive disorder may be less likely to respond when treated with citalopram, fluoxetine, paroxetine or sertraline as compared to patients with the GG genotype, or more likely to respond when treated with citalopram, fluoxetine, paroxetine or sertraline as compared to patients with the AA genotype. Other genetic and clinical factors may also influence response to citalopram, fluoxetine, paroxetine or sertraline.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"citalopram"}],"evidenceLevel":"3","genotype":"AG","literature":["22795047"],"pgxCategory":"efficacy","studyId":"1183590959","targetFromSourceId":"ENSG00000153037","variantFunctionalConsequenceId":"SO_0001627","variantRsId":"rs495794","phenotypeText":"less likely to respond"} +{"genotypeId":"22_19963748_G_A,G","variantId":"22_19963748_G_A","genotypeAnnotationText":"Patients with the AG genotype and major Depressive Disorder may have an increased response to fluvoxamine treatment as compared to patients with the GG genotype. Other genetic and clinical factors may also influence a patient's response to fluvoxamine.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"fluvoxamine"}],"evidenceLevel":"3","genotype":"AG","literature":["20619611"],"pgxCategory":"efficacy","studyId":"1043880039","targetFromSourceId":"ENSG00000093010","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs4680","phenotypeText":"increased response"} +{"genotypeId":"10_94761900_C_T,T","variantId":"10_94761900_C_T","genotypeAnnotationText":"Patients with the TT genotype and breast cancer may have a decreased risk for leukopenia when treated with cyclophosphamide, doxorubicin and fluorouracil (FAC) as compared to patients with CC genotype. Other genetic and clinical factors may also influence risk for leukopenia in patients taking FAC chemotherapy.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"cyclophosphamide"}],"evidenceLevel":"3","genotype":"TT","literature":["29507678"],"pgxCategory":"toxicity","studyId":"1449718265","targetFromSourceId":"ENSG00000165841","variantFunctionalConsequenceId":"SO_0001631","variantRsId":"rs12248560","phenotypeText":"decreased risk for leukopenia"} +{"genotypeId":"3_114171968_C_T,T","variantId":"3_114171968_C_T","genotypeAnnotationText":"Patients with the TT genotype and Schizophrenia who are treated with clozapine may have a better response to treatment as compared to patients with the CC or CT genotype. Please note; this association was not found in a meta-analysis. Other genetic and clinical factors may also influence a patient's response to clozapine treatment.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"clozapine"}],"evidenceLevel":"3","genotype":"TT","literature":["21332319","20029384"],"pgxCategory":"efficacy","studyId":"981203578","targetFromSourceId":"ENSG00000151577","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs6280","phenotypeText":"better response to treatment"} +{"genotypeId":"19_45409478_C_A,G","variantId":"19_45409478_C_A","genotypeAnnotationText":"Patients with Mesothelioma and the AC genotype may have worse overall and progression-free survival when treated with cisplatin and gemcitabine as compared to patients with the CC genotype. Other clinical and genetic factors may also influence response to cisplatin and gemcitabine in patients with mesothelioma.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"cisplatin"}],"evidenceLevel":"3","genotype":"AG","literature":["28422153"],"pgxCategory":"efficacy","studyId":"1449004745","targetFromSourceId":"ENSG00000012061","variantFunctionalConsequenceId":"SO_0001624","variantRsId":"rs3212986","phenotypeText":"worse overall and progression-free survival"} +{"genotypeId":"2_233671807_A_A,AT","variantId":"2_233671807_A_AT","genotypeAnnotationText":"Patients with the rs3832043 T/del genotype and non-small cell lung cancer may have decreased glucuronidation of SN-38 as compared to patients with the TT genotype, or increased glucuronidation of SN-38 as compared to patients with the del/del genotype. SN-38 is the active metabolite of irinotecan, and is glucuronidated by UGT1A9 into an inactive form (SN-38G). This annotation only covers the pharmacokinetic relationship between rs3832043 and SN-38 and does not include evidence about clinical outcomes. Other genetic and clinical factors may also influence SN-38 metabolism.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"SN-38"}],"evidenceLevel":"3","genotype":"T/del","literature":["24897286","18221820","16636344"],"pgxCategory":"metabolism/pk","studyId":"1183615199","targetFromSourceId":"ENSG00000242515","variantFunctionalConsequenceId":"SO_0001627","variantRsId":"rs3832043","phenotypeText":"decreased glucuronidation"} +{"genotypeId":"18_673016_C_T,T","variantId":"18_673016_C_T","genotypeAnnotationText":"Patients with the TT genotype and cancer who are treated with Capecitabine may have an increased risk of of nausea and vomiting as compared to patients with the CC or CT genotypes and a decreased likelihood of asthenia as compared to the CC genotype. Other clinical and genetic factors may also influence nausea and vomiting in patients with cancer who are treated with Capecitabine.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"capecitabine"}],"evidenceLevel":"3","genotype":"TT","literature":["28347776","28347776"],"pgxCategory":"toxicity","studyId":"1448616922","targetFromSourceId":"ENSG00000132199","variantFunctionalConsequenceId":"SO_0001624","variantRsId":"rs699517","phenotypeText":"decreased likelihood of asthenia"} +{"genotypeId":"17_5379957_A_G,G","variantId":"17_5379957_A_G","genotypeAnnotationText":"Patients with bipolar, depressive, psychotic, or schizoaffective disorders and the GG genotype who are administered amisulpride, aripiprazole, clozapine, olanzapine, quetiapine, paliperidone, risperidone, lithium, valproate or/and mirtazapine may have smaller elevations of fasting glucose concentrations as compared to patients with the AA genotype. Other clinical and genetic factors may also influence fasting glucose concentrations in patients administered these medications.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"valproic acid"}],"evidenceLevel":"3","genotype":"GG","literature":["28694205"],"pgxCategory":"other","studyId":"1449005382","targetFromSourceId":"ENSG00000108559","variantFunctionalConsequenceId":"SO_0001627","variantRsId":"rs1000940","phenotypeText":"smaller elevations of fasting glucose concentrations"} +{"genotypeId":"20_54684529_G_A,G","variantId":"20_54684529_G_A","genotypeAnnotationText":"Pediatric patients with acute lymphoblastic leukemia (ALL) and the AG genotype may have an increased risk of developing osteonecrosis when treated with cyclophosphamide, cytarabine, daunorubicin, dexamethasone, doxorubicin, methotrexate, pegaspargase, prednisone, thioguanine and vincristine as compared to pediatric ALL patients with the GG genotypes and a decreased risk of osteonecrosis as compared to pediatric patients with the AA genotype. Other clinical and genetic factors may also influence the risk of developing osteonecrosis in pediatric ALL patients.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"pegaspargase"}],"evidenceLevel":"3","genotype":"AG","literature":["26590194"],"pgxCategory":"toxicity","studyId":"1448099093","targetFromSourceId":null,"variantFunctionalConsequenceId":null,"variantRsId":"rs117532069","phenotypeText":"increased risk of developing osteonecrosis"} +{"genotypeId":"8_18390208_T_A,A","variantId":"8_18390208_T_A","genotypeAnnotationText":"Patients with the AA genotype and tuberculosis (TB) may have an increased risk for anti-TB drug-induced hepatitis as compared to patients with the TT genotype. Cells with the A allele have been shown to result in decreased transcription of the NAT2 gene as compared to those with the T allele. Other genetic and clinical factors may also influence risk of hepatitis in patients taking anti-TB drugs.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"rifampin"}],"evidenceLevel":"3","genotype":"AA","literature":["19891553","19891553","19891553"],"pgxCategory":"metabolism/pk","studyId":"1447964123","targetFromSourceId":"ENSG00000156006","variantFunctionalConsequenceId":"SO_0001631","variantRsId":"rs4646244","phenotypeText":"increased risk for anti-TB drug-induced hepatitis"} +{"genotypeId":"21_45537880_T_C,T","variantId":"21_45537880_T_C","genotypeAnnotationText":"Patients with the rs1051266 CT genotype and rheumatoid arthritis may have decreased response when treated with methotrexate as compared to patients with the TT genotype. However, conflicting evidence has been reported. Other genetic and clinical factors may also influence methotrexate response.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"methotrexate"}],"evidenceLevel":"2A","genotype":"CT","literature":["19827168","15677700","22450926","15457444","18322994","17325736","17325736","31099054","26616421","27992285"],"pgxCategory":"efficacy","studyId":"1451245360","targetFromSourceId":"ENSG00000173638","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs1051266","phenotypeText":"decreased response"} +{"genotypeId":"9_9687487_C_T,T","variantId":"9_9687487_C_T","genotypeAnnotationText":"Patients with the TT genotype may have higher risk for resistant hypertension in whites and Hispanics patients treated with verapamil and trandolapril as compared to patients with genotype CC. Other genetic and clinical factors may also influence the response to verapamil.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"trandolapril"}],"evidenceLevel":"3","genotype":"TT","literature":["26425837"],"pgxCategory":"efficacy","studyId":"1446902927","targetFromSourceId":"ENSG00000153707","variantFunctionalConsequenceId":"SO_0001627","variantRsId":"rs4742610","phenotypeText":"higher risk for resistant hypertension"} +{"genotypeId":"12_21178615_T_C,C","variantId":"12_21178615_T_C","genotypeAnnotationText":"Patients with precursor cell lymphoblastic leukemia-lymphoma and the rs4149056 CC genotype may have a decreased response to methotrexate as compared to patients with the CT and TT genotypes. However, conflicting evidence has been reported. Other clinical and genetic factors may also influence response to methotrexate.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"methotrexate"}],"evidenceLevel":"3","genotype":"CC","literature":["24386571","28525903"],"pgxCategory":"efficacy","studyId":"1449004841","targetFromSourceId":"ENSG00000134538","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs4149056","phenotypeText":"decreased response to methotrexate"} +{"genotypeId":"X_114584047_C_T","variantId":"X_114584047_C_T","genotypeAnnotationText":"Patients with one X-chromosome, the T genotype and psychiatric disorders who are treated with olanzapine may have a decreased risk of weight gain as compared to patients with the C genotype. This gene is on the X chromosome and males have only one allele. However, some studies find no association with weight gain. Other genetic and clinical factors may also influence a patient's risk for weight gain with antipsychotic treatment.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"olanzapine"}],"evidenceLevel":"3","genotype":"T","literature":["17702092","22967772","21121776","19636338","15666332","19434072","25152019","18718676","17016522","20504252","19193342"],"pgxCategory":"toxicity","studyId":"1451256440","targetFromSourceId":"ENSG00000147246","variantFunctionalConsequenceId":"SO_0001631","variantRsId":"rs3813929","phenotypeText":"decreased risk of weight gain"} +{"genotypeId":"5_148826877_G_A,G","variantId":"5_148826877_G_A","genotypeAnnotationText":"Patients with the AG genotype and hypertension may have a greater decrease in diastolic blood pressure when treated with benazepril as compared to patients with the AA genotype. No significant results have been seen for systolic blood pressure. Additionally, the same study reported no significant differences in systolic or diastolic blood pressure between genotypes in a different cohort. Other genetic and clinical factors may also influence change in diastolic or systolic blood pressure.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"benazepril"}],"evidenceLevel":"4","genotype":"AG","literature":["15554460","15554460","15554460"],"pgxCategory":"efficacy","studyId":"1183614850","targetFromSourceId":"ENSG00000169252","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs1042713","phenotypeText":"greater decrease in diastolic blood pressure"} +{"genotypeId":"16_28606193_C_C,T","variantId":"16_28606193_C_T","genotypeAnnotationText":"Patients with the CT genotype may have a decreased response to acetaminophen (paracetamol) as compared to patients with the CC genotype. Other genetic and clinical factors may also affect a patient's response to acetaminophen.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"acetaminophen"}],"evidenceLevel":"3","genotype":"CT","literature":["30908574"],"pgxCategory":"efficacy","studyId":"1450374089","targetFromSourceId":"ENSG00000196502","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs1042028","phenotypeText":"decreased response to acetaminophen (paracetamol)"} +{"genotypeId":"13_46834899_G_A,A","variantId":"13_46834899_G_A","genotypeAnnotationText":"Patients with the AA genotype and major depressive disorder who are treated with antidepressants and other treatments may have a reduced response and reduced likelihood of remission as compared to patients with the AG or GG genotype. Other genetic and clinical factors may also influence a patient's response to treatment for major depressive disorder and likelihood of remission.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"antidepressants"}],"evidenceLevel":"3","genotype":"AA","literature":["11311507","18253134","18253134","20075642"],"pgxCategory":"efficacy","studyId":"1183618924","targetFromSourceId":"ENSG00000102468","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs6314","phenotypeText":"reduced response and reduced likelihood of remission"} +{"genotypeId":"16_55810697_G_G,T","variantId":"16_55810697_G_T","genotypeAnnotationText":"Patients with the GT genotype and Atrial Fibrillation who are treated with dabigatran may have 1) a decreased adjusted trough concentrations of dabigatran, 2) a decreased, but not absent, risk for bleeding when treated with dabigatran as compared to patients with the TT genotype. Other genetic and clinical factors may also influence a patient's risk for bleeding.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"dabigatran"}],"evidenceLevel":"3","genotype":"GT","literature":["23467860","23467860","27261537"],"pgxCategory":"toxicity","studyId":"1183490952","targetFromSourceId":"ENSG00000198848","variantFunctionalConsequenceId":"SO_0001627","variantRsId":"rs2244613","phenotypeText":"decreased, but not absent, risk for bleeding"} +{"genotypeId":"22_19963748_G_A,A","variantId":"22_19963748_G_A","genotypeAnnotationText":"Patients with the AA genotype with substance withdrawal syndrome may have a decreased likelihood of headache when discontinuing the use of analgesics (such as opioids, NSAIDs, triptans, ergot) as compared to patients with the AG and GG genotypes. Other clinical and genetic factors may also influence likelihood of headache in patients with withdrawal syndrome who discontinue the use of analgesics.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"sumatriptan"}],"evidenceLevel":"3","genotype":"AA","literature":["25096645"],"pgxCategory":"other","studyId":"1184987573","targetFromSourceId":"ENSG00000093010","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs4680","phenotypeText":"decreased likelihood of headache"} +{"genotypeId":"10_99804058_G_A,A","variantId":"10_99804058_G_A","genotypeAnnotationText":"Patients with the AA genotype and gastrointestinal stromal tumors may have increased progression-free survival times when treated with imatinib as compared to patients with the GG genotype. Other genetic and clinical factors may also influence progression-free survival tumes in patients receiving imatinib.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"imatinib"}],"evidenceLevel":"3","genotype":"AA","literature":["30237583"],"pgxCategory":"efficacy","studyId":"1450373604","targetFromSourceId":"ENSG00000023839","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs2273697","phenotypeText":"increased progression-free survival times"} +{"genotypeId":"6_154039662_A_G,G","variantId":"6_154039662_A_G","genotypeAnnotationText":"Patients with the rs1799971 GG genotype may have increased alfentanil dose requirements as compared to patients with the AA genotype. This drug-variant pair has been assigned a β€œno recommendation” by CPIC, as it was determined to be not clinically actionable. Other genetic or clinical factors may also affect a alfentanil dose requirements.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"alfentanil"}],"evidenceLevel":"3","genotype":"GG","literature":["19605407"],"pgxCategory":"dosage","studyId":"1450826899","targetFromSourceId":"ENSG00000112038","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs1799971","phenotypeText":"increased alfentanil dose requirements"} +{"genotypeId":"14_103699416_G_A,A","variantId":"14_103699416_G_A","genotypeAnnotationText":"Patients with the AA genotype and non-small cell lung cancer may have an improved response when treated with platinum compounds as compared to patients with the GG genotype, although this is contradicted in one study. Other clinical or genetic factors may also influence a patient's response to platinum compounds in people with non-small cell lung cancer.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"Platinum compounds"}],"evidenceLevel":"3","genotype":"AA","literature":["23940523","23940523","27248474"],"pgxCategory":"efficacy","studyId":"1043872993","targetFromSourceId":"ENSG00000126215","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs861539","phenotypeText":"improved response"} +{"genotypeId":"11_67585218_A_A,G","variantId":"11_67585218_A_G","genotypeAnnotationText":"Patients with the AG genotype and Ovarian Neoplasms who are treated with cisplatin and cyclophosphamide may have a decreased likelihood of progression free survival as compared to patients with the AA genotype. However, this association was contradicted in other studies. Other genetic and clinical factors may also influence a patient's response to cisplatin and cyclophosphamide treatment.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"cyclophosphamide"}],"evidenceLevel":"3","genotype":"AG","literature":["22188361","19786980"],"pgxCategory":"efficacy","studyId":"981237950","targetFromSourceId":"ENSG00000084207","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs1695","phenotypeText":"decreased likelihood of progression free survival"} +{"genotypeId":"7_87509329_A_A,G","variantId":"7_87509329_A_G","genotypeAnnotationText":"Patients with the AG genotype and schizophrenia who responded to treatment with antipsychotics may require a decreased dose of antipsychotics as compared to patients with the GG genotype, or an increased dose as compared to patients with the AA genotype. Other genetic and clinical factors may also influence dose of antipsychotics.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"antipsychotics"}],"evidenceLevel":"3","genotype":"AG","literature":["22909202"],"pgxCategory":"dosage","studyId":"1447983818","targetFromSourceId":"ENSG00000085563","variantFunctionalConsequenceId":"SO_0001819","variantRsId":"rs1045642","phenotypeText":"require a decreased dose of antipsychotics"} +{"genotypeId":"12_21178615_T_C,C","variantId":"12_21178615_T_C","genotypeAnnotationText":"Patients with the rs4149056 CC genotype may have increased concentrations of pitavastatin when treated with pitavastatin as compared to patients with TT genotype. Other genetic and clinical factors may also influence the metabolism of pitavastatin. This annotation only covers the pharmacokinetic relationship between rs4149056 and pitavastatin and does not include evidence about clinical outcomes.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"pitavastatin"}],"evidenceLevel":"1A","genotype":"CC","literature":["23556337","17460607"],"pgxCategory":"metabolism/pk","studyId":"1451678210","targetFromSourceId":"ENSG00000134538","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs4149056","phenotypeText":"increased concentrations of pitavastatin"} +{"genotypeId":"8_18400860_G_A,A","variantId":"8_18400860_G_A","genotypeAnnotationText":"Patients with the AA genotype may have decreased but not absent risk of toxicity with docetaxel and thalidomide as compared to patients with the AG or GG genotypes. Other genetic and clinical factors may also influence treatment response.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"thalidomide"}],"evidenceLevel":"3","genotype":"AA","literature":["20038957"],"pgxCategory":"toxicity","studyId":"655386176","targetFromSourceId":"ENSG00000156006","variantFunctionalConsequenceId":"SO_0001583","variantRsId":"rs1799931","phenotypeText":"decreased risk of toxicity"} +{"genotypeId":"13_46895805_G_A,A","variantId":"13_46895805_G_A","genotypeAnnotationText":"Patients with the rs6313 AA genotype and major depressive disorder may be more likely to develop sexual dysfunction and less likely to develop heart palpitations and when treated with citalopram as compared to patients with the AG or GG genotype. The current evidence base suggests that there is no association between the genotype and gastrointestinal toxicity. Other genetic and clinical factors may also influence likelihood of developing sexual dysfunction when treated with citalopram.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"citalopram"}],"evidenceLevel":"3","genotype":"AA","literature":["31792367","23158458","23158458","30221791"],"pgxCategory":"toxicity","studyId":"1451407329","targetFromSourceId":"ENSG00000102468","variantFunctionalConsequenceId":"SO_0001819","variantRsId":"rs6313","phenotypeText":"less likely to develop heart palpitations"} +{"genotypeId":"2_233772999_G_C,C","variantId":"2_233772999_G_C","genotypeAnnotationText":"Patients with the CC genotype and HIV may have a decreased risk of nephrolithiasis when treated with atazanavir and ritonavir as compared to patients with the CG and GG genotypes. Other genetic and clinical factors may also affect risk of nephrolithiasis in patients with HIV who are taking atazanavir and ritonavir.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"ritonavir"}],"evidenceLevel":"3","genotype":"CC","literature":["25151207"],"pgxCategory":"toxicity","studyId":"1185000369","targetFromSourceId":"ENSG00000244474","variantFunctionalConsequenceId":"SO_0001624","variantRsId":"rs8330","phenotypeText":"decreased risk of nephrolithiasis"} +{"genotypeId":"1_161215085_A_A,G","variantId":"1_161215085_A_G","genotypeAnnotationText":"Patients with the AG genotype and asthma may have a decreased risk for aspirin sensitivity but patients with chronic urticaria may have an increased risk for aspirin sensitivity as compared to patients with the AA genotype. Other genetic and clinical factors may also influence a patient's risk for aspirin sensitivity.","datasourceId":"pharmgkb","datasourceVersion":"2024-07-05","datatypeId":"clinical_annotation","drugs":[{"drugFromSource":"aspirin"}],"evidenceLevel":"3","genotype":"AG","literature":["18534082","18595682"],"pgxCategory":"toxicity","studyId":"1043858680","targetFromSourceId":"ENSG00000158869","variantFunctionalConsequenceId":"SO_0001631","variantRsId":"rs11587213","phenotypeText":"decreased risk for aspirin sensitivity"} diff --git a/tests/gentropy/data_samples/variant_sources/uniprot-test-sort.jsonl b/tests/gentropy/data_samples/variant_sources/uniprot-test-sort.jsonl new file mode 100644 index 000000000..c408b4fe4 --- /dev/null +++ b/tests/gentropy/data_samples/variant_sources/uniprot-test-sort.jsonl @@ -0,0 +1,9 @@ +{"confidence":"high","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"Pontocerebellar hypoplasia, hypotonia, and respiratory insufficiency syndrome, neonatal lethal","diseaseFromSourceId":"OMIM:618810","diseaseFromSourceMappedId":"MONDO_0032931","literature":"['31727539']","targetFromSourceId":"Q9NVI7","targetModulation":"up_or_down","variantRsId":"rs1570345942","variantFunctionalConsequenceId":"SO_0001583","variantId":"1_1525242_T_G"} +{"confidence":"high","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"Charcot-Marie-Tooth disease, demyelinating, 1B","diseaseFromSourceId":"OMIM:118200","diseaseFromSourceMappedId":"MONDO_0007307","literature":"['8797476', '7688964', '10737979', '11437164', '12221176', '14711881']","targetFromSourceId":"P25189","targetModulation":"up_or_down","variantRsId":"rs121913589","variantFunctionalConsequenceId":"SO_0001583","variantId":"1_161306863_C_T"} +{"confidence":"high","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"Achromatopsia 2","diseaseFromSourceId":"OMIM:216900","diseaseFromSourceMappedId":null,"literature":"['9662398', '11536077', '18521937']","targetFromSourceId":"Q16281","targetModulation":"up_or_down","variantRsId":"rs104893614","variantFunctionalConsequenceId":"SO_0001583","variantId":"2_98396018_G_A"} +{"confidence":"high","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"Brugada syndrome 1","diseaseFromSourceId":"OMIM:601144","diseaseFromSourceMappedId":"MONDO_0011001","literature":"['20129283']","targetFromSourceId":"Q14524","targetModulation":"up_or_down","variantRsId":"rs199473172","variantFunctionalConsequenceId":"SO_0001583","variantId":"3_38585800_C_T"} +{"confidence":"high","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"Ataxia telangiectasia","diseaseFromSourceId":"OMIM:208900","diseaseFromSourceMappedId":"Orphanet_100","literature":"['10817650', '10873394', '9288106', '9443866']","targetFromSourceId":"Q13315","targetModulation":"up_or_down","variantRsId":"rs587779872","variantFunctionalConsequenceId":"SO_0001583","variantId":"11_108345818_C_G"} +{"confidence":"high","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"Autoimmune polyendocrine syndrome 1, with or without reversible metaphyseal dysplasia","diseaseFromSourceId":"OMIM:240300","diseaseFromSourceMappedId":"Orphanet_3453","literature":"['11524733', '11836330', '15712268', '14974083', '11524731']","targetFromSourceId":"O43918","targetModulation":"up_or_down","variantRsId":"rs179363880","variantFunctionalConsequenceId":"SO_0001583","variantId":"21_44286656_T_A"} +{"confidence":"high","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"Lowe oculocerebrorenal syndrome","diseaseFromSourceId":"OMIM:309000","diseaseFromSourceMappedId":"MONDO_0010645","literature":"['10923037']","targetFromSourceId":"Q01968","targetModulation":"up_or_down","variantRsId":"rs137853854","variantFunctionalConsequenceId":"SO_0001583","variantId":"X_129562612_G_A"} +{"confidence":"high","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"46,XY sex reversal 1","diseaseFromSourceId":"OMIM:400044","diseaseFromSourceMappedId":"MONDO_0020712","literature":"['2247149', '1570829']","targetFromSourceId":"Q05066","targetModulation":"up_or_down","variantRsId":"rs104894957","variantFunctionalConsequenceId":"SO_0001583","variantId":"Y_2787426_C_G"} +{"confidence":"medium","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"Colorectal cancer","diseaseFromSourceId":"OMIM:114500","diseaseFromSourceMappedId":"MONDO_0005575","literature":"['16407113', '19218458']","targetFromSourceId":"P00395","targetModulation":"up_or_down","variantRsId":"rs281865417","variantFunctionalConsequenceId":"SO_0001583","variantId":"MT_6277_G_A"} diff --git a/tests/gentropy/data_samples/variant_sources/uniprot-test.jsonl b/tests/gentropy/data_samples/variant_sources/uniprot-test.jsonl new file mode 100644 index 000000000..f3a637b1b --- /dev/null +++ b/tests/gentropy/data_samples/variant_sources/uniprot-test.jsonl @@ -0,0 +1,50 @@ +{"confidence":"high","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"Brugada syndrome 1","diseaseFromSourceId":"OMIM:601144","diseaseFromSourceMappedId":"MONDO_0011001","literature":"['20129283']","targetFromSourceId":"Q14524","targetModulation":"up_or_down","variantRsId":"rs199473172","variantFunctionalConsequenceId":"SO_0001583","variantId":"3_38585800_C_T"} +{"confidence":"high","datasourceId":"uniprot_variants","datatypeId":"genetic_association","diseaseFromSource":"Lowe 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2K","diseaseFromSourceId":"OMIM:607831","diseaseFromSourceMappedId":"MONDO_0011916","literature":"['22206013']","targetFromSourceId":"Q8TB36","targetModulation":"up_or_down","variantRsId":"rs375431837","variantFunctionalConsequenceId":"SO_0001583","variantId":"8_74364135_G_A"} diff --git a/tests/gentropy/data_samples/vep_consequences_sample.tsv b/tests/gentropy/data_samples/vep_consequences_sample.tsv deleted file mode 100644 index 7b5de68b2..000000000 --- a/tests/gentropy/data_samples/vep_consequences_sample.tsv +++ /dev/null @@ -1,45 +0,0 @@ -Accession Term Description Display term IMPACT v2g_score eco_score -http://purl.obolibrary.org/obo/SO_0001893 transcript_ablation A feature ablation whereby the deleted region includes a transcript feature Transcript ablation HIGH 1 1 -http://identifiers.org/eco/cttv_mapping_pipeline cttv_mapping_pipeline 1 -http://purl.obolibrary.org/obo/ECO_0000205 curator_inference 1 -http://purl.obolibrary.org/obo/SO_0002165 trinucleotide_repeat_expansion 1 -http://purl.obolibrary.org/obo/SO_0001574 splice_acceptor_variant A splice variant that changes the 2 base region at the 3' end of an intron Splice acceptor variant HIGH 1 0.95 -http://purl.obolibrary.org/obo/SO_0001575 splice_donor_variant A splice variant that changes the 2 base region at the 5' end of an intron Splice donor variant HIGH 1 0.95 -http://purl.obolibrary.org/obo/SO_0001587 stop_gained A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript Stop gained HIGH 1 0.95 -http://purl.obolibrary.org/obo/SO_0001589 frameshift_variant A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three Frameshift variant HIGH 1 0.95 -http://purl.obolibrary.org/obo/SO_0002012 start_lost A codon variant that changes at least one base of the canonical start codon Start lost HIGH 1 0.95 -http://purl.obolibrary.org/obo/SO_0001578 stop_lost A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript Stop lost HIGH 1 0.9 -http://purl.obolibrary.org/obo/SO_0001889 transcript_amplification A feature amplification of a region containing a transcript Transcript amplification HIGH 1 0.9 -http://purl.obolibrary.org/obo/SO_0001894 regulatory_region_ablation A feature ablation whereby the deleted region includes a regulatory region Regulatory region ablation MODERATE 0.66 0.9 -http://purl.obolibrary.org/obo/SO_0001583 missense_variant A sequence variant, that changes one or more bases, resulting in a different amino acid sequence but where the length is preserved Missense variant MODERATE 0.66 0.7 -http://purl.obolibrary.org/obo/SO_0001818 protein_altering_variant A sequence_variant which is predicted to change the protein encoded in the coding sequence Protein altering variant MODERATE 0.66 0.7 -http://purl.obolibrary.org/obo/SO_0001821 inframe_insertion An inframe non synonymous variant that inserts bases into in the coding sequence Inframe insertion MODERATE 0.66 0.7 -http://purl.obolibrary.org/obo/SO_0001822 inframe_deletion An inframe non synonymous variant that deletes bases from the coding sequence Inframe deletion MODERATE 0.66 0.7 -http://purl.obolibrary.org/obo/SO_0001582 initiator_codon_variant 0.7 -http://purl.obolibrary.org/obo/SO_0001630 splice_region_variant A sequence variant in which a change has occurred within the region of the splice site, either within 1-3 bases of the exon or 3-8 bases of the intron Splice region variant LOW 0.33 0.65 -http://purl.obolibrary.org/obo/SO_0001626 incomplete_terminal_codon_variant A sequence variant where at least one base of the final codon of an incompletely annotated transcript is changed Incomplete terminal codon variant LOW 0.33 0.65 -http://purl.obolibrary.org/obo/SO_0001567 stop_retained_variant A sequence variant where at least one base in the terminator codon is changed, but the terminator remains Stop retained variant LOW 0.33 0.65 -http://purl.obolibrary.org/obo/SO_0001819 synonymous_variant A sequence variant where there is no resulting change to the encoded amino acid Synonymous variant LOW 0.33 0.65 -http://purl.obolibrary.org/obo/SO_0002019 start_retained_variant A sequence variant where at least one base in the start codon is changed, but the start remains Start retained variant LOW 0.33 0.65 -http://purl.obolibrary.org/obo/SO_0001619 non_coding_transcript_variant A transcript variant of a non coding RNA gene Non coding transcript variant MODIFIER 0 0.65 -http://purl.obolibrary.org/obo/SO_0001620 mature_miRNA_variant A transcript variant located with the sequence of the mature miRNA Mature miRNA variant MODIFIER 0 0.65 -http://purl.obolibrary.org/obo/SO_0001621 NMD_transcript_variant A variant in a transcript that is the target of NMD NMD transcript variant MODIFIER 0.1 0.65 -http://purl.obolibrary.org/obo/SO_0001623 5_prime_UTR_variant A UTR variant of the 5' UTR 5 prime UTR variant MODIFIER 0.1 0.65 -http://purl.obolibrary.org/obo/SO_0001624 3_prime_UTR_variant A UTR variant of the 3' UTR 3 prime UTR variant MODIFIER 0.1 0.65 -http://purl.obolibrary.org/obo/SO_0001627 intron_variant A transcript variant occurring within an intron Intron variant MODIFIER 0.1 0.65 -http://purl.obolibrary.org/obo/SO_0001792 non_coding_transcript_exon_variant A sequence variant that changes non-coding exon sequence in a non-coding transcript Non coding transcript exon variant MODIFIER 0 0.65 -http://purl.obolibrary.org/obo/SO_0001580 coding_sequence_variant A sequence variant that changes the coding sequence Coding sequence variant MODIFIER 0 0.6 -http://purl.obolibrary.org/obo/SO_0001566 regulatory_region_variant A sequence variant located within a regulatory region Regulatory region variant MODIFIER 0 0.6 -http://purl.obolibrary.org/obo/SO_0001631 upstream_gene_variant A sequence variant located 5' of a gene Upstream gene variant MODIFIER 0 0.6 -http://purl.obolibrary.org/obo/SO_0001632 downstream_gene_variant A sequence variant located 3' of a gene Downstream gene variant MODIFIER 0 0.6 -http://purl.obolibrary.org/obo/SO_0001782 TF_binding_site_variant A sequence variant located within a transcription factor binding site TF binding site variant MODIFIER 0 0.6 -http://purl.obolibrary.org/obo/SO_0001891 regulatory_region_amplification A feature amplification of a region containing a regulatory region Regulatory region amplification MODIFIER 0 0.6 -http://purl.obolibrary.org/obo/SO_0001892 TFBS_amplification A feature amplification of a region containing a transcription factor binding site TFBS amplification MODIFIER 0 0.6 -http://purl.obolibrary.org/obo/SO_0001895 TFBS_ablation A feature ablation whereby the deleted region includes a transcription factor binding site TFBS ablation MODIFIER 0 0.6 -http://purl.obolibrary.org/obo/SO_0001906 feature_truncation A sequence variant that causes the reduction of a genomic feature, with regard to the reference sequence Feature truncation MODIFIER 0 0.6 -http://purl.obolibrary.org/obo/SO_0001907 feature_elongation A sequence variant that causes the extension of a genomic feature, with regard to the reference sequence Feature elongation MODIFIER 0 0.6 -http://targetvalidation.org/sequence/regulatory_nearest_gene_five_prime_end Regulatory nearest gene 5' end 0.6 -http://purl.obolibrary.org/obo/SO_0001628 intergenic_variant A sequence variant located in the intergenic region, between genes Intergenic variant MODIFIER 0 0.5 -http://purl.obolibrary.org/obo/SO_0001060 sequence_variant 0.5 -http://purl.obolibrary.org/obo/SO_0001825 conservative_inframe_deletion 0.5 -http://targetvalidation.org/sequence/nearest_gene_five_prime_end Nearest gene counting from 5' end 0.5 diff --git a/tests/gentropy/data_samples/vep_sample.jsonl b/tests/gentropy/data_samples/vep_sample.jsonl new file mode 100644 index 000000000..ec8ab7dbe --- /dev/null +++ b/tests/gentropy/data_samples/vep_sample.jsonl @@ -0,0 +1,4 @@ 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diff --git a/tests/gentropy/dataset/test_biosample_index.py b/tests/gentropy/dataset/test_biosample_index.py new file mode 100644 index 000000000..c647710d1 --- /dev/null +++ b/tests/gentropy/dataset/test_biosample_index.py @@ -0,0 +1,8 @@ +"""Tests on Biosample index.""" + +from gentropy.dataset.biosample_index import BiosampleIndex + + +def test_biosample_index_creation(mock_biosample_index: BiosampleIndex) -> None: + """Test biosample index creation with mock biosample index.""" + assert isinstance(mock_biosample_index, BiosampleIndex) diff --git a/tests/gentropy/dataset/test_colocalisation.py b/tests/gentropy/dataset/test_colocalisation.py index 2937efbca..c15653787 100644 --- a/tests/gentropy/dataset/test_colocalisation.py +++ b/tests/gentropy/dataset/test_colocalisation.py @@ -1,9 +1,133 @@ """Test colocalisation dataset.""" + from __future__ import annotations +from typing import TYPE_CHECKING + +import pytest + from gentropy.dataset.colocalisation import Colocalisation +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_locus import StudyLocus + +if TYPE_CHECKING: + from pyspark.sql import SparkSession def test_colocalisation_creation(mock_colocalisation: Colocalisation) -> None: """Test colocalisation creation with mock data.""" assert isinstance(mock_colocalisation, Colocalisation) + + +def test_append_study_metadata_study_locus( + mock_colocalisation: Colocalisation, + mock_study_locus: StudyLocus, + mock_study_index: StudyIndex, + metadata_cols: list[str] | None = None, +) -> None: + """Test appending right study metadata.""" + if metadata_cols is None: + metadata_cols = ["studyType"] + expected_extra_col = ["rightStudyType", "rightStudyId"] + res_df = mock_colocalisation.append_study_metadata( + mock_study_locus, + mock_study_index, + metadata_cols=metadata_cols, + colocalisation_side="right", + ) + for col in expected_extra_col: + assert col in res_df.columns, f"Column {col} not found in result DataFrame." + + +class TestAppendStudyMetadata: + """Test Colocalisation.append_study_metadata method.""" + + @pytest.mark.parametrize( + ("colocalisation_side", "expected_geneId"), [("right", "g1"), ("left", None)] + ) + def test_append_study_metadata_right( + self: TestAppendStudyMetadata, + colocalisation_side: str, + expected_geneId: str | None, + metadata_cols: list[str] | None = None, + ) -> None: + """Test appending right study metadata.""" + if metadata_cols is None: + metadata_cols = ["geneId"] + observed_df = self.sample_colocalisation.append_study_metadata( + self.sample_study_locus, + self.sample_study_index, + metadata_cols=metadata_cols, + colocalisation_side=colocalisation_side, + ) + assert ( + observed_df.select(f"{colocalisation_side}GeneId").collect()[0][0] + == expected_geneId + ), f"Expected {colocalisation_side}GeneId {expected_geneId}, but got {observed_df.select(f'{colocalisation_side}GeneId').collect()[0][0]}" + + @pytest.fixture(autouse=True) + def _setup(self: TestAppendStudyMetadata, spark: SparkSession) -> None: + """Setup fixture.""" + self.sample_study_locus = StudyLocus( + _df=spark.createDataFrame( + [ + ( + "1", + "var1", + "gwas1", + ), + ( + "2", + "var2", + "eqtl1", + ), + ], + ["studyLocusId", "variantId", "studyId"], + ), + _schema=StudyLocus.get_schema(), + ) + self.sample_study_index = StudyIndex( + _df=spark.createDataFrame( + [("gwas1", "gwas", None, "p1"), ("eqtl1", "eqtl", "g1", "p2")], + [ + "studyId", + "studyType", + "geneId", + "projectId", + ], + ), + _schema=StudyIndex.get_schema(), + ) + self.sample_colocalisation = Colocalisation( + _df=spark.createDataFrame( + [("1", "2", "eqtl", "X", "COLOC", 1, 0.9)], + [ + "leftStudyLocusId", + "rightStudyLocusId", + "rightStudyType", + "chromosome", + "colocalisationMethod", + "numberColocalisingVariants", + "h4", + ], + ), + _schema=Colocalisation.get_schema(), + ) + + +def test_extract_maximum_coloc_probability_per_region_and_gene( + mock_colocalisation: Colocalisation, + mock_study_locus: StudyLocus, + mock_study_index: StudyIndex, + filter_by_colocalisation_method: str | None = None, +) -> None: + """Test extracting maximum coloc probability per region and gene returns a dataframe with the correct columns: studyLocusId, geneId, h4.""" + filter_by_colocalisation_method = filter_by_colocalisation_method or "Coloc" + res_df = mock_colocalisation.extract_maximum_coloc_probability_per_region_and_gene( + mock_study_locus, + mock_study_index, + filter_by_colocalisation_method=filter_by_colocalisation_method, + ) + expected_cols = ["studyLocusId", "geneId", "h4"] + for col in expected_cols: + assert col in res_df.columns, f"Column {col} not found in result DataFrame." diff --git a/tests/gentropy/dataset/test_dataset.py b/tests/gentropy/dataset/test_dataset.py index 340906322..7c61f3f52 100644 --- a/tests/gentropy/dataset/test_dataset.py +++ b/tests/gentropy/dataset/test_dataset.py @@ -1,12 +1,15 @@ """Test Dataset class.""" + from __future__ import annotations +import numpy as np import pyspark.sql.functions as f import pytest +from pyspark.sql import SparkSession +from pyspark.sql.types import DoubleType, IntegerType, StructField, StructType + from gentropy.dataset.dataset import Dataset from gentropy.dataset.study_index import StudyIndex -from pyspark.sql import SparkSession -from pyspark.sql.types import IntegerType, StructField, StructType class MockDataset(Dataset): @@ -56,3 +59,31 @@ def test_dataset_filter(mock_study_index: StudyIndex) -> None: filtered.df.select("studyType").distinct().toPandas()["studyType"].to_list()[0] == expected_filter_value ), "Filtering failed." + + +def test_dataset_drop_infinity_values() -> None: + """drop_infinity_values method shoud remove inf value from standardError field.""" + spark = SparkSession.getActiveSession() + data = [np.Infinity, -np.Infinity, np.inf, -np.inf, np.Inf, -np.Inf, 5.1] + rows = [(v,) for v in data] + schema = StructType([StructField("field", DoubleType())]) + input_df = spark.createDataFrame(rows, schema=schema) + assert input_df.count() == 7 + # run without specifying *cols results in no filtering + ds = MockDataset(_df=input_df, _schema=schema) + assert ds.drop_infinity_values().df.count() == 7 + # otherwise drop all columns + assert ds.drop_infinity_values("field").df.count() == 1 + + +def test__process_class_params(spark: SparkSession) -> None: + """Test splitting of parameters between class and spark parameters.""" + params = { + "_df": spark.createDataFrame([(1,)], schema=MockDataset.get_schema()), + "recursiveFileLookup": True, + } + class_params, spark_params = Dataset._process_class_params(params) + assert "_df" in class_params, "Class params should contain _df" + assert ( + "recursiveFileLookup" in spark_params + ), "Spark params should contain recursiveFileLookup" diff --git a/tests/gentropy/dataset/test_dataset_exclusion.py b/tests/gentropy/dataset/test_dataset_exclusion.py new file mode 100644 index 000000000..1b6fce967 --- /dev/null +++ b/tests/gentropy/dataset/test_dataset_exclusion.py @@ -0,0 +1,96 @@ +"""Test dataset validation/exclusion.""" + +from __future__ import annotations + +import pyspark.sql.functions as f +import pytest +from pyspark.sql import SparkSession + +from gentropy.dataset.study_index import StudyIndex, StudyQualityCheck + + +class TestDataExclusion: + """Testing Dataset exclusion. + + Calling `dataset.valid_rows` methods on a mock datasets to test if + the right rows are excluded. + """ + + CORRECT_FLAG = ["DUPLICATED_STUDY"] + INCORRECT_FLAG = ["UNKNOWN_CATEGORY"] + ALL_FLAGS = [member.name for member in StudyQualityCheck] + + DATASET = [ + # Good study no flag: + ("S1", None), + # Good study permissive flag: + ("S2", "This type of study is not supported"), + ("S2", "No valid disease identifier found"), + # Bad study: + ("S3", "The identifier of this study is not unique"), + ("S3", "This type of study is not supported"), + ] + + @pytest.fixture(autouse=True) + def _setup(self: TestDataExclusion, spark: SparkSession) -> None: + """Setup study the mock index for testing.""" + self.study_index = StudyIndex( + _df=( + spark.createDataFrame(self.DATASET, ["studyId", "flag"]) + .groupBy("studyId") + .agg(f.collect_list("flag").alias("qualityControls")) + .select( + "studyId", + "qualityControls", + f.lit("project1").alias("projectId"), + f.lit("gwas").alias("studyType"), + ) + ), + _schema=StudyIndex.get_schema(), + ) + + @pytest.mark.parametrize( + "filter_, expected", + [ + (CORRECT_FLAG, ["S1", "S2"]), + (ALL_FLAGS, ["S1"]), + ], + ) + def test_valid_rows( + self: TestDataExclusion, filter_: list[str], expected: list[str] + ) -> None: + """Test valid rows.""" + passing_studies = [ + study["studyId"] + for study in self.study_index.valid_rows( + filter_, invalid=False + ).df.collect() + ] + + assert passing_studies == expected + + @pytest.mark.parametrize( + "filter_, expected", + [ + (CORRECT_FLAG, ["S3"]), + (ALL_FLAGS, ["S2", "S3"]), + ], + ) + def test_invalid_rows( + self: TestDataExclusion, filter_: list[str], expected: list[str] + ) -> None: + """Test invalid rows.""" + failing_studies = [ + study["studyId"] + for study in self.study_index.valid_rows(filter_, invalid=True).df.collect() + ] + + assert failing_studies == expected + + def test_failing_quality_flag(self: TestDataExclusion) -> None: + """Test invalid quality flag.""" + with pytest.raises(ValueError): + self.study_index.valid_rows(self.INCORRECT_FLAG, invalid=True).df.collect() + + with pytest.raises(ValueError): + self.study_index.valid_rows(self.INCORRECT_FLAG, invalid=False).df.collect() diff --git a/tests/gentropy/dataset/test_gene_index.py b/tests/gentropy/dataset/test_gene_index.py index 68c279cab..e4ae8e581 100644 --- a/tests/gentropy/dataset/test_gene_index.py +++ b/tests/gentropy/dataset/test_gene_index.py @@ -1,9 +1,11 @@ """Tests on LD index.""" + from __future__ import annotations -from gentropy.dataset.gene_index import GeneIndex from pyspark.sql import DataFrame +from gentropy.dataset.gene_index import GeneIndex + def test_gene_index_creation(mock_gene_index: GeneIndex) -> None: """Test gene index creation with mock gene index.""" diff --git a/tests/gentropy/dataset/test_intervals.py b/tests/gentropy/dataset/test_intervals.py deleted file mode 100644 index bfa12ad69..000000000 --- a/tests/gentropy/dataset/test_intervals.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Tests on LD index.""" -from __future__ import annotations - -from typing import TYPE_CHECKING - -from gentropy.dataset.v2g import V2G - -if TYPE_CHECKING: - from gentropy.dataset.intervals import Intervals - from gentropy.dataset.variant_index import VariantIndex - - -def test_interval_v2g_creation( - mock_intervals: Intervals, mock_variant_index: VariantIndex -) -> None: - """Test creation of V2G from intervals.""" - assert isinstance(mock_intervals.v2g(mock_variant_index), V2G) diff --git a/tests/gentropy/dataset/test_l2g.py b/tests/gentropy/dataset/test_l2g.py index 045b98d62..293735edd 100644 --- a/tests/gentropy/dataset/test_l2g.py +++ b/tests/gentropy/dataset/test_l2g.py @@ -1,8 +1,11 @@ """Tests on L2G datasets.""" + from __future__ import annotations from typing import TYPE_CHECKING +from pyspark.sql.types import FloatType + from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix from gentropy.dataset.l2g_gold_standard import L2GGoldStandard from gentropy.dataset.l2g_prediction import L2GPrediction @@ -31,7 +34,7 @@ def test_process_gene_interactions(sample_otp_interactions: DataFrame) -> None: ), "Gene interactions has a different schema." -def test_predictions(mock_l2g_predictions: L2GFeatureMatrix) -> None: +def test_predictions(mock_l2g_predictions: L2GPrediction) -> None: """Test L2G predictions creation with mock data.""" assert isinstance(mock_l2g_predictions, L2GPrediction) @@ -40,44 +43,47 @@ def test_filter_unique_associations(spark: SparkSession) -> None: """Test filter_unique_associations.""" mock_l2g_gs_df = spark.createDataFrame( [ - (1, "variant1", "study1", "gene1", "positive"), + ("1", "variant1", "study1", "gene1", "positive"), ( - 2, + "2", "variant2", "study1", "gene1", "negative", ), # in the same locus as sl1 and pointing to same gene, has to be dropped ( - 3, + "3", "variant3", "study1", "gene1", "positive", ), # in diff locus as sl1 and pointing to same gene, has to be kept ( - 4, + "4", "variant4", "study1", "gene2", "positive", ), # in same locus as sl1 and pointing to diff gene, has to be kept ], - "studyLocusId LONG, variantId STRING, studyId STRING, geneId STRING, goldStandardSet STRING", + "studyLocusId STRING, variantId STRING, studyId STRING, geneId STRING, goldStandardSet STRING", ) mock_sl_overlap_df = spark.createDataFrame( - [(1, 2, "variant2"), (1, 4, "variant4")], - "leftStudyLocusId LONG, rightStudyLocusId LONG, tagVariantId STRING", + [ + ("1", "2", "eqtl", "CHROM1", "variant2", None), + ("1", "4", "eqtl", "CHROM1", "variant4", None), + ], + StudyLocusOverlap.get_schema(), ) expected_df = spark.createDataFrame( [ - (1, "variant1", "study1", "gene1", "positive"), - (3, "variant3", "study1", "gene1", "positive"), - (4, "variant4", "study1", "gene2", "positive"), + ("1", "variant1", "study1", "gene1", "positive"), + ("3", "variant3", "study1", "gene1", "positive"), + ("4", "variant4", "study1", "gene2", "positive"), ], - "studyLocusId LONG, variantId STRING, studyId STRING, geneId STRING, goldStandardSet STRING", + "studyLocusId STRING, variantId STRING, studyId STRING, geneId STRING, goldStandardSet STRING", ) mock_l2g_gs = L2GGoldStandard( @@ -96,30 +102,30 @@ def test_remove_false_negatives(spark: SparkSession) -> None: """Test `remove_false_negatives`.""" mock_l2g_gs_df = spark.createDataFrame( [ - (1, "variant1", "study1", "gene1", "positive"), + ("1", "variant1", "study1", "gene1", "positive"), ( - 2, + "2", "variant2", "study1", "gene2", "negative", ), # gene2 is a partner of gene1, has to be dropped ( - 3, + "3", "variant3", "study1", "gene3", "negative", ), # gene 3 is not a partner of gene1, has to be kept ( - 4, + "4", "variant4", "study1", "gene4", "positive", ), # gene 4 is a partner of gene1, has to be kept because it's positive ], - "studyLocusId LONG, variantId STRING, studyId STRING, geneId STRING, goldStandardSet STRING", + "studyLocusId STRING, variantId STRING, studyId STRING, geneId STRING, goldStandardSet STRING", ) mock_interactions_df = spark.createDataFrame( @@ -133,11 +139,11 @@ def test_remove_false_negatives(spark: SparkSession) -> None: expected_df = spark.createDataFrame( [ - (1, "variant1", "study1", "gene1", "positive"), - (3, "variant3", "study1", "gene3", "negative"), - (4, "variant4", "study1", "gene4", "positive"), + ("1", "variant1", "study1", "gene1", "positive"), + ("3", "variant3", "study1", "gene3", "negative"), + ("4", "variant4", "study1", "gene4", "positive"), ], - "studyLocusId LONG, variantId STRING, studyId STRING, geneId STRING, goldStandardSet STRING", + "studyLocusId STRING, variantId STRING, studyId STRING, geneId STRING, goldStandardSet STRING", ) mock_l2g_gs = L2GGoldStandard( @@ -151,24 +157,36 @@ def test_remove_false_negatives(spark: SparkSession) -> None: assert observed_df.collect() == expected_df.collect() -def test_calculate_feature_missingness_rate(spark: SparkSession) -> None: - """Test L2GFeatureMatrix.calculate_feature_missingness_rate.""" +def test_l2g_feature_constructor_with_schema_mismatch( + spark: SparkSession, +) -> None: + """Test if provided schema mismatch is converted to right type in the L2GFeatureMatrix constructor.""" fm = L2GFeatureMatrix( _df=spark.createDataFrame( [ - (1, "gene1", 100.0, None), - (2, "gene2", 1000.0, 0.0), + ("1", "gene1", 100.0), + ("2", "gene2", 1000.0), ], - "studyLocusId LONG, geneId STRING, distanceTssMean DOUBLE, distanceTssMinimum DOUBLE", + "studyLocusId STRING, geneId STRING, distanceTssMean DOUBLE", ), - _schema=L2GFeatureMatrix.get_schema(), + with_gold_standard=False, ) + assert ( + fm._df.schema["distanceTssMean"].dataType == FloatType() + ), "Feature `distanceTssMean` is not being casted to FloatType. Check L2GFeatureMatrix constructor." - expected_missingness = {"distanceTssMean": 0.0, "distanceTssMinimum": 1.0} - observed_missingness = fm.calculate_feature_missingness_rate() + +def test_calculate_feature_missingness_rate( + spark: SparkSession, mock_l2g_feature_matrix: L2GFeatureMatrix +) -> None: + """Test L2GFeatureMatrix.calculate_feature_missingness_rate.""" + expected_missingness = {"distanceTssMean": 0.0, "distanceSentinelTssMinimum": 1.0} + observed_missingness = mock_l2g_feature_matrix.calculate_feature_missingness_rate() assert isinstance(observed_missingness, dict) - assert len(observed_missingness) == len( - fm.features_list # type: ignore + assert mock_l2g_feature_matrix.features_list is not None and len( + observed_missingness + ) == len( + mock_l2g_feature_matrix.features_list ), "Missing features in the missingness rate dictionary." assert ( observed_missingness == expected_missingness diff --git a/tests/gentropy/dataset/test_l2g_feature.py b/tests/gentropy/dataset/test_l2g_feature.py new file mode 100644 index 000000000..0637b4a86 --- /dev/null +++ b/tests/gentropy/dataset/test_l2g_feature.py @@ -0,0 +1,1092 @@ +# pylint: disable=too-few-public-methods +# isort: skip_file + +"""Test locus-to-gene feature generation.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +import pyspark.sql.functions as f +import pytest +from pyspark.sql.types import ( + ArrayType, + BooleanType, + FloatType, + IntegerType, + LongType, + StringType, + StructField, + StructType, +) + +from gentropy.dataset.colocalisation import Colocalisation +from gentropy.dataset.gene_index import GeneIndex +from gentropy.dataset.l2g_features.colocalisation import ( + EQtlColocClppMaximumFeature, + EQtlColocClppMaximumNeighbourhoodFeature, + EQtlColocH4MaximumFeature, + EQtlColocH4MaximumNeighbourhoodFeature, + PQtlColocClppMaximumFeature, + PQtlColocClppMaximumNeighbourhoodFeature, + PQtlColocH4MaximumFeature, + PQtlColocH4MaximumNeighbourhoodFeature, + SQtlColocClppMaximumFeature, + SQtlColocClppMaximumNeighbourhoodFeature, + SQtlColocH4MaximumFeature, + SQtlColocH4MaximumNeighbourhoodFeature, + common_colocalisation_feature_logic, + common_neighbourhood_colocalisation_feature_logic, + extend_missing_colocalisation_to_neighbourhood_genes, +) +from gentropy.dataset.l2g_features.distance import ( + DistanceFootprintMeanFeature, + DistanceFootprintMeanNeighbourhoodFeature, + DistanceSentinelFootprintFeature, + DistanceSentinelFootprintNeighbourhoodFeature, + DistanceSentinelTssFeature, + DistanceSentinelTssNeighbourhoodFeature, + DistanceTssMeanFeature, + DistanceTssMeanNeighbourhoodFeature, + common_distance_feature_logic, + common_neighbourhood_distance_feature_logic, +) +from gentropy.dataset.l2g_features.l2g_feature import L2GFeature +from gentropy.dataset.l2g_features.vep import ( + VepMaximumFeature, + VepMaximumNeighbourhoodFeature, + VepMeanFeature, + VepMeanNeighbourhoodFeature, + common_neighbourhood_vep_feature_logic, + common_vep_feature_logic, +) +from gentropy.dataset.l2g_features.other import ( + common_genecount_feature_logic, + is_protein_coding_feature_logic, + GeneCountFeature, + ProteinGeneCountFeature, + CredibleSetConfidenceFeature, + ProteinCodingFeature, +) +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_locus import StudyLocus +from gentropy.dataset.variant_index import VariantIndex +from gentropy.method.l2g.feature_factory import L2GFeatureInputLoader + +if TYPE_CHECKING: + from pyspark.sql import SparkSession + + +@pytest.mark.parametrize( + "feature_class", + [ + EQtlColocH4MaximumFeature, + PQtlColocH4MaximumFeature, + SQtlColocH4MaximumFeature, + EQtlColocClppMaximumFeature, + PQtlColocClppMaximumFeature, + SQtlColocClppMaximumFeature, + EQtlColocClppMaximumNeighbourhoodFeature, + PQtlColocClppMaximumNeighbourhoodFeature, + SQtlColocClppMaximumNeighbourhoodFeature, + EQtlColocH4MaximumNeighbourhoodFeature, + PQtlColocH4MaximumNeighbourhoodFeature, + SQtlColocH4MaximumNeighbourhoodFeature, + DistanceTssMeanFeature, + DistanceTssMeanNeighbourhoodFeature, + DistanceFootprintMeanFeature, + DistanceFootprintMeanNeighbourhoodFeature, + DistanceSentinelTssFeature, + DistanceSentinelTssNeighbourhoodFeature, + DistanceSentinelFootprintFeature, + DistanceSentinelFootprintNeighbourhoodFeature, + VepMaximumFeature, + VepMeanFeature, + VepMaximumNeighbourhoodFeature, + VepMeanNeighbourhoodFeature, + GeneCountFeature, + ProteinGeneCountFeature, + CredibleSetConfidenceFeature, + ProteinCodingFeature, + ], +) +def test_feature_factory_return_type( + feature_class: Any, + mock_study_locus: StudyLocus, + mock_colocalisation: Colocalisation, + mock_study_index: StudyIndex, + mock_variant_index: VariantIndex, + mock_gene_index: GeneIndex, +) -> None: + """Test that every feature factory returns a L2GFeature dataset.""" + loader = L2GFeatureInputLoader( + colocalisation=mock_colocalisation, + study_index=mock_study_index, + variant_index=mock_variant_index, + study_locus=mock_study_locus, + gene_index=mock_gene_index, + ) + feature_dataset = feature_class.compute( + study_loci_to_annotate=mock_study_locus, + feature_dependency=loader.get_dependency_by_type( + feature_class.feature_dependency_type + ), + ) + assert isinstance(feature_dataset, L2GFeature) + + +@pytest.fixture(scope="module") +def sample_gene_index(spark: SparkSession) -> GeneIndex: + """Create a sample gene index for testing.""" + return GeneIndex( + _df=spark.createDataFrame( + [ + { + "geneId": "gene1", + "biotype": "protein_coding", + "chromosome": "1", + }, + { + "geneId": "gene2", + "biotype": "lncRNA", + "chromosome": "1", + }, + { + "geneId": "gene3", + "biotype": "protein_coding", + "chromosome": "1", + }, + ], + GeneIndex.get_schema(), + ), + _schema=GeneIndex.get_schema(), + ) + + +@pytest.fixture(scope="module") +def sample_variant_index(spark: SparkSession) -> VariantIndex: + """Create a sample variant index for testing.""" + return VariantIndex( + _df=spark.createDataFrame( + [ + ( + "var1", + "chrom", + 1, + "A", + "T", + [ + { + "targetId": "gene1", + "consequenceScore": 0.66, + "isEnsemblCanonical": True, + }, + { + "targetId": "gene2", + "consequenceScore": 1.0, + "isEnsemblCanonical": True, + }, + { + "targetId": "gene3", + "consequenceScore": 0.0, + "isEnsemblCanonical": True, + }, + ], + ), + ], + schema=StructType( + [ + StructField("variantId", StringType(), True), + StructField("chromosome", StringType(), True), + StructField("position", IntegerType(), True), + StructField("referenceAllele", StringType(), True), + StructField("alternateAllele", StringType(), True), + StructField( + "transcriptConsequences", + ArrayType( + StructType( + [ + StructField("targetId", StringType(), True), + StructField( + "isEnsemblCanonical", BooleanType(), True + ), + StructField("consequenceScore", FloatType(), True), + ] + ) + ), + True, + ), + ] + ), + ), + _schema=VariantIndex.get_schema(), + ) + + +@pytest.fixture(scope="module") +def sample_variant_index_schema() -> StructType: + """Partial schema of the variant index.""" + return StructType( + [ + StructField("variantId", StringType(), True), + StructField("chromosome", StringType(), True), + StructField("position", IntegerType(), True), + StructField("referenceAllele", StringType(), True), + StructField("alternateAllele", StringType(), True), + StructField( + "transcriptConsequences", + ArrayType( + StructType( + [ + StructField("distanceFromTss", LongType(), True), + StructField("targetId", StringType(), True), + StructField("isEnsemblCanonical", BooleanType(), True), + ] + ) + ), + True, + ), + ] + ) + + +class TestCommonColocalisationFeatureLogic: + """Test the common logic of the colocalisation features.""" + + def test__common_colocalisation_feature_logic( + self: TestCommonColocalisationFeatureLogic, + spark: SparkSession, + ) -> None: + """Test the common logic of the colocalisation features. + + The test data associates studyLocusId1 with gene1 based on the colocalisation with studyLocusId2 and studyLocusId3. + The H4 value of number 2 is higher, therefore the feature value should be based on that. + """ + feature_name = "eQtlColocH4Maximum" + observed_df = common_colocalisation_feature_logic( + self.sample_study_loci_to_annotate, + self.colocalisation_method, + self.colocalisation_metric, + feature_name, + self.qtl_type, + colocalisation=self.sample_colocalisation, + study_index=self.sample_studies, + study_locus=self.sample_study_locus, + ) + expected_df = spark.createDataFrame( + [ + { + "studyLocusId": "1", + "geneId": "gene1", + "eQtlColocH4Maximum": 0.81, + }, + { + "studyLocusId": "1", + "geneId": "gene2", + "eQtlColocH4Maximum": 0.9, + }, + ], + ).select("studyLocusId", "geneId", "eQtlColocH4Maximum") + assert ( + observed_df.collect() == expected_df.collect() + ), "The feature values are not as expected." + + def test_extend_missing_colocalisation_to_neighbourhood_genes( + self: TestCommonColocalisationFeatureLogic, + spark: SparkSession, + sample_gene_index: GeneIndex, + sample_variant_index: VariantIndex, + ) -> None: + """Test the extend_missing_colocalisation_to_neighbourhood_genes function.""" + local_features = spark.createDataFrame( + [ + { + "studyLocusId": "1", + "geneId": "gene1", + "eQtlColocH4Maximum": 0.81, + }, + { + "studyLocusId": "1", + "geneId": "gene2", + "eQtlColocH4Maximum": 0.9, + }, + ], + ) + observed_df = extend_missing_colocalisation_to_neighbourhood_genes( + feature_name="eQtlColocH4Maximum", + local_features=local_features, + variant_index=sample_variant_index, + gene_index=sample_gene_index, + study_locus=self.sample_study_locus, + ).select("studyLocusId", "geneId", "eQtlColocH4Maximum") + expected_df = spark.createDataFrame( + [{"geneId": "gene3", "studyLocusId": "1", "eQtlColocH4Maximum": 0.0}] + ).select("studyLocusId", "geneId", "eQtlColocH4Maximum") + assert ( + observed_df.collect() == expected_df.collect() + ), "The feature values are not as expected." + + def test_common_neighbourhood_colocalisation_feature_logic( + self: TestCommonColocalisationFeatureLogic, + spark: SparkSession, + sample_gene_index: GeneIndex, + sample_variant_index: VariantIndex, + ) -> None: + """Test the common logic of the neighbourhood colocalisation features.""" + feature_name = "eQtlColocH4MaximumNeighbourhood" + observed_df = common_neighbourhood_colocalisation_feature_logic( + self.sample_study_loci_to_annotate, + self.colocalisation_method, + self.colocalisation_metric, + feature_name, + self.qtl_type, + colocalisation=self.sample_colocalisation, + study_index=self.sample_studies, + study_locus=self.sample_study_locus, + gene_index=sample_gene_index, + variant_index=sample_variant_index, + ).withColumn(feature_name, f.round(f.col(feature_name), 3)) + # expected average is (0.81 + 0)/2 = 0.405 + expected_df = spark.createDataFrame( + [ + { + "studyLocusId": "1", + "geneId": "gene1", + "eQtlColocH4MaximumNeighbourhood": 0.405, # 0.81 - 0.405 + }, + { + "studyLocusId": "1", + "geneId": "gene2", + "eQtlColocH4MaximumNeighbourhood": 0.495, # 0.9 - 0.405 + }, + ], + ).select("studyLocusId", "geneId", "eQtlColocH4MaximumNeighbourhood") + assert ( + observed_df.collect() == expected_df.collect() + ), "The expected and observed dataframes do not match." + + @pytest.fixture(autouse=True) + def _setup(self: TestCommonColocalisationFeatureLogic, spark: SparkSession) -> None: + """Set up the test variables.""" + self.colocalisation_method = "Coloc" + self.colocalisation_metric = "h4" + self.qtl_type = "eqtl" + + self.sample_study_loci_to_annotate = StudyLocus( + _df=spark.createDataFrame( + [ + { + "studyLocusId": "1", + "variantId": "var1", + "studyId": "study1", # this is a GWAS + "chromosome": "1", + }, + ] + ), + _schema=StudyLocus.get_schema(), + ) + + self.sample_colocalisation = Colocalisation( + _df=spark.createDataFrame( + [ + { + "leftStudyLocusId": "1", + "rightStudyLocusId": "2", + "chromosome": "1", + "colocalisationMethod": "COLOC", + "numberColocalisingVariants": 1, + "h4": 0.81, + "rightStudyType": "eqtl", + }, + { + "leftStudyLocusId": "1", + "rightStudyLocusId": "3", # qtl linked to the same gene as studyLocusId 2 with a lower score + "chromosome": "1", + "colocalisationMethod": "COLOC", + "numberColocalisingVariants": 1, + "h4": 0.50, + "rightStudyType": "eqtl", + }, + { + "leftStudyLocusId": "1", + "rightStudyLocusId": "4", # qtl linked to a diff gene and with the highest score + "chromosome": "1", + "colocalisationMethod": "COLOC", + "numberColocalisingVariants": 1, + "h4": 0.90, + "rightStudyType": "eqtl", + }, + ], + schema=Colocalisation.get_schema(), + ), + _schema=Colocalisation.get_schema(), + ) + self.sample_study_locus = StudyLocus( + _df=spark.createDataFrame( + [ + { + "studyLocusId": "1", + "variantId": "var1", + "studyId": "study1", # this is a GWAS + "chromosome": "1", + }, + { + "studyLocusId": "2", + "variantId": "var1", + "studyId": "study2", # this is a QTL (same gene) + "chromosome": "1", + }, + { + "studyLocusId": "3", + "variantId": "var1", + "studyId": "study3", # this is another QTL (same gene) + "chromosome": "1", + }, + { + "studyLocusId": "4", + "variantId": "var1", + "studyId": "study4", # this is another QTL (diff gene) + "chromosome": "1", + }, + ] + ), + _schema=StudyLocus.get_schema(), + ) + self.sample_studies = StudyIndex( + _df=spark.createDataFrame( + [ + { + "studyId": "study1", + "studyType": "gwas", + "geneId": None, + "traitFromSource": "trait1", + "projectId": "project1", + }, + { + "studyId": "study2", + "studyType": "eqtl", + "geneId": "gene1", + "traitFromSource": "trait2", + "projectId": "project2", + }, + { + "studyId": "study3", + "studyType": "eqtl", + "geneId": "gene1", + "traitFromSource": "trait3", + "projectId": "project3", + }, + { + "studyId": "study4", + "studyType": "eqtl", + "geneId": "gene2", + "traitFromSource": "trait4", + "projectId": "project4", + }, + ] + ), + _schema=StudyIndex.get_schema(), + ) + + +class TestCommonDistanceFeatureLogic: + """Test the CommonDistanceFeatureLogic methods.""" + + @pytest.mark.parametrize( + ("feature_name", "expected_data"), + [ + ( + "distanceSentinelTss", + [ + { + "studyLocusId": "1", + "geneId": "gene1", + "distanceSentinelTss": 0.0, + }, + { + "studyLocusId": "1", + "geneId": "gene2", + "distanceSentinelTss": 0.92, + }, + ], + ), + ( + "distanceTssMean", + [ + {"studyLocusId": "1", "geneId": "gene1", "distanceTssMean": 0.52}, + {"studyLocusId": "1", "geneId": "gene2", "distanceTssMean": 0.63}, + ], + ), + ], + ) + def test_common_distance_feature_logic( + self: TestCommonDistanceFeatureLogic, + spark: SparkSession, + feature_name: str, + expected_data: dict[str, Any], + ) -> None: + """Test the logic of the function that extracts features from distance. + + 2 tests: + - distanceSentinelTss: distance of the sentinel is 10, the max distance is 10. In log scale, the score is 0. + - distanceTssMean: avg distance of any variant in the credible set, weighted by its posterior. + """ + observed_df = ( + common_distance_feature_logic( + self.sample_study_locus, + variant_index=self.sample_variant_index, + feature_name=feature_name, + distance_type=self.distance_type, + genomic_window=10, + ) + .withColumn(feature_name, f.round(f.col(feature_name), 2)) + .orderBy(feature_name) + ) + expected_df = ( + spark.createDataFrame(expected_data) + .select("studyLocusId", "geneId", feature_name) + .orderBy(feature_name) + ) + assert ( + observed_df.collect() == expected_df.collect() + ), f"Expected and observed dataframes are not equal for feature {feature_name}." + + def test_common_neighbourhood_distance_feature_logic( + self: TestCommonDistanceFeatureLogic, + spark: SparkSession, + ) -> None: + """Test the logic of the function that extracts the distance between the sentinel of a credible set and the nearby genes.""" + feature_name = "distanceSentinelTssNeighbourhood" + observed_df = ( + common_neighbourhood_distance_feature_logic( + self.sample_study_locus, + variant_index=self.sample_variant_index, + feature_name=feature_name, + distance_type=self.distance_type, + genomic_window=10, + ) + .withColumn(feature_name, f.round(f.col(feature_name), 2)) + .orderBy(f.col(feature_name).asc()) + ) + expected_df = spark.createDataFrame( + (["1", "gene1", -0.44], ["1", "gene2", 0.44]), + ["studyLocusId", "geneId", feature_name], + ).orderBy(feature_name) + assert ( + observed_df.collect() == expected_df.collect() + ), "Output doesn't meet the expectation." + + @pytest.fixture(autouse=True) + def _setup( + self: TestCommonDistanceFeatureLogic, + spark: SparkSession, + sample_variant_index_schema: StructType, + ) -> None: + """Set up testing fixtures.""" + self.distance_type = "distanceFromTss" + self.sample_study_locus = StudyLocus( + _df=spark.createDataFrame( + [ + { + "studyLocusId": "1", + "variantId": "lead1", + "studyId": "study1", + "locus": [ + { + "variantId": "lead1", + "posteriorProbability": 0.5, + }, + { + "variantId": "tag1", + "posteriorProbability": 0.5, + }, + ], + "chromosome": "1", + }, + ], + StudyLocus.get_schema(), + ), + _schema=StudyLocus.get_schema(), + ) + self.sample_variant_index = VariantIndex( + _df=spark.createDataFrame( + [ + ( + "lead1", + "chrom", + 1, + "A", + "T", + [ + { + "distanceFromTss": 10, + "targetId": "gene1", + "isEnsemblCanonical": True, + }, + { + "distanceFromTss": 2, + "targetId": "gene2", + "isEnsemblCanonical": True, + }, + ], + ), + ( + "tag1", + "chrom", + 1, + "A", + "T", + [ + { + "distanceFromTss": 5, + "targetId": "gene1", + "isEnsemblCanonical": True, + }, + ], + ), + ], + sample_variant_index_schema, + ), + _schema=VariantIndex.get_schema(), + ) + + +class TestCommonVepFeatureLogic: + """Test the common_vep_feature_logic methods.""" + + @pytest.mark.parametrize( + ("feature_name", "expected_data"), + [ + ( + "vepMean", + [ + { + "studyLocusId": "1", + "geneId": "gene3", + "vepMean": "0.00", + }, + { + "studyLocusId": "1", + "geneId": "gene1", + "vepMean": "0.33", + }, + { + "studyLocusId": "1", + "geneId": "gene2", + "vepMean": "0.50", + }, + ], + ), + ( + "vepMaximum", + [ + { + "studyLocusId": "1", + "geneId": "gene3", + "vepMaximum": "0.00", + }, + { + "studyLocusId": "1", + "geneId": "gene1", + "vepMaximum": "0.66", + }, + { + "studyLocusId": "1", + "geneId": "gene2", + "vepMaximum": "1.00", + }, + ], + ), + ], + ) + def test_common_vep_feature_logic( + self: TestCommonVepFeatureLogic, + spark: SparkSession, + feature_name: str, + expected_data: dict[str, Any], + sample_variant_index: VariantIndex, + ) -> None: + """Test the logic of the function that extracts features from VEP's functional consequences.""" + observed_df = ( + common_vep_feature_logic( + self.sample_study_locus, + variant_index=sample_variant_index, + feature_name=feature_name, + ) + .orderBy(feature_name) + .withColumn( + feature_name, f.format_number(f.round(f.col(feature_name), 2), 2) + ) + ) + expected_df = ( + spark.createDataFrame(expected_data) + .orderBy(feature_name) + .select("studyLocusId", "geneId", feature_name) + ) + assert ( + observed_df.collect() == expected_df.collect() + ), f"Expected and observed dataframes are not equal for feature {feature_name}." + + def test_common_neighbourhood_vep_feature_logic_no_protein_coding( + self: TestCommonVepFeatureLogic, + spark: SparkSession, + sample_gene_index: GeneIndex, + sample_variant_index: VariantIndex, + ) -> None: + """Test the logic of the function that extracts the maximum severity score for a gene given the average of the maximum scores for all protein coding genes in the vicinity. + + Because the genes in the vicinity are all non coding, the neighbourhood features should equal the local ones. + """ + feature_name = "vepMaximumNeighbourhood" + non_protein_coding_gene_index = GeneIndex( + _df=sample_gene_index.df.filter(f.col("geneId") != "gene3"), + _schema=GeneIndex.get_schema(), + ) + observed_df = ( + common_neighbourhood_vep_feature_logic( + self.sample_study_locus, + variant_index=sample_variant_index, + gene_index=non_protein_coding_gene_index, + feature_name=feature_name, + ) + .withColumn(feature_name, f.round(f.col(feature_name), 2)) + .orderBy(f.col(feature_name).asc()) + .select("studyLocusId", "geneId", feature_name) + ) + expected_df = ( + spark.createDataFrame( + # regional mean is 0.66 + ( + ["1", "gene1", 0.0], + ["1", "gene2", 0.34], + ), # (0.66-0.66) and (1.0-0.66) + ["studyLocusId", "geneId", feature_name], + ) + .orderBy(feature_name) + .select("studyLocusId", "geneId", feature_name) + ) + assert ( + observed_df.collect() == expected_df.collect() + ), "Output doesn't meet the expectation." + + def test_common_neighbourhood_vep_feature_logic( + self: TestCommonVepFeatureLogic, + spark: SparkSession, + sample_gene_index: GeneIndex, + sample_variant_index: VariantIndex, + ) -> None: + """Test the logic of the function that extracts the maximum severity score for a gene given the average of the maximum scores for all protein coding genes in the vicinity.""" + feature_name = "vepMaximumNeighbourhood" + observed_df = ( + common_neighbourhood_vep_feature_logic( + self.sample_study_locus, + variant_index=sample_variant_index, + gene_index=sample_gene_index, + feature_name=feature_name, + ) + .withColumn(feature_name, f.round(f.col(feature_name), 2)) + .orderBy(f.col(feature_name).asc()) + ) + expected_df = ( + spark.createDataFrame( + # regional mean is 0.66/2 = 0.33 + ( + ["1", "gene3", -0.33], + ["1", "gene1", 0.33], + ["1", "gene2", 0.67], + ), # (0 - 0.33) and (0.66-0.33) and (1.0 -0.33) + ["studyLocusId", "geneId", feature_name], + ) + .orderBy(feature_name) + .select("studyLocusId", "geneId", feature_name) + ) + assert ( + observed_df.collect() == expected_df.collect() + ), "Output doesn't meet the expectation." + + @pytest.fixture(autouse=True) + def _setup(self: TestCommonVepFeatureLogic, spark: SparkSession) -> None: + """Set up testing fixtures.""" + self.sample_study_locus = StudyLocus( + _df=spark.createDataFrame( + [ + { + "studyLocusId": "1", + "variantId": "var1", + "studyId": "study1", + "locus": [ + { + "variantId": "var1", + "posteriorProbability": 0.5, + }, + ], + "chromosome": "1", + }, + ], + StudyLocus.get_schema(), + ), + _schema=StudyLocus.get_schema(), + ) + + +class TestCommonGeneCountFeatureLogic: + """Test the CommonGeneCountFeatureLogic methods.""" + + @pytest.mark.parametrize( + ("feature_name", "expected_data", "protein_coding_only"), + [ + ( + "geneCount", + [ + {"studyLocusId": "1", "geneId": "gene1", "geneCount": 3}, + {"studyLocusId": "1", "geneId": "gene2", "geneCount": 3}, + {"studyLocusId": "1", "geneId": "gene3", "geneCount": 3}, + ], + False, # Test case for all genes + ), + ( + "geneCountProteinCoding", + [ + { + "studyLocusId": "1", + "geneId": "gene1", + "geneCountProteinCoding": 2, + }, + { + "studyLocusId": "1", + "geneId": "gene2", + "geneCountProteinCoding": 2, + }, + ], + True, # Test case for protein-coding genes only + ), + ], + ) + def test_common_genecount_feature_logic( + self: TestCommonGeneCountFeatureLogic, + spark: SparkSession, + feature_name: str, + expected_data: list[dict[str, Any]], + protein_coding_only: bool, + ) -> None: + """Test the common logic of the gene count features.""" + observed_df = common_genecount_feature_logic( + study_loci_to_annotate=self.sample_study_locus, + gene_index=self.sample_gene_index, + feature_name=feature_name, + genomic_window=500000, + protein_coding_only=protein_coding_only, + ).orderBy("studyLocusId", "geneId") + expected_df = ( + spark.createDataFrame(expected_data) + .select("studyLocusId", "geneId", feature_name) + .orderBy("studyLocusId", "geneId") + ) + + assert ( + observed_df.collect() == expected_df.collect() + ), f"Expected and observed dataframes do not match for feature {feature_name}." + + @pytest.fixture(autouse=True) + def _setup(self: TestCommonGeneCountFeatureLogic, spark: SparkSession) -> None: + """Set up testing fixtures.""" + self.sample_study_locus = StudyLocus( + _df=spark.createDataFrame( + [ + { + "studyLocusId": "1", + "variantId": "var1", + "studyId": "study1", + "chromosome": "1", + "position": 1000000, + }, + ], + StudyLocus.get_schema(), + ), + _schema=StudyLocus.get_schema(), + ) + self.sample_gene_index = GeneIndex( + _df=spark.createDataFrame( + [ + { + "geneId": "gene1", + "chromosome": "1", + "tss": 950000, + "biotype": "protein_coding", + }, + { + "geneId": "gene2", + "chromosome": "1", + "tss": 1050000, + "biotype": "protein_coding", + }, + { + "geneId": "gene3", + "chromosome": "1", + "tss": 1010000, + "biotype": "non_coding", + }, + ], + GeneIndex.get_schema(), + ), + _schema=GeneIndex.get_schema(), + ) + + +class TestCommonProteinCodingFeatureLogic: + """Test the CommonGeneCountFeatureLogic methods.""" + + @pytest.mark.parametrize( + ("expected_data"), + [ + ( + [ + {"studyLocusId": "1", "geneId": "gene1", "isProteinCoding500kb": 1}, + {"studyLocusId": "1", "geneId": "gene2", "isProteinCoding500kb": 1}, + {"studyLocusId": "1", "geneId": "gene3", "isProteinCoding500kb": 0}, + ] + ), + ], + ) + def test_is_protein_coding_feature_logic( + self: TestCommonProteinCodingFeatureLogic, + spark: SparkSession, + expected_data: list[dict[str, Any]], + ) -> None: + """Test the logic of the is_protein_coding_feature_logic function.""" + observed_df = ( + is_protein_coding_feature_logic( + study_loci_to_annotate=self.sample_study_locus, + gene_index=self.sample_gene_index, + feature_name="isProteinCoding500kb", + genomic_window=500000, + ) + .select("studyLocusId", "geneId", "isProteinCoding500kb") + .orderBy("studyLocusId", "geneId") + ) + + expected_df = ( + spark.createDataFrame(expected_data) + .select("studyLocusId", "geneId", "isProteinCoding500kb") + .orderBy("studyLocusId", "geneId") + ) + assert ( + observed_df.collect() == expected_df.collect() + ), "Expected and observed DataFrames do not match." + + @pytest.fixture(autouse=True) + def _setup(self: TestCommonProteinCodingFeatureLogic, spark: SparkSession) -> None: + """Set up sample data for the test.""" + # Sample study locus data + self.sample_study_locus = StudyLocus( + _df=spark.createDataFrame( + [ + { + "studyLocusId": "1", + "variantId": "var1", + "studyId": "study1", + "chromosome": "1", + "position": 1000000, + }, + ], + StudyLocus.get_schema(), + ), + _schema=StudyLocus.get_schema(), + ) + + # Sample gene index data with biotype + self.sample_gene_index = GeneIndex( + _df=spark.createDataFrame( + [ + { + "geneId": "gene1", + "chromosome": "1", + "tss": 950000, + "biotype": "protein_coding", + }, + { + "geneId": "gene2", + "chromosome": "1", + "tss": 1050000, + "biotype": "protein_coding", + }, + { + "geneId": "gene3", + "chromosome": "1", + "tss": 1010000, + "biotype": "non_coding", + }, + ], + GeneIndex.get_schema(), + ), + _schema=GeneIndex.get_schema(), + ) + + +class TestCredibleSetConfidenceFeatureLogic: + """Test the CredibleSetConfidenceFeature method.""" + + def test_compute( + self: TestCredibleSetConfidenceFeatureLogic, + spark: SparkSession, + ) -> None: + """Test the logic of the function that scores a credible set's confidence.""" + sample_study_loci_to_annotate = self.sample_study_locus + observed_df = CredibleSetConfidenceFeature.compute( + study_loci_to_annotate=sample_study_loci_to_annotate, + feature_dependency={ + "study_locus": self.sample_study_locus, + "variant_index": self.sample_variant_index, + }, + ) + assert observed_df.df.first()["featureValue"] == 0.25 + + @pytest.fixture(autouse=True) + def _setup( + self: TestCredibleSetConfidenceFeatureLogic, + spark: SparkSession, + sample_variant_index_schema: StructType, + ) -> None: + """Set up testing fixtures.""" + self.sample_study_locus = StudyLocus( + _df=spark.createDataFrame( + [ + { + "studyLocusId": "1", + "variantId": "lead1", + "studyId": "study1", + "confidence": "PICS fine-mapped credible set based on reported top hit", + "chromosome": "1", + "locus": [ + { + "variantId": "lead1", + }, + ], + }, + ], + StudyLocus.get_schema(), + ), + _schema=StudyLocus.get_schema(), + ) + self.sample_variant_index = VariantIndex( + _df=spark.createDataFrame( + [ + ( + "lead1", + "chrom", + 1, + "A", + "T", + [ + { + "distanceFromTss": 10, + "targetId": "gene1", + "isEnsemblCanonical": True, + }, + ], + ) + ], + sample_variant_index_schema, + ), + _schema=VariantIndex.get_schema(), + ) diff --git a/tests/gentropy/dataset/test_l2g_feature_matrix.py b/tests/gentropy/dataset/test_l2g_feature_matrix.py new file mode 100644 index 000000000..4fe338254 --- /dev/null +++ b/tests/gentropy/dataset/test_l2g_feature_matrix.py @@ -0,0 +1,243 @@ +"""Test L2G feature matrix methods.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pyspark.sql.functions as f +import pytest +from pyspark.sql.types import ( + ArrayType, + DoubleType, + IntegerType, + StringType, + StructField, + StructType, +) + +from gentropy.dataset.colocalisation import Colocalisation +from gentropy.dataset.gene_index import GeneIndex +from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix +from gentropy.dataset.l2g_gold_standard import L2GGoldStandard +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_locus import StudyLocus +from gentropy.method.l2g.feature_factory import L2GFeatureInputLoader + +if TYPE_CHECKING: + from pyspark.sql import SparkSession + + +def test_select_features_inheritance( + spark: SparkSession, mock_l2g_feature_matrix: L2GFeatureMatrix +) -> None: + """Test L2GFeatureMatrix.select_features method inherits the instance attributes in the new instance.""" + new_instance = mock_l2g_feature_matrix.select_features( + features_list=["distanceTssMean"] + ) + assert new_instance.features_list == ["distanceTssMean"] + # Because the feature matrix contains the gold standard flag information, the new fixed colums should be the same + assert "goldStandardSet" in new_instance.fixed_cols + + +class TestFromFeaturesList: + """Test L2GFeatureMatrix.from_features_list method. + + If the columns from the features list are there, it means that the business logic is working (the dataframe is not empty when converting from long to wide). + """ + + def test_study_locus( + self: TestFromFeaturesList, + ) -> None: + """Test building feature matrix for a SL with the eQtlColocH4Maximum feature.""" + features_list = ["eQtlColocH4Maximum", "geneCount500kb"] + loader = L2GFeatureInputLoader( + colocalisation=self.sample_colocalisation, + study_index=self.sample_study_index, + study_locus=self.sample_study_locus, + gene_index=self.sample_gene_index, + ) + fm = L2GFeatureMatrix.from_features_list( + self.sample_study_locus, features_list, loader + ) + for feature in features_list: + assert ( + feature in fm._df.columns + ), f"Feature {feature} not found in feature matrix." + + def test_gold_standard( + self: TestFromFeaturesList, + ) -> None: + """Test building feature matrix for a gold standard with the eQtlColocH4Maximum feature.""" + features_list = ["eQtlColocH4Maximum"] + loader = L2GFeatureInputLoader( + colocalisation=self.sample_colocalisation, + study_index=self.sample_study_index, + study_locus=self.sample_study_locus, + ) + fm = L2GFeatureMatrix.from_features_list( + self.sample_gold_standard, features_list, loader + ) + for feature in features_list: + assert ( + feature in fm._df.columns + ), f"Feature {feature} not found in feature matrix." + + @pytest.fixture(autouse=True) + def _setup(self: TestFromFeaturesList, spark: SparkSession) -> None: + """Setup fixture.""" + self.sample_gold_standard = L2GGoldStandard( + _df=spark.createDataFrame( + [(1, "var1", "gwas1", "g1", "positive", ["a_source"])], + L2GGoldStandard.get_schema(), + ), + _schema=L2GGoldStandard.get_schema(), + ) + self.sample_study_locus = StudyLocus( + _df=spark.createDataFrame( + [ + ( + "1", + "var1", + "gwas1", + "X", + 2, + [ + {"variantId": "var1", "posteriorProbability": 0.8}, + {"variantId": "var12", "posteriorProbability": 0.2}, + ], + ), + ( + "2", + "var2", + "eqtl1", + "X", + 10, + [ + {"variantId": "var2", "posteriorProbability": 1.0}, + ], + ), + ], + schema=StructType( + [ + StructField("studyLocusId", StringType(), True), + StructField("variantId", StringType(), True), + StructField("studyId", StringType(), True), + StructField("chromosome", StringType(), True), + StructField("position", IntegerType(), True), + StructField( + "locus", + ArrayType( + StructType( + [ + StructField("variantId", StringType(), True), + StructField( + "posteriorProbability", DoubleType(), True + ), + ] + ) + ), + True, + ), + ] + ), + ), + _schema=StudyLocus.get_schema(), + ) + self.sample_study_index = StudyIndex( + _df=spark.createDataFrame( + [("gwas1", "gwas", None, "p1"), ("eqtl1", "eqtl", "g1", "p2")], + [ + "studyId", + "studyType", + "geneId", + "projectId", + ], + ), + _schema=StudyIndex.get_schema(), + ) + self.sample_colocalisation = Colocalisation( + _df=spark.createDataFrame( + [("1", "2", "eqtl", "X", "COLOC", 1, 0.9)], + [ + "leftStudyLocusId", + "rightStudyLocusId", + "rightStudyType", + "chromosome", + "colocalisationMethod", + "numberColocalisingVariants", + "h4", + ], + ), + _schema=Colocalisation.get_schema(), + ) + self.sample_gene_index = GeneIndex( + _df=spark.createDataFrame( + [ + ("g1", "X", "protein_coding", 200), + ("g2", "X", "protein_coding", 300), + ], + [ + "geneId", + "chromosome", + "biotype", + "tss", + ], + ), + _schema=GeneIndex.get_schema(), + ) + + +def test_fill_na(spark: SparkSession) -> None: + """Tests L2GFeatureMatrix.fill_na, particularly the imputation logic.""" + sample_fm = L2GFeatureMatrix( + _df=spark.createDataFrame( + [ + { + "studyLocusId": "1", + "geneId": "gene1", + "proteinGeneCount500kb": 3.0, + "geneCount500kb": 8.0, + "isProteinCoding": 1.0, + "anotherFeature": None, + }, + { + "studyLocusId": "1", + "geneId": "gene2", + "proteinGeneCount500kb": 4.0, + "geneCount500kb": 10.0, + "isProteinCoding": 1.0, + "anotherFeature": None, + }, + { + "studyLocusId": "1", + "geneId": "gene3", + "proteinGeneCount500kb": None, + "geneCount500kb": None, + "isProteinCoding": None, + "anotherFeature": None, + }, + ], + schema="studyLocusId STRING, geneId STRING, proteinGeneCount500kb DOUBLE, geneCount500kb DOUBLE, isProteinCoding DOUBLE, anotherFeature DOUBLE", + ), + ) + observed_df = sample_fm.fill_na()._df.filter(f.col("geneId") == "gene3") + expected_df_missing_row = spark.createDataFrame( + [ + { + "studyLocusId": "1", + "geneId": "gene3", + "proteinGeneCount500kb": 3.5, + "geneCount500kb": 9.0, + "isProteinCoding": 0.0, + "anotherFeature": 0.0, + }, + ], + ).select( + "studyLocusId", + "geneId", + "proteinGeneCount500kb", + "geneCount500kb", + "isProteinCoding", + "anotherFeature", + ) + assert observed_df.collect() == expected_df_missing_row.collect() diff --git a/tests/gentropy/dataset/test_ld_index.py b/tests/gentropy/dataset/test_ld_index.py index c3eff6a4d..dc2b49c09 100644 --- a/tests/gentropy/dataset/test_ld_index.py +++ b/tests/gentropy/dataset/test_ld_index.py @@ -1,4 +1,5 @@ """Tests on LD index.""" + from __future__ import annotations from gentropy.dataset.ld_index import LDIndex diff --git a/tests/gentropy/dataset/test_pairwise_ld.py b/tests/gentropy/dataset/test_pairwise_ld.py new file mode 100644 index 000000000..9a57c129b --- /dev/null +++ b/tests/gentropy/dataset/test_pairwise_ld.py @@ -0,0 +1,103 @@ +"""Testing pairwise LD dataset.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import pytest +from pyspark.sql import functions as f +from pyspark.sql.window import Window + +from gentropy.dataset.pairwise_ld import PairwiseLD + +if TYPE_CHECKING: + from pyspark.sql import SparkSession + + +class TestPairwiseLD: + """Test suit for pairwise LD dataset and associated methods.""" + + variants = [ + "1_8_A_C", + "1_9_A_C", + "1_10_A_C", + "1_99_A_C", + ] + + @pytest.fixture(scope="class") + def mock_pairwise_ld(self: TestPairwiseLD, spark: SparkSession) -> PairwiseLD: + """Generate a mock pairwise LD dataset. + + Args: + spark (SparkSession): _description_ + + Returns: + PairwiseLD: _description_ + """ + spark = spark.builder.getOrCreate() + + data = [(v1, v2) for v1 in self.variants for v2 in self.variants] + return PairwiseLD( + _df=( + spark.createDataFrame(data, ["variantIdI", "variantIdJ"]) + .withColumn( + "r", + f.row_number() + .over(Window.partitionBy(f.lit("x")).orderBy("variantIdI")) + .cast("double"), + ) + .withColumn( + "r", + f.when(f.col("variantIdI") == f.col("variantIdJ"), 1.0).otherwise( + f.col("r") + ), + ) + .persist() + ), + _schema=PairwiseLD.get_schema(), + ) + + @staticmethod + def test_pairwise_ld__type(mock_pairwise_ld: PairwiseLD) -> None: + """Testing type.""" + assert isinstance(mock_pairwise_ld, PairwiseLD) + + def test_pariwise_ld__get_variants( + self: TestPairwiseLD, mock_pairwise_ld: PairwiseLD + ) -> None: + """Testing function that returns list of variants from the LD table. + + Args: + mock_pairwise_ld (PairwiseLD): _description_ + """ + variant_set_expected = set(self.variants) + variant_set_from_data = set(mock_pairwise_ld.get_variant_list()) + + assert variant_set_from_data == variant_set_expected + + def test_pairwise_ld__r_to_numpy_matrix__type( + self: TestPairwiseLD, mock_pairwise_ld: PairwiseLD + ) -> None: + """Testing the returned numpy array.""" + assert isinstance(mock_pairwise_ld.r_to_numpy_matrix(), np.ndarray) + + def test_pairwise_ld__r_to_numpy_matrix__dimensions( + self: TestPairwiseLD, mock_pairwise_ld: PairwiseLD + ) -> None: + """Testing the returned numpy array.""" + assert mock_pairwise_ld.r_to_numpy_matrix().shape == ( + len(self.variants), + len(self.variants), + ) + + def test_pairwise_ld__overlap_with_locus( + self: TestPairwiseLD, mock_pairwise_ld: PairwiseLD + ) -> None: + """Testing the returned numpy array.""" + variant_subset = self.variants[1:3] + + assert ( + mock_pairwise_ld.overlap_with_locus(variant_subset).df.count() + == len(variant_subset) ** 2 + ) diff --git a/tests/gentropy/dataset/test_study_index.py b/tests/gentropy/dataset/test_study_index.py index 1d43b3b07..05b652752 100644 --- a/tests/gentropy/dataset/test_study_index.py +++ b/tests/gentropy/dataset/test_study_index.py @@ -1,10 +1,15 @@ """Test study index dataset.""" + from __future__ import annotations -from gentropy.dataset.study_index import StudyIndex +import pytest from pyspark.sql import DataFrame, SparkSession from pyspark.sql import functions as f +from gentropy.dataset.biosample_index import BiosampleIndex +from gentropy.dataset.gene_index import GeneIndex +from gentropy.dataset.study_index import StudyIndex + def test_study_index_creation(mock_study_index: StudyIndex) -> None: """Test study index creation with mock data.""" @@ -137,3 +142,365 @@ def test_aggregate_samples_by_ancestry__correctness(spark: SparkSession) -> None ) == 300.0 ) + + +class TestQTLValidation: + """A small test suite to ensure the QTL study validation works as intended.""" + + GENE_DATA = [ + ("ENSG00000102021", "1"), + ("ENSG000001020", "1"), + ] + GENE_COLUMNS = ["geneId", "chromosome"] + + BIOSAMPLE_DATA = [("UBERON_00123", "lung"), ("CL_00321", "monocyte")] + BIOSAMPLE_COLUMNS = ["biosampleId", "biosampleName"] + + STUDY_DATA = [ + ("s1", "eqtl", "p", "ENSG00000102021", "UBERON_00123"), + # This is the only study to be flagged: QTL + Wrong gene + ("s2", "eqtl", "p", "cicaful", "UBERON_00123"), + # This is the only study to be flagged: QTL + Wrong biosample + ("s3", "sqtl", "p", "ENSG00000102021", "jibberish"), + ("s4", "gwas", "p", None, "anything"), + ("s5", "gwas", "p", "pocok", None), + ] + STUDY_COLUMNS = [ + "studyId", + "studyType", + "projectId", + "geneId", + "biosampleFromSourceId", + ] + + @pytest.fixture(autouse=True) + def _setup(self: TestQTLValidation, spark: SparkSession) -> None: + """Setup fixture.""" + + def create_study_index(drop_column: str) -> StudyIndex: + df = spark.createDataFrame(self.STUDY_DATA, self.STUDY_COLUMNS) + df = df.withColumn("qualityControls", f.array().cast("array")) + if drop_column != "": + df = df.drop(drop_column) + return StudyIndex(_df=df, _schema=StudyIndex.get_schema()) + + self.study_index = create_study_index("") + self.study_index_no_gene = create_study_index("geneId") + self.study_index_no_biosample_id = create_study_index("biosampleFromSourceId") + + self.gene_index = GeneIndex( + _df=spark.createDataFrame(self.GENE_DATA, self.GENE_COLUMNS), + _schema=GeneIndex.get_schema(), + ) + self.biosample_index = BiosampleIndex( + _df=spark.createDataFrame(self.BIOSAMPLE_DATA, self.BIOSAMPLE_COLUMNS), + _schema=BiosampleIndex.get_schema(), + ) + + def test_gene_validation_type(self: TestQTLValidation) -> None: + """Testing if the target validation runs and returns the expected type.""" + validated = self.study_index.validate_target(self.gene_index) + assert isinstance(validated, StudyIndex) + + def test_biosample_validation_type(self: TestQTLValidation) -> None: + """Testing if the biosample validation runs and returns the expected type.""" + validated = self.study_index.validate_biosample(self.biosample_index) + assert isinstance(validated, StudyIndex) + + @pytest.mark.parametrize("test", ["gene", "biosample"]) + def test_qtl_validation_correctness(self: TestQTLValidation, test: str) -> None: + """Testing if the QTL validation only flags the expected studies.""" + if test == "gene": + validated = self.study_index.validate_target(self.gene_index).persist() + bad_study = "s2" + if test == "biosample": + validated = self.study_index.validate_biosample( + self.biosample_index + ).persist() + bad_study = "s3" + + # Make sure there's only one flagged: + assert validated.df.filter(f.size("qualityControls") != 0).count() == 1 + + # Make sure there's only one flagged: + flagged_study = validated.df.filter(f.size("qualityControls") != 0).collect()[ + 0 + ]["studyId"] + + assert flagged_study == bad_study + + def test_gene_validation_correctness(self: TestQTLValidation) -> None: + """Testing if the gene validation only flags the expected studies.""" + self.test_qtl_validation_correctness("gene") + + def test_biosample_validation_correctness(self: TestQTLValidation) -> None: + """Testing if the biosample validation only flags the expected studies.""" + self.test_qtl_validation_correctness("biosample") + + @pytest.mark.parametrize( + "drop,test", + [ + ("gene", "gene"), + ("gene", "biosample"), + ("biosample", "biosample"), + ("biosample", "gene"), + ], + ) + def test_qtl_validation_drop_relevant_column( + self: TestQTLValidation, drop: str, test: str + ) -> None: + """Testing what happens if an expected column is not present.""" + if drop == "gene": + if test == "gene": + validated = self.study_index_no_gene.validate_target(self.gene_index) + if test == "biosample": + validated = self.study_index_no_gene.validate_biosample( + self.biosample_index + ) + if drop == "biosample": + if test == "gene": + validated = self.study_index_no_biosample_id.validate_target( + self.gene_index + ) + if test == "biosample": + validated = self.study_index_no_biosample_id.validate_biosample( + self.biosample_index + ) + + # Asserty type: + assert isinstance(validated, StudyIndex) + + # Assert count: + assert validated.df.count() == self.study_index.df.count() + + def test_qtl_validation_no_gene_column(self: TestQTLValidation) -> None: + """Testing what happens if no gene column is present.""" + self.test_qtl_validation_drop_relevant_column(test="gene", drop="gene") + + def test_qtl_validation_no_biosample_from_source_column( + self: TestQTLValidation, + ) -> None: + """Testing what happens if no biosampleFromSourceId column is present.""" + self.test_qtl_validation_drop_relevant_column( + test="biosample", drop="biosample" + ) + + def test_qtl_validation_existing_gene_column(self: TestQTLValidation) -> None: + """Testing what happens if no gene column is present.""" + self.test_qtl_validation_drop_relevant_column(test="gene", drop="biosample") + + def test_qtl_validation_existing_biosample_from_source_column( + self: TestQTLValidation, + ) -> None: + """Testing what happens if a biosampleFromSourceId column is present.""" + self.test_qtl_validation_drop_relevant_column(test="biosample", drop="gene") + + def test_qtl_validation_existing_biosample_column(self: TestQTLValidation) -> None: + """Testing what happens if a biosampleId column is present in study index as well as biosampleFromSourceId.""" + # Append a biosample column filled with null to the self.study_index then validate: + validated = StudyIndex( + _df=self.study_index.df.withColumn( + "biosampleId", f.lit(None).cast("string") + ), + _schema=StudyIndex.get_schema(), + ).validate_biosample(self.biosample_index) + assert isinstance(validated, StudyIndex) + + +class TestUniquenessValidation: + """A small test suite to ensure the gene validation works as intended.""" + + STUDY_DATA = [ + # This is the only study to be flagged: + ("s1", "eqtl", "p"), + ("s1", "eqtl", "p"), # Duplicate -> one should be flagged + ("s3", "gwas", "p"), + ("s4", "gwas", "p"), + ] + STUDY_COLUMNS = ["studyId", "studyType", "projectId"] + + @pytest.fixture(autouse=True) + def _setup(self: TestUniquenessValidation, spark: SparkSession) -> None: + """Setup fixture.""" + self.study_index = StudyIndex( + _df=spark.createDataFrame(self.STUDY_DATA, self.STUDY_COLUMNS).withColumn( + "qualityControls", f.array().cast("array") + ), + _schema=StudyIndex.get_schema(), + ) + + def test_uniqueness_return_type(self: TestUniquenessValidation) -> None: + """Testing if the function returns the right type.""" + assert isinstance(self.study_index.validate_unique_study_id(), StudyIndex) + + def test_uniqueness_correct_data(self: TestUniquenessValidation) -> None: + """Testing if the function returns the right type.""" + validated = self.study_index.validate_unique_study_id().persist() + + # We have only one flagged study: + assert validated.df.filter(f.size(f.col("qualityControls")) > 0).count() == 1 + + # The flagged study identifiers are found more than once: + flagged_ids = { + study["studyId"]: study["count"] + for study in validated.df.filter(f.size(f.col("qualityControls")) > 0) + .groupBy("studyId") + .count() + .collect() + } + + for _, count in flagged_ids.items(): + assert count == 1 + + # the right study is found: + assert "s1" in flagged_ids + + +class TestStudyTypeValidation: + """Testing study type validation.""" + + STUDY_DATA = [ + # This study is flagged because of unexpected type: + ("s1", "cicaful", "p", "gene1"), + ("s3", "eqtl", "p", "gene1"), + ("s4", "gwas", "p", None), + ] + STUDY_COLUMNS = ["studyId", "studyType", "projectId", "geneId"] + + @pytest.fixture(autouse=True) + def _setup(self: TestStudyTypeValidation, spark: SparkSession) -> None: + """Setup fixture.""" + self.study_index = StudyIndex( + _df=spark.createDataFrame(self.STUDY_DATA, self.STUDY_COLUMNS).withColumn( + "qualityControls", f.array().cast("array") + ), + _schema=StudyIndex.get_schema(), + ) + + def test_study_type_validation_return_type(self: TestStudyTypeValidation) -> None: + """Testing if the function returns the expected type.""" + assert isinstance(self.study_index.validate_study_type(), StudyIndex) + + def test_study_type_validation_correctness(self: TestStudyTypeValidation) -> None: + """Test if the correct study is flagged.""" + flagged_study_ids = [ + study["studyId"] + for study in self.study_index.validate_study_type() + .df.filter(f.size("qualityControls") > 0) + .collect() + ] + assert "s1" in flagged_study_ids + # Check if any + flagged_study_types = [ + study["studyType"] + for study in self.study_index.validate_study_type() + .df.filter(f.size("qualityControls") != 0) + .collect() + ] + for study_type in flagged_study_types: + assert study_type != "gwas" + assert "qtl" not in study_type + + +class TestDiseaseValidation: + """Testing the disease validation.""" + + DISEASE_DATA = [ + ("EFO_old", "EFO_new"), + ("EFO_new", "EFO_new"), + ("EFO_new2", "EFO_new2"), + ] + + DISEASE_HEADER = ["efo", "diseaseId"] + + STUDY_DATA = [ + # Old EFO mapped to new: + ("s1", "gwas", "p", "EFO_old"), + # List of EFOs some mapped, some not: + ("s2", "gwas", "p", "EFO_old"), + ("s2", "gwas", "p", "EFO_new2"), + ("s2", "gwas", "p", "EFO_invalid"), + # single EFO mapped as the same: + ("s3", "gwas", "p", "EFO_new2"), + # Invalid study: + ("s4", "gwas", "p", "EFO_invalid"), + # Invalid study - no EFO: + ("s5", "gwas", "p", None), + # Valid study, missing efo, not gwas: + ("s6", "eqtl", "p2", None), + ] + + STUDY_COLUMNS = ["studyId", "studyType", "projectId", "efo"] + + @pytest.fixture(autouse=True) + def _setup(self: TestDiseaseValidation, spark: SparkSession) -> None: + """Setup fixture.""" + study_df = ( + spark.createDataFrame(self.STUDY_DATA, self.STUDY_COLUMNS) + .groupBy("studyId", "studyType", "projectId") + .agg(f.collect_set("efo").alias("traitFromSourceMappedIds")) + .withColumn("qualityControls", f.array().cast("array")) + .withColumn( + "backgroundTraitFromSourceMappedIds", f.array().cast("array") + ) + ) + # Mock study index: + self.study_index = StudyIndex( + _df=study_df, + _schema=StudyIndex.get_schema(), + ) + + # Disease mapping: + self.disease = spark.createDataFrame(self.DISEASE_DATA, self.DISEASE_HEADER) + + # Validated data: + self.validated = self.study_index.validate_disease(self.disease).persist() + + def test_disease_validation_return_type(self: TestDiseaseValidation) -> None: + """Testing if the disease validation returns the right type.""" + assert isinstance(self.validated, StudyIndex) + + def test_disease_validation_right_flag(self: TestDiseaseValidation) -> None: + """Testing if the right studies are flagged in the validation step.""" + # Testing flagged studies: + for study in self.validated.df.filter(f.size("qualityControls") > 0).collect(): + # All flagged studies are from gwas: + assert study["studyType"] == "gwas" + # None of the flagged studies have assigned valid disease: + assert len(study["diseaseIds"]) == 0 + + # Testing unflagged studies: + for study in self.validated.df.filter(f.size("qualityControls") == 0).collect(): + # If a valid study has no disease, it cannot be gwas: + if len(study["diseaseIds"]) == 0: + assert study["studyType"] != "gwas" + + def test_disease_validation_disease_mapping(self: TestDiseaseValidation) -> None: + """Testing if old disease identifiers can be rescued.""" + example_study_id = "s1" + + test_study = self.validated.df.filter( + f.col("studyId") == example_study_id + ).collect()[0] + + # Assert validation: + assert len(test_study["qualityControls"]) == 0 + + # Assert disease mapping: + assert test_study["traitFromSourceMappedIds"][0] != test_study["diseaseIds"][0] + + def test_disease_validation_disease_removal(self: TestDiseaseValidation) -> None: + """Testing if not all diseases can be mapped, the study still passes QC.""" + example_study_id = "s2" + + test_study = self.validated.df.filter( + f.col("studyId") == example_study_id + ).collect()[0] + + # Assert validation: + assert len(test_study["qualityControls"]) == 0 + + # Assert not all diseases could be mapped to disease index: + assert len(test_study["traitFromSourceMappedIds"]) > len( + test_study["diseaseIds"] + ) diff --git a/tests/gentropy/dataset/test_study_locus.py b/tests/gentropy/dataset/test_study_locus.py index 037ede068..1d34479e1 100644 --- a/tests/gentropy/dataset/test_study_locus.py +++ b/tests/gentropy/dataset/test_study_locus.py @@ -1,29 +1,36 @@ """Test study locus dataset.""" + from __future__ import annotations from typing import Any import pyspark.sql.functions as f +import pyspark.sql.types as t import pytest -from gentropy.dataset.ld_index import LDIndex -from gentropy.dataset.study_index import StudyIndex -from gentropy.dataset.study_locus import CredibleInterval, StudyLocus -from gentropy.dataset.study_locus_overlap import StudyLocusOverlap -from pyspark.sql import Column, SparkSession +from pyspark.sql import Column, Row, SparkSession from pyspark.sql.types import ( ArrayType, BooleanType, DoubleType, - LongType, StringType, StructField, StructType, ) - -def test_study_locus_creation(mock_study_locus: StudyLocus) -> None: - """Test study locus creation with mock data.""" - assert isinstance(mock_study_locus, StudyLocus) +from gentropy.dataset.colocalisation import Colocalisation +from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix +from gentropy.dataset.ld_index import LDIndex +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_locus import ( + CredibleInterval, + CredibleSetConfidenceClasses, + StudyLocus, + StudyLocusQualityCheck, +) +from gentropy.dataset.study_locus_overlap import StudyLocusOverlap +from gentropy.dataset.summary_statistics import SummaryStatistics +from gentropy.dataset.variant_index import VariantIndex +from gentropy.method.l2g.feature_factory import L2GFeatureInputLoader @pytest.mark.parametrize( @@ -34,8 +41,9 @@ def test_study_locus_creation(mock_study_locus: StudyLocus) -> None: True, [ { - "leftStudyLocusId": 1, - "rightStudyLocusId": 2, + "leftStudyLocusId": "1", + "rightStudyLocusId": "2", + "rightStudyType": "eqtl", "chromosome": "1", "tagVariantId": "commonTag", "statistics": { @@ -44,8 +52,9 @@ def test_study_locus_creation(mock_study_locus: StudyLocus) -> None: }, }, { - "leftStudyLocusId": 1, - "rightStudyLocusId": 2, + "leftStudyLocusId": "1", + "rightStudyLocusId": "2", + "rightStudyType": "eqtl", "chromosome": "1", "tagVariantId": "nonCommonTag", "statistics": { @@ -69,18 +78,20 @@ def test_find_overlaps_semantic( # 2 associations with a common variant in the locus [ { - "studyLocusId": 1, + "studyLocusId": "1", "variantId": "lead1", "studyId": "study1", + "studyType": "gwas", "locus": [ {"variantId": "commonTag", "posteriorProbability": 0.9}, ], "chromosome": "1", }, { - "studyLocusId": 2, + "studyLocusId": "2", "variantId": "lead2", "studyId": "study2", + "studyType": "eqtl", "locus": [ {"variantId": "commonTag", "posteriorProbability": 0.6}, {"variantId": "nonCommonTag", "posteriorProbability": 0.6}, @@ -98,18 +109,20 @@ def test_find_overlaps_semantic( # 2 associations with no common variants in the locus [ { - "studyLocusId": 1, + "studyLocusId": "1", "variantId": "lead1", "studyId": "study1", + "studyType": "gwas", "locus": [ {"variantId": "var1", "posteriorProbability": 0.9}, ], "chromosome": "1", }, { - "studyLocusId": 2, + "studyLocusId": "2", "variantId": "lead2", "studyId": "study2", + "studyType": "eqtl", "locus": None, "chromosome": "1", }, @@ -119,25 +132,6 @@ def test_find_overlaps_semantic( _schema=StudyLocus.get_schema(), ) - studies = StudyIndex( - _df=spark.createDataFrame( - [ - { - "studyId": "study1", - "studyType": "gwas", - "traitFromSource": "trait1", - "projectId": "project1", - }, - { - "studyId": "study2", - "studyType": "eqtl", - "traitFromSource": "trait2", - "projectId": "project2", - }, - ] - ), - _schema=StudyIndex.get_schema(), - ) expected_overlaps_df = spark.createDataFrame( expected, StudyLocusOverlap.get_schema() ) @@ -147,71 +141,25 @@ def test_find_overlaps_semantic( "statistics.right_posteriorProbability", ] assert ( - credset.find_overlaps(studies).df.select(*cols_to_compare).collect() + credset.find_overlaps().df.select(*cols_to_compare).collect() == expected_overlaps_df.select(*cols_to_compare).collect() ), "Overlaps differ from expected." -def test_find_overlaps( - mock_study_locus: StudyLocus, mock_study_index: StudyIndex -) -> None: +def test_find_overlaps(mock_study_locus: StudyLocus) -> None: """Test study locus overlaps.""" - assert isinstance( - mock_study_locus.find_overlaps(mock_study_index), StudyLocusOverlap - ) + assert isinstance(mock_study_locus.find_overlaps(), StudyLocusOverlap) -@pytest.mark.parametrize( - "study_type, expected_sl_count", [("gwas", 1), ("eqtl", 1), ("pqtl", 0)] -) -def test_filter_by_study_type( - spark: SparkSession, study_type: str, expected_sl_count: int +def test_annotate_locus_statistics( + mock_study_locus: StudyLocus, mock_summary_statistics: SummaryStatistics ) -> None: - """Test filter by study type.""" - # Input data - sl = StudyLocus( - _df=spark.createDataFrame( - [ - { - # from gwas - "studyLocusId": 1, - "variantId": "lead1", - "studyId": "study1", - }, - { - # from eqtl - "studyLocusId": 2, - "variantId": "lead2", - "studyId": "study2", - }, - ], - StudyLocus.get_schema(), - ), - _schema=StudyLocus.get_schema(), - ) - studies = StudyIndex( - _df=spark.createDataFrame( - [ - { - "studyId": "study1", - "studyType": "gwas", - "traitFromSource": "trait1", - "projectId": "project1", - }, - { - "studyId": "study2", - "studyType": "eqtl", - "traitFromSource": "trait2", - "projectId": "project2", - }, - ] - ), - _schema=StudyIndex.get_schema(), + """Test annotate locus statistics returns a StudyLocus.""" + assert isinstance( + mock_study_locus.annotate_locus_statistics(mock_summary_statistics, 100), + StudyLocus, ) - observed = sl.filter_by_study_type(study_type, studies) - assert observed.df.count() == expected_sl_count - def test_filter_credible_set(mock_study_locus: StudyLocus) -> None: """Test credible interval filter.""" @@ -220,86 +168,84 @@ def test_filter_credible_set(mock_study_locus: StudyLocus) -> None: ) -def test_assign_study_locus_id__null_variant_id(spark: SparkSession) -> None: - """Test assign study locus id when variant id is null for the same study.""" - df = spark.createDataFrame( - [("GCST000001", None), ("GCST000001", None)], - schema="studyId: string, variantId: string", - ).withColumn( - "studyLocusId", - StudyLocus.assign_study_locus_id(f.col("studyId"), f.col("variantId")), - ) - assert ( - df.select("studyLocusId").distinct().count() == 2 - ), "studyLocusId is not unique when variantId is null" +def test_qc_abnormal_pips(mock_study_locus: StudyLocus) -> None: + """Test that the qc_abnormal_pips method returns a StudyLocus object.""" + assert isinstance(mock_study_locus.qc_abnormal_pips(0.99, 1), StudyLocus) -@pytest.mark.parametrize( - ("observed", "expected"), +# Used primarily for test_unique_variants_in_locus but also for other tests +test_unique_variants_in_locus_test_data = [ + ( + # Locus is not null, should return union between variants in locus and lead variant + [ + ( + "1", + "traitA", + "22_varA", + [ + {"variantId": "22_varA", "posteriorProbability": 0.44}, + {"variantId": "22_varB", "posteriorProbability": 0.015}, + ], + ), + ], + [ + ( + "22_varA", + "22", + ), + ( + "22_varB", + "22", + ), + ], + ), + ( + # locus is null, should return lead variant + [ + ("1", "traitA", "22_varA", None), + ], + [ + ( + "22_varA", + "22", + ), + ], + ), +] + +test_unique_variants_in_locus_test_schema = StructType( [ - ( - # Locus is not null, should return union between variants in locus and lead variant - [ - ( - 1, - "traitA", - "22_varA", + StructField("studyLocusId", StringType(), True), + StructField("studyId", StringType(), True), + StructField("variantId", StringType(), True), + StructField( + "locus", + ArrayType( + StructType( [ - {"variantId": "22_varA", "posteriorProbability": 0.44}, - {"variantId": "22_varB", "posteriorProbability": 0.015}, - ], - ), - ], - [ - ( - "22_varA", - "22", - ), - ( - "22_varB", - "22", - ), - ], - ), - ( - # locus is null, should return lead variant - [ - (1, "traitA", "22_varA", None), - ], - [ - ( - "22_varA", - "22", - ), - ], + StructField("variantId", StringType(), True), + StructField("posteriorProbability", DoubleType(), True), + ] + ) + ), + True, ), - ], + ] +) + + +@pytest.mark.parametrize( + ("observed", "expected"), + test_unique_variants_in_locus_test_data, ) def test_unique_variants_in_locus( spark: SparkSession, observed: list[Any], expected: list[Any] ) -> None: """Test unique variants in locus.""" # assert isinstance(mock_study_locus.test_unique_variants_in_locus(), DataFrame) - schema = StructType( - [ - StructField("studyLocusId", LongType(), True), - StructField("studyId", StringType(), True), - StructField("variantId", StringType(), True), - StructField( - "locus", - ArrayType( - StructType( - [ - StructField("variantId", StringType(), True), - ] - ) - ), - True, - ), - ] - ) data_sl = StudyLocus( - _df=spark.createDataFrame(observed, schema), _schema=StudyLocus.get_schema() + _df=spark.createDataFrame(observed, test_unique_variants_in_locus_test_schema), + _schema=StudyLocus.get_schema(), ) expected_df = spark.createDataFrame( expected, schema="variantId: string, chromosome: string" @@ -317,205 +263,929 @@ def test_clump(mock_study_locus: StudyLocus) -> None: assert isinstance(mock_study_locus.clump(), StudyLocus) -@pytest.mark.parametrize( - ("observed", "expected"), +# Used primarily for test_annotate_credible_sets but also for other tests +test_annotate_credible_sets_test_data = [ + ( + # Simple case + [ + # Observed + ( + "1", + "traitA", + "leadB", + [{"variantId": "tagVariantA", "posteriorProbability": 1.0}], + ), + ], + [ + # Expected + ( + "1", + "traitA", + "leadB", + [ + { + "variantId": "tagVariantA", + "posteriorProbability": 1.0, + "is95CredibleSet": True, + "is99CredibleSet": True, + } + ], + ) + ], + ), + ( + # Unordered credible set + [ + # Observed + ( + "1", + "traitA", + "leadA", + [ + {"variantId": "tagVariantA", "posteriorProbability": 0.44}, + {"variantId": "tagVariantB", "posteriorProbability": 0.015}, + {"variantId": "tagVariantC", "posteriorProbability": 0.04}, + {"variantId": "tagVariantD", "posteriorProbability": 0.005}, + {"variantId": "tagVariantE", "posteriorProbability": 0.5}, + {"variantId": "tagVariantNull", "posteriorProbability": None}, + {"variantId": "tagVariantNull", "posteriorProbability": None}, + ], + ) + ], + [ + # Expected + ( + "1", + "traitA", + "leadA", + [ + { + "variantId": "tagVariantE", + "posteriorProbability": 0.5, + "is95CredibleSet": True, + "is99CredibleSet": True, + }, + { + "variantId": "tagVariantA", + "posteriorProbability": 0.44, + "is95CredibleSet": True, + "is99CredibleSet": True, + }, + { + "variantId": "tagVariantC", + "posteriorProbability": 0.04, + "is95CredibleSet": True, + "is99CredibleSet": True, + }, + { + "variantId": "tagVariantB", + "posteriorProbability": 0.015, + "is95CredibleSet": False, + "is99CredibleSet": True, + }, + { + "variantId": "tagVariantD", + "posteriorProbability": 0.005, + "is95CredibleSet": False, + "is99CredibleSet": False, + }, + { + "variantId": "tagVariantNull", + "posteriorProbability": None, + "is95CredibleSet": False, + "is99CredibleSet": False, + }, + { + "variantId": "tagVariantNull", + "posteriorProbability": None, + "is95CredibleSet": False, + "is99CredibleSet": False, + }, + ], + ) + ], + ), + ( + # Null credible set + [ + # Observed + ( + "1", + "traitA", + "leadB", + None, + ), + ], + [ + # Expected + ( + "1", + "traitA", + "leadB", + None, + ) + ], + ), + ( + # Empty credible set + [ + # Observed + ( + "1", + "traitA", + "leadB", + [], + ), + ], + [ + # Expected + ( + "1", + "traitA", + "leadB", + None, + ) + ], + ), +] +test_annotate_credible_sets_test_schema = StructType( [ - ( - # Simple case - [ - # Observed - ( - 1, - "traitA", - "leadB", - [{"variantId": "tagVariantA", "posteriorProbability": 1.0}], - ), - ], - [ - # Expected - ( - 1, - "traitA", - "leadB", + StructField("studyLocusId", StringType(), True), + StructField("studyId", StringType(), True), + StructField("variantId", StringType(), True), + StructField( + "locus", + ArrayType( + StructType( [ - { - "variantId": "tagVariantA", - "posteriorProbability": 1.0, - "is95CredibleSet": True, - "is99CredibleSet": True, - } - ], + StructField("variantId", StringType(), True), + StructField("posteriorProbability", DoubleType(), True), + StructField("is95CredibleSet", BooleanType(), True), + StructField("is99CredibleSet", BooleanType(), True), + ] ) - ], + ), + True, ), + ] +) + + +@pytest.mark.parametrize( + ("observed", "expected"), + test_annotate_credible_sets_test_data, +) +def test_annotate_credible_sets( + spark: SparkSession, observed: list[Any], expected: list[Any] +) -> None: + """Test annotate_credible_sets.""" + data_sl = StudyLocus( + _df=spark.createDataFrame(observed, test_annotate_credible_sets_test_schema), + _schema=StudyLocus.get_schema(), + ) + expected_sl = StudyLocus( + _df=spark.createDataFrame(expected, test_annotate_credible_sets_test_schema), + _schema=StudyLocus.get_schema(), + ) + assert data_sl.annotate_credible_sets().df.collect() == expected_sl.df.collect() + + +def test_qc_abnormal_pips_good_locus(spark: SparkSession) -> None: + """Test qc_abnormal_pips with a well-behaving locus.""" + # Input data + sl = StudyLocus( + _df=spark.createDataFrame( + test_annotate_credible_sets_test_data[1][0], + test_annotate_credible_sets_test_schema, + ), + _schema=StudyLocus.get_schema(), + ) + assert ( + sl.qc_abnormal_pips().df.filter(f.size("qualityControls") > 0).count() == 0 + ), "Expected number of rows differ from observed." + + +def test_qc_abnormal_pips_bad_locus(spark: SparkSession) -> None: + """Test qc_abnormal_pips with an abnormal locus.""" + # Input data + sl = StudyLocus( + _df=spark.createDataFrame( + test_unique_variants_in_locus_test_data[0][0], + test_unique_variants_in_locus_test_schema, + ), + _schema=StudyLocus.get_schema(), + ) + assert ( + sl.qc_abnormal_pips().df.filter(f.size("qualityControls") > 0).count() == 1 + ), "Expected number of rows differ from observed." + + +def test_annotate_ld( + mock_study_locus: StudyLocus, mock_study_index: StudyIndex, mock_ld_index: LDIndex +) -> None: + """Test annotate_ld.""" + assert isinstance( + mock_study_locus.annotate_ld(mock_study_index, mock_ld_index), StudyLocus + ) + + +def test__qc_no_population(mock_study_locus: StudyLocus) -> None: + """Test _qc_no_population.""" + assert isinstance(mock_study_locus._qc_no_population(), StudyLocus) + + +def test_qc_MHC_region(mock_study_locus: StudyLocus) -> None: + """Test qc_MHC_region.""" + assert isinstance(mock_study_locus.qc_MHC_region(), StudyLocus) + + +def test_ldannotate( + mock_study_locus: StudyLocus, mock_study_index: StudyIndex, mock_ld_index: LDIndex +) -> None: + """Test ldannotate.""" + assert isinstance( + mock_study_locus.annotate_ld(mock_study_index, mock_ld_index), StudyLocus + ) + + +def test_filter_ld_set(spark: SparkSession) -> None: + """Test filter_ld_set.""" + observed_data = [ + Row(studyLocusId="sl1", ldSet=[{"tagVariantId": "tag1", "r2Overall": 0.4}]) + ] + observed_df = spark.createDataFrame( + observed_data, ["studyLocusId", "ldSet"] + ).withColumn("ldSet", StudyLocus.filter_ld_set(f.col("ldSet"), 0.5)) + expected_tags_in_ld = 0 + assert ( + observed_df.filter(f.size("ldSet") > 1).count() == expected_tags_in_ld + ), "Expected tags in ld set differ from observed." + + +def test_annotate_locus_statistics_boundaries( + mock_study_locus: StudyLocus, mock_summary_statistics: SummaryStatistics +) -> None: + """Test annotate locus statistics returns a StudyLocus.""" + df = mock_study_locus.df + df = df.withColumn("locusStart", f.col("position") - 10) + df = df.withColumn("locusEnd", f.col("position") + 10) + slt = StudyLocus(df, StudyLocus.get_schema()) + assert isinstance( + slt.annotate_locus_statistics_boundaries(mock_summary_statistics), + StudyLocus, + ) + + +class TestStudyLocusVariantValidation: + """Collection of tests for StudyLocus variant validation.""" + + VARIANT_DATA = [ + ("v1", "c1", 1, "r", "a"), + ("v2", "c1", 2, "r", "a"), + ("v3", "c1", 3, "r", "a"), + ("v4", "c1", 4, "r", "a"), + ] + VARIANT_HEADERS = [ + "variantId", + "chromosome", + "position", + "referenceAllele", + "alternateAllele", + ] + + STUDYLOCUS_DATA = [ + # First studylocus passes qc: + ("1", "v1", "s1", "v1"), + ("1", "v1", "s1", "v2"), + ("1", "v1", "s1", "v3"), + # Second studylocus passes qc: + ("2", "v1", "s1", "v1"), + ("2", "v1", "s1", "v5"), + ] + STUDYLOCUS_HEADER = ["studyLocusId", "variantId", "studyId", "tagVariantId"] + + @pytest.fixture(autouse=True) + def _setup(self: TestStudyLocusVariantValidation, spark: SparkSession) -> None: + """Setup study locus for testing.""" + self.variant_index = VariantIndex( + _df=spark.createDataFrame( + self.VARIANT_DATA, self.VARIANT_HEADERS + ).withColumn("position", f.col("position").cast(t.IntegerType())), + _schema=VariantIndex.get_schema(), + ) + + self.credible_set = StudyLocus( + _df=( + spark.createDataFrame(self.STUDYLOCUS_DATA, self.STUDYLOCUS_HEADER) + .withColumn("studyLocusId", f.col("studyLocusId").cast(t.StringType())) + .withColumn("qualityControls", f.array()) + .groupBy("studyLocusId", "variantId", "studyId") + .agg( + f.collect_set( + f.struct(f.col("tagVariantId").alias("variantId")) + ).alias("locus") + ) + ), + _schema=StudyLocus.get_schema(), + ) + + def test_validation_return_type(self: TestStudyLocusVariantValidation) -> None: + """Testing if the validation returns the right type.""" + assert isinstance( + self.credible_set.validate_variant_identifiers(self.variant_index), + StudyLocus, + ) + + def test_validation_no_data_loss(self: TestStudyLocusVariantValidation) -> None: + """Testing if the validation returns same number of rows.""" + assert ( + self.credible_set.validate_variant_identifiers( + self.variant_index + ).df.count() + == self.credible_set.df.count() + ) + + def test_validation_correctness(self: TestStudyLocusVariantValidation) -> None: + """Testing if the validation flags the right number of variants.""" + # Execute validation: + validated = self.credible_set.validate_variant_identifiers( + self.variant_index + ).df + + # Make sure there's only one study locus with a failed variants: + assert validated.filter(f.size("qualityControls") > 0).count() == 1 + + # Check that the right one is flagged: + assert ( + validated.filter( + (f.size("qualityControls") > 0) & (f.col("studyLocusId") == "2") + ).count() + == 1 + ) + + +class TestStudyLocusValidation: + """Collection of tests for StudyLocus validation.""" + + STUDY_LOCUS_DATA = [ + # Won't be flagged: + ("1", "v1", "s1", 1.0, -8, [], "pics"), + # Already flagged, needs to be tested if the flag reamins unique: ( - # Unordered credible set - [ - # Observed - ( - 1, - "traitA", - "leadA", - [ - {"variantId": "tagVariantA", "posteriorProbability": 0.44}, - {"variantId": "tagVariantB", "posteriorProbability": 0.015}, - {"variantId": "tagVariantC", "posteriorProbability": 0.04}, - {"variantId": "tagVariantD", "posteriorProbability": 0.005}, - {"variantId": "tagVariantE", "posteriorProbability": 0.5}, - {"variantId": "tagVariantNull", "posteriorProbability": None}, - {"variantId": "tagVariantNull", "posteriorProbability": None}, - ], + "2", + "v2", + "s2", + 5.0, + -4, + [StudyLocusQualityCheck.SUBSIGNIFICANT_FLAG.value], + "pics", + ), + # To be flagged: + ("3", "v3", "s3", 1.0, -4, [], "SuSiE-inf"), + ("4", "v4", "s4", 5.0, -3, [], "unknown"), + ] + + STUDY_LOCUS_SCHEMA = t.StructType( + [ + t.StructField("studyLocusId", t.StringType(), False), + t.StructField("variantId", t.StringType(), False), + t.StructField("studyId", t.StringType(), False), + t.StructField("pValueMantissa", t.FloatType(), False), + t.StructField("pValueExponent", t.IntegerType(), False), + t.StructField("qualityControls", t.ArrayType(t.StringType()), False), + t.StructField("finemappingMethod", t.StringType(), False), + ] + ) + + STUDY_DATA = [ + # Unflagged: + ("s1", "p1", "gwas", []), + # Flagged: + ("s2", "p1", "gwas", ["some_flag"]), + ("s3", "p1", "gwas", ["some_flag"]), + ] + + STUDY_SCHEMA = t.StructType( + [ + t.StructField("studyId", t.StringType(), False), + t.StructField("projectId", t.StringType(), False), + t.StructField("studyType", t.StringType(), False), + t.StructField("qualityControls", t.ArrayType(t.StringType(), True), True), + ] + ) + + @pytest.fixture(autouse=True) + def _setup(self: TestStudyLocusValidation, spark: SparkSession) -> None: + """Setup study locus for testing.""" + self.study_locus = StudyLocus( + _df=spark.createDataFrame( + self.STUDY_LOCUS_DATA, schema=self.STUDY_LOCUS_SCHEMA + ), + _schema=StudyLocus.get_schema(), + ) + + self.study_index = StudyIndex( + _df=spark.createDataFrame(self.STUDY_DATA, schema=self.STUDY_SCHEMA), + _schema=StudyIndex.get_schema(), + ) + + @pytest.mark.parametrize( + "test_pvalues", + [1e-6, 1e-5], + ) + def test_return_type_pval_validation( + self: TestStudyLocusValidation, test_pvalues: float + ) -> None: + """Testing if the p-value validation returns the right type.""" + assert isinstance( + self.study_locus.validate_lead_pvalue(test_pvalues), StudyLocus + ) + + def test_confidence_flag_return_type(self: TestStudyLocusValidation) -> None: + """Testing if the confidence flagging returns the right type.""" + assert isinstance(self.study_locus.assign_confidence(), StudyLocus) + + def test_confidence_flag_new_column(self: TestStudyLocusValidation) -> None: + """Testing if the confidence flagging adds a new column.""" + assert ( + self.study_locus.assign_confidence().df.columns + == self.study_locus.df.columns + ["confidence"] + ) + + def test_confidence_flag_unknown_confidence(self: TestStudyLocusValidation) -> None: + """Testing if the confidence flagging adds a new column.""" + assert ( + self.study_locus.assign_confidence() + .df.filter( + f.col("confidence") == CredibleSetConfidenceClasses.UNKNOWN.value + ) + .count() + == 1 + ) + + @pytest.mark.parametrize( + ("test_pvalues", "flagged_count"), + [(1e-5, 3), (1e-4, 2)], + ) + def test_flagged_count( + self: TestStudyLocusValidation, test_pvalues: float, flagged_count: int + ) -> None: + """Testing if the p-value validation flags the right number of variants.""" + assert ( + self.study_locus.validate_lead_pvalue(test_pvalues) + .df.filter(f.size("qualityControls") > 0) + .count() + == flagged_count + ) + + def test_flag_uniqueness(self: TestStudyLocusValidation) -> None: + """Testing if the flag remains unique although the locus should be flagged twice.""" + assert ( + self.study_locus.validate_lead_pvalue(1e-10) + .df.filter(f.size("qualityControls") > 1) + .count() + ) == 0 + + def test_study_validation_return_type(self: TestStudyLocusValidation) -> None: + """Testing if the study validation returns the right type.""" + assert isinstance(self.study_locus.validate_study(self.study_index), StudyLocus) + + def test_study_validation_no_data_loss(self: TestStudyLocusValidation) -> None: + """Testing if the study validation returns same number of rows.""" + assert ( + self.study_locus.validate_study(self.study_index).df.count() + ) == self.study_locus.df.count() + + def test_study_validation_correctness(self: TestStudyLocusValidation) -> None: + """Testing if the study validation flags the right number of studies.""" + assert ( + self.study_locus.validate_study(self.study_index) + .df.filter( + f.array_contains( + f.col("qualityControls"), StudyLocusQualityCheck.FLAGGED_STUDY.value ) - ], - [ - # Expected - ( - 1, - "traitA", - "leadA", - [ - { - "variantId": "tagVariantE", - "posteriorProbability": 0.5, - "is95CredibleSet": True, - "is99CredibleSet": True, - }, - { - "variantId": "tagVariantA", - "posteriorProbability": 0.44, - "is95CredibleSet": True, - "is99CredibleSet": True, - }, - { - "variantId": "tagVariantC", - "posteriorProbability": 0.04, - "is95CredibleSet": True, - "is99CredibleSet": True, - }, - { - "variantId": "tagVariantB", - "posteriorProbability": 0.015, - "is95CredibleSet": False, - "is99CredibleSet": True, - }, - { - "variantId": "tagVariantD", - "posteriorProbability": 0.005, - "is95CredibleSet": False, - "is99CredibleSet": False, - }, - { - "variantId": "tagVariantNull", - "posteriorProbability": None, - "is95CredibleSet": False, - "is99CredibleSet": False, - }, - { - "variantId": "tagVariantNull", - "posteriorProbability": None, - "is95CredibleSet": False, - "is99CredibleSet": False, - }, - ], + ) + .count() + ) == 2 + + assert ( + self.study_locus.validate_study(self.study_index) + .df.filter( + f.array_contains( + f.col("qualityControls"), StudyLocusQualityCheck.MISSING_STUDY.value + ) + ) + .count() + ) == 1 + + +class TestStudyLocusWindowClumping: + """Testing window-based clumping on study locus.""" + + TEST_DATASET = [ + ("s1", "c1", 1, -1), + ("s1", "c1", 2, -2), + ("s1", "c1", 3, -3), + ("s2", "c2", 2, -2), + ("s3", "c2", 2, -2), + ] + + TEST_SCHEMA = t.StructType( + [ + t.StructField("studyId", t.StringType(), False), + t.StructField("chromosome", t.StringType(), False), + t.StructField("position", t.IntegerType(), False), + t.StructField("pValueExponent", t.IntegerType(), False), + ] + ) + + @pytest.fixture(autouse=True) + def _setup(self: TestStudyLocusWindowClumping, spark: SparkSession) -> None: + """Setup study locus for testing.""" + self.study_locus = StudyLocus( + _df=( + spark.createDataFrame( + self.TEST_DATASET, schema=self.TEST_SCHEMA + ).withColumns( + { + "studyLocusId": f.monotonically_increasing_id().cast( + t.StringType() + ), + "pValueMantissa": f.lit(1).cast(t.FloatType()), + "variantId": f.concat( + f.lit("v"), + f.monotonically_increasing_id().cast(t.StringType()), + ), + } ) + ), + _schema=StudyLocus.get_schema(), + ) + + def test_clump_return_type(self: TestStudyLocusWindowClumping) -> None: + """Testing if the clumping returns the right type.""" + assert isinstance(self.study_locus.window_based_clumping(3), StudyLocus) + + def test_clump_no_data_loss(self: TestStudyLocusWindowClumping) -> None: + """Testing if the clumping returns same number of rows.""" + assert ( + self.study_locus.window_based_clumping(3).df.count() + == self.study_locus.df.count() + ) + + def test_correct_flag(self: TestStudyLocusWindowClumping) -> None: + """Testing if the clumping flags are for variants.""" + assert ( + self.study_locus.window_based_clumping(3) + .df.filter( + f.array_contains( + f.col("qualityControls"), + StudyLocusQualityCheck.WINDOW_CLUMPED.value, + ) + ) + .count() + ) == 2 + + +def test_build_feature_matrix( + mock_study_locus: StudyLocus, + mock_colocalisation: Colocalisation, + mock_study_index: StudyIndex, +) -> None: + """Test building feature matrix with the eQtlColocH4Maximum feature.""" + features_list = ["eQtlColocH4Maximum"] + loader = L2GFeatureInputLoader( + colocalisation=mock_colocalisation, + study_index=mock_study_index, + study_locus=mock_study_locus, + ) + fm = mock_study_locus.build_feature_matrix(features_list, loader) + assert isinstance( + fm, L2GFeatureMatrix + ), "Feature matrix should be of type L2GFeatureMatrix" + + +class TestStudyLocusRedundancyFlagging: + """Collection of tests related to flagging redundant credible sets.""" + + STUDY_LOCUS_DATA = [ + ("1", "v1", "s1", "pics", []), + ("2", "v2", "s1", "pics", [StudyLocusQualityCheck.TOP_HIT.value]), + ("3", "v3", "s1", "pics", []), + ("3", "v3", "s1", "pics", []), + ("1", "v1", "s1", "pics", [StudyLocusQualityCheck.TOP_HIT.value]), + ("1", "v1", "s2", "pics", [StudyLocusQualityCheck.TOP_HIT.value]), + ("1", "v1", "s2", "pics", [StudyLocusQualityCheck.TOP_HIT.value]), + ("1", "v1", "s3", "SuSie", []), + ("1", "v1", "s3", "pics", [StudyLocusQualityCheck.TOP_HIT.value]), + ("1", "v1", "s4", "pics", []), + ("1", "v1", "s4", "SuSie", []), + ("1", "v1", "s4", "pics", [StudyLocusQualityCheck.TOP_HIT.value]), + ] + + STUDY_LOCUS_SCHEMA = t.StructType( + [ + t.StructField("studyLocusId", t.StringType(), False), + t.StructField("variantId", t.StringType(), False), + t.StructField("studyId", t.StringType(), False), + t.StructField("finemappingMethod", t.StringType(), False), + t.StructField("qualityControls", t.ArrayType(t.StringType()), False), + ] + ) + + @pytest.fixture(autouse=True) + def _setup(self: TestStudyLocusRedundancyFlagging, spark: SparkSession) -> None: + """Setup study locus for testing.""" + self.study_locus = StudyLocus( + _df=spark.createDataFrame( + self.STUDY_LOCUS_DATA, schema=self.STUDY_LOCUS_SCHEMA + ), + _schema=StudyLocus.get_schema(), + ) + + def test_qc_redundant_top_hits_from_PICS_returntype( + self: TestStudyLocusRedundancyFlagging, + ) -> None: + """Test qc_redundant_top_hits_from_PICS.""" + assert isinstance( + self.study_locus.qc_redundant_top_hits_from_PICS(), StudyLocus + ) + + def test_qc_redundant_top_hits_from_PICS_no_data_loss( + self: TestStudyLocusRedundancyFlagging, + ) -> None: + """Testing if the redundancy flagging returns the same number of rows.""" + assert ( + self.study_locus.qc_redundant_top_hits_from_PICS().df.count() + == self.study_locus.df.count() + ) + + def test_qc_redundant_top_hits_from_PICS_correctness( + self: TestStudyLocusRedundancyFlagging, + ) -> None: + """Testing if the study validation flags the right number of studies.""" + assert ( + self.study_locus.qc_redundant_top_hits_from_PICS() + .df.filter( + f.array_contains( + f.col("qualityControls"), + StudyLocusQualityCheck.REDUNDANT_PICS_TOP_HIT.value, + ) + ) + .count() + ) == 3 + + +class TestStudyLocusSuSiERedundancyFlagging: + """Collection of tests related to flagging redundant credible sets.""" + + STUDY_LOCUS_DATA: Any = [ + # to be flagged due to v4 + ( + "1", + "v1", + "s1", + "X", + "pics", + 1, + 3, + [ + {"variantId": "X_1_A_A"}, + {"variantId": "X_2_A_A"}, + {"variantId": "X_3_A_A"}, ], + [], ), + # to be flagged due to v4 ( - # Null credible set + "2", + "v2", + "s1", + "X", + "pics", + 4, + 5, [ - # Observed - ( - 1, - "traitA", - "leadB", - None, - ), + {"variantId": "X_4_A_A"}, + {"variantId": "X_5_A_A"}, ], + [], + ), + # NOT to be flagged (outside regions) + ( + "3", + "v3", + "s1", + "X", + "pics", + 6, + 7, [ - # Expected - ( - 1, - "traitA", - "leadB", - None, - ) + {"variantId": "X_6_A_A"}, + {"variantId": "X_7_A_A"}, ], + [], ), + # NOT to be flagged (SuSie-Inf credible set) ( - # Empty credible set + "4", + "v4", + "s1", + "X", + "SuSiE-inf", + 3, + 5, + [{"variantId": "X_3_A_A"}, {"variantId": "X_5_A_A"}], + [], + ), + # To be flagged (Unresolved LD flag on it) + ( + "5", + "v5", + "s1", + "X", + "pics", + 5, + 5, [ - # Observed - ( - 1, - "traitA", - "leadB", - [], - ), + {"variantId": "X_5_A_A"}, ], + [StudyLocusQualityCheck.UNRESOLVED_LD.value], + ), + # NOT to be flagged (different study) + ( + "6", + "v6", + "s2", + "X", + "pics", + 3, + 5, [ - # Expected - ( - 1, - "traitA", - "leadB", - None, - ) + {"variantId": "X_3_A_A"}, + {"variantId": "X_5_A_A"}, ], + [], ), - ], -) -def test_annotate_credible_sets( - spark: SparkSession, observed: list[Any], expected: list[Any] -) -> None: - """Test annotate_credible_sets.""" - schema = StructType( + ] + + STUDY_LOCUS_SCHEMA = t.StructType( [ - StructField("studyLocusId", LongType(), True), - StructField("studyId", StringType(), True), - StructField("variantId", StringType(), True), + t.StructField("studyLocusId", t.StringType(), False), + t.StructField("variantId", t.StringType(), False), + t.StructField("studyId", t.StringType(), False), + t.StructField("chromosome", t.StringType(), False), + t.StructField("finemappingMethod", t.StringType(), False), + t.StructField("locusStart", t.IntegerType(), False), + t.StructField("locusEnd", t.IntegerType(), False), StructField( "locus", ArrayType( StructType( [ StructField("variantId", StringType(), True), - StructField("posteriorProbability", DoubleType(), True), - StructField("is95CredibleSet", BooleanType(), True), - StructField("is99CredibleSet", BooleanType(), True), ] ) ), True, ), + t.StructField("qualityControls", t.ArrayType(t.StringType()), False), ] ) - data_sl = StudyLocus( - _df=spark.createDataFrame(observed, schema), _schema=StudyLocus.get_schema() - ) - expected_sl = StudyLocus( - _df=spark.createDataFrame(expected, schema), _schema=StudyLocus.get_schema() - ) - assert data_sl.annotate_credible_sets().df.collect() == expected_sl.df.collect() + @pytest.fixture(autouse=True) + def _setup( + self: TestStudyLocusSuSiERedundancyFlagging, spark: SparkSession + ) -> None: + """Setup study locus for testing.""" + self.study_locus = StudyLocus( + _df=spark.createDataFrame( + self.STUDY_LOCUS_DATA, schema=self.STUDY_LOCUS_SCHEMA + ), + _schema=StudyLocus.get_schema(), + ) -def test_annotate_ld( - mock_study_locus: StudyLocus, mock_study_index: StudyIndex, mock_ld_index: LDIndex + def test_qc_qc_explained_by_SuSiE_returntype( + self: TestStudyLocusSuSiERedundancyFlagging, + ) -> None: + """Test qc_explained_by_SuSiE.""" + assert isinstance(self.study_locus.qc_explained_by_SuSiE(), StudyLocus) + + def test_qc_explained_by_SuSiE_no_data_loss( + self: TestStudyLocusSuSiERedundancyFlagging, + ) -> None: + """Test qc_explained_by_SuSiE no data loss.""" + assert ( + self.study_locus.qc_explained_by_SuSiE().df.count() + == self.study_locus.df.count() + ) + + def test_qc_explained_by_SuSiE_correctness( + self: TestStudyLocusSuSiERedundancyFlagging, + ) -> None: + """Testing if the study validation flags the right number of studies.""" + assert ( + self.study_locus.qc_explained_by_SuSiE() + .df.filter( + f.array_contains( + f.col("qualityControls"), + StudyLocusQualityCheck.EXPLAINED_BY_SUSIE.value, + ) + ) + .count() + ) == 3 + + +def test_qc_valid_chromosomes( + spark: SparkSession, ) -> None: - """Test annotate_ld.""" - assert isinstance( - mock_study_locus.annotate_ld(mock_study_index, mock_ld_index), StudyLocus + """Testing if chredible sets with invalid chromosomes are properly flagged.""" + df = spark.createDataFrame( + [ + # Chromosome is fine: + ("1", "v1", "s1", "X", []), + ("2", "v2", "s1", "1", []), + # Should be flagged: + ("3", "v3", "s1", "11325", []), + ("4", "v4", "s1", "CICAFUL", []), + ], + schema=t.StructType( + [ + t.StructField("studyLocusId", t.StringType(), False), + t.StructField("variantId", t.StringType(), False), + t.StructField("studyId", t.StringType(), False), + t.StructField("chromosome", t.StringType(), False), + t.StructField("qualityControls", t.ArrayType(t.StringType()), False), + ] + ), ) + sl = StudyLocus(_df=df, _schema=StudyLocus.get_schema()).validate_chromosome_label() -def test__qc_no_population(mock_study_locus: StudyLocus) -> None: - """Test _qc_no_population.""" - assert isinstance(mock_study_locus._qc_no_population(), StudyLocus) + # Assert return type: + assert isinstance(sl, StudyLocus) + # Assert flagging correctness: + for row in sl.df.collect(): + if row["chromosome"] in ["1", "X"]: + assert not row["qualityControls"] + else: + assert ( + StudyLocusQualityCheck.INVALID_CHROMOSOME.value + in row["qualityControls"] + ) -def test_ldannotate( - mock_study_locus: StudyLocus, mock_study_index: StudyIndex, mock_ld_index: LDIndex -) -> None: - """Test ldannotate.""" - assert isinstance( - mock_study_locus.annotate_ld(mock_study_index, mock_ld_index), StudyLocus + +class TestStudyLocusDuplicationFlagging: + """Collection of tests related to flagging redundant credible sets.""" + + STUDY_LOCUS_DATA = [ + # Non-duplicated: + ("1", "v1", "s1", "pics"), + # Triplicate: + ("3", "v3", "s1", "pics"), + ("3", "v3", "s1", "pics"), + ("3", "v3", "s1", "pics"), + ] + + STUDY_LOCUS_SCHEMA = t.StructType( + [ + t.StructField("studyLocusId", t.StringType(), False), + t.StructField("variantId", t.StringType(), False), + t.StructField("studyId", t.StringType(), False), + t.StructField("finemappingMethod", t.StringType(), False), + ] ) + + @pytest.fixture(autouse=True) + def _setup(self: TestStudyLocusDuplicationFlagging, spark: SparkSession) -> None: + """Setup study locus for testing.""" + self.study_locus = StudyLocus( + _df=spark.createDataFrame( + self.STUDY_LOCUS_DATA, schema=self.STUDY_LOCUS_SCHEMA + ).withColumn( + "qualityControls", f.array().cast(t.ArrayType(t.StringType())) + ), + _schema=StudyLocus.get_schema(), + ) + + # Run validation: + self.validated = self.study_locus.validate_unique_study_locus_id() + + def test_duplication_flag_type(self: TestStudyLocusDuplicationFlagging) -> None: + """Test duplication flagging return type.""" + assert isinstance(self.validated, StudyLocus) + + def test_duplication_flag_no_data_loss( + self: TestStudyLocusDuplicationFlagging, + ) -> None: + """Test duplication flagging no data loss.""" + assert self.validated.df.count() == self.study_locus.df.count() + + def test_duplication_flag_correctness( + self: TestStudyLocusDuplicationFlagging, + ) -> None: + """Make sure that the end, there are two study loci that pass the validation.""" + assert self.validated.df.filter(f.size("qualityControls") == 0).count() == 2 + + assert self.validated.df.filter(f.size("qualityControls") > 0).count() == 2 diff --git a/tests/gentropy/dataset/test_study_locus_overlap.py b/tests/gentropy/dataset/test_study_locus_overlap.py index ca49fc067..c517c023d 100644 --- a/tests/gentropy/dataset/test_study_locus_overlap.py +++ b/tests/gentropy/dataset/test_study_locus_overlap.py @@ -1,9 +1,11 @@ """Test study locus overlap dataset.""" + from __future__ import annotations -from gentropy.dataset.study_locus_overlap import StudyLocusOverlap from pyspark.sql import SparkSession +from gentropy.dataset.study_locus_overlap import StudyLocusOverlap + def test_study_locus_overlap_creation( mock_study_locus_overlap: StudyLocusOverlap, @@ -17,19 +19,19 @@ def test_convert_to_square_matrix(spark: SparkSession) -> None: mock_sl_overlap = StudyLocusOverlap( _df=spark.createDataFrame( [ - (1, 2, "variant2"), + ("1", "2", "eqtl", "CHROM1", "variant2", None), ], - "leftStudyLocusId LONG, rightStudyLocusId LONG, tagVariantId STRING", + StudyLocusOverlap.get_schema(), ), _schema=StudyLocusOverlap.get_schema(), ) expected_df = spark.createDataFrame( [ - (1, 2, "variant2"), - (2, 1, "variant2"), + ("1", "2", "eqtl", "CHROM1", "variant2", None), + ("2", "1", "eqtl", "CHROM1", "variant2", None), ], - "leftStudyLocusId LONG, rightStudyLocusId LONG, tagVariantId STRING", + StudyLocusOverlap.get_schema(), ) observed_df = mock_sl_overlap._convert_to_square_matrix().df diff --git a/tests/gentropy/dataset/test_study_locus_overlaps.py b/tests/gentropy/dataset/test_study_locus_overlaps.py index 6a812fb39..669af476f 100644 --- a/tests/gentropy/dataset/test_study_locus_overlaps.py +++ b/tests/gentropy/dataset/test_study_locus_overlaps.py @@ -1,15 +1,17 @@ """Test colocalisation dataset.""" + from __future__ import annotations from typing import TYPE_CHECKING, Any +import pyspark.sql.functions as f import pyspark.sql.types as t import pytest + from gentropy.dataset.study_locus import StudyLocus from gentropy.dataset.study_locus_overlap import StudyLocusOverlap if TYPE_CHECKING: - from gentropy.dataset.study_index import StudyIndex from pyspark.sql import SparkSession @@ -20,66 +22,147 @@ def test_study_locus_overlap_creation( assert isinstance(mock_study_locus_overlap, StudyLocusOverlap) -def test_study_locus_overlap_from_associations( - mock_study_locus: StudyLocus, mock_study_index: StudyIndex -) -> None: +def test_study_locus_overlap_from_associations(mock_study_locus: StudyLocus) -> None: """Test colocalisation creation from mock associations.""" - overlaps = StudyLocusOverlap.from_associations(mock_study_locus, mock_study_index) + overlaps = StudyLocusOverlap.from_associations(mock_study_locus) assert isinstance(overlaps, StudyLocusOverlap) @pytest.mark.parametrize( - ("observed", "expected"), + ("observed", "intrastudy", "expected"), [ ( # observed - input DataFrame representing gwas and nongwas data to find overlapping signals [ { - "studyLocusId": 1, + "studyLocusId": "1", + "studyId": "A", "studyType": "gwas", "chromosome": "1", "tagVariantId": "A", }, { - "studyLocusId": 2, + "studyLocusId": "2", + "studyId": "B", "studyType": "eqtl", "chromosome": "1", "tagVariantId": "A", }, { - "studyLocusId": 3, + "studyLocusId": "3", + "studyId": "C", "studyType": "gwas", "chromosome": "1", "tagVariantId": "B", }, ], + # intrastudy - bool of whether or not to use inter-study or intra-study logic + False, # expected - output DataFrame with overlapping signals [ - {"leftStudyLocusId": 1, "rightStudyLocusId": 2, "chromosome": "1"}, + { + "leftStudyLocusId": "1", + "rightStudyLocusId": "2", + "rightStudyType": "eqtl", + "chromosome": "1", + }, + ], + ), + ( + # observed - input DataFrame representing intra-study data to find overlapping signals in the same study + [ + { + "studyLocusId": "1", + "studyId": "A", + "studyType": "gwas", + "chromosome": "1", + "region": "X", + "tagVariantId": "A", + }, + { + "studyLocusId": "2", + "studyId": "A", + "studyType": "gwas", + "chromosome": "1", + "region": "Y", + "tagVariantId": "A", + }, + { + "studyLocusId": "3", + "studyId": "B", + "studyType": "gwas", + "chromosome": "1", + "region": "X", + "tagVariantId": "A", + }, + ], + # intrastudy - bool of whether or not to use inter-study or intra-study logic + True, + # expected - output DataFrame with overlapping signals + [ + { + "leftStudyLocusId": "2", + "rightStudyLocusId": "1", + "rightStudyType": "gwas", + "chromosome": "1", + } ], ), ], ) def test_overlapping_peaks( - spark: SparkSession, observed: list[dict[str, Any]], expected: list[dict[str, Any]] + spark: SparkSession, + observed: list[dict[str, Any]], + intrastudy: bool, + expected: list[dict[str, Any]], ) -> None: """Test overlapping signals between GWAS-GWAS and GWAS-Molecular trait to make sure that mQTLs are always on the right.""" mock_schema = t.StructType( [ - t.StructField("studyLocusId", t.LongType()), + t.StructField("studyLocusId", t.StringType()), + t.StructField("studyId", t.StringType()), t.StructField("studyType", t.StringType()), t.StructField("chromosome", t.StringType()), + t.StructField("region", t.StringType()), t.StructField("tagVariantId", t.StringType()), ] ) expected_schema = t.StructType( [ - t.StructField("leftStudyLocusId", t.LongType()), - t.StructField("rightStudyLocusId", t.LongType()), + t.StructField("leftStudyLocusId", t.StringType()), + t.StructField("rightStudyLocusId", t.StringType()), + t.StructField("rightStudyType", t.StringType()), t.StructField("chromosome", t.StringType()), ] ) observed_df = spark.createDataFrame(observed, mock_schema) - result_df = StudyLocus._overlapping_peaks(observed_df) + result_df = StudyLocus._overlapping_peaks(observed_df, intrastudy) expected_df = spark.createDataFrame(expected, expected_schema) assert result_df.collect() == expected_df.collect() + + +class TestStudyLocusOverlap: + """Test the overlapping of StudyLocus dataset.""" + + @pytest.fixture(autouse=True) + def setup( + self: TestStudyLocusOverlap, study_locus_sample_for_colocalisation: StudyLocus + ) -> None: + """Get sample dataset.""" + # Store imput dataset: + self.study_locus = study_locus_sample_for_colocalisation + + # Call locus overlap: + self.overlaps = study_locus_sample_for_colocalisation.find_overlaps() + + def test_coloc_return_type(self: TestStudyLocusOverlap) -> None: + """Test get_schema.""" + assert isinstance(self.overlaps, StudyLocusOverlap) + + def test_coloc_not_null(self: TestStudyLocusOverlap) -> None: + """Test get_schema.""" + assert self.overlaps.df.count() != 0 + + def test_coloc_study_type_not_null(self: TestStudyLocusOverlap) -> None: + """Test get_schema.""" + assert self.overlaps.filter(f.col("rightStudyType").isNull()).df.count() == 0 diff --git a/tests/gentropy/dataset/test_summary_statistics.py b/tests/gentropy/dataset/test_summary_statistics.py index b148994b6..b1b06442b 100644 --- a/tests/gentropy/dataset/test_summary_statistics.py +++ b/tests/gentropy/dataset/test_summary_statistics.py @@ -1,11 +1,14 @@ """Test study index dataset.""" + from __future__ import annotations from typing import TYPE_CHECKING +from pyspark.sql import types as t + +from gentropy.common.genomic_region import GenomicRegion from gentropy.dataset.study_locus import StudyLocus from gentropy.dataset.summary_statistics import SummaryStatistics -from pyspark.sql import types as t if TYPE_CHECKING: from pyspark.sql import SparkSession @@ -42,7 +45,10 @@ def test_summary_statistics__exclude_region__return_type( ) -> None: """Testing if the exclude region method returns the right datatype.""" assert isinstance( - mock_summary_statistics.exclude_region("chr12:124-1245"), SummaryStatistics + mock_summary_statistics.exclude_region( + GenomicRegion.from_string("chr12:124-1245") + ), + SummaryStatistics, ) @@ -83,7 +89,7 @@ def test_summary_statistics__exclude_region__correctness( df = spark.createDataFrame(data, schema=schema) filtered_sumstas = SummaryStatistics( _df=df, _schema=SummaryStatistics.get_schema() - ).exclude_region("c1:9-16") + ).exclude_region(GenomicRegion.from_string("c1:9-16")) # Test for the correct number of rows returned: assert filtered_sumstas.df.count() == 8 diff --git a/tests/gentropy/dataset/test_v2g.py b/tests/gentropy/dataset/test_v2g.py deleted file mode 100644 index e53395fe4..000000000 --- a/tests/gentropy/dataset/test_v2g.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Tests V2G dataset.""" -from __future__ import annotations - -from typing import TYPE_CHECKING - -from gentropy.dataset.v2g import V2G - -if TYPE_CHECKING: - from gentropy.dataset.gene_index import GeneIndex - - -def test_v2g_creation(mock_v2g: V2G) -> None: - """Test v2g creation with mock data.""" - assert isinstance(mock_v2g, V2G) - - -def test_v2g_filter_by_genes(mock_v2g: V2G, mock_gene_index: GeneIndex) -> None: - """Test v2g filter by genes.""" - assert isinstance( - mock_v2g.filter_by_genes(mock_gene_index), - V2G, - ) diff --git a/tests/gentropy/dataset/test_variant_annotation.py b/tests/gentropy/dataset/test_variant_annotation.py deleted file mode 100644 index 563d3c05c..000000000 --- a/tests/gentropy/dataset/test_variant_annotation.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Tests variant annotation dataset.""" -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from gentropy.dataset.gene_index import GeneIndex - -from gentropy.dataset.v2g import V2G -from gentropy.dataset.variant_annotation import VariantAnnotation - - -def test_variant_index_creation(mock_variant_annotation: VariantAnnotation) -> None: - """Test gene index creation with mock gene index.""" - assert isinstance(mock_variant_annotation, VariantAnnotation) - - -def test_get_plof_v2g( - mock_variant_annotation: VariantAnnotation, mock_gene_index: GeneIndex -) -> None: - """Test get_plof_v2g with mock variant annotation.""" - assert isinstance(mock_variant_annotation.get_plof_v2g(mock_gene_index), V2G) - - -def test_get_distance_to_tss( - mock_variant_annotation: VariantAnnotation, mock_gene_index: GeneIndex -) -> None: - """Test get_distance_to_tss with mock variant annotation.""" - assert isinstance(mock_variant_annotation.get_distance_to_tss(mock_gene_index), V2G) diff --git a/tests/gentropy/dataset/test_variant_index.py b/tests/gentropy/dataset/test_variant_index.py index c79a04092..43c409ea6 100644 --- a/tests/gentropy/dataset/test_variant_index.py +++ b/tests/gentropy/dataset/test_variant_index.py @@ -1,13 +1,17 @@ """Tests on variant index generation.""" + from __future__ import annotations from typing import TYPE_CHECKING +import pytest +from pyspark.sql import functions as f +from pyspark.sql import types as t + from gentropy.dataset.variant_index import VariantIndex if TYPE_CHECKING: - from gentropy.dataset.study_locus import StudyLocus - from gentropy.dataset.variant_annotation import VariantAnnotation + from pyspark.sql import SparkSession def test_variant_index_creation(mock_variant_index: VariantIndex) -> None: @@ -15,11 +19,157 @@ def test_variant_index_creation(mock_variant_index: VariantIndex) -> None: assert isinstance(mock_variant_index, VariantIndex) -def test_from_variant_annotation( - mock_variant_annotation: VariantAnnotation, mock_study_locus: StudyLocus -) -> None: - """Test variant index creation from variant annotation.""" - variant_index = VariantIndex.from_variant_annotation( - mock_variant_annotation, mock_study_locus +class TestVariantIndex: + """Collection of tests around the functionality and shape of the variant index.""" + + MOCK_ANNOTATION_DATA = [ + ("v1", "c1", 2, "T", "A", ["rs5"], "really bad consequence"), + ( + "v4_long", + "c1", + 5, + "T", + "A", + ["rs6"], + "mild consequence", + ), # should be hashed automatically + ] + + MOCK_DATA = [ + ("v1", "c1", 2, "T", "A", ["rs1"]), + ("v2", "c1", 3, "T", "A", ["rs2", "rs3"]), + ("v3", "c1", 4, "T", "A", None), + ("v4_long", "c1", 5, "T", "A", None), # should be hashed automatically + ] + + MOCK_SCHEMA = t.StructType( + [ + t.StructField("variantId", t.StringType(), False), + t.StructField("chromosome", t.StringType(), False), + t.StructField("position", t.IntegerType(), False), + t.StructField("referenceAllele", t.StringType(), False), + t.StructField("alternateAllele", t.StringType(), False), + t.StructField("rsIds", t.ArrayType(t.StringType(), True), True), + ] + ) + + # The mock annotation has an extra column that needs to be propagated to the annotated data: + MOCK_ANNOTATION_SCHEMA = t.StructType( + [ + t.StructField("variantId", t.StringType(), False), + t.StructField("chromosome", t.StringType(), False), + t.StructField("position", t.IntegerType(), False), + t.StructField("referenceAllele", t.StringType(), False), + t.StructField("alternateAllele", t.StringType(), False), + t.StructField("rsIds", t.ArrayType(t.StringType(), True), True), + t.StructField("mostSevereConsequenceId", t.StringType(), False), + ] + ) + + @pytest.fixture(autouse=True) + def _setup(self: TestVariantIndex, spark: SparkSession) -> None: + """Setting up the test. + + Args: + spark (SparkSession): Spark session. + """ + # Create dataframe: + self.df = spark.createDataFrame(self.MOCK_DATA, schema=self.MOCK_SCHEMA) + # Loading variant index: + self.variant_index = VariantIndex( + _df=self.df, _schema=VariantIndex.get_schema(), id_threshold=2 + ) + + # Loading annotation variant index: + self.annotation = VariantIndex( + _df=spark.createDataFrame( + self.MOCK_ANNOTATION_DATA, schema=self.MOCK_ANNOTATION_SCHEMA + ), + _schema=VariantIndex.get_schema(), + id_threshold=2, + ) + + def test_init_type(self: TestVariantIndex) -> None: + """Just make sure the right datatype is created.""" + assert isinstance(self.variant_index, VariantIndex) + + def test_removed_null_values(self: TestVariantIndex) -> None: + """Making sure upon initialisation, the null values are removed and were replaced with empty arrays.""" + # RsIds column cannot be null: + assert self.variant_index.df.filter(f.col("rsIds").isNull()).count() == 0 + + # However can be empty array: + assert self.variant_index.df.filter(f.size("rsIds") == 0).count() > 0 + + def test_annotation_return_type(self: TestVariantIndex) -> None: + """Make sure the annotation method returns a dataframe.""" + assert isinstance( + self.variant_index.add_annotation(self.annotation), VariantIndex + ) + + def test_new_column_added(self: TestVariantIndex) -> None: + """Make sure the annotation method adds a new column.""" + assert ( + "mostSevereConsequenceId" + in self.variant_index.add_annotation(self.annotation).df.columns + ) + + def test_new_column_correct(self: TestVariantIndex) -> None: + """Make sure the annotation method adds the correct values.""" + assert ( + self.variant_index.add_annotation(self.annotation) + .df.filter(f.col("mostSevereConsequenceId").isNotNull()) + .count() + == 2 + ) + + def test_rsid_column_updated(self: TestVariantIndex) -> None: + """Make sure the annotation method updates the rsId column.""" + # RsId added to a new row: + assert ( + self.variant_index.add_annotation(self.annotation) + .df.filter(f.size("rsIds") > 0) + .count() + == 3 + ) + + # RsID added to an existing row: + assert ( + self.variant_index.add_annotation(self.annotation) + .df.filter(f.size("rsIds") > 1) + .count() + == 2 + ) + + def test_variantid_column_hashed(self: TestVariantIndex) -> None: + """Make sure the variantId column is hashed during initialisation. Threshold is set to 2, so var_4_long should be hashed.""" + assert ( + self.variant_index.df.filter(f.col("variantId").startswith("OTVAR")).count() + != 0 + ) + + @pytest.mark.parametrize( + "distance_type", ["distanceFromTss", "distanceFromFootprint"] ) - assert isinstance(variant_index, VariantIndex) + def test_get_distance_to_gene( + self: TestVariantIndex, mock_variant_index: VariantIndex, distance_type: str + ) -> None: + """Assert that the function returns a df with the requested columns.""" + expected_cols = ["variantId", "targetId", distance_type] + observed = mock_variant_index.get_distance_to_gene(distance_type=distance_type) + for col in expected_cols: + assert col in observed.columns, f"Column {col} not in {observed.columns}" + + def test_get_loftee( + self: TestVariantIndex, mock_variant_index: VariantIndex + ) -> None: + """Assert that the function returns a df with the requested columns.""" + expected_cols = [ + "variantId", + "targetId", + "lofteePrediction", + "isHighQualityPlof", + ] + observed = mock_variant_index.get_loftee() + for col in expected_cols: + assert col in observed.columns, f"Column {col} not in {observed.columns}" diff --git a/tests/gentropy/datasource/biosample_ontologies/test_biosample_ontology.py b/tests/gentropy/datasource/biosample_ontologies/test_biosample_ontology.py new file mode 100644 index 000000000..a9099048c --- /dev/null +++ b/tests/gentropy/datasource/biosample_ontologies/test_biosample_ontology.py @@ -0,0 +1,54 @@ +"""Tests for biosample index dataset.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from gentropy.dataset.biosample_index import BiosampleIndex +from gentropy.datasource.biosample_ontologies.utils import extract_ontology_from_json + +if TYPE_CHECKING: + from pyspark.sql import SparkSession + + +class TestOntologyParger: + """Testing ontology parser.""" + + SAMPLE_CELL_ONTOLOGY_PATH = "tests/gentropy/data_samples/cell_ontology_sample.json" + SAMPLE_UBERON_PATH = "tests/gentropy/data_samples/uberon_sample.json" + SAMPLE_EFO_PATH = "tests/gentropy/data_samples/efo_biosample_sample.json" + + def test_ontology_parser(self: TestOntologyParger, spark: SparkSession) -> None: + """Test all ontology parsers.""" + cell_ontology = extract_ontology_from_json( + self.SAMPLE_CELL_ONTOLOGY_PATH, spark + ) + uberon = extract_ontology_from_json(self.SAMPLE_UBERON_PATH, spark) + efo_cell_line = extract_ontology_from_json( + self.SAMPLE_EFO_PATH, spark + ).retain_rows_with_ancestor_id(["CL_0000000"]) + + assert isinstance( + cell_ontology, BiosampleIndex + ), "Cell ontology subset is not parsed correctly to BiosampleIndex." + assert isinstance( + uberon, BiosampleIndex + ), "Uberon subset is not parsed correctly to BiosampleIndex." + assert isinstance( + efo_cell_line, BiosampleIndex + ), "EFO cell line subset is not parsed correctly to BiosampleIndex." + + def test_merge_biosample_indices( + self: TestOntologyParger, spark: SparkSession + ) -> None: + """Test merging of biosample indices.""" + cell_ontology = extract_ontology_from_json( + self.SAMPLE_CELL_ONTOLOGY_PATH, spark + ) + uberon = extract_ontology_from_json(self.SAMPLE_UBERON_PATH, spark) + efo = extract_ontology_from_json(self.SAMPLE_EFO_PATH, spark) + + merged = cell_ontology.merge_indices([uberon, efo]) + assert isinstance( + merged, BiosampleIndex + ), "Merging of biosample indices is not correct." diff --git a/tests/gentropy/datasource/ensembl/test_vep_variants.py b/tests/gentropy/datasource/ensembl/test_vep_variants.py new file mode 100644 index 000000000..556a22411 --- /dev/null +++ b/tests/gentropy/datasource/ensembl/test_vep_variants.py @@ -0,0 +1,183 @@ +"""Testing VEP parsing and variant index extraction.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pytest +from pyspark.sql import DataFrame +from pyspark.sql import functions as f +from pyspark.sql import types as t + +from gentropy.dataset.variant_index import VariantIndex +from gentropy.datasource.ensembl.vep_parser import VariantEffectPredictorParser + +if TYPE_CHECKING: + from pyspark.sql import SparkSession + + +class TestVEPParserInSilicoExtractor: + """Testing the _vep_in_silico_prediction_extractor method of the VEP parser class. + + These tests assumes that the _get_most_severe_transcript() method works correctly, as it's not tested. + + The test cases try to cover the following scenarios: + - Transcripts with no assessments. + - Transcripts without assessments flag. + - Transcripts with no score. + - Testing cases, where no score column is provided. + """ + + # Data prototype: + SAMPLE_DATA = [ + # Complete dataset: + ("v1", "deleterious", 0.1, "gene1", "flag"), + # No assessment: + ("v2", None, 0.1, "gene1", "flag"), + # No flag: + ("v3", "deleterious", 0.1, "gene1", None), + # No score: + ("v4", "deleterious", None, "gene1", "flag"), + ] + + SAMPLE_COLUMNS = ["variantId", "assessment", "score", "gene_id", "flag"] + + @pytest.fixture(autouse=True) + def _setup(self: TestVEPParserInSilicoExtractor, spark: SparkSession) -> None: + """Setup fixture.""" + parsed_df = ( + spark.createDataFrame(self.SAMPLE_DATA, self.SAMPLE_COLUMNS) + .groupBy("variantId") + .agg( + f.collect_list( + f.struct( + f.col("assessment").alias("assessment"), + f.col("score").alias("score"), + f.col("flag").alias("flag"), + f.col("gene_id").alias("gene_id"), + ) + ).alias("transcripts") + ) + .select( + "variantId", + VariantEffectPredictorParser._vep_in_silico_prediction_extractor( + "transcripts", "method_name", "score", "assessment", "flag" + ).alias("in_silico_predictions"), + ) + ).persist() + + self.df = parsed_df + + def test_in_silico_output_missing_value( + self: TestVEPParserInSilicoExtractor, + ) -> None: + """Test if the in silico output count is correct.""" + variant_with_missing_score = [ + x[0] for x in filter(lambda x: x[2] is None, self.SAMPLE_DATA) + ] + # Assert that the correct variants return null: + assert ( + [ + x["variantId"] + for x in self.df.filter( + f.col("in_silico_predictions").isNull() + ).collect() + ] + == variant_with_missing_score + ), "Not the right variants got nullified in-silico predictor object." + + +class TestVEPParser: + """Testing VEP parser class. + + Some of the input data: + - 6_151445307_C_T - complicated variant with numerous annotations. + - 2_140699625_G_GT - simple variant with no annotations whatsoever. + """ + + SAMPLE_VEP_DATA_PATH = "tests/gentropy/data_samples/vep_sample.jsonl" + + @pytest.fixture(autouse=True) + def _setup(self: TestVEPParser, spark: SparkSession) -> None: + """Setup fixture.""" + self.raw_vep_output = spark.read.json( + self.SAMPLE_VEP_DATA_PATH, + schema=VariantEffectPredictorParser.get_schema(), + ) + self.processed_vep_output = VariantEffectPredictorParser.process_vep_output( + self.raw_vep_output, 200 + ) + + def test_extract_variant_index_from_vep( + self: TestVEPParser, spark: SparkSession + ) -> None: + """Test if the variant index can be extracted from the VEP output.""" + variant_index = VariantEffectPredictorParser.extract_variant_index_from_vep( + spark, self.SAMPLE_VEP_DATA_PATH, hash_threshold=100 + ) + + assert isinstance( + variant_index, VariantIndex + ), "VariantIndex object not created." + in_silico_schema = t.ArrayType( + t.StructType( + [ + t.StructField("method", t.StringType(), True), + t.StructField("assessment", t.StringType(), True), + t.StructField("score", t.FloatType(), True), + t.StructField("assessmentFlag", t.StringType(), True), + t.StructField("targetId", t.StringType(), True), + ] + ) + ) + assert ( + variant_index.df.select("inSilicoPredictors").schema.fields[0].dataType + == in_silico_schema + ), "In silico schema is not correct." + + def test_process(self: TestVEPParser) -> None: + """Test process method.""" + df = VariantEffectPredictorParser.process_vep_output(self.raw_vep_output) + assert isinstance(df, DataFrame), "Processed VEP output is not a DataFrame." + assert df.count() > 0, "No variant data in processed VEP dataframe." + + def test_conversion(self: TestVEPParser) -> None: + """Test if processed data can be converted into a VariantIndex object.""" + variant_index = VariantIndex( + _df=self.processed_vep_output, + _schema=VariantIndex.get_schema(), + ) + + assert isinstance( + variant_index, VariantIndex + ), "VariantIndex object not created." + + def test_variant_count(self: TestVEPParser) -> None: + """Test if the number of variants is correct. + + It is expected that all rows from the parsed VEP output are present in the processed VEP output. + """ + assert ( + self.raw_vep_output.count() == self.processed_vep_output.count() + ), f"Incorrect number of variants in processed VEP output: expected {self.raw_vep_output.count()}, got {self.processed_vep_output.count()}." + + def test_collection(self: TestVEPParser) -> None: + """Test if the collection of VEP variantIndex runs without failures.""" + assert ( + len(self.processed_vep_output.collect()) + == self.processed_vep_output.count() + ), "Collection performed incorrectly." + + def test_ensembl_transcripts_no_duplicates(self: TestVEPParser) -> None: + """Test if in single row all ensembl target ids (gene ids) do not have duplicates.""" + targets = ( + self.processed_vep_output.limit(1) + .select(f.explode("transcriptConsequences").alias("t")) + .select("t.targetId") + .collect() + ) + + asserted_targets = [t["targetId"] for t in targets] + assert len(asserted_targets) == len( + set(asserted_targets) + ), "Duplicate ensembl transcripts in a single row." diff --git a/tests/gentropy/datasource/eqtl_catalogue/test_eqtl_catalogue.py b/tests/gentropy/datasource/eqtl_catalogue/test_eqtl_catalogue.py new file mode 100644 index 000000000..4e7a87cc4 --- /dev/null +++ b/tests/gentropy/datasource/eqtl_catalogue/test_eqtl_catalogue.py @@ -0,0 +1,73 @@ +"""Tests for study index dataset from eQTL Catalogue.""" + +from __future__ import annotations + +import pytest +from pyspark.sql import DataFrame +from pyspark.sql import functions as f + +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.study_locus import StudyLocus +from gentropy.datasource.eqtl_catalogue.finemapping import EqtlCatalogueFinemapping +from gentropy.datasource.eqtl_catalogue.study_index import EqtlCatalogueStudyIndex + + +@pytest.fixture +def processed_finemapping_df( + sample_eqtl_catalogue_studies_metadata: DataFrame, + sample_eqtl_catalogue_finemapping_credible_sets: DataFrame, + sample_eqtl_catalogue_finemapping_lbf: DataFrame, +) -> DataFrame: + """Return a DataFrame after joining credible sets and LBFs.""" + return EqtlCatalogueFinemapping.parse_susie_results( + sample_eqtl_catalogue_finemapping_credible_sets, + sample_eqtl_catalogue_finemapping_lbf, + sample_eqtl_catalogue_studies_metadata, + ) + + +def test_parse_susie_results( + sample_eqtl_catalogue_finemapping_credible_sets: DataFrame, + sample_eqtl_catalogue_finemapping_lbf: DataFrame, + sample_eqtl_catalogue_studies_metadata: DataFrame, +) -> None: + """Test parsing SuSIE results.""" + assert isinstance( + EqtlCatalogueFinemapping.parse_susie_results( + sample_eqtl_catalogue_finemapping_credible_sets, + sample_eqtl_catalogue_finemapping_lbf, + sample_eqtl_catalogue_studies_metadata, + ), + DataFrame, + ) + + +class TestEqtlCatalogueStudyLocus: + """Test the correctness of the study locus dataset from eQTL Catalogue.""" + + @pytest.fixture(autouse=True) + def _setup(self, processed_finemapping_df: DataFrame) -> DataFrame: + """Set up the test.""" + self.study_locus = EqtlCatalogueFinemapping.from_susie_results( + processed_finemapping_df + ) + + def test_credsets_from_susie_results(self: TestEqtlCatalogueStudyLocus) -> None: + """Test creating a study locus from SuSIE results.""" + assert isinstance(self.study_locus, StudyLocus) + + def test_locus_uniqueness(self: TestEqtlCatalogueStudyLocus) -> None: + """Test the uniqueness of the locus.""" + find_discrepancies = self.study_locus.df.select( + f.size("locus").alias("locus_size"), + f.size(f.array_distinct("locus")).alias("locus_distinct_size"), + ).filter(f.col("locus_size") != f.col("locus_distinct_size")) + assert find_discrepancies.count() == 0 + + +def test_studies_from_susie_results(processed_finemapping_df: DataFrame) -> None: + """Test creating a study index from SuSIE results.""" + assert isinstance( + EqtlCatalogueStudyIndex.from_susie_results(processed_finemapping_df), + StudyIndex, + ) diff --git a/tests/gentropy/datasource/eqtl_catalogue/test_eqtl_catalogue_study_index.py b/tests/gentropy/datasource/eqtl_catalogue/test_eqtl_catalogue_study_index.py deleted file mode 100644 index d68550e07..000000000 --- a/tests/gentropy/datasource/eqtl_catalogue/test_eqtl_catalogue_study_index.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Tests for study index dataset from eQTL Catalogue.""" - -from __future__ import annotations - -from gentropy.dataset.study_index import StudyIndex -from gentropy.datasource.eqtl_catalogue.study_index import EqtlCatalogueStudyIndex -from pyspark.sql import DataFrame - - -def test_eqtl_catalogue_study_index_from_source( - sample_eqtl_catalogue_studies: DataFrame, -) -> None: - """Test study index from source.""" - assert isinstance( - EqtlCatalogueStudyIndex.from_source( - sample_eqtl_catalogue_studies, - ), - StudyIndex, - ) diff --git a/tests/gentropy/datasource/eqtl_catalogue/test_eqtl_catalogue_summary_stats.py b/tests/gentropy/datasource/eqtl_catalogue/test_eqtl_catalogue_summary_stats.py deleted file mode 100644 index 3661f6b41..000000000 --- a/tests/gentropy/datasource/eqtl_catalogue/test_eqtl_catalogue_summary_stats.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Tests for study index dataset from eQTL Catalogue.""" - -from __future__ import annotations - -from gentropy.dataset.summary_statistics import SummaryStatistics -from gentropy.datasource.eqtl_catalogue.summary_stats import EqtlCatalogueSummaryStats -from pyspark.sql import DataFrame - - -def test_eqtl_catalogue_summary_stats_from_source( - sample_eqtl_catalogue_summary_stats: DataFrame, -) -> None: - """Test summary statistics from source.""" - assert isinstance( - EqtlCatalogueSummaryStats.from_source(sample_eqtl_catalogue_summary_stats), - SummaryStatistics, - ) diff --git a/tests/gentropy/datasource/finngen/test_finngen_finemapping.py b/tests/gentropy/datasource/finngen/test_finngen_finemapping.py index 089b10f74..4c7e12bf5 100644 --- a/tests/gentropy/datasource/finngen/test_finngen_finemapping.py +++ b/tests/gentropy/datasource/finngen/test_finngen_finemapping.py @@ -2,21 +2,88 @@ from __future__ import annotations +from pathlib import Path + +import hail as hl +import pytest +from pyspark.sql import SparkSession + +from gentropy.common.session import Session from gentropy.dataset.study_locus import StudyLocus from gentropy.datasource.finngen.finemapping import FinnGenFinemapping -from pyspark.sql import SparkSession +from gentropy.finngen_finemapping_ingestion import FinnGenFinemappingIngestionStep +@pytest.mark.parametrize( + [ + "finngen_susie_finemapping_snp_files", + "finngen_susie_finemapping_cs_summary_files", + ], + [ + pytest.param( + "tests/gentropy/data_samples/finngen_R9_AB1_EBV.SUSIE.snp.gz", + "tests/gentropy/data_samples/finngen_credset_summary_sample.tsv", + id="non block compressed files", + ), + pytest.param( + "tests/gentropy/data_samples/finngen_R9_AB1_EBV.SUSIE.snp.bgz", + "tests/gentropy/data_samples/finngen_credset_summary_sample.tsv.bgz", + id="block compressed files", + ), + ], +) def test_finngen_finemapping_from_finngen_susie_finemapping( spark: SparkSession, + finngen_susie_finemapping_snp_files: str, + finngen_susie_finemapping_cs_summary_files: str, ) -> None: """Test finemapping results (SuSie) from source.""" + hl.init(sc=spark.sparkContext, log="/dev/null", idempotent=True) assert isinstance( FinnGenFinemapping.from_finngen_susie_finemapping( spark=spark, - finngen_finemapping_df="tests/gentropy/data_samples/finngen_R9_AB1_EBV.SUSIE.snp.gz", - finngen_finemapping_summaries="tests/gentropy/data_samples/finngen_credset_summary_sample.tsv", - finngen_release_prefix="FINNGEN_R10", + finngen_susie_finemapping_snp_files=finngen_susie_finemapping_snp_files, + finngen_susie_finemapping_cs_summary_files=finngen_susie_finemapping_cs_summary_files, + finngen_release_prefix="FINNGEN_R11", ), StudyLocus, ) + + +@pytest.mark.parametrize( + [ + "finngen_susie_finemapping_snp_files", + "finngen_susie_finemapping_cs_summary_files", + ], + [ + pytest.param( + "tests/gentropy/data_samples/finngen_R9_AB1_EBV.SUSIE.snp.gz", + "tests/gentropy/data_samples/finngen_credset_summary_sample.tsv", + id="non block compressed files", + ), + ], +) +@pytest.mark.step_test +def test_finngen_finemapping_ingestion_step( + session: Session, + finngen_susie_finemapping_snp_files: str, + finngen_susie_finemapping_cs_summary_files: str, + tmp_path: Path, +) -> None: + """Test finngen finemapping ingestion step.""" + output_path = tmp_path / "output" + FinnGenFinemappingIngestionStep( + session=session, + finngen_finemapping_out=str(output_path), + finngen_susie_finemapping_cs_summary_files=finngen_susie_finemapping_cs_summary_files, + finngen_susie_finemapping_snp_files=finngen_susie_finemapping_snp_files, + finngen_finemapping_lead_pvalue_threshold=1e-5, + finngen_release_prefix="FINNGEN_R11", + ) + assert output_path.is_dir() + assert (output_path / "_SUCCESS").exists() + + cs = StudyLocus.from_parquet(session=session, path=str(output_path)) + assert cs.df.count() == 1 + study_id: str = cs.df.select("studyId").collect()[0]["studyId"] + assert study_id.startswith("FINNGEN_R11_") diff --git a/tests/gentropy/datasource/finngen/test_finngen_study_index.py b/tests/gentropy/datasource/finngen/test_finngen_study_index.py index 96a24db94..0d0537413 100644 --- a/tests/gentropy/datasource/finngen/test_finngen_study_index.py +++ b/tests/gentropy/datasource/finngen/test_finngen_study_index.py @@ -2,11 +2,391 @@ from __future__ import annotations +import json +from typing import TYPE_CHECKING +from unittest.mock import MagicMock + +import pytest +from pyspark.sql import DataFrame +from pyspark.sql import types as T + from gentropy.dataset.study_index import StudyIndex -from gentropy.datasource.finngen.study_index import FinnGenStudyIndex -from pyspark.sql import SparkSession +from gentropy.datasource.finngen.study_index import ( + FinngenPrefixMatch, + FinnGenStudyIndex, +) +from gentropy.finngen_studies import FinnGenStudiesStep + +if TYPE_CHECKING: + from pathlib import Path + from typing import Callable + + from pyspark.sql import SparkSession + + from gentropy.common.session import Session + + +@pytest.fixture() +def finngen_study_index_mock(spark: SparkSession) -> StudyIndex: + """Finngen minimal example for mocking join to the efo mappings.""" + data = [ + # NOTE: Study maps to a single EFO trait + ( + "FINNGEN_R11_STUDY_1", + "Actinomycosis", + "FINNGEN_R11", + "gwas", + ), + # NOTE: Study does not map to EFO traits + ( + "FINNGEN_R11_STUDY_2", + "Some other trait", + "FINNGEN_R11", + "gwas", + ), + # NOTE: Study maps to two EFO traits + ( + "FINNGEN_R11_STUDY_3", + "Glucose", + "FINNGEN_R11", + "gwas", + ), + ] + schema = T.StructType( + [ + T.StructField("studyId", T.StringType(), nullable=False), + T.StructField("traitFromSource", T.StringType(), nullable=False), + T.StructField("projectId", T.StringType(), nullable=False), + T.StructField("studyType", T.StringType(), nullable=False), + ] + ) + df = spark.createDataFrame(data=data, schema=schema) + return StudyIndex(_df=df, _schema=StudyIndex.get_schema()) + + +@pytest.fixture() +def finngen_phenotype_table_mock() -> str: + """This is the data extracted from https://r11.finngen.fi/api/phenos.""" + data = json.dumps( + [ + # NOTE: Study maps to single EFO trait. + { + "assoc_files": [ + "/cromwell_root/pheweb/generated-by-pheweb/pheno_gz/AB1_ACTINOMYCOSIS.gz" + ], + "category": "I Certain infectious and parasitic diseases (AB1_)", + "category_index": 1, + "gc_lambda": { + "0.001": 0.93878, + "0.01": 0.96727, + "0.1": 0.85429, + "0.5": 0.52544, + }, + "num_cases": 113, + "num_cases_prev": 101, + "num_controls": 399149, + "num_controls_prev": 363227, + "num_gw_significant": 0, + "num_gw_significant_prev": 0, + "phenocode": "AB1_ACTINOMYCOSIS", + "phenostring": "Actinomycosis", + }, + # NOTE: Study maps to multiple EFO traits. + { + "assoc_files": [ + "/cromwell_root/pheweb/generated-by-pheweb/pheno_gz/GLUCOSE.gz" + ], + "category": "Glucose", + "category_index": 28, + "gc_lambda": { + "0.001": 1.1251, + "0.01": 1.062, + "0.1": 1.0531, + "0.5": 1.0599, + }, + "num_cases": 43764, + "num_cases_prev": 39231, + "num_controls": 409969, + "num_controls_prev": 372950, + "num_gw_significant": 3, + "num_gw_significant_prev": 3, + "phenocode": "GLUCOSE", + "phenostring": "Glucose", + }, + # NOTE: Study does not map to EFO traits + { + "assoc_files": [ + "/cromwell_root/pheweb/generated-by-pheweb/pheno_gz/SOME_OTHER_TRAIT.gz" + ], + "category": "SomeOtherTrait", + "category_index": 28, + "gc_lambda": { + "0.001": 1.1251, + "0.01": 1.062, + "0.1": 1.0531, + "0.5": 1.0599, + }, + "num_cases": 43764, + "num_cases_prev": 39231, + "num_controls": 409969, + "num_controls_prev": 372950, + "num_gw_significant": 3, + "num_gw_significant_prev": 3, + "phenocode": "SOME_OTHER_TRAIT", + "phenostring": "Some other trait", + }, + ] + ) + return data + + +@pytest.fixture() +def efo_mappings_mock() -> list[tuple[str, str, str]]: + """EFO mappings mock based on https://raw.githubusercontent.com/opentargets/curation/24.09.1/mappings/disease/manual_string.tsv. + + Only required fields are extracted. + """ + data = [ + ( + "STUDY", + "PROPERTY_VALUE", + "SEMANTIC_TAG", + ), + ("FinnGen r11", "Actinomycosis", "http://www.ebi.ac.uk/efo/EFO_0007128"), + # NOTE: EFO does not map, as it's missing from the StudyIndex - hypothetical example. + ("FinnGen r11", "Bleeding", "http://purl.obolibrary.org/obo/MP_0001914"), + # NOTE: Two EFO traits for one disease should be collected to array - hypothetical example: + # Glucose tolerance test & NMR Glucose + ("FinnGen r11", "Glucose", "http://www.ebi.ac.uk/efo/EFO_0002571"), + ("FinnGen r11", "Glucose", "http://www.ebi.ac.uk/efo/EFO_0004468"), + # NOTE: EFO that does not map, due to study not from Finngen - hypothetical example. + ("PheWAS 2024", "Glucose", "http://www.ebi.ac.uk/efo/EFO_0000001"), + ] + return data + + +@pytest.fixture() +def efo_mappings_df_mock( + spark: SparkSession, efo_mappings_mock: list[tuple[str, str, str]] +) -> DataFrame: + """EFO mappings dataframe mock.""" + schema = T.StructType( + [ + T.StructField("STUDY", T.StringType(), nullable=False), + T.StructField("PROPERTY_VALUE", T.StringType(), nullable=False), + T.StructField("SEMANTIC_TAG", T.StringType(), nullable=False), + ] + ) + data = spark.createDataFrame(data=efo_mappings_mock, schema=schema) + return data + + +@pytest.fixture() +def urlopen_mock( + efo_mappings_mock: list[tuple[str, str, str, str]], + finngen_phenotype_table_mock: str, +) -> Callable[[str], MagicMock]: + """Mock object for requesting urlopen objects with proper encoding. + + This mock object allows to call `read` and `readlines` methods on two endpoints: + - https://finngen_phenotypes -> finngen_phenotype_table_mock + - https://efo_mappings -> efo_mappings_mock + The return values are mocks of the source data respectively. + """ -def test_finngen_study_index_from_source(spark: SparkSession) -> None: + def mock_response(url: str) -> MagicMock: + """Mock urllib.request.urlopen.""" + match url: + case "https://finngen_phenotypes": + value = finngen_phenotype_table_mock + case "https://efo_mappings": + value = "\n".join(["\t".join(row) for row in efo_mappings_mock]) + case _: + value = "" + mock_open = MagicMock() + mock_open.read.return_value = value.encode() + mock_open.readlines.return_value = value.encode().splitlines(keepends=True) + return mock_open + + return mock_response + + +@pytest.mark.step_test +def test_finngen_study_index_step( + monkeypatch: pytest.MonkeyPatch, + session: Session, + tmp_path: Path, + urlopen_mock: Callable[[str], MagicMock], +) -> None: + """Test step that generates finngen study index. + + FIXME: Currently we miss following columns when reading from source. + 'biosampleFromSourceId' + 'publicationTitle' + 'diseaseIds' + 'publicationDate' + 'geneId' + 'backgroundDiseaseIds' + 'pubmedId' + 'publicationJournal' + 'qualityControls' + 'backgroundTraitFromSourceMappedIds' + 'publicationFirstAuthor' + 'replicationSamples' + 'analysisFlags' + 'condition' + """ + with monkeypatch.context() as m: + m.setattr("gentropy.datasource.finngen.study_index.urlopen", urlopen_mock) + output_path = str(tmp_path / "study_index") + FinnGenStudiesStep( + session=session, + finngen_study_index_out=output_path, + finngen_phenotype_table_url="https://finngen_phenotypes", + finngen_release_prefix="FINNGEN_R11", + finngen_summary_stats_url_prefix="gs://finngen_data/sumstats", + finngen_summary_stats_url_suffix=".gz", + efo_curation_mapping_url="https://efo_mappings", + sample_size=5_000_000, + ) + study_index = StudyIndex.from_parquet(session=session, path=output_path) + # fmt: off + assert study_index.df.count() == 3, "Expected 3 rows that come from the input table." + assert "traitFromSourceMappedIds" in study_index.df.columns, "Expected that EFO terms were joined to the study_index table." + # fmt: on + + +def test_finngen_study_index_read_efo_curation( + monkeypatch: pytest.MonkeyPatch, + spark: SparkSession, + urlopen_mock: Callable[[str], MagicMock], +) -> None: + """Test reading efo curation.""" + with monkeypatch.context() as m: + m.setattr("gentropy.datasource.finngen.study_index.urlopen", urlopen_mock) + efo_df = FinnGenStudyIndex.read_efo_curation(spark, "https://efo_mappings") + assert isinstance(efo_df, DataFrame) + efo_df.show() + assert efo_df.count() == 5 + + +def test_finngen_study_index_from_source( + monkeypatch: pytest.MonkeyPatch, + spark: SparkSession, + urlopen_mock: Callable[[str], MagicMock], +) -> None: """Test study index from source.""" - assert isinstance(FinnGenStudyIndex.from_source(spark), StudyIndex) + with monkeypatch.context() as m: + m.setattr("gentropy.datasource.finngen.study_index.urlopen", urlopen_mock) + expected_sample_size = 5_000_000 + expected_project_id = "FINNGEN_R11" + study_index = FinnGenStudyIndex.from_source( + spark, + finngen_phenotype_table_url="https://finngen_phenotypes", + finngen_release_prefix=expected_project_id, + finngen_summary_stats_url_prefix="gs://finngen-public-data-r11/summary_stats/finngen_R11_", + finngen_summary_stats_url_suffix=".gz", + sample_size=expected_sample_size, + ) + # fmt: off + assert isinstance(study_index, StudyIndex), "Expect that we deal with StudyIndex object." + + all_columns = StudyIndex.get_schema().fieldNames() + assert set(all_columns).issuperset(set(study_index.df.columns)), "Expect all columns can be found in the schema of StudyIndex." + assert study_index.df.count() == 3, "Expect two rows at the study_index, as in the input." + + rows = study_index.df.collect() + expected_study_ids = ["FINNGEN_R11_AB1_ACTINOMYCOSIS", "FINNGEN_R11_GLUCOSE", "FINNGEN_R11_SOME_OTHER_TRAIT"] + assert "studyId" in study_index.df.columns, "Expect that studyId column exists." + assert sorted([v["studyId"] for v in rows]) == expected_study_ids, "Expect that studyIds are populated from input." + + assert "projectId" in study_index.df.columns, "Expect that projectId column exists." + assert {v["projectId"] for v in rows} == {expected_project_id}, "Expect projectId column is correctly populated." + + expected_sumstat_locations = [ + "gs://finngen-public-data-r11/summary_stats/finngen_R11_AB1_ACTINOMYCOSIS.gz", + "gs://finngen-public-data-r11/summary_stats/finngen_R11_GLUCOSE.gz", + "gs://finngen-public-data-r11/summary_stats/finngen_R11_SOME_OTHER_TRAIT.gz", + ] + assert "summarystatsLocation" in study_index.df.columns, "Expect that summarystatsLocation column exists." + sumstat_locations = sorted([v["summarystatsLocation"] for v in rows]) + assert sumstat_locations == expected_sumstat_locations, "Expect that summarystatsLocation is populated." + assert "ldPopulationStructure" in study_index.df.columns, "Expect that ldPopulationStructure column exists." + for row in rows: + ld_struct = row["ldPopulationStructure"][0] + assert ld_struct["ldPopulation"] == "fin", "Expect fin ld population structure." + assert ld_struct["relativeSampleSize"] == pytest.approx(1.0), "Expect relative sample size if fixed to be 1.0." + + assert "discoverySamples" in study_index.df.columns, "Expect that discoverySamples column exists." + for row in rows: + ds_struct = row["discoverySamples"][0] + assert ds_struct["ancestry"] == "Finnish", "Expect Finnish ancestry." + assert ds_struct["sampleSize"] == expected_sample_size, "Expect sample size to be fixed." + # fmt: on + + +@pytest.mark.parametrize( + ["prefix", "expected_output", "xfail"], + [ + pytest.param( + "FINNGEN_R11", + FinngenPrefixMatch(prefix="FINNGEN_R11", release="R11"), + False, + id="Correct prefix passed.", + ), + pytest.param( + "FINNGEN_R11_", + FinngenPrefixMatch(prefix="FINNGEN_R11", release="R11"), + False, + id="Underscore is removed from the prefix.", + ), + pytest.param( + "R11", + FinngenPrefixMatch(prefix="FINNGEN_R11", release="R11"), + True, + id="Incorrect prefix raises ValueError.", + ), + ], +) +def test_finngen_validate_release_prefix( + prefix: str, expected_output: FinngenPrefixMatch, xfail: bool +) -> None: + """Test validate_release_prefix.""" + if not xfail: + assert ( + FinnGenStudyIndex.validate_release_prefix(prefix) == expected_output + ), "Incorrect match object" + else: + with pytest.raises(ValueError): + FinnGenStudyIndex.validate_release_prefix(prefix) + + +def test_finngen_study_index_add_efos( + finngen_study_index_mock: StudyIndex, + efo_mappings_df_mock: DataFrame, +) -> None: + """Test finngen study index add efo ids.""" + efo_column_name = "traitFromSourceMappedIds" + # Expect that EFO column is not present when study index is generated. + assert efo_column_name not in finngen_study_index_mock.df.columns + study_index = FinnGenStudyIndex.join_efo_mapping( + finngen_study_index_mock, + finngen_release="R11", + efo_curation_mapping=efo_mappings_df_mock, + ) + # fmt: off + assert isinstance(study_index, StudyIndex), "Expect we have the StudyIndex object after joining EFOs." + assert efo_column_name in study_index.df.columns, "Expect that EFO column is present after joining EFOs." + assert study_index.df.count() == 3, "Expect we do not drop any studies, even if no EFO has been found." + # fmt: on + efos = { + row["studyId"]: sorted(row[efo_column_name]) + for row in study_index.df.select(efo_column_name, "studyId").collect() + } + expected_efos = { + "FINNGEN_R11_STUDY_1": ["EFO_0007128"], + "FINNGEN_R11_STUDY_2": [], + "FINNGEN_R11_STUDY_3": ["EFO_0002571", "EFO_0004468"], + } + assert expected_efos == efos, "Expect that EFOs are correctly assigned." diff --git a/tests/gentropy/datasource/finngen/test_finngen_summary_stats.py b/tests/gentropy/datasource/finngen/test_finngen_summary_stats.py index 624f66d66..73cffdc9f 100644 --- a/tests/gentropy/datasource/finngen/test_finngen_summary_stats.py +++ b/tests/gentropy/datasource/finngen/test_finngen_summary_stats.py @@ -2,9 +2,10 @@ from __future__ import annotations +from pyspark.sql import SparkSession + from gentropy.dataset.summary_statistics import SummaryStatistics from gentropy.datasource.finngen.summary_stats import FinnGenSummaryStats -from pyspark.sql import SparkSession def test_finngen_summary_stats_from_source(spark: SparkSession) -> None: diff --git a/tests/gentropy/datasource/gnomad/test_gnomad_ld.py b/tests/gentropy/datasource/gnomad/test_gnomad_ld.py index 5fa3b00a4..78b96ad84 100644 --- a/tests/gentropy/datasource/gnomad/test_gnomad_ld.py +++ b/tests/gentropy/datasource/gnomad/test_gnomad_ld.py @@ -7,10 +7,11 @@ import hail as hl import pytest -from gentropy.datasource.gnomad.ld import GnomADLDMatrix from pyspark.sql import DataFrame, SparkSession from pyspark.sql import functions as f +from gentropy.datasource.gnomad.ld import GnomADLDMatrix + @pytest.mark.parametrize( ("observed", "expected"), diff --git a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_associations.py b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_associations.py index de70e8b63..147f6c067 100644 --- a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_associations.py +++ b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_associations.py @@ -2,14 +2,15 @@ from __future__ import annotations -from gentropy.dataset.variant_annotation import VariantAnnotation +from pyspark.sql import DataFrame +from pyspark.sql import functions as f +from pyspark.sql.types import StringType + +from gentropy.dataset.variant_index import VariantIndex from gentropy.datasource.gwas_catalog.associations import ( GWASCatalogCuratedAssociationsParser, StudyLocusGWASCatalog, ) -from pyspark.sql import DataFrame -from pyspark.sql import functions as f -from pyspark.sql.types import LongType def test_study_locus_gwas_catalog_creation( @@ -50,29 +51,38 @@ def test_qc_ambiguous_study( def test_study_locus_gwas_catalog_from_source( - mock_variant_annotation: VariantAnnotation, + mock_variant_index: VariantIndex, sample_gwas_catalog_associations: DataFrame, ) -> None: """Test study locus from gwas catalog mock data.""" assert isinstance( GWASCatalogCuratedAssociationsParser.from_source( - sample_gwas_catalog_associations, mock_variant_annotation + sample_gwas_catalog_associations, mock_variant_index ), StudyLocusGWASCatalog, ) -def test__map_to_variant_annotation_variants( +def test_map_variants_to_variant_index( sample_gwas_catalog_associations: DataFrame, - mock_variant_annotation: VariantAnnotation, + mock_variant_index: VariantIndex, ) -> None: """Test mapping to variant annotation variants.""" assert isinstance( - GWASCatalogCuratedAssociationsParser._map_to_variant_annotation_variants( + GWASCatalogCuratedAssociationsParser._map_variants_to_gnomad_variants( sample_gwas_catalog_associations.withColumn( - "studyLocusId", f.monotonically_increasing_id().cast(LongType()) + "rowId", f.monotonically_increasing_id().cast(StringType()) ), - mock_variant_annotation, + mock_variant_index, ), DataFrame, ) + + +def test_qc_flag_all_tophits( + mock_study_locus_gwas_catalog: StudyLocusGWASCatalog, +) -> None: + """Test qc flag all top hits.""" + assert isinstance( + mock_study_locus_gwas_catalog.qc_flag_all_tophits(), StudyLocusGWASCatalog + ) diff --git a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_curation.py b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_curation.py index 764d003e8..0dd1a7363 100644 --- a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_curation.py +++ b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_curation.py @@ -7,9 +7,10 @@ import pyspark.sql.functions as f import pyspark.sql.types as t import pytest -from gentropy.datasource.gwas_catalog.study_index import StudyIndexGWASCatalog from pyspark.sql import DataFrame +from gentropy.datasource.gwas_catalog.study_index import StudyIndexGWASCatalog + if TYPE_CHECKING: from pyspark.sql import SparkSession @@ -142,39 +143,6 @@ def test_curation__study_type_update( assert expected == observed - # Test update qc flag - @staticmethod - def test_curation__quality_controls( - mock_gwas_study_index: StudyIndexGWASCatalog, mock_study_curation: DataFrame - ) -> None: - """Test for making sure the study type got updated.""" - curated = mock_gwas_study_index.annotate_from_study_curation( - mock_study_curation - ) - - # Expected studyIds: - expected = [ - row["studyId"] - for row in ( - mock_study_curation.filter(f.col("qualityControls").isNotNull()) - .select("studyId") - .distinct() - .collect() - ) - ] - - observed = [ - row["studyId"] - for row in ( - curated.df.filter(f.size(f.col("qualityControls")) > 0) - .select("studyId") - .distinct() - .collect() - ) - ] - - assert expected == observed - # Test updated method flag @staticmethod def test_curation__analysis_flags( diff --git a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_index.py b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_index.py index 96dccdf9d..b3ff4e486 100644 --- a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_index.py +++ b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_index.py @@ -2,11 +2,12 @@ from __future__ import annotations +from pyspark.sql import DataFrame + from gentropy.datasource.gwas_catalog.study_index import ( StudyIndexGWASCatalog, StudyIndexGWASCatalogParser, ) -from pyspark.sql import DataFrame def test_annotate_discovery_sample_sizes( @@ -30,25 +31,8 @@ def test_parse_study_table(sample_gwas_catalog_studies: DataFrame) -> None: ) -def test_annotate_sumstats( - mock_study_index_gwas_catalog: StudyIndexGWASCatalog, - sample_gwas_catalog_harmonised_sumstats_list: DataFrame, -) -> None: - """Test annotate sumstats of GWASCatalogStudyIndex.""" - mock_study_index_gwas_catalog.df = mock_study_index_gwas_catalog.df.drop( - "summarystatsLocation" - ) - assert isinstance( - mock_study_index_gwas_catalog.annotate_sumstats_info( - sample_gwas_catalog_harmonised_sumstats_list - ), - StudyIndexGWASCatalog, - ) - - def test_study_index_from_source( sample_gwas_catalog_studies: DataFrame, - sample_gwas_catalog_harmonised_sumstats_list: DataFrame, sample_gwas_catalog_ancestries_lut: DataFrame, ) -> None: """Test study index from source.""" @@ -56,7 +40,6 @@ def test_study_index_from_source( StudyIndexGWASCatalogParser.from_source( sample_gwas_catalog_studies, sample_gwas_catalog_ancestries_lut, - sample_gwas_catalog_harmonised_sumstats_list, ), StudyIndexGWASCatalog, ) diff --git a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_splitter.py b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_splitter.py index 2955cee91..c6bd03054 100644 --- a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_splitter.py +++ b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_study_splitter.py @@ -1,10 +1,12 @@ """Tests GWAS Catalog study splitter.""" + from __future__ import annotations from typing import TYPE_CHECKING, Any import pyspark.sql.functions as f import pytest + from gentropy.datasource.gwas_catalog.associations import StudyLocusGWASCatalog from gentropy.datasource.gwas_catalog.study_index import StudyIndexGWASCatalog from gentropy.datasource.gwas_catalog.study_splitter import GWASCatalogStudySplitter diff --git a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_summary_statistics.py b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_summary_statistics.py index 4ede62f36..867345455 100644 --- a/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_summary_statistics.py +++ b/tests/gentropy/datasource/gwas_catalog/test_gwas_catalog_summary_statistics.py @@ -6,6 +6,7 @@ import pyspark.sql.functions as f import pytest + from gentropy.dataset.summary_statistics import SummaryStatistics from gentropy.datasource.gwas_catalog.summary_statistics import ( GWASCatalogSummaryStatistics, diff --git a/tests/gentropy/datasource/intervals/test_andersson.py b/tests/gentropy/datasource/intervals/test_andersson.py index b7283597b..69575b7c3 100644 --- a/tests/gentropy/datasource/intervals/test_andersson.py +++ b/tests/gentropy/datasource/intervals/test_andersson.py @@ -1,12 +1,14 @@ """Test Andersson Intervals.""" + from __future__ import annotations import pytest +from pyspark.sql import DataFrame, SparkSession + from gentropy.common.Liftover import LiftOverSpark from gentropy.dataset.gene_index import GeneIndex from gentropy.dataset.intervals import Intervals from gentropy.datasource.intervals.andersson import IntervalsAndersson -from pyspark.sql import DataFrame, SparkSession @pytest.fixture(scope="module") diff --git a/tests/gentropy/datasource/intervals/test_javierre.py b/tests/gentropy/datasource/intervals/test_javierre.py index be8c528e2..886a28c52 100644 --- a/tests/gentropy/datasource/intervals/test_javierre.py +++ b/tests/gentropy/datasource/intervals/test_javierre.py @@ -1,12 +1,14 @@ """Test JavierreIntervals.""" + from __future__ import annotations import pytest +from pyspark.sql import DataFrame, SparkSession + from gentropy.common.Liftover import LiftOverSpark from gentropy.dataset.gene_index import GeneIndex from gentropy.dataset.intervals import Intervals from gentropy.datasource.intervals.javierre import IntervalsJavierre -from pyspark.sql import DataFrame, SparkSession @pytest.fixture(scope="module") diff --git a/tests/gentropy/datasource/intervals/test_jung.py b/tests/gentropy/datasource/intervals/test_jung.py index 81506ae55..e391b8f96 100644 --- a/tests/gentropy/datasource/intervals/test_jung.py +++ b/tests/gentropy/datasource/intervals/test_jung.py @@ -1,12 +1,14 @@ """Test Jung Intervals.""" + from __future__ import annotations import pytest +from pyspark.sql import DataFrame, SparkSession + from gentropy.common.Liftover import LiftOverSpark from gentropy.dataset.gene_index import GeneIndex from gentropy.dataset.intervals import Intervals from gentropy.datasource.intervals.jung import IntervalsJung -from pyspark.sql import DataFrame, SparkSession @pytest.fixture(scope="module") diff --git a/tests/gentropy/datasource/intervals/test_thurman.py b/tests/gentropy/datasource/intervals/test_thurman.py index f89c9d3da..616e1abec 100644 --- a/tests/gentropy/datasource/intervals/test_thurman.py +++ b/tests/gentropy/datasource/intervals/test_thurman.py @@ -1,12 +1,14 @@ """Test Thurman.""" + from __future__ import annotations import pytest +from pyspark.sql import DataFrame, SparkSession + from gentropy.common.Liftover import LiftOverSpark from gentropy.dataset.gene_index import GeneIndex from gentropy.dataset.intervals import Intervals from gentropy.datasource.intervals.thurman import IntervalsThurman -from pyspark.sql import DataFrame, SparkSession @pytest.fixture(scope="module") diff --git a/tests/gentropy/datasource/open_targets/test_l2g_gold_standard.py b/tests/gentropy/datasource/open_targets/test_l2g_gold_standard.py index 448e2a50c..e6afc942f 100644 --- a/tests/gentropy/datasource/open_targets/test_l2g_gold_standard.py +++ b/tests/gentropy/datasource/open_targets/test_l2g_gold_standard.py @@ -1,29 +1,46 @@ """Test Open Targets L2G gold standards data source.""" + from __future__ import annotations from typing import TYPE_CHECKING import pytest +from pyspark.sql import DataFrame +from pyspark.sql.types import ( + ArrayType, + BooleanType, + IntegerType, + LongType, + StringType, + StructField, + StructType, +) + +from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix from gentropy.dataset.l2g_gold_standard import L2GGoldStandard -from gentropy.dataset.v2g import V2G +from gentropy.dataset.study_index import StudyIndex +from gentropy.dataset.variant_index import VariantIndex from gentropy.datasource.open_targets.l2g_gold_standard import ( OpenTargetsL2GGoldStandard, ) -from pyspark.sql import DataFrame +from gentropy.method.l2g.feature_factory import L2GFeatureInputLoader if TYPE_CHECKING: from pyspark.sql.session import SparkSession + from gentropy.dataset.colocalisation import Colocalisation + from gentropy.dataset.study_locus import StudyLocus + def test_open_targets_as_l2g_gold_standard( sample_l2g_gold_standard: DataFrame, - mock_v2g: V2G, + mock_variant_index: VariantIndex, ) -> None: """Test L2G gold standard from OTG curation.""" assert isinstance( OpenTargetsL2GGoldStandard.as_l2g_gold_standard( sample_l2g_gold_standard, - mock_v2g, + mock_variant_index, ), L2GGoldStandard, ) @@ -73,19 +90,52 @@ def _setup(self: TestExpandGoldStandardWithNegatives, spark: SparkSession) -> No ["variantId", "geneId", "studyId"], ) - sample_v2g_df = spark.createDataFrame( + sample_variant_index_df = spark.createDataFrame( [ - ("variant1", "gene1", 5, "X", "X", "X"), - ("variant1", "gene3", 10, "X", "X", "X"), - ], - [ - "variantId", - "geneId", - "distance", - "chromosome", - "datatypeId", - "datasourceId", + ( + "variant1", + "chrom", + 1, + "A", + "T", + [ + { + "distanceFromTss": 5, + "targetId": "gene1", + "isEnsemblCanonical": True, + }, + { + "distanceFromTss": 10, + "targetId": "gene3", + "isEnsemblCanonical": True, + }, + ], + ), ], + StructType( + [ + StructField("variantId", StringType(), True), + StructField("chromosome", StringType(), True), + StructField("position", IntegerType(), True), + StructField("referenceAllele", StringType(), True), + StructField("alternateAllele", StringType(), True), + StructField( + "transcriptConsequences", + ArrayType( + StructType( + [ + StructField("distanceFromTss", LongType(), True), + StructField("targetId", StringType(), True), + StructField( + "isEnsemblCanonical", BooleanType(), True + ), + ] + ) + ), + True, + ), + ] + ), ) self.expected_expanded_gs = spark.createDataFrame( @@ -99,6 +149,28 @@ def _setup(self: TestExpandGoldStandardWithNegatives, spark: SparkSession) -> No self.observed_df = ( OpenTargetsL2GGoldStandard.expand_gold_standard_with_negatives( self.sample_positive_set, - V2G(_df=sample_v2g_df, _schema=V2G.get_schema()), + VariantIndex( + _df=sample_variant_index_df, _schema=VariantIndex.get_schema() + ), ) ) + + +def test_build_feature_matrix( + mock_l2g_gold_standard: L2GGoldStandard, + mock_study_locus: StudyLocus, + mock_colocalisation: Colocalisation, + mock_study_index: StudyIndex, +) -> None: + """Test building feature matrix with the eQtlColocH4Maximum feature.""" + features_list = ["eQtlColocH4Maximum"] + loader = L2GFeatureInputLoader( + colocalisation=mock_colocalisation, + study_index=mock_study_index, + study_locus=mock_study_locus, + ) + fm = mock_study_locus.build_feature_matrix(features_list, loader) + assert isinstance( + mock_l2g_gold_standard.build_feature_matrix(fm, mock_study_locus), + L2GFeatureMatrix, + ), "Feature matrix should be of type L2GFeatureMatrix" diff --git a/tests/gentropy/datasource/open_targets/test_target.py b/tests/gentropy/datasource/open_targets/test_target.py index 33bb6ddc9..091dcea53 100644 --- a/tests/gentropy/datasource/open_targets/test_target.py +++ b/tests/gentropy/datasource/open_targets/test_target.py @@ -1,9 +1,11 @@ """Test Open Targets target data source.""" + from __future__ import annotations +from pyspark.sql import DataFrame + from gentropy.dataset.gene_index import GeneIndex from gentropy.datasource.open_targets.target import OpenTargetsTarget -from pyspark.sql import DataFrame def test_open_targets_as_gene_index(sample_target_index: DataFrame) -> None: diff --git a/tests/gentropy/datasource/open_targets/test_variants.py b/tests/gentropy/datasource/open_targets/test_variants.py new file mode 100644 index 000000000..6aa22e628 --- /dev/null +++ b/tests/gentropy/datasource/open_targets/test_variants.py @@ -0,0 +1,90 @@ +"""Test suite for the variants module of Open Targets.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from gentropy.dataset.study_locus import StudyLocus +from gentropy.datasource.open_targets.variants import OpenTargetsVariant + +if TYPE_CHECKING: + from pyspark.sql import SparkSession + + from gentropy.common.session import Session + + +class TestOpenTargetsVariant: + """Test suite for the OpenTargetsVariant class.""" + + def test_as_vcf_df_credible_set( + self: TestOpenTargetsVariant, + spark: SparkSession, + session: Session, + ) -> None: + """Test the as_vcf_df method.""" + df_credible_set_df = spark.createDataFrame( + [ + { + "studyLocusId": "1", + "variantId": "1_2_C_G", + "studyId": "study1", + "locus": [ + { + "variantId": "1_3_A_T", + }, + ], + }, + ], + StudyLocus.get_schema(), + ) + observed_df = OpenTargetsVariant.as_vcf_df(session, df_credible_set_df).orderBy( + *["#CHROM", "POS", "REF", "ALT"] + ) + + vcf_cols = ["#CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO"] + df_credible_set_expected_df = spark.createDataFrame( + [ + ("1", 2, ".", "C", "G", ".", ".", "."), + ("1", 3, ".", "A", "T", ".", ".", "."), + ], + vcf_cols, + ) + assert ( + observed_df.collect() == df_credible_set_expected_df.collect() + ), "Unexpected VCF dataframe." + + def test_as_vcf_df_without_variant_id( + self: TestOpenTargetsVariant, + spark: SparkSession, + session: Session, + ) -> None: + """Test the as_vcf_df method.""" + df_without_variant_id_df = spark.createDataFrame( + [("rs75493593",)], ["variantRsId"] + ) + observed_df = OpenTargetsVariant.as_vcf_df( + session, df_without_variant_id_df + ).orderBy(*["#CHROM", "POS", "REF", "ALT"]) + + assert observed_df.count() == 0, "A variant ID should be present for VCF step." + + def test_as_vcf_df_without_rs_id( + self: TestOpenTargetsVariant, + spark: SparkSession, + session: Session, + ) -> None: + """Test the as_vcf_df method with a dataframe of variants without an annotated variantRsId.""" + df_without_rs_id_df = spark.createDataFrame([("1_2_G_GA",)], ["variantId"]) + observed_df = OpenTargetsVariant.as_vcf_df(session, df_without_rs_id_df) + + vcf_cols = ["#CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO"] + df_without_rs_id_expected_df = spark.createDataFrame( + [ + ("1", 2, ".", "G", "GA", ".", ".", "."), + ], + vcf_cols, + ) + + assert ( + observed_df.collect() == df_without_rs_id_expected_df.collect() + ), "Unexpected VCF dataframe." diff --git a/tests/gentropy/datasource/ukbiobank/test_ukbiobank_study_index.py b/tests/gentropy/datasource/ukbiobank/test_ukbiobank_study_index.py index ff07db5db..9f48ebef2 100644 --- a/tests/gentropy/datasource/ukbiobank/test_ukbiobank_study_index.py +++ b/tests/gentropy/datasource/ukbiobank/test_ukbiobank_study_index.py @@ -2,9 +2,10 @@ from __future__ import annotations +from pyspark.sql import DataFrame + from gentropy.dataset.study_index import StudyIndex from gentropy.datasource.ukbiobank.study_index import UKBiobankStudyIndex -from pyspark.sql import DataFrame def test_ukbiobank_study_index_from_source( diff --git a/tests/gentropy/docs/test_applying_methods.py b/tests/gentropy/docs/test_applying_methods.py index 255752f46..14bd70fd7 100644 --- a/tests/gentropy/docs/test_applying_methods.py +++ b/tests/gentropy/docs/test_applying_methods.py @@ -1,15 +1,16 @@ """Testing applying methods docs.""" + from typing import Any import pytest -from gentropy.dataset.study_locus import StudyLocus -from gentropy.dataset.summary_statistics import SummaryStatistics from docs.src_snippets.howto.python_api.c_applying_methods import ( apply_class_method_clumping, apply_class_method_pics, apply_instance_method, ) +from gentropy.dataset.study_locus import StudyLocus +from gentropy.dataset.summary_statistics import SummaryStatistics @pytest.mark.parametrize( diff --git a/tests/gentropy/docs/test_create_dataset.py b/tests/gentropy/docs/test_create_dataset.py index 663254753..256f11251 100644 --- a/tests/gentropy/docs/test_create_dataset.py +++ b/tests/gentropy/docs/test_create_dataset.py @@ -1,15 +1,16 @@ """Testing creating dataset docs.""" + from typing import Any import pytest -from gentropy.common.session import Session -from gentropy.dataset.summary_statistics import SummaryStatistics from docs.src_snippets.howto.python_api.b_create_dataset import ( create_from_pandas, create_from_parquet, create_from_source, ) +from gentropy.common.session import Session +from gentropy.dataset.summary_statistics import SummaryStatistics @pytest.mark.parametrize( diff --git a/tests/gentropy/docs/test_creating_spark_session.py b/tests/gentropy/docs/test_creating_spark_session.py index 6e1cf1ca5..088f23d38 100644 --- a/tests/gentropy/docs/test_creating_spark_session.py +++ b/tests/gentropy/docs/test_creating_spark_session.py @@ -1,10 +1,10 @@ """Testing creating spark session docs.""" -from gentropy.common.session import Session from docs.src_snippets.howto.python_api.a_creating_spark_session import ( custom_session, default_session, ) +from gentropy.common.session import Session def test_default_session() -> None: diff --git a/tests/gentropy/docs/test_inspect_dataset.py b/tests/gentropy/docs/test_inspect_dataset.py index daefa9793..ecfb6d85b 100644 --- a/tests/gentropy/docs/test_inspect_dataset.py +++ b/tests/gentropy/docs/test_inspect_dataset.py @@ -1,5 +1,5 @@ """Testing inspecting dataset docs.""" -from gentropy.dataset.summary_statistics import SummaryStatistics + from pyspark.sql.types import StructType from docs.src_snippets.howto.python_api.d_inspect_dataset import ( @@ -7,6 +7,7 @@ get_dataset_schema, interact_w_dataframe, ) +from gentropy.dataset.summary_statistics import SummaryStatistics def test_filter_dataset(mock_summary_statistics: SummaryStatistics) -> None: diff --git a/tests/gentropy/method/test_carma.py b/tests/gentropy/method/test_carma.py index c715eba47..8db2abe47 100644 --- a/tests/gentropy/method/test_carma.py +++ b/tests/gentropy/method/test_carma.py @@ -3,6 +3,7 @@ from __future__ import annotations import numpy as np + from gentropy.method.carma import CARMA @@ -54,3 +55,22 @@ def test_MCS_modified( num_causal=10, ) assert np.allclose(np.round(np.corrcoef(l1_pips, pips)[0, 1], decimals=2), 1) + + def test_time_limited_CARMA_spike_slab_noEM_pips_no_restriction( + self: TestCARMA, sample_data_for_carma: list[np.ndarray] + ) -> None: + """Test of CARMA PIPs with liberal (no) time restriction.""" + ld = sample_data_for_carma[0] + z = sample_data_for_carma[1] + pips = sample_data_for_carma[2] + _l = CARMA.time_limited_CARMA_spike_slab_noEM(z=z, ld=ld, sec_threshold=600) + assert np.allclose(np.round(np.corrcoef(_l["PIPs"], pips)[0, 1], decimals=2), 1) + + def test_time_limited_CARMA_spike_slab_noEM_pips_restriction( + self: TestCARMA, sample_data_for_carma: list[np.ndarray] + ) -> None: + """Test of CARMA PIPs with time restriction.""" + ld = sample_data_for_carma[0] + z = sample_data_for_carma[1] + _l = CARMA.time_limited_CARMA_spike_slab_noEM(z=z, ld=ld, sec_threshold=0.001) + assert _l["Outliers"] is None and _l["PIPs"] is None diff --git a/tests/gentropy/method/test_clump.py b/tests/gentropy/method/test_clump.py index af4e2d141..ed07608db 100644 --- a/tests/gentropy/method/test_clump.py +++ b/tests/gentropy/method/test_clump.py @@ -2,11 +2,12 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING import pyspark.sql.functions as f import pyspark.sql.types as t import pytest + from gentropy.dataset.study_locus import StudyLocus from gentropy.method.clump import LDclumping @@ -19,142 +20,77 @@ def test_clump(mock_study_locus: StudyLocus) -> None: assert isinstance(LDclumping.clump(mock_study_locus), StudyLocus) -@pytest.mark.parametrize( - ("observed_data", "expected_data"), - [ +class TestIsLeadLinked: + """Testing the is_lead_linked method.""" + + DATA = [ + # Linked to V2: ( - [ - ( - # Dependent locus - lead is correlated with a more significant variant - 1, - "L1", - "GCST005650_1", - 1.0, - -17, - [{"tagVariantId": "T1"}, {"tagVariantId": "L2"}], - None, - ), - ( - # Dependent locus - lead shows a stronger association than the row above - 2, - "L2", - "GCST005650_1", - 4.0, - -18, - [ - {"tagVariantId": "T2"}, - {"tagVariantId": "T3"}, - {"tagVariantId": "L1"}, - ], - None, - ), - ( - # Independent locus - 3, - "L2", - "GCST005650_1", - 4.0, - -18, - [ - {"tagVariantId": "L3"}, - {"tagVariantId": "T4"}, - {"tagVariantId": "L5"}, - ], - None, - ), - ( - # Empty credible set - 4, - "L3", - "GCST005650_1", - 4.0, - -18, - [], - None, - ), - ( - # Null credible set - 5, - "L4", - "GCST005650_1", - 4.0, - -18, - None, - None, - ), - ], - [ - ( - # Signal is linked to the next row - 1, - "L1", - "GCST005650_1", - 1.0, - -17, - [{"tagVariantId": "T1"}, {"tagVariantId": "L2"}], - True, - ), - ( - # Signal is the most significant - 2, - "L2", - "GCST005650_1", - 4.0, - -18, - [ - {"tagVariantId": "T2"}, - {"tagVariantId": "T3"}, - {"tagVariantId": "L1"}, - ], - False, - ), - ( - # Signal is not linked - 3, - "L2", - "GCST005650_1", - 4.0, - -18, - [ - {"tagVariantId": "L3"}, - {"tagVariantId": "T4"}, - {"tagVariantId": "L5"}, - ], - False, - ), - ( - # Empty credible set - signal is not linked - 4, - "L3", - "GCST005650_1", - 4.0, - -18, - [], - False, - ), - ( - # Null credible set - signal is not linked - 5, - "L4", - "GCST005650_1", - 4.0, - -18, - None, - False, - ), - ], - ) - ], -) -def test_is_lead_linked( - spark: SparkSession, observed_data: list[Any], expected_data: list[Any] -) -> None: - """Test function that annotates whether a studyLocusId is linked to a more statistically significant studyLocusId.""" - schema = t.StructType( + "s1", + 1, + "c1", + "v3", + 1.0, + -8, + [{"tagVariantId": "v3"}, {"tagVariantId": "v2"}, {"tagVariantId": "v4"}], + True, + ), + # True lead: + ( + "s1", + 2, + "c1", + "v1", + 1.0, + -10, + [{"tagVariantId": "v1"}, {"tagVariantId": "v2"}, {"tagVariantId": "v3"}], + False, + ), + # Linked to V1: + ( + "s1", + 3, + "c1", + "v2", + 1.0, + -9, + [{"tagVariantId": "v2"}, {"tagVariantId": "v1"}], + True, + ), + # Independent - No LD set: + ("s1", 4, "c1", "v10", 1.0, -10, [], False), + # Independent - No variantId: + ("s1", 5, "c1", None, 1.0, -10, [], False), + # An other independent variant on the same chromosome, but lead is not in ldSet: + ( + "s1", + 6, + "c1", + "v6", + 1.0, + -8, + [{"tagVariantId": "v7"}, {"tagVariantId": "v8"}, {"tagVariantId": "v9"}], + False, + ), + # An other independent variant on a different chromosome, but lead is not in ldSet: + ( + "s1", + 7, + "c2", + "v10", + 1.0, + -8, + [{"tagVariantId": "v2"}, {"tagVariantId": "v10"}], + False, + ), + ] + + SCHEMA = t.StructType( [ - t.StructField("studyLocusId", t.LongType(), True), - t.StructField("variantId", t.StringType(), True), t.StructField("studyId", t.StringType(), True), + t.StructField("studyLocusId", t.StringType(), True), + t.StructField("chromosome", t.StringType(), True), + t.StructField("variantId", t.StringType(), True), t.StructField("pValueMantissa", t.FloatType(), True), t.StructField("pValueExponent", t.IntegerType(), True), t.StructField( @@ -168,28 +104,49 @@ def test_is_lead_linked( ), True, ), - t.StructField("is_lead_linked", t.BooleanType(), True), + t.StructField("expected_flag", t.BooleanType(), True), ] ) - study_locus_df = spark.createDataFrame( - observed_data, - schema, - ) - observed_df = ( - study_locus_df.withColumn( + + @pytest.fixture(autouse=True) + def _setup(self: TestIsLeadLinked, spark: SparkSession) -> None: + """Setup study the mock index for testing.""" + # Store input data: + self.df = spark.createDataFrame(self.DATA, self.SCHEMA) + + def test_is_lead_correctness(self: TestIsLeadLinked) -> None: + """Test the correctness of the is_lead_linked method.""" + observed = self.df.withColumn( "is_lead_linked", LDclumping._is_lead_linked( f.col("studyId"), + f.col("chromosome"), f.col("variantId"), f.col("pValueExponent"), f.col("pValueMantissa"), f.col("ldSet"), ), - ) - .orderBy("studyLocusId") - .collect() - ) - expected_df = ( - spark.createDataFrame(expected_data, schema).orderBy("studyLocusId").collect() - ) - assert observed_df == expected_df + ).collect() + + for row in observed: + assert row["is_lead_linked"] == row["expected_flag"] + + def test_flagging(self: TestIsLeadLinked) -> None: + """Test flagging of lead variants.""" + # Create the study locus and clump: + sl_flagged = StudyLocus( + _df=self.df.drop("expected_flag").withColumn( + "qualityControls", f.array().cast("array") + ), + _schema=StudyLocus.get_schema(), + ).clump() + + # Assert that the clumped locus is a StudyLocus: + assert isinstance(sl_flagged, StudyLocus) + + # Assert that the clumped locus has the correct columns: + for row in sl_flagged.df.join(self.df, on="studylocusId").collect(): + if len(row["qualityControls"]) == 0: + assert not row["expected_flag"] + else: + assert row["expected_flag"] diff --git a/tests/gentropy/method/test_colocalisation_method.py b/tests/gentropy/method/test_colocalisation_method.py index a0e7508a4..5b05d724b 100644 --- a/tests/gentropy/method/test_colocalisation_method.py +++ b/tests/gentropy/method/test_colocalisation_method.py @@ -2,18 +2,258 @@ from __future__ import annotations -from typing import TYPE_CHECKING +from typing import Any + +import pytest +from pandas.testing import assert_frame_equal +from pyspark.sql import SparkSession +from pyspark.sql.types import DoubleType, StringType, StructField, StructType from gentropy.dataset.colocalisation import Colocalisation +from gentropy.dataset.study_locus_overlap import StudyLocusOverlap from gentropy.method.colocalisation import Coloc, ECaviar -if TYPE_CHECKING: - from gentropy.dataset.study_locus_overlap import StudyLocusOverlap - def test_coloc(mock_study_locus_overlap: StudyLocusOverlap) -> None: """Test coloc.""" assert isinstance(Coloc.colocalise(mock_study_locus_overlap), Colocalisation) + assert isinstance( + Coloc.colocalise( + mock_study_locus_overlap, priorc1=1e-4, priorc2=1e-4, priorc12=1e-5 + ), + Colocalisation, + ) + + +@pytest.mark.parametrize( + "observed_data, expected_data", + [ + # associations with a single overlapping SNP + ( + # observed overlap + [ + { + "leftStudyLocusId": "1", + "rightStudyLocusId": "2", + "rightStudyType": "eqtl", + "chromosome": "1", + "tagVariantId": "snp", + "statistics": { + "left_logBF": 10.3, + "right_logBF": 10.5, + "left_beta": 0.1, + "right_beta": 0.2, + }, + }, + ], + # expected coloc + [ + { + "h0": 9.254841951638903e-5, + "h1": 2.7517068829182966e-4, + "h2": 3.3609423764447284e-4, + "h3": 9.254841952564387e-13, + "h4": 0.9992961866536217, + }, + ], + ), + # associations with multiple overlapping SNPs + ( + # observed overlap + [ + { + "leftStudyLocusId": "1", + "rightStudyLocusId": "2", + "rightStudyType": "eqtl", + "chromosome": "1", + "tagVariantId": "snp1", + "statistics": { + "left_logBF": 10.3, + "right_logBF": 10.5, + "left_beta": 0.1, + "right_beta": 0.2, + }, + }, + { + "leftStudyLocusId": "1", + "rightStudyLocusId": "2", + "rightStudyType": "eqtl", + "chromosome": "1", + "tagVariantId": "snp2", + "statistics": { + "left_logBF": 10.3, + "right_logBF": 10.5, + "left_beta": 0.3, + "right_beta": 0.5, + }, + }, + ], + # expected coloc + [ + { + "h0": 4.6230151407950416e-5, + "h1": 2.749086942648107e-4, + "h2": 3.357742374172504e-4, + "h3": 9.983447421747411e-4, + "h4": 0.9983447421747356, + }, + ], + ), + ], +) +def test_coloc_semantic( + spark: SparkSession, + observed_data: list[Any], + expected_data: list[Any], +) -> None: + """Test our COLOC with the implementation in R.""" + observed_overlap = StudyLocusOverlap( + _df=spark.createDataFrame(observed_data, schema=StudyLocusOverlap.get_schema()), + _schema=StudyLocusOverlap.get_schema(), + ) + observed_coloc_pdf = ( + Coloc.colocalise(observed_overlap) + .df.select("h0", "h1", "h2", "h3", "h4") + .toPandas() + ) + expected_coloc_pdf = ( + spark.createDataFrame(expected_data) + .select("h0", "h1", "h2", "h3", "h4") + .toPandas() + ) + + assert_frame_equal( + observed_coloc_pdf, + expected_coloc_pdf, + check_exact=False, + check_dtype=True, + ) + + +def test_coloc_no_logbf( + spark: SparkSession, + minimum_expected_h0: float = 0.99, + maximum_expected_h4: float = 1e-5, +) -> None: + """Test COLOC output when the input data has irrelevant logBF.""" + observed_overlap = StudyLocusOverlap( + ( + spark.createDataFrame( + [ + { + "leftStudyLocusId": "1", + "rightStudyLocusId": "2", + "rightStudyType": "eqtl", + "chromosome": "1", + "tagVariantId": "snp", + "statistics": { + "left_logBF": None, + "right_logBF": None, + "left_beta": 0.1, + "right_beta": 0.2, + "left_posteriorProbability": None, + "right_posteriorProbability": None, + }, # irrelevant for COLOC + } + ], + schema=StructType( + [ + StructField("leftStudyLocusId", StringType(), False), + StructField("rightStudyLocusId", StringType(), False), + StructField("rightStudyType", StringType(), False), + StructField("chromosome", StringType(), False), + StructField("tagVariantId", StringType(), False), + StructField( + "statistics", + StructType( + [ + StructField("left_logBF", DoubleType(), True), + StructField("right_logBF", DoubleType(), True), + StructField("left_beta", DoubleType(), False), + StructField("right_beta", DoubleType(), False), + StructField( + "left_posteriorProbability", DoubleType(), True + ), + StructField( + "right_posteriorProbability", DoubleType(), True + ), + ] + ), + ), + ] + ), + ) + ), + StudyLocusOverlap.get_schema(), + ) + observed_coloc_df = Coloc.colocalise(observed_overlap).df + assert ( + observed_coloc_df.select("h0").collect()[0]["h0"] > minimum_expected_h0 + ), "COLOC should return a high h0 (no association) when the input data has irrelevant logBF." + assert ( + observed_coloc_df.select("h4").collect()[0]["h4"] < maximum_expected_h4 + ), "COLOC should return a low h4 (traits are associated) when the input data has irrelevant logBF." + + +def test_coloc_no_betas(spark: SparkSession) -> None: + """Test COLOC output when the input data has no betas.""" + observed_overlap = StudyLocusOverlap( + ( + spark.createDataFrame( + [ + { + "leftStudyLocusId": "1", + "rightStudyLocusId": "2", + "rightStudyType": "eqtl", + "chromosome": "1", + "tagVariantId": "snp", + "statistics": { + "left_logBF": 10.5, + "right_logBF": 10.3, + "left_beta": None, + "right_beta": None, + "left_posteriorProbability": None, + "right_posteriorProbability": None, + }, # irrelevant for COLOC + } + ], + schema=StructType( + [ + StructField("leftStudyLocusId", StringType(), False), + StructField("rightStudyLocusId", StringType(), False), + StructField("rightStudyType", StringType(), False), + StructField("chromosome", StringType(), False), + StructField("tagVariantId", StringType(), False), + StructField( + "statistics", + StructType( + [ + StructField("left_logBF", DoubleType(), False), + StructField("right_logBF", DoubleType(), False), + StructField("left_beta", DoubleType(), True), + StructField("right_beta", DoubleType(), True), + StructField( + "left_posteriorProbability", DoubleType(), True + ), + StructField( + "right_posteriorProbability", DoubleType(), True + ), + ] + ), + ), + ] + ), + ) + ), + StudyLocusOverlap.get_schema(), + ) + observed_coloc_df = Coloc.colocalise(observed_overlap).df + assert ( + observed_coloc_df.select("betaRatioSignAverage").collect()[0][ + "betaRatioSignAverage" + ] + is None + ), "No betas results in None type." def test_ecaviar(mock_study_locus_overlap: StudyLocusOverlap) -> None: diff --git a/tests/gentropy/method/test_ld.py b/tests/gentropy/method/test_ld.py index db0b5aba4..7e7237ac9 100644 --- a/tests/gentropy/method/test_ld.py +++ b/tests/gentropy/method/test_ld.py @@ -2,50 +2,197 @@ from __future__ import annotations -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any import pyspark.sql.functions as f import pyspark.sql.types as t import pytest +from pyspark.sql import Row + from gentropy.dataset.study_locus import StudyLocus from gentropy.method.ld import LDAnnotator -from pyspark.sql import Row if TYPE_CHECKING: + from pyspark.sql import SparkSession + from gentropy.dataset.ld_index import LDIndex from gentropy.dataset.study_index import StudyIndex - from pyspark.sql import SparkSession class TestLDAnnotator: """Test LDAnnotatorGnomad.""" - def test__add_population_size( + @pytest.mark.parametrize( + ("observed", "expected"), + [ + # no tie in relativeSampleSize + ( + # observed ldPopulationStructure + [ + Row( + ldPopulationStructure=[ + {"ldPopulation": "pop1", "relativeSampleSize": 0.5}, + {"ldPopulation": "pop2", "relativeSampleSize": 0.3}, + {"ldPopulation": "pop3", "relativeSampleSize": 0.2}, + ], + ) + ], + # expected majorPopulation + "pop1", + ), + # tie in relativeSampleSize, "nfe" is not one of the tied populations + ( + # observed ldPopulationStructure + [ + Row( + ldPopulationStructure=[ + {"ldPopulation": "pop1", "relativeSampleSize": 0.4}, + {"ldPopulation": "pop2", "relativeSampleSize": 0.4}, + {"ldPopulation": "pop3", "relativeSampleSize": 0.2}, + ], + ) + ], + # expected majorPopulation + "pop1", + ), + # tie in relativeSampleSize, "nfe" is one of the tied populations + ( + # observed ldPopulationStructure + [ + Row( + ldPopulationStructure=[ + {"ldPopulation": "pop1", "relativeSampleSize": 0.4}, + {"ldPopulation": "nfe", "relativeSampleSize": 0.4}, + {"ldPopulation": "pop3", "relativeSampleSize": 0.2}, + ], + ) + ], + # expected majorPopulation + "nfe", + ), + ], + ) + def test__get_major_population( self: TestLDAnnotator, + spark: SparkSession, + observed: list[Any], + expected: list[Any], ) -> None: - """Test _add_population_size.""" - result_df = self.observed_df.select( - LDAnnotator._add_population_size( - f.col("ldSet"), f.col("ldPopulationStructure") - ).alias("ldSet") + """Test _get_major_population.""" + schema = t.StructType( + [ + t.StructField( + "ldPopulationStructure", + t.ArrayType( + t.StructType( + [ + t.StructField("ldPopulation", t.StringType(), True), + t.StructField( + "relativeSampleSize", t.DoubleType(), True + ), + ] + ) + ), + True, + ), + ] ) - expected = [0.8, None] - for i, row in enumerate(result_df.collect()): - assert row["ldSet"][0]["rValues"][i]["relativeSampleSize"] == pytest.approx( - expected[i] - ) + observed_df = spark.createDataFrame(observed, schema) + result_df = observed_df.withColumn( + "majorPopulation", + LDAnnotator._get_major_population(f.col("ldPopulationStructure")), + ) + assert result_df.collect()[0]["majorPopulation"] == pytest.approx(expected) - def test__calculate_weighted_r_overall( + @pytest.mark.parametrize( + ("observed", "expected"), + [ + # r available for majorPopulation + ( + # observed ldSet and majorPopulation + [ + Row( + majorPopulation="pop1", + ldSet=[ + { + "tagVariantId": "tag1", + "rValues": [ + {"population": "pop1", "r": 0.5}, + {"population": "pop2", "r": 0.6}, + ], + } + ], + ) + ], + # expected r2Overall + 0.25, + ), + # r not available for majorPopulation + ( + # observed ldSet and majorPopulation + [ + Row( + majorPopulation="pop3", + ldSet=[ + { + "tagVariantId": "tag1", + "rValues": [ + {"population": "pop1", "r": 0.5}, + {"population": "pop2", "r": 0.6}, + ], + } + ], + ) + ], + # expected r2Overall + 0.0, + ), + ], + ) + def test__calculate_r2_major( self: TestLDAnnotator, + spark: SparkSession, + observed: list[Any], + expected: list[Any], ) -> None: - """Test _calculate_weighted_r_overall.""" - result_df = self.observed_df.withColumn( + """Test _calculate_r2_major.""" + schema = t.StructType( + [ + t.StructField("majorPopulation", t.StringType(), True), + t.StructField( + "ldSet", + t.ArrayType( + t.StructType( + [ + t.StructField("tagVariantId", t.StringType(), True), + t.StructField( + "rValues", + t.ArrayType( + t.StructType( + [ + t.StructField( + "population", t.StringType(), True + ), + t.StructField( + "r", t.DoubleType(), True + ), + ] + ) + ), + True, + ), + ] + ) + ), + True, + ), + ] + ) + observed_df = spark.createDataFrame(observed, schema) + result_df = observed_df.withColumn( "ldSet", - LDAnnotator._add_population_size( - f.col("ldSet"), f.col("ldPopulationStructure") - ), - ).withColumn("ldSet", LDAnnotator._calculate_weighted_r_overall(f.col("ldSet"))) - expected = 0.2 + LDAnnotator._calculate_r2_major(f.col("ldSet"), f.col("majorPopulation")), + ) assert result_df.collect()[0]["ldSet"][0]["r2Overall"] == pytest.approx( expected ) diff --git a/tests/gentropy/method/test_locus_breaker_clumping.py b/tests/gentropy/method/test_locus_breaker_clumping.py new file mode 100644 index 000000000..c2c23eca5 --- /dev/null +++ b/tests/gentropy/method/test_locus_breaker_clumping.py @@ -0,0 +1,133 @@ +"""Test locus-breaker clumping.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pytest +from pyspark.sql import functions as f +from pyspark.sql import types as t + +from gentropy.dataset.study_locus import StudyLocus +from gentropy.dataset.summary_statistics import SummaryStatistics + +if TYPE_CHECKING: + from pyspark.sql import SparkSession + + +def test_locus_breaker_return_type( + mock_summary_statistics: SummaryStatistics, +) -> None: + """Test locus clumping.""" + assert isinstance( + mock_summary_statistics.locus_breaker_clumping(), + StudyLocus, + ) + + +class TestLocusBreakerClumping: + """Test locus breaker clumping.""" + + # Some constants for testing: + distance_cutoff = 3 + pvalue_baseline_cutoff = 0.05 + pvalue_cutoff = 1e-3 + flanking = 2 + + @pytest.fixture(scope="class") + def mock_input( + self: TestLocusBreakerClumping, + spark: SparkSession, + ) -> SummaryStatistics: + """Prepare mock summary statistics for clumping.""" + data = [ + # Block 1: Study1, chromosome 1, expected boundaries: 0-5 + ("s1", "chr1", "v1", 1, -2), + ("s1", "chr1", "v1", 2, -4), + ("s1", "chr1", "top_loci", 3, -5), + ("s1", "chr1", "v1", 4, -1), # <- will be dropped: not reaching baseline + # Block 2: Study1, chromosome 1, expected boundaries: 5-10 + ("s1", "chr1", "top_loci", 7, -4), + ("s1", "chr1", "v1", 8, -2), + # Block 3: Study1, chromosome 2, expected boundaries: 6-12 + ("s1", "chr2", "v1", 8, -2), + ("s1", "chr2", "v1", 9, -3), + ("s1", "chr2", "top_loci", 10, -5), + # Block 4: Study1, chromosome 2 + ("s1", "chr2", "v1", 14, -2), # <- will be dropped: low p-value + # Block 5: Study2, chromosome 2, expected boundaries: 6-12 + ("s2", "chr2", "v1", 8, -2), + ("s2", "chr2", "v1", 9, -3), + ("s2", "chr2", "top_loci", 10, -6), + # Block 6: Study2, chromosome 2, expected boundaries: 12-16 + ("s2", "chr2", "top_loci", 14, -5), + ] + df = spark.createDataFrame( + data, ["studyId", "chromosome", "variantId", "position", "pValueExponent"] + ).select( + "studyId", + "chromosome", + "variantId", + f.col("position").cast(t.IntegerType()), + f.col("pValueExponent").cast(t.IntegerType()), + f.lit(1.0).cast(t.FloatType()).alias("pValueMantissa"), + f.lit(1.0).cast(t.DoubleType()).alias("beta"), + ) + + return SummaryStatistics(_df=df, _schema=SummaryStatistics.get_schema()) + + @pytest.fixture(scope="class") + def clumped_data( + self: TestLocusBreakerClumping, mock_input: SummaryStatistics + ) -> StudyLocus: + """Apply method and store for clumped data.""" + return mock_input.locus_breaker_clumping( + self.pvalue_baseline_cutoff, + self.distance_cutoff, + self.pvalue_cutoff, + self.flanking, + ).persist() + + def test_return_type( + self: TestLocusBreakerClumping, clumped_data: StudyLocus + ) -> None: + """Testing return type.""" + assert isinstance( + clumped_data, StudyLocus + ), f"Unexpected return type: {type(clumped_data)}" + + def test_number_of_loci( + self: TestLocusBreakerClumping, clumped_data: StudyLocus + ) -> None: + """Testing return type.""" + assert ( + clumped_data.df.count() == 5 + ), f"Unexpected number of loci: {clumped_data.df.count()}" + + def test_top_loci(self: TestLocusBreakerClumping, clumped_data: StudyLocus) -> None: + """Testing selected top-loci.""" + top_loci_variants = clumped_data.df.select("variantId").distinct().collect() + + assert ( + len(top_loci_variants) == 1 + ), f"Unexpected number of top loci: {len(top_loci_variants)} ({top_loci_variants})" + + assert ( + top_loci_variants[0]["variantId"] == "top_loci" + ), f"Unexpected top locus: {top_loci_variants[0]['variantId']}" + + def test_locus_boundaries( + self: TestLocusBreakerClumping, clumped_data: StudyLocus + ) -> None: + """Testing locus boundaries.""" + locus_start = [ + row["locusStart"] for row in clumped_data.df.select("locusStart").collect() + ] + + locus_end = [ + row["locusEnd"] for row in clumped_data.df.select("locusEnd").collect() + ] + + assert locus_start == [0, 5, 6, 6, 12], f"Unexpected locus start: {locus_start}" + + assert locus_end == [5, 10, 12, 12, 16], f"Unexpected locus end: {locus_end}" diff --git a/tests/gentropy/method/test_locus_to_gene.py b/tests/gentropy/method/test_locus_to_gene.py deleted file mode 100644 index d97f4dc4d..000000000 --- a/tests/gentropy/method/test_locus_to_gene.py +++ /dev/null @@ -1,230 +0,0 @@ -"""Test locus-to-gene model training.""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -import pytest -from gentropy.dataset.colocalisation import Colocalisation -from gentropy.dataset.l2g_feature import L2GFeature -from gentropy.dataset.l2g_feature_matrix import L2GFeatureMatrix -from gentropy.dataset.study_index import StudyIndex -from gentropy.dataset.study_locus import StudyLocus -from gentropy.method.l2g.feature_factory import ColocalisationFactory, StudyLocusFactory -from gentropy.method.l2g.model import LocusToGeneModel -from gentropy.method.l2g.trainer import LocusToGeneTrainer -from pyspark.ml import PipelineModel -from pyspark.ml.tuning import ParamGridBuilder -from xgboost.spark import SparkXGBClassifier - -if TYPE_CHECKING: - from gentropy.dataset.v2g import V2G - from pyspark.sql import SparkSession - - -@pytest.fixture(scope="module") -def model() -> LocusToGeneModel: - """Creates an instance of the LocusToGene class.""" - estimator = SparkXGBClassifier( - eval_metric="logloss", - features_col="features", - label_col="label", - max_depth=5, - ) - return LocusToGeneModel(estimator=estimator, features_list=["distanceTssMean"]) - - -class TestLocusToGeneTrainer: - """Test the L2GTrainer methods using a logistic regression model as estimation algorithm.""" - - def test_cross_validate( - self: TestLocusToGeneTrainer, - mock_l2g_feature_matrix: L2GFeatureMatrix, - model: LocusToGeneModel, - ) -> None: - """Test the k-fold cross-validation function.""" - param_grid = ( - ParamGridBuilder() - .addGrid(model.estimator.learning_rate, [0.1, 0.01]) - .build() - ) - best_model = LocusToGeneTrainer.cross_validate( - model, mock_l2g_feature_matrix.fill_na(), num_folds=2, param_grid=param_grid - ) - assert isinstance( - best_model, LocusToGeneModel - ), "Unexpected model type returned from cross_validate" - # Check that the best model's hyperparameters are among those in the param_grid - assert best_model.model.getOrDefault("learning_rate") in [ # type: ignore - 0.1, - 0.01, - ], "Unexpected learning rate in the best model" - - def test_train( - self: TestLocusToGeneTrainer, - mock_l2g_feature_matrix: L2GFeatureMatrix, - model: LocusToGeneModel, - ) -> None: - """Test the training function.""" - trained_model = LocusToGeneTrainer.train( - mock_l2g_feature_matrix.fill_na(), - model, - features_list=["distanceTssMean"], - evaluate=False, - ) - # Check that `model` is a PipelineModel object and not None - assert isinstance( - trained_model.model, PipelineModel - ), "Model is not a PipelineModel object." - - -class TestColocalisationFactory: - """Test the ColocalisationFactory methods.""" - - @pytest.mark.parametrize( - "colocalisation_method", - [ - "COLOC", - "eCAVIAR", - ], - ) - def test_get_max_coloc_per_study_locus( - self: TestColocalisationFactory, - mock_study_locus: StudyLocus, - mock_study_index: StudyIndex, - mock_colocalisation: Colocalisation, - colocalisation_method: str, - ) -> None: - """Test the function that extracts the maximum log likelihood ratio for each pair of overlapping study-locus returns the right data type.""" - coloc_features = ColocalisationFactory._get_max_coloc_per_study_locus( - mock_study_locus, - mock_study_index, - mock_colocalisation, - colocalisation_method, - ) - assert isinstance( - coloc_features, L2GFeature - ), "Unexpected model type returned from _get_max_coloc_per_study_locus" - - def test_get_max_coloc_per_study_locus_semantic( - self: TestColocalisationFactory, - spark: SparkSession, - ) -> None: - """Test logic of the function that extracts the maximum log likelihood ratio for each pair of overlapping study-locus.""" - # Prepare mock datasets based on 2 associations - credset = StudyLocus( - _df=spark.createDataFrame( - # 2 associations with a common variant in the locus - [ - { - "studyLocusId": 1, - "variantId": "lead1", - "studyId": "study1", # this is a GWAS - "locus": [ - {"variantId": "commonTag", "posteriorProbability": 0.9}, - ], - "chromosome": "1", - }, - { - "studyLocusId": 2, - "variantId": "lead2", - "studyId": "study2", # this is a eQTL study - "locus": [ - {"variantId": "commonTag", "posteriorProbability": 0.9}, - ], - "chromosome": "1", - }, - ], - StudyLocus.get_schema(), - ), - _schema=StudyLocus.get_schema(), - ) - - studies = StudyIndex( - _df=spark.createDataFrame( - [ - { - "studyId": "study1", - "studyType": "gwas", - "traitFromSource": "trait1", - "projectId": "project1", - }, - { - "studyId": "study2", - "studyType": "eqtl", - "geneId": "gene1", - "traitFromSource": "trait2", - "projectId": "project2", - }, - ] - ), - _schema=StudyIndex.get_schema(), - ) - coloc = Colocalisation( - _df=spark.createDataFrame( - [ - { - "leftStudyLocusId": 1, - "rightStudyLocusId": 2, - "chromosome": "1", - "colocalisationMethod": "eCAVIAR", - "numberColocalisingVariants": 1, - "clpp": 0.81, # 0.9*0.9 - } - ] - ), - _schema=Colocalisation.get_schema(), - ) - expected_coloc_features_df = spark.createDataFrame( - [ - (1, "gene1", "eqtlColocClppMaximum", 0.81), - (1, "gene1", "eqtlColocClppMaximumNeighborhood", -4.0), - ], - L2GFeature.get_schema(), - ) - # Test - coloc_features = ColocalisationFactory._get_max_coloc_per_study_locus( - credset, - studies, - coloc, - "eCAVIAR", - ) - assert coloc_features.df.collect() == expected_coloc_features_df.collect() - - def test_get_coloc_features( - self: TestColocalisationFactory, - mock_study_locus: StudyLocus, - mock_study_index: StudyIndex, - mock_colocalisation: Colocalisation, - ) -> None: - """Test the function that calls all the methods to produce colocalisation features.""" - coloc_features = ColocalisationFactory._get_coloc_features( - mock_study_locus, mock_study_index, mock_colocalisation - ) - assert isinstance( - coloc_features, L2GFeature - ), "Unexpected model type returned from _get_coloc_features" - - -class TestStudyLocusFactory: - """Test the StudyLocusFactory methods.""" - - def test_get_tss_distance_features( - self: TestStudyLocusFactory, mock_study_locus: StudyLocus, mock_v2g: V2G - ) -> None: - """Test the function that extracts the distance to the TSS.""" - tss_distance = StudyLocusFactory._get_tss_distance_features( - mock_study_locus, mock_v2g - ) - assert isinstance( - tss_distance, L2GFeature - ), "Unexpected model type returned from _get_tss_distance_features" - - def test_get_vep_features( - self: TestStudyLocusFactory, mock_study_locus: StudyLocus, mock_v2g: V2G - ) -> None: - """Test the function that extracts the VEP features.""" - vep_features = StudyLocusFactory._get_vep_features(mock_study_locus, mock_v2g) - assert isinstance( - vep_features, L2GFeature - ), "Unexpected model type returned from _get_vep_features" diff --git a/tests/gentropy/method/test_pics.py b/tests/gentropy/method/test_pics.py index ee0425b61..d5a8eb5d0 100644 --- a/tests/gentropy/method/test_pics.py +++ b/tests/gentropy/method/test_pics.py @@ -3,9 +3,12 @@ from __future__ import annotations import pyspark.sql.functions as f +import pyspark.sql.types as t +import pytest +from pyspark.sql import Row, SparkSession + from gentropy.dataset.study_locus import StudyLocus from gentropy.method.pics import PICS -from pyspark.sql import Row class TestFinemap: @@ -54,8 +57,8 @@ def test_finemap_quality_control( def test__finemap_udf() -> None: """Test the _finemap UDF with a simple case.""" ld_set = [ - Row(variantId="var1", r2Overall=0.8), - Row(variantId="var2", r2Overall=1), + Row(tagVariantId="var1", r2Overall=0.8), + Row(tagVariantId="var2", r2Overall=1), ] result = PICS._finemap(ld_set, lead_neglog_p=10.0, k=6.4) expected = [ @@ -80,3 +83,75 @@ def test__finemap_udf() -> None: def test_finemap(mock_study_locus: StudyLocus) -> None: """Test finemap function returns study-locus.""" assert isinstance(PICS.finemap(mock_study_locus), StudyLocus) + + +class TestLeadPropagation: + """This test suite is designed to test that the statistics of the lead variant are propagated correctly.""" + + DATA = [ + ("v1", "v1", 1.0), + ("v1", "v2", 0.9), + ("v1", "v3", 0.3), + ] + + @pytest.fixture(autouse=True) + def setup(self, spark: SparkSession) -> None: + """Set up the test suite. + + Args: + spark (SparkSession): The spark session. + """ + df = ( + spark.createDataFrame(self.DATA, ["variantId", "tagVariantId", "r2Overall"]) + .groupBy("variantId") + .agg( + f.collect_list( + f.struct( + f.col("tagVariantId"), + f.col("r2Overall").cast(t.DoubleType()).alias("r2Overall"), + ) + ).alias("ldSet") + ) + .withColumns( + { + "studyLocusId": f.lit("l1"), + "studyId": f.lit("s1"), + "chromosome": f.lit("1"), + "pValueMantissa": f.lit(1.0).cast(t.FloatType()), + "pValueExponent": f.lit(-4).cast(t.IntegerType()), + "beta": f.lit(0.234).cast(t.DoubleType()), + "qualityControls": f.lit(None).cast(t.ArrayType(t.StringType())), + "ldSet": f.filter( + f.col("ldSet"), lambda x: x.tagVariantId.isNotNull() + ), + } + ) + ) + + self.study_locus = StudyLocus(_df=df, _schema=StudyLocus.get_schema()) + + def test_lead_propagation(self: TestLeadPropagation) -> None: + """Testing if all the lead variant statistics are propagated to the tag variants.""" + # Explode all the tags: + finemapped = ( + PICS.finemap(self.study_locus) + .df.select( + f.col("variantId"), + f.col("pValueMantissa"), + f.col("pValueExponent"), + f.col("beta"), + f.explode("locus").alias("locus"), + ) + .collect() + ) + + # Looping through all the tags and checking if the statistics are propagated correctly: + for row in finemapped: + if row["locus"]["variantId"] == row["variantId"]: + assert row["locus"]["pValueMantissa"] == row["pValueMantissa"] + assert row["locus"]["pValueExponent"] == row["pValueExponent"] + assert row["locus"]["beta"] == row["beta"] + else: + assert row["locus"]["pValueMantissa"] is None + assert row["locus"]["pValueExponent"] is None + assert row["locus"]["beta"] is None diff --git a/tests/gentropy/method/test_qc_of_sumstats.py b/tests/gentropy/method/test_qc_of_sumstats.py new file mode 100644 index 000000000..cb8d3443e --- /dev/null +++ b/tests/gentropy/method/test_qc_of_sumstats.py @@ -0,0 +1,102 @@ +"""Test of the qc of summary statistics.""" + +from __future__ import annotations + +import numpy as np +import pyspark.sql.functions as f +import pytest +from pyspark.sql.functions import rand, when + +from gentropy.common.session import Session +from gentropy.dataset.summary_statistics import SummaryStatistics +from gentropy.method.sumstat_quality_controls import SummaryStatisticsQC + + +def test_qc_functions( + sample_summary_statistics: SummaryStatistics, +) -> None: + """Test all sumstat qc functions.""" + gwas = sample_summary_statistics.sanity_filter() + QC = SummaryStatisticsQC.get_quality_control_metrics(gwas=gwas, pval_threshold=5e-8) + QC = QC.toPandas() + + assert QC["n_variants"].iloc[0] == 1663 + assert QC["n_variants_sig"].iloc[0] == 29 + assert np.round(QC["gc_lambda"].iloc[0], 4) == 1.916 + assert np.round(QC["mean_beta"].iloc[0], 4) == 0.0013 + assert np.round(QC["mean_diff_pz"].iloc[0], 6) == 0 + assert np.round(QC["se_diff_pz"].iloc[0], 6) == 0 + + +def test_neff_check_eaf( + sample_summary_statistics: SummaryStatistics, +) -> None: + """Test N_eff check using mock EAFs.""" + gwas = sample_summary_statistics.sanity_filter() + gwas_df = gwas._df + gwas_df = gwas_df.withColumn("effectAlleleFrequencyFromSource", f.lit(0.5)) + gwas._df = gwas_df + + QC = SummaryStatisticsQC.sumstat_n_eff_check( + gwas_for_qc=gwas, limit=100000, min_count=100, n_total=100000 + ) + QC = QC.toPandas() + assert np.round(QC["se_N"].iloc[0], 4) == 0.5586 + + +def test_several_studyid( + sample_summary_statistics: SummaryStatistics, +) -> None: + """Test stability when several studyIds are present.""" + gwas = sample_summary_statistics.sanity_filter() + gwas_df = gwas._df + gwas_df = gwas_df.withColumn( + "studyId", when(rand() < 0.5, "new_value").otherwise(gwas_df["studyId"]) + ) + gwas._df = gwas_df + + QC = SummaryStatisticsQC.get_quality_control_metrics(gwas=gwas) + QC = QC.toPandas() + assert QC.shape == (2, 7) + + +def test_sanity_filter_remove_inf_values( + session: Session, +) -> None: + """Sanity filter remove inf value from standardError field.""" + data = [ + ( + "GCST012234", + "10_73856419_C_A", + 10, + 73856419, + np.Infinity, + 1, + 3.1324, + -650, + None, + 0.4671, + ), + ( + "GCST012234", + "14_98074714_G_C", + 14, + 98074714, + 6.697, + 2, + 5.4275, + -2890, + None, + 0.4671, + ), + ] + input_df = session.spark.createDataFrame( + data=data, schema=SummaryStatistics.get_schema() + ) + summary_stats = SummaryStatistics( + _df=input_df, _schema=SummaryStatistics.get_schema() + ) + stats_after_filter = summary_stats.sanity_filter().df.collect() + assert input_df.count() == 2 + assert len(stats_after_filter) == 1 + assert stats_after_filter[0]["beta"] - 6.697 == pytest.approx(0) diff --git a/tests/gentropy/method/test_sumstat_imputation.py b/tests/gentropy/method/test_sumstat_imputation.py new file mode 100644 index 000000000..93df23abc --- /dev/null +++ b/tests/gentropy/method/test_sumstat_imputation.py @@ -0,0 +1,32 @@ +"""Test of sumstat imputation functions.""" + +from __future__ import annotations + +import numpy as np + +from gentropy.method.sumstat_imputation import SummaryStatisticsImputation + + +class TestSSImp: + """Test of RAISS sumstat imputation main function.""" + + def test_sumstat_imputation( + self: TestSSImp, sample_data_for_carma: list[np.ndarray] + ) -> None: + """Test of RAISS.""" + ld = sample_data_for_carma[0] + z = sample_data_for_carma[1] + + unknowns = [5] + known = [index for index in list(range(21)) if index not in unknowns] + sig_t = ld[known, :][:, known] + sig_i_t = ld[unknowns, :][:, known] + zt = z[known] + + _l = SummaryStatisticsImputation.raiss_model( + zt, sig_t, sig_i_t, lamb=0.01, rtol=0.01 + ) + assert ( + np.round(_l["imputation_r2"][0], decimals=4) == 0.9304 + and np.round(_l["mu"][0], decimals=4) == 9.7215 + ) diff --git a/tests/gentropy/method/test_susie_inf.py b/tests/gentropy/method/test_susie_inf.py index d8e855a5b..4885a3d8a 100644 --- a/tests/gentropy/method/test_susie_inf.py +++ b/tests/gentropy/method/test_susie_inf.py @@ -3,7 +3,13 @@ from __future__ import annotations import numpy as np +import pyspark.sql.functions as f + +from gentropy.common.session import Session +from gentropy.dataset.study_locus import StudyLocus +from gentropy.dataset.summary_statistics import SummaryStatistics from gentropy.method.susie_inf import SUSIE_inf +from gentropy.susie_finemapper import SusieFineMapperStep class TestSUSIE_inf: @@ -16,7 +22,7 @@ def test_SUSIE_inf_lbf_moments( ld = sample_data_for_susie_inf[0] z = sample_data_for_susie_inf[1] lbf_moments = sample_data_for_susie_inf[2] - susie_output = SUSIE_inf.susie_inf(z=z, LD=ld, method="moments") + susie_output = SUSIE_inf.susie_inf(z=z, LD=ld, est_tausq=True, method="moments") lbf_calc = susie_output["lbf_variable"][:, 0] assert np.allclose( lbf_calc, lbf_moments @@ -29,7 +35,7 @@ def test_SUSIE_inf_lbf_mle( ld = sample_data_for_susie_inf[0] z = sample_data_for_susie_inf[1] lbf_mle = sample_data_for_susie_inf[3] - susie_output = SUSIE_inf.susie_inf(z=z, LD=ld, method="MLE") + susie_output = SUSIE_inf.susie_inf(z=z, LD=ld, est_tausq=True, method="MLE") lbf_calc = susie_output["lbf_variable"][:, 0] assert np.allclose( lbf_calc, lbf_mle, atol=1e-1 @@ -41,6 +47,47 @@ def test_SUSIE_inf_cred( """Test of SuSiE-inf credible set generator.""" ld = sample_data_for_susie_inf[0] z = sample_data_for_susie_inf[1] - susie_output = SUSIE_inf.susie_inf(z=z, LD=ld) + susie_output = SUSIE_inf.susie_inf( + z=z, + LD=ld, + est_tausq=True, + ) cred = SUSIE_inf.cred_inf(susie_output["PIP"], LD=ld) assert cred[0] == [5] + + def test_SUSIE_inf_convert_to_study_locus( + self: TestSUSIE_inf, + sample_data_for_susie_inf: list[np.ndarray], + sample_summary_statistics: SummaryStatistics, + session: Session, + ) -> None: + """Test of SuSiE-inf credible set generator.""" + ld = sample_data_for_susie_inf[0] + z = sample_data_for_susie_inf[1] + susie_output = SUSIE_inf.susie_inf( + z=z, + LD=ld, + est_tausq=False, + ) + gwas_df = sample_summary_statistics._df.withColumn( + "z", f.col("beta") / f.col("standardError") + ).filter(f.col("z").isNotNull()) + gwas_df = gwas_df.limit(21) + + L1 = SusieFineMapperStep.susie_inf_to_studylocus( + susie_output=susie_output, + session=session, + studyId="sample_id", + region="sample_region", + variant_index=gwas_df, + cs_lbf_thr=2, + ld_matrix=ld, + lead_pval_threshold=1, + purity_mean_r2_threshold=0, + purity_min_r2_threshold=0, + sum_pips=0.99, + ld_min_r2=1, + locusStart=1, + locusEnd=2, + ) + assert isinstance(L1, StudyLocus), "L1 is not an instance of StudyLocus" diff --git a/tests/gentropy/method/test_window_based_clumping.py b/tests/gentropy/method/test_window_based_clumping.py index 59bf0277a..382dce1e9 100644 --- a/tests/gentropy/method/test_window_based_clumping.py +++ b/tests/gentropy/method/test_window_based_clumping.py @@ -1,18 +1,20 @@ """Test window-based clumping.""" + from __future__ import annotations from typing import TYPE_CHECKING -from gentropy.dataset.study_locus import StudyLocus -from gentropy.method.window_based_clumping import WindowBasedClumping from pyspark.ml import functions as fml from pyspark.ml.linalg import VectorUDT +from pyspark.sql import SparkSession from pyspark.sql import functions as f from pyspark.sql.window import Window +from gentropy.dataset.study_locus import StudyLocus +from gentropy.method.window_based_clumping import WindowBasedClumping + if TYPE_CHECKING: from gentropy.dataset.summary_statistics import SummaryStatistics - from pyspark.sql import SparkSession def test_window_based_clump__return_type( @@ -20,7 +22,14 @@ def test_window_based_clump__return_type( ) -> None: """Test window-based clumping.""" assert isinstance( - WindowBasedClumping.clump_with_locus(mock_summary_statistics, 250_000), + WindowBasedClumping.clump(mock_summary_statistics, distance=250_000), + StudyLocus, + ) + assert isinstance( + WindowBasedClumping.clump( + mock_summary_statistics, + distance=250_000, + ), StudyLocus, ) @@ -43,7 +52,10 @@ def test_window_based_clump_with_locus__correctness( ) -> None: """Test window-based clumping.""" clumped = sample_summary_statistics.window_based_clumping( - distance=250_000, locus_collect_distance=250_000 + distance=250_000, + ) + clumped = clumped.annotate_locus_statistics( + sample_summary_statistics, collect_locus_distance=250_000 ) # Asserting the presence of locus key: @@ -56,7 +68,7 @@ def test_window_based_clump_with_locus__correctness( assert (clumped.df.filter(f.col("variantId") == "18_12843138_T_C").count()) == 1 # Assert the number of variants in the locus: - assert (clumped.df.select(f.explode_outer("locus").alias("loci")).count()) == 132 + assert (clumped.df.select(f.explode_outer("locus").alias("loci")).count()) == 218 def test_prune_peak(spark: SparkSession) -> None: diff --git a/tests/gentropy/step/test_clump_step.py b/tests/gentropy/step/test_clump_step.py index 701afc950..ab40de901 100644 --- a/tests/gentropy/step/test_clump_step.py +++ b/tests/gentropy/step/test_clump_step.py @@ -1,4 +1,5 @@ """Test clump step.""" + from __future__ import annotations import tempfile diff --git a/tests/gentropy/step/test_colocalisation_step.py b/tests/gentropy/step/test_colocalisation_step.py new file mode 100644 index 000000000..e74dee234 --- /dev/null +++ b/tests/gentropy/step/test_colocalisation_step.py @@ -0,0 +1,293 @@ +"""Test colocalisation step.""" + +from pathlib import Path +from typing import Type + +import pytest + +from gentropy.colocalisation import ColocalisationStep +from gentropy.common.session import Session +from gentropy.dataset.colocalisation import Colocalisation +from gentropy.dataset.study_locus import StudyLocus +from gentropy.method.colocalisation import Coloc, ColocalisationMethodInterface, ECaviar + + +@pytest.mark.step_test +class TestColocalisationStep: + """Test colocalisation steps.""" + + @pytest.fixture(autouse=True) + def _setup(self, session: Session, tmp_path: Path) -> None: + """Setup StudyLocus for testing.""" + credible_set_data = [ + ( + "-1299941111165481046", + "gwas", + "1_62634374_G_GA", + "1", + 62634374, + "1:62116600-63176657", + "GCST90269661", + None, + -18.026562155233105, + 8.294741, + -72, + None, + None, + None, + [ + "Variant not found in LD reference, Study locus finemapped without in-sample LD reference" + ], + "SuSiE-inf", + 2, + 128.08235878972883, + 1.0, + 1.0, + 62116600, + 63176657, + None, + [("1_62634374_G_GA", 1.0)], + [ + ( + True, + True, + 303.2017476882394, + 1.0, + "1_62634374_G_GA", + None, + None, + -0.07779708137213309, + None, + None, + ) + ], + "SuSiE fine-mapped credible set with out-of-sample LD", + ), + ( + "-1245591334543437941", + "gwas", + "1_62725906_C_A", + "1", + 62725906, + "1:62275115-62861709", + "GCST90024601", + None, + 6.818181818181818, + 1.0845997, + -12, + None, + None, + None, + [ + "Variant not found in LD reference, Study locus finemapped without in-sample LD reference" + ], + "SuSiE-inf", + 3, + 903.4374513916813, + 1.0, + 1.0, + 62275115, + 62861709, + None, + [("1_62725906_C_A", 1.0)], + [ + ( + True, + True, + 2087.4457573345685, + 0.9999999999381545, + "1_62725906_C_A", + None, + None, + 0.20241232721094407, + None, + None, + ) + ], + "SuSiE fine-mapped credible set with out-of-sample LD", + ), + ( + "-0.20241232721094407", + "gwas", + "1_62725906_C_A", + "1", + 62725906, + "1:62335572-62883302", + "GCST90025461", + None, + 6.363636363636364, + 5.0753098, + -10, + None, + None, + None, + [ + "Variant not found in LD reference, Study locus finemapped without in-sample LD reference" + ], + "SuSiE-inf", + 2, + 912.1598183692258, + 1.0, + 1.0, + 62335572, + 62883302, + None, + [("1_62725906_C_A", 1.0)], + [ + ( + True, + True, + 2107.38950418228, + 0.9999999999454303, + "1_62725906_C_A", + None, + None, + 0.20330391077149534, + None, + None, + ) + ], + "SuSiE fine-mapped credible set with out-of-sample LD", + ), + ( + "-2271857845883525223", + "gwas", + "1_62634374_G_GA", + "1", + 62634374, + "1:62192511-63034021", + "GCST90269580", + None, + -15.43232373355239, + 1.0077391, + -54, + None, + None, + None, + [ + "Variant not found in LD reference, Study locus finemapped without in-sample LD reference" + ], + "SuSiE-inf", + 2, + 104.77639852123883, + 1.0, + 1.0, + 62192511, + 63034021, + None, + [("1_62634374_G_GA", 1.0)], + [ + ( + True, + True, + 249.20354469210795, + 1.0, + "1_62634374_G_GA", + None, + None, + -0.07071263272378725, + None, + None, + ) + ], + "SuSiE fine-mapped credible set with out-of-sample LD", + ), + ] + self.credible_set_path = str(tmp_path / "credible_set_datasets") + session.spark.createDataFrame( + credible_set_data, schema=StudyLocus.get_schema() + ).write.parquet(self.credible_set_path) + self.coloc_path = str(tmp_path / "colocalisation") + + @pytest.mark.parametrize( + ["label", "expected_method"], + [ + pytest.param("coloc", Coloc, id="coloc method"), + pytest.param("ecaviar", ECaviar, id="ecaviar method"), + pytest.param("ECaviar", ECaviar, id="uppercase label"), + ], + ) + def test_get_colocalisation_class( + self, label: str, expected_method: Type[ColocalisationMethodInterface] + ) -> None: + """Test _get_colocalisation_class method on ColocalisationStep.""" + method = ColocalisationStep._get_colocalisation_class(label) + assert ( + method is expected_method + ), "Incorrect colocalisation class returned by ColocalisationStep._get_colocalisation_class(label)" + + def test_label_with_invalid_method(self) -> None: + """Test what happens when invalid method_label is passed to the _get_colocalisation_class.""" + with pytest.raises(ValueError): + ColocalisationStep._get_colocalisation_class("NewMethod") + + @pytest.mark.parametrize( + ["coloc_method", "expected_data"], + [ + pytest.param( + "ecaviar", + { + "clpp": [1.0, 1.0], + "colocalisationMethod": ["eCAVIAR", "eCAVIAR"], + "leftStudyLocusId": [ + "-1245591334543437941", + "-2271857845883525223", + ], + "rightStudyLocusId": [ + "-0.20241232721094407", + "-1299941111165481046", + ], + }, + id="ecaviar", + ), + pytest.param( + "coloc", + { + "h4": [1.0, 1.0], + "h3": [0.0, 0.0], + "h2": [0.0, 0.0], + "h1": [0.0, 0.0], + "h0": [0.0, 0.0], + "colocalisationMethod": ["COLOC", "COLOC"], + "leftStudyLocusId": [ + "-1245591334543437941", + "-2271857845883525223", + ], + "rightStudyLocusId": [ + "-0.20241232721094407", + "-1299941111165481046", + ], + }, + id="coloc", + ), + ], + ) + def test_colocalise( + self, + coloc_method: str, + expected_data: dict[str, list[float] | list[str]], + session: Session, + ) -> None: + """Test colocalise method.""" + ColocalisationStep( + session=session, + credible_set_path=self.credible_set_path, + coloc_path=self.coloc_path, + colocalisation_method=coloc_method, + ) + + coloc_dataset = Colocalisation.from_parquet( + session, self.coloc_path, recursiveFileLookup=True + ) + for column in expected_data: + values = [c[column] for c in coloc_dataset.df.collect()] + expected_values = expected_data[column] + for v, e in zip(values, expected_values): + if isinstance(e, float): + assert ( + e == pytest.approx(v, 1e-1) + ), f"Incorrect value {v} at {column} found in {coloc_method}, expected {e}" + else: + assert ( + e == v + ), f"Incorrect value {v} at {column} found in {coloc_method}, expected {e}" diff --git a/tests/gentropy/step/test_convert_to_vcf_step.py b/tests/gentropy/step/test_convert_to_vcf_step.py new file mode 100644 index 000000000..cc4ec800d --- /dev/null +++ b/tests/gentropy/step/test_convert_to_vcf_step.py @@ -0,0 +1,157 @@ +"""Test convert to vcf step.""" + +from __future__ import annotations + +from pathlib import Path +from typing import TYPE_CHECKING + +import pandas as pd +import pytest + +from gentropy.common.session import Session +from gentropy.variant_index import ConvertToVcfStep + +if TYPE_CHECKING: + from typing import Any, Literal + + +@pytest.mark.step_test +class TestConvertToVcfStep: + """Test ConvertToVcfStep. + + Test if the step correctly read multiple variant sources and extracts + non duplicated variants and collects to sorted vcf partitions. + """ + + @pytest.mark.parametrize( + ["sources", "partition_size", "expected_partition_number"], + [ + pytest.param( + [ + { + "path": "tests/gentropy/data_samples/variant_sources/uniprot-test.jsonl", + "format": "json", + "n_variants": 50, # 2 variants per chromosome + }, + { + "path": "tests/gentropy/data_samples/variant_sources/eva-test.jsonl", + "format": "json", + "n_variants": 50, # 2 variants per chromosome + }, + { + "path": "tests/gentropy/data_samples/variant_sources/pharmacogenomics-test.jsonl", + "format": "json", + "n_variants": 44, # missing Y and MT, input contains two duplicated variants 22_19963748_G_A and 12_21178615_T_C and + }, + { + "path": "tests/gentropy/data_samples/variant_sources/credible-sets", + "format": "parquet", + "n_variants": 42, # after loci explosion + }, + ], + 10, + 19, # 186 variants / 10 size ~ 19 partitions + id="Multiple OT datasets", + ), + pytest.param( + [ + { + "path": "tests/gentropy/data_samples/variant_sources/credible-sets-extended", + "format": "parquet", + "n_variants": 1187, # after deduplication of locus object + } + ], + 2000, + 1, # 1199 variants / 2000 size ~ 1 partition + id="More variants than spark default partition size", + ), + ], + ) + def test_step( + self, + session: Session, + tmp_path: Path, + sources: list[dict[Literal["path", "format", "n_variants"], Any]], + partition_size: int, + expected_partition_number: int, + ) -> None: + """Test step. + + Expect that step outputs asserted number of partitions, where each partition + contains expected number of variants. + """ + source_paths = [s["path"] for s in sources] + source_formats = [s["format"] for s in sources] + output_path = str(tmp_path / "variants") + ConvertToVcfStep( + session, source_paths, source_formats, output_path, partition_size + ) + + variants_df = session.spark.read.csv(output_path, sep="\t", header=True) + # 40 variants (10 variants from each source) + expected_variant_count = sum(c["n_variants"] for c in sources) + assert ( + variants_df.count() == expected_variant_count + ), "Found incorrect number of variants" + partitions = [ + str(p) for p in Path(output_path).iterdir() if str(p).endswith("csv") + ] + assert ( + len(partitions) == expected_partition_number + ), "Found incorrect number of partitions" + + def test_sorting( + self, + session: Session, + tmp_path: Path, + ) -> None: + """Test sorting in partitions. + + Test if variants within single partition are sorted correctly. + The partition should be naturally sorted by #CHROM and POS fields. + """ + source_path = ( + "tests/gentropy/data_samples/variant_sources/uniprot-test-sort.jsonl" + ) + output_path = str(tmp_path / "variants") + ConvertToVcfStep(session, [source_path], ["json"], output_path, 10) + partitions = [ + str(p) for p in Path(output_path).iterdir() if str(p).endswith("csv") + ] + assert len(partitions) == 1, "Must be one partition to test variant sorting" + df = pd.read_csv( + partitions[0], + usecols=[0, 1], # just read #CHROM and POS + sep="\t", + ) + # values comes from input file tests/gentropy/data_samples/variant_sources/uniprot-test-sorting.jsonl + # NOTE: Natural ordering in CHROM (str) and POS (int) + with open(partitions[0]) as fp: + assert fp.readline().startswith("CHROM\tPOS") + + expected_df = pd.DataFrame( + [ + ("1", 1525242), + ("1", 161306863), + ("11", 108345818), + ("2", 98396018), + ("21", 44286656), + ("3", 38585800), + ("MT", 6277), + ("X", 129562612), + ("Y", 2787426), + ], + columns=["CHROM", "POS"], + ) + + assert list(df.columns) == list(expected_df.columns) + assert df.equals(expected_df), "Variant sorting does not match expectations." + + def test_raises_assertion_imbalanced_arg_ratios(self, session: Session) -> None: + """Test imbalanced argument ratio exception. + + Test if passing uneven number of sources to paths, not 1:1 ratio should result in assertion + """ + with pytest.raises(AssertionError) as e: + ConvertToVcfStep(session, ["dummy_path"], ["json", "json"], "output", 10) + assert e.value[0] == "Must provide format for each source path." diff --git a/tests/gentropy/step/test_credible_set_qc.py b/tests/gentropy/step/test_credible_set_qc.py new file mode 100644 index 000000000..c7fb58c8c --- /dev/null +++ b/tests/gentropy/step/test_credible_set_qc.py @@ -0,0 +1,151 @@ +"""Test credible set qc step.""" + +from pathlib import Path + +import pytest +from pyspark.sql import functions as f +from pyspark.sql import types as t + +from gentropy.common.session import Session +from gentropy.credible_set_qc import CredibleSetQCStep +from gentropy.dataset.study_locus import StudyLocus + + +@pytest.mark.step_test +class TestCredibleSetQCStep: + """Test credible set qc.""" + + @pytest.fixture(autouse=True) + def _setup(self, session: Session, tmp_path: Path) -> None: + """Setup StudyLocus for testing.""" + # NOTE: About the input dataset for tests + # Entry dataset contains 6 loci (3 of them contains duplicated studyLocusId, 2 contains the same studyId) + # The step is expected to remove the duplicates of the studyLocus (1 row) + # THe step is expected to remove rows which pValue <= p_val_threshold (1 row) + # The step is expected to remove rows which purityMinR2 >= to purity_min_r2 (1 row) + # at the end we should end up with 2 non duplicated loci + self.purity_min_r2 = 0.01 + self.p_value_threshold = 1e-5 + self.n_partitions = 1 + credible_set_data = [ + ( + "A", # duplicated credibleSetId + "1_100_G_GA", # variantId + "GCST1", # duplicated studyId + 1.0, # pValMantissa + -6, # pValExponent + 1.0, # credibleSetlog10BF -> should be skipped due to the lowest Log10BF + 1.0, # purityMinR2 + ), + ( + "A", # duplicated credibleSetId + "1_100_G_GA", # variantId + "GCST1", # duplicated studyId + 1.0, # pValMantissa + -6, # pValExponent + 2.0, # credibleSetlog10BF -> highest log10BF within duplicates considering single study + 1.0, # purityMinR2 + ), + ( + "A", # duplicated credibleSetId + "1_100_G_GA", # variantId + "GCST2", # studyId + 1.0, # pValMantissa + -6, # pValExponent + 3.0, # credibleSetlog10BF -> highest log10BF within duplicates + 1.0, # purityMinR2 + ), + ( + "B", # credibleSetId + "1_200_G_GA", # variantId + "GCST3", # studyId + 1.0, # pValMantissa + -4, # too high pValExponent => pVal = 1.0e-4 < p_val_threshold + 1.0, # credibleSetlog10BF + 1.0, # purityMinR2 + ), + ( + "C", # credibleSetId + "1_300_G_GA", # variantId + "GCST3", # studyId + 1.0, # pValMantissa + -6, # pValExponent + 1.0, # credibleSetlog10BF + 0.001, # purityMinR2 < purity_min_r2 + ), + ( + # full row OK! + "D", # credibleSetId + "1_400_G_GA", # variantId + "GCST3", # studyId + 1.0, # pValMantissa + -6, # pValExponent + 1.0, # credibleSetlog10BF + 1.0, # purityMinR2 + ), + ] + cs_schema = t.StructType( + [ + t.StructField("studyLocusId", t.StringType(), True), + t.StructField("variantId", t.StringType(), True), + t.StructField("studyId", t.StringType(), True), + t.StructField("pValueMantissa", t.FloatType(), True), + t.StructField("pValueExponent", t.IntegerType(), True), + t.StructField("credibleSetlog10BF", t.DoubleType(), True), + t.StructField("purityMinR2", t.DoubleType(), True), + ] + ) + + # NOTE: Use proper input! + # Ensure the input dataset is saved per studyLocusId in recursive manner. + # This mimics the dataset with multiple loci evaluated separately. + self.credible_set_path = str(tmp_path / "credible_set_datasets") + cs_df = session.spark.createDataFrame(credible_set_data, schema=cs_schema) + cs_path = tmp_path / "credible_set_dataset" + loci_ids = {row["studyLocusId"] for row in cs_df.collect()} + for loci_id in loci_ids: + loci_path = str(cs_path / loci_id) + cs_df.filter(f.col("studyLocusId") == loci_id).write.parquet(loci_path) + self.input_cs_df = cs_df + self.cs_path = str(cs_path) + self.output_path = str(tmp_path / "clean_credible_sets") + + def test_step(self, session: Session) -> None: + """Invoke the step to check if it works correctly.""" + assert not Path(self.output_path).exists(), "Input for qc does not exists." + assert Path(self.cs_path).exists(), "Output of qc is not emptied before test." + assert self.input_cs_df.count() == 6, "Incorrect number of rows." + CredibleSetQCStep( + session=session, + credible_sets_path=self.cs_path, + output_path=self.output_path, + p_value_threshold=self.p_value_threshold, + purity_min_r2=self.purity_min_r2, + clump=False, + ld_index_path=None, + study_index_path=None, + ld_min_r2=None, + n_partitions=self.n_partitions, + ) + + assert Path(self.output_path).exists(), "Output of qc does not exists." + # check the number of partitions + partitions = [ + str(p) + for p in Path(self.output_path).iterdir() + if str(p).endswith(".parquet") + ] + assert ( + len(partitions) == self.n_partitions + ), "Incorrect number of partitions in the output." + cs = StudyLocus.from_parquet( + session, self.output_path, recursiveFileLookup=True + ) + assert cs.df.count() == 2 # Row A where LogBF == 3.0 and row D + assert cs.df.rdd.getNumPartitions() == self.n_partitions + data = { + row["studyLocusId"]: row["credibleSetlog10BF"] for row in cs.df.collect() + } + assert sorted(data.keys()) == ["A", "D"] + # ensure the Locus A with highest credibleSetlog10BF was chosen + assert data["A"] == 3.0 diff --git a/tests/gentropy/test_cli.py b/tests/gentropy/test_cli.py index 92effa840..dbb5a8cac 100644 --- a/tests/gentropy/test_cli.py +++ b/tests/gentropy/test_cli.py @@ -1,11 +1,13 @@ """Test the command-line interface (CLI).""" + from unittest.mock import patch import pytest -from gentropy.cli import main from hydra.errors import ConfigCompositionException from omegaconf.errors import MissingMandatoryValue +from gentropy.cli import main + def test_main_no_step() -> None: """Test the main function of the CLI without a valid step.""" diff --git a/tests/gentropy/test_schemas.py b/tests/gentropy/test_schemas.py index 768a3d4df..1b06076d0 100644 --- a/tests/gentropy/test_schemas.py +++ b/tests/gentropy/test_schemas.py @@ -1,4 +1,5 @@ """Tests on spark schemas.""" + from __future__ import annotations import json @@ -11,10 +12,13 @@ import pytest from pyspark.sql.types import StructType +from gentropy.common.schemas import SchemaValidationError + if TYPE_CHECKING: from _pytest.fixtures import FixtureRequest + from gentropy.dataset.gene_index import GeneIndex - from gentropy.dataset.v2g import V2G + from gentropy.dataset.l2g_prediction import L2GPrediction SCHEMA_DIR = "src/gentropy/assets/schemas" @@ -56,6 +60,9 @@ def test_schema_columns_camelcase(schema_json: str) -> None: Args: schema_json (str): schema filename """ + if schema_json == "vep_json_output.json": + pytest.skip("VEP schema is exempt from camelCase check.") + core_schema = json.loads(Path(SCHEMA_DIR, schema_json).read_text(encoding="utf-8")) schema = StructType.fromJson(core_schema) # Use a regular expression to check if the identifier is in camelCase @@ -68,61 +75,69 @@ def test_schema_columns_camelcase(schema_json: str) -> None: class TestValidateSchema: - """Test validate_schema method using V2G (unnested) and GeneIndex (nested) as a testing dataset.""" + """Test validate_schema method using L2GPrediction (unnested) and GeneIndex (nested) as a testing dataset.""" @pytest.fixture() def mock_dataset_instance( self: TestValidateSchema, request: FixtureRequest - ) -> V2G | GeneIndex: + ) -> L2GPrediction | GeneIndex: """Meta fixture to return the value of any requested fixture.""" return request.getfixturevalue(request.param) @pytest.mark.parametrize( - "mock_dataset_instance", ["mock_v2g", "mock_gene_index"], indirect=True + "mock_dataset_instance", + ["mock_l2g_predictions", "mock_gene_index"], + indirect=True, ) def test_validate_schema_extra_field( self: TestValidateSchema, - mock_dataset_instance: V2G | GeneIndex, + mock_dataset_instance: L2GPrediction | GeneIndex, ) -> None: """Test that validate_schema raises an error if the observed schema has an extra field.""" - with pytest.raises(ValueError, match="extraField"): + with pytest.raises(SchemaValidationError, match="extraField"): mock_dataset_instance.df = mock_dataset_instance.df.withColumn( "extraField", f.lit("extra") ) @pytest.mark.parametrize( - "mock_dataset_instance", ["mock_v2g", "mock_gene_index"], indirect=True + "mock_dataset_instance", + ["mock_l2g_predictions", "mock_gene_index"], + indirect=True, ) def test_validate_schema_missing_field( self: TestValidateSchema, - mock_dataset_instance: V2G | GeneIndex, + mock_dataset_instance: L2GPrediction | GeneIndex, ) -> None: """Test that validate_schema raises an error if the observed schema is missing a required field, geneId in this case.""" - with pytest.raises(ValueError, match="geneId"): + with pytest.raises(SchemaValidationError, match="geneId"): mock_dataset_instance.df = mock_dataset_instance.df.drop("geneId") @pytest.mark.parametrize( - "mock_dataset_instance", ["mock_v2g", "mock_gene_index"], indirect=True + "mock_dataset_instance", + ["mock_l2g_predictions", "mock_gene_index"], + indirect=True, ) def test_validate_schema_duplicated_field( self: TestValidateSchema, - mock_dataset_instance: V2G | GeneIndex, + mock_dataset_instance: L2GPrediction | GeneIndex, ) -> None: """Test that validate_schema raises an error if the observed schema has a duplicated field, geneId in this case.""" - with pytest.raises(ValueError, match="geneId"): + with pytest.raises(SchemaValidationError, match="geneId"): mock_dataset_instance.df = mock_dataset_instance.df.select( "*", f.lit("A").alias("geneId") ) @pytest.mark.parametrize( - "mock_dataset_instance", ["mock_v2g", "mock_gene_index"], indirect=True + "mock_dataset_instance", + ["mock_l2g_predictions", "mock_gene_index"], + indirect=True, ) def test_validate_schema_different_datatype( self: TestValidateSchema, - mock_dataset_instance: V2G | GeneIndex, + mock_dataset_instance: L2GPrediction | GeneIndex, ) -> None: """Test that validate_schema raises an error if any field in the observed schema has a different type than expected.""" - with pytest.raises(ValueError, match="geneId"): + with pytest.raises(SchemaValidationError, match="geneId"): mock_dataset_instance.df = mock_dataset_instance.df.withColumn( "geneId", f.lit(1) ) diff --git a/tests/gentropy/test_spark_helpers.py b/tests/gentropy/test_spark_helpers.py index 76abcbe56..1adb686cd 100644 --- a/tests/gentropy/test_spark_helpers.py +++ b/tests/gentropy/test_spark_helpers.py @@ -1,16 +1,18 @@ """Tests on helper spark functions.""" + from __future__ import annotations from typing import TYPE_CHECKING, Any import pyspark.sql.functions as f import pytest +from pyspark.sql.types import ArrayType, DoubleType, StructField, StructType + from gentropy.common.spark_helpers import ( get_record_with_maximum_value, get_record_with_minimum_value, order_array_of_structs_by_field, ) -from pyspark.sql.types import ArrayType, DoubleType, StructField, StructType if TYPE_CHECKING: from pyspark.sql import DataFrame, SparkSession diff --git a/utils/install_dependencies.sh b/utils/install_dependencies.sh index e17290f1f..33c5cf8b1 100644 --- a/utils/install_dependencies.sh +++ b/utils/install_dependencies.sh @@ -30,7 +30,6 @@ poetry install --sync echo "Setting up pre-commit..." poetry run pre-commit install -poetry run pre-commit autoupdate poetry run pre-commit install --hook-type commit-msg echo "Activating the Poetry environment..." diff --git a/utils/install_dependencies_on_cluster.sh b/utils/install_dependencies_on_cluster.sh index 9f26b9f17..849dee0c3 100644 --- a/utils/install_dependencies_on_cluster.sh +++ b/utils/install_dependencies_on_cluster.sh @@ -3,7 +3,6 @@ set -exo pipefail readonly PACKAGE=$(/usr/share/google/get_metadata_value attributes/PACKAGE || true) -readonly CONFIGTAR=$(/usr/share/google/get_metadata_value attributes/CONFIGTAR || true) function err() { echo "[$(date +'%Y-%m-%dT%H:%M:%S%z')]: $*" >&2 @@ -61,11 +60,10 @@ function main() { echo "Uninstalling previous version if it exists" pip uninstall -y gentropy echo "Install package..." - run_with_retry pip install --upgrade ${PACKAGENAME} + # NOTE: ensure the gentropy is reinstalled each time without version cache + # see https://pip.pypa.io/en/stable/cli/pip_install/#cmdoption-force-reinstall + run_with_retry pip install --force-reinstall --ignore-installed ${PACKAGENAME} - echo "Downloading and uncompressing config..." - gsutil cp ${CONFIGTAR} . || err "Failed to download CONFIGTAR" - tar -xvf $(basename ${CONFIGTAR}) || err "Failed to extract CONFIGTAR" } main diff --git a/utils/update_GWAS_Catalog_data.sh b/utils/update_GWAS_Catalog_data.sh index 1e380d30c..00ee44d8f 100755 --- a/utils/update_GWAS_Catalog_data.sh +++ b/utils/update_GWAS_Catalog_data.sh @@ -15,7 +15,7 @@ get_release_url(){ # Function to get the Ensembl and EFO version which used to ground GWAS data: get_release_info(){ - curl -s https://www.ebi.ac.uk/gwas/api/search/stats | jq -r '"\(.ensemblbuild) \(.efoversion)"' + curl -s "${1}" | jq -r '"\(.ensemblbuild) \(.efoversion)"' } logging(){ @@ -41,6 +41,18 @@ upload_file_to_gcp(){ fi } +fetch_from_ftp(){ + URL=${1} + TARGET=${2} + wget -q ${URL} -O ${TARGET} + if [ $? -ne 0 ]; then + logging "Failed to fetch ${URL}" + return + else + logging "File ${TARGET} saved." + fi +} + # Resources: export BASE_URL=ftp://ftp.ebi.ac.uk/pub/databases/gwas export RELEASE_INFO_URL=https://www.ebi.ac.uk/gwas/api/search/stats @@ -71,7 +83,7 @@ read YEAR MONTH DAY < <(get_release_url) logging "Most recent GWAS Catalog release: ${YEAR}/${MONTH}/${DAY}" # Capturing release metadata: -read ENSEMBL EFO < <(get_release_info) +read ENSEMBL EFO < <(get_release_info ${RELEASE_INFO_URL}) logging "Genes were mapped to v${ENSEMBL} Ensembl release." logging "Diseases were mapped to ${EFO} EFO release." @@ -80,26 +92,19 @@ RELEASE_URL=${BASE_URL}/releases/${YEAR}/${MONTH}/${DAY} logging "Datafiles are fetching from ${RELEASE_URL}" # Fetching files while assigning properly dated and annotated names: -wget -q ${RELEASE_URL}/gwas-catalog-associations_ontology-annotated.tsv -O ${ASSOCIATION_FILE} -logging "File ${ASSOCIATION_FILE} saved." +fetch_from_ftp ${RELEASE_URL}/gwas-catalog-associations_ontology-annotated.tsv ${ASSOCIATION_FILE} -wget -q ${RELEASE_URL}/gwas-catalog-download-studies-v1.0.3.txt -O ${PUBLISHED_STUDIES_FILE} -logging "File ${PUBLISHED_STUDIES_FILE} saved." +fetch_from_ftp ${RELEASE_URL}/gwas-catalog-download-studies-v1.0.3.1.txt ${PUBLISHED_STUDIES_FILE} -wget -q ${RELEASE_URL}/gwas-catalog-unpublished-studies-v1.0.3.tsv -O ${UNPUBLISHED_STUDIES_FILE} -logging "File ${UNPUBLISHED_STUDIES_FILE} saved." +fetch_from_ftp ${RELEASE_URL}/gwas-catalog-unpublished-studies-v1.0.3.1.tsv ${UNPUBLISHED_STUDIES_FILE} -wget -q ${RELEASE_URL}/gwas-catalog-download-ancestries-v1.0.3.txt -O ${PUBLISHED_ANCESTRIES_FILE} -logging "File ${PUBLISHED_ANCESTRIES_FILE} saved." +fetch_from_ftp ${RELEASE_URL}/gwas-catalog-download-ancestries-v1.0.3.1.txt ${PUBLISHED_ANCESTRIES_FILE} -wget -q ${RELEASE_URL}/gwas-catalog-unpublished-ancestries-v1.0.3.tsv -O ${UNPUBLISHED_ANCESTRIES_FILE} -logging "File ${UNPUBLISHED_ANCESTRIES_FILE} saved." +fetch_from_ftp ${RELEASE_URL}/gwas-catalog-unpublished-ancestries-v1.0.3.1.tsv ${UNPUBLISHED_ANCESTRIES_FILE} -wget -q ${BASE_URL}/summary_statistics/harmonised_list.txt -O ${HARMONISED_LIST_FILE} -logging "File ${HARMONISED_LIST_FILE} saved." +fetch_from_ftp ${BASE_URL}/summary_statistics/harmonised_list.txt ${HARMONISED_LIST_FILE} -wget -q ${GWAS_CATALOG_STUDY_CURATION_URL} -O ${GWAS_CATALOG_STUDY_CURATION_FILE} -logging "In-house GWAS Catalog study curation file fetched from GitHub." +fetch_from_ftp ${GWAS_CATALOG_STUDY_CURATION_URL} ${GWAS_CATALOG_STUDY_CURATION_FILE} logging "Copying files to GCP..."