diff --git a/.github/labeler.yml b/.github/labeler.yml new file mode 100644 index 000000000..197d1f923 --- /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 + + + +## 🛠 What does this PR implement + + + +## 🙈 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/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/config/datasets/ot_gcp.yaml b/config/datasets/ot_gcp.yaml index 54c7dd2fe..ebc9fc9c2 100644 --- a/config/datasets/ot_gcp.yaml +++ b/config/datasets/ot_gcp.yaml @@ -39,7 +39,6 @@ thurman: ${datasets.static_assets}/thurman2012/genomewideCorrs_above0.7_promoter 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 diff --git a/config/step/ot_eqtl_catalogue.yaml b/config/step/ot_eqtl_catalogue.yaml index 141c94ee3..06076d228 100644 --- a/config/step/ot_eqtl_catalogue.yaml +++ b/config/step/ot_eqtl_catalogue.yaml @@ -1,6 +1,9 @@ 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} +eqtl_catalogue_paths_imported: ??? +eqtl_catalogue_study_index_out: ??? +eqtl_catalogue_credible_sets_out: ??? +session: + extended_spark_conf: + "spark.sql.shuffle.partitions": "3200" diff --git a/docs/python_api/datasources/eqtl_catalogue/_eqtl_catalogue.md b/docs/python_api/datasources/eqtl_catalogue/_eqtl_catalogue.md index 5382d9d57..c2e7c79ce 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 mQTLs 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/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/src/airflow/dags/eqtl_preprocess.py b/src/airflow/dags/eqtl_preprocess.py new file mode 100644 index 000000000..40a3c1d6a --- /dev/null +++ b/src/airflow/dags/eqtl_preprocess.py @@ -0,0 +1,74 @@ +"""Airflow DAG to extract credible sets and a study index from eQTL Catalogue's finemapping results.""" + +from __future__ import annotations + +from pathlib import Path + +import common_airflow as common +from airflow.models.dag import DAG +from airflow.providers.google.cloud.operators.dataflow import ( + DataflowTemplatedJobStartOperator, +) +from airflow.providers.google.cloud.operators.gcs import GCSDeleteBucketOperator + +CLUSTER_NAME = "otg-preprocess-eqtl" +AUTOSCALING = "do-ld-explosion" +PROJECT_ID = "open-targets-genetics-dev" + +EQTL_CATALOG_SUSIE_LOCATION = "gs://eqtl_catalog_data/ebi_ftp/susie" +TEMP_DECOMPRESS_LOCATION = "gs://eqtl_catalog_data/susie_decompressed_tmp" +DECOMPRESS_FAILED_LOG = f"{TEMP_DECOMPRESS_LOCATION}.log" +STUDY_INDEX_PATH = "gs://eqtl_catalog_data/study_index" +CREDIBLE_SET_PATH = "gs://eqtl_catalog_data/credible_set_datasets/susie" + +with DAG( + dag_id=Path(__file__).stem, + description="Open Targets Genetics — eQTL preprocess", + default_args=common.shared_dag_args, + **common.shared_dag_kwargs, +): + # SuSIE fine mapping results are stored as gzipped files in a GCS bucket. + # To improve processing performance, we decompress the files before processing to a temporary location in GCS. + decompression_job = DataflowTemplatedJobStartOperator( + task_id="decompress_susie_outputs", + template="gs://dataflow-templates/latest/Bulk_Decompress_GCS_Files", + location="europe-west1", + project_id=PROJECT_ID, + parameters={ + "inputFilePattern": f"{EQTL_CATALOG_SUSIE_LOCATION}/**/*.gz", + "outputDirectory": TEMP_DECOMPRESS_LOCATION, + "outputFailureFile": DECOMPRESS_FAILED_LOG, + }, + ) + + ingestion_job = common.submit_step( + cluster_name=CLUSTER_NAME, + step_id="ot_eqtl_catalogue", + task_id="ot_eqtl_ingestion", + other_args=[ + f"step.eqtl_catalogue_paths_imported={TEMP_DECOMPRESS_LOCATION}", + f"step.eqtl_catalogue_study_index_out={STUDY_INDEX_PATH}", + f"step.eqtl_catalogue_credible_sets_out={CREDIBLE_SET_PATH}", + ], + ) + + delete_decompressed_job = GCSDeleteBucketOperator( + task_id="delete_decompressed_files", + bucket_name=TEMP_DECOMPRESS_LOCATION, + force=True, + user_project=PROJECT_ID, + ) + + ( + decompression_job + >> common.create_cluster( + CLUSTER_NAME, + autoscaling_policy=AUTOSCALING, + num_workers=4, + worker_machine_type="n1-highmem-8", + ) + >> common.install_dependencies(CLUSTER_NAME) + >> ingestion_job + >> delete_decompressed_job + >> common.delete_cluster(CLUSTER_NAME) + ) diff --git a/src/airflow/requirements.txt b/src/airflow/requirements.txt index e9fd45142..97bbcc796 100644 --- a/src/airflow/requirements.txt +++ b/src/airflow/requirements.txt @@ -1,2 +1,3 @@ apache-airflow-providers-google==10.10.1 +apache-airflow-providers-apache-beam==5.6.1 psycopg2-binary==2.9.9 diff --git a/src/gentropy/assets/schemas/study_index.json b/src/gentropy/assets/schemas/study_index.json index 08f61c072..e529fafd2 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": {} }, { @@ -41,6 +41,12 @@ "nullable": true, "metadata": {} }, + { + "name": "tissueFromSourceId", + "type": "string", + "nullable": true, + "metadata": {} + }, { "name": "pubmedId", "type": "string", diff --git a/src/gentropy/config.py b/src/gentropy/config.py index 552c50d1d..5e82055cb 100644 --- a/src/gentropy/config.py +++ b/src/gentropy/config.py @@ -112,7 +112,12 @@ class EqtlCatalogueConfig(StepConfig): 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: list[str] = field( + default_factory=lambda: [ + "ge", + ] + ) _target_: str = "gentropy.eqtl_catalogue.EqtlCatalogueStep" @@ -263,7 +268,7 @@ class VariantAnnotationConfig(StepConfig): } ) variant_annotation_path: str = MISSING - _target_: str = "gentropytropy.variant_annotation.VariantAnnotationStep" + _target_: str = "gentropy.variant_annotation.VariantAnnotationStep" @dataclass diff --git a/src/gentropy/datasource/eqtl_catalogue/finemapping.py b/src/gentropy/datasource/eqtl_catalogue/finemapping.py new file mode 100644 index 000000000..af814d94f --- /dev/null +++ b/src/gentropy/datasource/eqtl_catalogue/finemapping.py @@ -0,0 +1,267 @@ +"""Process SuSIE finemapping results from eQTL Catalogue.""" +from __future__ import annotations + +from dataclasses import dataclass + +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.utils import parse_pvalue +from gentropy.dataset.study_locus import StudyLocus +from gentropy.datasource.eqtl_catalogue.study_index import EqtlCatalogueStudyIndex + + +@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 gene (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.withColumn( + "dataset_id", + cls._extract_dataset_id_from_file_path(f.input_file_name()), + ) + .join( + f.broadcast( + credible_sets.withColumn( + "dataset_id", + cls._extract_dataset_id_from_file_path(f.input_file_name()), + ) + .withColumn( + "credibleSetIndex", + cls._extract_credible_set_index(f.col("cs_id")), + ) + .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("gene_id").alias("geneId"), + f.array(f.col("molecular_trait_id")).alias("traitFromSourceMappedIds"), + f.col("dataset_id"), + f.concat_ws( + "_", + f.col("study_label"), + f.col("sample_group"), + f.col("gene_id"), + ).alias("studyId"), + f.col("tissue_id").alias("c"), + EqtlCatalogueStudyIndex._identify_study_type( + f.col("study_label") + ).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"), + ) + ) + + @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 + ] + ["studyLocusId", "locus"] + return StudyLocus( + _df=( + processed_finemapping_df.withColumn( + "isLead", + f.row_number().over( + lead_w.orderBy( + *[ + f.col("pValueExponent").asc(), + f.col("pValueMantissa").asc(), + ] + ) + ) + == 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") + .withColumn( + "studyLocusId", + StudyLocus.assign_study_locus_id( + f.col("studyId"), f.col("variantId") + ), + ) + .select(study_locus_cols) + ), + _schema=StudyLocus.get_schema(), + ).annotate_credible_sets() diff --git a/src/gentropy/datasource/eqtl_catalogue/study_index.py b/src/gentropy/datasource/eqtl_catalogue/study_index.py index bcfc5e50f..611bcdff7 100644 --- a/src/gentropy/datasource/eqtl_catalogue/study_index.py +++ b/src/gentropy/datasource/eqtl_catalogue/study_index.py @@ -1,9 +1,11 @@ """Study Index for eQTL Catalogue data source.""" + from __future__ import annotations -from typing import TYPE_CHECKING, Type +from typing import TYPE_CHECKING import pyspark.sql.functions as f +from pyspark.sql.types import IntegerType, StringType, StructField, StructType from gentropy.dataset.study_index import StudyIndex @@ -13,167 +15,88 @@ 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. - @classmethod - def get_study_attribute( - cls: Type[EqtlCatalogueStudyIndex], attribute_key: str - ) -> Column: - """Returns a Column expression that dynamically assigns the attribute based on the study. + 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/). - Args: - attribute_key (str): The attribute key to assign. + One study from the eQTL Catalogue clusters together all the molecular QTLs (mQTLs) that were found: - Returns: - Column: The dynamically assigned attribute. + - in the same publication (e.g. Alasoo_2018) + - in the same cell type or tissue (e.g. monocytes) + - and for the same gene associated with the measured molecular trait (e.g. ENSG00000141510) - Raises: - ValueError: If the attribute key is not known for the study. - """ - 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) + """ + + 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), + ] + ) + raw_studies_metadata_path = "https://raw.githubusercontent.com/eQTL-Catalogue/eQTL-Catalogue-resources/master/data_tables/dataset_metadata.tsv" @classmethod - def _all_attributes(cls: Type[EqtlCatalogueStudyIndex]) -> list[Column]: - """A helper function to return all study index attribute expressions. + def _identify_study_type( + cls: type[EqtlCatalogueStudyIndex], study_label_col: Column + ) -> Column: + """Identify the study type based on the study label. + + Args: + study_label_col (Column): column with the study label that identifies the supporting publication. Returns: - list[Column]: A list of all study index attribute expressions. + Column: The study type. """ - 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 f.when( + study_label_col == "Sun_2018", + f.lit("pqtl"), + ).otherwise(f.lit("eqtl")) @classmethod - def add_gene_to_study_id( - cls: type[EqtlCatalogueStudyIndex], - study_index_df: DataFrame, - summary_stats_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. + def get_studies_of_interest( + cls: type[EqtlCatalogueStudyIndex], studies_metadata: DataFrame + ) -> list[str]: + """Filter studies of interest from the raw studies metadata. Args: - study_index_df (DataFrame): preliminary study index for eQTL Catalogue studies. - summary_stats_df (DataFrame): summary statistics dataframe for eQTL Catalogue data. + studies_metadata (DataFrame): raw studies metadata filtered with studies of interest. Returns: - StudyIndex: final study index for eQTL Catalogue studies. + list[str]: QTD IDs defining the studies of interest for ingestion. """ - 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)) + return ( + studies_metadata.select("dataset_id") + .distinct() + .toPandas()["dataset_id"] + .tolist() ) - return StudyIndex(_df=study_index_df, _schema=StudyIndex.get_schema()) @classmethod - def from_source( + def from_susie_results( cls: type[EqtlCatalogueStudyIndex], - eqtl_studies: DataFrame, + processed_finemapping_df: DataFrame, ) -> StudyIndex: """Ingest study level metadata from eQTL Catalogue. Args: - eqtl_studies (DataFrame): ingested but unprocessed eQTL Catalogue studies. + processed_finemapping_df (DataFrame): processed fine mapping results with study metadata. Returns: - StudyIndex: preliminary processed study index for eQTL Catalogue studies. + StudyIndex: eQTL Catalogue study index dataset derived from the selected SuSIE results. """ + study_index_cols = [ + field.name + for field in StudyIndex.get_schema().fields + if field.name in processed_finemapping_df.columns + ] return StudyIndex( - _df=eqtl_studies.select(*cls._all_attributes()), + _df=processed_finemapping_df.select(study_index_cols).distinct(), _schema=StudyIndex.get_schema(), ) diff --git a/src/gentropy/eqtl_catalogue.py b/src/gentropy/eqtl_catalogue.py index 22c8880f0..bfbfd79c2 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 +import pandas as pd +import pyspark.sql.functions as f + from gentropy.common.session import Session +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: 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, ) -> 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 (list[str]): Molecular trait quantification methods 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. """ - # Fetch study index. - df = session.spark.read.option("delimiter", "\t").csv( - eqtl_catalogue_paths_imported, header=True + # Extract + pd.DataFrame.iteritems = pd.DataFrame.items + studies_metadata = session.spark.createDataFrame( + pd.read_csv(EqtlCatalogueStudyIndex.raw_studies_metadata_path, sep="\t"), + schema=EqtlCatalogueStudyIndex.raw_studies_metadata_schema, + ).filter(f.col("quant_method").isin(list(mqtl_quantification_methods))) + + # 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 = session.spark.read.csv( + [ + f"{eqtl_catalogue_paths_imported}/{qtd_id}.credible_sets.tsv" + for qtd_id in studies_to_ingest + ], + sep="\t", + header=True, + schema=EqtlCatalogueFinemapping.raw_credible_set_schema, + ) + lbf_df = session.spark.read.csv( + [ + f"{eqtl_catalogue_paths_imported}/{qtd_id}.lbf_variable.txt" + for qtd_id in studies_to_ingest + ], + sep="\t", + header=True, + schema=EqtlCatalogueFinemapping.raw_lbf_schema, ) - # 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 + # Transform + processed_susie_df = EqtlCatalogueFinemapping.parse_susie_results( + credible_sets_df, lbf_df, studies_metadata + ) + credible_sets = EqtlCatalogueFinemapping.from_susie_results(processed_susie_df) + study_index = EqtlCatalogueStudyIndex.from_susie_results(processed_susie_df) - # Write study index. - study_index_df.write.mode(session.write_mode).parquet( + # Load + study_index.df.write.mode(session.write_mode).parquet( eqtl_catalogue_study_index_out ) - # Write summary stats. - ( - summary_stats_df.sortWithinPartitions("position") - .write.partitionBy("chromosome") - .mode(session.write_mode) - .parquet(eqtl_catalogue_summary_stats_out) + credible_sets.df.write.mode(session.write_mode).parquet( + eqtl_catalogue_credible_sets_out ) diff --git a/src/gentropy/method/carma.py b/src/gentropy/method/carma.py index 85722d24d..75cb32c79 100644 --- a/src/gentropy/method/carma.py +++ b/src/gentropy/method/carma.py @@ -1,6 +1,7 @@ """CARMA outlier detection method.""" from __future__ import annotations +import concurrent.futures from itertools import combinations from math import floor, lgamma from typing import Any @@ -14,6 +15,34 @@ 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 + ) -> 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. + + 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. + """ + 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) + 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, @@ -105,7 +134,6 @@ def CARMA_spike_slab_noEM( "Outliers": all_C_list["conditional_S_list"], } - return results_list @staticmethod diff --git a/tests/gentropy/conftest.py b/tests/gentropy/conftest.py index f3dc197f9..c7af5a589 100644 --- a/tests/gentropy/conftest.py +++ b/tests/gentropy/conftest.py @@ -24,6 +24,8 @@ 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 @@ -470,28 +472,32 @@ 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(spark: SparkSession) -> DataFrame: + """Sample raw eQTL Catalogue credible sets outputted by SuSIE.""" + return spark.read.option("delimiter", "\t").csv( + "tests/gentropy/data_samples/QTD000584.credible_sets.tsv", + header=True, + schema=EqtlCatalogueFinemapping.raw_credible_set_schema, + ) + + +@pytest.fixture() +def sample_eqtl_catalogue_finemapping_lbf(spark: SparkSession) -> DataFrame: + """Sample raw eQTL Catalogue table with logBayesFactors outputted by SuSIE.""" + return spark.read.option("delimiter", "\t").csv( + "tests/gentropy/data_samples/QTD000584.lbf_variable.txt", + header=True, + schema=EqtlCatalogueFinemapping.raw_lbf_schema, + ) @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, ) 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 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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 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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 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+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..47023f5b6 --- /dev/null +++ b/tests/gentropy/data_samples/QTD000584.lbf_variable.txt @@ -0,0 +1,201 @@ +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 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124823679_A_C 10 124823679 -1.26935233091278 0.00552916857251828 0.00670686749504501 0.00684844231246418 0.00642940186674279 0.00573212028335401 0.0049294931404007 0.00412586958588079 0.00338040036796583 0.00272239661658613 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124823817_T_C 10 124823817 -2.25582222123641 -0.00208125858550989 -0.00256052488318437 -0.00261904135127056 -0.00244640743271951 -0.00216292043253841 -0.00184233048896054 -0.00152734817415645 -0.00124041502049366 -0.000991265768420568 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124824245_G_A 10 124824245 -2.25249260789328 -0.00197882128489812 -0.00243573380317663 -0.00249154951399166 -0.00232690101650856 -0.00205664557252794 -0.00175120086684633 -0.00145129303789115 -0.00117826513141051 -0.00094132823193549 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124824364_C_A 10 124824364 -2.25582222123641 -0.00208125858550989 -0.00256052488318437 -0.00261904135127056 -0.00244640743271951 -0.00216292043253841 -0.00184233048896054 -0.00152734817415645 -0.00124041502049366 -0.000991265768420568 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124824534_C_CA 10 124824534 -2.26318575514626 -0.00759055742912418 -0.00927290986259788 -0.00947680517976357 -0.00887433723066655 -0.00787871618900615 -0.00674313127256188 -0.00561710571236462 -0.00458216509091836 -0.00367617563083877 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124825317_C_CA 10 124825317 -0.577170243131913 0.0161425873788299 0.0196348326829869 0.0200560085606836 0.0188102366798635 0.0167429348752397 0.0143719021033193 0.0120069483938527 0.00982103212513996 0.00789777641993306 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124825317_C_CAA 10 124825317 -1.93134671459752 0.00808255585983675 0.00982087088974337 0.0100302701012764 0.00941074157608668 0.00838160756220585 0.00719967534544841 0.0060190946946368 0.00492641412078276 0.00396386932238579 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124825317_CA_C 10 124825317 -0.532610050446922 0.0206967338320014 0.0251845551687073 0.0257260629026321 0.0241245358917253 0.0214679440558103 0.0184226737957691 0.015386928044514 0.0125825075687986 0.0101162494818747 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124825356_A_G 10 124825356 -0.698810919271326 0.0188588353573147 0.0229460465266103 0.0234391645719034 0.0219807191027961 0.0195612559074494 0.0167874827544021 0.0140220418245414 0.0114670266520394 0.00921986192434243 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124826915_CA_C 10 124826915 -2.31321458683551 -0.00840871648295538 -0.0102690109215051 -0.0104943894955198 -0.00982839321766171 -0.00872744617829202 -0.00747120691421399 -0.00622499048851344 -0.00507909365762194 -0.00407558751563553 +UROS.11248.43.3..1 chr10:124823285-126823285 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-0.00473362071415639 -0.00579071933489494 -0.00591902499045105 -0.00554002302072565 -0.00491448643477943 -0.00420222218601563 -0.00349723797950663 -0.00285041846016343 -0.00228509379823727 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124880634_A_G 10 124880634 -2.42263994346138 -0.00980890127822365 -0.0119749417421993 -0.0122372647243512 -0.011462035241383 -0.0101801143860145 -0.00871675898083257 -0.0072644269715183 -0.00592843026074208 -0.00475799007795086 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124880658_C_G 10 124880658 -1.54338394539326 0.0054732181143331 0.006636598101601 0.00677638902011513 0.00636258344555207 0.00567375159401484 0.00488046004011222 0.0040857800176437 0.00334825903324143 0.00269700196257672 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124880660_C_G 10 124880660 -2.42416829065956 -0.00990841040868284 -0.0120961267053583 -0.0123610679168187 -0.0115780975040773 -0.0102833454460893 -0.00880529657188056 -0.0073383332879815 -0.00598883475236445 -0.00480653243049112 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124880710_T_C 10 124880710 -2.40511428331389 -0.0101195205057487 -0.0123543389114853 -0.01262499780101 -0.0118251391319895 -0.0105025123221472 -0.00899272201284296 -0.00749433786698495 -0.00611600569828985 -0.00490849710312347 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124881424_C_T 10 124881424 -1.36919701492274 0.00999673883925034 0.0121489779011763 0.0124082824073661 0.0116411348917662 0.0103670205340629 0.00890407623996703 0.00744315622964464 0.00609131454102307 0.00490071372014 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124882052_C_T 10 124882052 -1.82420838934042 0.0022928177461039 0.0027633036003798 0.00281940697305805 0.00265306005708954 0.00237438141671209 0.00205074471555555 0.00172371164138019 0.00141772511562177 0.00114558890483929 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124883060_G_A 10 124883060 -1.90172214488359 0.000577834630465457 0.000674749179790268 0.000685735811911226 0.000652797970321473 0.000595242745631719 0.000524817245715514 0.00044992479634498 0.000376620248644155 0.000308915212811733 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124883112_C_G 10 124883112 -1.89887216225045 0.000537991937034743 0.000626233357059025 0.000636172694886472 0.000606331851326747 0.000553910421083437 0.000489364593859509 0.000420327911155205 0.000352428055699683 0.00028947216448838 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124883394_T_TATTC 10 124883394 -2.38825389806601 -0.00878270817768545 -0.0107237598647796 -0.0109588724170124 -0.0102640787576536 -0.00911532698727369 -0.00780423307672162 -0.00650327561321129 -0.00530675905511302 -0.00425869852260474 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124883535_T_A 10 124883535 -2.38883035182704 -0.00876769840310532 -0.0107054703526681 -0.01094018654477 -0.0102465647594068 -0.009099754282758 -0.00779088174651577 -0.00649213456308928 -0.00529765622368883 -0.00425138528198943 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124883669_A_G 10 124883669 -2.02483811858609 0.00281448798610162 0.00340305047823719 0.0034735314263088 0.00326474527569998 0.00291618593853871 0.00251323571720308 0.00210796785899481 0.00173043429351782 0.00139594610687688 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124883741_G_T 10 124883741 -1.90172214488359 0.000577834630465457 0.000674749179790268 0.000685735811911226 0.000652797970321473 0.000595242745631719 0.000524817245715514 0.00044992479634498 0.000376620248644155 0.000308915212811733 +UROS.11248.43.3..1 chr10:124823285-126823285 chr10_124883757_G_T 10 124883757 -2.23798452352228 -0.00323497440754217 -0.00396937981280354 -0.00405881135783215 -0.00379482448051105 -0.00336031726431107 -0.00286740186100642 -0.00238146493585711 -0.00193733324513268 -0.00155051237063519 diff --git a/tests/gentropy/data_samples/coloc_test_data.snappy.parquet b/tests/gentropy/data_samples/coloc_test_data.snappy.parquet new file mode 100644 index 000000000..71b3913eb Binary files /dev/null and b/tests/gentropy/data_samples/coloc_test_data.snappy.parquet differ diff --git a/tests/gentropy/data_samples/eqtl_catalogue_studies_sample.tsv b/tests/gentropy/data_samples/eqtl_catalogue_studies_sample.tsv deleted file mode 100644 index f756ecd6e..000000000 --- a/tests/gentropy/data_samples/eqtl_catalogue_studies_sample.tsv +++ /dev/null @@ -1,11 +0,0 @@ -study qtl_group tissue_ontology_id tissue_ontology_term tissue_label condition_label quant_method ftp_path -GTEx_V8 Adipose_Subcutaneous UBER_0002190 subcutaneous adipose tissue Adipose - Subcutaneous naive ge ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Adipose_Subcutaneous.tsv.gz -GTEx_V8 Adipose_Visceral_Omentum UBER_0010414 omental fat pad Adipose - Visceral (Omentum) naive ge ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Adipose_Visceral_Omentum.tsv.gz -GTEx_V8 Adrenal_Gland UBER_0002369 adrenal gland Adrenal Gland naive ge ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Adrenal_Gland.tsv.gz -GTEx_V8 Artery_Aorta UBER_0001496 ascending aorta Artery - Aorta naive ge ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Artery_Aorta.tsv.gz -GTEx_V8 Artery_Coronary UBER_0001621 coronary artery Artery - Coronary naive ge ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Artery_Coronary.tsv.gz -GTEx_V8 Artery_Tibial UBER_0007610 tibial artery Artery - Tibial naive ge ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Artery_Tibial.tsv.gz -GTEx_V8 Brain_Amygdala UBER_0001876 amygdala Brain - Amygdala naive ge ftp://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/Brain_Amygdala.tsv.gz -GTEx_V8 Brain_Anterior_cingulate_cortex_BA24 UBER_0009835 anterior 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/sample_eqtl_catalogue_studies.tsv b/tests/gentropy/data_samples/sample_eqtl_catalogue_studies.tsv new file mode 100644 index 000000000..08a06803b --- /dev/null +++ b/tests/gentropy/data_samples/sample_eqtl_catalogue_studies.tsv @@ -0,0 +1,2 @@ +study_id dataset_id study_label sample_group tissue_id tissue_label condition_label sample_size quant_method +QTS000035 QTD000584 Sun_2018 plasma UBERON_0001969 plasma naive 3301 aptamer 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..a488ab78a --- /dev/null +++ b/tests/gentropy/datasource/eqtl_catalogue/test_eqtl_catalogue.py @@ -0,0 +1,56 @@ +"""Tests for study index dataset from eQTL Catalogue.""" + +from __future__ import annotations + +import pytest +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 +from pyspark.sql import DataFrame + + +@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, + ) + + +def test_credsets_from_susie_results(processed_finemapping_df: DataFrame) -> None: + """Test creating a study locus from SuSIE results.""" + assert isinstance( + EqtlCatalogueFinemapping.from_susie_results(processed_finemapping_df), + StudyLocus, + ) + + +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/method/test_carma.py b/tests/gentropy/method/test_carma.py index c715eba47..b3aecf6c8 100644 --- a/tests/gentropy/method/test_carma.py +++ b/tests/gentropy/method/test_carma.py @@ -54,3 +54,22 @@ 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_colocalisation_method.py b/tests/gentropy/method/test_colocalisation_method.py index a0e7508a4..a4fe07646 100644 --- a/tests/gentropy/method/test_colocalisation_method.py +++ b/tests/gentropy/method/test_colocalisation_method.py @@ -2,13 +2,11 @@ from __future__ import annotations -from typing import TYPE_CHECKING - 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 +from pyspark.sql import SparkSession +from pyspark.sql import functions as f def test_coloc(mock_study_locus_overlap: StudyLocusOverlap) -> None: @@ -16,6 +14,33 @@ def test_coloc(mock_study_locus_overlap: StudyLocusOverlap) -> None: assert isinstance(Coloc.colocalise(mock_study_locus_overlap), Colocalisation) +def test_coloc_colocalise( + spark: SparkSession, + threshold: float = 1e-5, +) -> None: + """Compare COLOC results with R implementation, using provided sample dataset from R package (StudyLocusOverlap).""" + test_overlap_df = spark.read.parquet( + "tests/gentropy/data_samples/coloc_test_data.snappy.parquet", header=True + ) + test_overlap = StudyLocusOverlap(test_overlap_df, StudyLocusOverlap.get_schema()) + test_result = Coloc.colocalise(test_overlap) + + expected = spark.createDataFrame( + [ + { + "h0": 1.3769995397857477e-18, + "h1": 2.937336451601565e-10, + "h2": 8.593226431647826e-12, + "h3": 8.338916748775843e-4, + "h4": 0.9991661080227981, + } + ] + ) + difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected) + for col in difference.columns: + assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0 + + def test_ecaviar(mock_study_locus_overlap: StudyLocusOverlap) -> None: """Test eCAVIAR.""" assert isinstance(ECaviar.colocalise(mock_study_locus_overlap), Colocalisation)