pip install pandaspgs
MIT License
The PGS Catalog and all its contents are available under the general terms of use for EMBL-EBI services
from pandaspgs import *
from plotnine import*traits = get_traits(term='diabetes')
traits
# Trait is running in fat mode. It has 6 DataFrames with hierarchical dependencies.
# traits: 6 rows
# |
# -associated_pgs_ids: 186 rows
# |
# -child_associated_pgs_ids:265 rows
# |
# -trait_categories: 13 rows
# |
# -trait_mapped_terms: 57 rows
# |
# -trait_synonyms: 66 rowstraits.traits
# id label description url
# 0 EFO_0000400 diabetes mellitus A metabolic disorder characterized by abnormal... http://www.ebi.ac.uk/efo/EFO_0000400
# 1 EFO_0006842 diabetes mellitus biomarker http://www.ebi.ac.uk/efo/EFO_0006842
# 2 EFO_0003770 diabetic retinopathy A chronic, pathological complication associate... http://www.ebi.ac.uk/efo/EFO_0003770
# 3 EFO_0004593 gestational diabetes Carbohydrate intolerance first diagnosed durin... http://www.ebi.ac.uk/efo/EFO_0004593
# 4 MONDO_0005147 type 1 diabetes mellitus A chronic condition characterized by minimal o... http://purl.obolibrary.org/obo/MONDO_0005147
# 5 MONDO_0005148 type 2 diabetes mellitus A type of diabetes mellitus that is characteri... http://purl.obolibrary.org/obo/MONDO_0005148score1 = get_scores(trait_id='EFO_0000400')
score2 = get_scores(trait_id='EFO_0006842')
score3 = get_scores(trait_id='EFO_0003770')
score4 = get_scores(trait_id='EFO_0004593')
score5 = get_scores(trait_id='MONDO_0005147')
score6 = get_scores(trait_id='MONDO_0005148')
diabetes_score = score1+score2+score3+score4+score5+score6
diabetes_score
# Score is running in fat mode. It has 7 DataFrames with hierarchical dependencies.
# scores:186 rows
# |
# -samples_variants: 253 rows
# |
# -samples_variants_cohorts: 386 rows
# |
# -samples_training: 107 rows
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# -samples_training_cohorts: 97 rows
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# -trait_efo: 195 rows
# |
# -ancestry_distribution: 447 rowspic=ggplot(diabetes_score.trait_efo)+geom_bar(aes(x='label'))+coord_flip()
pic.save(filename='Additional file 1.png',dpi=300)from pandaspgs import *traits = get_traits(term='gestational diabetes')
traits.traits
# id label description url
# 0 EFO_0004593 gestational diabetes Carbohydrate intolerance first diagnosed durin... http://www.ebi.ac.uk/efo/EFO_0004593
traits.traits['id'][0]
# 'EFO_0004593'
traits.traits['description'][0]
# 'Carbohydrate intolerance first diagnosed during pregnancy. [NCIT: P378]'gd_pgs = get_scores(trait_id='EFO_0004593')
gd_pgs.scores
# id name ftp_scoring_file matches_publication trait_reported trait_additional method_name method_params variants_number variants_interactions variants_genomebuild weight_type date_release license publication.id publication.title publication.doi publication.PMID publication.journal publication.firstauthor publication.date_publication ftp_harmonized_scoring_files.GRCh38.positions ftp_harmonized_scoring_files.GRCh37.positions
# 0 PGS002256 GRS4_GDM https://ftp.ebi.ac.uk/pub/databases/spot/pgs/s... True Gestational diabetes mellitus in early pregnancy None Genome-wide significant variants p < 0.05 4 0 NR log(OR) 2022-02-16 PGS obtained from the Catalog should be cited ... PGP000282 An early prediction model for gestational diab... 10.1186/s13098-022-00788-y 35073990 Diabetol Metab Syndr Wu Q 2022-01-24 https://ftp.ebi.ac.uk/pub/databases/spot/pgs/s... https://ftp.ebi.ac.uk/pub/databases/spot/pgs/s...
gd_pgs.scores['id'][0]
# PGS002256
gd_pgs.scores['name'][0]
# 'GRS4_GDM'
gd_pgs.scores['matches_publication'][0]
# True
gd_pgs.scores['trait_reported'][0]
# 'Gestational diabetes mellitus in early pregnancy'
gd_pgs.scores['variants_number'][0]
# 4gd_pgs.scores['publication.id'][0]
# PGP000282
gd_pgs.scores['publication.PMID'][0]
# 35073990
gd_pgs.scores['publication.date_publication'][0]
# '2022-01-24'
gd_pgs.scores['publication.journal'][0]
# 'Diabetol Metab Syndr'
gd_pgs.scores['publication.title'][0]
# 'An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China.'
open_in_pubmed(gd_pgs.scores['publication.PMID'][0])gd_pgs.samples_variants
# sample_number sample_cases sample_controls sample_percent_male sample_age phenotyping_free followup_time ancestry_broad ancestry_free ancestry_country ancestry_additional source_GWAS_catalog source_PMID source_DOI cohorts_additional id score_id followup_time.estimate_type followup_time.estimate followup_time.interval.type followup_time.interval.lower followup_time.interval.upper followup_time.variability_type followup_time.variability followup_time.unit
# 0 671 332 339 0.0 None None None East Asian Chinese China None None None None None 0 PGS002256 NaN NaN NaN NaN NaN NaN NaN NaN
gd_pgs.samples_variants['sample_number'][0]
# 671
gd_pgs.samples_variants['ancestry_broad'][0]
# 'East Asian'gd_file = read_scoring_file('PGS002256')
gd_file
# rsID effect_allele other_allele effect_weight hm_source hm_rsID hm_chr hm_pos hm_inferOtherAllele
# 0 rs10830963 G C 1.327 ENSEMBL rs10830963 11 92708710 NaN
# 1 rs1436953 T C 1.292 ENSEMBL rs1436953 15 62414014 NaN
# 2 rs7172432 G A 1.283 ENSEMBL rs7172432 15 62396389 NaN
# 3 rs16955379 C T 1.220 ENSEMBL rs16955379 16 81489373 NaN snp1=gd_file[['rsID','effect_allele','other_allele','effect_weight']].loc[0]
snp1
# rsID rs10830963
# effect_allele G
# other_allele C
# effect_weight 1.327
# Name: 0, dtype: object
from pandaspgs.file_operation import genotype_weighted_score
genotype_weighted_score(snp1)
# rs10830963_genotype rs10830963_weighted_score
# 0 G/G 2.654
# 1 G/C 1.327
# 2 C/C 0.000 df_list = []
for x in range(len(gd_file)):
snp_x = gd_file[['rsID','effect_allele','other_allele','effect_weight']].loc[x]
df_x = genotype_weighted_score(snp_x)
df_x['key'] = 1
df_list.append(df_x)
from functools import reduce
combination_df = reduce(lambda x, y: x.merge(y,on='key'), df_list)
del combination_df['key']
combination_df
# rs10830963_genotype rs10830963_weighted_score rs1436953_genotype rs1436953_weighted_score rs7172432_genotype rs7172432_weighted_score rs16955379_genotype rs16955379_weighted_score
# 0 G/G 2.654 T/T 2.584 G/G 2.566 C/C 2.44
# 1 G/G 2.654 T/T 2.584 G/G 2.566 C/T 1.22
# 2 G/G 2.654 T/T 2.584 G/G 2.566 T/T 0.00
# 3 G/G 2.654 T/T 2.584 G/A 1.283 C/C 2.44
# 4 G/G 2.654 T/T 2.584 G/A 1.283 C/T 1.22
# .. ... ... ... ... ... ... ... ...
# 76 C/C 0.000 C/C 0.000 G/A 1.283 C/T 1.22
# 77 C/C 0.000 C/C 0.000 G/A 1.283 T/T 0.00
# 78 C/C 0.000 C/C 0.000 A/A 0.000 C/C 2.44
# 79 C/C 0.000 C/C 0.000 A/A 0.000 C/T 1.22
# 80 C/C 0.000 C/C 0.000 A/A 0.000 T/T 0.00
# [81 rows x 8 columns]combination_df['genotypes']=combination_df['rs10830963_genotype']+"-"+combination_df['rs1436953_genotype']+"-"+combination_df['rs7172432_genotype']+"-"+combination_df['rs16955379_genotype']
combination_df['scores']= combination_df['rs10830963_weighted_score']+combination_df['rs1436953_weighted_score']+combination_df['rs7172432_weighted_score']+combination_df['rs16955379_weighted_score']
combination_df[['genotypes','scores']].sort_values(by='scores', ascending=False)
# genotypes scores
# 0 G/G-T/T-G/G-C/C 10.244
# 1 G/G-T/T-G/G-C/T 9.024
# 3 G/G-T/T-G/A-C/C 8.961
# 9 G/G-T/C-G/G-C/C 8.952
# 27 G/C-T/T-G/G-C/C 8.917
# .. ... ...
# 53 G/C-C/C-A/A-T/T 1.327
# 71 C/C-T/C-A/A-T/T 1.292
# 77 C/C-C/C-G/A-T/T 1.283
# 79 C/C-C/C-A/A-C/T 1.220
# 80 C/C-C/C-A/A-T/T 0.000
#
# [81 rows x 2 columns]