pandaspgs 1.0.0

Citation

Zhang Z, Zhou J, Cao T, Huang Y, Huang C, Xia Y. 2025. pandasPGS: a Python package for easy retrieval of Polygenic Score Catalog data. PeerJ 13:e18985 https://doi.org/10.7717/peerj.18985

Installation

pip install pandaspgs

Documentation

See pandasPGS Documentation

Licensing information

Source code

MIT License

Data from PGS Catalog

The PGS Catalog and all its contents are available under the general terms of use for EMBL-EBI services

Example 1. Investigating trends in diabetes-related polygenic risk scores

Step 1

from pandaspgs import *
from plotnine import*

Step 2

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 rows

Step 3

traits.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_0005148

Step 4

score1 = 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
#   |
#    -samples_training_cohorts: 97 rows
# |
#  -trait_efo: 195 rows
# |
#  -ancestry_distribution: 447 rows

Step 5

pic=ggplot(diabetes_score.trait_efo)+geom_bar(aes(x='label'))+coord_flip()
pic.save(filename='Additional file 1.png',dpi=300)

Example 2: Investigating polygenic risk scores for gestational diabetes

Step 1

from pandaspgs import *

Step 2

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]'

Step 3

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]
# 4

Step 4

gd_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])

Step 5

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'

Step 6

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 

Step 7

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 

Step 8

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]

Step 9

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]