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Data from PGS Catalog

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

Step 1

frompandaspgsimport*fromplotnineimport*

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+score6diabetes_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

frompandaspgsimport*

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_0004593traits.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]# PGS002256gd_pgs.scores['name'][0]# 'GRS4_GDM'gd_pgs.scores['matches_publication'][0]# Truegd_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]# PGP000282gd_pgs.scores['publication.PMID'][0]# 35073990gd_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                     NaNgd_pgs.samples_variants['sample_number'][0]# 671gd_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: objectfrompandaspgs.file_operationimportgenotype_weighted_scoregenotype_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= []forxinrange(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']=1df_list.append(df_x)fromfunctoolsimportreducecombination_df=reduce(lambdax,y:x.merge(y,on='key'),df_list)delcombination_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]