Polygenic scores
A polygenic score (PGS) aggregates the effects of many genetic variants into a single number which predicts genetic predisposition for a phenotype. PGS are typically composed of hundreds-to-millions of genetic variants (usually SNPs) which are combined using a weighted sum of allele dosages multiplied by their corresponding effect sizes, as estimated from a relevant genome-wide association study (GWAS).
PGS nomenclature is heterogeneous: they can also be referred to as genetic scores or genomic scores, and as polygenic risk scores (PRS) or genomic risk scores (GRS) if they predict a discrete phenotype, such as a disease.
PGS Catalog
The PGS Catalog is an open database of published polygenic scores (PGS). Each PGS in the Catalog is consistently annotated with relevant metadata; including scoring files (variants, effect alleles/weights), annotations of how the PGS was developed and applied, and evaluations of their predictive performance.
Getting PGS scores
You can search PGS scores by three criteria:
-
pgs_id: PGS identifier -
efo_id: EFO trait identifier -
pubmed_id: PubMed identifier
While these criteria are not terribly useful, as normally you do not know these identifiers beforehand, these are the search criteria provided by the PGS Catalog REST API (the service quincunx communicates with).
Instead of using these criteria directly, we show you how you may retrieve polygenic score information by starting with a trait or disease of interest. See vignette('pgs-scores-mavaddat') for how to get polygenic scores if starting with a publication of interest.
Let’s say you are interested in basophil count. Basophils are one of the several kinds of white blood cells and make up less than 1% of all circulating white blood cells. Basophils play a part in immune surveillance, and varying levels of basophils are associated with different medical conditions, e.g., allergies, inflammation, infection, leukemia or anemia.
We start by querying the PGS Catalog for traits with the term "basophil" — you may check vignette('getting-traits') for more details on how to use the get_traits() function.
(basophil_traits <- get_traits(trait_term = 'basophil', exact_term = FALSE))
#> An object of class "traits"
#> Slot "traits":
#> # A tibble: 2 × 6
#> efo_id parent_efo_id is_child trait descr…¹ url
#> <chr> <chr> <lgl> <chr> <chr> <chr>
#> 1 EFO_0005090 NA FALSE basophil count The nu… http…
#> 2 EFO_0007992 NA FALSE basophil percentage of leuko… A calc… http…
#> # … with abbreviated variable name ¹description
#>
#> Slot "pgs_ids":
#> # A tibble: 6 × 4
#> efo_id parent_efo_id is_child pgs_id
#> <chr> <chr> <lgl> <chr>
#> 1 EFO_0005090 NA FALSE PGS000088
#> 2 EFO_0005090 NA FALSE PGS000163
#> 3 EFO_0005090 NA FALSE PGS001378
#> 4 EFO_0007992 NA FALSE PGS000089
#> 5 EFO_0007992 NA FALSE PGS000164
#> 6 EFO_0007992 NA FALSE PGS001377
#>
#> Slot "child_pgs_ids":
#> # A tibble: 3 × 4
#> efo_id parent_efo_id is_child child_pgs_id
#> <chr> <chr> <lgl> <chr>
#> 1 EFO_0005090 NA FALSE PGS000089
#> 2 EFO_0005090 NA FALSE PGS000164
#> 3 EFO_0005090 NA FALSE PGS001377
#>
#> Slot "trait_categories":
#> # A tibble: 4 × 4
#> efo_id parent_efo_id is_child trait_categories
#> <chr> <chr> <lgl> <chr>
#> 1 EFO_0005090 NA FALSE Hematological measurement
#> 2 EFO_0005090 NA FALSE Inflammatory measurement
#> 3 EFO_0007992 NA FALSE Hematological measurement
#> 4 EFO_0007992 NA FALSE Inflammatory measurement
#>
#> Slot "trait_synonyms":
#> # A tibble: 6 × 4
#> efo_id parent_efo_id is_child trait_synonyms
#> <chr> <chr> <lgl> <chr>
#> 1 EFO_0005090 NA FALSE blood basophil count
#> 2 EFO_0007992 NA FALSE basophil count as percentage of total whit…
#> 3 EFO_0007992 NA FALSE basophil count to total WBC count ratio
#> 4 EFO_0007992 NA FALSE basophil percentage
#> 5 EFO_0007992 NA FALSE basophil percentage of white cells
#> 6 EFO_0007992 NA FALSE blood basophil count to total leukocyte co…
#>
#> Slot "trait_mapped_terms":
#> # A tibble: 5 × 4
#> efo_id parent_efo_id is_child trait_mapped_terms
#> <chr> <chr> <lgl> <chr>
#> 1 EFO_0005090 NA FALSE CMO:0000034
#> 2 EFO_0005090 NA FALSE CMO:0000111
#> 3 EFO_0005090 NA FALSE MedDRA:10049695
#> 4 EFO_0005090 NA FALSE SNOMEDCT:42351005
#> 5 EFO_0007992 NA FALSE CMO:0000368The table (slot) pgs_ids in basophil_traits provides the associated PGS identifiers.
basophil_traits@pgs_ids
#> # A tibble: 6 × 4
#> efo_id parent_efo_id is_child pgs_id
#> <chr> <chr> <lgl> <chr>
#> 1 EFO_0005090 NA FALSE PGS000088
#> 2 EFO_0005090 NA FALSE PGS000163
#> 3 EFO_0005090 NA FALSE PGS001378
#> 4 EFO_0007992 NA FALSE PGS000089
#> 5 EFO_0007992 NA FALSE PGS000164
#> 6 EFO_0007992 NA FALSE PGS001377These identifiers can now be used to query score information using the function get_scores():
get_scores(pgs_id = basophil_traits@pgs_ids$pgs_id)
#> An object of class "scores"
#> Slot "scores":
#> # A tibble: 6 × 12
#> pgs_id pgs_n…¹ scori…² match…³ repor…⁴ trait…⁵ pgs_m…⁶ pgs_m…⁷ n_var…⁸ n_var…⁹
#> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> <chr> <int> <int>
#> 1 PGS00… baso https:… TRUE Basoph… BASO# Regula… "Bi-al… 9121 0
#> 2 PGS00… baso https:… TRUE Basoph… BASO# Genome… "Condi… 185 0
#> 3 PGS00… GBE_IN… https:… TRUE Basoph… https:… snpnet NA 3050 0
#> 4 PGS00… baso_p https:… TRUE Basoph… BASO% Regula… "Bi-al… 5248 0
#> 5 PGS00… baso_p https:… TRUE Basoph… BASO% Genome… "Condi… 150 0
#> 6 PGS00… GBE_IN… https:… TRUE Basoph… https:… snpnet NA 3205 0
#> # … with 2 more variables: assembly <chr>, license <chr>, and abbreviated
#> # variable names ¹pgs_name, ²scoring_file, ³matches_publication,
#> # ⁴reported_trait, ⁵trait_additional_description, ⁶pgs_method_name,
#> # ⁷pgs_method_params, ⁸n_variants, ⁹n_variants_interactions
#> # ℹ Use `colnames()` to see all variable names
#>
#> Slot "publications":
#> # A tibble: 6 × 8
#> pgs_id pgp_id pubmed_id publication_date publication title autho…¹ doi
#> <chr> <chr> <chr> <date> <chr> <chr> <chr> <chr>
#> 1 PGS000088 PGP000051 35072137 2022-01-12 Cell Genom Mach… Xu Y 10.1…
#> 2 PGS000163 PGP000078 32888494 2020-09-01 Cell The … Vuckov… 10.1…
#> 3 PGS001378 PGP000244 35324888 2022-03-24 PLoS Genet Sign… Taniga… 10.1…
#> 4 PGS000089 PGP000051 35072137 2022-01-12 Cell Genom Mach… Xu Y 10.1…
#> 5 PGS000164 PGP000078 32888494 2020-09-01 Cell The … Vuckov… 10.1…
#> 6 PGS001377 PGP000244 35324888 2022-03-24 PLoS Genet Sign… Taniga… 10.1…
#> # … with abbreviated variable name ¹author_fullname
#>
#> Slot "samples":
#> # A tibble: 8 × 15
#> pgs_id sampl…¹ stage sampl…² sampl…³ sampl…⁴ sampl…⁵ pheno…⁶ ances…⁷ ances…⁸
#> <chr> <int> <chr> <int> <int> <int> <dbl> <chr> <chr> <chr>
#> 1 PGS0000… 1 gwas 404718 NA NA 46 NA Europe… NA
#> 2 PGS0000… 2 dev 323774 NA NA 46 NA Europe… NA
#> 3 PGS0001… 1 gwas 408112 NA NA NA NA Europe… NA
#> 4 PGS0013… 1 dev 261890 NA NA NA NA Europe… NA
#> 5 PGS0000… 1 gwas 404532 NA NA 46 NA Europe… NA
#> 6 PGS0000… 2 dev 323626 NA NA 46 NA Europe… NA
#> 7 PGS0001… 1 gwas 408112 NA NA NA NA Europe… NA
#> 8 PGS0013… 1 dev 261893 NA NA NA NA Europe… NA
#> # … with 5 more variables: country <chr>,
#> # ancestry_additional_description <chr>, study_id <chr>, pubmed_id <chr>,
#> # cohorts_additional_description <chr>, and abbreviated variable names
#> # ¹sample_id, ²sample_size, ³sample_cases, ⁴sample_controls,
#> # ⁵sample_percent_male, ⁶phenotype_description, ⁷ancestry_category, ⁸ancestry
#> # ℹ Use `colnames()` to see all variable names
#>
#> Slot "demographics":
#> # A tibble: 4 × 11
#> pgs_id sampl…¹ varia…² estim…³ estim…⁴ unit varia…⁵ varia…⁶ inter…⁷ inter…⁸
#> <chr> <int> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> <dbl>
#> 1 PGS0000… 1 age mean 57.2 years NA NA range 38.9
#> 2 PGS0000… 2 age mean 57.2 years NA NA range 38.9
#> 3 PGS0000… 1 age mean 57.2 years NA NA range 38.9
#> 4 PGS0000… 2 age mean 57.2 years NA NA range 38.9
#> # … with 1 more variable: interval_upper <dbl>, and abbreviated variable names
#> # ¹sample_id, ²variable, ³estimate_type, ⁴estimate, ⁵variability_type,
#> # ⁶variability, ⁷interval_type, ⁸interval_lower
#> # ℹ Use `colnames()` to see all variable names
#>
#> Slot "cohorts":
#> # A tibble: 8 × 4
#> pgs_id sample_id cohort_symbol cohort_name
#> <chr> <int> <chr> <chr>
#> 1 PGS000088 1 UKB UK Biobank
#> 2 PGS000088 2 UKB UK Biobank
#> 3 PGS000163 1 UKB UK Biobank
#> 4 PGS001378 1 UKB UK Biobank
#> 5 PGS000089 1 UKB UK Biobank
#> 6 PGS000089 2 UKB UK Biobank
#> 7 PGS000164 1 UKB UK Biobank
#> 8 PGS001377 1 UKB UK Biobank
#>
#> Slot "traits":
#> # A tibble: 6 × 5
#> pgs_id efo_id trait description url
#> <chr> <chr> <chr> <chr> <chr>
#> 1 PGS000088 EFO_0005090 basophil count The number of g… http…
#> 2 PGS000163 EFO_0005090 basophil count The number of g… http…
#> 3 PGS001378 EFO_0005090 basophil count The number of g… http…
#> 4 PGS000089 EFO_0007992 basophil percentage of leukocytes A calculated me… http…
#> 5 PGS000164 EFO_0007992 basophil percentage of leukocytes A calculated me… http…
#> 6 PGS001377 EFO_0007992 basophil percentage of leukocytes A calculated me… http…
#>
#> Slot "stages_tally":
#> # A tibble: 14 × 4
#> pgs_id stage sample_size n_sample_sets
#> <chr> <chr> <int> <int>
#> 1 PGS000088 gwas 404718 NA
#> 2 PGS000088 dev 323774 NA
#> 3 PGS000088 eval NA 2
#> 4 PGS000163 gwas 408112 NA
#> 5 PGS000163 eval NA 2
#> 6 PGS001378 dev 261890 NA
#> 7 PGS001378 eval NA 5
#> 8 PGS000089 gwas 404532 NA
#> 9 PGS000089 dev 323626 NA
#> 10 PGS000089 eval NA 2
#> 11 PGS000164 gwas 408112 NA
#> 12 PGS000164 eval NA 1
#> 13 PGS001377 dev 261893 NA
#> 14 PGS001377 eval NA 5
#>
#> Slot "ancestry_frequencies":
#> # A tibble: 20 × 4
#> pgs_id stage ancestry_class_symbol frequency
#> <chr> <chr> <chr> <dbl>
#> 1 PGS000088 gwas EUR 100
#> 2 PGS000088 dev EUR 100
#> 3 PGS000088 eval EUR 100
#> 4 PGS000163 gwas EUR 100
#> 5 PGS000163 eval EUR 100
#> 6 PGS001378 dev EUR 100
#> 7 PGS001378 eval AFR 20
#> 8 PGS001378 eval EAS 20
#> 9 PGS001378 eval EUR 40
#> 10 PGS001378 eval SAS 20
#> 11 PGS000089 gwas EUR 100
#> 12 PGS000089 dev EUR 100
#> 13 PGS000089 eval EUR 100
#> 14 PGS000164 gwas EUR 100
#> 15 PGS000164 eval EUR 100
#> 16 PGS001377 dev EUR 100
#> 17 PGS001377 eval AFR 20
#> 18 PGS001377 eval EAS 20
#> 19 PGS001377 eval EUR 40
#> 20 PGS001377 eval SAS 20
#>
#> Slot "multi_ancestry_composition":
#> # A tibble: 0 × 4
#> # … with 4 variables: pgs_id <chr>, stage <chr>,
#> # multi_ancestry_class_symbol <chr>, ancestry_class_symbol <chr>
#> # ℹ Use `colnames()` to see all variable names