Polygenic Score (PGS) ID: PGS004168

Predicted Trait
Reported Trait Breast cancer
Mapped Trait(s) breast carcinoma (EFO_0000305)
Additional Trait Information UK Biobank codes: cancer codes: 1002; ICD9: 174, 1749; ICD10: C50, C500-C506, C508, C509; field ID 40001: C50, C500-C506, C508, C509; field ID40002: C50, C500-C506, C508, C509; field ID 40006: C50, C500-C506, C508, C509; field ID 40013: 174, 1749
Released in PGS Catalog: Dec. 15, 2023
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Score Details

Score Construction
PGS Name bc_3
Development Method
Name LASSO
Parameters To reduce the number of candidate SNPs to a computationally feasible set, a gwas was performed on the raw phenotype and the 50,000 snps with smallest p-value were retained. The raw phenotype was regressed on age and the top 20 UK Biobank PCs and a residual phenotype was then built. The LASSO model was trained using these 50k SNPs and the adjusted phenotype.
Variants
Original Genome Build GRCh37
Number of Variants 4,618
Effect Weight Type beta
PGS Source
PGS Catalog Publication (PGP) ID PGP000520
Citation (link to publication) Raben TG et al. Sci Rep (2023)
Ancestry Distribution
Score Development/Training
European: 100%
200,000 individuals (100%)
PGS Evaluation
European: 100%
1 Sample Sets

Development Samples

Score Development/Training
Study Identifiers Sample Numbers Sample Ancestry Cohort(s) Phenotype Definitions & Methods Age of Study Participants Participant Follow-up Time Additional Ancestry Description Additional Sample/Cohort Information
—
[
  • 16,408 cases
  • , 183,592 controls
]
,
0.0 % Male samples
 European NR UK Biobank codes: cancer codes: 1002; ICD9: 174, 1749; ICD10: C50, C500-C506, C508, C509; field ID 40001: C50, C500-C506, C508, C509; field ID40002: C50, C500-C506, C508, C509; field ID 40006: C50, C500-C506, C508, C509; field ID 40013: 174, 1749 — used UK self report 'white' category and then used an adjusted phenotype that includes a regression on the top 20 PCs —

Performance Metrics

Disclaimer: The performance metrics are displayed as reported by the source studies. It is important to note that metrics are not necessarily comparable with each other. For example, metrics depend on the sample characteristics (described by the PGS Catalog Sample Set [PSS] ID), phenotyping, and statistical modelling. Please refer to the source publication for additional guidance on performance.

PGS Performance
Metric ID (PPM) 		PGS Sample Set ID
(PSS) 		Performance Source Trait PGS Effect Sizes
(per SD change) 		Classification Metrics Other Metrics Covariates Included in the Model PGS Performance:
Other Relevant Information
PPM020101 PSS011296|
European Ancestry|
45,334 individuals
 PGP000520 |
Raben TG et al. Sci Rep (2023)
 Reported Trait: Breast cancer — AUROC: 0.64699 — year of birth —

Evaluated Samples

PGS Sample Set ID
(PSS) 		Phenotype Definitions and Methods Participant Follow-up Time Sample Numbers Age of Study Participants Sample Ancestry Additional Ancestry Description Cohort(s) Additional Sample/Cohort Information
PSS011296 22,667 sibling pairs — 45,334 individuals — European — UKB —