Predicted Trait | |
Reported Trait | Prostate cancer |
Mapped Trait(s) | prostate cancer (MONDO_0008315) |
Score Construction | |
PGS Name | PRState_Eur |
Development Method | |
Name | Genome-wide significant SNPs |
Parameters | p<5e-08, r2=0.1 |
Variants | |
Original Genome Build | GRCh37 |
Number of Variants | 7 |
Effect Weight Type | log(OR) |
PGS Source | |
PGS Catalog Publication (PGP) ID | PGP000430 |
Citation (link to publication) | Pagadala MS et al. BMC Cancer (2022) |
Ancestry Distribution | |
Source of Variant Associations (GWAS) | European: 100% 5,393 individuals (100%) |
PGS Evaluation | African: 100% 1 Sample Sets |
Study Identifiers | Sample Numbers | Sample Ancestry | Cohort(s) |
---|---|---|---|
— | 5,393 individuals, 100.0 % Male samples |
European | ELLIPSE |
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 |
---|---|---|---|---|---|---|---|---|
PPM017071 | PSS010114| African Ancestry| 4,533 individuals |
PGP000430 | Pagadala MS et al. BMC Cancer (2022) |
Reported Trait: Prostate cancer | — | AUROC: 0.52 [0.5, 0.53] | — | genetics, age and family history | — |
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 |
---|---|---|---|---|---|---|---|---|
PSS010114 | — | — | 4,533 individuals, 100.0 % Male samples |
— | African unspecified | — | ELLIPSE | — |