Predicted Trait | |
Reported Trait | Gout |
Mapped Trait(s) | gout (EFO_0004274) |
Additional Trait Information | https://biobankengine.stanford.edu/RIVAS_HG19/snpnet/HC328 |
Score Construction | |
PGS Name | GBE_HC328 |
Development Method | |
Name | snpnet |
Parameters | NR |
Variants | |
Original Genome Build | GRCh37 |
Number of Variants | 880 |
Effect Weight Type | NR |
PGS Source | |
PGS Catalog Publication (PGP) ID | PGP000244 |
Citation (link to publication) | Tanigawa Y et al. PLoS Genet (2022) |
Ancestry Distribution | |
Score Development/Training | European: 100% 269,704 individuals (100%) |
PGS Evaluation | European: 40% African: 20% East Asian: 20% South Asian: 20% 5 Sample Sets |
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 |
---|---|---|---|---|---|---|---|---|
— | [
|
European | UKB | — | — | — | white British ancestry | — |
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 |
---|---|---|---|---|---|---|---|---|
PPM008749 | PSS004452| African Ancestry| 6,497 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Gout | — | AUROC: 0.8285 [0.79138, 0.86562] | R²: 0.15624 Incremental AUROC (full-covars): 0.01741 PGS R2 (no covariates): 0.02051 PGS AUROC (no covariates): 0.62118 [0.56505, 0.67731] |
age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method |
PPM008750 | PSS004453| East Asian Ancestry| 1,704 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Gout | — | AUROC: 0.84973 [0.79458, 0.90489] | R²: 0.22864 Incremental AUROC (full-covars): 0.02603 PGS R2 (no covariates): 0.04315 PGS AUROC (no covariates): 0.65938 [0.56975, 0.74901] |
age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method |
PPM008751 | PSS004454| European Ancestry| 24,905 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Gout | — | AUROC: 0.83619 [0.82126, 0.85113] | R²: 0.17414 Incremental AUROC (full-covars): 0.02904 PGS R2 (no covariates): 0.03561 PGS AUROC (no covariates): 0.66092 [0.63744, 0.6844] |
age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method |
PPM008752 | PSS004455| South Asian Ancestry| 7,831 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Gout | — | AUROC: 0.77913 [0.74649, 0.81177] | R²: 0.11671 Incremental AUROC (full-covars): 0.03258 PGS R2 (no covariates): 0.0283 PGS AUROC (no covariates): 0.63533 [0.59256, 0.6781] |
age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method |
PPM008753 | PSS004456| European Ancestry| 67,425 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Gout | — | AUROC: 0.81572 [0.80628, 0.82516] | Incremental AUROC (full-covars): 0.04061 PGS R2 (no covariates): 0.04014 R²: 0.15436 PGS AUROC (no covariates): 0.66908 [0.65556, 0.6826] |
age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method |
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 |
---|---|---|---|---|---|---|---|---|
PSS004452 | — | — | [
|
— | African unspecified | — | UKB | — |
PSS004453 | — | — | [
|
— | East Asian | — | UKB | — |
PSS004454 | — | — | [
|
— | European | non-white British ancestry | UKB | — |
PSS004455 | — | — | [
|
— | South Asian | — | UKB | — |
PSS004456 | — | — | [
|
— | European | white British ancestry | UKB | Testing cohort (heldout set) |