Predicted Trait | |
Reported Trait | Gout |
Mapped Trait(s) | gout (EFO_0004274) |
Score Construction | |
PGS Name | prscs.auto.GCST008972.Gout |
Development Method | |
Name | PRS-CS-auto |
Parameters | phi:auto |
Variants | |
Original Genome Build | GRCh38 |
Number of Variants | 976,777 |
Effect Weight Type | beta |
PGS Source | |
PGS Catalog Publication (PGP) ID | PGP000517 |
Citation (link to publication) | Monti M R et al. medRxiv (2023) Preprint |
Ancestry Distribution | |
Score Development/Training | European: 100% 6,704 individuals (100%) |
PGS Evaluation | European: 66.7% South Asian: 33.3% 6 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 |
---|---|---|---|---|---|---|---|---|
— | 6,704 individuals | European | UKB | — | — | — | — | — |
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 |
---|---|---|---|---|---|---|---|---|
PPM019892 | PSS011217| European Ancestry| 199,274 individuals |
PGP000517 | Monti M R et al. medRxiv (2023) |Pre |
Reported Trait: Gout | OR: 1.50656 β: 0.40983 |
AUROC: 0.61 | — | 0 | beta = log(or)/sd_pgs |
PPM019893 | PSS011229| European Ancestry| 257,781 individuals |
PGP000517 | Monti M R et al. medRxiv (2023) |Pre |
Reported Trait: Gout | OR: 1.57446 β: 0.45391 |
AUROC: 0.63 | — | 0 | beta = log(or)/sd_pgs |
PPM019895 | PSS011240| South Asian Ancestry| 44,057 individuals |
PGP000517 | Monti M R et al. medRxiv (2023) |Pre |
Reported Trait: Gout | OR: 1.6051 β: 0.47319 |
AUROC: 0.65 | — | 0 | beta = log(or)/sd_pgs |
PPM019896 | PSS011256| European Ancestry| 66,865 individuals |
PGP000517 | Monti M R et al. medRxiv (2023) |Pre |
Reported Trait: Gout | OR: 1.68882 β: 0.52403 |
AUROC: 0.64 | — | 0 | beta = log(or)/sd_pgs |
PPM019898 | PSS011284| South Asian Ancestry| 9,326 individuals |
PGP000517 | Monti M R et al. medRxiv (2023) |Pre |
Reported Trait: Gout | OR: 1.42666 β: 0.35534 |
AUROC: 0.6 | — | 0 | beta = log(or)/sd_pgs |
PPM019900 | PSS011270| European Ancestry| 90,274 individuals |
PGP000517 | Monti M R et al. medRxiv (2023) |Pre |
Reported Trait: Gout | OR: 1.93464 β: 0.65992 |
AUROC: 0.68 | — | 0 | beta = log(or)/sd_pgs |
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 |
---|---|---|---|---|---|---|---|---|
PSS011240 | — | — | [
|
— | South Asian | — | G&H | — |
PSS011217 | — | — | [
|
— | European | — | EB | — |
PSS011284 | — | — | [
|
— | South Asian | — | UKB | — |
PSS011256 | — | — | [
|
— | European | — | HUNT | — |
PSS011229 | M13_GOUT, ICD10: M10, ICD9: 2740 | — | [
|
— | European | — | FinnGen | — |
PSS011270 | — | — | [
|
— | European | — | UKB | — |