| Predicted Trait | |
| Reported Trait | Gout |
| Mapped Trait(s) | gout (MONDO_0005393) |
| Score Construction | |
| PGS Name | TPMI_274.1_PRS-CS |
| Development Method | |
| Name | PRS-CS |
| Parameters | phi1e-05 |
| Variants | |
| Original Genome Build | GRCh38 |
| Number of Variants | 983,787 |
| Effect Weight Type | beta |
| PGS Source | |
| PGS Catalog Publication (PGP) ID | PGP000835 |
| Citation (link to publication) | Chen HH et al. Nature (2025) |
| Study Identifiers | Sample Numbers | Sample Ancestry | Cohort(s) |
|---|---|---|---|
| — | [ ,
43.58 % Male samples |
East Asian (Han Chinese) |
TPMI |
| 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 |
|---|---|---|---|---|---|---|---|---|
| — | [ ,
44.71 % Male samples |
East Asian (Han Chinese) |
TPMI | 274.0, 274.10, 274.11, 274.19,M10.0, M10.1, M10.2, M10.30, M10.31, M10.32, M10.33, M10.34, M10.35, M10.36, M10.37, M10.38, M10.39, M1A | — | — | — | — |
|
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 |
|---|---|---|---|---|---|---|---|---|
| PPM037007 | PSS012727| East Asian Ancestry| 19,671 individuals |
PGP000835 | Chen HH et al. Nature (2025) |
Reported Trait: Gout | — | AUROC: 0.7268 | R²: 0.15299 | sex, age, array, PCs 1-10 | — |
|
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 |
|---|---|---|---|---|---|---|---|---|
| PSS012727 | 274,M10, M1A | — | [ ,
45.74 % Male samples |
— | East Asian (Han Chinese) |
— | TPMI | — |