| Predicted Trait | |
| Reported Trait | Other metabolic disorders (time-to-event) | 
| Mapped Trait(s) | metabolic disease (EFO_0000589) | 
| Additional Trait Information | https://biobankengine.stanford.edu/RIVAS_HG19/snpnet/HC708 | 
| Score Construction | |
| PGS Name | GBE_HC708 | 
| Development Method | |
| Name | snpnet | 
| Parameters | NR | 
| Variants | |
| Original Genome Build | GRCh37 | 
| Number of Variants | 2 | 
| 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: 50% African: 25% South Asian: 25% 4 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  | 
                    
                    
|---|---|---|---|---|---|---|---|---|
| PPM009088 | PSS004610| African Ancestry| 6,497 individuals  | 
                        
                            PGP000244 | Tanigawa Y et al. PLoS Genet (2022)  | 
                        
                            Reported Trait: TTE other metabolic disorders | — | AUROC: 0.77406 [0.68678, 0.86135] | R²: 0.06649 Incremental AUROC (full-covars): -0.00199 PGS R2 (no covariates): 0.00603 PGS AUROC (no covariates): 0.44259 [0.3516, 0.53358]  | 
                        
                            age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method | 
| PPM009089 | PSS004611| European Ancestry| 24,905 individuals  | 
                        
                            PGP000244 | Tanigawa Y et al. PLoS Genet (2022)  | 
                        
                            Reported Trait: TTE other metabolic disorders | — | AUROC: 0.70958 [0.62744, 0.79171] | R²: 0.04504 Incremental AUROC (full-covars): 0.02495 PGS R2 (no covariates): 0.01479 PGS AUROC (no covariates): 0.54987 [0.46099, 0.63876]  | 
                        
                            age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method | 
| PPM009090 | PSS004612| South Asian Ancestry| 7,831 individuals  | 
                        
                            PGP000244 | Tanigawa Y et al. PLoS Genet (2022)  | 
                        
                            Reported Trait: TTE other metabolic disorders | — | AUROC: 0.64684 [0.54842, 0.74526] | R²: 0.02913 Incremental AUROC (full-covars): -0.00029 PGS R2 (no covariates): 0.00067 PGS AUROC (no covariates): 0.52382 [0.42834, 0.61931]  | 
                        
                            age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method | 
| PPM009091 | PSS004613| European Ancestry| 67,425 individuals  | 
                        
                            PGP000244 | Tanigawa Y et al. PLoS Genet (2022)  | 
                        
                            Reported Trait: TTE other metabolic disorders | — | AUROC: 0.60186 [0.54294, 0.66079] | R²: 0.02952 Incremental AUROC (full-covars): 0.09428 PGS R2 (no covariates): 0.05151 PGS AUROC (no covariates): 0.62356 [0.56795, 0.67918]  | 
                        
                            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 | 
|---|---|---|---|---|---|---|---|---|
| PSS004612 | — | — | [ 
  | 
                        
                            — | South Asian | — | UKB | — | 
| PSS004613 | — | — | [ 
  | 
                        
                            — | European | white British ancestry | UKB | Testing cohort (heldout set) | 
| PSS004610 | — | — | [ 
  | 
                        
                            — | African unspecified | — | UKB | — | 
| PSS004611 | — | — | [ 
  | 
                        
                            — | European | non-white British ancestry | UKB | — |