| Trait Information | |
| Identifier | MONDO_0005377 |
| Description | A collection of symptoms that include severe edema, proteinuria, and hypoalbuminemia; it is indicative of renal dysfunction. [NCIT: C34845] | Trait category |
Other trait
|
| Synonyms |
5 synonyms
|
| Polygenic Score ID & Name | PGS Publication ID (PGP) | Reported Trait | Mapped Trait(s) (Ontology) | Number of Variants |
Ancestry distribution GWAS Dev Eval |
Scoring File (FTP Link) |
|---|---|---|---|---|---|---|
| PGS003354 (SSNS-GRS) |
PGP000407 | Downie ML et al. Pediatr Nephrol (2022) |
Childhood steroid-sensitive nephrotic syndrome | nephrotic syndrome | 5 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003354/ScoringFiles/PGS003354.txt.gz |
| PGS012539 (pSSNS) |
PGP000789 | Barry A et al. Nat Commun (2023) |
Pediatric steroid-sensitive nephrotic syndrome | nephrotic syndrome | 1,081,086 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS012539/ScoringFiles/PGS012539.txt.gz |
|
PGS Performance Metric ID (PPM) |
Evaluated Score |
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 |
|---|---|---|---|---|---|---|---|---|---|
| PPM016206 | PGS003354 (SSNS-GRS) |
PSS010057| European Ancestry| 597 individuals |
PGP000407 | Downie ML et al. Pediatr Nephrol (2022) |
Reported Trait: Non-monogenic idiopathic nephrotic syndrome | — | AUROC: 0.638 [0.543, 0.733] | — | — | — |
| PPM030645 | PGS012539 (pSSNS) |
PSS012215| European Ancestry| 233 individuals |
PGP000789 | Barry A et al. Nat Commun (2023) |
Reported Trait: Age of onset of Pediatric Steroid-Sensitive Nephrotic Syndrome | β: -2.05 (0.68) | — | — | sex, relapse pattern, 4PCs | — |
| PPM030644 | PGS012539 (pSSNS) |
PSS012214| European Ancestry| 565 individuals |
PGP000789 | Barry A et al. Nat Commun (2023) |
Reported Trait: Pediatric Steroid-Sensitive Nephrotic Syndrome | — | AUROC: 0.73 | F-measure: 0.35 | — | While our goal of this analysis was to explore the relationships between the PRS and clinical correlates within case cohorts, not case/control prediction, we used prediction accuracy to optimize the gamma-gamma priors and the global shrinkage parameter used in the PRS-CSx model. We varied the hyper parameters and chose the model with the best prediction accuracy (F-measure; Supp. Table 15). The regression betas from the best model were used to weight the population-specific PRS. |
|
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 |
|---|---|---|---|---|---|---|---|---|
| PSS012215 | — | — | 233 individuals | — | European | — | NR | — |
| PSS012214 | same as above | — | [
|
— | European | — | NR | — |
| PSS010057 | — | — | 597 individuals | — | European | — | NR | BRIDGE |