| Trait Information | |
| Identifier | MONDO_0024625 |
| Description | A disease that involves the lacrimal gland. [MONDO: patterns/location] | Trait category |
Other trait
|
| Synonyms |
5 synonyms
|
| Child trait(s) | Sjogren syndrome |
| 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) |
|---|---|---|---|---|---|---|
| PGS001308 (GBE_HC321) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Sjogren's syndrome/sicca syndrome | Sjogren syndrome | 7 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001308/ScoringFiles/PGS001308.txt.gz |
| PGS019645 (TPMI_709.2_Lassosum2) |
PGP000835 | Chen HH et al. Nature (2025) |
Sicca syndrome | Sjogren syndrome | 1,224 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS019645/ScoringFiles/PGS019645.txt.gz | |
| PGS019646 (TPMI_709.2_LDpred2) |
PGP000835 | Chen HH et al. Nature (2025) |
Sicca syndrome | Sjogren syndrome | 939,810 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS019646/ScoringFiles/PGS019646.txt.gz | |
| PGS019647 (TPMI_709.2_MegaPRS) |
PGP000835 | Chen HH et al. Nature (2025) |
Sicca syndrome | Sjogren syndrome | 49,185 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS019647/ScoringFiles/PGS019647.txt.gz | |
| PGS019648 (TPMI_709.2_PRS-CS) |
PGP000835 | Chen HH et al. Nature (2025) |
Sicca syndrome | Sjogren syndrome | 983,765 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS019648/ScoringFiles/PGS019648.txt.gz | |
| PGS019649 (TPMI_709.2_PRSmix+) |
PGP000835 | Chen HH et al. Nature (2025) |
Sicca syndrome | Sjogren syndrome | 1,071,405 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS019649/ScoringFiles/PGS019649.txt.gz | |
| PGS019650 (TPMI_709.2_SBayesR) |
PGP000835 | Chen HH et al. Nature (2025) |
Sicca syndrome | Sjogren syndrome | 202,225 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS019650/ScoringFiles/PGS019650.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 |
|---|---|---|---|---|---|---|---|---|---|
| PPM009034 | PGS001308 (GBE_HC321) |
PSS004437| African Ancestry| 6,497 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Sjogren's syndrome/sicca syndrome | — | AUROC: 0.79846 [0.71474, 0.88218] | R²: 0.07345 Incremental AUROC (full-covars): 0.0079 PGS R2 (no covariates): 0.01471 PGS AUROC (no covariates): 0.59027 [0.45551, 0.72504] |
age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method |
| PPM009035 | PGS001308 (GBE_HC321) |
PSS004438| East Asian Ancestry| 1,704 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Sjogren's syndrome/sicca syndrome | — | AUROC: 0.75824 [0.55089, 0.96558] | R²: 0.05686 Incremental AUROC (full-covars): 0.00691 PGS R2 (no covariates): 0.0091 PGS AUROC (no covariates): 0.69794 [0.63241, 0.76347] |
age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method |
| PPM009036 | PGS001308 (GBE_HC321) |
PSS004439| European Ancestry| 24,905 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Sjogren's syndrome/sicca syndrome | — | AUROC: 0.77174 [0.71988, 0.82361] | R²: 0.07435 Incremental AUROC (full-covars): 0.01461 PGS R2 (no covariates): 0.01453 PGS AUROC (no covariates): 0.65693 [0.58901, 0.72485] |
age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method |
| PPM009037 | PGS001308 (GBE_HC321) |
PSS004440| South Asian Ancestry| 7,831 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Sjogren's syndrome/sicca syndrome | — | AUROC: 0.8014 [0.74455, 0.85826] | R²: 0.08893 Incremental AUROC (full-covars): 0.00953 PGS R2 (no covariates): 0.03189 PGS AUROC (no covariates): 0.61034 [0.48809, 0.73259] |
age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method |
| PPM009038 | PGS001308 (GBE_HC321) |
PSS004441| European Ancestry| 67,425 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Sjogren's syndrome/sicca syndrome | — | AUROC: 0.73313 [0.69647, 0.76978] | R²: 0.04771 Incremental AUROC (full-covars): 0.01551 PGS R2 (no covariates): 0.01074 PGS AUROC (no covariates): 0.60303 [0.55292, 0.65315] |
age, sex, UKB array type, Genotype PCs | Full Model & PGS R2 is estimated using Nagelkerke's method |
| PPM037827 | PGS019645 (TPMI_709.2_Lassosum2) |
PSS013542| East Asian Ancestry| 18,012 individuals |
PGP000835 | Chen HH et al. Nature (2025) |
Reported Trait: Sicca syndrome | — | AUROC: 0.77927 | R²: 0.24978 | sex, age, array, PCs 1-10 | — |
| PPM037828 | PGS019646 (TPMI_709.2_LDpred2) |
PSS013541| East Asian Ancestry| 18,012 individuals |
PGP000835 | Chen HH et al. Nature (2025) |
Reported Trait: Sicca syndrome | — | AUROC: 0.78396 | R²: 0.25747 | sex, age, array, PCs 1-10 | — |
| PPM037829 | PGS019647 (TPMI_709.2_MegaPRS) |
PSS013543| East Asian Ancestry| 18,012 individuals |
PGP000835 | Chen HH et al. Nature (2025) |
Reported Trait: Sicca syndrome | — | AUROC: 0.77815 | R²: 0.24514 | sex, age, array, PCs 1-10 | — |
| PPM037830 | PGS019648 (TPMI_709.2_PRS-CS) |
PSS013544| East Asian Ancestry| 18,012 individuals |
PGP000835 | Chen HH et al. Nature (2025) |
Reported Trait: Sicca syndrome | — | AUROC: 0.78347 | R²: 0.25531 | sex, age, array, PCs 1-10 | — |
| PPM037831 | PGS019649 (TPMI_709.2_PRSmix+) |
PSS013545| East Asian Ancestry| 18,012 individuals |
PGP000835 | Chen HH et al. Nature (2025) |
Reported Trait: Sicca syndrome | — | AUROC: 0.78759 | R²: 0.26384 | sex, age, array, PCs 1-10 | — |
| PPM037832 | PGS019650 (TPMI_709.2_SBayesR) |
PSS013546| East Asian Ancestry| 18,012 individuals |
PGP000835 | Chen HH et al. Nature (2025) |
Reported Trait: Sicca syndrome | — | AUROC: 0.77877 | R²: 0.2473 | 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 |
|---|---|---|---|---|---|---|---|---|
| PSS013541 | 710.2,M35.0 | — | [ ,
46.5 % Male samples |
— | East Asian (Han Chinese) |
— | TPMI | — |
| PSS013542 | 710.2,M35.0 | — | [ ,
46.5 % Male samples |
— | East Asian (Han Chinese) |
— | TPMI | — |
| PSS013543 | 710.2,M35.0 | — | [ ,
46.5 % Male samples |
— | East Asian (Han Chinese) |
— | TPMI | — |
| PSS013544 | 710.2,M35.0 | — | [ ,
46.5 % Male samples |
— | East Asian (Han Chinese) |
— | TPMI | — |
| PSS013545 | 710.2,M35.0 | — | [ ,
46.5 % Male samples |
— | East Asian (Han Chinese) |
— | TPMI | — |
| PSS013546 | 710.2,M35.0 | — | [ ,
46.5 % Male samples |
— | East Asian (Han Chinese) |
— | TPMI | — |
| PSS004437 | — | — | [
|
— | African unspecified | — | UKB | — |
| PSS004438 | — | — | [
|
— | East Asian | — | UKB | — |
| PSS004439 | — | — | [
|
— | European | non-white British ancestry | UKB | — |
| PSS004440 | — | — | [
|
— | South Asian | — | UKB | — |
| PSS004441 | — | — | [
|
— | European | white British ancestry | UKB | Testing cohort (heldout set) |