| Predicted Trait | |
| Reported Trait | Body mass index (BMI) |
| Mapped Trait(s) | body mass index (EFO_0004340) |
| Score Construction | |
| PGS Name | G23_BMI_2025_PRSCSx_LC_SAS |
| Development Method | |
| Name | PRS-CSx |
| Parameters | This score was generated using PRS-CSx v1.0.0 with default settings and phi = 1e-2, based on five ancestral GWAS results (AMR, AFR, EAS, EUR, SAS) and corresponding 1000 Genomes reference panels. Variants were filtered to those with INFO>0.3 in the overall UK Biobank and ancestry-specific summary statistics further restricted to the variants with a variant-specific sample size of at least a third of the maximum sample size for that ancestry. This entry includes only the weight file for the SAS subscore (with its specific training sample size). To compute the final score for individuals of SAS ancestry, all five ancestry-specific scores must be standardized in your testing population and linearly combined as follows: SAS_PGS = (0.68 × zAMR) + (0.21 × zAFR) + (0.87 × zEAS) + (2.11 × zEUR) + (1 × zSAS). These combination weights were derived from UK Biobank participants of the target population ancestry (e.g., SAS here). Weight files for the other four subscores are available under the catalog entries: G23_BMI_2025_PRSCSx_LC_AFR, *_AMR, *_EAS, and *_EUR. Performance metrics reflect the combined score (i.e., SAS_PGS). |
| Variants | |
| Original Genome Build | hg19 |
| Number of Variants | 1,129,666 |
| Effect Weight Type | beta |
| PGS Source | |
| PGS Catalog Publication (PGP) ID | PGP000724 |
| Citation (link to publication) | Smit RAJ et al. Nat Med (2025) |
| Study Identifiers | Sample Numbers | Sample Ancestry | Cohort(s) |
|---|---|---|---|
| — | 75,715 individuals | South Asian | 9 cohorts
|
| 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 |
|---|---|---|---|---|---|---|---|---|
| — | 8,005 individuals, 53.6 % Male samples |
South Asian | UKB | — | Mean = 53.5 years Sd = 8.51 years |
— | — | — |
|
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 |
|---|---|---|---|---|---|---|---|---|
| PPM022567 | PSS012018| Additional Asian Ancestries| 12,603 individuals |
PGP000724 | Smit RAJ et al. Nat Med (2025) |
Reported Trait: body mass index (BMI) | β: 0.34 [0.31, 0.38] | — | R²: 9.0 [7.4, 10.8] | age, sex, 10 PCs | The beta [95%CI] reflects the per-PGS-SD linear regression coefficient with BMI (inverse-normalized by sex), adjusting for the covariates. R2 [95%CI] reflects the adjusted R2, incremental to base model including the covariates. |
|
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 |
|---|---|---|---|---|---|---|---|---|
| PSS012018 | — | — | 4,201 individuals, 92.6 % Male samples |
Mean = 52.1 years Sd = 16.9 years |
Asian unspecified | — | MVP | — |
| PSS012018 | — | — | 4,201 individuals, 92.6 % Male samples |
Mean = 52.1 years Sd = 16.9 years |
Asian unspecified | — | MVP | — |
| PSS012018 | — | — | 4,201 individuals, 92.6 % Male samples |
Mean = 52.1 years Sd = 16.9 years |
Asian unspecified | — | MVP | — |