| Title: | Trans-ancestry polygenic models for the prediction of LDL blood levels: an analysis of the United Kingdom Biobank and Taiwan Biobank |
| Journal: | Frontiers in Genetics |
| Published: | 23 Nov 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38075701/ |
| DOI: | https://doi.org/10.3389/fgene.2023.1286561 |
| URL: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1286561/pdf?isPublishedV2=False |
Publication 8264
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Abstract
Polygenic risk score (PRS) predictions often show bias toward the population of available genome-wide association studies (GWASs), which is typically of European ancestry. This study aimed to assess the performance differences of ancestry-specific PRS and test the implementation of multi-ancestry PRS to enhance the generalizability of low-density lipoprotein (LDL) cholesterol predictions in the East Asian (EAS) population. In this study, we computed ancestry-specific and multi-ancestry PRSs for LDL using data obtained from the Global Lipid Genetics Consortium, while accounting for population-specific linkage disequilibrium patterns using the PRS-CSx method in the United Kingdom Biobank dataset (UKB, n = 423,596) and Taiwan Biobank dataset (TWB, n = 68,978). Population-specific PRSs were able to predict LDL levels better within the target population, whereas multi-ancestry PRSs were more generalizable. In the TWB dataset, covariate-adjusted R 2 values were 9.3% for ancestry-specific PRS, 6.7% for multi-ancestry PRS, and 4.5% for European-specific PRS. Similar trends (8.6%, 7.8%, and 6.2%) were observed in the smaller EAS population of the UKB (n = 1,480). Consistent with R 2 values, PRS stratification in EAS regions (TWB) effectively captured a heterogenous variability in LDL blood cholesterol levels across PRS strata. The mean difference in LDL levels between the lowest and highest EAS-specific PRS (EAS_PRS) deciles was 0.82, compared to 0.59 for European-specific PRS (EUR_PRS) and 0.76 for multi-ancestry PRS. Notably, the mean LDL values in the top decile of multi-ancestry PRS were comparable to those of EAS_PRS (3.543 vs. 3.541, p = 0.86). Our analysis of the PRS prediction model for LDL cholesterol further supports the issue of PRS generalizability across populations. Our targeted analysis of the EAS population revealed that integrating non-European genotyping data with a powerful European-based GWAS can enhance the generalizability of LDL PRS.</p>
10 Authors
- Emadeldin Hassanin
- Ko-Han Lee
- Tzung-Chien Hsieh
- Rana Aldisi
- Yi-Lun Lee
- Dheeraj Bobbili
- Peter Krawitz
- Patrick May
- Chien-Yu Chen
- Carlo Maj
1 Application
| Application ID | Title |
|---|---|
| 52446 | Predictive and integrative models for disease prevention using the UK Biobank Data |
Enabling scientific discoveries that improve human health