| Title: | Cross-ancestry genetic architecture and prediction for cholesterol traits |
| Journal: | Human Genetics |
| Published: | 27 Mar 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38536467/ |
| DOI: | https://doi.org/10.1007/s00439-024-02660-7 |
| URL: | https://doi.org/10.1101/2023.01.31.23285307 |
Publication 10046
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
While cholesterol is essential, a high level of cholesterol is associated with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have proven successful in identifying genetic variants that are linked to cholesterol levels, predominantly in white European populations. However, the extent to which genetic effects on cholesterol vary across different ancestries remains largely unexplored. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries. We find significant genetic heterogeneity between ancestries for cholesterol traits. Furthermore, we demonstrate that single nucleotide polymorphisms (SNPs) with concordant effects across ancestries for cholesterol are more frequently found in regulatory regions compared to other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog. These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings.</p>
5 Authors
- Md. Moksedul Momin
- Xuan Zhou
- Elina Hyppönen
- Beben Benyamin
- S. Hong Lee
1 Application
| Application ID | Title |
|---|---|
| 14575 | Whole-genome approaches for dissecting (shared) genetic architecture and individual risk prediction of complex traits in human populations |
Enabling scientific discoveries that improve human health