| Title: | Polygenic prediction for underrepresented populations through transfer learning by utilizing genetic similarity shared with European populations |
| Journal: | Briefings in Bioinformatics |
| Published: | 22 Nov 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39905953/ |
| DOI: | https://doi.org/10.1093/bib/bbaf048 |
Publication 14666
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
Because current genome-wide association studies are primarily conducted in individuals of European ancestry and information disparities exist among different populations, the polygenic score derived from Europeans thus exhibits poor transferability. Borrowing the idea of transfer learning, which enables the utilization of knowledge acquired from auxiliary samples to enhance learning capability in target samples, we propose transPGS, a novel polygenic score method, for genetic prediction in underrepresented populations by leveraging genetic similarity shared between the European and non-European populations while explaining the trans-ethnic difference in linkage disequilibrium (LD) and effect sizes. We demonstrate the usefulness and robustness of transPGS in elevated prediction accuracy via individual-level and summary-level simulations and apply it to seven continuous phenotypes and three diseases in the African, Chinese, and East Asian populations of the UK Biobank and Genetic Epidemiology Research Study on Adult Health and Aging cohorts. We further reveal that distinct LD and minor allele frequency patterns across ancestral groups are responsible for the dissatisfactory portability of PGS.</p>
8 Keywords
- Gene Frequency
- Genetics, Population
- Genome-Wide Association Study
- Humans
- Linkage Disequilibrium
- Multifactorial Inheritance
- Polymorphism, Single Nucleotide
- White People
6 Authors
- Yiyang Zhu
- Wenying Chen
- Kexuan Zhu
- Yuxin Liu
- Shuiping Huang
- Ping Zeng
1 Application
| Application ID | Title |
|---|---|
| 88159 | Fine-mapping, genetic correlation and genetic risk prediction for complex traits in different populations |
Enabling scientific discoveries that improve human health