| Title: | The construction of cross-population polygenic risk scores using transfer learning |
| Journal: | American Journal of Human Genetics |
| Published: | 13 Oct 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36240765/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2022.09.010 |
| URL: | http://www.cell.com/article/S000292972200413X/pdf |
| Citations: | 30 (29 in last 2 years) as of 8 Aug 2024 |
Publication 10569
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
As most existing genome-wide association studies (GWASs) were conducted in European-ancestry cohorts, and as the existing polygenic risk score (PRS) models have limited transferability across ancestry groups, PRS research on non-European-ancestry groups needs to make efficient use of available data until we attain large sample sizes across all ancestry groups. Here we propose a PRS method using transfer learning techniques. Our approach, TL-PRS, uses gradient descent to fine-tune the baseline PRS model from an ancestry group with large sample GWASs to the dataset of target ancestry. In our application of constructing PRS for seven quantitative and two dichotomous traits for 10,285 individuals of South Asian ancestry and 8,168 individuals of African ancestry in UK Biobank, TL-PRS using PRS-CS as a baseline method obtained 25% average relative improvement for South Asian samples and 29% for African samples compared to the standard PRS-CS method in terms of predicted R2. Our approach increases the transferability of PRSs across ancestries and thereby helps reduce existing inequities in genetics research.</p>
5 Authors
- Zhangchen Zhao
- Lars G. Fritsche
- Jennifer A. Smith
- Bhramar Mukherjee
- Seunggeun Lee
1 Application
| Application ID | Title |
|---|---|
| 45227 | Scalable and Robust methods for biobank data analysis |
Enabling scientific discoveries that improve human health