| Title: | Improving polygenic score prediction for underrepresented groups through transfer learning |
| Journal: | Nature Communications |
| Published: | 23 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41577700/ |
| DOI: | https://doi.org/10.1038/s41467-026-68696-7 |
| URL: | https://www.nature.com/articles/s41467-026-68696-7_reference.pdf |
Publication 18485
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Abstract
The advent of large biobanks has substantially increased the accuracy of polygenic scores (PGS). However, most existing PGSs were derived from European-ancestry data and often exhibit reduced predictive performance when applied to individuals of non-European ancestries. Transfer Learning offers a promising strategy to address this limitation by leveraging information learned in one population to improve prediction in another. Here, we introduce GPTL, an R package that implements three Transfer Learning based approaches for developing PGS: (1) gradient descent with early stopping, (2) a penalized regression model that shrinks variant-effect estimates toward prior values, and (3) a Bayesian method with a finite-mixture prior that enables integration of multiple prior sources of information. Using both simulated data and real data from the UK-Biobank and All of Us, we demonstrate that PGS generated with GPTL's Transfer Learning algorithms consistently outperform single-ancestry PGS and, in many settings, match or exceed the performance of multi-ancestry ensemble-based PGS. Our software can be used with either individual genotype-phenotype data or summary statistics from genome-wide association studies.</p>
7 Authors
- Hao Wu
- Paulino Pérez-Rodríguez
- Michael Boehnke
- Yuehua Cui
- Xiaoyu Liang
- Ana I. Vazquez
- Gustavo de los Campos
1 Application
| Application ID | Title |
|---|---|
| 15326 | Compressed Sensing and high-dimensional statistical methods in complex trait genomics |
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