| Title: | JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation |
| Journal: | Nature Communications |
| Published: | 24 Apr 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40268942/ |
| DOI: | https://doi.org/10.1038/s41467-025-59243-x |
| URL: | https://www.nature.com/articles/s41467-025-59243-x.pdf |
Publication 15758
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Abstract
Genetic risk prediction for non-European populations is hindered by limited Genome-Wide Association Study (GWAS) sample sizes and small tuning datasets. We propose JointPRS, a data-adaptive framework that leverages genetic correlations across multiple populations using GWAS summary statistics. It achieves accurate predictions without individual-level tuning data and remains effective in the presence of a small tuning set thanks to its data-adaptive approach. Through extensive simulations and real data applications to 22 quantitative and four binary traits in five continental populations evaluated using the UK Biobank (UKBB) and All of Us (AoU), JointPRS consistently outperforms six state-of-the-art methods across three data scenarios: no tuning data, same-cohort tuning and testing, and cross-cohort tuning and testing. Notably, in the Admixed American population, JointPRS improves lipid trait prediction in AoU by 6.46%-172.00% compared to the other existing methods.</p>
7 Keywords
- Computer Simulation
- Genetic Predisposition to Disease
- Genetics, Population
- Genome-Wide Association Study
- Humans
- Models, Genetic
- Polymorphism, Single Nucleotide
7 Authors
- Leqi Xu
- Geyu Zhou
- Wei Jiang
- Haoyu Zhang
- Yikai Dong
- Leying Guan
- Hongyu Zhao
1 Application
| Application ID | Title |
|---|---|
| 29900 | Defining physical activity phenotypes based on objective measures and dissecting the genetic and phenotypic relationships of physical activity with diseases and health outcomes |
Enabling scientific discoveries that improve human health