JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation

Leqi Xu, Geyu Zhou, Wei Jiang, Haoyu Zhang, Yikai Dong, Leying Guan () and Hongyu Zhao ()
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Leqi Xu: Yale School of Public Health
Geyu Zhou: Yale School of Public Health
Wei Jiang: Yale School of Public Health
Haoyu Zhang: National Cancer Institute
Yikai Dong: Yale School of Public Health
Leying Guan: Yale School of Public Health
Hongyu Zhao: Yale School of Public Health

Nature Communications, 2025, vol. 16, issue 1, 1-20

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-59243-x

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