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

Author

Listed:
  • Leqi Xu

    (Yale School of Public Health)

  • Geyu Zhou

    (Yale School of Public Health)

  • Wei Jiang

    (Yale School of Public Health
    University of Texas at Arlington
    University of Texas at Arlington)

  • Haoyu Zhang

    (National Cancer Institute)

  • Yikai Dong

    (Yale School of Public Health)

  • Leying Guan

    (Yale School of Public Health
    Yale University)

  • Hongyu Zhao

    (Yale School of Public Health
    Yale University)

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.

Suggested Citation

  • Leqi Xu & Geyu Zhou & Wei Jiang & Haoyu Zhang & Yikai Dong & Leying Guan & Hongyu Zhao, 2025. "JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59243-x
    DOI: 10.1038/s41467-025-59243-x
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