Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI

Quan Sun, Bryce T. Rowland, Jiawen Chen, Anna V. Mikhaylova, Christy Avery, Ulrike Peters, Jessica Lundin, Tara Matise, Steve Buyske, Ran Tao, Rasika A. Mathias, Alexander P. Reiner, Paul L. Auer, Nancy J. Cox, Charles Kooperberg, Timothy A. Thornton, Laura M. Raffield and Yun Li ()
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Quan Sun: University of North Carolina at Chapel Hill
Bryce T. Rowland: University of North Carolina at Chapel Hill
Jiawen Chen: University of North Carolina at Chapel Hill
Anna V. Mikhaylova: University of Washington
Christy Avery: University of North Carolina at Chapel Hill
Ulrike Peters: Fred Hutchinson Cancer Center
Jessica Lundin: Fred Hutchinson Cancer Center
Tara Matise: Rutgers University
Steve Buyske: Rutgers University
Ran Tao: Vanderbilt University Medical Center
Rasika A. Mathias: Johns Hopkins University
Alexander P. Reiner: University of Washington
Paul L. Auer: and Cancer Center, Medical College of Wisconsin
Nancy J. Cox: Vanderbilt University Medical Center
Charles Kooperberg: Fred Hutchinson Cancer Center
Timothy A. Thornton: University of Washington
Laura M. Raffield: University of North Carolina at Chapel Hill
Yun Li: University of North Carolina at Chapel Hill

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women’s Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.

Date: 2024
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DOI: 10.1038/s41467-024-45135-z

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