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; Yun Li

doi:10.1038/s41467-024-45135-z

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

Nat Commun. 2024 Feb 3;15(1):1016. doi: 10.1038/s41467-024-45135-z.

Authors

Quan Sun^#¹, Bryce T Rowland^#¹, Jiawen Chen¹, Anna V Mikhaylova², Christy Avery³, Ulrike Peters⁴, Jessica Lundin⁴, Tara Matise⁵, Steve Buyske⁶, Ran Tao^{7

8}, Rasika A Mathias⁹, Alexander P Reiner¹⁰, Paul L Auer¹¹, Nancy J Cox^{7

12}, Charles Kooperberg⁴, Timothy A Thornton², Laura M Raffield¹³, Yun Li^{14

15}

Affiliations

¹ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
² Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA.
³ Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
⁴ Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
⁵ Department of Genetics, Rutgers University, New Brunswick, NJ, 08901, USA.
⁶ Department of Statistics, Rutgers University, New Brunswick, NJ, 08901, USA.
⁷ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
⁸ Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
⁹ Department of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA.
¹⁰ Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA.
¹¹ Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
¹² Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
¹³ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
¹⁴ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. yun_li@med.unc.edu.
¹⁵ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. yun_li@med.unc.edu.

^# Contributed equally.

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.

MeSH terms

Black or African American* / genetics
Computer Simulation
Genetic Predisposition to Disease
Genetic Risk Score*
Genome, Human
Genome-Wide Association Study / methods
Humans
Phenotype
Risk Factors
Software*

Abstract

MeSH terms

Grants and funding