Admixture-informed polygenic risk reporting using the ePRS framework

Polygenic risk score values vary with genetic ancestry due to differences in population-specific allele frequencies and linkage disequilibrium patterns. We present a framework to calibrate polygenic risk scores based on ancestral makeup. We propose the "expected polygenic risk score" or ePRS, defined as the expected value of a polygenic risk score based on one's global or local admixture patterns. We further define the "residual polygenic risk score" or rPRS as measuring the deviation of the polygenic risk score from the ePRS. The ePRS reflects the baseline ancestry-driven component of genetic risk, whereas the rPRS isolates an ancestry-agnostic measure of genetic liability. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the polygenic risk score-outcome association without further adjusting for principal components. Using the TOPMed and the All of Us datasets, effect size estimates for the rPRS (adjusted for ePRS) are similar to those obtained from polygenic risk scores adjusting for genetic principal components. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to interpret genetic risk across diverse populations.

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Publication Details

Journal
Nature Communications
Published
2026-04-30
DOI
https://doi.org/10.1038/s41467-026-72457-x
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00

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article

Admixture-informed polygenic risk reporting using the ePRS framework

Brian Spitzer, Nuzulul Kurniansyah, Yu‐Jyun Huang, Matthew Goodman et al.
Nature Communications
Genetic Associations and Epidemiology
article

Admixture-informed polygenic risk reporting using the ePRS framework

Brian Spitzer, Nuzulul Kurniansyah, Yu‐Jyun Huang, Matthew Goodman, Jiongming Wang, Adrienne M. Stilp, Cecelia Laurie, Yuan-I Min, Nora Franceschini, Laura M Raffield, Sebastian Zöllner, Paul S de Vries, Daniel Levy, Xiuqing Guo, Gina M Peloso, Jerome I Rotter, Alanna C Morrison, Eric Boerwinkle, Han Chen, Jennifer A Smith, Joshua C Bis, Tamar Sofer, Mario Sims, Susan Redline, Wei Zhao, Stephen S Rich, Robert C Kaplan, Charles Kooperberg, Bruce M Psaty, Myriam Fornage, Jennifer A Brody, Nicholas L Smith, Tamar Sofer
article en

Abstract

Polygenic risk score values vary with genetic ancestry due to differences in population-specific allele frequencies and linkage disequilibrium patterns. We present a framework to calibrate polygenic risk scores based on ancestral makeup. We propose the "expected polygenic risk score" or ePRS, defined as the expected value of a polygenic risk score based on one's global or local admixture patterns. We further define the "residual polygenic risk score" or rPRS as measuring the deviation of the polygenic risk score from the ePRS. The ePRS reflects the baseline ancestry-driven component of genetic risk, whereas the rPRS isolates an ancestry-agnostic measure of genetic liability. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the polygenic risk score-outcome association without further adjusting for principal components. Using the TOPMed and the All of Us datasets, effect size estimates for the rPRS (adjusted for ePRS) are similar to those obtained from polygenic risk scores adjusting for genetic principal components. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to interpret genetic risk across diverse populations.

Nature Communications
University of California, Riverside (US), Boston University (US), University of North Carolina at Chapel Hill (US), Brigham and Women's Hospital (US), Albert Einstein College of Medicine (US), Beth Israel Deaconess Medical Center (US), Harvard University (US), Kaiser Permanente Washington Health Research Institute (US), University of Washington (US), University of Michigan (US), UCLA Medical Center (US), VA Office of Research and Development (US), Jackson Memorial Hospital (US), University of Mississippi Medical Center (US), Fred Hutch Cancer Center (US), Cancer Research And Biostatistics (US), National Heart, Lung, and Blood Institute (US), Brown Foundation (US), Harbor–UCLA Medical Center (US), University of Virginia (US), The University of Texas Health Science Center at Houston (US)
National Institute on Aging
Openalex Percentile: Top 12%
Genetic Associations and Epidemiology
DOIOpen Access PDFOpenAlex
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