Admixture-informed polygenic risk reporting using the ePRS framework
Abstract
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.
Article Details
Authors (32)
Yu-Jyun Huang
Nuzulul Kurniansyah
Matthew O. Goodman
Brian W. Spitzer
Jiongming Wang
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Adrienne Stilp
Cecelia Laurie
University of Washington, Seattle, WA, USA.
Han Chen
GBRCE for Functional Molecular Engineering, LIFM, IGCME, School of Chemistry
Yuan-I Min
Mario Sims
Gina M. Peloso
Xiuqing Guo
Joshua C. Bis
Jennifer A. Brody
Laura M. Raffield
Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Jennifer A. Smith
Wei Zhao
Jerome I. Rotter
Stephen S. Rich
Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA, USA.
Susan Redline
Myriam Fornage
Robert Kaplan
Nora Franceschini
University of North Carolina, Chapel Hill, NC, USA.
Daniel Levy
Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Alanna C. Morrison
Eric Boerwinkle
Nicholas L. Smith
Charles Kooperberg
Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Bruce M. Psaty
Sebastian Zöllner
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Paul S. de Vries
Tamar Sofer