Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI
Quan Sun, Bryce Rowland, Jiawen Chen, Anna V. Mikhaylova, Christy L. Avery, Ulrike Peters, Jessica Lundin, Tara C. Matise, Steven Buyske, Ran Tao, Rasika A. Mathias, Alex P. Reiner, Paul L. Auer, Nancy J. Cox, Charles Kooperberg, Timothy A. Thornton, Laura M. Raffield, Yun Li
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.