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Novel Methods for Multi-ancestry Polygenic Prediction and their Evaluations in 5.1 Million Individuals of Diverse Ancestry
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Novel Methods for Multi-ancestry Polygenic Prediction and their Evaluations in 5.1 Million Individuals of Diverse Ancestry
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Novel Methods for Multi-ancestry Polygenic Prediction and their Evaluations in 5.1 Million Individuals of Diverse Ancestry
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Paper
Novel Methods for Multi-ancestry Polygenic Prediction and their Evaluations in 5.1 Million Individuals of Diverse Ancestry
2023
Overview
Polygenic risk scores are becoming increasingly predictive of complex traits, but their suboptimal performance in non-European ancestry populations raises questions about their clinical applications and impact on health inequities. We develop CT-SLEB, a powerful and scalable method to calculate PRS based on ancestry-specific GWAS summary statistics from multi-ancestry training samples by integrating multiple techniques, including clumping and thresholding, empirical Bayes and super learning. We evaluate the performance of the proposed method and nine alternatives using large-scale simulated GWAS on ~19 million common variants and large datasets from 23andMe Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, which include up to 1.18 million individuals from four non-European populations across thirteen complex traits. Results show that the proposed method can substantially improve the performance of PRS in non-European populations relative to simple alternatives and has comparable or superior performance relative to a recent method that requires a higher order of computational time. Furthermore, our simulation studies provide novel insights to sample size requirements and the effect of SNP density on multi-ancestry risk prediction.Competing Interest StatementJianan Zhan, Yunxuan Jiang, Jared O. Connell, and Betram L. Koelsch are employed by and hold stock or stock options in 23andMe, Inc.Footnotes* Update author information* https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/COXHAP
Publisher
Cold Spring Harbor Laboratory Press
