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Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics

J. Miao,Hanmin Guo,Gefei Song,Zijie Zhao,Lin Hou,Q. Lu

Published 2022 in bioRxiv

ABSTRACT

Polygenic risk scores are used to improve risk prediction for common diseases but typically have reduced accuracy for individuals of non-European ancestry. Here, the authors present an approach that improves polygenic risk score performance in ancestrally diverse populations. Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting their clinical utility and raising concerns about health disparities across ancestral populations. Here, we introduce a statistical framework named X-Wing to improve predictive performance in ancestrally diverse populations. X-Wing quantifies local genetic correlations for complex traits between populations, employs an annotation-dependent estimation procedure to amplify correlated genetic effects between populations, and combines multiple population-specific PRS into a unified score with GWAS summary statistics alone as input. Through extensive benchmarking, we demonstrate that X-Wing pinpoints portable genetic effects and substantially improves PRS performance in non-European populations, showing 14.1%–119.1% relative gain in predictive R^2 compared to state-of-the-art methods based on GWAS summary statistics. Overall, X-Wing addresses critical limitations in existing approaches and may have broad applications in cross-population polygenic risk prediction.

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