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

Jiacheng Miao, Hanmin Guo, Gefei Song, Zijie Zhao, Lin Hou () and Qiongshi Lu ()
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Jiacheng Miao: University of Wisconsin–Madison
Hanmin Guo: Tsinghua University
Gefei Song: University of Wisconsin–Madison
Zijie Zhao: University of Wisconsin–Madison
Lin Hou: Tsinghua University
Qiongshi Lu: University of Wisconsin–Madison

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract 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 R2 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.

Date: 2023
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DOI: 10.1038/s41467-023-36544-7

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