Quantifying Portable Genetic Effects and Improving Cross-ancestry Genetic Prediction with GWAS Summary Statistics

Overview

Journal Nat Commun

Specialty Biology

Date 2023 Feb 14

PMID 36788230

Authors

Jiacheng Miao

Hanmin Guo

Gefei Song

Zijie Zhao

Lin Hou

Qiongshi Lu

Affiliations

Soon will be listed here.

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 R 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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