A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations. - Sheng Fu; William Wheeler; Xiaoyu Wang; Xing Hua; Devika Godbole; Jubao Duan; Bin Zhu; Lu Deng; Fei Qin; Haoyu Zhang; Jianxin Shi; Kai Yu (PLoS Genetics)
As more multi-ancestry GWAS summary data become available, we have developed a comprehensive trans-ancestry pathway analysis framework that effectively utilizes this diverse genetic information. Within this framework, we evaluated various strategies for integrating genetic data at different levels-SNP, gene, and pathway-from multiple ancestry groups. Through extensive simulation studies, we have identified robust strategies that demonstrate superior performance across diverse scenarios. Applying these methods, we analyzed 6,970 pathways for their association with schizophrenia, incorporating data from African, East Asian, and European populations. Our analysis identified over 200 pathways significantly associated with schizophrenia, even after excluding genes near genome-wide significant loci. This approach substantially enhances detection efficiency compared to traditional single-ancestry pathway analysis and the conventional approach that amalgamates single-ancestry pathway analysis results across different ancestry groups. Our framework provides a flexible and effective tool for leveraging the expanding pool of multi-ancestry GWAS summary data, thereby improving our ability to identify biologically relevant pathways that contribute to disease susceptibility.