A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations.

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. Author summary: Pathway analysis is a powerful tool used to understand genetic associations with diseases. Instead of looking at individual genetic markers (such as single nucleotide polymorphisms, SNPs), it examines the combined effects of multiple markers within biological pathways. This method is more effective for detecting subtle genetic influences on diseases that might be missed when looking at individual markers alone. Our study expands pathway analysis to include data from diverse ancestry groups, which is often overlooked in traditional single-ancestry genetic studies. We developed a comprehensive trans-ancestry pathway analysis framework to effectively utilize diverse genetic data. In our framework, we explore various strategies for integrating genetic data at different levels—SNP, gene, and pathway—from multiple ancestry groups. Through extensive simulations, we identified robust strategies that perform well in diverse scenarios. Applying these methods, we analyzed around 7,000 pathways for their association with schizophrenia, using 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. Our approach significantly improves detection efficiency compared to traditional single-ancestry pathway analysis and the conventional approach that amalgamates single-ancestry pathway analysis results across different ancestry groups. This framework offers a flexible and effective tool for leveraging the growing pool of multi-ancestry GWAS data, enhancing our ability to identify biologically relevant pathways contributing to disease susceptibility. [ABSTRACT FROM AUTHOR]