School of Statistics and Data Science, Nankai University, Tianjin, China.
Key Laboratory of Pure Mathematics and Combinatorics, Nankai University, Tianjin, China.
PLoS Genet. 2024 Oct 23;20(10):e1011322. doi: 10.1371/journal.pgen.1011322. eCollection 2024 Oct.
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.
随着越来越多的多血统 GWAS 汇总数据可用，我们开发了一个全面的跨血统途径分析框架，该框架有效地利用了这种多样化的遗传信息。在这个框架内，我们评估了从多个血统群体整合不同层次的遗传数据（SNP、基因和途径）的各种策略。通过广泛的模拟研究，我们确定了稳健的策略，这些策略在各种情况下表现出卓越的性能。应用这些方法，我们分析了与精神分裂症相关的 6970 个途径，其中包括来自非洲、东亚和欧洲人群的数据。我们的分析发现，即使排除了全基因组显著位点附近的基因，也有 200 多个途径与精神分裂症显著相关。与传统的单血统途径分析和跨不同血统群体综合单血统途径分析结果的常规方法相比，这种方法大大提高了检测效率。我们的框架提供了一种灵活有效的工具，可利用不断扩大的多血统 GWAS 汇总数据池，从而提高我们识别与疾病易感性相关的生物学上相关途径的能力。
