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

IF 4 2区 生物学 Q1 GENETICS & HEREDITY
PLoS Genetics Pub Date : 2024-10-23 eCollection Date: 2024-10-01 DOI:10.1371/journal.pgen.1011322
Sheng Fu, William Wheeler, Xiaoyu Wang, Xing Hua, Devika Godbole, Jubao Duan, Bin Zhu, Lu Deng, Fei Qin, Haoyu Zhang, Jianxin Shi, Kai Yu
{"title":"A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations.","authors":"Sheng Fu, William Wheeler, Xiaoyu Wang, Xing Hua, Devika Godbole, Jubao Duan, Bin Zhu, Lu Deng, Fei Qin, Haoyu Zhang, Jianxin Shi, Kai Yu","doi":"10.1371/journal.pgen.1011322","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49007,"journal":{"name":"PLoS Genetics","volume":"20 10","pages":"e1011322"},"PeriodicalIF":4.0000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11534268/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PLoS Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1371/journal.pgen.1011322","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

Abstract

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.

利用来自不同人群的 GWAS 摘要数据进行跨祖先通路分析的综合框架。
随着越来越多的多祖先 GWAS 总结数据的出现,我们开发了一个全面的跨祖先通路分析框架,以有效利用这些不同的遗传信息。在这一框架内,我们评估了整合来自多个祖先群体的不同层次--NNP、基因和通路--遗传数据的各种策略。通过广泛的模拟研究,我们确定了在各种情况下都能表现出卓越性能的稳健策略。应用这些方法,我们结合非洲、东亚和欧洲人群的数据,分析了 6,970 条通路与精神分裂症的关联。即使排除了全基因组重要位点附近的基因,我们的分析也发现了 200 多条与精神分裂症显著相关的通路。与传统的单一祖先通路分析和将不同祖先群体的单一祖先通路分析结果合并的传统方法相比,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,可用于利用不断扩大的多祖先 GWAS 摘要数据池,从而提高我们识别导致疾病易感性的生物相关通路的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
PLoS Genetics
PLoS Genetics GENETICS & HEREDITY-
自引率
2.20%
发文量
438
期刊介绍: PLOS Genetics is run by an international Editorial Board, headed by the Editors-in-Chief, Greg Barsh (HudsonAlpha Institute of Biotechnology, and Stanford University School of Medicine) and Greg Copenhaver (The University of North Carolina at Chapel Hill). Articles published in PLOS Genetics are archived in PubMed Central and cited in PubMed.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信