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大规模基因研究中基因与环境相互作用的多性状分析。

Multi-trait analysis of gene-by-environment interactions in large-scale genetic studies.

作者信息

Luo Lan, Mehrotra Devan V, Shen Judong, Tang Zheng-Zheng

机构信息

Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA.

Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA 19454, USA.

出版信息

Biostatistics. 2024 Apr 15;25(2):504-520. doi: 10.1093/biostatistics/kxad004.

Abstract

Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene-Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene-environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB.

摘要

识别基因-环境相互作用(GEI)具有挑战性,因为GEI分析通常功效较低。最终需要开展基于大型联盟的研究,以获得足够的功效来识别GEI。我们引入了基因-环境相互作用多性状分析(MTAGEI),这是一个强大、稳健且计算高效的框架,用于在诸如英国生物银行(UKB)等大数据集中测试多个性状的基因-环境相互作用。为便于联盟中GEI研究的荟萃分析,MTAGEI能高效生成不同环境条件下多个性状的遗传关联汇总统计量,并整合这些汇总统计量用于GEI分析。MTAGEI通过汇总多个性状和变异体间的GEI信号来增强GEI分析的功效,否则这些信号很难单独检测到。MTAGEI通过在广泛的遗传结构下结合互补检验来实现稳健性。我们通过广泛的模拟研究以及对UKB全外显子测序数据的分析,证明了MTAGEI相对于现有基于单性状的GEI检验的优势。

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