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fastGxE:助力生物样本库研究中全基因组范围内基因-环境相互作用的检测。

fastGxE: Powering genome-wide detection of genotype-environment interactions in biobank studies.

作者信息

Ning Chao, Zhou Xiang

机构信息

Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.

出版信息

Res Sq. 2025 Mar 20:rs.3.rs-5952773. doi: 10.21203/rs.3.rs-5952773/v1.

Abstract

Traditional genome-wide association studies (GWAS) have primarily focused on detecting main genotype effects, often overlooking genotype-environment interactions (GxE), which are essential for understanding context-specific genetic effects and refining disease etiology. Here, we present fastGxE, a scalable and effective genome-wide GxE method designed to identify genetic variants that interact with environmental factors to influence traits of interest. fastGxE controls for both polygenic effects and polygenic interaction effects, is robust to the number of environmental factors involved in GxE interactions, and ensures scalability for genome-wide GxE analysis in large biobank studies, achieving speed improvements of 32.98-126.49 times over existing approaches. We illustrate the benefits of fastGxE through extensive simulations and an in-depth analysis of 32 physical traits and 67 blood biomarkers from the UK Biobank. In real data applications, fastGxE identifies nine genomic loci associated with physical traits, including six novel ones, and 26 genomic loci associated with blood biomarkers, 19 of which are novel. The new discoveries highlight the dynamic interplay between genetics and the environment, uncovering potentially clinically significant pathways that could inform personalized interventions and treatment strategies.

摘要

传统的全基因组关联研究(GWAS)主要集中于检测主要基因型效应,常常忽略基因型-环境相互作用(GxE),而这对于理解特定背景下的遗传效应和完善疾病病因至关重要。在此,我们提出了fastGxE,这是一种可扩展且有效的全基因组GxE方法,旨在识别与环境因素相互作用以影响感兴趣性状的遗传变异。fastGxE能够控制多基因效应和多基因相互作用效应,对GxE相互作用中涉及的环境因素数量具有稳健性,并确保在大型生物样本库研究中进行全基因组GxE分析时具有可扩展性,与现有方法相比速度提高了32.98至126.49倍。我们通过广泛的模拟以及对英国生物样本库中32种身体特征和67种血液生物标志物的深入分析,阐述了fastGxE的优势。在实际数据应用中,fastGxE识别出9个与身体特征相关的基因组位点,其中包括6个新位点,以及26个与血液生物标志物相关的基因组位点,其中19个是新位点。这些新发现突出了基因与环境之间的动态相互作用,揭示了可能为个性化干预和治疗策略提供信息的潜在临床重要途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9bd/11957207/32cc5826cc2c/nihpp-rs5952773v1-f0001.jpg

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