Argos Maria, Tong Lin, Roy Shantanu, Sabarinathan Mekala, Ahmed Alauddin, Islam Md Tariqul, Islam Tariqul, Rakibuz-Zaman Muhammad, Sarwar Golam, Shahriar Hasan, Rahman Mahfuzar, Yunus Md, Graziano Joseph H, Jasmine Farzana, Kibriya Muhammad G, Zhou Xiang, Ahsan Habibul, Pierce Brandon L
Division of Epidemiology and Biostatistics, University of Illinois at Chicago, 1603 West Taylor Street, MC 923, Chicago, IL, 60612, USA.
Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA.
Mamm Genome. 2018 Feb;29(1-2):101-111. doi: 10.1007/s00335-018-9737-8. Epub 2018 Feb 16.
Identifying gene-environment interactions is a central challenge in the quest to understand susceptibility to complex, multi-factorial diseases. Developing an understanding of how inter-individual variability in inherited genetic variation alters the effects of environmental exposures will enhance our knowledge of disease mechanisms and improve our ability to predict disease and target interventions to high-risk sub-populations. Limited progress has been made identifying gene-environment interactions in the epidemiological setting using existing statistical approaches for genome-wide searches for interaction. In this paper, we describe a novel two-step approach using omics data to conduct genome-wide searches for gene-environment interactions. Using existing genome-wide SNP data from a large Bangladeshi cohort study specifically designed to assess the effect of arsenic exposure on health, we evaluated gene-arsenic interactions by first conducting genome-wide searches for SNPs that modify the effect of arsenic on molecular phenotypes (gene expression and DNA methylation features). Using this set of SNPs showing evidence of interaction with arsenic in relation to molecular phenotypes, we then tested SNP-arsenic interactions in relation to skin lesions, a hallmark characteristic of arsenic toxicity. With the emergence of additional omics data in the epidemiologic setting, our approach may have the potential to boost power for genome-wide interaction research, enabling the identification of interactions that will enhance our understanding of disease etiology and our ability to develop interventions targeted at susceptible sub-populations.
识别基因-环境相互作用是理解复杂多因素疾病易感性过程中的一项核心挑战。深入了解遗传变异中的个体间差异如何改变环境暴露的影响，将增进我们对疾病机制的认识，并提高我们预测疾病以及针对高危亚人群进行干预的能力。在流行病学环境中，使用现有的全基因组搜索相互作用的统计方法来识别基因-环境相互作用，进展有限。在本文中，我们描述了一种新颖的两步法，利用组学数据进行全基因组范围的基因-环境相互作用搜索。利用来自一项专门设计用于评估砷暴露对健康影响的大型孟加拉队列研究的现有全基因组单核苷酸多态性（SNP）数据，我们首先通过全基因组搜索来评估基因-砷相互作用，以寻找那些能改变砷对分子表型（基因表达和DNA甲基化特征）影响的SNP。利用这组显示出与砷在分子表型方面存在相互作用证据的SNP，我们随后测试了SNP-砷在皮肤病变方面的相互作用，皮肤病变是砷中毒的一个标志性特征。随着流行病学环境中更多组学数据的出现，我们的方法可能有潜力提高全基因组相互作用研究的效能，从而能够识别出那些将增进我们对疾病病因理解以及开发针对易感亚人群干预措施能力的相互作用。
