Identifying arsenic susceptibility variants using a functional screening approach

描述（由申请人提供）：确定基因与环境（GxE）的相互作用是寻求了解复杂的多因素疾病易感性的核心挑战。 了解遗传变异如何改变环境暴露的影响（反之亦然）将增强我们对疾病机制的了解，提高我们预测疾病和针对高危亚群进行干预的能力。不幸的是，在流行病学背景下确定GxE相互作用的进展有限。大多数全基因组相互作用（GWI）研究仅依赖于相互作用的统计证据，并且通常可能无法检测到适度的相互作用。在这个建议中，我们描述了一种新的两步“GxE-omic”的方法，解决了标准GWI方法的局限性。我们将应用我们的方法，利用现有的遗传和分子数据，从一个大型孟加拉国队列研究，专门设计来评估砷暴露对健康的影响。我们建议通过首先在全基因组范围内搜索改变砷对分子（“组学”）表型的影响的SNP来搜索基因-砷相互作用（即，基因表达和DNA甲基化表型，全基因组测量）（目的1）。使用这组与砷相互作用以影响分子表型的SNP，然后我们将测试与砷相关的健康状况有关的SNP-砷相互作用：皮肤病变状态和糖尿病相关表型（目的2）。作为第二个目标，我们将尝试通过对我们选择的临床表型进行常规GWI分析来鉴定与砷相互作用以影响疾病但在Aim 1“GxE-omic”筛选中未被选择的SNP，使用已建立的“两步”统计方法，利用病例和对照中基因-环境相关性以及边缘基因-疾病关联的信息。通过使用高质量的砷暴露措施，并限制分析与砷相互作用的可能性增强的SNP，我们很有可能克服标准GWI方法的局限性。我们的团队非常适合实现这些目标，因为我们已经对砷暴露的健康影响和砷毒性的遗传易感性进行了广泛的研究，并在环境流行病学，统计遗传学和分子基因组学方面拥有丰富的经验。我们相信，将GxE研究的重点从不可知的全基因组相互作用测试转移到了解遗传变异如何影响人类对分子水平暴露的反应是很有希望的。我们的方法具有很高的潜力，以提高权力的GWI研究，使识别的相互作用，这将提高我们对疾病病因的理解和我们的能力，制定针对易感亚群的干预措施。此外，本文所述的方法可能用于研究我们正在进行的纵向研究中各种暴露和疾病结局的GxE相互作用。

DESCRIPTION (provided by applicant): Identifying gene-by-environment (GxE) interactions is a central challenge in the quest to understand susceptibility to complex, multi-factorial diseases. Developing an understanding of how genetic variation alters the effects of environmental exposures (and vice versa) will enhance our knowledge of disease mechanisms and improve our ability to predict disease and target interventions to high-risk sub-populations. Unfortunately limited progress has been made identifying GxE interactions in the epidemiological setting. Most genome-wide interaction (GWI) studies rely on statistical evidence of interaction alone and are often likely to be underpowered to detect modest interactions. In this proposal, we describe a novel two-step "GxE-omic" approach that addresses the limitations of standard GWI approaches. We will apply our approach using existing genetic and molecular data from a large Bangladeshi cohort study specifically designed to assess the effect of arsenic exposure on health. We propose to search for gene-arsenic interactions by first conducting a genome-wide search for SNPs that modify the effect of arsenic on molecular ("omic") phenotypes (i.e., gene expression and DNA methylation phenotypes, measured genome-wide) (Aim 1). Using this set of SNPs that interact with arsenic to influence molecular phenotypes, we will then test SNP-arsenic interactions in relation to arsenic-related health conditions: skin lesion status and diabetes-related phenotypes (Aim 2). As a secondary aim, we will attempt to identify SNPs that interact with arsenic to influence disease but were not selected in the Aim 1 "GxE-omic" screen by conducting conventional GWI analyses of our selected clinical phenotypes, using established "two-step" statistical approaches that leverage information on gene-environment correlation in cases and controls as well as marginal gene-disease associations. By using high-quality measures of arsenic exposure and restricting analyses to SNPs with enhanced probability of interaction with arsenic, we are highly likely to overcome the limitations of standard GWI approaches. Our team is ideally positioned to accomplish these aims, as we have conducted extensive research on the health effects of arsenic exposure and genetic susceptibility to arsenic toxicity and have extensive experience in environmental epidemiology, statistical genetics, and molecular genomics. We believe there is great promise in shifting the focus of GxE research from agnostic genome-wide interaction testing to understanding how genetic variants influence humans' response to an exposure at the molecular level. Our approach has very high potential to boost power for GWI 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. Moreover, the approach described here could potentially be used to investigate GxE interactions for a wide array of exposures and disease outcomes within our ongoing longitudinal study.