Identifying arsenic susceptibility variants using a functional screening approach

使用功能筛选方法识别砷敏感性变异

• 批准号: 8989537
• 负责人: Maria Argos
• 金额: $ 15.85万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8989537
• 关键词: Address; Affect; Arsenic; Arsenicals; Bangladeshi; Biological Markers; Cohort Studies; Complex; DNA Methylation; Data; Diabetes Mellitus; Disease; Disease Outcome; Environment; Environmental Epidemiology; Environmental Exposure; Epidemiology; Etiology; Exposure to; Gene Expression; Genes; Genetic Predisposition to Disease; Genetic Variation; Genomics; Genotype; Glycosylated Hemoglobin; Glycosylated hemoglobin A; Health; Human; Intervention; Knowledge; Linear Models; Longitudinal Studies; Measures; Methylation; Molecular; Molecular Genetics; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Participant; Phenotype; Population; Positioning Attribute; Predisposition; Premalignant; Probability; Research; Risk; Sample Size; Site; Step Tests; Susceptibility Gene; Testing; Toxic effect; Transcript; Variant; Work; case control; clinical phenotype; design; disease phenotype; disorder risk; epigenome; experience; gene environment interaction; genetic variant; genome wide association study; genome-wide; genomic data; high risk; improved; insight; molecular phenotype; novel; response; screening; skin lesion

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.

描述（由申请人提供）：识别基因与环境（GxE）的相互作用是了解复杂、多因素疾病易感性的一个核心挑战。 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，并提高我们预测疾病和针对高风险亚人群进行针对性干预的能力。不幸的是，在流行病学环境中识别 GxE 相互作用方面取得的进展有限。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，并且通常可能不足以检测适度的相互作用。在本提案中，我们描述了一种新颖的两步“GxE-omic”方法，该方法解决了标准 GWI 方法的局限性。我们将利用孟加拉国一项大型队列研究的现有遗传和分子数据来应用我们的方法，该研究专门设计用于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（即基因表达和 DNA 甲基化表型，在全基因组范围内测量）的影响的 SNP 来寻找基因-砷相互作用。 1).利用这组与砷相互作用影响分子表型的 SNP，我们将测试 SNP-砷相互作用与砷相关健康状况的关系：皮肤病变状态和糖尿病相关表型（目标 2）。作为次要目标，我们将尝试通过使用已建立的“两步法”对我们选定的临床表型进行常规 GWI 分析来识别与砷相互作用以影响疾病但在目标 1“GxE-omic”筛选中未选择的 SNP利用病例和对照中基因-环境相关性以及边缘基因-疾病关联信息的统计方法。通过使用高质量的砷暴露测量方法并限制对与砷相互作用的可能性增强的 SNP 进行分析，我们很有可能克服标准 GWI 方法的局限性。我们的团队非常有能力实现这些目标，因为我们对砷暴露对健康的影响和砷毒性的遗传易感性进行了广泛的研究，并且在环境流行病学、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有很大的希望。我们的方法具有很大的潜力来增强 GWI 研究的力量，能够识别相互作用，从而增强我们对疾病病因学的理解以及我们针对易感亚群制定干预措施的能力。此外，本文描述的方法有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果的 GxE 相互作用。