Identifying inherited genetic determinants of arsenic metabolism efficiency and their mechanisms

确定砷代谢效率的遗传决定因素及其机制

• 批准号: 10157648
• 负责人: Meytal Batya Chernoff
• 金额: $ 5.1万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10157648
• 关键词: Address; Affect; Arsenic; Arsenites; Automobile Driving; Bangladesh; Bangladeshi; Biological; Carcinogens; Cardiovascular Diseases; Cells; Chronic; Code; Consumption; DNA Methylation; Data; Data Sources; Diabetes Mellitus; Disease; Enzymes; Excretory function; Exposure to; Future; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Determinism; Genetic Predisposition to Disease; Genetic Variation; Genomics; Genotype; Genotype-Tissue Expression Project; Health; Heart; Individual; Inherited; Knowledge; Linkage Disequilibrium; Longitudinal Studies; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of urinary bladder; Measures; Metabolism; Methylation; Methyltransferase; New Hampshire; Pattern; Population; Positioning Attribute; Predisposition; Pregnancy Complications; Probability; Public Health; Quantitative Trait Loci; Reaction; Regulation; Risk; Sample Size; Series; Skin Cancer; Skin Squamous Cell; Squamous cell carcinoma; Subgroup; Testing; Tissues; Toxic effect; Urine; Variant; Work; cancer type; causal variant; cohort; contaminated drinking water; data integration; drinking water; epidemiology study; exposed human population; follow-up; gene environment interaction; genetic variant; genome editing; genome wide association study; health data; high risk; improved; inter-individual variation; nervous system disorder; skin lesion; targeted sequencing; urinary

PROJECT SUMMARY/ABSTRACT This project seeks to identify the causal variants and mechanisms underlying the association between inherited genetic variants in the 10q24.32 region and arsenic metabolism efficiency (AME). Exposure to arsenic- contaminated drinking water impacts approximately 140 million individuals across the globe including 13 million in the U.S and 56 million in Bangladesh. This exposure increases the risk of multiple cancers as well as cardiovascular and neurologic diseases, pregnancy complications, and diabetes. There is considerable inter- individual variation in AME with lower AME associated with a higher risk of arsenic toxicity. A genome-wide association study (GWAS) of AME in a Bangladeshi population conducted by our group identified multiple genetic variants in the 10q24.32 region independently associated with AME (measured as relative concentrations of arsenic metabolites in urine). These variants reside in close proximity to AS3MT, a gene coding for arsenite methyltransferase, a key enzyme involved in arsenic metabolism. However, neither the causal variants in this region nor the mechanisms underlying their effects are known. These gaps in our knowledge are likely due to the small sample size, incomplete genetic data, and single-population focus of many previous studies, as well as a lack of integration of data sources on the potential functional effects of associated variants. To address the limitations of prior studies, we will analyze dense genotyping data from targeted sequencing of the 10q24.32 region for ~4100 individuals from 3 arsenic-exposed cohorts: the Health Effects of Arsenic Longitudinal Study, the Strong Heart Study, and the New Hampshire Skin Cancer Study of Squamous Cell Carcinoma. First, we will use association analysis approaches to identify potential causal variants underlying observed associations between AME and inherited variation in the 10q24.32 region. Our use of targeted sequencing data from three distinct ancestry groups will enable us to leverage differences in their patterns of linkage disequilibrium to identify shared causal variants through Bayesian fine-mapping. Second, we will identify potential mechanisms by which causal variants impact arsenic metabolism efficiency by integrating our association results (from Aim 1) with results from analyses of expression and methylation quantitative trait loci (eQTL, meQTL) in multiple tissue types as well as with expression and methylation data from a Bangladeshi population. Finally, we will determine how AS3MT genotype modifies the association between arsenic-exposure and risk for arsenic-related diseases (i.e., arsenic-induced skin lesions and skin cancer). Our work will be the first to use targeted sequencing to identify potential causal variants and mechanisms underlying the association between the 10q24.32 region and AME. Our results will provide a more complete picture of genetic susceptibility to arsenic toxicity, enabling us to better identify individuals at high risk for arsenic toxicities and improve the focus of public health efforts in arsenic-exposed populations.

项目摘要/摘要 该项目旨在确定继承之间关联的基础的因果变体和机制 10q24.32区域和砷代谢效率（AME）中的遗传变异。暴露于砷 受污染的饮用水影响全球约1.4亿人，其中包括1300万 在美国，在孟加拉国5600万。这种暴露增加了多种癌症的风险以及 心血管和神经系统疾病，妊娠并发症和糖尿病。有很大的间 AME的个体变异与砷毒性的较高风险有关。全基因组 在我们的小组进行的孟加拉国人口中，AME的协会研究（GWAS）确定了多个 10q24.32区域中与AME独立相关的遗传变异 尿液中砷代谢物的浓度）。这些变体与AS3MT紧邻（一个基因）密切相处 编码砷甲基转移酶，这是一种参与砷代谢的关键酶。但是，都不是 该区域的因果变异或其作用的基础机制是已知的。这些差距 知识可能是由于样本量较小，遗传数据不完整以及单人群的重点 许多先前的研究以及缺乏有关数据源的整合在 相关变体。为了解决先前研究的局限性，我们将分析来自 来自3个暴露砷队列的〜4100个个人的10q24.32区域的靶向测序：健康 砷纵向研究，强大的心脏研究和新罕布什尔州皮肤癌研究的影响 鳞状细胞癌。首先，我们将使用关联分析方法来识别潜在的因果关系 在10q24.32区域中AME与遗传变异之间观察到的相关性的变体。我们的 使用来自三个不同祖先的目标测序数据的使用将使我们能够利用差异 他们的连锁模式是不平衡的，无法通过贝叶斯细图来识别共享的因果变体。 其次，我们将确定因果变异影响砷代谢效率的潜在机制 通过将我们的关联结果（来自AIM 1）与表达和甲基化分析的结果整合在一起 多种组织类型的定量性状基因座（EQTL，MEQTL）以及表达和甲基化数据 来自孟加拉国人口。最后，我们将确定AS3MT基因型如何修饰关联 在砷暴露与砷相关疾病的风险之间（即，砷诱导的皮肤病变和皮肤 癌症）。我们的工作将是第一个使用靶向测序来识别潜在的因果变体和 10q24.32区域与AME之间关联之间的机制。我们的结果将提供 对砷毒性的遗传敏感性的更完整图片，使我们能够更好地识别个人 砷毒性的高风险，并改善了暴露于砷的人群中公共卫生努力的重点。