Identifying inherited genetic determinants of arsenic metabolism efficiency and their mechanisms

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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.