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.
This project aims to identify genetic variants that interact with arsenic exposure in relation to premalignant skin lesions and diabetes mellitus, using a novel 'GxE-omic' screening approach. The results of this work may help develop an understanding of how genetic variation alters the effects of arsenic exposure, provide insights for underlying disease mechanisms, and improve our ability to predict disease and target interventions to high-risk sub-populations.
