Although results of many genetic association studies have identified numerous gene variants involved in disease risk, recognition of the complex interaction between genome and environment is now more common. Such gene-environment (GxE) interactions show allele-specific alteration of disease risk and likely often act by affecting gene expression in response to key environmental factors (EF) such as diet, exercise and alcohol and tobacco use. Hence, this study's short- term goal is to use bioinformatics to prioritize genetic variants with a strong likelihood of responding to dietary components, physical activity, or alcohol use in an allele-specific manner based on analysis of gene expression data and gene/protein interaction networks.
Three specific aims are proposed: One, identify putative GxE interaction SNPs by merging genes with published expression QTL with genes showing consistent altered expression in published experiments centered on specific environmental challenges, e.g. high-fat diet or caloric restriction. Two, identify genes with strong likelihood to exhibit GxE interactions by building gene/protein networks seeded by genes harboring SNPs directing allele- specific interactions to important phenotypes or EFs: diet, exercise, or alcohol or smoking use. Three, test for actual GxE interactions in two deeply phenotyped populations (Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) and Framingham Heart (FHS)) using genes/SNPs prioritized in Aims 1 &2. While GxE interactions are known and their role in disease risk is accepted as more commonplace, methods are lacking for rapid detection across a wide range of common environmental exposures. The work proposed here is significant because it describes and assesses two methods for quick and efficient prioritization of genetic variants with high likelihood of partaking in GxE interactions relevant to heart disease, diabetes, hypertension and obesity. Adoption of genomics data to predict novel GxEs based on computational approaches is lacking. Thus, two aspects that, in our opinion, qualify this proposal as innovative are its application of systems biology with gene networks and mining of gene expression data to identify genes most responsive to a given EF, where variants of those genes are likely GxE participants. This innovation arises from leveraging gene behavior (expression changes after EF challenge or interacting partners in a network) filtered through genetic variants (eQTL, GxE- based networks) to prioritize SNPs for the GxE interaction test. This proposal will use integrated genomics methodology to identify putative GxE variants, which will be based on merging large, genome-wide datasets with subsequent filtering to identify the genes with the most/best attributes. In this case, eQTL genes give a genetic context to active genes and genes with consistent mRNA changes are those responding to an environmental cue while elements within the EF-specific networks become candidates for further analysis and bottlenecks are critical regulators of information flow. The proposed research will be performed within our group of scientists who are skilled in computational biology, human population genetics and statistics and are leaders in the field of nutrigenomics at a world-renown research institute. Also, we have access to two key populations deeply phenotyped for both clinical measures of health status and lifestyle choices of diet, exercise and alcohol/tobacco use.
This study proposes to identify genetic variants, mainly single nucleotide polymorphisms that are likely to participate in gene-environment interactions for phenotypes relevant to metabolic syndrome: blood lipids, blood pressure, obesity (body mass index) and plasma glucose and insulin levels. This study, of original and novel design, will use expression QTL SNPs and gene expression changes induced by EF exposure coupled with gene networks built with genes participating in specific types of published GxE interactions in order to identify new putative GxE SNPs. Those SNPs will be tested with genotyping data available from two deeply phenotyped populations. This project will provide techniques enabling our understanding of how widespread afflictions such as cardiovascular disease, type 2 diabetes and hypertension/stroke are and to what extent the genetic risk of these sicknesses is modulated by environmental factors. In essence, this study will help to define how the genome senses and responds to diet, exercise and alcohol/tobacco use.
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