Understanding the Genetic Architecture of Gene Expression Regulation

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DESCRIPTION (provided by applicant): Gene expression is a fundamental determinant of phenotypic variation in humans and model organisms. Regulation of gene expression is known to have a substantial heritable component, and is believed to underly much of the genetic contribution to disease risk and other phenotypic variation. Thus, in order to understand how genetic variation affects phenotypic variation, it is important to understand how genetic variation affects gene expression levels. Although previous studies have identified individual regulatory variants, the overall genetic architecture of gene expression regulation is poorly understood. Genetic control of gene expression may include individual cis variants, individual trans variants, as well as epistatic interactions between networks of multiple cis and/or trans variants. In this proposal, we will apply population admixture, family structure and model organism approaches to understanding the contributions of these components to genetic heritability. We will also draw conclusions about long-range cis effects, cis-cis interactions, and shared genetic regulation across tissue types. Due to large differences in power to detect different effects, the approaches we propose have a greater chance of yielding unbiased conclusions about the genetic architecture of gene expression than the approach of counting the number of robustly identified associations in these categories. Our research will lead to a greater understanding of how genetic variation affects gene expression and disease risk. PUBLIC HEALTH RELEVANCE: Gene expression regulation refers to how our genetic code affects which genes are turned on and off, and is widely believed to explain much of the genetic contribution to disease risk. Gene expression regulation may be affected by genetic variation in the part of the genome close to the gene being turned on and off, or by genetic variation in other parts of the genome, or by combinations of these effects. In this proposal, we will analyze data from multiple populations and family cohorts to understand how these factors influence gene expression.