Title: Implications of population structure and regulatory variation for genetic association studies
Description: Complex traits and common diseases, such as obesity and type 2 diabetes, are influenced by multiple genetic and environmental factors. Association analysis is a powerful method to discover genetic variants that influence complex traits. In a case-control association study, allele or genotype frequencies are compared between subjects with a disease or high trait values (the cases) and subjects that are healthy or have low trait values (the controls). Case-control association studies are potentially susceptible to population stratification when phenotype and ancestry are correlated and ancestry differs between cases and controls. In this dissertation, I show that stratification can occur in an apparently "homogeneous" European American population. This result highlights that stratification is necessary for the design of association studies, including studies in populations without a strong recent history of admixture. Another issue in the design of association studies is whether regulatory, non-coding variants influence complex traits. In my dissertation, I show that a substantial number of genes have genetically determined gene expression differences in mice. These differences can be tissue-specific and transcript-specific, and genes can be more susceptible to expression differences across species. These data indicate that non-coding variations should be considered in association studies and tissue type should be considered when interpreting the effects of non-coding variants on gene expression. Finally, I present association studies of genes in the Neuropeptide Y pathway, which is involved in appetite control. Consistent with the importance of testing non-coding variation, I have found a modest association between variation upstream of NPY2R and type 2 diabetes in men.

These data suggest that this gene should be considered a candidate gene for type 2 diabetes. Taken together, the results presented in this dissertation emphasize the importance of considering population stratification and regulatory variation in the design of genetic association studies, and these findings have implications for future genetic studies of common diseases and complex traits.