Statistical Methods for Genome-Wide Association Studies of Gene Expression, with Applications to the Genetic Study of Psoriasis.

Abstract

Gene transcript levels can bridge genotypes and more complex phenotypes, including common human diseases and traits. Understanding the processes that regulate the expression of disease associated transcripts and, in parallel, understanding the impact of disease associated genetic variants on gene expression, could enhance our understanding of the biology of these complex traits. Advances in high-throughput gene expression profiling and genotyping technologies have made it possible to search for these connections on a genomic scale. My dissertation focuses on statistical methods for genome-wide studies that aim to identify genetic variants associated with gene expression levels. Such variants are called expression quantitative trait loci (eQTLs). In Chapter 1, I use two case studies to discuss how genome-wide association studies of gene expression have the potential to address some of the new challenges raised by current genetic studies. In Chapter 2, I describe a practical method to identify genetic variants that are associated with the levels of many transcripts. In Chapter 3, I propose a novel method for estimating the eQTL overlap between two tissues. In Chapter 4, I extend the method proposed in the previous chapter by removing the constraint on the sample-splitting strategy and use simulation studies to assess the performance of the method. In Chapter 5, I perform eQTL mapping in skin tissues from psoriatic patients and normal controls, and build a catalog of genetic variants influencing transcript levels in both normal and psoriatic skin. My work has the potential to lead to a better understanding of the mechanisms of gene regulation and a better dissection of the effects of genetic variants on complex phenotypes, such as many common diseases.