Knowledge-Driven Genome-Wide Analysis of Multigenic Interactions Impacting HDL Cholesterol Level

Abstract

Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability. I begin this dissertation with a review of study designs and analytical methods for genetic association studies. Next, I characterize and present a series of improvements in using grammatical evolution to train neural networks for discovering gene-gene interactions in disease gene association studies. I then present an analysis of cis-epistasis - nonadditive multi-SNP interactions that influence gene expression. Next, I present a cohesive set of quality control procedures to be used for genome-wide association studies. Finally, I conclude by presenting results from a knowledge-driven gene-gene interaction analysis of HDL level in two clinical practice-based population biobanks.