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Title page for ETD etd-12162010-145118


Type of Document Dissertation
Author Turner, Stephen Dale
URN etd-12162010-145118
Title Knowledge-Driven Genome-Wide Analysis of Multigenic Interactions Impacting HDL Cholesterol Level
Degree PhD
Department Human Genetics
Advisory Committee
Advisor Name Title
Dana Crawford Committee Chair
Erik Boczko Committee Member
Jonathan Haines Committee Member
Marylyn D. Ritchie Committee Member
Yu Shyr Committee Member
Keywords
  • Human genetics
  • bioinformatics
  • machine learning
  • neural networks
  • cardiovascular disease
  • complex disease
Date of Defense 2010-12-03
Availability unrestricted
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.
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