Wang, Wen2017-11-272017-11-272017-08https://hdl.handle.net/11299/191394University of Minnesota Ph.D. dissertation. 2017. Major: Computer Science. Advisor: Chad Myers. 1 computer file (PDF); 181 pages + 1 zip file of supplementary tables.Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, the global genetic networks mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. We examined BridGE approach with seven different diseases, and were able to discover significant interactions in six of them including Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data. An application of BridGE with a focus on breast cancer was also extensively explored. We applied the BridGE method to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests or single pathway enrichment analyses that do not consider SNP-SNP interactions. Finally, we describe an application of the BridGE framework to test a specific hypothesis derived from studies of genetic interactions in yeast, which found that the proteasome complex was a genetic interaction hub. Given that proteasome function is highly conserved between yeast and humans, we predicted that natural variation in the homologous human proteasome genes would be involved in a number of disease-modifying genetic interactions. Using BridGE, we evaluated genetic interactions across seven different diseases, and indeed found that the proteasome pathway was the top positive interaction hub among ~800 pathways examined. Overall, this thesis demonstrates the potential for novel computational approaches to translate systems-level insights across species to better elucidate the genetic basis of human disease.enThesis or Dissertation