Date

May 2011

Document Type

Dissertation

Degree Name

Ph.D.

Department

Dept. of Medical Informatics and Clinical Epidemiology

Institution

Oregon Health & Science University

Abstract

Enormous data collection efforts and improvements in technology have made large-scale genome-wide association studies (GWAS) a promising approach to better understanding the genetics of common, complex diseases. However, the limited success of these studies so far suggests that genetic susceptibility may be due to a combined effect of multiple genetic variants (or interactions between variants), and that there may be a significant amount of genetic heterogeneity among those affected with complex diseases. It is clear that new data analysis methods are needed to address these hypotheses. Using data from the NIMH-sponsored Bipolar Genome Study, this project attempted to discover groups of SNPs that are jointly associated with the disease, thereby explaining a greater portion of disease susceptibility than can be achieved by examining SNPs individually. A machine-learning technique, known as a genetic algorithm, was used to search for these multi-locus associations, and was guided by a va

Identifier

doi:10.6083/M4PC30CW

School

School of Medicine

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