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dc.contributor.advisor Furey, Terrence en_US
dc.contributor.author Crosslin, David Russell en_US
dc.date.accessioned 2009-05-01T18:24:26Z
dc.date.available 2009-05-01T18:24:26Z
dc.date.issued 2009 en_US
dc.identifier.uri http://hdl.handle.net/10161/1100
dc.description Dissertation en_US
dc.description.abstract <p>Leukotrienes are arachidonic acid derivatives long known for their inflammatory properties and their involvement with a number of human diseases, most notably asthma. Recently, leukotriene-based inflammation has also been implicated in atherosclerosis: ALOX5AP and LTA4H, two genes in the leukotriene biosynthesis pathway, have been associated with various cardiovascular disease (CVD) phenotypes. To assess the role of the leukotriene pathway in CVD pathogenesis, we performed genetic association studies of ALOX5AP and LTA4H in a non-familial data set of early onset coronary artery disease. Our results support a modest role for the leukotriene pathway in atherosclerosis pathogenesis, reveal important genomic interactions within the pathway, and suggest the importance of using pathway-based modeling for evaluating the genomics of atherosclerosis susceptibility. Motivated by this need, we investigated the statistical properties of a class of matrix-based statistics to assess epistasis. We simulated multiple two-variant disease models with haplotypes to gain an understanding of pathway interactions in terms of correlation patterns. Our goal was to detect an interaction between multiple disease-causing variants by means of their linkage disequlibrium (LD) patterns with other haplotype markers. The simulated models can be summarized into three categories: 1. No epistasis in the presence of marginal effects and LD; 2. Epistasis in the presence of LD and no marginal effects; and 3. Epistasis in the presence marginal effects and LD. We then assessed previously introduced single-gene methods that compare whole matrices of Single Nucleotide Polymorphism (SNP) LD between two samples. These methods include comparing two sets of principal components, a sum-of-squared-differences comparing pairwise LD, and a contrast test that controls for background LD. We also considered a partial least-square (PLS) approach for modeling gene-gene interactions. Our results indicate that these measures can be used to assess epistasis as well as marginal effects under certain disease models. Understanding and quantifying whole-gene variation and association to disease using multiple SNPs remains a difficult task. Providing a single statistical measure per gene will facilitate combining multiple types of genomic data at a gene-level and will serve as an alternative approach to assess epistasis in genome-wide association studies. The matrix-based measures can also be used in pathway ascertainment tools that require scores on a gene-level.</p> en_US
dc.format.extent 1344721 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Biology, Biostatistics en_US
dc.subject Biology, Genetics en_US
dc.subject Biology, Bioinformatics en_US
dc.subject Cardiovascular disease (CVD) en_US
dc.subject Complex diseases en_US
dc.subject Epistasis en_US
dc.subject Gene en_US
dc.subject gene interaction en_US
dc.subject Genetic association studies en_US
dc.subject Linkage disequlibrium (LD) en_US
dc.title Characterization of Gene Interaction and Assessment of Ld Matrix Measures for the Analysis of Biological Pathway Association en_US
dc.type Dissertation en_US
dc.department Computational Biology and Bioinformatics en_US

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