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dc.contributor.advisor Iversen, Edwin S. en_US
dc.contributor.author Huang, Weizi en_US
dc.date.accessioned 2011-01-05T15:23:11Z
dc.date.available 2011-09-01T04:30:11Z
dc.date.issued 2010 en_US
dc.identifier.uri http://hdl.handle.net/10161/3058
dc.description Thesis en_US
dc.description.abstract <p>Tremendous effort has been put into study of the etiology of complex</p><p>diseases including the breast cancer, type 2 diabetes,</p><p>cardiovascular diseases, and prostate cancers. Despite large numbers of reported disease-associated loci,</p><p>few associated loci have been replicated, and some true associations</p><p>does not belong to the group of the most significant loci</p><p>reported to be associated. We built a Bayesian hierarchical model incorporated</p><p>with SNP-level functional data that can help identify associated SNPs in pathway-wide association studies.</p><p>We applied the model to an association study for the serous invasive ovarian cancer based on the DNA repair and apoptosis pathways. We found that using our model, blocks of SNPs located in regions enriched for missense SNPs or gene inversions were more likely to be identified as candidates of the association.</p> en_US
dc.subject Biology, Bioinformatics en_US
dc.subject Biology, Biostatistics en_US
dc.subject Association Study en_US
dc.subject Bayesian Hierarchical Modeling en_US
dc.subject Functional Annotation en_US
dc.subject MCMC en_US
dc.subject SNP en_US
dc.title A Bayesian Hierarchical Model with SNP-level Functional Priors Applied to a Pathway-wide Association Study. en_US
dc.type Thesis en_US
dc.department Computational Biology and Bioinformatics en_US
duke.embargo.months 12 en_US

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