A Bayesian Hierarchical Model with SNP-level Functional Priors Applied to a Pathway-wide Association Study.
Tremendous effort has been put into study of the etiology of complex
diseases including the breast cancer, type 2 diabetes,
cardiovascular diseases, and prostate cancers. Despite large numbers of reported disease-associated loci,
few associated loci have been replicated, and some true associations
does not belong to the group of the most significant loci
reported to be associated. We built a Bayesian hierarchical model incorporated
with SNP-level functional data that can help identify associated SNPs in pathway-wide association studies.
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.
DepartmentComputational Biology and Bioinformatics
Bayesian Hierarchical Modeling
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Rights for Collection: Masters Theses