A Bayesian Hierarchical Model with SNP-level Functional Priors Applied to a Pathway-wide Association Study.
| dc.contributor.advisor | Iversen, Edwin S | |
| dc.contributor.author | Huang, Weizi | |
| dc.date.accessioned | 2011-01-05T15:23:11Z | |
| dc.date.available | 2011-09-01T04:30:11Z | |
| dc.date.issued | 2010 | |
| dc.department | Computational Biology and Bioinformatics | |
| dc.description.abstract | 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. | |
| dc.identifier.uri | ||
| dc.subject | Biology, Bioinformatics | |
| dc.subject | Biology, Biostatistics | |
| dc.subject | Association Study | |
| dc.subject | Bayesian hierarchical modeling | |
| dc.subject | Functional Annotation | |
| dc.subject | MCMC | |
| dc.subject | SNP | |
| dc.title | A Bayesian Hierarchical Model with SNP-level Functional Priors Applied to a Pathway-wide Association Study. | |
| dc.type | Master's thesis | |
| duke.embargo.months | 12 |