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Localized Correlation Analysis and Genetic Association with Cardiovascular Disease

dc.contributor.advisor Hauser, Elizabeth R
dc.contributor.author Ou, Chern-Han
dc.date.accessioned 2010-05-13T17:49:41Z
dc.date.available 2010-05-13T17:49:41Z
dc.date.issued 2010
dc.identifier.uri https://hdl.handle.net/10161/2501
dc.description.abstract <p>Variations in gene expression are potential risk factors for atherosclerosis, which is one of the most common forms of cardiovascular disease. We performed a localized Pearson correlation test in 372 individuals from seven datasets relevant to cardiovascular disease studies. The genomes of samples were separated into 20Mb windows and correlation tests were performed locally in these windows. The localized Pearson correlation test found chr3:115Mb&ndash;135Mb was tightly connected by significantly high proportion of highly correlated pairs (<italic>P</italic> value = 0.0266 with Z-test). LSAMP, GATA2, MBD4, and other genes in the region were considered associated with cardiovascular disease because they were involved in highly correlated pairs. Furthermore, these genes were also associated with cardiovascular disease by having significantly high SNP odds ratios (<italic>P</italic> value < 0.1) between patients and controls in an independent Duke University Medical Center database. In addition, a permutation test was performed to demonstrate that chr3:115Mb&ndash;135Mb might underlie the regulation of cardiovascular disease. Finally, the localized Pearson correlation test also found some other regions that could be associated with cardiovascular disease.</p>
dc.format.extent 8483044 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Biology, Bioinformatics
dc.subject Biology, Genetics
dc.subject Biology, Biostatistics
dc.subject Cardiovascular Disease
dc.subject Correlation
dc.subject Gene expression
dc.subject Genetics
dc.subject Genomics
dc.title Localized Correlation Analysis and Genetic Association with Cardiovascular Disease
dc.type Master's thesis
dc.department Computational Biology and Bioinformatics


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