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Computational Selection and Prioritization of Disease Candidate Genes

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2008, PhD, University of Cincinnati, Engineering : Biomedical Engineering.

Identifying causal genes underlying susceptibility to human disease is a problem of primary importance in post-genomic era and current biomedical research. Recently, there has been a paradigm shift of such gene-discovery efforts from rare, monogenic conditions to common "oligogenic" or "multifactorial" conditions such as asthma, diabetes, cancers and neurological disorders. These conditions are referred as multifactorial because, susceptibility to these diseases is attributed to the combinatorial effects of genetic variation at a number of different genes and their interaction with relevant environmental exposures. The expectation is that identification and characterization of the causal genes implicated in the inherited component of disease susceptibility will lead to substantial advances in our understanding of disease. These advances in turn can lead to improvements in diagnostic accuracy, prognostic precision, the range and targeting of available therapeutic options and ultimately realize the promise of personalized or "tailor-made" medicine. The objective of my thesis therefore is to design, develop, and validate computational approaches for identification and prioritization of these causal genes.

The first approach tests the hypothesis that the majority of genes that impact or cause disease share membership in any of several functional relationships. We use a p-value-based meta-analysis method to prioritize the candidate genes based on functional annotation. For the very first time, we use and demonstrate, the utility of mouse phenotype annotations in human disease gene prioritization. Since this approach is limited to only genes with functional annotation, and because many human genes are yet to be functionally classified, we have developed another approach that is independent of gene functional annotations. We implemented a set of new algorithms to prioritize genes based on protein-protein interaction networks. Large scale cross-validation were performed for comparison and evaluation of the methods, and to determine the associated parameters. Our results demonstrate that the functional annotation-based method performs better than other approaches. Although the performance of the network-based method was not as good as functional annotation-based method, it is much simpler to implement, apply, and execute. The best performance was however achieved, as demonstrated through asthma test case, by combining the results from the two methods.

Bruce Aronow (Committee Chair)
Anil Jegga (Committee Co-Chair)
Marepalli Rao (Committee Member)
125 p.

Recommended Citations

Citations

  • Chen, J. (2008). Computational Selection and Prioritization of Disease Candidate Genes [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1211228557

    APA Style (7th edition)

  • Chen, Jing. Computational Selection and Prioritization of Disease Candidate Genes. 2008. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1211228557.

    MLA Style (8th edition)

  • Chen, Jing. "Computational Selection and Prioritization of Disease Candidate Genes." Doctoral dissertation, University of Cincinnati, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1211228557

    Chicago Manual of Style (17th edition)