Bayesian Approach for Nonlinear Dynamic System and Genome-Wide Association Study

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dc.contributor.advisor Sujit K. Ghosh, Committee Chair en_US
dc.contributor.advisor Jung-Ying Tzeng, Committee Co-Chair en_US Ouyang, Haojun en_US 2010-08-19T18:14:32Z 2010-08-19T18:14:32Z 2010-04-28 en_US
dc.identifier.other etd-04142009-123323 en_US
dc.description.abstract Genome-wide association studies (GWAS) have been widely used to identify single-nucleotide polymorphisms (SNPs) that are responsible for diseases. A challenging aspect of this study is to resolve the various issues related to multiple tests. We propose a new Bayesian method to measure statistical significance in these genome-wide studies based on the concept of false discovery rate (FDR). Our proposed method provides a convenient way to integrate prior knowledge obtained from external resources into current study. By controlling Bayesian positive FDR at a given level, the realized FDR is controlled. Our simulations show that the power can be substantially improved with correct prior information while the FDR is controlled at the desired level. When prior information is imprecise, our method can still improve the power of detecting signals, while keeping the FDR under control. The modified Bayesian method is applied to a GWAS for schizophrenia. Meta-analysis is another approach to utilize information from multiple sources by combining results from multiple independent studies. A major concern in carrying out meta-analysis involves the proper characterization of heterogeneity among population. To account for heterogeneity, the most commonly used approach is to implement a random-effects model, where the random-effects are assumed to be normally distributed with an unknown population mean and an unknown variance. We relax the normality assumption and show that a broad class of distributions can be approximated by a class of mixture distributions. The population mean and variance estimates based on the mixture density are then obtained by the EM algorithm. Our results show that the proposed method greatly improves the accuracy in estimating overall mean effect and heterogeneity variance in various realistic cases. We illustrate our method to a study on DRD2 gene in multiple association studies with schizophrenia. Dynamic system defined by ordinary differential equations is an important tool to modeling complicated biology system. To estimate parameters in the dynamic system which analytic, close form solution is not available and involving missing or censored data, we extend Bayesian Euler's Approximation method based on data augmentation algorithm. Our simulation study shown the method is robust in both cases. The proposed method is applied to analyze HIV viral load dataset, which enable us to retrieve information from the censored data. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject dynamic system en_US
dc.subject multiple testing en_US
dc.subject genome-wide association study en_US
dc.subject heterogeneity en_US
dc.subject EM algorithm en_US
dc.subject meta-analysis en_US
dc.subject false discovery rate en_US
dc.title Bayesian Approach for Nonlinear Dynamic System and Genome-Wide Association Study en_US PhD en_US dissertation en_US Bioinformatics en_US

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