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dc.contributor.authorShen, Tongen_US
dc.contributor.otherDepartment of Applied Mathematics and Statisticsen_US
dc.date.accessioned2012-05-17T12:22:17Z
dc.date.available2012-05-17T12:22:17Z
dc.date.issued1-Aug-11en_US
dc.date.submittedAug-11en_US
dc.identifierShen_grad.sunysb_0771E_10609.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1951/56116
dc.description.abstractIn a genome-wide association study (GWAS) for a longitudinal quantitative trait, the trait is measured at multiple time points. GWAS is the examination of marker loci to identify loci associated with the progression of the quantitative trait. I use two models, a single locus model and a multi locus model, to simulate a longitudinal quantitative trait. I use the growth mixture modeling (GMM) method to assign each member of a sample into one of a small number of trajectory groups. The clinically important trajectory group is the one with fastest progression. The Bayesian posterior probability (BPP) of being in the clinically important group is used as a quantitative trait. I test for association with marker loci. I also use the modal BPP in the association test and perform a case/control association analysis. Finally, I compare these methods with the contingency table method. I evaluate the empirical type I error and empirical power using null simulations and power simulations. The principal results are that: (1) Both the BPP method and modal BPP method maintain the correct type I error rate, but the empirical null rejection rate is increasing less than the nominal rate as the nominal type I error rate increases. (2) Both the BPP and modal BPP methods have very high power to detect the disease locus in the single locus model. (3) Both the BPP and modal BPP methods have significant power to detect the disease loci in the multi locus model. The powers of detecting a specific locus are proportional to minor allele frequency (MAF) of loci. (4) Both the BPP and modal BPP methods are better than the contingency table method with regard to the empirical power and the power of the BPP is essentially equal to the power of the modal BPP.en_US
dc.description.sponsorshipStony Brook University Libraries. SBU Graduate School in Department of Applied Mathematics and Statistics. Lawrence Martin (Dean of Graduate School).en_US
dc.formatElectronic Resourceen_US
dc.language.isoen_USen_US
dc.publisherThe Graduate School, Stony Brook University: Stony Brook, NY.en_US
dc.subject.lcshStatisticsen_US
dc.titleUsing Growth Mixture Modeling to identify loci associated with the progression of diseaseen_US
dc.typeDissertationen_US
dc.description.advisorAdvisor(s): Stephen J. Finch. Committee Member(s): Nancy R. Mendell; Wei Zhu; Derek Gordon.en_US
dc.mimetypeApplication/PDFen_US


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