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Memorial University - Electronic Theses and Dissertations 4
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Document Description
TitleBayesian analysis of mixture models with application to genetic linkage
AuthorFang, Fang, 1983-
DescriptionThesis (M.A.S)--Memorial University of Newfoundland, 2010. Mathematics and Statistics
Date2010
Paginationviii, 52 leaves
SubjectBayesian statistical decision theory; Linkage (Genetics)--Mathematical models; Markov processes--Numerical solutions; Monte Carlo method;
DegreeM.A.S
Degree GrantorMemorial University of Newfoundland. Dept. of Mathematics and Statistics
DisciplineMathematics and Statistics
LanguageEng
NotesIncludes bibliographical references (leaves 47-52)
AbstractThrough an application to genetic linkage analysis, this project describes how the Bayesian approach can be used for the mixture model with an unknown number of components. Genetic linkage analysis based on a complex model can be difficult to manage when a large number of markers loci and/or large pedigrees are involved, due to computation limitations. However, Markov chain Monte Carlo (MCMC) schemes are one alternative, utilizing a reversible jump steps that allow change on the dimension of parameter space. Thus, the MCMC samplers with a different numbers of quantitative trait loci based on complex large pedigrees can be obtained using reversible jump MCMC methodology. The application of the MCMC scheme is illustrated with a case study of genetic linkage to hypercalciuria. This analysis report found strong evidence for linkage of hypercalciuria to calibrated estimates of Bayes factors, the so-called L-Scores. To my knowledge this is the first time that urinary calcium excretion has been clearly linked to a narrow region of the genome. Nevertheless, further study is needed to confirm this finding.
TypeText
Resource TypeElectronic thesis or dissertation
FormatImage/jpeg; Application/pdf
SourcePaper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifiera3475041
RightsThe author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
CollectionElectronic Theses and Dissertations
Scanning StatusCompleted
PDF File(6.01 MB) -- http://collections.mun.ca/PDFs/theses/Fang_Fang.pdf
CONTENTdm file name31693.cpd