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" Exploring evolutionary heterogeneity with change -point models, Gaussian Markov random fields, and Markov chain induced counting processes "
Volodymyr Minin
M. A. Suchard
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Latin Dissertation
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English
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| Record Number
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53001
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TL22955
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3257221
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| Main Entry
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Volodymyr Minin
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| Title & Author
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Exploring evolutionary heterogeneity with change -point models, Gaussian Markov random fields, and Markov chain induced counting processes\ Volodymyr Minin
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| College
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University of California, Los Angeles
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| Date
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2007
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| Degree
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Ph.D.
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2007
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| Page No
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195
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| Abstract
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Signatures of spatial variation, left by evolutionary processes in genomic sequences, provide important information about the function and structure of genomic regions. I discuss statistical methods for detection of such signatures in a Bayesian framework. I start with phylogenetic analysis of recombination. I present a recombination detection method that simultaneously incorporates discrepancies in phylogenies, caused by recombination, and spatial variation in evolutionary pressure across the alignment using a dual multiple change-point (DMCP) model. Next, I turn to mapping recombination hot-spots. Based on the DMCP model, I build a hierarchical framework for simultaneous inference of break-point locations and spatial variation in recombination frequency from multiple putative recombinant sequences. To overcome the sparseness of break-point data, dictated by the modest number of available recombinant sequences, I a priori impose a biologically relevant correlation structure on recombination location log-odds via a Gaussian Markov random field. Applied to HIV sequences, this approach reveals a previously unknown recombination hot-spot. I show that GMRF smoothing can also be successfully combined with Kingman's coalescent to estimate temporal variation of the population demographic: history. GMRF temporal smoothing does not require strong prior decisions and is robust to prior perturbations. I apply GMRF smoothing to hepatitis C sequences, contemporaneously sampled in Egypt, and human influenza A hemagglutinin sequences, serially sampled throughout three flu seasons. I conclude with posterior predictive model diagnostics for locating spatial patterns of variation in genomic sequences. The evolutionary counting processes that keep track of a priori labeled mutations provide very useful discrepancy measures for detecting model inadequacies. I take an algorithmic probability approach that allows for an exact and efficient computation of certain properties of evolutionary counting processes. I demonstrate that these properties allow detection of periodic patterns of mutation rate variation in nucleotide sequence alignments.
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| Subject
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Biological sciences; Change-point models; Induced counting; Markov random fields; Biostatistics; Genetics; 0369:Genetics; 0308:Biostatistics
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M. A. Suchard
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University of California, Los Angeles
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