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Document Type:Latin Dissertation
Language of Document:English
Record Number:53001
Doc. No:TL22955
Call number:‭3257221‬
Main Entry:Volodymyr Minin
Title & Author:Exploring evolutionary heterogeneity with change -point models, Gaussian Markov random fields, and Markov chain induced counting processesVolodymyr Minin
College:University of California, Los Angeles
Date:2007
Degree:Ph.D.
student score:2007
Page No:195
Abstract: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.
Subject:Biological sciences; Change-point models; Induced counting; Markov random fields; Biostatistics; Genetics; 0369:Genetics; 0308:Biostatistics
Added Entry:M. A. Suchard
Added Entry:University of California, Los Angeles