Title page for ETD etd-07072004-094725

Type of Document Master's Thesis
Author Krishnan, Neeraja M
Author's Email Address nkrish2@lsu.edu
URN etd-07072004-094725
Title Phylogenetic Influence of Complex, Evolutionary Models: A Bayesian Approach
Degree Master of Science (M.S.)
Department Biochemistry (Biological Sciences)
Advisory Committee
Advisor Name Title
David D. Pollock Committee Chair
Donald H. Kraft Committee Member
Marcia E. Newcomer Committee Member
Michael E. Hellberg Committee Member
  • Markov chain Monte Carlo
  • site-specific models
  • ancestral reconstruction
  • phylogeny
  • substitution matrices
  • mitochondria
  • Bayesian
Date of Defense 2004-06-18
Availability unrestricted
Molecular evolution recovers the history of living species by comparing genetic information, exploring genome structure and function from an evolutionary perspective. Here we infer substitution rates and ancestral reconstructions, to better understand mutation responses to some known biochemical phenomena. Mutation processes are commonly inferred using parsimony, maximum likelihood and Bayesian. Parsimony is not explicitly model-based, and is statistically biased due to unrealistic assumptions. The model-based maximum likelihood approaches become computationally inefficient while analyzing large or high-dimensional datasets, leaving little opportunities to incorporate complex evolutionary models. We implemented a posterior probability (Bayesian) approach that evaluates evolutionary models, applying it to primate mitochondrial genomes. The species nucleotide sequence data were augmented with ancestral states at the internal nodes of the phylogeny. We simplified probability calculations for substitution events along the branches by assuming that only up to one or two substitution events occurred per branch per site. These conditional pathway calculations introduce very little bias into the inferred reconstructions, while increasing the feasibility of incorporating complex evolutionary models with higher dimensions. Compositional bias tests, including functional predictions of ancestral tRNAs, show that ancestral sequences from the Bayesian approach are more biologically realistic than those reconstructed by maximum likelihood. To explore other model complexity, we allowed substitution rates to vary among sites by having a different model at each site. With a strand-symmetric model as the base model, asymmetric substitution probabilities for specific substitution types were varied among sites. This model would not be feasible with standard matrix exponentiation methods, particularly maximum likelihood. We observed for A-->G and C-->T substitutions almost linear, respectively, almost asymptotic responses (with some regional deviations). Note that the HMM models had no a priori response built in them. Observed responses fitted predictions from earlier gene by gene likelihood analyses. For A-->G substitutions, deviations from the expected linear response correlated positively with the loop-forming propensity of the corresponding site in the mRNA secondary structure. In the COI region, C-->T substitutions have a prominent dip, suggesting protection against mutations. The C-->T substitution responses differed significantly between primate sub-groups defined based on their single genome A-->G responses.
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