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Statistical evaluation of model-based phylogenetic methods
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Dissertation
Statistical evaluation of model-based phylogenetic methods
2009
Overview
Due to theoretical advancements and a rapid increase in computing power, phylogenetic inference has become a primarily statistical endeavor. Methods such as maximum-likelihood (ML) and Bayesian Markov chain Monte Carlo (Bayesian MCMC) analysis utilize explicit models of molecular evolution. Because confidence in the accuracy of ML and Bayesian MCMC analysis depends on using an appropriate model of sequence evolution, models must adequately capture the evolutionary process and be selected in a statistically rigorous manner. The research conducted for this dissertation focuses on the statistical evaluation of common substitution models and model-based phylogenetic methods. In Chapter I, we investigate the effects of using four model-selection methods on ML analysis. We find that although model-selection methods normally choose different substitution models for the same data and that use of alternative best-fit models often leads to different ML trees, use of any model-selection method is preferable to using a simple default model that does not incorporate among-site rate variation (ASRV). Although model-selection methods are useful, they cannot reject all models if they provide an inadequate fit to the data. In Chapter II, we evaluate the adequacy of common substitution models using frequentist and Bayesian methods and compare the results with four model-selection methods. We find that tests of model adequacy normally fail to reject less complex substitution models than those chosen by model-selection methods, especially when the models incorporate ASRV. Bipartition support in ML analysis is normally assessed using the nonparametric bootstrap. Although bootstrap replicates should be analyzed in the same manner as the original data, model selection is virtually never conducted for bootstrap replicates and replicates are often analyzed using a less rigorous tree-search strategy than the original data. In Chapter III, we investigate the effects of forgoing model-selection and using reduced tree-search strategies on ML bootstrap analysis and find that while using a less-intensive analysis may lead to significant differences among bootstrap values, these differences are normally small and would not change biological inferences drawn from the data.
