Statistical methods of phylogenetic analysis : including Hadamard conjugations, LogDet transforms and maximum likelihood : a thesis presented in partial fulfilment of the requirements for the degree of Ph.D. in Biology at Massey University

Abstract

This thesis studies phylogenetics from a biological-statistical perspective. Chapter 1 offers an overview of the field, with particular emphasis upon the classification and interrelationships of phylogenetic methods. Separating tree selection criteria from 'corrections' for multiple hits is crucial to understanding the behaviour of different methods. Chapter 2 extends Hadamard conjugations to allow for a distribution of unequal rates at different sites in a DNA sequence. This can be done, with minimal additional computational effort, assuming a gamma, lognormal etc. distribution of site rates. The result is either 'correction' of observed sequences assuming a certain distribution of rates, or prediction of sequence probabilities given a distribution of rates and a tree. A new set of faster Hadamard conjugations for correcting four state data are presented. These conjugations also allow unequal rates across sites, transition to transversion weighting and fixing the transition to transversion ratio. Chapter 3 considers the more general time reversible and LogDet-Paralinear distances. These are extended to accommodate unequal rates across sites. It is shown that removing a proportion of constant sites gives the LogDet a high degree of robustness to unequal rates across sites even if the true model is not invariant sites plus identical rates. Analyses of 16S-like rRNA with constant site removal (CSR) LogDet reveals surprising results, including good evidence that Microsporidia are the most distantly related (i.e. first branch) eukaryotes. Chapter 4 deals with understanding the sampling properties of transformations, especially the Hadamard conjugation. Results include forcing the Hadamard conjugation to the Kimura 2ST and Jukes Cantor models, thereby reducing sampling variance. In doing this families of tree informative linear invariants were found. It is also shown that replacing log functions with truncated power series can reduce sampling errors (RMSE) substantially. Chapter 5 deals with tree selection criteria. Studies reveal some interesting interrelationships between Hadamard conjugation, distance and maximum likelihood (ML) based methods. Calculation of likelihoods with unequal rates across sites (e.g. a gamma distribution) are also developed. This can be done quickly with Hadamard conjugations, and a variety of sequences and models are studied. ML solutions to inferring reticulate phytogenies are described, and in an application are used to infer the population size of our ancestors with chimps and gorillas. A wide variety of methods, including ML, are shown to be inconsistent in the Felsenstein zone when site rates are unequal (in a similar situation ML is also seen to be inconsistent under a molecular clock). Overcorrecting the data is also a potential pitfall, and the concept of the 'anti-Felsenstein zone' is introduced, illustrated, and developed. A related phenomena is that two or more optimal binary trees can predict exactly the same sequences when rates across sites are unequal, and examples are provided. Chapter 6 describes new statistical tests. These include faster model based resampling to evaluate fit of model to data and tests of whether two data sets came from the same tree. A Bayesian view of support for different trees is presented. The thesis is large, but well illustrated, and looking at the figures alone should provide a useful overview of new results.