A Phylogenetic Mixture Model for Heterotachy

Abstract

We present a likelihood-based phylogenetic mixture model designed to analyse data that exhibit within-site rate variation or heterotachy. Heterotachy refers to the phenomenon of a site in a gene-sequence or other alignment changing its rate of evolution throughout the tree. The method accounts for heterotachy by summing the likelihood of the data at each site over more than one set of branch lengths on the same tree. A branch length set that is best for one site may differ from the branch length set that is best for some other site, thereby allowing different sites to have different rates of change throughout the tree. We show that the model improves the accuracy of phylogenetic reconstruction when the sequence data are not derived from a single underlying evolutionary process. We apply the method to a number of simulated and published data sets and show that many sequence data sets have complex evolutionary signals of heterotachy. The presence of such signals has important consequences for the correct reconstruction of phylogenies as well as for tests of hypotheses that rely on accurate branch length information. These include molecular clocks, analyses of tempo and mode of evolution, comparative studies and ancestral state reconstruction. The model is implemented in a Bayesian Markov chain Monte Carlo framework and is available from the authors’ Web site, and can be used for the analysis of both nucleotide and morphological data.