1 Combining Multiple Datasets in a Likelihood Analysis : Which Models are Best ?

Until recently, phylogenetic analyses have been routinely based on homologous sequences of a single gene. Given the vast number of gene sequences now available, phylogenetic studies are now based on the analysis of multiple genes. Thus, it has become necessary to devise statistical methods to combine multiple molecular datasets. Here, we compare several models for combining different genes for the purpose of evaluating the likelihood of tree topologies. Three methods of branch length estimation were studied: assuming all genes have the same branch lengths (concatenate model); assuming that branch lengths are proportional among genes (proportional model); or assuming that each gene has a separate set of branch lengths (separate model). We also compared three models of among-site rate variation: the homogenous model, a model that assumes one gamma parameter for all genes, and a model that assumes one gamma parameter for each gene. On the basis of two nuclear and one mitochondrial amino-acid datasets, our results suggest that, depending on the dataset chosen, either the separate model or the proportional model represent the most appropriate method for branch length analysis. For all datasets examined, one gamma parameter to each gene represents the best model for among-site rate variation. Using these models, we analyzed alternative mammalian tree topologies and describe the effect of the assumed model on the maximum likelihood tree. We show that the choice of the model has an impact on the best phylogeny obtained.