EVALUATION OF THE RESTRICTED MAXIMUM-LIKELIHOOD METHOD FOR ESTIMATING PHYLOGENETIC TREES USING SIMULATED ALLELE-FREQUENCY DATA

Abstract

Comparisons are made of the accuracy of the restricted maximum-likelihood, Wagner parsimony, and UPGMA (unweighted pair-group method using arithmetic averages) clustering methods to estimate phylogenetic trees. Data matrices were generated by constructing simulated stochastic evolution in a multidimensional gene-frequency space using a simple genetic-drift model (Brownian-motion, random-walk) with constant rates of divergence in all lineages. Ten differentphylogenetic tree topologies of 20 operational taxonomic units (OTU's), representing a range of tree shapes, were used. Felsenstein's restricted maximum-likelihood method, Wagner parsimony, and UPGMA clustering were used to construct trees from the resulting data matrices. The computations for the restricted maximum-likelihood method were performed on a Cray-1 supercomputer since the required calculations (especially when optimized for the vector hardware) are performed substantially faster than on more conventional computing systems. The overall level of accuracy of tree reconstruction depends on the topology of the true phylogenetic tree. The UPGMA clustering method, especially when genetic-distance coefficients are used, gives the most accurate estimates of the true phylogeny (for our model with constant evolutionary rates). For large numbers of loci, all methods give similar results, but trends in the results imply that the restricted maximum-likelihood method would produce the most accurate trees if sample sizes were large enough.