Plesiomorphic character states cause systematic errors in molecular phylogenetic analyses: a simulation study

Abstract

Analysis of sequence data using time-reversible substitution models and maximum likelihood (ML) algorithms is currently the most popular method to infer phylogenies, despite the fact that results often contradict each other. Searching for sources of error we focus on a hitherto neglected feature of these methods: character polarity is usually thought to be irrelevant in ML analyses. Mechanisms that lead to wrong tree topologies were analysed at the level of split-supporting site patterns. In simulations, plesiomorphic site patterns can be identified by comparison with known root sequences. These patterns cause some surprising effects: Using data sets generated with simulations of sequence evolution along a variety of topologies and inferring trees using the same (correct) model, we show for cases of branch-length heterogeneity that (i) as already known, ML analyses can fail to recover the correct tree even when the correct substitution model is used, but also that (ii) plesiomorphic character states cause substantial mistakes and therefore character polarity is relevant, and (iii) accumulating chance similarities on long branches are far less misleading than plesiomorphic states accumulating on shorter branches. The artefacts occur when branch lengths are heterogeneous. The systematic errors disappear for the most part when the sites with symplesiomorphies supporting false clades are deleted from the data set. We conclude that many of the phylogenies published during the past decades may be false due to the neglected effects of symplesiomorphies.