Efficiencies of maximum likelihood methods of phylogenetic inferences when different substitution models are used

Abstract

Choice of a substitution model is a crucial step in the maximum likelihood (ML) method of phylogenetic inference, and investigators tend to prefer complex mathematical models to simple ones. However, when complex models with many parameters are used, the extent of noise in statistical inferences increases, and thus complex models may not produce the true topology with a higher probability than simple ones. This problem was studied using computer simulation. When the number of nucleotides used was relatively large (1000 bp), the HKY + Γ model showed smaller d T (topological distance between the inferred and the true trees) than the JC and Kimura models. In the cases of shorter sequences (300 bp) simpler model and search algorithm such as JC model and SA + NNI search were found to be as efficient as more complicated searches and models in terms of topological distances, although the topologies obtained under HKY + Γ model had the highest likelihood values. The performance of relatively simple search algorithm SA + NNI was found to be essentially the same as that of more extensive SA + TBR search under all models studied. Similarly to the conclusions reached by Takahashi and Nei [Mol. Biol. Evol. 17 (2000) 1251], our results indicate that simple models can be as efficient as complex models, and that use of complex models does not necessarily give more reliable trees compared with simple models.