Approximating Model Probabilities in Bayesian Information Criterion and Decision-Theoretic Approaches to Model Selection in Phylogenetics

Abstract

A priori selection of models for use in phylogeny estimation from molecular sequence data is increasingly important as the number and complexity of available models increases. The Bayesian information criterion (BIC) and the derivative decision-theoretic (DT) approaches rely on a conservative approximation to estimate the posterior probability of a given model. Here, we extended the DT method by using reversible jump Markov chain Monte Carlo approaches to directly estimate model probabilities for an extended candidate pool of all 406 special cases of the general time reversible + Γ family. We analyzed 250 diverse data sets in order to evaluate the effectiveness of the BIC approximation for model selection under the BIC and DT approaches. Model choice under DT differed between the BIC approximation and direct estimation methods for 45% of the data sets (113/250), and differing model choice resulted in significantly different sets of trees in the posterior distributions for 26% of the data sets (64/250). The model with the lowest BIC score differed from the model with the highest posterior probability in 30% of the data sets (76/250). When the data indicate a clear model preference, the BIC approximation works well enough to result in the same model selection as with directly estimated model probabilities, but a substantial proportion of biological data sets lack this characteristic, which leads to selection of underparametrized models.