Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo.

By John P. Huelsenbeck, Bret R. Larget, Michael E. Alfaro. Molecular biology and evolution, 2004

535 citations recorded by the source corpus.

A common problem in molecular phylogenetics is choosing a model of DNA substitution that does a good job of explaining the DNA sequence alignment without introducing superfluous parameters. A number of methods have been used to choose among a small set of candidate substitution models, such as the likelihood ratio test, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and Bayes factors. Current implementations of any of these criteria suffer from the limitation that only a small set of models are examined, or that the test does not allow easy comparison of non-nested models. In this article, we expand the pool of candidate substitution models to include all possible time-reversible models. This set includes seven models that have already been described. We show how Bayes factors can be calculated for these models using reversible jump Markov chain Monte Carlo, and apply the method to 16 DNA sequence alignments. For each data set, we compare the model with the best Bayes factor to the best models chosen using AIC and BIC. We find that the best model under any of these criteria is not necessarily the most complicated one; models with an intermediate number of substitution types typically do best. Moreover, almost all of the models that are chosen as best do not constrain a transition rate to be the same as a transversion rate, suggesting that it is the transition/transversion rate bias that plays the largest role in determining which models are selected. Importantly, the reversible jump Markov chain Monte Carlo algorithm described here allows estimation of phylogeny (and other phylogenetic model parameters) to be performed while accounting for uncertainty in the model of DNA substitution.

Subfields: Artificial Intelligence, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/12132e6bb0e1971c8e40f56aba46c6096b01c480

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Citations in the corpus

Mitochondrial DNA from Hemlock Woolly Adelgid (Hemiptera: Adelgidae) Suggests Cryptic Speciation and Pinpoints the Source of the Introduction to Eastern North America (2006, background): “Using a complex and more realistic evolutionary model for multilocus data sets rather than a single, homogeneous model has been shown to result in more accurate and precise estimates of posterior probabilities (Castoe et al. 2004, Huelsenbeck et al. 2004 ).”
A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data (2014, methodology): “We implement a reversible-jump algorithm that jointly estimates the location, number and magnitude of shifts in adaptive optima from phylogenetic comparative data (Green 1995; Huelsenbeck et al. 2000, 2004; Eastman et al. 2011, 2013; Rabosky et al. 2013).”
Bayesian Analysis of Correlated Evolution of Discrete Characters by Reversible‐Jump Markov Chain Monte Carlo (2006, methodology): “Our implementation of the merge and split moves is similar to that described for moving among models of gene-sequence evolution (Huelsenbeck et al. 2004); both involve the same continuous-time Markov model, except here we manipulate an eight-parameter non-timereversible model rather than the usual…”
The genetic diversity, phylogeography and morphology of Elphidiidae (Foraminifera) in the Northeast Atlantic (2016, methodology): “A mixed model was used for BA which sampled across the GTR model space (Huelsenbeck et al., 2004).”
Bayesian Analysis of Molecular Evolution Using MrBayes (2005, methodology): “In 82 cases, the model of Jones et al. (1992) was chosen as best.”
MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space (2012, methodology): “An alternative is to use Bayesian model jumping during the MCMC simulation to integrate out the uncertainty concerning the correct substitution model (Huelsenbeck et al. 2004).”
Computing Bayes factors using thermodynamic integration. (2006, background): “This requirement can be met quite easily as long as the models being compared are formulated along similar parameterizations; for instance, alternative substitution matrices ( Huelsenbeck et al., 2004 ), or different number of classes for a mixture model (Lartillot and Philippe, 2004).”
Not so different after all: a comparison of methods for detecting amino acid sites under selection. (2005, methodology): “Muse (1999) and Huelsenbeck, Larget, and Alfaro (2004) demonstrated that evolution of many genes is best explained by ‘‘nonstandard’’ models of nucleotide substitution.”
Frequentist properties of Bayesian posterior probabilities of phylogenetic trees under simple and complex substitution models. (2004, methodology): “It may still be possible to choose a model that best explains the alignment without introducing superfluous parameters using Bayesian model choice (e.g., Huelsenbeck et al., 2004 ) or information criteria (see Burnham and Anderson, 1998).”
Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests. (2004, methodology): “In a Bayesian framework one could also directly obtain a model-averaged estimate of phylogeny by using reversible-jump MCMC, an algorithm that moves through both parameter and model space (Green, 1995), and very recently implemented by Huelsenbeck et al. (2004), for phylogenetic model selection.”
MODELING STABILIZING SELECTION: EXPANDING THE ORNSTEIN–UHLENBECK MODEL OF ADAPTIVE EVOLUTION (2012, methodology): “As with Huelsenbeck et al. (2004) and Pagel and Meade (2006), a reversible jump MCMC approach could also be used to get the posterior probabilities of various models, ranging from BM1 to OUMVA, in a single analysis.”
bModelTest: Bayesian phylogenetic site model averaging and model comparison (2015, methodology): “There are 203 reversible models in total [7].”