Abstract
Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as “convergence”) and in estimating the correct proportions of the different types of them (known as “mixing”). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical data sets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these data sets, ranging in size from 357 to 934 taxa and from 1740 to 5681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account. [Bayesian phylogenetic inference; MCMC; parsimony; tree proposal.]
Highlights
Introduced to the field two decades ago (Rannala and Yang, 1996; Yang and Rannala, 1997; Mau and Newton, 1997; Li et al, 2000), Bayesian estima[49] tion of phylogeny has become widely used by evolutionary biologists
Here, we describe a couple of variants of parsimony-guided subtree pruning and regrafting (SPR) and parsimony-guided tree bisection and reconnection (TBR) proposals, which have been implemented in MrBayes
Evaluating the parsimony score can be done in time 506 proportional to cn, which is small in comparison
Summary
Introduced to the field two decades ago (Rannala and Yang, 1996; Yang and Rannala, 1997; Mau and Newton, 1997; Li et al, 2000), Bayesian estima[49] tion of phylogeny has become widely used by evolutionary biologists (see Huelsenbeck et al, 2001; Holder and Lewis, 2003; Yang and Rannala, 2012; Yang, 2014; Nascimento et al, 2017, for reviews). The Bayesian framework is attractive for many reasons, including the simple interpretation of results, the ability to address interesting evolutionary questions, and the availability of efficient and easy-to-use computer programs that implement it (Ronquist and Huelsenbeck, 2003; Ronquist et al, 2012b; Drummond and Rambaut, 2007; Bouckaert et al, 2014; Hohna et al, 2016). For any reasonable number of tips, a MCMC proposal has a huge space of tree topologies to explore. Most MCMC tree proposals studied and used in current Bayesian MCMC phylo-
Sign-in/Register to view highlights & summary 
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call