Abstract
Motivation The coalescent model is now widely accepted as an effective model for incorporating variation in the evolutionary histories of individual genes into methods for phylogenetic inference from genome-scale data. However, because model-based analysis under the coalescent can be computationally expensive for large data sets, a variety of inferential frameworks and corresponding algorithms have been proposed for estimation of species-level phylogenies and the associated parameters, including the speciation times and effective population sizes.
Results We consider the problem of estimating the timing of speciation events along a phylogeny in a coalescent framework. We propose a maximum a posteriori estimator based on composite likelihood (MAPCL) for inferring these speciation times under a model of DNA sequence evolution for which exact site pattern probabilities can be computed. We demonstrate that the MAPCL estimates are statistically consistent and asymptotically normally distributed, and we show how this result can be used to estimate their asymptotic variance. We also provide a more computationally efficient estimator of the asymptotic variance based on the nonparametric bootstrap. We evaluate the performance of our method using simulation and by application to an empirical dataset for gibbons.
Availability and implementation The method has been implemented in the PAUP* program, freely available at https://paup.phylosolutions.com for Macintosh, Windows, and Linux operating systems.
Contact peng.650@osu.edu
Supplementary information Supplementary data are available at Bioinformatics online.
Competing Interest Statement
The authors have declared no competing interest.
