Bayesian inference of species trees from multilocus data

Item Type Journal Article
Author Joseph Heled
Author Alexei J Drummond
URL http://www.ncbi.nlm.nih.gov/pubmed/19906793
Publication Molecular Biology and Evolution
ISSN 1537-1719
Date Nov 20, 2009
Extra PMID: 19906793
Journal Abbr Mol. Biol. Evol
DOI 10.1093/molbev/msp274
Accessed 2009-12-30 14:27:05
Library Catalog NCBI PubMed
Abstract Until recently it has been common practice for a phylogenetic analysis to use a single gene sequence from a single individual organism as a proxy for an entire species. With technological advances it is now becoming more common to collect data sets containing multiple gene loci and multiple individuals per species. These data sets often reveal the need to directly model intraspecies polymorphism and incomplete lineage sorting in phylogenetic estimation procedures. For a single species, coalescent theory is widely used in contemporary population genetics to model intraspecific gene trees. Here we present a Bayesian MCMC method for the multispecies coalescent. Our method co-estimates multiple gene trees embedded in a shared species tree along with the effective population size of both extant and ancestral species. The inference is made possible by multi-locus data from multiple individuals per species. Using a multi-individual data set and a series of simulations of rapid species radiations, we demonstrate the efficacy of our new method. These simulations give some insight into the behavior of the method as a function of sampled individuals, sampled loci and sequence length. Finally, we compare our new method to both an existing method (BEST) with similar goals and the supermatrix (concatenation) method. We demonstrate that both BEST and our method have much better estimation accuracy for species tree topology than concatenation and our method outperforms BEST in divergence time and population size estimation.
Title Bayesian inference of species trees from multilocus data
Date Added 2009-12-30 09:27
Date Modified 2009-12-30 09:27