MBE Advance Access published online on October 15, 2009
Molecular Biology and Evolution, doi:10.1093/molbev/msp250
Research Article |
Fast and consistent estimation of species trees using supermatrix rooted triples
* Center for Computational Medicine and Bioinformatics, University of Michigan, 2017 Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA
Department of Mathematics and Statistics, Private Bag 4800, University of Canterbury, Christchurch 8140 New Zealand
Corresponding authors: Michael DeGiorgio, Center for Computational Medicine and Bioinformatics, University of Michigan, 2017 Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA, Phone: +1 734 615 9551, Fax: +1 734 615 6553, E-mail: degiormi{at}umich.edu
James H. Degnan, Department of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand, Phone: +64 (03) 364 2600, Fax: +64 (03) 364 2587, E-mail: j.degnan{at}math.canterbury.ac.nz
Received for publication June 19, 2009. Revision received September 6, 2009. Accepted for publication October 5, 2009.
Concatenated sequence alignments are often used to infer species-level relationships. Previous studies have shown that analysis of concatenated data using maximum likelihood (ML) can produce misleading results when loci have differing gene tree topologies due to incomplete lineage sorting. Here, we develop a polynomial-time method that utilizes the modified mincut supertree algorithm to construct an estimated species tree from inferred rooted triples of concatenated alignments. We term this method SuperMatrix Rooted Triple (SMRT) and use the notation SMRT-ML when rooted triples are inferred by ML. We use simulations to investigate the performance of SMRT-ML under Jukes-Cantor and General Time-Reversible substitution models for four- and five-taxon species trees, and also apply the method to an empirical dataset of yeast genes. We find that SMRT-ML converges to the correct species tree in many cases in which maximum likelihood on the full concatenated dataset fails to do so. SMRT-ML can be conservative in that its output tree is often partially unresolved for problematic clades. We show analytically that when the species tree is clocklike and mutations occur under the Cavender-Farris-Neyman substitution model, as the number of genes increases, SMRT-ML is increasingly likely to infer the correct species tree even when the most likely gene tree does not match the species tree. SMRT-ML is therefore a computationally efficient and statistically consistent estimator of the species tree when gene trees are distributed according to the multispecies coalescent model.
Key Words: phylogenetics • phylogenomics • anomaly zone • anomalous gene tree • statistical consistency • lineage sorting