Coalescent methods for estimating multilocus phylogenetic trees

Liang Liu, Lili Yu, Laura Kubatko, Dennis K. Pearl, Scott V. Edwards

Research output: Contribution to journalArticlepeer-review

301 Scopus citations

Abstract

We review recent models to estimate phylogenetic trees under the multispecies coalescent. Although the distinction between gene trees and species trees has come to the fore of phylogenetics, only recently have methods been developed that explicitly estimate species trees. Of the several factors that can cause gene tree heterogeneity and discordance with the species tree, deep coalescence due to random genetic drift in branches of the species tree has been modeled most thoroughly. Bayesian approaches to estimating species trees utilizes two likelihood functions, one of which has been widely used in traditional phylogenetics and involves the model of nucleotide substitution, and the second of which is less familiar to phylogeneticists and involves the probability distribution of gene trees given a species tree. Other recent parametric and nonparametric methods for estimating species trees involve parsimony criteria, summary statistics, supertree and consensus methods. Species tree approaches are an appropriate goal for systematics, appear to work well in some cases where concatenation can be misleading, and suggest that sampling many independent loci will be paramount. Such methods can also be challenging to implement because of the complexity of the models and computational time. In addition, further elaboration of the simplest of coalescent models will be required to incorporate commonly known issues such as deviation from the molecular clock, gene flow and other genetic forces.

Original languageAmerican English
JournalMolecular Phylogenetics and Evolution
Volume53
DOIs
StatePublished - Jun 9 2009

Keywords

  • Bayesian statistics
  • Coalescent model
  • Maximum likelihood
  • Molecular clock
  • Phylogenomics

DC Disciplines

  • Public Health
  • Biostatistics
  • Environmental Public Health
  • Epidemiology

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