Molecular Biology and Evolution, Vol 14, 717-724, Copyright © 1997 by Society for Molecular Biology and Evolution
Z Yang and B Rannala
An improved Bayesian method is presented for estimating phylogenetic trees
using DNA sequence data. The birth-death process with species sampling is
used to specify the prior distribution of phylogenies and ancestral
speciation times, and the posterior probabilities of phylogenies are used
to estimate the maximum posterior probability (MAP) tree. Monte Carlo
integration is used to integrate over the ancestral speciation times for
particular trees. A Markov Chain Monte Carlo method is used to generate the
set of trees with the highest posterior probabilities. Methods are
described for an empirical Bayesian analysis, in which estimates of the
speciation and extinction rates are used in calculating the posterior
probabilities, and a hierarchical Bayesian analysis, in which these
parameters are removed from the model by an additional integration. The
Markov Chain Monte Carlo method avoids the requirement of our earlier
method for calculating MAP trees to sum over all possible topologies (which
limited the number of taxa in an analysis to about five). The methods are
applied to analyze DNA sequences for nine species of primates, and the MAP
tree, which is identical to a maximum-likelihood estimate of topology, has
a probability of approximately 95%.
ORIGINAL ARTICLE
Bayesian phylogenetic inference using DNA sequences: a Markov Chain Monte Carlo Method
Department of Integrative Biology, University of California, Berkeley 94720-3140, USA.
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J. P. Bollback Bayesian Model Adequacy and Choice in Phylogenetics Mol. Biol. Evol., July 1, 2002; 19(7): 1171 - 1180. [Abstract] [Full Text] [PDF]
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R. Nielsen Mutations as Missing Data: Inferences on the Ages and Distributions of Nonsynonymous and Synonymous Mutations Genetics, September 1, 2001; 159(1): 401 - 411. [Abstract] [Full Text] [PDF]
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K. Nieselt-Struwe and A. von Haeseler Quartet-Mapping, a Generalization of the Likelihood-Mapping Procedure Mol. Biol. Evol., July 1, 2001; 18(7): 1204 - 1219. [Abstract] [Full Text] [PDF]
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M. A. Suchard, R. E. Weiss, and J. S. Sinsheimer Bayesian Selection of Continuous-Time Markov Chain Evolutionary Models Mol. Biol. Evol., June 1, 2001; 18(6): 1001 - 1013. [Abstract] [Full Text]
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H. Kishino, J. L. Thorne, and W. J. Bruno Performance of a Divergence Time Estimation Method under a Probabilistic Model of Rate Evolution Mol. Biol. Evol., March 1, 2001; 18(3): 352 - 361. [Abstract] [Full Text]
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L. Markovtsova, P. Marjoram, and S. Tavaré The Effects of Rate Variation on Ancestral Inference in the Coalescent Genetics, November 1, 2000; 156(3): 1427 - 1436. [Abstract] [Full Text]
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S. C. Thomas and W. G. Hill Estimating Quantitative Genetic Parameters Using Sibships Reconstructed From Marker Data Genetics, August 1, 2000; 155(4): 1961 - 1972. [Abstract] [Full Text]
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M. Steel and D. Penny Parsimony, Likelihood, and the Role of Models in Molecular Phylogenetics Mol. Biol. Evol., June 1, 2000; 17(6): 839 - 850. [Abstract] [Full Text] [PDF]
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M. A. Beaumont Detecting Population Expansion and Decline Using Microsatellites Genetics, December 1, 1999; 153(4): 2013 - 2029. [Abstract] [Full Text]
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G. Cooper, N. J. Burroughs, D. A. Rand, D. C. Rubinsztein, and W. Amos Markov Chain Monte Carlo analysis of human Y-chromosome microsatellites provides evidence of biased mutation PNAS, October 12, 1999; 96(21): 11916 - 11921. [Abstract] [Full Text] [PDF]
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P. W. Diaconis and S. P. Holmes Matchings and phylogenetic trees PNAS, December 8, 1998; 95(25): 14600 - 14602. [Abstract] [Full Text] [PDF]
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P. Liò and N. Goldman Models of Molecular Evolution and Phylogeny Genome Res., December 1, 1998; 8(12): 1233 - 1244. [Abstract] [Full Text]
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J. Shoemaker, I. Painter, and B. S. Weir A Bayesian Characterization of Hardy-Weinberg Disequilibrium Genetics, August 1, 1998; 149(4): 2079 - 2088. [Abstract] [Full Text] [PDF]
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