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Bayesian inference of species trees from multilocus data.

Heled J, Drummond AJ - Mol. Biol. Evol. (2009)

Bottom Line: Our method coestimates multiple gene trees embedded in a shared species tree along with the effective population size of both extant and ancestral species.Finally, we compare our new method to both an existing method (BEST 2.2) 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Auckland, New Zealand. jheled@gmail.com

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 Markov chain Monte Carlo method for the multispecies coalescent. Our method coestimates 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 multilocus data from multiple individuals per species. Using a multiindividual 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 2.2) 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.

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(a) Relative error and (b) credible interval size for both population size and speciation time point estimates. The number of individuals sampled per species is four for all experiments. Each graph point is obtained by averaging over 100 analyses of simulated data sets (see main text for details).
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fig4: (a) Relative error and (b) credible interval size for both population size and speciation time point estimates. The number of individuals sampled per species is four for all experiments. Each graph point is obtained by averaging over 100 analyses of simulated data sets (see main text for details).

Mentions: Although the tree score measures overall performance, some specific point estimates such as speciation times are of interest as well. However, note that evaluating the errors in point estimates of speciation times and population sizes can be done only for clades that appear in the true species tree. Figure 4a summarizes the estimation errors in speciation times and population sizes for all runs.


Bayesian inference of species trees from multilocus data.

Heled J, Drummond AJ - Mol. Biol. Evol. (2009)

(a) Relative error and (b) credible interval size for both population size and speciation time point estimates. The number of individuals sampled per species is four for all experiments. Each graph point is obtained by averaging over 100 analyses of simulated data sets (see main text for details).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2822290&req=5

fig4: (a) Relative error and (b) credible interval size for both population size and speciation time point estimates. The number of individuals sampled per species is four for all experiments. Each graph point is obtained by averaging over 100 analyses of simulated data sets (see main text for details).
Mentions: Although the tree score measures overall performance, some specific point estimates such as speciation times are of interest as well. However, note that evaluating the errors in point estimates of speciation times and population sizes can be done only for clades that appear in the true species tree. Figure 4a summarizes the estimation errors in speciation times and population sizes for all runs.

Bottom Line: Our method coestimates multiple gene trees embedded in a shared species tree along with the effective population size of both extant and ancestral species.Finally, we compare our new method to both an existing method (BEST 2.2) 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Auckland, New Zealand. jheled@gmail.com

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 Markov chain Monte Carlo method for the multispecies coalescent. Our method coestimates 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 multilocus data from multiple individuals per species. Using a multiindividual 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 2.2) 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.

Show MeSH