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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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Phylogeny for seven groups of western pocket gophers (Geomyidae, Thomomys). The analysis is based on seven noncoding nuclear genes from 28 individuals. Clade posterior probability is indicated on the branch. (a) Analysis with no monophyly constraints and (b) analysis with ingroup monophyly enforced.
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fig8: Phylogeny for seven groups of western pocket gophers (Geomyidae, Thomomys). The analysis is based on seven noncoding nuclear genes from 28 individuals. Clade posterior probability is indicated on the branch. (a) Analysis with no monophyly constraints and (b) analysis with ingroup monophyly enforced.

Mentions: For the analysis, we used a general time reversible substitution model and strict molecular clock with a separate mutation rate for each locus as described in the simulations section. The highest posterior tree is shown in figure 8a. There are four strongly supported clades, but the outgroup is not where we expect it to be.


Bayesian inference of species trees from multilocus data.

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

Phylogeny for seven groups of western pocket gophers (Geomyidae, Thomomys). The analysis is based on seven noncoding nuclear genes from 28 individuals. Clade posterior probability is indicated on the branch. (a) Analysis with no monophyly constraints and (b) analysis with ingroup monophyly enforced.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig8: Phylogeny for seven groups of western pocket gophers (Geomyidae, Thomomys). The analysis is based on seven noncoding nuclear genes from 28 individuals. Clade posterior probability is indicated on the branch. (a) Analysis with no monophyly constraints and (b) analysis with ingroup monophyly enforced.
Mentions: For the analysis, we used a general time reversible substitution model and strict molecular clock with a separate mutation rate for each locus as described in the simulations section. The highest posterior tree is shown in figure 8a. There are four strongly supported clades, but the outgroup is not where we expect it to be.

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