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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 sizes, as a function of number of individuals sample from each species. Each graph point is obtained by averaging over 100 analyses of simulated data sets. The analysis used the true gene trees to reduce the computational cost.
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fig7: (a) Relative error and (b) credible interval sizes, as a function of number of individuals sample from each species. Each graph point is obtained by averaging over 100 analyses of simulated data sets. The analysis used the true gene trees to reduce the computational cost.

Mentions: Next we considered the effect of varying the number of individuals sampled from each species. Gene trees for 32 individuals per species and four loci were simulated for each of the 100 species trees, then 16 individuals were removed to leave 16, then halved again to 8, and so on down to 2 individuals per species. To reduce the considerable computational cost involved, the analysis was carried out using the gene trees directly, that is, without sequence data. The results are shown in figure 7.


Bayesian inference of species trees from multilocus data.

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

(a) Relative error and (b) credible interval sizes, as a function of number of individuals sample from each species. Each graph point is obtained by averaging over 100 analyses of simulated data sets. The analysis used the true gene trees to reduce the computational cost.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: (a) Relative error and (b) credible interval sizes, as a function of number of individuals sample from each species. Each graph point is obtained by averaging over 100 analyses of simulated data sets. The analysis used the true gene trees to reduce the computational cost.
Mentions: Next we considered the effect of varying the number of individuals sampled from each species. Gene trees for 32 individuals per species and four loci were simulated for each of the 100 species trees, then 16 individuals were removed to leave 16, then halved again to 8, and so on down to 2 individuals per species. To reduce the considerable computational cost involved, the analysis was carried out using the gene trees directly, that is, without sequence data. The results are shown in figure 7.

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