Estimating species trees using multiple-allele DNA sequence data

Several techniques, such as concatenation and consensus methods, are available for combining data from multiple loci to produce

a single statement of phylogenetic relationships. However, when multiple alleles are sampled from individual species, it becomes

more challenging to estimate relationships at the level of species, either because concatenation becomes inappropriate due to

conflicts among individual gene trees, or because the species from which multiple alleles have been sampled may not form

monophyletic groups in the estimated tree. We propose a Bayesian hierarchical model to reconstruct species trees from multiple-

allele, multilocus sequence data, building on a recently proposed method for estimating species trees from single allele multilocus

data. A two-step Markov Chain Monte Carlo (MCMC) algorithm is adopted to estimate the posterior distribution of the species tree.

The model is applied to estimate the posterior distribution of species trees for two multiple-allele datasets—yeast (

Saccharomyces

)

and birds (

Manacus

—manakins). The estimates of the species trees using our method are consistent with those inferred from other

methods and genetic markers, but in contrast to other species tree methods, it provides credible regions for the species tree. The

Bayesian approach described here provides a powerful framework for statistical testing and integration of population genetics

and phylogenetics.