Bayesian Inference of Species Networks from Multilocus Sequence Data.

Abstract:

:Reticulate species evolution, such as hybridization or introgression, is relatively common in nature. In the presence of reticulation, species relationships can be captured by a rooted phylogenetic network, and orthologous gene evolution can be modeled as bifurcating gene trees embedded in the species network. We present a Bayesian approach to jointly infer species networks and gene trees from multilocus sequence data. A novel birth-hybridization process is used as the prior for the species network, and we assume a multispecies network coalescent prior for the embedded gene trees. We verify the ability of our method to correctly sample from the posterior distribution, and thus to infer a species network, through simulations. To quantify the power of our method, we reanalyze two large data sets of genes from spruces and yeasts. For the three closely related spruces, we verify the previously suggested homoploid hybridization event in this clade; for the yeast data, we find extensive hybridization events. Our method is available within the BEAST 2 add-on SpeciesNetwork, and thus provides an extensible framework for Bayesian inference of reticulate evolution.

journal_name

Mol Biol Evol

authors

Zhang C,Ogilvie HA,Drummond AJ,Stadler T

doi

10.1093/molbev/msx307

subject

Has Abstract

pub_date

2018-02-01 00:00:00

pages

504-517

issue

2

eissn

0737-4038

issn

1537-1719

pii

4705834

journal_volume

35

pub_type

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