Statistical Inference of Allopolyploid Species Networks in the Presence of Incomplete Lineage Sorting

TL;DR

This paper introduces two new statistical models for inferring the evolutionary history of allopolyploid species, accounting for hybridization events and incomplete lineage sorting, validated through simulations and empirical data.

Contribution

The paper develops and evaluates two novel models, AlloppMUL and AlloppNET, for inferring allopolyploid species networks, extending existing species tree models to include hybridization.

Findings

Both models are useful for inferring allopolyploid histories.

AlloppNET provides more accurate results when assumptions are met.

Models are successfully applied to empirical datasets from Pachycladon and Silene.

Abstract

Polyploidy is an important speciation mechanism, particularly in land plants. Allopolyploid species are formed after hybridization between otherwise intersterile parental species. Recent theoretical progress has led to successful implementation of species tree models that take population genetic parameters into account. However, these models have not included allopolyploid hybridization and the special problems imposed when species trees of allopolyploids are inferred. Here, two new models for the statistical inference of the evolutionary history of allopolyploids are evaluated using simulations and demonstrated on two empirical data sets. It is assumed that there has been a single hybridization event between two diploid species resulting in a genomic allotetraploid. The evolutionary history can be represented as a network or as a multiply labeled tree, in which some pairs of tips are labeled with the same species. In one of the models (AlloppMUL), the multiply labeled tree is inferred directly. This is the simplest model and the most widely applicable, since fewer assumptions are made. The second model (AlloppNET) incorporates the hybridization event explicitly which means that fewer parameters need to be estimated. Both models are implemented in the BEAST framework. Simulations show that both models are useful and that AlloppNET is more accurate if the assumptions it is based on are valid. The models are demonstrated on previously analyzed data from the genus Pachycladon (Brassicaceae) and from the genus Silene (Caryophyllaceae).