Data from: Statistical inference of allopolyploid species networks in the presence of incomplete lineage sorting

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 species network or as a multi-labeled species tree, in which some pairs of tips are labeled with the same species. In one of the models (AlloppMUL), the multi-labeled species 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).

多倍体化是一种重要的物种形成机制，在陆生植物中尤为关键。异源多倍体物种由原本生殖隔离的亲本物种间的杂交产生。近年来的理论进展已成功实现了纳入种群遗传参数的物种树模型，但此类模型尚未涵盖异源多倍体杂交，以及推断异源多倍体物种树时所面临的特殊难题。本文通过模拟评估了两款用于异源多倍体演化历史统计推断的新型模型，并在两个实证数据集上进行了演示。研究假设两个二倍体物种间仅发生一次杂交事件，进而形成基因组异源四倍体。其演化历史可通过物种网络或多标记物种树（multi-labeled species tree）进行表征，其中部分类群端点会被标记为同一物种。其中一款模型（AlloppMUL）可直接推断多标记物种树，该模型假设条件最少，是结构最简且适用性最广的方案。第二款模型（AlloppNET）则显式纳入了杂交事件，因此需要估计的参数数量更少。两款模型均基于BEAST框架实现。模拟结果表明，两款模型均具备实用价值；若所基于的假设成立，AlloppNET的推断精度更高。本研究以十字花科（Brassicaceae）薄地芥属（Pachycladon）和石竹科（Caryophyllaceae）蝇子草属（Silene）的已分析数据集为例，演示了两款模型的应用。