Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure
Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure
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DOI：
10.1093/gbe/evaa022
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发表时间：
2020-02-01
影响因子：
3.3
通讯作者：
DeGiorgio，Michael
中科院分区：
文献类型：
--
作者：
Koch，Hillary;DeGiorgio，Michael
虽然大型多位点基因组数据集导致了系统发育推断的整体改进，但它们也提出了解决整个基因组中相互冲突的信号的新挑战。特别是，在许多不同物种中发现的祖先种群结构可能会扭曲基因树的频率，从而阻碍物种树估计器的性能。在这里，我们发展了一种新的最大似然方法，称为TASTI(TATA WITH ASTERAL STORY STORY STORE STORY INFING)，它可以在这种情况下推断系统发育，并发现它随着输入基因树数量的增加而具有越来越高的精度，而不是为祖先结构量身定制的方法性能相对较差。此外，我们提出了一种超树方法，允许TASTI随着输入分类群数量的增加而在计算上进行扩展。我们使用遗传模拟来评估TASTI在三分类和四分类环境中的表现，并演示了TASI在六种非洲热带蚊子数据集上的应用。最后，为了便于科学界使用，我们在开放源码软件包中实现了TASTI。
Though large multilocus genomic data sets have led to overall improvements in phylogenetic inference, they have posed the new challenge of addressing conflicting signals across the genome. In particular, ancestral population structure, which has been uncovered in a number of diverse species, can skew gene tree frequencies, thereby hindering the performance of species tree estimators. Here we develop a novel maximum likelihood method, termed TASTI (Taxa with Ancestral structure Species Tree Inference), that can infer phylogenies under such scenarios, and find that it has increasing accuracy with increasing numbers of input gene trees, contrasting with the relatively poor performances of methods not tailored for ancestral structure. Moreover, we propose a supertree approach that allows TASTI to scale computationally with increasing numbers of input taxa. We use genetic simulations to assess TASTI’s performance in the three- and four-taxon settings and demonstrate the application of TASTI on a six-species Afrotropical mosquito data set. Finally, we have implemented TASTI in an open-source software package for ease of use by the scientific community.