Inferring Phylogenetic Networks with Maximum Pseudolikelihood under Incomplete Lineage Sorting.
Inferring Phylogenetic Networks with Maximum Pseudolikelihood under Incomplete Lineage Sorting.
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DOI：
10.1371/journal.pgen.1005896
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发表时间：
2016-03
期刊：
影响因子：
4.5
通讯作者：
Ané C
中科院分区：
文献类型：
--
作者：
Solís-Lemus C;Ané C
系统发育网络是必要的，以代表由边缘扩展的生命树，以代表诸如水平基因转移、杂交或基因流动等事件。并非所有物种都遵循其遗传物质垂直遗传的范例。虽然大量的研究已经活跃在系统发育树的推断上，但推断系统发育网络的统计方法仍然有限，而且还在发展中。现有方法的主要缺点是缺乏可扩展性。在这里，我们提出了一种统计方法来推断系统发育网络从多位点遗传数据在一个假遗传框架。我们的模型解释了通过合并模型进行的不完全谱系排序，以及通过网络中的网状节点进行基因的水平遗传。伪似然的计算是快速和简单的，它避免了繁琐的全似然计算，这对于许多物种来说可能是棘手的。此外，四元组级别的估计具有额外的计算优势，即它很容易并行化。仿真研究表明，与完全似然方法相比，我们的伪似然方法在不影响精度的前提下，速度要快得多。我们应用我们的方法重建了剑尾和鸭嘴鱼之间的进化关系，这是以广泛的杂交为特征的。系统发育网络显示了个体(物种或种群)群体的进化历史，包括杂交、水平基因转移或迁徙等网状事件。在这里，我们提出了一种从多个基因的分子序列中学习网络的似然方法。我们的模型解释了几个生物过程：突变，祖先种群中等位基因的不完全世系分类，以及网络中的网状结构。将似然性分解为4个分类子集，使分析范围扩大到多个物种和多个基因。我们的工作使利用统计学方法和生物学相关模型从大数据集中学习大型系统发育网络成为可能。
Phylogenetic networks are necessary to represent the tree of life expanded by edges to represent events such as horizontal gene transfers, hybridizations or gene flow. Not all species follow the paradigm of vertical inheritance of their genetic material. While a great deal of research has flourished into the inference of phylogenetic trees, statistical methods to infer phylogenetic networks are still limited and under development. The main disadvantage of existing methods is a lack of scalability. Here, we present a statistical method to infer phylogenetic networks from multi-locus genetic data in a pseudolikelihood framework. Our model accounts for incomplete lineage sorting through the coalescent model, and for horizontal inheritance of genes through reticulation nodes in the network. Computation of the pseudolikelihood is fast and simple, and it avoids the burdensome calculation of the full likelihood which can be intractable with many species. Moreover, estimation at the quartet-level has the added computational benefit that it is easily parallelizable. Simulation studies comparing our method to a full likelihood approach show that our pseudolikelihood approach is much faster without compromising accuracy. We applied our method to reconstruct the evolutionary relationships among swordtails and platyfishes (Xiphophorus: Poeciliidae), which is characterized by widespread hybridizations. Phylogenetic networks display the evolutionary history of groups of individuals (species or populations) including reticulation events such as hybridization, horizontal gene transfer or migration. Here, we present a likelihood method to learn networks from molecular sequences at multiple genes. Our model accounts for several biological processes: mutations, incomplete lineage sorting of alleles in ancestral populations, and reticulations in the network. The likelihood is decomposed into 4-taxon subsets to make the analyses scale to many species and many genes. Our work makes it possible to learn large phylogenetic networks from large data sets, with a statistical approach and a biologically relevant model.