Department of Biological Sciences, New York City College of Technology, The City University of New York, 285 Jay Street, Brooklyn, NY 11201, USA.
Biology PhD Program, CUNY Graduate Center, 365 5th Ave., New York, NY 10016, USA.
Syst Biol. 2020 May 1;69(3):593-601. doi: 10.1093/sysbio/syz056.
Genomic data have had a profound impact on nearly every biological discipline. In systematics and phylogenetics, the thousands of loci that are now being sequenced can be analyzed under the multispecies coalescent model (MSC) to explicitly account for gene tree discordance due to incomplete lineage sorting (ILS). However, the MSC assumes no gene flow post divergence, calling for additional methods that can accommodate this limitation. Explicit phylogenetic network methods have emerged, which can simultaneously account for ILS and gene flow by representing evolutionary history as a directed acyclic graph. In this point of view, we highlight some of the strengths and limitations of phylogenetic networks and argue that tree-based inference should not be blindly abandoned in favor of networks simply because they represent more parameter rich models. Attention should be given to model selection of reticulation complexity, and the most robust conclusions regarding evolutionary history are likely obtained when combining tree- and network-based inference.
基因组数据对几乎所有的生物学科都产生了深远的影响。在系统分类学和系统发生学中，现在可以对数千个正在测序的基因座进行分析，以多物种合并模型（MSC）为基础，明确解释由于不完全谱系分选（ILS）而导致的基因树分歧。然而，MSC 假设在分歧后没有基因流动，因此需要额外的方法来适应这一限制。明确的系统发生网络方法已经出现，它可以通过将进化历史表示为有向无环图，同时解释 ILS 和基因流动。在这个观点中，我们强调了系统发生网络的一些优点和局限性，并认为仅仅因为它们代表了更丰富的参数模型，就盲目地放弃基于树的推断是不合理的。应该注意网络复杂性的模型选择，当结合基于树和网络的推断时，可能会得到关于进化历史的最稳健的结论。
