论文标题
基于四重奏的推理方法在统一重复渗透模型下在统计上是一致的
Quartet-Based Inference Methods are Statistically Consistent Under the Unified Duplication-Loss-Coalescence Model
论文作者
论文摘要
经典的多种聚合物（MSC）模型为不完整的谱系分类物种树推理方法提供了理论上的理由。系统发育学上的大量作品专门用于在MSC下统计上一致的推理方法的设计。这种特别受欢迎的方法之一是星体，一种基于四重奏的物种树推断方法。最近的一些研究表明，在模拟研究中给出了多洛克斯基因树时，星体也表现良好。此外，Legried等人。最近证明，在基因复制和损失模型（GDL）下，星体在统计上是一致的。请注意，GDL在进化史中很普遍，是Rasmussen和Kellis的强大重复 - 钙化进化模型（DLCoal）的一部分。在这项工作中，我们证明，在一般DLCoal模型下，星体在统计上是一致的。因此，我们的结果支持基于仿真的研究的经验证据。更广泛地说，我们证明，与其他两个四重奏相比，从基因树（具有独特分类单元）中随机选择的四重奏更有可能与各自的物种树四重奏一致。
The classic multispecies coalescent (MSC) model provides the means for theoretical justification of incomplete lineage sorting-aware species tree inference methods. A large body of work in phylogenetics is dedicated to the design of inference methods that are statistically consistent under MSC. One of such particularly popular methods is ASTRAL, a quartet-based species tree inference method. A few recent studies suggested that ASTRAL also performs well when given multi-locus gene trees in simulation studies. Further, Legried et al. recently demonstrated that ASTRAL is statistically consistent under the gene duplication and loss model (GDL). Note that GDL is prevalent in evolutionary histories and is a part of the powerful duplication-loss-coalescence evolutionary model (DLCoal) by Rasmussen and Kellis. In this work we prove that ASTRAL is statistically consistent under the general DLCoal model. Therefore, our result supports the empirical evidence from the simulation-based studies. More broadly, we prove that a randomly chosen quartet from a gene tree (with unique taxa) is more likely to agree with the respective species tree quartet than any of the two other quartets.
