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区分三个分类群的趋同进化和分子钟违背

Distinguishing Between Convergent Evolution and Violation of the Molecular Clock for Three Taxa.

机构信息

School of Natural Sciences (Maths/Physics), University of Tasmania, Private Bag 37, Hobart, Tasmania 7001, Australia.

Department of Mathematics and Statistics, University of Alaska Fairbanks, P.O. Box 756660, Fairbanks, Alaska 99775-6660, USA.

出版信息

Syst Biol. 2018 Sep 1;67(5):905-915. doi: 10.1093/sysbio/syy038.

Abstract

We give a non-technical introduction to convergence-divergence models, a new modeling approach for phylogenetic data that allows for the usual divergence of lineages after lineage-splitting but also allows for taxa to converge, i.e. become more similar over time. By examining the $3$-taxon case in some detail, we illustrate that phylogeneticists have been "spoiled" in the sense of not having to think about the structural parameters in their models by virtue of the strong assumption that evolution is tree-like. We show that there are not always good statistical reasons to prefer the usual class of tree-like models over more general convergence-divergence models. Specifically, we show many $3$-taxon data sets can be equally well explained by supposing violation of the molecular clock due to change in the rate of evolution along different edges, or by keeping the assumption of a constant rate of evolution but instead assuming that evolution is not a purely divergent process. Given the abundance of evidence that evolution is not strictly tree-like, our discussion is an illustration that as phylogeneticists we need to think clearly about the structural form of the models we use. For cases with four taxa, we show that there will be far greater ability to distinguish models with convergence from non-clock-like tree models. [Akaike information criterion; convergence-divergence models; distinguishability; identifiability; likelihood; molecular clock; phylogeny.].

摘要

我们对收敛分歧模型进行了非技术性介绍,这是一种新的系统发育数据分析方法,它允许谱系分裂后通常的分歧,但也允许分类群收敛,即随着时间的推移变得更加相似。通过详细研究三分类群的情况,我们说明系统发育学家一直以来“受到优待”,因为他们不必考虑模型中的结构参数,这得益于进化是树状的强烈假设。我们表明,并不总是有充分的统计理由偏好通常的树状模型类,而不是更一般的收敛分歧模型。具体来说,我们表明许多三分类群数据集可以通过假设由于不同边缘上进化率的变化而违反分子钟,或者通过保持进化率恒定的假设,但假设进化不是纯粹的分歧过程,来同样好地解释。鉴于有大量证据表明进化不是严格的树状,我们的讨论说明了作为系统发育学家,我们需要清楚地思考我们使用的模型的结构形式。对于具有四个分类群的情况,我们表明,从非钟状树模型中区分具有收敛性的模型的能力将大大增强。 [Akaike 信息准则;收敛分歧模型;可区分性;可识别性;似然;分子钟;系统发育]。

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