Accounting for uncertainty in the tree topology has little effect on the decision-theoretic approach to model selection in phylogeny estimation
Accounting for uncertainty in the tree topology has little effect on the decision-theoretic approach to model selection in phylogeny estimation
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DOI：
10.1093/molbev/msi050
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发表时间：
2005-03-01
影响因子：
10.7
通讯作者：
Sullivan, J
中科院分区：
文献类型：
--
作者：
Abdo, Z;Minin, VN;Sullivan, J
目前用于系统发育分析的模型选择方法是基于初始的固定树拓扑结构。一旦基于这种拓扑选择了模型，就在该模型下对树空间进行严格搜索，以找到树(拓扑和分支长度)的最大似然估计和模型参数的最大似然估计。在这篇文章中，我们提出了两个扩展的决策理论(DT)的方法，放宽了固定拓扑限制。我们还放宽了贝叶斯信息准则(BIC)和Akaike信息准则(AIC)方法的固定拓扑限制。我们使用模拟数据比较了不同方法(松弛、受限和似然比检验[LRT])的性能。这种比较是通过评估每种方法产生的模型的相对复杂性以及比较所选模型在估计真实树方面的性能来完成的。我们还通过测量在这些方法下不同选择的模型对应的估计树的贴近度来比较这些方法之间的相对关系。我们表明，改变拓扑结构不会对模型选择产生重大影响。我们还证明了这两个扩展的结果是相同的，并且与BIC、扩展的BIC和DT的结果相当。因此，使用更简单的方法选择用于分析数据的模型在计算上更可行，结果与计算更密集的方法相当。这项研究的另一个结果是，关于DT方法的早期结论得到了强化。也就是说，LRT、Extended-AIC和AIC导致了更复杂的模型，这些模型不会对系统发育推断的性能做出贡献，但会显著增加数据分析所需的时间。
Currently available methods for model selection used in phylogenetic analysis are based on an initial fixed-tree topology. Once a model is picked based on this topology, a rigorous search of the tree space is run under that model to find the maximum-likelihood estimate of the tree (topology and branch lengths) and the maximum-likelihood estimates of the model parameters. In this paper, we propose two extensions to the decision-theoretic (DT) approach that relax the fixed-topology restriction. We also relax the fixed-topology restriction for the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) methods. We compare the performance of the different methods (the relaxed, restricted, and the likelihood-ratio test [LRT]) using simulated data. This comparison is done by evaluating the relative complexity of the models resulting from each method and by comparing the performance of the chosen models in estimating the true tree. We also compare the methods relative to one another by measuring the closeness of the estimated trees corresponding to the different chosen models under these methods. We show that varying the topology does not have a major impact on model choice. We also show that the outcome of the two proposed extensions is identical and is comparable to that of the BIC, Extended-BIC, and DT. Hence, using the simpler methods in choosing a model for analyzing the data is more computationally feasible, with results comparable to the more computationally intensive methods. Another outcome of this study is that earlier conclusions about the DT approach are reinforced. That is, LRT, Extended-AIC, and AIC result in more complicated models that do not contribute to the performance of the phylogenetic inference, yet cause a significant increase in the time required for data analysis.