Posada D
Department of Zoology, Brigham Young University, Provo, UT 84602-5255, USA.
J Mol Evol. 2001 May;52(5):434-44. doi: 10.1007/s002390010173.
Models of sequence evolution play an important role in molecular evolutionary studies. The use of inappropriate models of evolution may bias the results of the analysis and lead to erroneous conclusions. Several procedures for selecting the best-fit model of evolution for the data at hand have been proposed, like the likelihood ratio test (LRT) and the Akaike (AIC) and Bayesian (BIC) information criteria. The relative performance of these model-selecting algorithms has not yet been studied under a range of different model trees. In this study, the influence of branch length variation upon model selection is characterized. This is done by simulating sequence alignments under a known model of nucleotide substitution, and recording how often this true model is recovered by different model-fitting strategies. Results of this study agree with previous simulations and suggest that model selection is reasonably accurate. However, different model selection methods showed distinct levels of accuracy. Some LRT approaches showed better performance than the AIC or BIC information criteria. Within the LRTs, model selection is affected by the complexity of the initial model selected for the comparisons, and only slightly by the order in which different parameters are added to the model. A specific hierarchy of LRTs, which starts from a simple model of evolution, performed overall better than other possible LRT hierarchies, or than the AIC or BIC.
序列进化模型在分子进化研究中起着重要作用。使用不恰当的进化模型可能会使分析结果产生偏差并导致错误结论。已经提出了几种为手头数据选择最佳拟合进化模型的方法，如似然比检验（LRT）以及赤池信息准则（AIC）和贝叶斯信息准则（BIC）。这些模型选择算法在一系列不同的模型树情况下的相对性能尚未得到研究。在本研究中，表征了分支长度变化对模型选择的影响。这是通过在已知的核苷酸替换模型下模拟序列比对，并记录不同的模型拟合策略能够恢复该真实模型的频率来实现的。本研究结果与先前的模拟结果一致，并表明模型选择具有合理的准确性。然而，不同的模型选择方法显示出不同程度的准确性。一些LRT方法表现出比AIC或BIC信息准则更好的性能。在LRT方法中，模型选择受为比较所选初始模型的复杂性影响，而受不同参数添加到模型中的顺序影响较小。一种从简单进化模型开始的特定LRT层次结构总体上比其他可能的LRT层次结构或比AIC或BIC表现更好。
