Performance of criteria for selecting evolutionary models in phylogenetics: a comprehensive study based on simulated datasets

BMC Evolutionary Biology

Research Article

Simon YW Ho¹  Aibing Zhang²  Chaodong Zhu³  Yanzhou Zhang³  Huijie Qiao⁴  Arong Luo⁴  Weifeng Shi⁵  Weijun Xu⁶ 

[1] Centre for Macroevolution and Macroecology, Research School of Biology, Australian National University, 0200, Canberra, ACT, Australia;School of Biological Sciences, University of Sydney, 2006, Sydney, NSW, Australia;College of Life Sciences, Capital Normal University, 100048, Beijing, China;Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China;Institute of Zoology, Chinese Academy of Sciences, 100101, Beijing, China;Graduate University of Chinese Academy of Sciences, 100049, Beijing, China;UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin 4, Ireland;Zhongbei College, Nanjing Normal University, 210046, Nanjing, China;

关键词: Model Selection;    Akaike Information Criterion;    Bayesian Information Criterion;    Decision Theory;    Simulated Dataset;   

DOI  :  10.1186/1471-2148-10-242

 received in 2010-03-18, accepted in 2010-08-09,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundExplicit evolutionary models are required in maximum-likelihood and Bayesian inference, the two methods that are overwhelmingly used in phylogenetic studies of DNA sequence data. Appropriate selection of nucleotide substitution models is important because the use of incorrect models can mislead phylogenetic inference. To better understand the performance of different model-selection criteria, we used 33,600 simulated data sets to analyse the accuracy, precision, dissimilarity, and biases of the hierarchical likelihood-ratio test, Akaike information criterion, Bayesian information criterion, and decision theory.ResultsWe demonstrate that the Bayesian information criterion and decision theory are the most appropriate model-selection criteria because of their high accuracy and precision. Our results also indicate that in some situations different models are selected by different criteria for the same dataset. Such dissimilarity was the highest between the hierarchical likelihood-ratio test and Akaike information criterion, and lowest between the Bayesian information criterion and decision theory. The hierarchical likelihood-ratio test performed poorly when the true model included a proportion of invariable sites, while the Bayesian information criterion and decision theory generally exhibited similar performance to each other.ConclusionsOur results indicate that the Bayesian information criterion and decision theory should be preferred for model selection. Together with model-adequacy tests, accurate model selection will serve to improve the reliability of phylogenetic inference and related analyses.

【 授权许可】

Unknown   
© Luo et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.