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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-08-09

AUTHORS

Arong Luo, Huijie Qiao, Yanzhou Zhang, Weifeng Shi, Simon YW Ho, Weijun Xu, Aibing Zhang, Chaodong Zhu

ABSTRACT

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. More... »

PAGES

242

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2148-10-242

DOI

http://dx.doi.org/10.1186/1471-2148-10-242

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004149138

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20696057


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