Performance of criteria for selecting evolutionary models in phylogenetics: a comprehensive study based on simulated datasets.
Bottom Line:
Our results also indicate that in some situations different models are selected by different criteria for the same dataset.Our 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.
Affiliation: Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
ABSTRACT
Show MeSH
Background: Explicit 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. Results: We 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. Conclusions: Our 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. |
Related In:
Results -
Collection
License getmorefigures.php?uid=PMC2925852&req=5
Mentions: The hLRT exhibited high accuracy in recovering some models, but unexpectedly, it was always incapable of recovering the four SYM-like models (i.e., SYM, SYM + I, SYM + Γ and SYM + I + Γ; Table 1) (Figure 1). The AIC showed moderate or low accuracy except for a few complex models (e.g., GTR + I + Γ) for which the accuracy was even as high as 1.00 in certain simulations. The accuracy of the BIC and DT differed among simulations. In most cases, they showed high accuracy in recovering almost all of the 24 models (Figure 1A). Compared with the other models, however, two (SYM + I + Γ and GTR + I + Γ) were only moderately recovered in simulations derived from parameter set-B; even all of the SYM-like and GTR-like models were recovered less frequently in simulation I-8 (tree topology with a height of 0.1; Figure 2D) (Figure 1B). The BIC and DT exhibited similar accuracy in simulation I-4 (ultrametric tree topology of 30 taxa, 0.1 tree height; Figure 2D) and simulation IV-1 (non-clock tree topology of 22 taxa; Figure 2G), both recovering less than 35% of models of base + I + Γ category (i.e., JC + I + Γ, K80 + I + Γ, etc.; Table 1) (Figure 1C). In fact, they always selected models of base + Γ category (i.e., JC + Γ, K80 + Γ, etc.). Their accuracy values were high when the value of the parameter for proportion of invariable sites (pinv) was altered from 0.25 in parameter set-A to 0.5 in parameter set-B when simulating datasets (data not shown). Notably, the hLRT and AIC in these two simulations, especially the former criterion, also showed lower accuracy in recovering models of base + I + Γ category than in the other simulations. |
View Article: PubMed Central - HTML - PubMed
Affiliation: Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
Background: Explicit 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.
Results: We 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.
Conclusions: Our 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.