Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, New York
Correspondence: E-mail: djtaylor{at}buffalo.edu.
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Abstract |
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Key Words: Bootstrap incongruence Bayesian phylogenetic inference yeast genome
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Introduction |
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The assumption of a conservative support value and its implementation in phylogenetics software has positioned nonparametric bootstrapping to be the gold standard by which phylogenetic assumptions such as among-gene conflict are assessed. For closely related organisms, the extent of conflict has been difficult to assess because genome projects have been largely restricted to distantly related disease-causing organisms. Recently, Rokas et al. (2003) concluded that phylogenetic conflict among 106 genes of seven closely related yeast species was strongly supported and genomically widespread. They assumed that the nonparametric bootstrap was biased downwards and used a cutoff of 70% or higher<--?1-->to designate "strong support" in their 106-gene data set. Rokas et al. (2003) concluded that more than 20 genes were necessary to attain confidence in yeast phylogenies and that there was no identifiable molecular evolutionary source of phylogenetic conflict. Phillips, Delsuc, and Penny (2004), however, identified a compositional bias in the concatenated yeast data that strongly misleads minimum-evolution (ME) results, but not maximum-likelihood (ML) or maximum-parsimony (MP) results. The role of other biases (such as long-branch attraction from substitution rate differences) in generating conflict among yeast genes has not been explored in detail. The implications of the pervasive conflict finding are profound (Gee 2003). If among-gene conflict is generally strongly supported and widespread, then the results of thousands of existing studies and major biodiversity projects may be unreliable.
Nevertheless, among-gene conflict could be the result of chance alone if based on a bootstrap support value of 70% that is not an underestimate. Here we use the yeast genome data and its strongly supported reference tree to address three questions. What are the estimated accuracies and error rates of nonparametric bootstraps and Bayesian posterior probabilities for yeast? Is among-gene phylogenetic conflict for yeast strongly supported by bootstrap values? Can branch-length biases be ruled out as a source of conflict among yeast genes?
No reliability method yielded support values that clearly underestimated accuracy (fig. 1AF). Indeed, MP and ML bootstrap values were not significantly different from ideal values (table 1). This suggests that yeast bootstrap values of 70% should not be treated as strong support, because there is a 30% probability that such a value is caused by chance alone. The generality of the observed lack of underestimation is unknown because this is the first genome-scale assessment of bootstrap accuracy among closely related taxa. The yeast data did have at least two features shown by Hillis and Bull (1993) to erode underestimation: asymmetric trees and unequal rates of external branch evolution (the average external branch length ratio was 3.29).
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The yeast data show that the assumption of underestimated support values can be a dangerous one. Conflict support can be grossly overestimated if the values are, in fact, accurate. Despite detecting a possible branch-artifact bias, our results revealed no evidence that clades with strong bootstrap support from a single gene are pervasively unreliable. We note that Daubin, Moran, and Ochman (2003) also found negligible (<5%) incongruence among an average of 1,067 genes for closely related genomes of bacteria. Although we agree with Rokas et al. (2003) that genome-scale data will make phylogenetic results more robust, we conclude that studies using less than 20 loci, tests and corrections for biases (Phillips, Delsuc, and Penny 2004), and strong support values (>95%) are not inherently unreliable.
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Methods |
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Nucleotide substitution models were fitted for each of the 106 genes by using two methods: decision theory (for Bayes nt1), as implemented in DT-ModSel (Minin et al. 2003), and likelihood ratio tests (for Bayes nt2 and ML), as implemented in Modeltest version 3.06 (Posada and Crandall 1998). For nt1, we used six substitution parameters when the number of substitution parameters for the model chosen was estimated to be three. For nt2, all parameter values estimated by Modeltest were fixed in the priors to improve comparisons with ML bootstraps. The amino acid rate matrix prior was fixed to the WAG model (Whelan and Goldman 2001) and an among-site rate variation parameter (gamma) was estimated for each gene. All other priors were the default values. Posterior probabilities were estimated with MrBayes version 3.0 or version 3.1 (Huelsenbeck and Ronquist 2001). We ran 500,000 generations, sampled every 100th generation, and conservatively set the burn-in value at 2,500. Where parameters were estimated (nt1), we ran 100,000 generations, sampled every 10th generation, and set the burn-in value at 1,000. The nonparametric bootstrap values were from Rokas et al. (2003).
To assess the accuracy of each method, the support values were binned by increments of 5% between 51 and 100. These mean support values were then plotted against the proportion correct (the median value for each bin) determined by presence in the reference tree. Nonparametric Wilcoxon signed-ranks tests were used to determine the statistical significance of the support levels from ideal (Simmons, Pickett, and Miya 2004).
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Acknowledgements |
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Footnotes |
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Literature Cited |
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