Accuracy and Power of Statistical Methods for Detecting Adaptive Evolution in Protein Coding Sequences and for Identifying Positively Selected Sites

Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14850, USA.
Genetics (Impact Factor: 5.96). 10/2004; 168(2):1041-51. DOI: 10.1534/genetics.104.031153
Source: PubMed


The parsimony method of Suzuki and Gojobori (1999) and the maximum likelihood method developed from the work of Nielsen and Yang (1998) are two widely used methods for detecting positive selection in homologous protein coding sequences. Both methods consider an excess of nonsynonymous (replacement) substitutions as evidence for positive selection. Previously published simulation studies comparing the performance of the two methods show contradictory results. Here we conduct a more thorough simulation study to cover and extend the parameter space used in previous studies. We also reanalyzed an HLA data set that was previously proposed to cause problems when analyzed using the maximum likelihood method. Our new simulations and a reanalysis of the HLA data demonstrate that the maximum likelihood method has good power and accuracy in detecting positive selection over a wide range of parameter values. Previous studies reporting poor performance of the method appear to be due to numerical problems in the optimization algorithms and did not reflect the true performance of the method. The parsimony method has a very low rate of false positives but very little power for detecting positive selection or identifying positively selected sites.

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    • "ssified into 3 classes : 0 < ω < 1 , ω = 1 and ω > 1 ) . These two models were com - pared by LRT and when the MA best fitted the data , we proceeded to one additional comparison ; between MA and MA ω=1 ( where ω is forced to be 1 in the third class ) to distinguish relaxed selective constraints from posi - tive selection ( Yang and Nielsen 2002 ; Wong et al . 2004 ) ."
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