Genetics, Vol. 168, 1041-1051, October 2004, Copyright © 2004
doi:10.1534/genetics.104.031153

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

Wendy S. W. Wong*,1, Ziheng Yang{dagger}, Nick Goldman{ddagger} and Rasmus Nielsen*,§

* Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14850
{dagger} Department of Biology, University College London, London WC1E 6BT, United Kingdom
{ddagger} European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
§ Center for Bioinformatics, University of Copenhagen, Copenhagen 2100 Kbh Ø, Denmark

1 Corresponding author: Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850.
E-mail: sww8{at}cornell.edu

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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