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

Accuracy and Power of Statistical Methods for Detecting Adaptive Evolution in Protein Coding... The parsimony method of S uzuki and G ojobori (1999) and the maximum likelihood method developed from the work of N ielsen and Y ang (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. Footnotes Communicating editor: J. W akeley Received May 12, 2004. Accepted June 23, 2004. Genetics Society of America http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Genetics Genetics Society of America

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

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Publisher
Genetics Society of America
Copyright
Copyright © 2004 by the Genetics Society of America
ISSN
0016-6731
eISSN
1943-2631
D.O.I.
10.1534/genetics.104.031153
Publisher site
See Article on Publisher Site

Abstract

The parsimony method of S uzuki and G ojobori (1999) and the maximum likelihood method developed from the work of N ielsen and Y ang (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. Footnotes Communicating editor: J. W akeley Received May 12, 2004. Accepted June 23, 2004. Genetics Society of America

Journal

GeneticsGenetics Society of America

Published: Oct 1, 2004

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