MBE Advance Access first published online on April 29, 2007
This version published online on May 7, 2007
Molecular Biology and Evolution, doi:10.1093/molbev/msm085
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Research Article |
Population genetics without intraspecific data
1 Wissenschaftskolleg zu Berlin, Institute for Advanced Study
2 Bioinformatics Research Center, North Carolina State University
3 Bioinformatics Centre, University of Copenhagen
4 Department of Physics and Astronomy, McMaster University
5 Laboratory of Biometrics, University of Tokyo
Corresponding Author: Jeffrey L. Thorne (thorne{at}statgen.ncsu.edu), Contact Information (until Jan. 30, 2007): Wissenschaftskolleg zu Berlin, Wallotstrasse 19, 14193 Berlin, Germany, Phone: +49-30-89001-231, FAX: +49-30-89001-300, Contact Information (after Jan. 30, 2007): Bioinformatics Research Center, Box 7566, N.C. State University, Raleigh NC, 27695-7566, USA, Phone +1-919-515-1946, FAX: +1-919-515-7315
Received for publication November 28, 2006. Revision received April 13, 2007. Accepted for publication April 18, 2007.
A central goal of computational biology is the prediction of phenotype from DNA and protein sequence data. Recent models of sequence change use in silico prediction systems to incorporate the effects of phenotype on evolutionary rates. These models have been designed for analyzing sequence data from different species and have been accompanied by statistical techniques for estimating model parameters when the incorporation of phenotype induces dependent change among sequence positions. A difficulty with these efforts to link phenotype and interspecific evolution is that evolution occurs within populations and parameters of interspecific models should have population genetic interpretations. We show with two examples how population genetic interpretations can be assigned to evolutionary models. The first example considers the impact of RNA secondary structure on sequence change and the second reflects the tendency for protein tertiary structure to influence nonsynonymous substitution rates. We argue that statistical fit to data should not be the sole criterion for assessing models of sequence change. A good interspecific model should also yield a clear and biologically plausible population genetic interpretation.
Key Words: Dependence among sites • Fixation probability • Protein structure • RNA structure
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