Population genetics without intraspecific data.
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.
Thorne
Jeffrey L
JL
Wissenschaftskolleg zu Berlin, Institute for Advanced Study, Berlin, Germany. thorne@statgen.ncsu.edu
Choi
Sang Chul
SC
Yu
Jiaye
J
Higgs
Paul G
PG
Kishino
Hirohisa
H
D000818:Animals; D017304:Annexin A5 / Q000235:genetics*; D019295:Computational Biology; D003198:Computer Simulation; D019143:Evolution, Molecular; D014644:Genetic Variation; D005828:Genetics, Population; D016013:Likelihood Functions; D051379:Mice; D008954:Models, Biological; D008957:Models, Genetic; D009690:Nucleic Acid Conformation; D010641:Phenotype; D010802:Phylogeny; D012313:RNA / Q000737:chemistry / Q000235:genetics*; D051381:Rats; D012641:Selection, Genetic
D017304:Annexin A5; D012313:RNA