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Testing for Covarion-like Evolution in Protein Sequences
Author(s) -
Huai-Chun Wang,
Matthew Spencer,
Edward Susko,
Andrew J. Roger
Publication year - 2006
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/msl155
Subject(s) - tree (set theory) , biology , phylogenetic tree , variable (mathematics) , markov chain , markov model , process (computing) , protein evolution , substitution (logic) , biological system , rate of evolution , computer science , mathematics , statistics , genetics , combinatorics , gene , mathematical analysis , programming language , operating system
The covarion hypothesis of molecular evolution proposes that selective pressures on an amino acid or nucleotide site change through time, thus causing changes of evolutionary rate along the edges of a phylogenetic tree. Several kinds of Markov models for the covarion process have been proposed. One model, proposed by Huelsenbeck (2002), has 2 substitution rate classes: the substitution process at a site can switch between a single variable rate, drawn from a discrete gamma distribution, and a zero invariable rate. A second model, suggested by Galtier (2001), assumes rate switches among an arbitrary number of rate classes but switching to and from the invariable rate class is not allowed. The latter model allows for some sites that do not participate in the rate-switching process. Here we propose a general covarion model that combines features of both models, allowing evolutionary rates not only to switch between variable and invariable classes but also to switch among different rates when they are in a variable state. We have implemented all 3 covarion models in a maximum likelihood framework for amino acid sequences and tested them on 23 protein data sets. We found significant likelihood increases for all data sets for the 3 models, compared with a model that does not allow site-specific rate switches along the tree. Furthermore, we found that the general model fit the data better than the simpler covarion models in the majority of the cases, highlighting the complexity in modeling the covarion process. The general covarion model can be used for comparing tree topologies, molecular dating studies, and the investigation of protein adaptation.

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