title: Detecting Adaptation in Protein-Coding Genes Using a Bayesian Site-Heterogeneous Mutation-Selection Codon Substitution Model.
abstract: Codon substitution models have traditionally attempted to uncover signatures of adaptation within protein-coding genes by contrasting the rates of synonymous and non-synonymous substitutions. Another modeling approach, known as the mutation-selection framework, attempts to explicitly account for selective patterns at the amino acid level, with some approaches allowing for heterogeneity in these patterns across codon sites. Under such a model, substitutions at a given position occur at the neutral or nearly neutral rate when they are synonymous, or when they correspond to replacements between amino acids of similar fitness; substitutions from high to low (low to high) fitness amino acids have comparatively low (high) rates. Here, we study the use of such a mutation-selection framework as a null model for the detection of adaptation. Following previous works in this direction, we include a deviation parameter that has the effect of capturing the surplus, or deficit, in non-synonymous rates, relative to what would be expected under a mutation-selection modeling framework that includes a Dirichlet process approach to account for across-codon-site variation in amino acid fitness profiles. We use simulations, along with a few real data sets, to study the behavior of the approach, and find it to have good power with a low false-positive rate. Altogether, we emphasize the potential of recent mutation-selection models in the detection of adaptation, calling for further model refinements as well as large-scale applications.
authors.affiliation: Department of Biology, Institute of Biochemistry, and School of Mathematics and Statistics, Carleton University, Ottawa, Canada nicolas.rodrigue@carleton.ca.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Rodrigue
authors.affiliation: Université de Lyon, Laboratoire de Biométrie, Biologie Évolutive, Villeurbanne, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Lartillot
mesh_terms: D000220:Adaptation, Biological / Q000235:genetics*; D019943:Amino Acid Substitution; D000596:Amino Acids / Q000235:genetics; D001499:Bayes Theorem; D003062:Codon; D003198:Computer Simulation; D004843:Epistasis, Genetic; D019143:Evolution, Molecular; D018740:Genetic Heterogeneity; D008957:Models, Genetic; D009154:Mutation; D059645:Mutation Rate; D010802:Phylogeny; D012641:Selection, Genetic / Q000235:genetics*
chemical_list: D000596:Amino Acids; D003062:Codon
keywords: Dirichlet process; Markov chain Monte Carlo; epistasis; fitness landscape; nearly neutral evolution