Detecting adaptation in protein-coding genes using a Bayesian site-heterogeneous mutation-selection codon substitution model
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.
|Keywords||Dirichlet process, epistasis, fitness landscape, Markov chain Monte Carlo, Nearly neutral evolution|
|Journal||Molecular Biology and Evolution|
Rodrigue, N, & Lartillot, N. (Nicolas). (2017). Detecting adaptation in protein-coding genes using a Bayesian site-heterogeneous mutation-selection codon substitution model. Molecular Biology and Evolution, 34(1), 204–214. doi:10.1093/molbev/msw220