title: On the statistical interpretation of site-specific variables in phylogeny-based substitution models.
abstract: Phylogeny-based modeling of heterogeneity across the positions of multiple-sequence alignments has generally been approached from two main perspectives. The first treats site specificities as random variables drawn from a statistical law, and the likelihood function takes the form of an integral over this law. The second assigns distinct variables to each position, and, in a maximum-likelihood context, adjusts these variables, along with global parameters, to optimize a joint likelihood function. Here, it is emphasized that while the first approach directly enjoys the statistical guaranties of traditional likelihood theory, the latter does not, and should be approached with particular caution when the site-specific variables are high dimensional. Using a phylogeny-based mutation-selection framework, it is shown that the difference in interpretation of site-specific variables explains the incongruities in recent studies regarding distributions of selection coefficients.
authors.affiliation: Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada. nicolas.rodrigue@agr.gc.ca
authors.forename: Nicolas
authors.initials: N
authors.lastname: Rodrigue
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D004272:DNA, Mitochondrial / Q000235:genetics; D003627:Data Interpretation, Statistical; D019143:Evolution, Molecular; D014644:Genetic Variation; D016013:Likelihood Functions; D008322:Mammals / Q000235:genetics; D008390:Markov Chains; D015233:Models, Statistical; D009975:Orthomyxoviridae / Q000235:genetics; D010802:Phylogeny; D016415:Sequence Alignment; D014764:Viral Proteins / Q000235:genetics
chemical_list: D004272:DNA, Mitochondrial; C502893:PB2 protein, influenza virus; D014764:Viral Proteins