RESUMO
Explicit evaluation of the accuracy and power of maximum likelihood and Bayesian methods for detecting site-specific positive Darwinian selection presents a challenge because selective consequences of single amino acid changes are generally unknown. We exploited extensive molecular and functional characterization of amino acid substitutions in the plant gene eIF4E to evaluate the performance of these methods in detecting site-specific positive selection. We documented for the first time a molecular signature of positive selection within a recessive resistance gene in plants. We then used two statistical platforms, Phylogenetic Analysis Using Maximum Likelihood and Hypothesis Testing Using Phylogenies (HyPhy), to look for site-specific positive selection. Their relative power and accuracy are assessed by comparing the sites they identify as being positively selected with those of resistance-determining amino acids. Our results indicate that although both methods are surprisingly accurate in their identification of resistance sites, HyPhy appears to more accurately identify biologically significant amino acids using our data set.
