Bayesian hierarchical models in characterizing molecular adaptation

Assessing the selective influence of amino acid properties is important in understanding evolution at the molecular level. A collection of methods and models have been developed in recent years to determine if amino acid sites in a given DNA sequence alignment display substitutions that are altering or conserving a pre-specified set of amino acid properties. Residues showing an increased number of substitutions that favorably alter a physicochemical property are considered targets of positive natural selection. Such approaches usually perform independent analyses for each amino acid property under consideration, without taking into account the fact that some of the properties may be highly correlated.;We propose a suite of Bayesian hierarchical regression models that allow us to determine which sites display substitutions that conserve or radically change a set of amino acid properties. The proposed models take into account correlations that may be present in the data, both at the level of sites and properties. The models also properly adjust for multiple comparisons. We illustrate our approaches by analyzing simulated data sets and real data examples. Our analyses indicate that a more complete quantitative and qualitative characterization of molecular adaptation is achieved by taking into account changes in amino acid properties.