Characterizing Molecular Evolution via Bayesian Nonparametric Mixture Models

• Activity Number: 657
• Type: Contributed
• Date/Time: Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Saheli Datta*+ and Abel Rodriguez and Raquel Prado
• Companies: University of California at Santa Cruz and University of California at Santa Cruz and University of California at Santa Cruz
• Keywords: Bayesian ; Hierarchical ; Evolution ; Nonparametric

Abstract

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 elevated 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 Bayesian hierarchical regression model that allows us to determine which sites display substitutions that conserve or radically change a set of amino acid properties. Our model uses nested nonparametric priors to account for similarities across properties and across sites. We illustrate our approach by analyzing simulated data sets and real data examples.