JSM 2005 - Toronto

Abstract #304649

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Activity Number: 474
Type: Topic Contributed
Date/Time: Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304649
Title: Detecting Differential Selection on Predefined Groups of Sequences
Author(s): Karin Dorman*+ and Xun Gu
Companies: Iowa State University and Iowa State University
Address: Department of Statistics, Ames, IA, 50011, United States
Keywords: Bayesian phylogenetics ; functional divergence ; HIV ; selection ; evolution
Abstract:

Since Aristotle's first classification efforts, biologists have been organizing the diversity of life. With modern high-throughput technology, genetic sequences now abound and they too are classified, for example, into protein families demarcated by ancient gene duplication events or viral genotypes generated by evolution in relative geographic isolation. While it is second nature to classify sequences, it is far more difficult to determine when or if functional differences accompany these groupings. We describe a Bayesian phylogenetic method for identifying nucleotide or amino acid positions that experience significantly disparate selection pressures in alternative, predefined groups. In the simplest model of two groups, the mutation rates at a sequence position in the two groups are either correlated or uncorrelated. Uncorrelated mutation rates indicate differentially selected positions that may be the reason for functional differences or the target of disparate environmental forces. We demonstrate the method through simulation and application to HIV nucleotide sequences from multiple subtypes.

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