Skip to main content

A Population Genetics-Phylogenetics Approach to Inferring Natural Selection in Coding Sequences

Author(s): Wilson, Daniel J.; Hernandez, Ryan D.; Andolfatto, Peter; Przeworski, Molly

To refer to this page use:
Abstract: Through an analysis of polymorphism within and divergence between species, we can hope to learn about the distribution of selective effects of mutations in the genome, changes in the fitness landscape that occur over time, and the location of sites involved in key adaptations that distinguish modern-day species. We introduce a novel method for the analysis of variation in selection pressures within and between species, spatially along the genome and temporally between lineages. We model codon evolution explicitly using a joint population genetics-phylogenetics approach that we developed for the construction of multiallelic models with mutation, selection, and drift. Our approach has the advantage of performing direct inference on coding sequences, inferring ancestral states probabilistically, utilizing allele frequency information, and generalizing to multiple species. We use a Bayesian sliding window model for intragenic variation in selection coefficients that efficiently combines information across sites and captures spatial clustering within the genome. To demonstrate the utility of the method, we infer selective pressures acting in Drosophila melanogaster and D. simulans from polymorphism and divergence data for 100 X-linked coding regions.
Publication Date: 1-Dec-2011
Electronic Publication Date: 1-Dec-2011
Citation: Wilson, Daniel J., Hernandez, Ryan D., Andolfatto, Peter, Przeworski, Molly. (2011). A Population Genetics-Phylogenetics Approach to Inferring Natural Selection in Coding Sequences. PLoS Genetics, 7 (12), e1002395 - e1002395. doi:10.1371/journal.pgen.1002395
DOI: doi:10.1371/journal.pgen.1002395
EISSN: 1553-7404
Pages: e1002395 - e1002395
Type of Material: Journal Article
Journal/Proceeding Title: PLoS Genetics
Version: Final published version. This is an open access article.

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.