A bayesian outlier criterion to detect SNPs under selection in large data sets

Mathieu Gautier, Toby Dylan Hocking, Jean Louis Foulley

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Background: The recent advent of high-throughput SNP genotyping technologies has opened new avenues of research for population genetics. In particular, a growing interest in the identification of footprints of selection, based on genome scans for adaptive differentiation, has emerged. Methodology/Principal Findings: The purpose of this study is to develop an efficient model-based approach to perform Bayesian exploratory analyses for adaptive differentiation in very large SNP data sets. The basic idea is to start with a very simple model for neutral loci that is easy to implement under a Bayesian framework and to identify selected loci as outliers via Posterior Predictive P-values (PPP-values). Applications of this strategy are considered using two different statistical models. The first one was initially interpreted in the context of populations evolving respectively under pure genetic drift from a common ancestral population while the second one relies on populations under migration-drift equilibrium. Robustness and power of the two resulting Bayesian model-based approaches to detect SNP under selection are further evaluated through extensive simulations. An application to a cattle data set is also provided. Conclusions/Significance: The procedure described turns out to be much faster than former Bayesian approaches and also reasonably efficient especially to detect loci under positive selection.

Original languageEnglish (US)
Article numbere11913
JournalPLoS ONE
Volume5
Issue number8
DOIs
StatePublished - 2010
Externally publishedYes

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'A bayesian outlier criterion to detect SNPs under selection in large data sets'. Together they form a unique fingerprint.

Cite this