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

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.

SUBMITTER: Gautier M 

PROVIDER: S-EPMC2914027 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature