a bayesian outlier criterion to detect snps under selection in large data sets贝叶斯的异常判据检测单核苷酸多态性在大型数据集选择.pdf

A Bayesian Outlier Criterion to Detect SNPs under Selection in Large Data Sets 1,2 1 1 Mathieu Gautier *, Toby Dylan Hocking , Jean-Louis Foulley 1 INRA, UMR1313 GABI, Jouy-en-Josas, France, 2 INRA, UMR1031 CBGP, Montferrier-sur-Lez, France 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. Citation: Gautier M, Hocking TD, Foulley J-L (2010) A Bayesian Ou