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


RESULTS
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
The procedure described turns out to be much faster than former bayesian approaches and also reasonably efficient especially to detect loci under positive selection.
Gautier
Mathieu
M
INRA, UMR1313 GABI, Jouy-en-Josas, France. mathieu.gautier@jouy.inra.fr
Hocking
Toby Dylan
TD
Foulley
Jean-Louis
JL
D000222:Adaptation, Physiological; D000818:Animals; D001499:Bayes Theorem; D002417:Cattle; D030541:Databases, Genetic; D017353:Gene Deletion; D056426:Genetic Loci / Q000235:genetics; D023281:Genomics / Q000379:methods*; D005838:Genotype; D016013:Likelihood Functions; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012641:Selection, Genetic