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

• Published in: PloS one Vol. 5; no. 8; p. e11913
• Main Authors: Gautier, Mathieu, Hocking, Toby Dylan, Foulley, Jean-Louis
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.08.2010; Public Library of Science (PLoS)
• Subjects: Adaptation; Adaptation, Physiological; Algorithms; Animals; Bayes Theorem; Bayesian analysis; Bias; Binomial distribution; Biological evolution; Bovidae; Cattle; Computational Biology/Population Genetics; Computer simulation; Data analysis; Data processing; Databases, Genetic; Datasets; Differentiation; Drift; Equilibrium; Gene Deletion; Genetic drift; Genetic Loci - genetics; Genetics; Genetics and Genomics/Population Genetics; Genomes; Genomics; Genomics - methods; Genotype; Genotyping; Life Sciences; Likelihood Functions; Loci; Mathematical models; Mathematics/Statistics; Migration; Models, Genetic; Outliers (statistics); Polymorphism, Single Nucleotide; Population (statistical); Population genetics; Positive selection; Probability; Reproducibility of Results; Selection, Genetic; Single-nucleotide polymorphism; Statistical analysis; Statistical models; Studies; France; data; snp; bayesian
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0011913

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. 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. The procedure described turns out to be much faster than former bayesian approaches and also reasonably efficient especially to detect loci under positive selection.