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A Bayesian Outlier Criterion to Detect SNPs under Selection in Large Data Sets

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  • Mathieu Gautier
  • Toby Dylan Hocking
  • Jean-Louis Foulley

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.

Suggested Citation

  • Mathieu Gautier & Toby Dylan Hocking & Jean-Louis Foulley, 2010. "A Bayesian Outlier Criterion to Detect SNPs under Selection in Large Data Sets," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-16, August.
  • Handle: RePEc:plo:pone00:0011913
    DOI: 10.1371/journal.pone.0011913
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    References listed on IDEAS

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    1. George Nicholson & Albert V. Smith & Frosti Jónsson & Ómar Gústafsson & Kári Stefánsson & Peter Donnelly, 2002. "Assessing population differentiation and isolation from single‐nucleotide polymorphism data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 695-715, October.
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    Cited by:

    1. Vinicius Francisco Rofatto & Marcelo Tomio Matsuoka & Ivandro Klein & Maurício Roberto Veronez & Luiz Gonzaga da Silveira Junior, 2020. "On the effects of hard and soft equality constraints in the iterative outlier elimination procedure," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-29, August.
    2. Soraggi, Samuele & Wiuf, Carsten, 2019. "General theory for stochastic admixture graphs and F-statistics," Theoretical Population Biology, Elsevier, vol. 125(C), pages 56-66.

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