Abstract

BackgroundThe 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 FindingsThe 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/SignificanceThe procedure described turns out to be much faster than former Bayesian approaches and also reasonably efficient especially to detect loci under positive selection.

Highlights

  • The recent advent of high-throughput Single Nucleotide Polymorphism (SNP) genotyping technologies has opened new avenues of research for population genetics

  • We investigate two different statistical models: i) a model interpreted in the context of populations evolving under pure genetic drift from a common ancestral population [17] and ii) a model interpreted in the context of populations under migrationdrift equilibrium [10,18]

  • The statistical model 1 was proposed to deal with this latter kind of non-equilibrium demographic scenarios [17,18]

Read more

Summary

Introduction

The recent advent of high-throughput Single Nucleotide Polymorphism (SNP) genotyping technologies has opened new avenues of research for population genetics. Markers affected by selection are expected to display an unexpectedly high or low value relative to the null distribution of FST for markers not under selection This null distribution typically depends on the (usually unknown) demographic history of the populations surveyed and two main types of strategies have been reported to estimate it, using either i) data simulated under demographic models [6] which are generally simple and restrictive or ii) directly from the observed data under the assumption that most of the analyzed markers are neutral [7,8,9]. A growing interest in the identification of footprints of selection, based on genome scans for adaptive differentiation, has emerged

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call