Genome scan methods against more complex models: when and how much should we trust them?

DOI Web Site Web Site 被引用文献3件
  • Pierre de Villemereuil
    Centre National de la Recherche Scientifique Université Jospeh Fourier LECA UMR 5553 2233 rue de la piscine 38400 Saint Martin d'Hères France
  • Éric Frichot
    Centre National de la Recherche Scientifique Université Joseph Fourier Grenoble TIMC‐IMAG UMR 5525 38042 Grenoble France
  • Éric Bazin
    Centre National de la Recherche Scientifique Université Jospeh Fourier LECA UMR 5553 2233 rue de la piscine 38400 Saint Martin d'Hères France
  • Olivier François
    Centre National de la Recherche Scientifique Université Joseph Fourier Grenoble TIMC‐IMAG UMR 5525 38042 Grenoble France
  • Oscar E. Gaggiotti
    Centre National de la Recherche Scientifique Université Jospeh Fourier LECA UMR 5553 2233 rue de la piscine 38400 Saint Martin d'Hères France

書誌事項

公開日
2014-04
権利情報
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
  • http://doi.wiley.com/10.1002/tdm_license_1.1
DOI
  • 10.1111/mec.12705
公開者
Wiley

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説明

<jats:title>Abstract</jats:title><jats:p>The recent availability of next‐generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual‐based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)‐based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies <jats:italic>versus</jats:italic> individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci.</jats:p>

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