Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations : [W633]

De Mita Stéphane, Thuillet Anne-Céline, Gay Laurène, Ahmadi Nourollah, Manel Stéphanie, Ronfort Joëlle, Vigouroux Yves. 2014. Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations : [W633]. In : Plant and Animal Genomes Conference XXII Conference, San diego, United States, San Diego, United States, January 11-15, 2014. s.l. : s.n., Résumé Plant and Animal Genome Conference. 22, San Diego, États-Unis, 11 January 2014/15 January 2014.

Paper without proceedings
Published version - Anglais
Use under authorization by the author or CIRAD.

Télécharger (19kB) | Preview

Abstract : Thanks to genome-scale diversity data, present-day studies can provide a detailed view of how natural and cultivated species adapt to their environment and particularly to environmental gradients. However, due to their sensitivity, up-to-date studies might be more sensitive to undocumented demographic effects such as the pattern of migration and the reproduction regime. In this study, we provide guidelines for the use of popular or recently developed statistical methods to detect footprints of selection. We simulated 100 populations along a selective gradient and explored different migration models, sampling schemes and rates of self-fertilization. We investigated the power and robustness of eight methods to detect loci potentially under selection: three designed to detect genotype-environment correlations and five designed to detect adaptive differentiation (based on F(ST) or similar measures). We show that genotype-environment correlation methods have substantially more power to detect selection than differentiation-based methods but that they generally suffer from high rates of false positives. This effect is exacerbated whenever allele frequencies are correlated, either between populations or within populations. Our results suggest that, when the underlying genetic structure of the data is unknown, a number of robust methods are preferable. Moreover, in the simulated scenario we used, sampling many populations led to better results than sampling many individuals per population. Finally, care should be taken when using methods to identify genotype-environment correlations without correcting for allele frequency autocorrelation because of the risk of spurious signals due to allele frequency correlations between populations. (Texte intégral)

Classification Agris : F30 - Plant genetics and breeding
L10 - Animal genetics and breeding
U10 - Mathematical and statistical methods

Auteurs et affiliations

  • De Mita Stéphane, INRA (FRA)
  • Thuillet Anne-Céline, IRD (FRA)
  • Gay Laurène, INRA (FRA)
  • Ahmadi Nourollah, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0003-0072-6285
  • Manel Stéphanie, Université Aix-Marseille I (FRA)
  • Ronfort Joëlle, INRA (FRA)
  • Vigouroux Yves, IRD (FRA)

Source : Cirad - Agritrop (

View Item (staff only) View Item (staff only)

[ Page générée et mise en cache le 2019-10-05 ]