Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time

  • Nature Ecology & Evolutionvolume 1pages15511561 (2017)
  • doi:10.1038/s41559-017-0297-1
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Rapid phenotypic evolution of quantitative traits can occur within years, but its underlying genetic architecture remains uncharacterized. Here we test the theoretical prediction that genes with intermediate pleiotropy drive adaptive evolution in nature. Through a resurrection experiment, we grew Arabidopsis thaliana accessions collected across an 8-year period in six micro-habitats representative of that local population. We then used genome-wide association mapping to identify the single-nucleotide polymorphisms (SNPs) associated with evolved and unevolved traits in each micro-habitat. Finally, we performed a selection scan by testing for temporal differentiation in these SNPs. Phenotypic evolution was consistent across micro-habitats, but its associated genetic bases were largely distinct. Adaptive evolutionary change was most strongly driven by a small number of quantitative trait loci (QTLs) with intermediate degrees of pleiotropy; this pleiotropy was synergistic with the per-trait effect size of the SNPs, increasing with the degree of pleiotropy. In addition, weak selection was detected for frequent micro-habitat-specific QTLs that shape single traits. In this population, A. thaliana probably responded to local warming and increased competition, in part mediated by central regulators of flowering time. This genetic architecture, which includes both synergistic pleiotropic QTLs and distinct QTLs within particular micro-habitats, enables rapid phenotypic evolution while still maintaining genetic variation in wild populations.

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Change history

  • Correction 05 December 2017

    In the version of this Article previously published, there was a typographical error (‘4’ instead of ‘2’) in the equations relating F ST and effective population size (N e) in the Methods section ‘Genome-wide scan for selection based on temporal differentiation’. The correct equations are given below. $${F}_{{\rm{ST}}}\approx \frac{T}{T+2}=\frac{\tau }{\tau +2{N}_{{\rm{e}}}}$$ F ST ≈ T T + 2 = τ τ + 2 N e $${\hat{N}}_{{\rm{e}}}=\frac{\tau \left(1-{\hat{\bar{F}}}_{{\rm{ST}}}\right)}{2{\hat{\bar{F}}}_{{\rm{ST}}}}$$ N ^ e = τ 1 - F ̄ ^ ST 2 F ̄ ^ ST


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We thank B. Brachi for his helpful discussions on the enrichment analysis in biological processes. This work was funded by the Région Midi-Pyrénées (CLIMARES project), the INRA Santé des Plantes et Environnement department (RESURRECTION project), the INRA-ACCAF metaprogram (SELFADAPT project), the LABEX TULIP (ANR-10-LABX-41, ANR-11-IDEX-0002-02) and the National Institute of Health.

Author information

Author notes

  1. Léa Frachon and Cyril Libourel contributed equally to this work.


  1. LIPM, Université de Toulouse, INRA, CNRS, 31326, Castanet-Tolosan, France

    • Léa Frachon
    • , Cyril Libourel
    • , Sébastien Carrère
    • , Carine Huard-Chauveau
    • , Dominique Roby
    •  & Fabrice Roux
  2. Laboratoire Evolution, Ecologie et Paléontologie, UMR CNRS 8198, Université de Lille, 59655, Villeneuve d’Ascq Cedex, France

    • Romain Villoutreix
    • , Cédric Glorieux
    • , Etienne Baron
    • , Laurent Amsellem
    •  & Fabrice Roux
  3. INRA, UMR CBGP, 34988, Montferrier-sur-Lez, France

    • Miguel Navascués
    •  & Renaud Vitalis
  4. Institut de Biologie Computationnelle, Montpellier, 34095, France

    • Miguel Navascués
    •  & Renaud Vitalis
  5. UMR AGAP, INRA, 34060, Montpellier, France

    • Laurène Gay
  6. INRA, GeT-PlaGe, Genotoul, 31326, Castanet-Tolosan, France

    • Olivier Bouchez
    •  & Marie Vidal
  7. GenPhySE, Université de Toulouse, INRA, INPT, INP-ENVT, 31326, Castanet-Tolosan, France

    • Olivier Bouchez
  8. INRA, UAR1209, 31326, Castanet-Tolosan, France

    • Marie Vidal
  9. INRA, UMR1347, Agroécologie, 21065, Dijon, France

    • Valérie Le Corre
  10. Department of Ecology and Evolution, University of Chicago, Chicago, IL, 60637, USA

    • Joy Bergelson


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F.R. supervised the project. F.R. conceived and designed the experiments. E.B., L.A., R.Vil. and F.R. conducted the in situ experiment. L.F., C.G., C.H.-C. and F.R. measured the phenotypic traits. L.F. and F.R. analysed the phenotypic traits. O.B. and M.V. generated the sequencing data. S.C. and C.L. performed the bioinformatics analyses. L.F., C.L. and F.R. performed the GWA mapping. L.F., C.L., D.R. and F.R. performed and analysed the enrichment tests. M.N., L.G. and R.Vit. developed a methodology in selfing species to perform a genome-wide scan for selection based on temporal differentiation. V.L.C. and J.B. guided the analysis of phenotypic and genomic data. F.R. and J.B. wrote the manuscript, with contributions from L.F., C.L., R.Vil., M.N., L.G., R.Vit. and D.R. All authors contributed to the revisions.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Fabrice Roux.

Electronic supplementary material

  1. Supplementary Information

    Supplementary Methods, Supplementary Figures 1–21 and Supplementary Tables 1–7.