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Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time

Nature Ecology & Evolution volume 1, pages 1551–1561 (2017)Cite this article

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Abstract

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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Fig. 1: Genetic variation among accessions and phenotypic evolution between 2002 and 2010.
Fig. 2: Phenotypic changes in the TOU-A population over eight generations.
Fig. 3: Genomic patterns of the TOU-A population.
Fig. 4: Identification of genomic regions associated with bolting time variation in the TOU-A population.
Fig. 5: Genetic architecture underlying in situ phenotypic evolution in the TOU-A population when considering a threshold of 200 top SNPs.

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Change history

  • 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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Acknowledgements

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.

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Author notes

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

Authors and Affiliations

  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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  1. Léa Frachon
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Contributions

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.

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Correspondence to Fabrice Roux.

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A correction to this article is available online at https://doi.org/10.1038/s41559-017-0405-2.

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

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Frachon, L., Libourel, C., Villoutreix, R. et al. Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time. Nat Ecol Evol 1, 1551–1561 (2017). https://doi.org/10.1038/s41559-017-0297-1

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