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Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time

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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https://hal.archives-ouvertes.fr/hal-01723277
Contributor : Anne-Gestion Labo Duputié <>
Submitted on : Monday, March 5, 2018 - 2:17:52 PM
Last modification on : Thursday, May 28, 2020 - 8:04:04 PM

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Léa Frachon, Cyril Libourel, Romain Villoutreix, Sébastien Carrere, Cédric Glorieux, et al.. Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time. Nature Ecology & Evolution, Nature, 2017, 1 (10), pp.1551 - 1561. ⟨10.1038/s41559-017-0297-1⟩. ⟨hal-01723277⟩

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