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21 juillet 2023
- doi: 10.57745/J0ZRRI
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Agricultural Sciences Experimental evolutionSujets proches
Plants--Flowering time Blooming dates Blooming time Flowering time Time, Flowering Nurture and nature Nature versus nurture Genetics and environment Nature--Nurture Environment Heredity and environment Environment Ecological science Environmental biology Bionomics Ecological sciences Environment Balance of nature Oecology Biology--Ecology Ecological processes Environment Time of floweringCiter ce document
Arnaud Desbiez-Piat et al., « DATA from Saclay's Divergent Selection Experiments analysed in Desbiez-Piat et al. 2023 », Recherche Data Gouv, ID : 10.57745/J0ZRRI
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Résumé
Empirical description of phenotypic shifts in selection experiments have nourished quantitative genetics models. These models have shown that long-term selection responses depend both on the initial standing variance as well as the flux of incoming mutations, and that quantifying the proportion of genetic variance due to latter is essential to understanding the seeming absence of selection limits. There are however at least two important things to keep in mind when interpreting selection experiments: first correlative responses to selection on non-focal traits can perturb the selection response on the focal trait and second, generations and selection environments are confounded so that genotype by environment interactions are ignored. Here we used the Saclay Divergent Selection Experiments (DSE) on maize flowering time that display striking selection responses over 18 generations and combined yearly measurements of flowering time (DSEYM), measurements of 11 traits on a subset of 308 DSE genotypes measured for two consecutive years in common gardens (DSECG), as well as genotyping data to track the fate of individual mutations in pedigrees, with four main objectives: (1) to quantify the relative contribution of standing variation and de novo mutational variance to the selection response; (2) to approximate the distribution of phenotypic effects of de novo mutations; (3) to study the impact of GxE interactions in the observed selection response; (4) to describe how trait correlations modulate the exploration of the phenotypic space. In agreement with theoretical predictions, we experimentally validated two successive phases in the selection response: one governed by the fixation of standing variants whose contribution quickly reached a plateau, and the second governed by the fixation of de novo mutations sustaining the selection response. The distribution of selected phenotypic effects confirmed the expected enrichment of fixation of beneficial mutations with an average effect of +0.278 and +0.299 days to flowering, depending on the genetic background; but also revealed fixation of unfavorable mutations reaching up to 25% of incoming mutations. Accumulation of genetic load may be due to antagonistic pleiotropy whereby mutations that were fixed in the selection environment (DSEYM) turn to be unfavorable in evaluation environment (DSECG). Finally, we found that global patterns of correlations between-traits are conserved across genetic backgrounds but exhibit a temporal pattern. Traits that are weakly or uncorrelated with flowering time allow stochastic exploration of the phenotypic space, owing to the microenvironment-specific fixation of standing variants and a pleiotropic mutational input.