DATA from Saclay's Divergent Selection Experiments analysed in Desbiez-Piat et al. 2023
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".