Genetic variance in contrasting environments
Bibliographic record
Abstract
The evolutionary response of a trait to directional selection depends upon the level of additive genetic variance. It has been long argued that sustained selection will tend to deplete additive genetic variance as favoured alleles approach fixation. Non-additive genetic variance, due to interactions among alleles within and between loci, does not immediately contribute to an evolutionary response, although shifts in the allele frequencies within and between interacting loci may convert interaction variance into additive variance. Here we consider the possibility that an environmental shift may alter allelic interactions in ways that convert nonadditive into additive genetic variance. Specifically, we performed experiments that used a Bayesian implementation of the animal model to estimate the additive, dominance, and maternal components of variance for a pedigreed population of Brassica rapa. One experiment was performed in a field that mimicked agricultural conditions from which the base population was drawn, while the other was performed in the benign conditions of a greenhouse. Although the additive genetic variance was elevated in the greenhouse condition, no consistent pattens emerged that would indicate a conversion of dominance variance. The unusually low genetic variance and broad confidence intervals for the variance estimates obtained through this analysis preclude definitive interpretations. Thus, we promote further investigation to determine if between-environment changes in additive genetic variance can be traced to conversion of nonadditive variance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.190 | 0.002 |
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; both teacher heads agree on what is shown here.
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".