Strong amplification of quantitative genetic variation under a balance between mutation and fluctuating stabilizing selection
Bibliographic record
Abstract
Abstract The observation of high heritability in most quantitative traits has been a long-standing puzzle. There is a general consensus that simple models of quantitative genetic variation, which are mostly founded on the assumption of mutation-selection balance in a constant environment, have failed to explain high heritability. To make matters worse, the reasons for failure are unknown, leaving little to guide future model developments. Here we revisit this puzzle by taking the canonical Latter-Bulmer model and relaxing the assumption of perfect environmental stasis. Instead we assume that the trait optimum changes slowly but steadily in a random walk (specifically, an Ornstein-Uhlenbeck process), similar to standard models used for phylogenetic comparative methods. We show that our model behaves qualitatively differently to its stationary optimum counterpart even though the optimum only changes slowly. This is the result of a feedback between the adaptation rate and selection coefficient fluctuations. The heritability predictions resulting from this feedback are more consistent with observations and also less sensitive to evolutionary parameters than the classical LB model. We derive a simple formula to predict genetic variation under random walk optimum fluctuations which helps to explain some of our counter-intuitive results and which should be useful for understanding the potential influence of fluctuations in future work. Since the feedback driving our results should also occur in more complex models e.g. with multiple traits, we argue that environmental change has been an essential biological ingredient missing in previous mutation-selection balance models of quantitative trait heritability.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".