An experimental test of the mutation-selection balance model for the maintenance of genetic variance in fitness components
Bibliographic record
Abstract
Despite decades of research, the factors that maintain genetic variation for fitness are poorly understood. It is unclear what fraction of the variance in a typical fitness component can be explained by mutation-selection balance (MSB) and whether fitness components differ in this respect. In theory, the level of standing variance in fitness due to MSB can be predicted using the rate of fitness decline under mutation accumulation, and this prediction can be directly compared to the standing variance observed. This approach allows for controlled statistical tests of the sufficiency of the MSB model, and could be used to identify traits or populations where genetic variance is maintained by other factors. For example, some traits may be influenced by sexually antagonistic balancing selection, resulting in an excess of standing variance beyond that generated by deleterious mutations. We describe the underlying theory and use it to test the MSB model for three traits in Drosophila melanogaster . We find evidence for differences among traits, with MSB being sufficient to explain genetic variance in larval viability but not male mating success or female fecundity. Our results are consistent with balancing selection on sexual fitness components, and demonstrate the feasibility of rigorous statistical tests of the MSB model.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".