An experimental test of the mutation-selection balance model for the maintenance of genetic variance in fitness components
Bibliographic record
Abstract
ABSTRACT Despite decades of research, the factors that maintain genetic variation for fitness are poorly understood. Mutation selection balance will always contribute to standing variance, but it is unclear what fraction of the variance in a typical fitness component can be explained by mutation-selection balance and whether fitness components differ in this respect. In theory, the level of standing variance in fitness due to mutation-selection balance can be predicted using the rate of fitness decline under mutation accumulation, and this prediction can be directly compared to the actual standing variance observed. This approach allows for controlled statistical tests of the sufficiency of the mutation-selection balance model, and could be used to identify traits or populations where genetic variance is maintained by factors beyond mutation-selection balance. 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. To encourage the application of this approach, we describe the underlying theory and use it to test the mutation selection balance model for three traits in Drosophila melanogaster . We find some evidence for non-mutational variance in male mating success and female fecundity relative to larval viability, which is consistent with balancing selection on sexual fitness components. Finally, we discuss the theoretical and practical limitations to this approach, and discuss how to apply it successfully.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".