The Role of Advantageous Mutations in Enhancing the Evolution of a Recombination Modifier
Bibliographic record
Abstract
Although the evolution of recombination is still a major problem in evolutionary genetics, recent theoretical studies have shown that recombination can evolve by breaking down interference ("Hill-Robertson effects") among multiple loci. This leads to selection on a recombination modifier in a population subject to recurrent deleterious mutation. Here, we use computer simulations to investigate the evolution of a recombination modifier under three different scenarios of recurrent mutation in a finite population: (1) mutations are deleterious only, (2) mutations are advantageous only, and (3) there is a mixture of deleterious and advantageous mutations. We also investigate how linkage disequilibrium, the strength of selection acting on a modifier, and effective population size change under the different scenarios. We observe that adding even a small number of advantageous mutations increases the fixation rate of modifiers that increase recombination, especially if the effects of deleterious mutations are weak. However, the strength of selection on a modifier is less than the summed strengths had there been deleterious mutations only and advantageous mutations only.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".