Selection can favor a recombination landscape that limits polygenic adaptation
Bibliographic record
Abstract
Abstract Meiotic crossover positions are uneven along eukaryotic chromosomes, giving rise to heterogeneous recombination rate landscapes. Genetic modifiers of local and genome-wide crossover positions have been described, but the selective pressures acting on them and their potential effect on adaptation in already-recombining populations remain unclear. We performed experimental evolution using a mutant that modifies the position of crossovers along chromosomes in the nematode Caenorhabditis elegans , without any detectable direct fitness effect. Our results show that when the recombination landscape is fixed, adaptation is facilitated by the modifier allele that, on average, increases recombination rates in genomic regions containing heritable fitness variation. However, in polymorphic populations containing both the wild-type and mutant modifier alleles, the allele that facilitates adaptation tends to decrease in frequency. This is likely because the allele that reduces recombination between selected loci at the genome-wide scale increases recombination in its chromosomal vicinity, and may thus benefit from local associations it establishes with beneficial genotype combinations. These results demonstrate that indirect selection acting on a recombination modifier mainly depends on its local effect, which may be decoupled from its consequences on genome-wide polygenic adaptation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".