Exceptionally high levels of recombination across the honey bee genome
Bibliographic record
Abstract
The first draft of the honey bee genome sequence and improved genetic maps are utilized to analyze a genome displaying 10 times higher levels of recombination (19 cM/Mb) than previously analyzed genomes of higher eukaryotes. The exceptionally high recombination rate is distributed genome-wide, but varies by two orders of magnitude. Analysis of chromosome, sequence, and gene parameters with respect to recombination showed that local recombination rate is associated with distance to the telomere, GC content, and the number of simple repeats as described for low-recombining genomes. Recombination rate does not decrease with chromosome size. On average 5.7 recombination events per chromosome pair per meiosis are found in the honey bee genome. This contrasts with a wide range of taxa that have a uniform recombination frequency of about 1.6 per chromosome pair. The excess of recombination activity does not support a mechanistic role of recombination in stabilizing pairs of homologous chromosome during chromosome pairing. Recombination rate is associated with gene size, suggesting that introns are larger in regions of low recombination and may improve the efficacy of selection in these regions. Very few transposons and no retrotransposons are present in the high-recombining genome. We propose evolutionary explanations for the exceptionally high genome-wide recombination rate.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".