Analysis of the winter oilseed rape recombination landscape suggests maternal–paternal bias
Bibliographic record
Abstract
Recombination, the reciprocal exchange of DNA between homologous chromosomes, is a mandatory step necessary for meiosis progression. Crossovers between homologous chromosomes generate new combinations of alleles and maintain genetic diversity. Due to genetic, epigenetic, and environmental factors, the recombination landscape is highly heterogeneous along the chromosomes and it also differs between populations and between sexes. Here, we investigated recombination characteristics across the 19 chromosomes of the model allopolyploid crop species oilseed rape ( Brassica napus L.), using two unique multiparental populations derived from two genetically divergent founder pools, each of which comprised 50 genetically diverse founder accessions. A fully balanced, pairwise chain-crossing scheme was utilized to create each of the two populations. A total of 3213 individuals, spanning five successive generations, were genotyped using a 15K SNP array. We observed uneven distribution of recombination along chromosomes, with some genomic regions undergoing substantially more frequent recombination in both populations. In both populations, maternal recombination events were more frequent than paternal recombination. This study provides unique insight into the recombination landscape at chromosomal level and reveals a maternal–paternal bias for recombination number with implications for breeding.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".