Influence of genetic background and heterozygosity on meiotic recombination in<i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
Plant breeding relies on genetic variability generated by meiotic recombination. Control of recombination frequencies is not yet possible, but would significantly extend the options for plant-breeding strategies. A prerequisite would be variability of recombination frequencies. In this study, 15 transgenic kanamycin (KR) and hygromycin (HR) resistance gene insertions mapping to the five Arabidopsis thaliana chromosomes were used as genetic markers. Recombination frequencies were determined from the frequencies of resistance phenotypes within populations segregating for linked KR and HR markers. Recombination frequencies of marker pairs were compared among these four ecotypes, among F1s in both reciprocal forms derived from these ecotypes, and between F1s and their parent lines. On average, the recombination frequencies in F1 crosses were substantially higher (up to 2-fold) than in the homozygous parental ecotypes. A strong negative correlation between genetic similarities of ecotypes and recombination frequencies was detected for two adjacent marker pairs located on the long arm of chromosome 3, but not for marker pairs in other genomic regions. Our results suggest that heterozygosity influences recombination in plant breeding, and cannot be ignored in genetic mapping of genomes.
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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 | 0.001 | 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 | 0.000 | 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".