Mapping of seed quality traits in the C genome of <i>Brassica napus</i> by using a population carrying genome content of <i>B. oleracea</i> and their effect on other traits
Bibliographic record
Abstract
Increasing seed oil and protein contents and reducing the content of seed glucosinolates (GSLs) in Brassica oilseed crops are important objectives in breeding. By using an oilseed rape (B. napus L.) doubled-haploid (DH) population carrying genome content introgressed from Chinese kale (B. oleracea L.), we mapped quantitative trait loci (QTL) for these seed quality traits and investigated their effect on other traits including seed yield. A stable QTL for seed oil content was identified on chromosome C5 at 40-42 Mb position and a QTL for seed GSL content was identified on C9 at 7-8 Mb position. The C5 and C9 QTL alleles for high oil and GSL contents were derived from Chinese kale, demonstrating that high-oil QTL allele can be found in the parental species of oilseed rape. The low-GSL QTL allele of C9 exerted a significant positive effect on seed protein content, demonstrating that selection for this QTL allele contributed to higher protein content in canola seed. These two QTL were not affected by field environmental conditions and did not exert a significant effect on days to flowering and seed yield. Thus, the genomic regions and the molecular markers identified in this study should be useful in molecular breeding of the seed quality traits in oilseed rape.
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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 | 0.000 | 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".