Identification of quantitative trait loci associated with oil content and development of near isogenic lines for stable <i>qOC-A10</i> in <i>Brasscia napus</i> L.
Bibliographic record
Abstract
Seed oil content is a key seed quality trait determining the economic value of rapeseed (Brassica napus L.). However, it is a complex quantitative trait controlled by multiple genes. To this point, its genetic mechanism in rapeseed remains to be revealed. In the present study, we separately identified the quantitative trait loci (QTL) controlling seed oil content of B. napus using three generations of recombinant inbred line (RIL) populations (F4:5, F5:6, and F6:7) derived from a cross of two contrasting parents (M201, a high-oil parent, and M202, a low-oil parent) in four trials. The results indicated that the additive effects may be the primary factors contributing to the variation in seed oil content in B. napus. A total of 15 QTL for seed oil content were mapped. Two of them, namely qOC-A9-3 and qOC-A10, were consistently detected across two and all four environments, respectively. Meanwhile, qOC-A10 showed a large effect on phenotypic variation in seed oil content. The stability and significance of qOC-A10 was also validated in the near isogenic lines (NILs-qOC-A10) developed from the RIL population (F4:5) using marker-assisted selection. The qOC-A10 is of particular interest for further fine mapping and map-based cloning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".