Discovery of quantitative trait loci associated with erucic acid content in Brassica napus L. seed
Bibliographic record
Abstract
Brassica napus L. is a crucial oilseed crop and holds significant economic value for Canada. There are several seed quality types of B. napus, including canola with low erucic acid (used for edible purposes), as well as genotypes with high erucic acid, mainly used for industrial applications. This research was directed at increasing erucic acid levels in high erucic acid rapeseed (HEAR). The goal was to identify and characterize minor-effect quantitative trait loci (QTL) related to erucic acid content in two distinct doubled haploid (DH) populations, CBLD2 and CBER2. Both populations utilized two HEAR parents to fix the known FAE1 genes and focus on identifying minor QTL impacting erucic acid content. The populations were phenotype in two locations over two years. Erucic acid content was evaluated using gas chromatography and the DH populations were genotyped using genotyping-by-sequencing (GBS). QTL were detected on various chromosomes, illustrating the polygenic regulation of erucic acid. Specifically, significant QTL were discovered on chromosomes A04, A07, and C08 in CBLD2, explaining up to 27 %, 21 %, and 22 % of phenotypic variation, respectively. In the CBER2 population, stable QTL on chromosomes A01, A02, and A05 accounted for 7-9 %, 7-9 %, and 11-18 % of variation, respectively. Analysis also revealed a positive correlation between erucic acid and oil content, and no significant correlation between erucic acid and other agronomic traits. This suggests erucic acid content is regulated independently from other agronomic characteristics. These insights into the genetic foundation of erucic acid content in B. napus are crucial for marker-assisted breeding, aiming to enhance erucic acid levels for improved agricultural and industrial uses.
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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 | 0.001 |
| 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".