Construction of a genetic linkage map and QTL analysis of erucic acid content and glucosinolate components in yellow mustard (Sinapis alba L.)
Bibliographic record
Abstract
BACKGROUND: Yellow mustard (Sinapis alba L.) is an important condiment crop for the spice trade in the world. It has lagged behind oilseed Brassica species in molecular marker development and application. Intron length polymorphism (ILP) markers are highly polymorphic, co-dominant and cost-effective. The cross-species applicability of ILP markers from Brassica species and Arabidopsis makes them possible to be used for genetic linkage mapping and further QTL analysis of agronomic traits in yellow mustard. RESULTS: A total of 250 ILP and 14 SSR markers were mapped on 12 linkage groups and designated as Sal01-12 in yellow mustard. The constructed map covered a total genetic length of 890.4 cM with an average marker interval of 3.3 cM. The QTL for erucic content co-localized with the fatty acid elongase 1 (FAE1) gene on Sal03. The self-(in)compatibility gene was assigned to Sal08. The 4-hydroxybenzyl, 3-indolylmethyl and 4-hydroxy-3-indolylmethyl glucosinolate contents were each controlled by one major QTL, all of which were located on Sal02. Two QTLs, accounting for the respective 20.4% and 19.2% of the total variation of 2-hydroxy-3-butenyl glucosinolate content, were identified and mapped to Sal02 and Sal11. Comparative synteny analysis revealed that yellow mustard was phylogenetically related to Arabidopsis thaliana and had undergone extensive chromosomal rearrangements during speciation. CONCLUSION: The linkage map based on ILP and SSR markers was constructed and used for QTL analysis of seed quality traits in yellow mustard. The markers tightly linked with the genes for different glucosinolate components will be used for marker-assisted selection and map-based cloning. The ILP markers and linkage map provide useful molecular tools for yellow mustard breeding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".