Mapping of quantitative trait loci (QTL) in Brassica napus L. for tolerance to water stress
Bibliographic record
Abstract
Brassica napus L. plants are sensitive to water stress throughout their life cycle from seed germination to seed setting. This study aims to identify quantitative trait loci (QTL) linked to B. napus tolerance to water stress mimicked by application of 10 % polyethylene glycol-6000 (PEG-6000). Two doubled haploid populations (1901 and 1904), each consisting of 150 genotypes, were used for this research. Plants at the two-true leaf stage of development were grown in the absence (control) or presence (water stress) of PEG-6000 under controlled environmental conditions for 48 h, and the drought stress index (DSI) was calculated for the fresh weight and dry weight of whole plant, root, and shoot of each genotype. The 300 genotypes along with their parents were genotyped using the Brassica Infinium 90K SNP BeadChip Array. Inclusive Composite Interval Mapping was used to identify and confirm QTL. Eighteen QTL associated with water stress tolerance were identified across six chromosomes (A2, A3, A4, A9, C3, and C7). Collectively, 2,154 putative candidate genes for drought stress tolerance in B. napus were identified for all the identified QTL. Among them, 213 genes were chosen as directly associated with drought stress tolerance based on nine functional annotations. These results can be incorporated into future breeding initiatives to select plant material with the ability to effectively cope with water stress.
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 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".