Mapping QTL Associated with Stripe Rust, Leaf Rust, and Leaf Spotting in a Canadian Spring Wheat Population
Bibliographic record
Abstract
Stripe rust, leaf rust, and the leaf spot complex are economically important diseases of wheat (Triticum aestivum L.) in western Canada, and genetic host resistance is the most successful management strategy. This study was conducted to identify quantitative trait loci (QTL) associated with these diseases and to provide wheat breeders with sources of potential disease resistance genes. A total of 208 recombinant inbred lines derived from a cross between Canadian spring wheat cultivars ‘Peace’ and ‘Carberry’ were evaluated from 2014 to 2017 in stripe rust, leaf rust, and leaf spot nurseries in Alberta and British Columbia. All lines were genotyped with sequencing‐based Diversity Arrays Technology (DArTseq) markers. Using the least square means of the combined environments, two stripe rust resistance QTL, two leaf rust resistance QTL, and three leaf spot resistance QTL were identified. The stripe rust QTL were located on chromosomes 3A and 4B, the leaf rust QTL were located on chromosomes 4A and 3D, and the leaf spot QTL were located on 2A, 4B and 7D. The stripe rust resistance QTL on 4B, contributed by ‘Carberry’, was previously identified in other studies using a population derived from ‘Carberry’. Results from this study suggest that ‘Carberry’ may be an attractive parental source for breeders to enhance resistance against stripe rust and leaf spot with minor resistance alleles.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".