Multi-environment QTL studies suggest a role for cysteine-rich protein kinase genes in quantitative resistance to blackleg disease in Brassica napus
Bibliographic record
Abstract
BACKGROUND: Resistance to the blackleg disease of Brassica napus (canola/oilseed rape), caused by the hemibiotrophic fungal pathogen Leptosphaeria maculans, is determined by both race-specific resistance (R) genes and quantitative resistance loci (QTL), or adult-plant resistance (APR). While the introgression of R genes into breeding material is relatively simple, QTL are often detected sporadically, making them harder to capture in breeding programs. For the effective deployment of APR in crop varieties, resistance QTL need to have a reliable influence on phenotype in multiple environments and be well defined genetically to enable marker-assisted selection (MAS). RESULTS: Doubled-haploid populations produced from the susceptible B. napus variety Topas and APR varieties AG-Castle and AV-Sapphire were analysed for resistance to blackleg in two locations over 3 and 4 years, respectively. Three stable QTL were detected in each population, with two loci appearing to be common to both APR varieties. Physical delineation of three QTL regions was sufficient to identify candidate defense-related genes, including a cluster of cysteine-rich receptor-like kinases contained within a 49 gene QTL interval on chromosome A01. Individual L. maculans isolates were used to define the physical intervals for the race-specific R genes Rlm3 and Rlm4 and to identify QTL common to both field studies and the cotyledon resistance response. CONCLUSION: Through multi-environment QTL analysis we have identified and delineated four significant and stable QTL suitable for MAS of quantitative blackleg resistance in B. napus, and identified candidate genes which potentially play a role in quantitative defense responses to L. maculans.
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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 | 0.000 |
| 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) | 0.002 | 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".