Mapping of quantitative trait loci controlling broomrape (<i>Orobanche crenata</i>Forsk.) resistance in faba bean (<i>Vicia faba</i>L.)
Bibliographic record
Abstract
Orobanche crenata Forsk. is a root parasite that produces devastating effects on many crop legumes and has become a limiting factor for faba bean production in the Mediterranean region. The efficacy of available control methods is minimal and breeding for broomrape resistance remains the most promising method of control. Resistance seems to be scarce and complex in nature, being a quantitative characteristic difficult to manage in breeding programmes. To identify and map the QTLs (quantitative trait loci) controlling the trait, 196 F2 plants derived from the cross between a susceptible and a resistant parent were analysed using isozymes, RAPD, seed protein genes, and microsatellites. F2-derived F3 lines were studied for broomrape resistance under field conditions. Of the 130 marker loci segregating in the F2 population, 121 could be mapped into 16 linkage groups. Simple interval mapping (SIM) and composite interval mapping (CIM) were performed using QTL Cartographer. Composite interval mapping using the maximum number of markers as cofactors was clearly the most efficient way to locate putative QTLs. Three QTLs for broomrape resistance were detected. One of the three QTLs explained more than 35% of the phenotypic variance, whereas the others accounted for 11.2 and 25.5%, respectively. This result suggests that broomrape resistance in faba bean can be considered a polygenic trait with major effects of a few single genes.
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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 | 0.001 |
| 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) | 0.001 | 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".