Characterization of genetic loci conferring adult plant resistance to leaf rust and stripe rust in spring wheat
Bibliographic record
Abstract
Leaf (brown) and stripe (yellow) rusts, caused by Puccinia triticina and Puccinia striiformis, respectively, are fungal diseases of wheat (Triticum aestivum) that cause significant yield losses annually in many wheat-growing regions of the world. The objectives of our study were to characterize genetic loci associated with resistance to leaf and stripe rusts using molecular markers in a population derived from a cross between the rust-susceptible cultivar 'Avocet S' and the resistant cultivar 'Pavon76'. Using bulked segregant analysis and partial linkage mapping with AFLPs, SSRs and RFLPs, we identified 6 independent loci that contributed to slow rusting or adult plant resistance (APR) to the 2 rust diseases. Using marker information available from existing linkage maps, we have identified additional markers associated with resistance to these 2 diseases and established several linkage groups in the 'Avocet S' x 'Pavon76' population. The putative loci identified on chromosomes 1BL, 4BL, and 6AL influenced resistance to both stripe and leaf rust. The loci on chromosomes 3BS and 6BL had significant effects only on stripe rust, whereas another locus, characterized by AFLP markers, had minor effects on leaf rust only. Data derived from Interval mapping indicated that the loci identified explained 53% of the total phenotypic variation (R2) for stripe rust and 57% for leaf rust averaged across 3 sets of field data. A single chromosome recombinant line population segregating for chromosome 1B was used to map Lr46/Yr29 as a single Mendelian locus. Characterization of slow-rusting genes for leaf and stripe rust in improved wheat germplasm would enable wheat breeders to combine these additional loci with known slow-rusting loci to generate wheat cultivars with higher levels of slow-rusting resistance.
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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 | 0.000 |
| 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) | 0.000 | 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 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".