Molecular and phenotypic characterization of seedling and adult plant leaf rust resistance in a world wheat collection
Bibliographic record
Abstract
Genetic resistance is the most effective approach to managing wheat leaf rust. The aim of this study was to characterize seedling and adult plant leaf rust resistance of a world wheat collection. Using controlled inoculation with ten races of Puccinia triticina , 14 seedling resistance genes were determined or postulated to be present in the collection. Lr1 , Lr3 , Lr10 and Lr20 were the most prevalent genes around the world while Lr9 , Lr14b , Lr3ka and/or Lr30 and Lr26 were rare. To confirm some gene postulations, the collection was screened with gene-specific molecular markers for Lr1 , Lr10 , Lr21 and Lr34 . Although possessing the Lr1 and/or Lr10 gene-specific marker, 51 accessions showed unexpected high infection types to P. triticina race BBBD. The collection was tested in the field, where rust resistance ranged from nearly immune or highly resistant with severity of 1 % and resistant host response to highly susceptible with severity of 84 % and susceptible host response. The majority of the accessions possessing the adult plant resistance (APR) gene Lr34 had a maximum rust severity of 0–35 %, similar to or better than accession RL6058, a Thatcher- Lr34 near-isogenic line. Many accessions displayed an immune response or a high level of resistance under field conditions, likely as a result of synergy between APR genes or between APR and seedling resistance genes. However, accessions with three or more seedling resistance genes had an overall lower field severity than those with two or fewer. Immune or highly resistant accessions are potential sources for improvement of leaf rust resistance. In addition, some lines were postulated to have known but unidentified genes/alleles or novel genes, also constituting potentially important sources of novel 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 | 0.001 |
| 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".