Interactions Between Lr67 or Lr34 and Other Leaf Rust Resistance Genes in Wheat (Triticum aestivum)
Bibliographic record
Abstract
The wheat multi-pest resistance genes Lr67 and Lr34 are similar in that they both condition resistance to many diseases, in a non-race-specific manner, and code for cellular transporters. Lr34 plays a critical role in breeding wheat for disease resistance in large part because it interacts with other resistance genes to result in effective and durable resistance. To determine if Lr67 interacts with other resistance genes in a similar manner as Lr34 six different doubled haploid populations were developed which segregated for either Lr67 or Lr34 along with a second resistance gene, either Lr13, Lr16, or Lr32. The presence or absence of each of these genes in the progeny lines was determined by molecular marker analysis. These six populations were tested for leaf rust field resistance in the same environments to compare the effects of Lr34 and Lr67 alone, and in combination with Lr13, Lr16 or Lr32. Lr67 and Lr34 significantly reduced the levels of rust severity, Lr34 showed a significant interaction with Lr13 but Lr67 did not. Both genes interacted with Lr16, and Lr67 had a significant interaction with Lr32. This analysis demonstrates the similar effect of Lr67, as seen with Lr34, on the interaction with other resistance genes to give a better level of resistance than with single resistance genes. While Lr67 is not widely deployed in agriculture, it could play an important role in disease resistance in future wheat cultivars.
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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 | 0.000 | 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 | 0.001 |
| 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".