Systematic Characterization of Multi-Rust Resistance Genes from a ‘Parula × Thatcher’ Population with a High-Density Genetic Map
Bibliographic record
Abstract
Pyramiding multiple resistant genes has been proposed as the most effective way to control wheat rust diseases globally. Identifying the most effective pyramids is challenged by the large pool of rust resistance genes and limited information about their mechanisms of resistance and interactions. Here, using a high-density genetic map, a double haploid population, and multi-rust field testing, we aimed to systematically characterize the most effective gene pyramids for rust resistance from the durable multi-rust resistant CIMMYT cultivar Parula. We revealed that the Parula resistance gene pyramid contains Lr34/Yr18/Sr57 ( Lr34), Lr46/Yr29/Sr58 ( Lr46), Lr27/Yr30/Sr2 ( Sr2), and Lr68. The efficacy, magnitude of effect, and interactions varied for the three rust diseases. A subpopulation mapping approach was applied to characterize the complex interactions of the resistance genes by controlling for the effect of Lr34. Using this approach, we found that Lr34 and Lr68 have a strong additive effect for leaf rust, whereas no additive effects were observed for any rusts between Lr34 and Lr46. Lr34 combined synergistically with Sr12 from Thatcher for stem rust, whereas the additive effect of Lr34 and Sr2 was dependent on the type of rust and environment. Two novel leaf rust quantitative trait loci (QTLs) from Parula were identified in this study, a stable QTL QLr-7BS and QLr-5AS, which showed Lr34 dependent expression. With these findings, we propose combining two to three high-value genes from Canadian wheat (e.g., Sr12 from Thatcher) with a foundational multi-adult plant resistance cassette for desirable and durable resistance to all three rusts in Canadian wheat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".