Molecular Breeding Strategies for Pyramiding Disease Resistance in Wheat
Bibliographic record
Abstract
Wheat is one of the most important food crops in the world, but its yield and quality are often seriously threatened by a variety of diseases such as rust, head blight and powdery mildew. Traditional single-gene disease resistance breeding faces huge challenges due to the rapid mutation of pathogen populations and the easy failure of resistance. In this context, the aggregation of multiple disease resistance genes through molecular breeding has become an effective strategy to improve wheat disease resistance. This study reviews the current research progress of wheat disease resistance genes, the application of molecular tools such as marker-assisted selection (MAS), genomic selection, and CRISPR gene editing, as well as the integration path of these technologies in the breeding of multi-resistant wheat varieties. Through actual cases such as the aggregation of rust resistance genes such as Lr , Sr , and Yr , and the combined application of Fhb1 and Fhb2 head blight resistance genes, the significant effect of gene aggregation in enhancing disease resistance was verified. At the same time, this study also analyzed the effects of aggregation on agronomic traits, explored the challenges faced by resistance persistence and gene interactions, and looked forward to the future direction of combining molecular breeding with sustainable agriculture, in order to achieve long-term control of wheat diseases and food security.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".