Developing Disease-Resistant Wheat Varieties Through Genomic Approaches
Bibliographic record
Abstract
Wheat diseases, particularly those caused by fungi, bacteria, and viruses, are one of the major threats to global food production. Advances in genomics have provided new opportunities for developing disease-resistant wheat varieties. By identifying resistance genes, quantitative trait loci (QTL), and utilizing gene-editing tools, breeders can accelerate the development of resistant varieties. This study reviews the latest progress in developing disease-resistant wheat using genomic approaches, with a focus on the application of genomic selection, genome-wide association studies (GWAS), gene editing, and multi-omics integration. These methods are crucial for improving wheat’s resistance to diseases. Research has shown that genomic selection and GWAS have been successfully applied in improving resistance to various wheat diseases, including leaf rust, stripe rust, and powdery mildew. Gene-editing technologies, such as CRISPR/Cas9, have enhanced wheat's disease resistance by targeting and editing resistance genes. The integration of multi-omics data provides new insights for precision breeding for disease resistance. Genomic approaches enable breeders to more accurately select disease-resistant wheat varieties, shorten the breeding cycle, and increase the durability of resistance genes. The application of genomic tools also helps to better understand wheat's response mechanisms to pathogens, thereby promoting global 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) | 0.003 | 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".