Breeding for Disease Resistance with Minor Genes: Case Studies and Success Stories
Bibliographic record
Abstract
Among the established disease control strategies, host resistance plays a critical role in disease management across numerous host-pathogen systems. Research on minor genes is important to enhance disease resistance and to prolong effectiveness of major resistance (R) genes. Plant breeders often prefer to use R gene-mediated resistance due to the relatively simple means of introgressing R genes into breeding material using markers, whereas quantitative resistance (QR), though more complex to introduce, offers potentially more durable resistance. In this review, we highlight several case studies evaluating their objectives, the challenges they faced, and the progress they have made towards breeding with minor genes for QR. For blackleg (Leptosphaeria maculans) in canola (Brassica napus) and white mold (Sclerotinia sclerotiorum) in common bean (Phaseolus vulgaris), we detail the minor genes and quantitative trait loci (QTL) procedures used for their detection, general challenges when breeding with QR, and the developed germplasm sources Following this, we review notable success stories in breeding efforts for minor genes against stem rust, leaf rust, and stripe rust (Puccinia spp.) in wheat (Triticum aestivum), late leaf spot, early leaf spot, tomato and spotted wilt, and southern stem rot in peanut (Arachis hypogaea), as well as common bacterial blight in common bean. Finally, we evaluate emerging technologies and research avenues that could further accelerate progress in breeding for QR. Key advancements discussed include high-throughput phenotyping, phenomic selection, genomic selection, genome editing, traditional/classical pre-breeding, TILLING and EcoTILLING, and strategies leveraging pathogen population genetics and host-pathogen interactions.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".