Breeding for quantitative disease resistance: Case studies, emerging approaches, and exploiting pathogen variation
Bibliographic record
Abstract
Abstract Host resistance, using qualitative genes with major effects, such as resistance ( R ) genes, is one of the most effective disease control strategies. However, because major gene‐derived resistance wanes over time, breeders must increasingly focus on quantitative trait loci and minor effect genes, which, when pyramided together, can confer stronger and longer lasting quantitative disease resistance (QDR). This review highlights the challenges of breeding for QDR in five case studies: blackleg (caused by Leptosphaeria maculans ) in canola ( Brassica napus ), white mold ( Sclerotinia sclerotiorum [ Ss ]) and common bacterial blight ( Xanthomonas citri pv . fuscans and Xanthomonas phaseoli pv. phaseoli ) in common bean ( Phaseolus vulgaris ), late leaf spot, early leaf spot, tomato spotted wilt virus, and southern stem rot in peanut ( Arachis hypogaea ), and stem, leaf, and stripe rusts ( Puccinia spp .) and powdery mildew ( Blumeria graminis ) in wheat ( Triticum aestivum ). Five emerging approaches for accelerating QDR breeding are discussed: high‐throughput phenotyping, phenomic selection, genomic selection, genome editing, and utilizing wild germplasm in pre‐breeding. Lastly, we highlight the importance for breeders of QDR to consider the phenotypic, genetic, genomic, and pathogenicity gene variation within the pathogen population, using Ss in common bean as an example. By doing so, breeders will save time and resources and develop locally adapted 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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".