Identification of Quantitative Trait Loci for Goss's Wilt of Maize
Bibliographic record
Abstract
Since its discovery in 1969, Goss's wilt, a foliar blight and vascular wilt disease caused by the Gram‐positive bacterium Clavibacter michiganensis (Smith) Davis et al. subsp. nebraskensis (Vidaver & Mandel) Davis et al. (Cmn), has emerged as one of the top four diseases of maize (Zea mays L.) in the United States and Ontario, Canada. No source of complete resistance has been described for Goss's wilt, and little is known about the genetic and mechanistic basis of host resistance to Cmn. Our objective was to perform linkage mapping on three populations to uncover the genomic regions associated with Goss's wilt resistance. We evaluated the intermated B73 × Mo17 population and two corresponding disease‐resistant introgression line populations: B73(4) × Mo17 and Mo17(4) × B73. We identified putative quantitative trait loci (QTLs) in bins 1.05 to 1.06, 2.06, 7.01 to 7.02, 8.05, and 10.04, both confirming previous findings and identifying novel resistance QTLs. The QTL on chromosome 1, designated qGW1.06, was identified in multiple environments and overlaps with a known multiple disease resistance locus. The QTL in bin 8.05 represents a novel region associated with Goss's wilt. Using the data from this study and previous studies, we found that Goss's wilt resistance was correlated with northern leaf blight [Setosphaeria turcica (Luttr.) K.J. Leonard & Suggs], but not gray leaf spot (Cercospora spp.) or southern leaf blight [Cochliobolus heterostrophus (Drechsler) Drechsler]. These results offer a deeper understanding of the genetic basis of resistance to Goss's wilt in maize that may facilitate breeding for resistance, and qGW1.06 is a strong candidate for further characterization and use.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".