Molecular Mapping of QTLs Conferring Fusarium Head Blight Resistance in Chinese Wheat Cultivar Jingzhou 66
Bibliographic record
Abstract
Fusarium head blight (FHB) is a destructive disease of wheat (Triticum aestivum L.), which not only significantly reduces grain yield, but also affects end-use quality. Breeding wheat cultivars with high FHB resistance is the most effective way to control the disease. The Chinese wheat cultivar Jingzhou 66 (JZ66) shows moderately high FHB resistance; however, the genetic basis of its resistance is unknown. A doubled haploid (DH) population consisting 209 lines was developed from a cross of JZ66 and Aikang 58 (AK58), a FHB susceptible wheat cultivar, to identify quantitative trait loci (QTL) that contribute to the FHB resistance. Five field experiments were established across two consecutive crop seasons (2018 and 2019) to evaluate the DH lines and parents for FHB response. The parents and DH population were genotyped with the wheat 55K single-nucleotide polymorphism (SNP) assay. Six QTLs associated with FHB resistance in JZ66 were mapped on chromosome 2DS, 3AS, 3AL, 3DL, 4DS, and 5DL, respectively. Four of the QTL (QFhb.hbaas-2DS, QFhb.hbaas-3AL, QFhb.hbaas-4DS, and QFhb.hbaas-5DL) were detected in at least two environments, and the QTL on 3AL and 5DL might be new. The QTL with major effects, QFhb.hbaas-2DS and QFhb.hbaas-4DS, explained up to 36.2% and 17.6% of the phenotypic variance, and were co-localized with the plant semi-dwarfing loci Rht8 and Rht-D1. The dwarfing Rht8 allele significantly increased spike compactness (SC) and FHB susceptibility causing a larger effect on FHB response than Rht-D1 observed in this study. PCR–based SNP markers for QFhb.hbaas-2DS, QFhb.hbaas-3AL, QFhb.hbaas-4DS, and QFhb.hbaas-5DL, were developed to facilitate their use in breeding for FHB resistance by marker-assisted selection.
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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 | 0.001 |
| 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".