Genetic characterization of QTL associated with resistance to Fusarium head blight in a doubled-haploid spring wheat population
Bibliographic record
Abstract
Fusarium head blight (FHB) is one of the most important fungal wheat diseases worldwide. Understanding the genetics of FHB resistance is the key to facilitating the introgression of different FHB resistance genes into adapted wheat. The objectives of the present study were to detect and map quantitative trait loci (QTL) associated with FHB resistance genes and characterize the genetic components of the QTL in a doubled-haploid (DH) spring wheat population using both single-locus and two-locus analysis. A mapping population, consisting of 174 DH lines from the cross between DH181 (resistant) and AC Foremost (susceptible), was evaluated for type I resistance to initial infection during a 2-year period in spray-inoculated field trials, for Type II resistance to fungal spread within the spike in 3 greenhouse experiments using single-floret inoculation, and for resistance to kernel infection in a 2001 field trial. One-locus QTL analysis revealed 7 QTL for type I resistance on chromosome arms 2DS, 3AS, 3BS, 3BC (centromeric), 4DL, 5AS, and 6BS, 4 QTL for type II resistance on chromosomes 2DS, 3BS, 6BS, and 7BL, and 6 QTL for resistance to kernel infection on chromosomes 1DL, 2DS, 3BS, 3BC, 4DL, and 6BS. Two-locus QTL analysis detected 8 QTL with main effects and 4 additive by additive epistatic interactions for FHB resistance and identified novel FHB resistance genes for the first time on chromosomes 1DL, 4AL, and 4DL. Neither significant QTL by environment interactions nor epistatic QTL by environment interactions were found for either type I or type II resistance. The additive effects of QTL explained most of the phenotypic variance for FHB resistance. Marker-assisted selection for the favored alleles at multiple genomic regions appears to be a promising tool to accelerate the introgression and pyramiding of different FHB resistance genes into adapted wheat genetic backgrounds.
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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 | 0.000 | 0.000 |
| 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".