Quantitative trait loci mapping of rust resistance and agronomic traits in the doubled haploid spring wheat population ‘HYAYT12-10’ × ‘GP146’
Bibliographic record
Abstract
Marker-assisted selection requires the identification of molecular markers associated with major genes and quantitative trait loci (QTL) using linkage analysis. In this study, we used 167 doubled haploid (DH) lines derived from two unregistered spring wheat (Triticum aestivum L.) parental lines that belong to the Canada Western Special Purpose (CWSP) class to map QTLs associated with five traits using inclusive composite interval mapping (ICIM). Using ICIM, least square means phenotype data across 3-4 environments, and a genetic map of 2,676 SNPs out of the wheat 90K SNP array, we identified ten QTLs associated with maturity (4A and 5B), plant lodging (4B, 5A, 5D, and 7D), grain yield (2D), leaf rust (4A) and stem rust (1A and 2B). Each QTL individually accounted for 6.0-22.3% of the phenotypic variance and together accounted for 8.6-38.2% of each trait. QTLs identified for rusts using ICIM had a minor effect (6.0-9.0%) or a major effect (22.3%). Our major effect QTL at 22.3% was discovered on chromosome 2B and contributed to stem rust response. Its physical location has been associated with disease response in previous studies. Results from this study provide additional valuable information to wheat researchers, in particular that the area on chromosome 2B should be considered for future analyses.
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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".