Mapping genomic regions controlling agronomic traits in spring wheat under conventional and organic managements
Bibliographic record
Abstract
Abstract Identification of quantitative trait loci (QTL) associated with important traits is one of the first steps towards deploying marker‐assisted selection, but the lack of stability and consistency in identifying QTL across environments and populations remains a limitation. We conducted this study to identify QTL associated with agronomic traits in wheat ( Triticum aestivum L.) across management‐specific environments. A total of 204 wheat lines derived from the ‘Peace’ × ‘Carberry’ cross were evaluated in 2016 and 2017 under conventional and organic managements in Edmonton, Canada, and genotyped with diversity arrays technology markers. Using the least‐squares means for each management system, 53 QTL were identified for nine agronomic traits, 14 of which were consistently identified in both managements. The largest QTL we identified was associated with plant height ( QPht.dms‐4B ), which might be the plant height‐reducing gene Rht‐B1b from ‘Carberry’. It explained 54 and 49% of the phenotypic variation in conventional and organic management, respectively. The second largest QTL was associated with gluten strength ( QSds.dms‐1A ) in both managements. We identified consistent QTL across both organic and conventional managements, even though they were generally minor‐effect QTL. Twelve organic management‐specific QTL were found for grain yield, days to heading and maturity, plant height, test weight, and thousand‐kernel weight, but most explained relatively low amount of phenotypic variation. The QTL identified across managements, especially the gluten strength QTL ( QSds.dms‐1A ), may serve as useful markers in selection. The QSds.dms‐1A region needs to be investigated further to confirm whether it is the Gli‐1 storage protein gene.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".