Mapping quantitative trait loci associated with stripe rust resistance from the Canadian wheat cultivar ‘AAC Innova’
Bibliographic record
Abstract
Stripe rust, caused by Puccinia striiformis Westend. f.sp. tritici Erikss. (Pst), is one of the most devastating diseases of wheat (Triticum aestivum L.) globally. Exploring and utilizing new sources of resistance is essential for breeding resistant wheat cultivars. Thus, a doubled haploid population (n = 291) derived from the cross ‘AAC Innova’/‘AAC Proclaim’ was evaluated to dissect the genetics of resistance in the cultivar ‘AAC Innova’. This population was evaluated for stripe rust severity in disease nurseries at Creston, British Columbia (in 2016 and 2020), and Lethbridge, Alberta (in 2016, 2017 and 2020), and genotyped using the wheat 90 K SNP assay. A high-density genetic map was constructed which consisted of 7112 SNP markers with an average marker interval of 2.3 cM. Quantitative trait loci (QTL) mapping identified one major (QYr.lrdc-2A) and 10 minor effect (QYr.lrdc-2B.1, QYr.lrdc-2B.2, QYr.lrdc-2B.3, QYr.lrdc-2B.4, QYr.lrdc-2D, QYr.lrdc-3B, QYr.lrdc-5A, QYr.lrdc-5B, QYr.lrdc-5D and QYr.lrdc-7D) loci. The QTL QYr.lrdc-2A was consistently effective against Pst races across all environments and explained up to 33.0% of the phenotypic variation. Other QTLs were either less consistent or environment specific. ‘AAC Innova’ contributed stripe rust resistance alleles for most of the QTLs except for QYr.lrdc-2D, QYr.lrdc-5A and QYr.lrdc-7D. The QTLs identified in this study should be valuable for diversifying the sources of genetic resistance against current and new virulent races of Pst. In particular, the ‘AAC Innova’-derived QTL QYr.lrdc-2A could be a valuable source of resistance to incorporate into commercial cultivars and combine with other genes using the associated SNP markers identified in this study.
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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 | 0.001 | 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".