Characterization of novel stripe rust resistance in wheat relatives
Bibliographic record
Abstract
Stripe rust of wheat is one of the most damaging wheat diseases, causing on average 2% yield losses every year around the world. Through plant breeding, resistant cultivars can be generated, which renders the use of fungicides obsolete, making it a durable and environmentally friendly way to control this disease. Due to the fast-evolving nature of the causal pathogen, Puccinia striiformis f. sp. tritici, genes conferring resistance to stripe rust are often defeated following years of deployment in a certain agroecosystem, and new sources of resistance are continuously required to limit the yield losses due to stripe rust. Here, I turned to two bread wheat relatives, Aegilops tauschii and spelt wheat (Triticum aestivum subsp. spelta) for novel sources of stripe rust resistance genes. The identification of two spelt wheat cultivars resistant to stripe rust, ‘CDC Silex’ and ‘10Spelt17’ motivated efforts to identify the genetic region responsible for resistance, and for the introgression of this resistance in Canadian Western Red Spring wheat cultivars. Through the phenotyping of multiple bi-parental mapping populations, I showed that the resistance in both ‘CDC Silex’ and ‘10Spelt17’ is the same or allelic and is conferred by two complementary dominant genes. Efforts to map the region conferring resistance through BSA-seq was unsuccessful, potentially due to the small segregating population phenotyped in this study. Additionally, I showed that the resistance in both resistant spelt cultivars was silenced when transferred to a bread wheat background, through the phenotyping of F₁, F₂, F₂∶₃, and F₅ bi-parental individuals. Additionally, we identified two stripe rust resistance genes and one QTL conferring resistance to stripe rust at the seedling stage in an Aegilops tauschii diversity panel. One gene, previously cloned and identified as YrAS2388, conferred non-race specific resistance to all tested isolates, representative of the global Pst population. This gene was introgressed into a CWRS wheat genetic background through the use of Kompetitive Allele-Specific PCR (KASP) markers developed as part of this study. An additional gene, temporally named YrW057, and a QTL on chromosome 6D, conferred race specific resistance to stripe rust isolates W057 and W052, respectively.
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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 | 0.000 | 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 | 0.001 |
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".