The analysis of <i>Puccinia triticina</i> field populations in Canada between 2018 and 2020 using restriction site‐associated <scp>DNA</scp> genotyping‐by‐sequencing
Bibliographic record
Abstract
Abstract Puccinia triticina , the causal agent of wheat leaf rust, is a dynamic pathogen causing significant yield losses worldwide. In this study, we analysed the virulence and genetic structure of 98 P . triticina isolates collected in Canada between 2018 and 2020. Isolates from Manitoba and Saskatchewan were found to be highly related for virulence, as were isolates from Quebec and Ontario. Isolates from Alberta had a virulence profile more similar to those from Manitoba and Saskatchewan than to Ontario and Quebec. To study the genetic structure of P . triticina populations, we used the restriction site‐associated DNA (RAD) genotyping‐by‐sequencing method to identify single‐nucleotide polymorphisms (SNPs). The Puccinia DNA sequences were aligned against the reference genome of P . triticina race BBBD and 1898 SNPs were identified. The phylogenetic analysis using SNP markers grouped these P . triticina into three genetic clades. The separation of P . triticina into three genetic clades was also supported by principal component analysis. Isolates from Clade 1 and Clade 2 were found throughout Canada, whereas Clade 3 isolates were only found in Ontario and Quebec. There were differences in virulence profiles among P . triticina isolates from the three genetic clades and a general correlation between virulence phenotypes and SNP genotypes was observed. These results indicate that the SNPs derived from RAD genotyping‐by‐sequencing could be useful in tracking the genetic and virulence dynamics of this pathogen in Canadian wheat.
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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 | 0.001 |
| 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) | 0.000 | 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".