Differentiation of Molecular Genotypes and Virulence Phenotypes of <i>Puccinia triticina</i> from Common Wheat in North America
Bibliographic record
Abstract
Wheat leaf rust caused by Puccinia triticina is widely distributed in the wheat growing regions of the United States and Canada, and is subject to selection for virulence phenotype by leaf rust resistance genes in wheat cultivars. The objective of this study was to determine the number of genetically differentiated groups of P. triticina that are currently present in North America. In total, 148 isolates of P. triticina from the 1980s to 2005 were collected from wheat-growing regions of the United States and Canada and tested for virulence on 20 lines of wheat with single genes for leaf rust resistance and for molecular genotype with 23 simple sequence repeat (SSR) markers. In total, 91 virulence phenotypes and 65 SSR genotypes were found. After removal of isolates with identical virulence and SSR genotypes, 125 isolates were included for further analysis. Bayesian cluster analysis indicated five different groups of isolates based on SSR genotypes that also differed for virulence to leaf rust resistance genes Lr2a, Lr2c, Lr3bg, Lr17, and Lr28. Isolates avirulent to Lr14a and Lr20 that have increased since 2003 had SSR genotypes identical or similar to older isolates in one of the five groups, indicating that these isolates were derived by mutation from the previously existing population of P. triticina. The representative collection of P. triticina isolates had characteristics consistent with an asexual dikaryotic population of genetically differentiated groups of SSR genotypes with high levels of heterozygosity and disequilibrium within which stepwise mutation at avirulence or virulence loci regularly occurs.
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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 | 0.001 | 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 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".