Molecular Characterization of Wheat Stripe Rust Pathogen (Puccinia striiformis f. sp. tritici) Collections from Nine Countries
Bibliographic record
Abstract
Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most important diseases of wheat worldwide. To understand the worldwide distribution of its molecular groups, as well as the diversity, differentiation, and migration of the Pst populations, 567 isolates collected from nine countries (China, Pakistan, Italy, Egypt, Ethiopia, Canada, Mexico, Ecuador, and the U.S.) in 2010–2018 were genotyped using 14 codominant simple sequence repeat markers. A total of 433, including 333 new multi-locus genotypes (MLGs), were identified, which were clustered into ten molecular groups (MGs). The MGs and country-wise populations differed in genetic diversity, heterozygosity, and correlation coefficient between the marker and virulence data. Many isolates from different countries, especially the isolates from Mexico, Ecuador, and the U.S., were found to be identical or closely related MLGs, and some of the MGs were present in all countries, indicating Pst migrations among different countries. The analysis of molecular variance revealed 78% variation among isolates, 12% variation among countries, and 10% variation within countries. Only low levels of differentiation were found by the pairwise comparisons of country populations. Of the 10 MGs, 5 were found to be involved in sexual and/or somatic recombination. Identical and closely related MLGs identified from different countries indicated international migrations. The study provides information on the distributions of various Pst genetic groups in different countries and evidence for the global migrations, which should be useful in understanding the pathogen evolution and in stressing the need for continual monitoring of the disease and pathogen populations at the global scale.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".