Genetic analysis of the population structure of the rice false smut fungus, <i>Villosiclava virens</i> , in China using microsatellite markers mined from a genome assembly
Bibliographic record
Abstract
Studies on population genetics of Villosiclava virens are limited because of the lack of polymorphic markers. Based on a draft genome sequence of isolate HWD ‐2 produced in this study, 20 of 403 potential simple sequence repeats ( SSR ) loci showed polymorphisms in preliminary screening using eight diverse V. virens isolates. Among polymorphic loci, most of them with tetra‐ to hexanucleotide unit motifs showed higher levels of polymorphism than loci with smaller motifs. After testing with 20 polymorphic SSR markers, the 87 isolates of V. virens from eight populations in China showed a high level of genetic diversity, with each as a unique haplotype. This differs from some previous findings showing little to no genetic variation based on random amplified polymorphic DNA and amplified fragment length polymorphism analyses. Among eight populations from major rice production areas of China, the population from Guangxi province in south China showed the highest levels of polymorphism, which led to the speculation that it might be closer to the centre of origin of this pathogen. The northern, central and eastern populations (Jilin, Liaoning, Hubei, Hunan, Jiangxi and Zhejiang), when considered together as a group, showed significant molecular variation compared to the southern populations (Fujian and Guangxi) (Φ PT = 0·043, P = 0·037). A significant relationship (Mantel test, P = 0·027) but with low correlation ( R 2 = 0·23) was also found between geographic distance and genetic distance. The 20 polymorphic SSR primer pairs designed in this study provide a tool for further research on the population diversity of this emerging fungal pathogen of rice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 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".