Genetic Diversity And Population Structure of Zymoseptoria Tritici Populations of Southern Ethiopia Using SSR Markers
Bibliographic record
Abstract
Abstract The fungal disease Zymoseptoria tritici causes Septoria tritici blotch, which is one of the most serious challenges to wheat production in Ethiopia and around the world. Understanding the pathogen's genetic structure is critical for developing and implementing effective management methods. Therefore, the present study targeted to explore the genetic structure of 51 Z. tritici isolates collected from four wheat producing zones of South and Southwestern parts of Ethiopia using nine microsatellite markers. In all of the examined isolates, a Z. tritici specific diagnostic marker that targets the ITS rDNA had amplified a predicted fragment size of 345bp. The number of alleles, gene diversity, and polymorphic information content per locus ranged from 9 to 14, 0.80 to 0.88, and 0.70 to 0.87, respectively, indicating a significant degree of genetic variety within populations. The results of an analysis AMOVA revealed a moderate (0.14) genetic differentiation, with 86 percent of total genetic variability (3.93) occurring within populations. Due to the existence of considerable gene flow, the dendrogram produced by UPGMA and PCoA also revealed a moderate population clustering in which the populations were not clearly clustered according to their sample areas. Furthermore, population structure analysis using a Bayesian model loosely grouped the population into five (K) sub-groups with substantial genetic mixing. The populations of the Kembata-Tembar and Hadiya zone have higher genetic variability than the other populations studied, and hence can be considered STB hot sites for future research on pathogen dynamics, germplasm screening, and host-pathogen interactions.
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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) | 0.000 | 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".