Whole genome sequencing reveals the genetic diversity and structure of Leptosphaeria maculans populations from the Western Cape province of South Africa
Bibliographic record
Abstract
BACKGROUND: Leptosphaeria maculans is the causal agent of blackleg, a globally important disease of canola. Investigating the genetic diversity and structure of L. maculans populations can provide insight into its evolutionary potential and genetic variability, which is important to develop effective blackleg management strategies. In this study, whole genome sequence data was generated for 230 L. maculans isolates collected between 2020 and 2022 across the canola production regions of the Western Cape of South Africa. A total of 27 419 informative single nucleotide polymorphisms was used to investigate the genetic diversity and structure of the pathogen population. RESULTS: Mating type distribution did not deviate statistically from a 1:1 ratio at any location, indicating no restriction on sexual reproduction. Genetic statistics calculated showed high genotypic diversity and evenness (Lambda and E.5 ≥ 0.98) and low linkage disequilibrium ( ≤ 2.71E-04) which is also associated with sexual reproduction. Discriminative analysis of principal components and sparse nonnegative matrix factorisation revealed genetic differentiation between the Swartland and Southern Cape canola production regions in the Western Cape. Analysis of molecular variance also indicated regions as the most important factor for population differentiation but suggested shallow population structure with only 3,71% of the total variation occurring between regions. To assess the phylogenomic position of South African isolates in the global context, data for 171 international isolates was included, and the clustering analyses repeated. Results showed a high similarity between Australian and Swartland isolates, while isolates from the Southern Cape formed a unique genetic cluster. CONCLUSION: The results from this study provide the basis for blackleg research in South Africa and enhances understanding of the pathogen, which will assist in developing improved blackleg management strategies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".