Applicability of start codon targeted (SCoT) and inter-simple sequence repeat (ISSR) markers for genetic diversity analysis in durum wheat genotypes
Bibliographic record
Abstract
Durum wheat (Triticum turgidum var. durum) is one of the most important cereal crops widely cultivated all over the world with high economic value. In the present study, genetic variation in a mini-core collection of durum wheat germplasm, including 25 breeding lines and 18 landraces, was evaluated using 15 inter-simple sequence repeat (ISSR) and six start codon targeted (SCoT) markers. High levels of polymorphism were observed; 98.70% (ISSR) and 100% (SCoT), which indicated that these markers are useful tools for detection of genetic variation in the collection. Analysis of molecular variance revealed that the major part of genetic variations (90% and 93% for ISSR and SCoT, respectively) occurred within genotypes set. Comparing the genetic variation of breeding lines and landraces based on genetic parameters showed that effective number of alleles (Ne), Nei's gene diversity (He) and Shannon's Information index (I) in landraces were higher than in breeding lines. Although cluster analysis, based on both markers, separated the genotypes in five groups, the dendrogram obtained from SCoT provided the best clustering pattern. Inter-population differentiation (Gst) estimated on the basis of two marker systems representing that a vast portion of the total genetic diversity refers to variation within two sets of genotypes. In conclusion, the results verified a high level of genetic variation among the durum wheat mini-core collection, particularly among landraces, which can be interesting for future breeding programmes.
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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".