Conversion of AFLP markers associated with FHB resistance in wheat into STS markers with an extension-AFLP method
Bibliographic record
Abstract
Amplified fragment length polymorphism (AFLP) has proven a powerful tool for tagging genes or quantitative trait loci (QTLs) of interest in plants. However, conversion of AFLP markers into sequence-tagged site (STS) markers is technically challenging in wheat owing to the complicated nature of its genome. In this study, we developed an "extension-AFLP" method to convert AFLP markers associated with Fusarium head blight (FHB) resistance into STS markers. When an AFLP marker of interest was detected with an EcoRI+3-MseI+4-selective primer combination, the PCR product was used as a template for an additional selective amplification with four primer pairs, in which one additional selective base (either A, C, G, or T) was added to the 3' end of one of the two primers. The extended primer pair that produced the targeted band was further extended by adding each of the four selective nucleotide bases for the next round of selective amplification. Extension selective amplification was performed until the target bands became clear enough for subsequent cloning and sequencing. By using the extension-AFLP method, we successfully converted two AFLP markers located on chromosome 3BS and associated with FHB resistance into STS markers. Our results indicated that the extension-AFLP method is an efficient approach for converting AFLP markers into STS markers in wheat. The developed STS markers might be used for marker-assisted selection (MAS) for FHB resistance in wheat breeding programs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".