Development of sequence-characterized amplified regions (SCARs) from amplified fragment length polymorphism (AFLP) markers tightly linked to the <i>Vf</i> gene in apple
Bibliographic record
Abstract
Amplified fragment length polymorphism (AFLP) markers have become widely used in saturating the region of a gene of interest for the ultimate goal of map-based cloning of the gene or for marker-assisted selection. However, conversion of AFLP markers into restriction fragment length polymorphism (RFLP) or polymerase chain reaction (PCR)-based markers will greatly expand their usefulness in genetic applications. Previously, we have identified 15 AFLP markers tightly linked to the Vf gene conferring scab resistance in apple. In this study, we have successfully converted 11 of these AFLPs into sequence-characterized amplified region (SCAR) markers. Of the remaining four nonconverted AFLP markers, one, ET2MC8-1, has been found to be very short (83 base pairs) and is an A/T rich (90%) marker; a second, EA2MG11-1, has shown identical sequences between Malus floribunda 821 (the original source of the Vf gene) and scab-susceptible apple cultivars; while the other two, EA12MG16-1 and ET8MG1-1, have not been cloned. Using the 11 converted SCAR markers along with 5 previously identified SCAR markers, a high-resolution linkage map around the Vf gene has been constructed, and found to be consistent with its corresponding AFLP map. Three converted SCAR markers (ACS-3, -7, and -9) are inseparable from the Vf gene; whereas one (ACS-6) is located left of, and the remaining seven (ACS-1, -2, -4, -5, -8, -10, and -11) are located right of the Vf gene at genetic distances of 0.4 and 0.2 cM, respectively. A reliable and robust procedure for development of SCAR markers from AFLP markers is presented.Key words: apple, AFLP, SCAR, apple scab disease, Vf gene.
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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 | 0.000 |
| 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 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".