Mapping of quantitative trait loci for fire blight resistance in the apple cultivars ‘Florina’ and ‘Nova Easygro’
Bibliographic record
Abstract
Fire blight is a devastating bacterial disease of rosaceous plants. Its damage to apple production is a major concern, since no existing control option has proven to be completely effective. Some commercial apple varieties, such as 'Florina' and 'Nova Easygro', exhibit a consistent level of resistance to fire blight. In this study, we used an F1 progeny of 'Florina' × 'Nova Easygro' to build parental genetic maps and identify quantitative trait loci (QTLs) related to fire blight resistance. Linkage maps were constructed using a set of microsatellites and enriched with amplified fragment length polymorphism (AFLP) markers. In parallel, progeny plants were artificially inoculated with Erwinia amylovora strain CFBP 1430 in a quarantine glasshouse. Shoot length measured 7 days after inoculation (DAI) and lesion length measured 7 and 14 DAI were used to calculate the lesion length as a percentage of the shoot length (PLL1 and PLL2, respectively). Percent lesion length data were log10-transformed (log10(PLL)) and used to perform the Kruskal-Wallis test, interval mapping (IM), and multiple QTL mapping (MQM). Two significant fire blight resistance QTLs were detected in 'Florina'. One QTL was mapped on linkage group 10 by IM and MQM; it explained 17.9% and 15.3% of the phenotypic variation by MQM with log10(PLL1) and log10(PLL2) data, respectively. A second QTL was identified on linkage group 5 by MQM with log10(PLL2) data; it explained 10.1% of the phenotypic variation. Genotyping the plants of 'Florina' pedigree with the microsatellites flanking the QTLs showed that the QTLs on linkage groups 5 and 10 were inherited from 'Jonathan' and 'Starking' (a 'Red Delicious' sport mutation), respectively. Other putative QTLs (defined as QTLs with LOD scores above the chromosomal threshold and below the genome-wide threshold) were detected by IM on linkage groups 5 and 9 of 'Nova Easygro'.
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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".