QTL analysis of soft scald in two apple populations
Bibliographic record
Abstract
The apple (Malus×domestica Borkh.) is one of the world's most widely grown and valuable fruit crops. With demand for apples year round, storability has emerged as an important consideration for apple breeding programs. Soft scald is a cold storage-related disorder that results in sunken, darkened tissue on the fruit surface. Apple breeders are keen to generate new cultivars that do not suffer from soft scald and can thus be marketed year round. Traditional breeding approaches are protracted and labor intensive, and therefore marker-assisted selection (MAS) is a valuable tool for breeders. To advance MAS for storage disorders in apple, we used genotyping-by-sequencing (GBS) to generate high-density genetic maps in two F1 apple populations, which were then used for quantitative trait locus (QTL) mapping of soft scald. In total, 900 million DNA sequence reads were generated, but after several data filtering steps, only 2% of reads were ultimately used to create two genetic maps that included 1918 and 2818 single-nucleotide polymorphisms. Two QTL associated with soft scald were identified in one of the bi-parental populations originating from parent 11W-12-11, an advanced breeding line. This study demonstrates the utility of next-generation DNA sequencing technologies for QTL mapping in F1 populations, and provides a basis for the advancement of MAS to improve storability of apples.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".