Large-scale apple GWAS reveals <i>NAC18.1</i> as a master regulator of ripening traits
Bibliographic record
Abstract
Apple quality traits such as fruit texture, sugar content, and firmness retention during storage are key targets for breeders. Understanding the genetic control of fruit quality traits can enable the development of genetic markers, useful for marker-assisted breeding of new apple cultivars. We made use of over 260,000 single nucleotide polymorphisms (SNPs) genotyped across 1,054 apple accessions from Canada's Apple Biodiversity Collection to perform genome-wide association for 21 fruit quality and phenology traits. We identified two loci on chromosome 15 and 16 associated with phenolic content and a locus on chromosome 10 associated with softening. In addition, we determined that allelic variation at the <italic>NAC18.1</italic> transcription factor was associated with numerous traits including harvest date, firmness at harvest, and firmness after storage. Our analyses suggest that <italic>NAC18.1</italic> independently acts as a high level regulator of multiple ripening related traits and we propose a model for the allelic effects at <italic>NAC18.1</italic> on apple ripening and softening.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".