Pool-seq of diverse apple germplasm reveals candidate loci underlying ripening time, phenolic content, and softening
Bibliographic record
Abstract
Ripening time, softening, and phenolic content are phenotypes of considerable commercial importance in apples. Identifying causal genetic variants controlling these traits not only advances marker-assisted breeding, but it is also an essential step for the application of gene editing technologies in apples. To advance the discovery of genetic variants associated with these phenotypes, we examined allele frequency differences between groups of phenotypically extreme individuals from Canada’s Apple Biodiversity Collection using pooled whole genome sequencing (pool-seq). We sequenced pooled DNA samples to an average read depth of 150x and scanned the genome for allelic differentiation between pools. For each phenotype, we identified >20 million genetic variants and identified numerous candidate genes. We identified loci on chromosomes 3 and 4 associated with ripening time, the former suggesting that regulatory variants upstream of a previously identified transcription factor <italic>NAC18.1</italic> may be causal. Our analysis identified candidate regions on chromosomes 4, 8, and 16 associated with phenolic content, and suggested a cluster of <italic>UDP-Glycosyltransferase</italic> family genes as candidates for polyphenol production. Further, we identified regions on chromosomes 17 and 10 associated with softening and suggest a <italic>Long-chain fatty alcohol dehydrogenase family</italic> gene as putatively causal.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".