Genome‐Wide Association Studies in Apple Reveal Loci for Aroma Volatiles, Sugar Composition, and Harvest Date
Bibliographic record
Abstract
Understanding the genetic architecture of fruit quality traits is crucial to target breeding of apple ( L.) cultivars. We linked genotype and phenotype information by combining genotyping-by-sequencing (GBS) generated single nucleotide polymorphism (SNP) markers with fruit flavor volatile data, sugar and acid content, and historical trait data from a gene bank collection. Using gas chromatography-mass spectrometry (GC-MS) analysis of apple juice samples, we identified 49 fruit volatile organic compounds (VOCs). We found a very variable content of VOCs, especially for the esters, among 149 apple cultivars. We identified convincing associations for the acetate esters especially butyl acetate and hexyl acetate on chromosome 2 in a region of several alcohol acyl-transferases including AAT1. For sucrose content and for fructose and sucrose in percentage of total sugars, we revealed significant SNP associations. Here, we suggest a vacuolar invertase close to significant SNPs for this association as candidate gene. Harvest date was in strong SNP association with a NAC transcription factor gene and sequencing identified two haplotypes associated with harvest date. The study shows that SNP marker characterization of a gene bank collection can be successfully combined with new and historical trait data for association studies. Suggested candidate genes may contribute to an improved understanding of the genetic basis for important traits and simultaneously provide tools for targeted breeding using marker-assisted selection (MAS).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".