A Genome‐Wide Association Study of Apple Quality and Scab Resistance
Bibliographic record
Abstract
The apple ( × Borkh.) is an economically and culturally important crop grown worldwide. Growers of this long-lived perennial must produce fruit of adequate quality while also combatting abiotic and biotic stress. Traditional apple breeding can take up to 20 yr from initial cross to commercial release, but genomics-assisted breeding can help accelerate this process. To advance genomics-assisted breeding in apple, we performed genome-wide association studies (GWAS) and genomic prediction in a collection of 172 apple accessions by linking over 55,000 single nucleotide polymorphisms (SNPs) with 10 phenotypes collected over 2 yr. Genome-wide association studies revealed several known loci for skin color, harvest date and firmness at harvest. Several significant GWAS associations were detected for resistance to a major fungal pathogen, apple scab ( [Cke.] Wint.), but we demonstrate that these hits likely represent a single ancestral source. Using genomic prediction, we show that most phenotypes are sufficiently predictable using genome-wide SNPs to be candidates for genomic selection. Finally, we detect a signal for firmness retention after storage on chromosome 10 and show that it may not stem from variation in , a gene repeatedly identified in bi-parental mapping studies and widely believed to underlie a major QTL for firmness on chromosome 10. We provide evidence that this major QTL is more likely due to variation in a neighboring ethylene response factor (ERF) gene. The present study showcases the superior mapping resolution of GWAS compared to bi-parental linkage mapping by identifying a novel candidate gene underlying a well-studied, major QTL involved in apple firmness.
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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 | 0.001 |
| 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) | 0.002 | 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".