From seed to algorithm: a comparative analysis of genome-wide association study and random forest for genomic trait prediction in apple
Bibliographic record
Abstract
Apples are one of Canada’s most economically important fruit (<em>Statistical Overview of the Canadian Fruit Industry, 2024</em>, 2024) and provides a healthy choice for consumption. However, when consumers are choosing which apple to eat, its physical appearance (shape and size) has a larger influence than it might seem. Breeding for new cultivars that focus on physical appearance requires understanding the apple genome for traits such as shape and size. To investigate the genome, this study uses approximately 19 million single nucleotides polymorphisms (SNPs) collected by whole genome sequencing (WGS) for 504 unique apple accessions from Canada’s Apple Biodiversity Collection. This genetic data was then analyzed through the use of two models to examine their performances while uncovering significant markers. Models used were the multi locus mixed model (MLMM) genome-wide association study (GWAS) and the Random Forest (RF) machine learning algorithm. Each approach was evaluated for its ability to identify significant SNPs. Results show that the GWAS model identified one SNP that was significantly associated with circularity. However, RF generalized poorly and could not conclude any significant SNPs. Future work could apply alternative machine learning algorithms to test their robustness in determining significant SNPs for complex trait discovery. Continuing to explore this dataset could also allow for breeders to gain more insights into different traits at a genomics level which could eventually successfully be integrated into their breeding targets.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".