Accuracy of genome-enabled polygenic risk score prediction of cruciate ligament rupture risk in the Labrador Retriever
Bibliographic record
Abstract
Cruciate ligament rupture (CR) is a prevalent and heritable orthopedic disease in dogs, especially in Labrador Retrievers. This study evaluates the accuracy of polygenic risk score (PRS) prediction for CR using genome-wide SNP data in a well-phenotyped Labrador Retriever population. A training set of 1,006 dogs and a validation set of 52 dogs were genotyped using the Illumina CanineHD array. Eight statistical models—including Bayesian regression and machine learning methods—were assessed for their ability to predict CR status based on genetic markers. Model performance was evaluated using AUC, accuracy, and R² metrics. The study further analyzed SNPs located in genic regions associated with CR and incorporated relevant covariates. Our results support the potential clinical utility of PRS in identifying dogs at high genetic risk for CR, enabling earlier intervention and improved breeding strategies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".