Accuracy of genome-enabled polygenic risk score prediction of cruciate ligament rupture risk in the Labrador Retriever
Bibliographic record
Abstract
Introduction: Canine cruciate ligament rupture (CR) is a common, complex, polygenic, orthopaedic disease in dogs that results in serious financial burden and patient morbidity even in the face of surgical correction. The goal of this study was to evaluate the clinical utility of CR polygenic risk score (PRS) prediction models using genome-wide SNP data from a large reference population of Labrador Retriever dogs. Methods: Using 10-fold cross-validation and an independent validation population, we assessed Bayesian and machine learning models with and without covariates using both genome-wide SNPs as well as genic SNPs. Models were tuned by optimizing numbers of CR risk SNPs selected by genome-wide association and adjusting posterior probability thresholds to maximize prediction accuracy. Results: Models that included clinical covariates (sex, neuter status, age, weight, withers height, as well as the first 10 principal components from the genetic relationship matrix) universally yielded higher accuracy up to 88.5% compared to 77% without covariates. Prediction accuracy for some models was reduced when only genic SNPs were used suggesting SNPs in non-coding regions could influence the CR disease risk. Discussion: Our results confirm that PRS models provide sufficient predictive accuracy for clinical application in veterinary medicine and offer a viable, early-life screening tool for personalized care and selective breeding to reduce CR incidence in high-risk breeds. Our results further confirm that CR is a complex polygenic disease in which genome-wide risk SNPs influence disease pathogenesis.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".