Genetic analyses of elbow and hip dysplasia in the German shepherd dog
Bibliographic record
Abstract
Results from radiographic screening for canine hip dysplasia (CHD) and elbow dysplasia (CED) of 48 367 German shepherd dogs born in 2001-07 were used for the population genetic analyses. Available information included CHD scores for 47 730 dogs, CED scores for 28 011 dogs and detailed veterinary diagnoses of primary ED lesions for a subsample of 18 899 dogs. Quasi-continuous traits were CHD, CED and cases of CED without radiographically visible primary lesion (CED-ARTH). Binary coding was used for fragmented medial coronoid process of the ulna (FCP), borderline findings and mild to severe signs of dysplasia in hip and elbow joints. Genetic parameters were estimated in univariate threshold and multivariate linear and mixed linear-threshold models using Gibbs sampling. Correlations between univariately predicted breeding values (BV) indicated genetic differences between borderline and affected disease status for both CHD (r(BV) = 0.5) and CED (r(BV) = 0.3). Multivariate genetic analyses with separate consideration of borderline findings revealed moderate heritabilities of 0.2-0.3 for the quasi-continuous traits with positive additive genetic correlation of 0.3 between CHD and both CED and CED-ARTH. For FCP, heritability of 0.6 and additive genetic correlations of +0.1 to CHD and -0.1 to CED-ARTH were estimated. Results supported the relevant genetic determination of CHD and CED, argued for both diseases against interpretation of borderline findings as healthy and implied genetic heterogeneity of CED. Accordingly, future breeding strategies to reduce the prevalences of CHD and CED in the German shepherd dog should be most efficient when based on BV from multivariate genetic evaluation for CHD, CED-ARTH and FCP with use of the whole scale of categories for classification of CHD and CED.
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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 | 0.002 | 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".