Genetic markers of canine hip dysplasia
Bibliographic record
Abstract
Canine hip dysplasia is a complex skeletal malformation caused by genetic and environmental factors. The prevalence of hip dysplasia in different canine breeds ranges widely, from 1% (for Whippet and Borzoi) to over 70% (for Bulldog and Pug). These differences indicate the presence of genetic variants predisposing to or preventing this disorder in gene pools of particular breeds. The importance of genetic factors is also confirmed by a high coefficient of heritability (h2) of canine hip dysplasia, which for most breeds oscillates around 0.5 – 0.6. Application of modern genomic methods, that is, mainly genome scanning (based previously on microsatellite markers and currently on SNP microarrays) has led in recent years to the identification of potential genetic markers associated with this disorder. Such studies were carried out mostly in two breeds: Labrador retriever and German shepherd. Some of the markers were found in the vicinity of genes involved in skeletal development. Following these achievements, the use of some markers has been suggested for early risk diagnosis of hip dysplasia. This shows that molecular testing is becoming important for not only monogenic, but also polygenic canine diseases and disorders. Identification of genetic markers associated with predisposition to hip dysplasia offers an opportunity for an early risk evaluation of this disorder (prior to its first signs). Moreover, it facilitates effective breeding selection aimed at eradicating undesirable genetic variants from the gene pool of a given breed..
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".