Polymorphic Microsatellite Markers for Genetic Analysis of Collagen Genes in Suspected Collagenopathies in Dogs
Bibliographic record
Abstract
Defects in collagen proteins cause a variety of disorders in humans. It can be expected that collagen gene mutations are involved in collagenopathies in dogs. The collagen genes COL3A1, COL5A1, COL5A2, COL6A1, COL6A3, COL9A1, COL9A2, COL9A3, COL10A1 and COL11A1 were identified on the canine genome based on the homology with the human genes. Simple sequence repeats (microsatellites) were found in the chromosomal regions of these genes and investigated for polymorphism in Labrador Retrievers, Bernese Mountain dogs, Boxer dogs and German Shepherd dogs by PCR and subsequent detection of the DNA products. Nine informative microsatellite markers were identified. The markers closely situated to COL9A1, COL9A2 and COL9A3 were used to investigate the involvement of the genes in cranial cruciate ligament rupture in Boxer dogs. It was found that these genes are probably not involved in this abnormality. The markers described here will be useful for a candidate gene approach of suspected collagenopathies specific to dog breeds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".