Detection of a genetic mutation for myotonia congenita among Miniature Schnauzers and identification of a common carrier ancestor
Bibliographic record
Abstract
OBJECTIVE: To develop a molecular genetic test to detect the mutant skeletal muscle chloride channel (CIC-1) allele that causes myotonia congenita in Miniature Schnauzers and to analyze the relationship of affected and carrier dogs. ANIMALS: 372 Miniature Schnauzers from the United States, Canada, Australia, and Europe that were tested between March 2000 and October 2001. PROCEDURE: The sequence surrounding the mutation in the CIC-1 allele was amplified by use of a unique pair of primers. Polymerase chain reaction (PCR) products were digested with the restriction enzyme Hpy CH4 III and separated on a 6% polyacrylamide gel. Pedigrees from all available carrier and affected dogs were analyzed, and a composite pedigree was established. RESULTS: Enzyme digestion of PCR products of the normal CIC-1 allele resulted in 3 fragments of 175, 135, and 30 bp, whereas PCR products of the mutant allele resulted in fragments of only 175 and 165 bp. Of the 372 Miniature Schnauzers, 292 (78.5%) were normal, 76 (20.4%) were carriers, and 4 (1.1%) were affected (myotonic) dogs. Frequency of the mutant allele was 0.113. Pedigree analysis revealed that a popular sire, documented to be a carrier, was a common ancestor of all carriers and affected dogs. CONCLUSIONS AND CLINICAL RELEVANCE: A PCR-based enzyme digestion DNA test was developed. The mutant allele for this disease is frequent in Miniature Schnauzers that are related to a common carrier ancestor. Breeding dogs should be tested by this specific DNA test to help limit the spread of this deleterious mutation.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".