A Candidate Gene Analysis of Canine Hypoadrenocorticism in 3 Dog Breeds
Bibliographic record
Abstract
Canine hypoadrenocorticism is believed to be an immune-related condition. It is rare in the overall dog population but shows a breed-related predisposition with Standard poodles and Portuguese water dogs having a greater prevalence of the condition. It shares many similarities with human primary adrenal insufficiency and is believed to be a naturally occurring, spontaneous model for the human condition. Short haplotype blocks and low levels of linkage disequilibrium in the human genome mean that the identification of genetic contributors to the condition requires large sample numbers. Pedigree dogs have high linkage disequilibrium and long haplotypes within a breed, increasing the potential of identifying novel genes that contribute to canine genetic disease. We investigated 222 SNPs from 42 genes that have been associated or may be implicated in human Addison's disease. We conducted case-control analyses in 3 pedigree dog breeds (Labrador retriever: affected n = 30, unaffected = 76; Cocker Spaniel: affected n = 19, unaffected = 53; Springer spaniel: affected n = 26, unaffected = 46) and identified 8 associated alleles in genes COL4A4, OSBPL9, CTLA4, PTPN22, and STXBP5 in 3 pedigree breeds. Association with immune response genes PTPN22 and CTLA4 in certain breeds suggests an underlying immunopathogenesis of the disease. These results suggest that canine hypoadrenocorticism could be a useful model for studying comparative genetics relevant to human Addison's disease.
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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 | 0.001 | 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".