Increased genetic risk or protection for canine autoimmune lymphocytic thyroiditis in Giant Schnauzers depends on DLA class II genotype
Bibliographic record
Abstract
Dogs represent an excellent comparative model for autoimmune thyroiditis as several dog breeds develop canine lymphocytic thyroiditis (CLT), which is clinically similar to Hashimoto's thyroiditis in human. We obtained evidence that dog leukocyte antigen (DLA) class II genotype function as either genetic risk factor that predisposes for CLT or as protective factor against the disease. Genetic diversity at their DLA-DRB1, -DQA1, and -DQB1 loci were defined and potential association to major histocompatibility complex II haplotypes and alleles was analyzed. Giant Schnauzers carrying the DLA-DRB1*01201/DQA1*00101/DQB1*00201 haplotype showed an increased risk (odds ratio of 6.5) for developing CLT. The same risk haplotype has, to date, been observed in three different breeds affected by this disease, Giant Schnauzer, Dobermann, and Labrador Retriever, indicating that it is a common genetic risk factor in a variety of breeds affected by this disease. Importantly, protection for development of the disease was found in dogs carrying the DLA-DRB1*01301/DQA1*00301/DQB1*00501 haplotype (odds ratio of 0.3).
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".