A splice donor variant in SLAMF1 is associated with canine atopic dermatitis
Bibliographic record
Abstract
Introduction Canine atopic dermatitis (CAD) is a common inflammatory skin condition in dogs. It is a lifelong issue that poses a significant welfare concern due to the chronic skin discomfort and pruritus (itching) experienced by affected animals. Excessive scratching, licking, and chewing cause self-inflicted injuries to the skin and increase the risk of secondary infections. Several dog breeds, including Labrador Retriever, Boxer, and French Bulldog, are known to be predisposed to these issues, suggesting a genetic link to the condition. Methods Access to a large population of dogs genotyped on a medium-density single-nucleotide polymorphism (SNP) array through commercial Wisdom Panel testing, along with their linked clinical records, allowed a large-scale, highly powered genome-wide association study (GWAS) to be performed. In this study, over 28,000 dogs were examined to identify genetic changes associated with CAD. Results A statistically significant signal on canine chromosome 38 was identified, with a particularly strong signal in French Bulldogs. Whole-genome resequencing revealed a compelling splice donor variant in the signaling lymphocytic activation molecule 1 (SLAMF1), a transmembrane receptor with important functions in immune cells. Further analysis of additional genome sequences and RNA samples from the MARS PETCARE BIOBANK confirmed that the SLAMF1 splice variant is a strong potential contributor to an increased risk of atopic dermatitis. Discussion The discovery represents the first compelling genetic variant associated with CAD to be validated in more than one breed of dog. The study identifies SLAMF1 as a potential pharmaceutical target and the associated variant as a biomarker to enable dog breeders to make informed breeding decisions to reduce risk of CAD in future generations. The presence of the SLAMF1 variant in many dog breeds and free-roaming dogs worldwide, indicates its potential role in contributing to the global risk of CAD.
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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 | 0.001 | 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) | 0.004 | 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".