Heritability of insidious uveitis in Appaloosa horses
Bibliographic record
Abstract
Abstract Equine recurrent uveitis (ERU) is a blinding ocular disorder among horses, and the Appaloosa horse breed is disproportionally affected by a chronic form of this intraocular inflammatory disease known as insidious uveitis. Strong breed predisposition and previous investigations suggest that there is a genetic component to the pathology of insidious uveitis among Appaloosa horses; however, no estimates of the heritability of the disease have previously been determined. This study aimed to characterize the genetic underpinning of the disease by estimating the heritability for insidious uveitis among Appaloosas. After combining two genotyping array datasets from the Illumina Equine SNP70 BeadChip and the Axiom Equine 670 K Genotyping Array, heritability was estimated for 59 affected and 83 unaffected horses using both restricted maximum likelihood (REML) and phenotype correlation – genotype correlation solvers from thelinkage disequilibrium adjusted kinshipsoftware. Based on previous research, age and sex were used as covariates, and the locus responsible for the characteristic Appaloosa coat pattern (LP), previously associated with ERU risk, was included as a fixed effect (‘top predictor’). Using prevalence values from 0.05 to 0.42, the heritability estimate for insidious uveitis ranged from 0.95 (SE = 0.14) to 1.74 (SE = 0.25) withLPcontributing 0.16–0.33 to the estimate. This study suggests that insidious uveitis is highly heritable (REML 95% CI,h2 = 0.68–1.0) and additional loci outside ofLPare contributing to the genetic risk for insidious uveitis for Appaloosas. Once identified, these other genetic factors may lead to new disease mitigation efforts in veterinary care and breeding practices.
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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 | 0.000 |
| 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) | 0.002 | 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".