Genetic investigation of equine recurrent uveitis in Appaloosa horses
Bibliographic record
Abstract
Summary Equine recurrent uveitis (ERU) is characterized by intraocular inflammation that often leads to blindness in horses. Appaloosas are more likely than any other breed to develop insidious ERU, distinguished by low‐grade chronic intraocular inflammation, suggesting a genetic predisposition. Appaloosas are known for their white coat spotting patterns caused by the leopard complex spotting allele (LP) and the modifierPATN1. A marker linked toLPon ECA1 and markers near MHC on ECA20 were previously associated with increased ERU risk. This study aims to further investigate these loci and identify additional genetic risk factors. A GWAS was performed using the Illumina Equine SNP70 BeadChip in 91 horses. Additive mixed model approaches were used to correct for relatedness. Although they do not reach a strict Bonferroni genome‐wide significance threshold, two SNPs on ECA1 and one SNP each on ECA12 and ECA29 were among the highest ranking SNPs and thus warranted further analysis (P = 1.20 × 10−5,P = 5.91 × 10−6,P = 4.91 × 10−5,P = 6.46 × 10−5). In a second cohort (n = 98), only an association with theLPallele on ECA1 was replicated (P = 5.33 × 10−5). Modeling disease risk withLP, age and additional depigmentation factors (PATN1genotype and extent of roaning) supports an additive role forLPand suggests an additive role forPATN1. Genotyping forLPandPATN1may help predict ERU risk (AUC = 0.83). The functional role ofLPandPATN1in ERU development requires further investigation. Testing samples across breeds with leopard complex spotting patterns and a denser set of markers is warranted to further refine the genetic components of ERU.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".