Sequence Analysis of Six Candidate Genes in Miniature Schnauzers with Primary Hypertriglyceridemia
Bibliographic record
Abstract
Miniature Schnauzers are predisposed to primary hypertriglyceridemia (HTG). In this study, we performed whole genome sequencing (WGS) of eight Miniature Schnauzers with primary HTG and screened for risk variants in six HTG candidate genes: LPL, APOC2, APOA5, GPIHBP1, LMF1, and APOE. Variants were filtered to identify those present in ≥2 Miniature Schnauzers with primary HTG and uncommon (<10% allele frequency) in a WGS variant database including 613 dogs from 61 other breeds. Three variants passed filtering: an APOE TATA box deletion, an LMF1 intronic SNP, and a GPIHBP1 missense variant. The APOE and GPIHBP1 variants were genotyped in a cohort of 108 Miniature Schnauzers, including 68 with primary HTG and 40 controls. A multivariable regression model, including age and sex, did not identify an effect of APOE (estimate = 0.18, std. error = 0.14; p = 0.20) or GPIHBP1 genotypes (estimate = −0.26, std. error = 0.42; p = 0.54) on triglyceride concentration. In conclusion, we did not identify a monogenic cause for primary HTG in Miniature Schnauzers in the six genes evaluated. However, if HTG in Miniature Schnauzers is a complex disease resulting from the cumulative effects of multiple variants and environment, the identified variants cannot be ruled out as contributing factors.
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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) | 0.001 | 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".