Investigation of Genetic Modifiers of Copper Toxicosis in Labrador Retrievers
Bibliographic record
Abstract
Copper toxicosis is a complex genetic disorder in Labrador retrievers characterized by hepatic copper accumulation eventually leading to liver cirrhosis. The variation of hepatic copper levels in Labrador retrievers has been partly explained by mutations in ATP7A c.980C>T and ATP7B c.4358G>A. To further elucidate the genetic background of this disease, we used targeted Next Generation Sequencing (NGS) in a cohort of 95 Labrador retrievers to analyze 72 potential modifier genes for variations associated with hepatic copper levels. Variants associated with copper levels were subsequently evaluated in a replication cohort of 144 Labrador retrievers. A total of 44 variants in 25 different genes were identified, of which four showed significant association with copper levels. Of the four variants found associated with hepatic copper levels in the NGS cohort, one was validated in the replication cohort. The non-reference allele of the variant NC_006602.3.g.52434480C>T in RETN resulting in amino-acid change p.Leu7Phe was associated with decreased hepatic copper levels. In humans, resistin is associated with severity of non-alcoholic fatty liver disease, fibrosis, cirrhosis and mitochondrial dysfunction in hepatocytes. Further studies are needed to investigate the biological function of RETN p.Leu7Phe in the development of copper toxicosis in Labrador retrievers.
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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) | 0.002 | 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".