Two <i><scp>MC</scp>1R</i> loss‐of‐function alleles in cream‐coloured Australian Cattle Dogs and white Huskies
Bibliographic record
Abstract
Summary Loss‐of‐function variants in the MC1R gene cause recessive red or yellow coat‐colour phenotypes in many species. The canine MC1R:c.916C>T (p.Arg306Ter) variant is widespread and found in a homozygous state in many uniformly yellow‐ or red‐coloured dogs. We investigated cream‐coloured Australian Cattle Dogs whose coat colour could not be explained by this variant. A genome‐wide association study with 10 cream and 123 red Australian Cattle Dogs confirmed that the cream locus indeed maps to MC1R. Whole‐genome sequencing of cream dogs revealed a single nucleotide variant within the MITF binding site of the canine MC1R promoter. We propose to designate the mutant alleles at MC1R:c.916C>T as e1 and at the new promoter variant as e2. Both alleles segregate in the Australian Cattle Dog breed. When we considered both alleles in combination, we observed perfect association between the MC1R genotypes and the cream coat colour phenotype in a cohort of 10 cases and 324 control dogs. Analysis of the MC1R transcript levels in an e1/e2 compound heterozygous dog confirmed that the transcript levels of the e2 allele were markedly reduced with respect to the e1 allele. We further report another MC1R loss‐of‐function allele in Alaskan and Siberian Huskies caused by a 2‐bp deletion in the coding sequence, MC1R:c.816_817delCT. We propose to term this allele e3. Huskies that carry two copies of MC1R loss‐of‐function alleles have a white coat colour.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".