Comprehensive genetic testing combined with citizen science reveals a recently characterized ancient MC1R mutation is associated with partial recessive red phenotypes in dog
Bibliographic record
Abstract
Abstract Background: The Melanocortin 1 Receptor (MC1R) plays a central role in regulation of coat color determination in dogs and is commonly referred to as the “E (extension) Locus”. Allelic variation of the MC1R gene is associated with coat color phenotypes EM (melanistic mask), EG (grizzle/domino) and e1-3 (recessive red) in dogs. In addition, a previous study of archeological dog specimens over 10,000 years of age identified a variant p.R301C in the MC1R gene that may have influenced coat color of early dogs.Results: Commercial genotyping of 11,750 dog samples showed the R301C variant of the MC1R gene was present in 35 breeds or breed varieties, at an allele frequency of 1.5% in the tested population. We detected no linkage disequilibrium between R301C and other tested alleles of the E locus. Based on current convention we propose that R301C should be considered a novel allele of the E locus, which we have termed eA for “e ancient red”. Phenotype analysis of owner-provided dog pictures reveals that the eA allele has an impact on coat color and is recessive to wild type E and dominant to the e alleles. In dominant black (KB/*) dogs it can prevent the expression of the K locus, and the expressed coat color is solely determined by the A locus. In the absence of dominant black, eA/eA and eA/e genotypes result in the coat color patterns referred to in their respective breed communities as domino in Alaskan Malamute and other Spitz breeds, grizzle in Chihuahua, and pied in Beagle.Conclusions: This study demonstrates a large genotype screening effort to identify the frequency and distribution of the MC1R R301C variant, one of the earliest mutations captured by canine domestication, and citizen science empowered characterization of its impact on coat color.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".