Linkage and Segregation Analysis of Black and Brindle Coat Color in Domestic Dogs
Bibliographic record
Abstract
Mutations of pigment type switching have provided basic insight into melanocortin physiology and evolutionary adaptation. In all vertebrates that have been studied to date, two key genes, Agouti and Melanocortin 1 receptor (Mc1r), encode a ligand-receptor system that controls the switch between synthesis of red-yellow pheomelanin vs. black-brown eumelanin. However, in domestic dogs, historical studies based on pedigree and segregation analysis have suggested that the pigment type-switching system is more complicated and fundamentally different from other mammals. Using a genomewide linkage scan on a Labrador x greyhound cross segregating for black, yellow, and brindle coat colors, we demonstrate that pigment type switching is controlled by an additional gene, the K locus. Our results reveal three alleles with a dominance order of black (K(B)) > brindle (k(br)) > yellow (k(y)), whose genetic map position on dog chromosome 16 is distinct from the predicted location of other pigmentation genes. Interaction studies reveal that Mc1r is epistatic to variation at Agouti or K and that the epistatic relationship between Agouti and K depends on the alleles being tested. These findings suggest a molecular model for a new component of the melanocortin signaling pathway and reveal how coat-color patterns and pigmentary diversity have been shaped by recent selection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".