Signature of Balancing Selection at the MC1R Gene in Kunming Dog Populations
Bibliographic record
Abstract
Coat color in dog breeds is an excellent character for revealing the power of artificial selection, as it is extremely diverse and likely the result of recent domestication. Coat color is generated by melanocytes, which synthesize pheomelanin (a red or yellow pigment) or eumelanin (a black or brown pigment) through the pigment type-switching pathway, and is regulated by three genes in dogs: MC1R (melanocortin receptor 1), CBD103 (β-defensin 103), and ASIP (agouti-signaling protein precursor). The genotypes of these three gene loci in dog breeds are associated with coat color pattern. Here, we resequenced these three gene loci in two Kunming dog populations and analyzed these sequences using population genetic approaches to identify evolutionary patterns that have occurred at these loci during the recent domestication and breeding of the Kunming dog. The analysis showed that MC1R undergoes balancing selection in both Kunming dog populations, and that the Fst value for MC1R indicates significant genetic differentiation across the two populations. In contrast, similar results were not observed for CBD103 or ASIP. These results suggest that high heterozygosity and allelic differences at the MC1R locus may explain both the mixed color coat, of yellow and black, and the difference in coat colors in both Kunming dog populations.
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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".