Synergy between<i>MC1R</i>and<i>ASIP</i>for coat color in horses (<i>Equus caballus</i>)1
Bibliographic record
Abstract
Through domestication and human selection, horses have acquired various coat colors, including seven phenotypes: black, brown, dark bay, bay, chestnut, white, and gray. Here we determined the genotypes for melanocortin-1 receptor (MC1R) and agouti signaling protein (ASIP) in 709 horses from 15 breeds. We found that the EEEE genotype frequency at MC1R decreased from dark to light colors (black = 64.5%, brown = 67.5%, dark bay = 47.0%, bay = 16.5%, and chestnut = 0.0%), whereas the AAAA genotype frequency at ASIP increased as coat color lightened (black = 0.0%, brown = 22.9%, dark bay = 69.2%, and bay = 83.0%). When combined genotypes at MC1R and ASIP were examined, different advantage genotype combinations were found for each color: black EEEE-AaAa (64.5%), brown EEEE-AAAa (47.0%), dark bay EEEE-AAAA, and EEEe-AAAA (36.2% and 33.0%, totally 69.2%), bay EEEe-AAAA (69.6%), and chestnut EeEe-AAAA (62.6%). The χ2 test showed that the phenotypes of horse coat colors were significantly related with the genotypes of MC1R and ASIP (p < 0.001). Furthermore, in contrast to a previous study where AaAa was only found in black, chestnut, and gray horses, we also found this allele in brown, dark bay, bay, and white horses. These results indicated that MC1R and ASIP may synergistically affect the levels of melanin in equine coat colors and that additional genes are likely involved in regulating coat colors, especially for white and gray colors. Our research provides new data for further studies on the synergetic actions of MC1R and ASIP in coat color of horses.
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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 | 0.000 |
| 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 | 0.000 | 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".