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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".