Atypical Genotypes for Canine Agouti Signaling Protein Suggest Novel Chromosomal Rearrangement
Bibliographic record
Abstract
Canine coat color is a readily observed phenotype of great interest to dog enthusiasts; it is also an excellent avenue to explore the mechanisms of genetics and inheritance. As such, multiple commercial testing laboratories include basic color alleles in their popular screening panels, allowing for the creation of genotyped datasets at a scale not before appreciated in canine genetic research. These vast datasets have revealed rare genotype anomalies that encourage further exploration of color and pattern inheritance. We previously reported the simultaneous presence of greater than two allele variants at the Agouti Signaling Protein (ASIP) locus in a commercial genotype cohort of 11,790 canids. Here we present additional data to characterize the occurrence of anomalous ASIP genotypes. We document the detection of combinations of three or four ASIP allele variants in 17 dog breeds and Dingoes, at within-breed frequencies of 1.32–63.34%. We analyze the potential impact on phenotype that these allele combinations present, and propose mechanisms that could account for the findings, including: gene recombination, duplication, and incorrect causal variant identification. These findings speak to the accuracy of industry-wide protocols for commercial ASIP genotyping and imply that ASIP should be analyzed via haplotype, rather than using only the existing allele hierarchy, in the future.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".