MITF and White Spotting in Dogs: A Population Study
Bibliographic record
Abstract
This study was designed to determine if one of the variants found in our laboratory, or previously reported in microphthalmia-associated transcription factor (MITF), was associated with one or more spotting patterns in dogs. None of the rare variants found in the coding sequence consistently occurred in dogs of any particular spotting pattern. However, an insertion of a short interspersed nucleotide element (SINE) over 3000 bp 5′ of the MITF-M start codon (Karlsson et al. 2007) did fit with random spotting in many dog breeds. Most (319/324) dogs of 45 breeds fit 1 of 2 inheritance patterns. All dogs that were homozygous for the SINE had white markings that either covered at least the ventral surface (mantle pattern) or most of the body (piebald or extreme white spotting). In most breeds, dogs heterozygous for the SINE insertion were solid colored or had minimal white, such as on the toes, but in some others, heterozygotes had white undersides, often with a white collar in the pattern called pseudo-Irish by Little (1957). However, none of the 15 dogs of 5 breeds in which all individuals have markings known as Irish spotting had the SINE insertion. Finally, we studied RNA expression in skin. The 2 MITF-M forms, M+ that contains an extra 18 bp that adds 6 amino acids between exons 5 and 6 and the M− form, were present. MITF-M is considered to be specific to melanocytes but was found in skin from a white Samoyed. A putative pseudogene containing exon 1M was also identified.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".