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Record W4413970733 · doi:10.3390/ani15162386

Identification of a Novel Haplotype Associated with Roan Coat Color in American Quarter Horses

2025· article· en· W4413970733 on OpenAlexaboutno aff
Robin E. Everts, Gabriel Foster, Kaitlyn McLoone, Laura Simiele, Katie Martin, Samantha A. Brooks, Christa Lafayette

Bibliographic record

VenueAnimals · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsnot available
Fundersnot available
KeywordsCoatQuarter (Canadian coin)HaplotypeIdentification (biology)BiologyGeneticsGeographyBotanyGenotypeGeneArchaeology

Abstract

fetched live from OpenAlex

Roan coat color is described as the dispersion of white hairs within an otherwise solid background-color coat. This phenotype is primarily expressed on the body of the horse, with the head and legs exhibiting few or no white hairs. Previous studies mapped the locus for roan to the KIT region and observed linked variants in a small number of breeds. Recently, we reported evidence for two independent haplotypes, RN1 and RN2, in the KIT region, which account for approximately 38% and 36% of roan horses, respectively. In the current report, using whole genome sequencing for unknown roan samples. We present a third novel haplotype, RN3, found in American Quarter Horses, that accounts for an additional 30% of American Quarter Horses negative for RN1 and RN2 that display the roan phenotype. Within this haplotype, we observe a variant chr3:79656505 (G > A), which we believe is a founder allele for the RN3 haplotype. In our sample set of horses, these three haplotypes account for more than 95% of the American Quarter Horse population studied and about 50–60% of roan horses in other breeds. Using whole genome sequencing of distantly related animals with a particular phenotype, together with a larger number of control horses, improves the odds of finding linked and/or causative variants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.276
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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