MétaCan
Menu
Back to cohort
Record W4416214979 · doi:10.1126/sciadv.adt5913

Structural and epistatic regulatory variants cause hallmark white spotting in cattle

2025· article· en· W4416214979 on OpenAlexaff
Swati Jivanji, Emma L. Wilkinson, Lijing Tang, Kathryn Tiplady, Anna Yeates, Chad Harland, Christine Couldrey, G.M. Worth, Isabelle Gamache, Jade Desjardins, John A. A. Tabares, Nobuko Yamanaka, L.R. McNaughton, Louise Brennan, Marie-Pier Cloutier, Mitra Cowan, Richard T. Ellison, Tony Fransen, Tracey Monehan, Richard Spelman, Russell G. Snell, Carole Charlier, Yojiro Yamanaka, Dorian J. Garrick, Richard L. Mort, Mathew D. Littlejohn

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsInstitute of GeneticsMcGill University
FundersMinistry of Business, Innovation and EmploymentMassey University
KeywordsEpistasisSpottingAlleleCoatTraitWhite (mutation)Causality (physics)Mutation

Abstract

fetched live from OpenAlex

Despite being one of the most iconic and immediately recognizable traits in domestic cattle, the variants underpinning the white-spotted coat pattern of Holstein-Friesian and related breeds remain uncharacterized. Here, we report two variants modulating these effects, comprising intronic and long-distance–acting regulatory variants of the MITF and KIT genes. We confirm causality through “Holsteinized” mouse models edited for these alleles and show that these variants are likely responsible for spotting traits in other bovine breeds. These effects include epistatic impacts on other bovine coat patterns, such as fine-scale speckling, “black socks,” and reversal of the otherwise dominant, “white-face” trait characteristic of Hereford cattle.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.287
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueScience AdvancesSame topicmelanin and skin pigmentationFrench-language works237,207