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Exclusion of EDNRB and KIT as the basis for white spotting in Border Collies

2000· article· en· W1538792413 on OpenAlexaboutno aff
Danika L. Metallinos, Jasper Rine

Bibliographic record

VenueGenome biology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyWhite (mutation)GeneticsPhenotypeCandidate genePopulationSpottingGene

Abstract

fetched live from OpenAlex

BACKGROUND: White spotting patterns in mammals can be caused by mutations in the genes for the endothelin B receptor and c-Kit, whose protein products are necessary for proper migration, differentiation or survival of the melanoblast population of cells. Although there are many different dog breeds that segregate white spotting patterns, no genes have been identified that are linked to these phenotypes. RESULTS: An intercross was generated from a female Newfoundland and a male Border Collie and the white spotting phenotypes of the intercross progeny were evaluated by measuring percentage surface area of white in the puppies. The Border Collie markings segregated as a simple autosomal recessive (7/25 intercross progeny had the phenotype). Two candidate genes, for the endothelin B receptor (EDNRB) and c-Kit (KIT), were evaluated for segregation with the white spotting pattern. Polymorphisms between the Border Collie and Newfoundland were identified for EDNRB using Southern analysis after a portion of the canine gene had been cloned. Polymorphisms for KIT were identified using a microsatellite developed from a bacterial artificial chromosome containing the canine gene. CONCLUSIONS: Both EDNRB and KIT were excluded as a cause of the white spotting pattern in at least two of the intercross progeny. Although these genes have been implicated in white spotting in other mammals, including horses, pigs, cows, mice and rats, they do not appear to be responsible for the white spotting pattern found in the Border Collie breed of dog.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.181

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 head0.007
GPT teacher head0.276
Teacher spread0.269 · 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

Citations17
Published2000
Admission routes1
Has abstractyes

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