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Record W2790547116 · doi:10.1111/age.12643

A splice site variant in the <i><scp>SUV</scp>39H2</i> gene in Greyhounds with nasal parakeratosis

2018· article· en· W2790547116 on OpenAlexaboutno aff
Anina Bauer, J. Nimmo, Ruchi M. Newman, Magdalena A. T. Brunner, Monika Welle, Vidhya Jagannathan, Tosso Leeb

Bibliographic record

VenueAnimal Genetics · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHedgehog Signaling Pathway Studies
Canadian institutionsnot available
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsBiologyGeneticsParakeratosisBreedGeneAnatomy

Abstract

fetched live from OpenAlex

Hereditary nasal parakeratosis (HNPK), described in the Labrador Retriever breed, is a monogenic autosomal recessive disorder that causes crusts and fissures on the nasal planum of otherwise healthy dogs. Our group previously showed that this genodermatosis may be caused by a missense variant located in the SUV39H2 gene encoding a histone 3 lysine 9 methyltransferase, a chromatin modifying enzyme with a potential role in keratinocyte differentiation. In the present study, we investigated a litter of Greyhounds in which six out of eight puppies were affected with parakeratotic lesions restricted to the nasal planum. Clinically and histologically, the lesions were comparable to HNPK in Labrador Retrievers. Whole genome sequencing of one affected Greyhound revealed a 4-bp deletion at the 5'-end of intron 4 of the SUV39H2 gene that was absent in 188 control dog and three wolf genomes. The variant was predicted to disrupt the 5'-splice site with subsequent loss of SUV39H2 function. The six affected puppies were homozygous for the variant, whereas the two non-affected littermates were heterozygous. Genotyping of a larger cohort of Greyhounds revealed that the variant is segregating in the breed and that this breed might benefit from genetic testing to avoid carrier × carrier matings.

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 score0.156
Threshold uncertainty score0.827

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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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