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Record W2777609012 · doi:10.21521/mw.6069

Genetic markers of canine hip dysplasia

2017· article· en· W2777609012 on OpenAlexaboutno aff
Paulina Krzemińska, Maciej Gogulski, R. Aleksiewicz, M. Świtoński

Bibliographic record

VenueMedycyna Weterynaryjna · 2017
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyGeneticsHip dysplasiaDysplasiaGenetic markerBreedMicrosatelliteCandidate geneSNPHeritabilityGenetic disorderGeneSingle-nucleotide polymorphismMedicineGenotypeAllele

Abstract

fetched live from OpenAlex

Canine hip dysplasia is a complex skeletal malformation caused by genetic and environmental factors. The prevalence of hip dysplasia in different canine breeds ranges widely, from 1% (for Whippet and Borzoi) to over 70% (for Bulldog and Pug). These differences indicate the presence of genetic variants predisposing to or preventing this disorder in gene pools of particular breeds. The importance of genetic factors is also confirmed by a high coefficient of heritability (h2) of canine hip dysplasia, which for most breeds oscillates around 0.5 – 0.6. Application of modern genomic methods, that is, mainly genome scanning (based previously on microsatellite markers and currently on SNP microarrays) has led in recent years to the identification of potential genetic markers associated with this disorder. Such studies were carried out mostly in two breeds: Labrador retriever and German shepherd. Some of the markers were found in the vicinity of genes involved in skeletal development. Following these achievements, the use of some markers has been suggested for early risk diagnosis of hip dysplasia. This shows that molecular testing is becoming important for not only monogenic, but also polygenic canine diseases and disorders. Identification of genetic markers associated with predisposition to hip dysplasia offers an opportunity for an early risk evaluation of this disorder (prior to its first signs). Moreover, it facilitates effective breeding selection aimed at eradicating undesirable genetic variants from the gene pool of a given breed..

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.313
Teacher spread0.260 · 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.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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