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Single nucleotide polymorphisms refine QTL intervals for hip joint laxity in dogs

2008· article· en· W1987300940 on OpenAlexaboutno aff
Lan Zhu, Zhiwu Zhang, Fan Feng, Peter A. Schweitzer, Janjira Phavaphutanon, Margaret Vernier‐Singer, Elizabeth Corey, Steven G. Friedenberg, Raluca G. Mateescu, Alwyn Williams, G. Lust, Gregory M. Acland, Rory J. Todhunter

Bibliographic record

VenueAnimal Genetics · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsnot available
FundersMorris Animal Foundation
KeywordsCrossbreedQuantitative trait locusBiologyHip dysplasiaGeneticsSingle-nucleotide polymorphismMicrosatelliteOsteoarthritisOffspringGenotypeMedicineGeneAllelePathologyPregnancy

Abstract

fetched live from OpenAlex

Hip laxity is one characteristic of canine hip dysplasia (CHD), an inheritable disease that leads to hip osteoarthritis. Using a genome-wide screen with 250 microsatellites in a crossbreed pedigree of 159 dysplastic Labrador retrievers and unaffected greyhounds, we previously identified putative (P < 0.01) QTL on canine chromosomes 11 and 29 (CFA11 and CFA29). To refine these QTL locations, we have genotyped 257 dogs including 105 Labrador retrievers, seven greyhounds, four generations of their crossbreed offspring and three German shepherds for 111 and 171 SNPs on CFA11 and CFA29 respectively. The distraction index (DI, a measure of maximum hip laxity) was used as an intermediate phenotype that predicts whether a hip joint will or will not develop osteoarthritis. Using a multipoint linkage analysis, significant evidence (95% posterior probability) was found for QTL contributing to hip laxity in the 16.2-21 cM region on CFA11 that explained 15-18% of the total variance in DI. Evidence for an independent QTL on CFA29 was weaker than that on CFA11. Identification of the causative mutation(s) will lead to better understanding of biochemical pathways in both dogs and humans with hip laxity and dysplasia.

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 score0.123
Threshold uncertainty score0.756

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 head0.066
GPT teacher head0.306
Teacher spread0.240 · 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

Citations23
Published2008
Admission routes1
Has abstractyes

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