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Record W1938515013 · doi:10.1002/jbmr.2604

Two Rare Mutations in the<i>COL1A2</i>Gene Associate With Low Bone Mineral Density and Fractures in Iceland

2015· article· en· W1938515013 on OpenAlexaff
Unnur Styrkársdóttir, Guðmar Þorleifsson, Berglind Eiríksdottir, Sigurjón A. Guðjónsson, Þorvaldur Ingvarsson, Tuan V. Nguyen, John A. Eisman, Claus Christiansen, Unnur Þorsteinsdóttir, Gunnar Sigurðsson

Bibliographic record

VenueJournal of Bone and Mineral Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsAmgen (Canada)
Fundersnot available
KeywordsOsteogenesis imperfectaBone mineralMissense mutationOdds ratioMedicineOsteoporosisConfidence intervalBone densityInternal medicineGeneticsEndocrinologyMutationGeneBiologyPathology

Abstract

fetched live from OpenAlex

We conducted a genome-wide association study of low bone mineral density (BMD) at the hip and spine utilizing sequence variants found through whole-genome sequencing of 2636 Icelanders. We found two rare missense mutations, p.Gly496Ala and p.Gly703Ser, in the COL1A2 gene that associate with measures of osteoporosis in Icelanders. Mutations in COL1A2 are known to cause the autosomal dominant disorder osteogenesis imperfecta. Both variants associate with low BMD and with osteoporotic fractures. p.Gly496Ala (frequency of 0.105%) shows the strongest association with low BMD at the spine (p = 1.8 × 10(-7) , odds ratio [OR] = 4.61 [95% confidence interval (CI) 2.59, 8.18]), whereas p.Gly703Ser (frequency of 0.050%) is most strongly associated with low BMD at the hip (p = 1.9 × 10(-8) , OR = 9.34 [95% CI 4.28, 20.3]). Association with fractures was p = 2.2 × 10(-5) , OR = 3.75 (95% CI 2.03, 6.93) and p = 0.0023, OR = 4.32 (95% CI 1.69, 11.1), respectively. The carriers of these variants do not have signs of osteogenesis imperfecta other than low BMD, demonstrating that similar mutations in COL1A2 can affect skeletal phenotypes in more than one way.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.670
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.032
GPT teacher head0.367
Teacher spread0.334 · 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 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

Citations43
Published2015
Admission routes1
Has abstractyes

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