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Record W2004027999 · doi:10.1371/journal.pgen.1004423

Phenotypic Dissection of Bone Mineral Density Reveals Skeletal Site Specificity and Facilitates the Identification of Novel Loci in the Genetic Regulation of Bone Mass Attainment

2014· article· en· W2004027999 on OpenAlexaff
John P. Kemp, Carolina Medina‐Gómez, Karol Estrada, Beaté St Pourcain, Denise H. M. Heppe, Nicole M. Warrington, Ling Oei, Susan M. Ring, Claudia J. Kruithof, Nicholas J. Timpson, Lisa E. Wolber, Sjur Reppe, Kaare M. Gautvik, Elin Grundberg, Bing Ge, Bram C. J. van der Eerden, Jeroen van de Peppel, Matthew Hibbs, Cheryl L. Ackert‐Bicknell, Kwangbom Choi, Daniel L. Koller, Michael J. Econs, Frances M. K. Williams, Tatiana Foroud, M. Carola Zillikens, Claes Ohlsson, Albert Hofman, André G. Uitterlinden, George Davey Smith, Vincent W. V. Jaddoe, Jonathan H. Tobias, Fernando Rivadeneira, David M. Evans

Bibliographic record

VenuePLoS Genetics · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsMcGill University and Génome Québec Innovation CentreMcGill University
FundersNational Center for Research ResourcesNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institute on AgingEducation, Audiovisual and Culture Executive AgencyNational Institute of Arthritis and Musculoskeletal and Skin DiseasesMedical Research CouncilU.S. Public Health ServiceNational Institutes of HealthUniversity of BristolErasmus Medisch CentrumEuropean CommissionNational Center for Advancing Translational SciencesNational Human Genome Research InstituteNederlandse Organisatie voor Wetenschappelijk OnderzoekWellcome TrustNovo Nordisk FondenSchool of Medicine, Boston UniversityZonMwErasmus Universiteit Rotterdam
KeywordsBone mineralHeritabilityBiologyLongitudinal studyBone densityAppendicular skeletonGenetic correlationInternal medicineGeneticsEndocrinologyGenetic variationAnatomyMedicineOsteoporosisGenePathology

Abstract

fetched live from OpenAlex

Heritability of bone mineral density (BMD) varies across skeletal sites, reflecting different relative contributions of genetic and environmental influences. To quantify the degree to which common genetic variants tag and environmental factors influence BMD, at different sites, we estimated the genetic (rg) and residual (re) correlations between BMD measured at the upper limbs (UL-BMD), lower limbs (LL-BMD) and skull (SK-BMD), using total-body DXA scans of ∼ 4,890 participants recruited by the Avon Longitudinal Study of Parents and their Children (ALSPAC). Point estimates of rg indicated that appendicular sites have a greater proportion of shared genetic architecture (LL-/UL-BMD rg = 0.78) between them, than with the skull (UL-/SK-BMD rg = 0.58 and LL-/SK-BMD rg = 0.43). Likewise, the residual correlation between BMD at appendicular sites (r(e) = 0.55) was higher than the residual correlation between SK-BMD and BMD at appendicular sites (r(e) = 0.20-0.24). To explore the basis for the observed differences in rg and re, genome-wide association meta-analyses were performed (n ∼ 9,395), combining data from ALSPAC and the Generation R Study identifying 15 independent signals from 13 loci associated at genome-wide significant level across different skeletal regions. Results suggested that previously identified BMD-associated variants may exert site-specific effects (i.e. differ in the strength of their association and magnitude of effect across different skeletal sites). In particular, variants at CPED1 exerted a larger influence on SK-BMD and UL-BMD when compared to LL-BMD (P = 2.01 × 10(-37)), whilst variants at WNT16 influenced UL-BMD to a greater degree when compared to SK- and LL-BMD (P = 2.31 × 10(-14)). In addition, we report a novel association between RIN3 (previously associated with Paget's disease) and LL-BMD (rs754388: β = 0.13, SE = 0.02, P = 1.4 × 10(-10)). Our results suggest that BMD at different skeletal sites is under a mixture of shared and specific genetic and environmental influences. Allowing for these differences by performing genome-wide association at different skeletal sites may help uncover new genetic influences on BMD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.015
GPT teacher head0.238
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations168
Published2014
Admission routes1
Has abstractyes

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