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Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture
Karol Estrada, Unnur Styrkársdóttir, Εvangelos Εvangelou, Yi‐Hsiang Hsu, Emma L. Duncan, Evangelia Ntzani, Ling Oei, Omar Albagha, Najaf Amin, John P. Kemp, Daniel L. Koller, Li Guo, Ching‐Ti Liu, Ryan L. Minster, Alireza Moayyeri, Liesbeth Vandenput, Dana Willner, Su‐Mei Xiao, Laura M. Yerges-Armstrong, Hou‐Feng Zheng, Nerea Alonso, Joel Eriksson, Candace M. Kammerer, Stephen K. Kaptoge, Paul Leo, Guðmar Þorleifsson, Scott G. Wilson, James F. Wilson, Ville Aalto, Markku Alén, Aaron K. Aragaki, Thor Aspelund, Zoe Dailiana, David Duggan, Melissa García, Natalia García‐Giralt, Sylvie Giroux, Göran Hallmans, Lynne J. Hocking, Lise B. Husted, Anthony James, Р. И. Хусаинова, Ghi Su Kim, Charles Kooperberg, Theodora Koromila, Marcin Kruk, Marika Laaksonen, Andrea Z. LaCroix, Seung Hun Lee, Ping‐Chung Leung, Joshua R. Lewis, Laura Masi, Simona Mencej-Bedrač, Tuan V. Nguyen, Xavier Nogués, Millan S. Patel, Janez Preželj, Lynda M. Rose, Serena Scollen, Kristín Siggeirsdóttir, Albert V. Smith, Olle Svensson, Stella Trompet, Olivia Trummer, Natasja M. van Schoor, Jean Woo, Kun Zhu, Susana Balcells, Maria Luisa Brandi, Brendan M. Buckley, Sulin Cheng, Claus Christiansen, Cyrus Cooper, George Dedoussis, Ian Ford, Morten Frost, David Goltzman, Jesús González-Macı́as, Mika Kähönen, Magnus K. Karlsson, Э. К. Хуснутдинова, Jung‐Min Koh, Panagoula Κollia, Bente Langdahl, William D. Leslie, Paul Lips, Östen Ljunggren, R Lorenc, Janja Marc, Dan Mellström, Barbara Obermayer‐Pietsch, José M. Olmos, U. Pettersson, David M. Reid, José A. Riancho, Paul M. Ridker, François Rousseau, P. Eline Slagboom, Nelson L.S. Tang, Roser Urreizti, Wim Van Hul, Jorma Viikari, Marı́a T. Zarrabeitia, Yurii S. Aulchenko, Martha C. Castaño‐Betancourt, Elin Grundberg, Lizbeth Herrera, Þorvaldur Ingvarsson, Hrefna Johannsdottir, Tony Kwan, Rui Li, Robert Luben, Carolina Medina‐Gómez, Stefan Palsson, Sjur Reppe, Jerome I. Rotter, Gunnar Sigurðsson, Joyce B. J. van Meurs, Dominique J. Verlaan, Frances M. K. Williams, Andrew R. Wood, Yanhua Zhou, Kaare M. Gautvik, Tomi Pastinen, Soumya Raychaudhuri, Jane A. Cauley, Daniel I. Chasman, Graeme R. Clark, Steven R. Cummings, Patrick Danoy, Elaine Dennison, Richard Eastell, John A. Eisman, Vilmundur Guðnason, Albert Hofman, Rebecca D. Jackson, Graeme Jones, J. Wouter Jukema, Kay‐Tee Khaw, Terho Lehtimäki, Mattias Lorentzon, Eugène McCloskey, Braxton D. Mitchell, Kannabiran Nandakumar, Geoffrey C. Nicholson, Ben A. Oostra, Munro Peacock, Huibert A. P. Pols, Richard L. Prince, Olli Raitakari, Ian R. Reid, John A. Robbins, Philip N. Sambrook, Pak C. Sham, Alan R. Shuldiner, Frances A. Tylavsky, Cornelia M. van Duijn, L. Adrienne Cupples, Michael J. Econs, David M. Evans, Tamara B. Harris, A W Kung, Bruce M. Psaty, J. Reeve, Timothy D. Spector, Elizabeth A. Streeten, M. Carola Zillikens, Unnur Þorsteinsdóttir, Claes Ohlsson, David Karasik, J. Brent Richards, Matthew A. Brown, Kāri Stefánsson, André G. Uitterlinden, Stuart H. Ralston, John P. A. Ioannidis, Douglas P. Kiel, Fernando Rivadeneira
Bibliographic record
VenueNature Genetics · 2012
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of ManitobaHôpital Saint-François d'AssiseCentre hospitalier universitaire de QuébecUniversity of British ColumbiaMcGill University
FundersNational Center for Advancing Translational SciencesNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Neurological Disorders and StrokeNational Cancer InstituteNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteInstituto de Salud Carlos IIIHealth Research Council of New ZealandCanadian Institutes of Health ResearchEli Lilly CanadaCollege of Pharmacy, University of MichiganNational Institutes of HealthHjartaverndKulturministerietNational Institute of Diabetes and Digestive and Kidney DiseasesTurun Yliopistollinen KeskussairaalaSyddansk UniversitetGöteborgs LäkaresällskapEmil Aaltosen SäätiöVetenskapsrådetPaavo Nurmen SäätiöAmgen CanadaCentre for Medical Systems BiologyNovo NordiskJuho Vainion SäätiöHealthwayKempe FoundationVersus ArthritisMinistry of Agriculture, Forestry and FisheriesNational Center for Research ResourcesNational and Kapodistrian University of AthensBundesministerium für Verkehr, Innovation und TechnologieNational Health and Medical Research CouncilJewish General HospitalSuomen KulttuurirahastoTampereen TuberkuloosisäätiöKelaMedical Research CouncilServierKing's College LondonAustralian Cancer Research FoundationGeelong Region Medical Research FoundationUniversity of MichiganFondation LeducqDairy Farmers of CanadaWorld Anti-Doping AgencySteirische WirtschaftsförderungsgesellschaftNational Institute for Health and Care ResearchCedars-Sinai Medical CenterAustrian Federal Ministry of Economy, Family and YouthNederlandse Organisatie voor Wetenschappelijk OnderzoekSvenska LäkaresällskapetArthritis SocietyStiftelsen för Strategisk ForskningNational Institute on AgingUniversity of Hong KongBritish Heart FoundationScottish GovernmentChronic Disease Research FoundationWellcome TrustProcter and GambleAmgenPfizerDonald W. Reynolds FoundationEli Lilly and CompanyErasmus Universitair Medisch Centrum RotterdamEuropean CommissionSanofi
KeywordsGenome-wide association studyBone mineralBiologyOsteoporosisBone densityGenetic associationGeneticsInternal medicineBioinformaticsSingle-nucleotide polymorphismEndocrinologyGeneMedicineGenotype
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.002
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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.008
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 head0.056
GPT teacher head0.361
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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 designMeta-analysis
Domainnot available
GenreReview
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
Citations1,215
Published2012
Admission routes2
Has abstractno