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Exome-wide association study of plasma lipids in >300,000 individuals
Dajiang J. Liu, Gina M. Peloso, Haojie Yu, Adam S. Butterworth, Xiao Wang, Anubha Mahajan, Danish Saleheen, Connor A. Emdin, Dewan S Alam, Alexessander Couto Alves, Philippe Amouyel, Emanuele Di Angelantonio, Dominique Arveiler, Themistocles L. Assimes, Paul L. Auer, Usman Baber, Christie M. Ballantyne, Lia E. Bang, Marianne Benn, Michael Boehnke, Eric Boerwinkle, Jette Bork‐Jensen, Erwin P. Böttinger, Ivan Brandslund, Morris J. Brown, Fabio Busonero, Mark J. Caulfield, John C. Chambers, Daniel I. Chasman, Y Eugene Chen, Yii‐Der Ida Chen, Rajiv Chowdhury, Cramer Christensen, Audrey Y. Chu, John Connell, Francesco Cucca, L. Adrienne Cupples, Scott M. Damrauer, Gail Davies, Ian J. Deary, George Dedoussis, Joshua C. Denny, Anna F. Dominiczak, Marie‐Pierre Dubé, Tapani Ebeling, Guðný Eiríksdóttir, Tõnu Esko, Aliki‐Eleni Farmaki, Mary F. Feitosa, Maurizio Ferrario, Jean Ferrières, Ian Ford, Myriam Fornage, Paul W. Franks, Timothy M. Frayling, Ruth Frikke‐Schmidt, Lars G. Fritsche, Philippe Frossard, Valentı́n Fuster, Santhi K. Ganesh, Wei Gao, Melissa E. Garcia, Christian Gieger, Franco Giulianini, Mark O. Goodarzi, Harald Grallert, Niels Grarup, Leif Groop, Megan L. Grove, Vilmundur Guðnason, Torben Hansen, Tamara B. Harris, Caroline Hayward, Joel N. Hirschhorn, Oddgeir L. Holmen, Jennifer E. Huffman, Yong Huo, Kristian Hveem, Sehrish Jabeen, Anne Jackson, Jóhanna Jakobsdóttir, Marjo‐Riitta Järvelin, Gorm Boje Jensen, Marit E. Jørgensen, J. Wouter Jukema, Johanne Marie Justesen, Pia R. Kamstrup, Stavroula Kanoni, Fredrik Karpe, Frank Kee, Amit V. Khera, Derek Klarin, Heikki A. Koistinen, Jaspal S. Kooner, Charles Kooperberg, Kari Kuulasmaa, Johanna Kuusisto, Markku Laakso, Timo A. Lakka, Claudia Langenberg, Anne Langsted, Lenore J. Launer, Torsten Lauritzen, David C. Liewald, Li An Lin, Allan Linneberg, Ruth J. F. Loos, Yingchang Lu, Xiangfeng Lu, Reedik Mägi, Anders Mälarstig, Ani Manichaikul, Alisa K. Manning, Pekka Mäntyselkä, Eirini Marouli, Nicholas G. D. Masca, Andrea Maschio, James B. Meigs, Olle Melander, Andres Metspalu, Andrew P. Morris, Alanna C. Morrison, Antonella Mulas, Martina Müller‐Nurasyid, Patricia B. Munroe, Matt J. Neville, Jonas B. Nielsen, Sune F. Nielsen, Børge G. Nordestgaard, José M. Ordovás, Roxana Mehran, Christoper J. O'Donnell, Marju Orho‐Melander, Cliona Molony, Pieter Muntendam, Sandosh Padmanabhan, Dorota Pasko, Aniruddh P. Patel, Oluf Pedersen, Markus Perola, Annette Peters, Charlotta Pisinger, Giorgio Pistis, Ozren Polašek, Neil R Poulter, Bruce M. Psaty, Daniel J. Rader, Asif Rasheed, Rainer Rauramaa, Dermot F. Reilly, Alex P. Reiner, Frida Renström, Stephen S. Rich, Paul M. Ridker, John D. Rioux, Neil R. Robertson, Dan M. Roden, Jerome I. Rotter, Igor Rudan, Veikko Salomaa, Nilesh J. Samani, Serena Sanna, Naveed Sattar, Ellen M. Schmidt, Robert A. Scott, Peter Sever, Raquel Sevilla, Christian M. Shaffer, Xueling Sim, Suthesh Sivapalaratnam, Kerrin S. Small, Albert V. Smith, Blair H. Smith, Sangeetha Somayajula, Lorraine Southam, Timothy D. Spector, Elizabeth K. Speliotes, John M. Starr, Kathleen Stirrups, Nathan O. Stitziel, Konstantin Strauch, Heather M. Stringham, Praveen Surendran, Hayato Tada, Alan R. Tall, Hua Tang, Jean‐Claude Tardif, Kent D. Taylor, Stella Trompet, Philip S. Tsao, Jaakko Tuomilehto, Anne Tybjærg‐Hansen, Natalie R. van Zuydam, Anette Varbo, Tibor V. Varga, Jarmo Virtamo, Mélanie Waldenberger, Nan Wang, Helen R. Warren, Peter Weeke, Joshua S. Weinstock, Jennifer Wessel, James G. Wilson, Peter W.F. Wilson, Ming Xu, Hanieh Yaghootkar, Robin Young, Eleftheria Zeggini, He Zhang, NingNing Zheng, Weihua Zhang, Yan Zhang, Wei Zhou, Yanhua Zhou, Magdalena Żołędziewska, Joanna M. M. Howson, John Danesh, Mark I. McCarthy, Chad A. Cowan, Gonçalo R. Abecasis, Panos Deloukas, Cristen J. Willer, Sekar Kathiresan
Bibliographic record
VenueNature Genetics · 2017
Typearticle
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersFP7 Ideas: European Research CouncilNational Institute of General Medical SciencesNational Cancer InstituteNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteNational Center for Advancing Translational SciencesMedical Research CouncilDirectorate for Biological SciencesNovo Nordisk FondenFaculty of Health and Medical Sciences, University of Western AustraliaRigshospitaletBritish Heart FoundationNIHR Exeter Clinical Research FacilityEuropean Hematology AssociationSarnoff Cardiovascular Research FoundationPerelman School of Medicine, University of PennsylvaniaVanderbilt University Medical CenterSteno Diabetes Center CopenhagenWellcome TrustNational Institutes of HealthRegeneron PharmaceuticalsLundbeckfondenEli Lilly and CompanyAstraZenecaNational Institute for Health and Care ResearchQuest DiagnosticsGentofte HospitalBayer HealthCareDonovan Family FoundationNational Institute on Drug AbuseUniversity of PennsylvaniaJapanese Circulation SocietyVanderbilt UniversityAlnylam PharmaceuticalsMassachusetts General HospitalBroad InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesSanofiU.S. Department of Health and Human Services
KeywordsBiologyInternal medicineEndocrinologyAlleleExomeGenome-wide association studyCoronary artery diseaseHigh-density lipoproteinCholesterolLipolysisType 2 diabetesGenotypingSingle-nucleotide polymorphismLipoproteinGeneticsExome sequencingDiabetes mellitusGenotypeGeneMedicineAdipose tissuePhenotype
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)0.001
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 score0.009
Threshold uncertainty score0.019
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 head0.013
GPT teacher head0.291
Teacher spread0.278 · 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 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
Citations595
Published2017
Admission routes1
Has abstractno