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Biological, clinical and population relevance of 95 loci for blood lipids
Tanya M. Teslovich, Albert V. Smith, Andrew C. Edmondson, Ioannis M. Stylianou, Masahiro Koseki, James P. Pirruccello, Samuli Ripatti, Daniel I. Chasman, Cristen J. Willer, Christopher T. Johansen, Sigrid W. Fouchier, Aaron Isaacs, Gina M. Peloso, Maja Barbalić, Sally L. Ricketts, Joshua C. Bis, Yurii S. Aulchenko, Guðmar Þorleifsson, Mary F. Feitosa, John C. Chambers, Marju Orho‐Melander, Olle Melander, Toby Johnson, Xiaohui Li, Xiuqing Guo, Mingyao Li, Yoon Shin Cho, Min Jin Go, Young Jin Kim, Jong‐Young Lee, Taesung Park, Kyunga Kim, Xueling Sim, Rick Twee‐Hee Ong, Damien C. Croteau‐Chonka, Leslie A. Lange, Joshua D. Smith, Kijoung Song, Jing Hua Zhao, Xin Yuan, Jian’an Luan, Claudia Lamina, Andreas Ziegler, Weihua Zhang, Robert Y.L. Zee, Alan F. Wright, Jacqueline C.M. Witteman, James F. Wilson, Gonneke Willemsen, H.‐Erich Wichmann, John B. Whitfield, Dawn Waterworth, Nicholas J. Wareham, Gérard Waeber, Péter Vollenweider, Benjamin F. Voight, Véronique Vitart, André G. Uitterlinden, Manuela Uda, Jaakko Tuomilehto, John R. Thompson, Toshiko Tanaka, Ida Surakka, Heather M. Stringham, Tim D. Spector, Nicole Soranzo, Johannes H. Smit, Juha Sinisalo, Kaisa Silander, Eric J.G. Sijbrands, Angelo Scuteri, David Schlessinger, Serena Sanna, Veikko Salomaa, Juha Saharinen, Chiara Sabatti, Aimo Ruokonen, Igor Rudan, Lynda M. Rose, Robert Roberts, Mark J. Rieder, Bruce M. Psaty, Peter P. Pramstaller, Irene Pichler, Markus Perola, Brenda W.J.H. Penninx, Nancy L. Pedersen, Cristian Pattaro, Alex Parker, Guillaume Paré, Ben A. Oostra, Christopher J. O’Donnell, Markku S. Nieminen, Deborah A. Nickerson, Grant W. Montgomery, Thomas Meitinger, Ruth McPherson, Mark I. McCarthy, Wendy L. McArdle, David Masson, Nicholas G. Martin, Fabio Marroni, Massimo Mangino, Patrik K. E. Magnusson, Gavin Lucas, Robert Luben, Ruth J. F. Loos, Marja‐Liisa Lokki, Guillaume Lettre, Claudia Langenberg, Lenore J. Launer, Edward G. Lakatta, Reijo Laaksonen, Kirsten Ohm Kyvik, Florian Kronenberg, Inke R. König, Kay‐Tee Khaw, Jaakko Kaprio, Lee M. Kaplan, Åsa Johansson, Marjo‐Riitta Järvelin, A. Cecile J.W. Janssens, Erik Ingelsson, Wilmar Igl, G. Kees Hovingh, Jouke‐Jan Hottenga, Albert Hofman, Andrew A. Hicks, Christian Hengstenberg, Iris M. Heid, Caroline Hayward, Aki S. Havulinna, Nicholas D. Hastie, Tamara B. Harris, Talin Haritunians, Alistair S. Hall, Ulf Gyllensten, Candace Guiducci, Leif Groop, Elena González, Christian Gieger, Nelson B. Freimer, Luigi Ferrucci, Jeanette Erdmann, Paul Elliott, Kenechi G. Ejebe, Angela Döring, Anna F. Dominiczak, Serkalem Demissie, Eco J. C. de Geus, Ulf dé Fairé, Gabriel Crawford, Francis S. Collins, Yii‐Der I. Chen, Mark J. Caulfield, Harry Campbell, Noël P. Burtt, Lori L. Bonnycastle, Dorret I. Boomsma, S. Matthijs Boekholdt, Richard N. Bergman, Inês Barroso, Stefania Bandinelli, Christie M. Ballantyne, Themistocles L. Assimes, Thomas Quertermous, David Altshuler, Mark Seielstad, Tien Yin Wong, E Shyong Tai, Alan B. Feranil, Christopher W. Kuzawa, Linda S. Adair, Herman A. Taylor, Ingrid B. Borecki, Stacey Gabriel, James G. Wilson, Hilma Hólm, Unnur Þorsteinsdóttir, Vilmundur Guðnason, Ronald M. Krauss, Karen L. Mohlke, José M. Ordovás, Patricia B. Munroe, Jaspal S. Kooner, Alan R. Tall, Robert A. Hegele, John J.P. Kastelein, Eric E. Schadt, Jerome I. Rotter, Eric Boerwinkle, David P. Strachan, Vincent Mooser, Kāri Stefánsson, Muredach P. Reilly, Nilesh J. Samani, Heribert Schunkert, L. Adrienne Cupples, Manjinder S. Sandhu, Paul M. Ridker, Daniel J. Rader, Cornelia M. van Duijn, Leena Peltonen, Gonçalo R. Abecasis, Michael Boehnke, Sekar Kathiresan
Bibliographic record
VenueNature · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversité de MontréalMcMaster UniversityMontreal Heart InstituteUniversity of OttawaWestern University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Research ResourcesNational Institute of Environmental Health SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesWellcome TrustNational Human Genome Research InstituteFogarty International CenterNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Institute of General Medical SciencesMedical Research CouncilBritish Heart FoundationNational Cancer InstituteNational Institutes of HealthU.S. Public Health Service
KeywordsSingle-nucleotide polymorphismBiologyGeneticsGenome-wide association studyLipoproteinCholesterolPhenotypeLipid metabolismPopulationCoronary artery diseaseGeneGenetic associationInternal medicineBioinformaticsEndocrinologyMedicineGenotype
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)0.003
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.011
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 head0.042
GPT teacher head0.394
Teacher spread0.352 · 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
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
Citations3,685
Published2010
Admission routes1
Has abstractno