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Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk
Dan E. Arking, Vasyl Pihur, Aravinda Chakravarti, Gina M. Hilton, Murielle Bochud, Toby Johnson, Mark J. Caulfield, Sue Shaw‐Hawkins, Patricia B. Munroe, Kenneth Rice, Ramachandran S. Vasan, Daniel Levy, Martin G. Larson, Thomas J. Wang, Daniel I. Chasman, Paul M. Ridker, Albert V. Smith, Vilmundur Guðnason, Thor Aspelund, María Soler Artigas, Nick Shrine, Louise V. Wain, Martin D. Tobin, André G. Uitterlinden, Najaf Amin, Yurii S. Aulchenko, M. Arfan Ikram, Eric J.G. Sijbrands, Cornelia M. van Duijn, Ayşe Demirkan, Albert Hofman, Aaron Isaacs, Germaine C. Verwoert, Péter Vollenweider, Peter Würtz, John C. Chambers, Paul Elliott, Weihua Zhang, Gonçalo R. Abecasis, Jennifer L. Bragg‐Gresham, Uwe Völker, Alexander Teumer, Joshua C. Bis, Nicole L. Glazer, Bruce M. Psaty, Tamara B. Harris, Melissa N. Garcia, Lenore J. Launer, Jing Zhao, Ruth J. F. Loos, Nicholas J. Wareham, Mark Lathrop, Diana Zélénika, Simon Heath, Siim Sõber, Maris Laan, Gudrun Veldre, Elin Org, Afshin Parsa, Nanette Steinle, Alan R. Shuldiner, Christopher Newton‐Cheh, Pankaj Arora, Sekar Kathiresan, Tim D. Spector, Feng Zhang, Guangju Zhai, Massimo Mangino, Gavin Lucas, Roberto Elosúa, Peter P. Pramstaller, Cristian Pattaro, Andrew A. Hicks, Anne Jackson, Heather M. Stringham, Michael Boehnke, Laura J. Scott, John F. Peden, Hugh Watkins, Halit Ongen, Anuj Goel, Martin Farrall, Yuri Milaneschi, Luigi Ferrucci, Toshiko Tanaka, James F. Wilson, Igor Rudan, Harry Campbell, Ulf Gyllensten, Wilmar Igl, Alex Parker, Marketa Sjögren, Peter M Nilsson, Olle Melander, Bo Hedblad, Cristiano Fava, Joban Sehmi, Ervin R. Fox, Albert W. Dreisbach, Meena Kumari, Andrew M. Taylor, Min Jin Go, Yoon Shin Cho, Pim van der Harst, Wiek H. van Gilst, Wen Hong Linda Kao, Giriraj R. Chandak, D. G. Vinay, Charles S. Janipalli, M. V. Kranthi Kumar, K. Radha Mani, Danish Saleheen, John Danesh, M Alexander, Tetsuro Miki, Yasuharu Tabara, David Hadley, Peter H. Whincup, David P. Strachan, YongMei Liu, Inga Prokopenko, Megan E. Rudock, Gang Shi, Markku Laakso, Johanna Kuusisto, Alena Stančáková, Bamidele O. Tayo, Mark Seielstad, Xueling Sim, E Shyong Tai, Terho Lehtimäki, Giuseppe Matullo, Simonetta Guarrera, Paolo Vineis, Karen L. Mohlke, Ying Wu, Ian N.M. Day, George Davey Smith, Debbie A. Lawlor, Tom R. Gaunt, Michiel L. Bots, Yvonne T. van der Schouw, Cuno S.P.M. Uiterwaal, Diederick E. Grobbee, Matthew N. Cooper, Robert Lawrence, Carl Platou, Kristian Hveem, Andrew Wong, Diana Kuh, Rebecca Hardy, Santosh Dahgam, Fredrik Nyberg, Jackie A. Cooper, Steve E. Humphries, Philippa J. Talmud, Jutta Palmen, Alan F. Wright, Caroline Hayward, Véronique Vitart, Maciej Tomaszewski, Nilesh J. Samani, Matthew Denniff, Tatiana Kuznetsova, Jan A. Staessen, Ayo P. Doumatey, Charles N. Rotimi, Adebowale Adeyemo, Daniel Shriner, Walter Palmas, Stefan R. Bornstein, Barbara Ludwig, Jürgen Gräßler, Ioanna Tzoulaki, Lynne E. Wagenknecht, Carl D. Langefeld, Sharon L.R. Kardia, Yan V. Sun, Alanna C. Morrison, Dena Hernandez, Andrew Singleton, Samer S. Najjar, Wendy L. McArdle, Morris J. Brown, John Connell, Aroon D. Hingorani, John Beilby, Jemma C. Hopewell, Robert Clarke, Yali Li, Xiaofeng Zhu, J. Hunter Young, Mika Kähönen, Jorma Viikari, Linda S. Adair, Nanette R. Lee, Matthias Olden, Anna Köttgen, Judith A. Hoffman Bolton, Richard N. Bergman, J. Beckmann, Vincent Mooser, Nish Chaturvedi, Timothy M Frayling, Muhammad Islam, Tazeen H. Jafar, Jeanette Erdmann, Chittaranjan S. Yajnik, Smita Kulkarni, Benjamin F. Voight, David Altshuler, Johannes Kettunen, Philip Howard, Andrew Plump, Valur Emilsson, Panos Deloukas, Inês Barroso, Alan B. Weder, Steven C. Hunt, Narisu Narisu, Amy J. Swift, Mario A. Morken, Francis S. Collins, Lori L. Bonnycastle, Erkki Vartiainen, Veikko Salomaa, Markus Perola, Yanbin Dong, Xiaoling Wang, Harold Snieder, Haidong Zhu, Kurt K. Lohman, Stephen M. Schwartz, Nicholas L. Smith, Kerri L. Wiggins, Sanjay Kinra, Vikal Tripathy, Dorairaj Prabhakaran, Annika Rosengren, Dag S. Thelle, A. Corsi, Colin A. McKenzie, Terrence Forrester, Tunde Salako, Naoharu Iwai, Takayoshi Ohkubo, Yoshikuni Kita
Bibliographic record
VenueNature · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Minority Health and Health DisparitiesAgence Nationale de la RechercheNational Center for Research ResourcesNational Institute of Dental and Craniofacial ResearchNational Institute of Environmental Health SciencesCanadian Institutes of Health ResearchFogarty International CenterNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational Institute for Health and Care ResearchNational Institute on AgingNational Institute of Diabetes and Digestive and Kidney DiseasesWellcome TrustBiotechnology and Biological Sciences Research CouncilAgency for Healthcare Research and QualityNational Institutes of HealthNational Institute of General Medical SciencesKidney Research UKBritish Heart FoundationNational Institute of Neurological Disorders and StrokeNational Cancer InstituteNational Center for Advancing Translational SciencesNational Human Genome Research InstituteHoward Hughes Medical Institute
KeywordsBlood pressureGenome-wide association studyPulse pressureInternal medicineMedicinePrehypertensionStroke (engine)CardiologyDiseaseKidney diseaseCoronary artery diseaseBiologyGeneticsSingle-nucleotide polymorphismGeneGenotype
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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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
Citations2,070
Published2011
Admission routes2
Has abstractno