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Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension
Praveen Surendran, Fotios Drenos, Robin Young, Helen R. Warren, James P. Cook, Alisa K. Manning, Niels Grarup, Xueling Sim, Daniel R. Barnes, Kate Witkowska, James R Staley, Vinicius Tragante, Taru Tukiainen, Hanieh Yaghootkar, Nicholas G. D. Masca, Daniel F. Freitag, Teresa Ferreira, Olga Giannakopoulou, Andrew Tinker, Magdaléna Harakaľová, Evelin Mihailov, Chunyu Liu, Aldi T. Kraja, Asif Rasheed, Maria Samuel, Wei Zhao, Lori L. Bonnycastle, Anne Jackson, Narisu Narisu, Amy J. Swift, Lorraine Southam, Jonathan Marten, Jeroen R. Huyghe, Alena Stančáková, Therese Ohlsson, Angela Matchan, Kathleen Stirrups, Jette Bork‐Jensen, Anette P. Gjesing, Jukka Kontto, Markus Perola, Susan Shaw-Hawkins, Aki S. Havulinna, He Zhang, Louise A. Donnelly, Christopher J. Groves, Nigel W. Rayner, Matt J. Neville, Neil R. Robertson, Andrianos M. Yiorkas, Karl‐Heinz Herzig, Eero Kajantie, Weihua Zhang, Sara M. Willems, Lars Lannfelt, Giovanni Malerba, Nicole Soranzo, Elisabetta Trabetti, Niek Verweij, Εvangelos Εvangelou, Alireza Moayyeri, Anne‐Claire Vergnaud, Christopher P. Nelson, Alaitz Poveda, Tibor V. Varga, Muriel Caslake, Anton J. M. de Craen, Stella Trompet, Jian’an Luan, Robert A. Scott, Sarah E. Harris, David C. Liewald, Riccardo E. Marioni, Cristina Menni, Aliki‐Eleni Farmaki, Göran Hallmans, Frida Renström, Jennifer E. Huffman, Maija Hassinen, Stephen Burgess, Ramachandran S. Vasan, Janine F. Felix, Maria Uria-Nickelsen, Anders Mälarstig, Dermot F. Reilly, Maarten Hoek, Thomas Vogt, Honghuang Lin, Wolfgang Lieb, Matthew Traylor, Hugh S. Markus, Heather M. Highland, Anne E. Justice, Eirini Marouli, Jaana Lindström, Matti Uusitupa, Pirjo Komulainen, Timo A. Lakka, Rainer Rauramaa, Ozren Polašek, Igor Rudan, Olov Rolandsson, Paul W. Franks, George Dedoussis, Timothy D. Spector, Satu Männistö, Ian J. Deary, John M. Starr, Claudia Langenberg, Morris J. Brown, Anna F. Dominiczak, John Connell, J. Wouter Jukema, Naveed Sattar, Ian Ford, Chris J. Packard, Tõnu Esko, Reedik Mägi, Andres Metspalu, Rudolf A. de Boer, Peter van der Meer, Pim van der Harst, Giovanni Gambaro, Erik Ingelsson, Lars Lind, Paul I. W. de Bakker, Mattijs E. Numans, Ivan Brandslund, Cramer Christensen, Annette Peters, Eeva Korpi-Hyövälti, Heikki Oksa, John C. Chambers, Jaspal S. Kooner, Alexandra I. F. Blakemore, Steve Franks, Marjo‐Riitta Järvelin, Lise Lotte N. Husemoen, Allan Linneberg, Tea Skaaby, Betina H. Thuesen, Fredrik Karpe, Jaakko Tuomilehto, Alex S. F. Doney, Andrew D. Morris, Oddgeir L. Holmen, Kristian Hveem, Cristen J. Willer, Leif Groop, Annemari Käräjämäki, Aarno Palotie, Samuli Ripatti, Veikko Salomaa, Dewan S Alam, Abdulla al Shafi Majumder, Emanuele Di Angelantonio, Rajiv Chowdhury, Mark I. McCarthy, Neil R Poulter, Alice Stanton, Peter Sever, Philippe Amouyel, Dominique Arveiler, Stefan Blankenberg, Jean Ferrières, Frank Kee, Kari Kuulasmaa, Martina Müller‐Nurasyid, Giovanni Veronesi, Jarmo Virtamo, Panos Deloukas, Paul Elliott, Eleftheria Zeggini, Sekar Kathiresan, Olle Melander, Johanna Kuusisto, Markku Laakso, Sandosh Padmanabhan, David J. Porteous, Caroline Hayward, Generation Scotland, Francis S. Collins, Karen L. Mohlke, Torben Hansen, Oluf Pedersen, Michael Boehnke, Heather M. Stringham, Philippe Frossard, Christopher Newton‐Cheh, Martin D. Tobin, Børge G. Nordestgaard, Mark J. Caulfield, Anubha Mahajan, Andrew P. Morris, Maciej Tomaszewski, Nilesh J. Samani, Danish Saleheen, Folkert W. Asselbergs, Cecilia M. Lindgren, John Danesh, Louise V. Wain, Adam S. Butterworth, Joanna M. M. Howson, Patricia B. Munroe
Bibliographic record
VenueNature Genetics · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsJaneway Children's Health and Rehabilitation Centre
FundersNational Cancer InstituteNational Institute on Drug AbuseNational Heart, Lung, and Blood InstituteNIHR Cambridge Biomedical Research CentreNational Center for Advancing Translational SciencesMedical Research CouncilCollege of Pharmacy, University of MichiganNational Institutes of HealthNovo Nordisk FondenTartu ÜlikoolErasmus Medisch CentrumNational Human Genome Research InstituteInternational Centre for Diarrhoeal Disease Research, BangladeshEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentZonMwSchool of Medicine, Boston UniversityEuropean CommissionNational Institute of Mental HealthHrvatska Zaklada za ZnanostUniversity of EdinburghNational Institute on AgingCentre for Cognitive Ageing and Cognitive EpidemiologyNational Institute for Health and Care ResearchScottish GovernmentU.S. National Library of MedicineNational Institute of Neurological Disorders and StrokeBritish Heart FoundationNorwegian Institute of Public HealthNederlandse Organisatie voor Wetenschappelijk OnderzoekBiotechnology and Biological Sciences Research CouncilWellcome TrustDirectorate for Biological SciencesImperial College LondonFoundation for Cardiovascular ResearchLundbeckfondenAge UKEuropean Regional Development FundUniversità degli Studi di VeronaUniversity of MichiganHome OfficeSydäntutkimussäätiöNational Institute of Diabetes and Digestive and Kidney DiseasesNorges Teknisk-Naturvitenskapelige UniversitetPfizer
KeywordsBiologyBlood pressureMeta-analysisGeneticsBioinformaticsComputational biologyInternal medicineEndocrinologyMedicine
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)0.005
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 score0.002
Threshold uncertainty score0.012
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 head0.089
GPT teacher head0.372
Teacher spread0.283 · 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
Citations330
Published2016
Admission routes1
Has abstractno