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Record W2807295499 · doi:10.1038/s41588-018-0133-9

Multi-ethnic genome-wide association study for atrial fibrillation

2018· review· en· W2807295499 on OpenAlexaff
Carolina Roselli, Mark Chaffin, Lu‐Chen Weng, Stefanie Aeschbacher, Gustav Ahlberg, Christine M. Albert, Peter Almgren, Álvaro Alonso, Christopher D. Anderson, Krishna G. Aragam, Dan E. Arking, John Barnard, Traci M. Bartz, Emelia J. Benjamin, Nathan A. Bihlmeyer, Joshua C. Bis, Heather L. Bloom, Eric Boerwinkle, Erwin B. Bottinger, Jennifer A. Brody, Hugh Calkins, Archie Campbell, Thomas P. Cappola, John F. Carlquist, Daniel I. Chasman, Lin Y. Chen, Yii-Der Ida Chen, Eue‐Keun Choi, Seung Hoan Choi, Ingrid E. Christophersen, Mina K. Chung, John W. Cole, David Conen, James P. Cook, Harry J. Crijns, Michael J. Cutler, Scott M. Damrauer, Brian R. Daniels, Dawood Darbar, Graciela Delgado, Joshua C. Denny, Martin Dichgans, Marcus Dörr, Elton Dudink, Samuel C. Dudley, Nada Esa, Tõnu Esko, Markku Eskola, Diane Fatkin, Stephan B. Felix, Ian Ford, Oscar H. Franco, Bastiaan Geelhoed, Raji P. Grewal, Vilmundur Guðnason, Xiuqing Guo, Namrata Gupta, Stefan Gustafsson, Rebecca Gutmann, Anders Hamsten, Tamara B. Harris, Caroline Hayward, Susan R. Heckbert, Jussi Hernesniemi, Lynne J. Hocking, Albert Hofman, Andréa R. V. R. Horimoto, Jie Huang, Paul L. Huang, Jennifer E. Huffman, Erik Ingelsson, Esra Gücük İpek, Kaoru Ito, Jordi Jiménez‐Conde, Renée Johnson, J. Wouter Jukema, Stefan Kääb, Mika Kähönen, Yoichiro Kamatani, John P. Kane, Adnan Kastrati, Sekar Kathiresan, Petra Katschnig‐Winter, Maryam Kavousi, Thorsten Kessler, Bas Kietselaer, Paulus Kirchhof, Marcus E. Kleber, Stacey Knight, José Eduardo Krieger, Michiaki Kubo, Lenore J. Launer, Jari Laurikka, Terho Lehtimäki, Kirsten Leineweber, Rozenn N. Lemaître, Man Li, Hong Euy Lim, Henry J. Lin, Honghuang Lin, Lars Lind, Cecilia M. Lindgren, Marja‐Liisa Lokki, Barry London, Ruth J. F. Loos, Siew‐Kee Low, Yingchang Lu, Leo‐Pekka Lyytikäinen, Peter W. Macfarlane, Patrik K. E. Magnusson, Anubha Mahajan, Rainer Malik, Alfredo José Mansur, Gregory M. Marcus, Lauren Margolin, Kenneth B. Margulies, Winfried März, David D. McManus, Olle Melander, Sanghamitra Mohanty, Jay A. Montgomery, Michael P. Morley, Andrew P. Morris, Martina Müller‐Nurasyid, Andrea Natale, Saman Nazarian, Benjamin Neumann, Christopher Newton‐Cheh, Maartje N. Niemeijer, Kjell Nikus, Peter M. Nilsson, Raymond Noordam, Heidi Oellers, Morten S. Olesen, Marju Orho‐Melander, Sandosh Padmanabhan, Hui‐Nam Pak, Guillaume Paré, Nancy L. Pedersen, Joanna Pera, Alexandre C. Pereira, David J. Porteous, Bruce M. Psaty, Sara L. Pulit, Clive R. Pullinger, Daniel J. Rader, Lena Refsgaard, Marta Ribasés, Paul M. Ridker, Michiel Rienstra, Lorenz Risch, Dan M. Roden, Jonathan Rosand, Michael A. Rosenberg, Natalia S. Rost, Jerome I. Rotter, Samir Saba, Roopinder K. Sandhu, Renate B. Schnabel, Katharina Schramm, Heribert Schunkert, Claudia Schurman, Stuart A. Scott, Ilkka Seppälä, Christian M. Shaffer, Svati H. Shah, Alaa Shalaby, Jaemin Shim, M. Benjamin Shoemaker, Joylene E. Siland, Juha Sinisalo, Moritz F. Sinner, Agnieszka Słowik, Albert V. Smith, Blair H. Smith, J. Gustav Smith, Jonathan D. Smith, Nicholas L. Smith, Elsayed Z. Soliman, Nona Sotoodehnia, Bruno H. Stricker, Albert Y. Sun, Han Sun, Jesper Hastrup Svendsen, Toshihiro Tanaka, Kahraman Tanrıverdi, Kent D. Taylor, Maris Teder‐Laving, Alexander Teumer, Sébastien Thériault, Stella Trompet, Nathan R. Tucker, Arnljot Tveit, André G. Uitterlinden, Pim van der Harst, Isabelle C. Van Gelder, David R. Van Wagoner, Niek Verweij, Efthymia Vlachopoulou, Uwe Völker, Biqi Wang, Peter Weeke, Bob Weijs, Raul Weiss, Stefan Weiß, Quinn S. Wells, Kerri L. Wiggins, Jorge Wong, Daniel Woo, Bradford B. Worrall, Pil‐Sung Yang, Jie Yao, Zachary T. Yoneda, Tanja Zeller, Lingyao Zeng, Steven A. Lubitz, Kathryn L. Lunetta, Patrick T. Ellinor

Bibliographic record

VenueNature Genetics · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsHamilton Health SciencesUniversity of AlbertaMcMaster UniversityPopulation Health Research Institute
FundersNational Institute of General Medical SciencesNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteMedical Research CouncilBiosense WebsterNational Institutes of HealthDoris Duke Charitable FoundationNovo Nordisk FondenYale UniversityFondation LeducqBroad InstituteEuropean CommissionQuest DiagnosticsAmarin CorporationBristol-Myers SquibbSt. Jude MedicalRegeneron PharmaceuticalsBritish Heart Foundation
KeywordsBiologyGenome-wide association studyAtrial fibrillationEthnic groupGeneticsGenetic associationAssociation (psychology)Computational biologyEvolutionary biologyInternal medicineGeneSingle-nucleotide polymorphismGenotypeAnthropologyMedicine

Abstract

fetched live from OpenAlex

. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.046
GPT teacher head0.377
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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".

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Citations810
Published2018
Admission routes1
Has abstractyes

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