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Meta-analysis of genome-wide associations and polygenic risk prediction for atrial fibrillation in more than 180,000 cases
Carolina Roselli, Ida Surakka, Morten S. Olesen, Garðar Sveinbjörnsson, Nicholas Marston, Seung Hoan Choi, Hilma Hólm, Mark Chaffin, Daníel F. Guðbjartsson, Matthew C. Hill, Hildur M. Aegisdottir, Christine M. Albert, Álvaro Alonso, Christopher D. Anderson, Dan E. Arking, Davíð O. Arnar, John Barnard, Emelia J. Benjamin, Eugene Braunwald, Ben Brumpton, Archie Campbell, Nathalie Chami, Daniel I. Chasman, Kelly Cho, Eue‐Keun Choi, Ingrid E. Christophersen, Mina K. Chung, David Conen, Harry J.G.M. Crijns, Michael J. Cutler, Tomasz Czuba, Scott M. Damrauer, Martin Dichgans, Marcus Dörr, Elton Dudink, ThuyVy Duong, Christian Erikstrup, Tõnu Esko, Diane Fatkin, Jessica D. Faul, Manuel A. R. Ferreira, Daniel F. Freitag, Santhi K. Ganesh, J. Michael Gaziano, Bastiaan Geelhoed, Jonas Ghouse, Christian Gieger, Franco Giulianini, Sarah E. Graham, Vilmundur Guðnason, Xiuqing Guo, Christopher M. Haggerty, Caroline Hayward, Susan R. Heckbert, Kristian Hveem, Kaoru Ito, Renée Johnson, J. Wouter Jukema, Sean J. Jurgens, Stefan Kääb, John P. Kane, Shinwan Kany, Sharon L. R. Kardia, Maryam Kavousi, Shaan Khurshid, Frederick Kamanu, Paulus Kirchhof, Marcus E. Kleber, Stacey Knight, Issei Komuro, José Eduardo Krieger, Lenore J. Launer, Dadong Li, Honghuang Lin, Henry J. Lin, Ruth J. F. Loos, Luca A. Lotta, Steven A. Lubitz, Kathryn L. Lunetta, Peter W. Macfarlane, Patrik K. E. Magnusson, Rainer Malik, Helene Mantineo, Gregory M. Marcus, Winfried März, David D. McManus, Olle Melander, Giorgio Melloni, Pascal Meyre, Kazuo Miyazawa, Sanghamitra Mohanty, Laia M. Monfort, Martina Müller‐Nurasyid, Navid A. Nafissi, Saman Nazarian, Sisse Rye Ostrowski, Hui‐Nam Pak, Shichao Pang, Ole Birger Pedersen, Nancy L. Pedersen, Alexandre C. Pereira, James P. Pirruccello, Michael Preuß, Bruce M. Psaty, Clive R. Pullinger, Daniel J. Rader, Joel Rämö, Paul M. Ridker, Michiel Rienstra, Lorenz Risch, Dan M. Roden, Jerome I. Rotter, Marc S. Sabatine, Heribert Schunkert, Svati H. Shah, Jaemin Shim, M. Benjamin Shoemaker, Bridget Simonson, Moritz F. Sinner, Roelof A. J. Smit, Jennifer A. Smith, Nicholas L. Smith, J. G. Smith, Elsayed Z. Soliman, Erik Sørensen, Nona Sotoodehnia, Daniel Strbian, Bruno H. Stricker, Maris Teder‐Laving, Yan V. Sun, Sébastien Thériault, Rósa B. Þórólfsdóttir, Unnur Thorsteinsdottir, Arnljot Tveit, Pim van der Harst, Joyce B. J. van Meurs, Biqi Wang, Stefan Weiß, Quinn S. Wells, Lu‐Chen Weng, Peter W.F. Wilson, Ling Xiao, Pil‐Sung Yang, Jie Yao, Zachary T. Yoneda, Tanja Zeller, Lingyao Zeng, Wei Zhao, Xiang Zhou, Sebastian Zöllner, Christian T. Ruff, Henning Bundgaard, Cristen J. Willer, Kāri Stefánsson, Patrick T. Ellinor
Bibliographic record
VenueNature Genetics · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversité LavalMcMaster UniversityPopulation Health Research Institute
FundersCommon FundNational Center for Research ResourcesNational Institute of Neurological Disorders and StrokeNational Center for Advancing Translational SciencesNational Human Genome Research InstituteNational Institute on Drug AbuseNational Institute of Diabetes and Digestive and Kidney DiseasesNIH Office of the DirectorNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Cancer InstituteNational Institutes of HealthOffice of Research and DevelopmentU.S. Department of Veterans Affairs
KeywordsBiologyAtrial fibrillationGenome-wide association studyPolygenic risk scoreMultifactorial InheritanceComputational biologyGenomeInternal medicineGeneticsBioinformaticsSingle-nucleotide polymorphismGeneMedicineGenotype
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)0.004
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.023
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 head0.043
GPT teacher head0.349
Teacher spread0.306 · 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
Citations45
Published2025
Admission routes1
Has abstractno