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Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression
Naomi R. Wray, Stephan Ripke, Manuel Mattheisen, Maciej Trzaskowski, Enda M. Byrne, Abdel Abdellaoui, Mark J. Adams, Esben Agerbo, Tracy Air, Till F. M. Andlauer, Silviu‐Alin Bacanu, Marie Bækvad‐Hansen, Aartjan T.F. Beekman, Tim B. Bigdeli, Elisabeth B. Binder, Douglas Blackwood, Julien Bryois, Henriette N. Buttenschøn, Jonas Bybjerg‐Grauholm, Na Cai, Enrique Castelao, Jane Christensen, Toni‐Kim Clarke, Jonathan R. I. Coleman, Lucía Colodro‐Conde, Baptiste Couvy‐Duchesne, Nick Craddock, Gregory E. Crawford, Cheynna Crowley, Hassan S. Dashti, Gail Davies, Ian J. Deary, Franziska Degenhardt, Eske M. Derks, Neşe Direk, Conor V. Dolan, Erin C. Dunn, Thalia C. Eley, Nicholas Eriksson, Valentina Escott‐Price, Farnush Hassan Farhadi Kiadeh, Hilary K. Finucane, Andreas J. Forstner, Josef Frank, Héléna A. Gaspar, Michael Gill, Paola Giusti‐Rodríguez, Fernando S. Goes, Scott D. Gordon, Jakob Grove, Lynsey S. Hall, Eilís Hannon, Christine Søholm Hansen, Thomas Folkmann Hansen, Stefan Herms, Ian B. Hickie, Per Hoffmann, Georg Homuth, Carsten Horn, Jouke‐Jan Hottenga, David M. Hougaard, Ming Hu, Craig Hyde, Marcus Ising, Rick Jansen, Fulai Jin, Eric Jorgenson, James A. Knowles, Isaac S. Kohane, Julia Kraft, Warren W. Kretzschmar, Jesper Krogh, Zoltán Kutalik, Jacqueline M. Lane, Yihan Li, Yun Li, Penelope A. Lind, Xiaoxiao Liu, Leina Lu, Donald J. MacIntyre, Dean F. MacKinnon, Robert Maier, Wolfgang Maier, Jonathan Marchini, Hamdi Mbarek, Patrick J. McGrath, Peter McGuffin, Sarah E. Medland, Divya Mehta, Christel M. Middeldorp, Evelin Mihailov, Yuri Milaneschi, Lili Milani, Jonathan Mill, Francis M. Mondimore, Grant W. Montgomery, Sara Mostafavi, Niamh Mullins, Matthias Nauck, Bernard Ng, Michel G. Nivard, Dale R. Nyholt, Paul F. O’Reilly, Högni Óskarsson, Michael J. Owen, Jodie N. Painter, Carsten Bøcker Pedersen, Marianne Giørtz Pedersen, Roseann E. Peterson, Erik Pettersson, Wouter J. Peyrot, Giorgio Pistis, Daniëlle Posthuma, Shaun Purcell, Jorge A. Quiroz, Per Qvist, John P. Rice, Brien P. Riley, Margarita Rivera, Saira Saeed Mirza, Richa Saxena, Robert A. Schoevers, Eva C. Schulte, Ling Shen, Jianxin Shi, Stanley I. Shyn, Engilbert Sigurðsson, Grant B. C. Sinnamon, Johannes H. Smit, Daniel J. Smıth, Hreinn Stefánsson, Stacy Steinberg, Craig A. Stockmeier, Fabian Streit, Jana Strohmaier, Katherine E. Tansey, Henning Teismann, Alexander Teumer, Wesley K. Thompson, Pippa A. Thomson, Thorgeir E. Thorgeirsson, Chao Tian, Matthew Traylor, Jens Treutlein, Vassily Trubetskoy, André G. Uitterlinden, Daniel Umbricht, Sandra Van der Auwera, Albert M. van Hemert, Alexander Viktorin, Peter M. Visscher, Yunpeng Wang, Bradley T. Webb, Shantel Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Stephanie H. Witt, Yang Wu, Hualin Simon Xi, Jian Yang, Futao Zhang, Volker Arolt, Bernhard T. Baune, Dorret I. Boomsma, Sven Cichon, Udo Dannlowski, E. C. J. de Geus, J. Raymond DePaulo, Enrico Domenici, Katharina Domschke, Tõnu Esko, Hans J. Grabe, Steven P. Hamilton, Caroline Hayward, Andrew C. Heath, David A. Hinds, Kenneth S. Kendler, Stefan Kloiber, Glyn Lewis, Qingqin S. Li, Susanne Lucae, Pamela Madden, Patrik K. E. Magnusson, Nicholas G. Martin, Andrew M. McIntosh, Andres Metspalu, Ole Mors, Preben Bo Mortensen, Bertram Müller‐Myhsok, Merete Nordentoft, Markus M. Nöthen, Michael O‘Donovan, Sara A. Paciga, Nancy L. Pedersen, Brenda W.J.H. Penninx, Roy H. Perlis, David J. Porteous, James B. Potash, Martin Preisig, Marcella Rietschel, Catherine Schaefer, Thomas G. Schulze, Jordan W. Smoller, Henning Tiemeier, Rudolf Uher, Henry Völzke, Myrna M. Weissman, Thomas Werge, Ashley R. Winslow, Cathryn M. Lewis, Douglas F. Levinson, Gerome Breen, Anders D. Børglum, Patrick F. Sullivan
Bibliographic record
VenueNature Genetics · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsDalhousie UniversityUniversity of TorontoCentre for Addiction and Mental HealthUniversity of British Columbia
FundersCilagNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeNational Cancer InstituteNovo NordiskNational Institute on Drug AbuseWellcome TrustNODAI Genome Research Center, Tokyo University of AgricultureMedical Research CouncilNational Institutes of HealthNational Institute of General Medical SciencesH. Lundbeck A/SWestfälische Wilhelms-Universität MünsterBundesministerium für Bildung und ForschungNational Institute for Health and Care ResearchAmgenSiemens HealthineersMax-Planck-GesellschaftLundbeckfondenNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNorthwestern UniversityPfizerNational Human Genome Research InstituteErasmus Medisch CentrumGlaxoSmithKline
KeywordsBiologyGenome-wide association studyGenetic architectureGenetic associationComputational biologyAssociation (psychology)GeneticsEvolutionary biologySingle-nucleotide polymorphismQuantitative trait locusGenotypeGenePsychology
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)0.002
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 score0.008
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.001 | 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 head0.021
GPT teacher head0.346
Teacher spread0.325 · 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
Citations3,303
Published2018
Admission routes1
Has abstractno