← Back to cohort
Rare coding variants in ten genes confer substantial risk for schizophrenia
Tarjinder Singh, Timothy Poterba, David Curtis, Huda Akil, Mariam Al Eissa, Jack D. Barchas, Nicholas Bass, Tim B. Bigdeli, Gerome Breen, Evelyn J. Bromet, P.F. Buckley, William E. Bunney, Jonas Bybjerg‐Grauholm, William Byerley, Sinéad B. Chapman, Wei J. Chen, Claire Churchhouse, Nicholas Craddock, Caroline Cusick, Lynn E. DeLisi, Sheila Dodge, Michael Escamilla, Saana Eskelinen, Ayman H. Fanous, Stephen V. Faraone, Alessia Fiorentino, Laurent C. Francioli, Stacey Gabriel, Diane Gage, Sarah A. Gagliano Taliun, Andrea Ganna, Giulio Genovese, David C. Glahn, Jakob Grove, Mei‐Hua Hall, Eija Hämäläinen, Henrike Heyne, Matti Holi, David M. Hougaard, Daniel P. Howrigan, Hailiang Huang, Hai‐Gwo Hwu, René S. Kahn, Hyun Min Kang, Konrad J. Karczewski, George Kirov, James A. Knowles, Francis S. Lee, Douglas S. Lehrer, Francesco Lescai, Dolores Malaspina, Stephen R. Marder, Steven A. McCarroll, Andrew M. McIntosh, Helena Medeiros, Lili Milani, Christopher P. Morley, Derek W. Morris, Preben Bo Mortensen, R Myers, Merete Nordentoft, Niamh L. O’Brien, Ana Maria Olivares, Döst Öngür, Willem H. Ouwehand, Duncan S. Palmer, Tiina Paunio, Digby Quested, Mark Hyman Rapaport, Elliott Rees, Brandi Rollins, F. Kyle Satterstrom, Alan F. Schatzberg, Edward M. Scolnick, Laura J. Scott, Sally I. Sharp, Pamela Sklar, Jordan W. Smoller, Janet L. Sobell, Matthew Solomonson, Eli A. Stahl, Christine Stevens, Jaana Suvisaari, Grace Tiao, Stanley J. Watson, Nicholas A. Watts, Douglas Blackwood, Anders D. Børglum, Bruce M. Cohen, Aiden Corvin, Tõnu Esko, Nelson B. Freimer, Stephen J. Glatt, Christina M. Hultman, Andrew McQuillin, Aarno Palotie, Carlos N. Pato, Michele T. Pato, Ann E. Pulver, David St Clair, Ming T. Tsuang, Marquis P. Vawter, James Walters, Thomas Werge, Roel A. Ophoff, Patrick F. Sullivan, Michael J. Owen, Michael Boehnke, Michael O‘Donovan, Benjamin M. Neale, Mark J. Daly
Bibliographic record
VenueNature · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersNational Human Genome Research InstituteNational Institute on Drug AbuseNational Institute on AgingNational Institute on Alcohol Abuse and AlcoholismCanadian Institutes of Health ResearchNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteInstitute of Psychiatry, Psychology and Neuroscience, King’s College LondonNational Institutes of HealthLundbeckfondenMedical Research CouncilBiogenDalio FoundationCardiff UniversityNational Institute of Neurological Disorders and StrokeMassachusetts General HospitalNational Institute for Health and Care ResearchKing's College LondonStanley Family FoundationGlaxoSmithKlineEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBroad Institute
KeywordsGeneGeneticsSchizophrenia (object-oriented programming)BiologyComputational biologyMedicinePsychiatry
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
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)0.004
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 score0.006
Threshold uncertainty score0.021
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 head0.006
GPT teacher head0.240
Teacher spread0.233 · 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
Citations884
Published2022
Admission routes2
Has abstractyes