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Analysis of shared heritability in common disorders of the brain
Verneri Anttila, Brendan Bulik‐Sullivan, Hilary K. Finucane, Raymond K. Walters, José Brás, Laramie E. Duncan, Valentina Escott‐Price, Guido J. Falcone, Padhraig Gormley, Rainer Malik, Nikolaos A. Patsopoulos, Stephan Ripke, Zhi Wei, Dongmei Yu, Phil H. Lee, Patrick Turley, Benjamin Grenier‐Boley, Vincent Chouraki, Claudine Berr, Luc Letenneur, Didier Hannequin, Philippe Amouyel, Anne Boland, Jean‐François Deleuze, Emmanuelle Duron, Badri N. Vardarajan, Christiane Reitz, Alison Goate, Matthew J. Huentelman, M. Ilyas Kamboh, Eric B. Larson, Ekaterina Rogaeva, Peter St George‐Hyslop, Håkon Håkonarson, Walter A. Kukull, Lindsay A. Farrer, Lisa L. Barnes, Thomas G. Beach, F. Yesim Demirci, Elizabeth Head, Christine M. Hulette, Gregory A. Jicha, John S.K. Kauwe, James B. Leverenz, Allan I. Levey, Andrew P. Lieberman, V. Shane Pankratz, Wayne W. Poon, Joseph F. Quinn, Andrew J. Saykin, Lon S. Schneider, Amanda Smith, Joshua A. Sonnen, Robert A. Stern, Vivianna M. Van Deerlin, Linda J. 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Bibliographic record
VenueScience · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsSunnybrook Health Science CentreUniversity of CalgaryBC Mental Health & Substance Use ServicesMontreal Neurological Institute and HospitalDalhousie UniversityUniversity of TorontoSickKids FoundationDouglas Mental Health University InstituteMcGill UniversityCentre Hospitalier de l’Université de MontréalUniversity of British ColumbiaHospital for Sick ChildrenUniversity Health NetworkOccupational Cancer Research Centre
FundersNational Institute of Mental HealthInstitute of Zoology, Chinese Academy of SciencesMenzies Centre for Australian Studies, King's College London, University of LondonNIHR Maudsley Biomedical Research CentreState University of New York Upstate Medical UniversityNational Institutes of HealthNovo Nordisk FondenUniversitätsklinikum JenaLee Kong Chian School of Medicine, Nanyang Technological UniversityState University of New YorkLundbeckfondenChinese University of Hong KongVirginia Commonwealth UniversityVetenskapsrådetAgence Nationale de la RechercheUniversiteit MaastrichtNational Center for Advancing Translational SciencesMedical Research CouncilUniversity of WollongongOxford Health NHS Foundation TrustNanyang Technological UniversityUniklinikum Giessen und MarburgKing's College LondonNational Institute of Neurological Disorders and StrokeSyracuse UniversityKunming Institute of Zoology, Chinese Academy of SciencesNSW Health PathologyNational University of SingaporeWellcome TrustUniversity College LondonUniversity of California, Santa BarbaraRoyal College of Surgeons in IrelandChinese Academy of SciencesUniversity of OxfordLawrence Berkeley National LaboratoryMotor Neurone Disease AssociationUniversity of North Carolina at Chapel HillParkinson's UKNorthShore University HealthSystemNational Institute on AgingNational Institute for Health and Care Research
KeywordsHeritabilityGenome-wide association studyMissing heritability problemEtiologyGenetic associationComorbidityTwin studyEpidemiologyPsychiatryPhenotypeMedicineClinical psychologyPsychologyBiologyGeneticsGenetic variantsSingle-nucleotide polymorphismInternal medicineGenotype
Abstract
Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.
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 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)0.007
metaresearch head score (Gemma)0.024
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 score0.007
Threshold uncertainty score0.039
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 head0.010
GPT teacher head0.295
Teacher spread0.284 · 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
Citations2,028
Published2018
Admission routes1
Has abstractyes