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Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries
Luis M. García‐Marín, Adrián I. Campos, Santiago Díaz‐Torres, Jill A. Rabinowitz, Zuriel Ceja, Brittany L. Mitchell, Katrina L. Grasby, Jackson G. Thorp, Ingrid Agartz, Saud Alhusaini, David Ames, Philippe Amouyel, Ole A. Andreassen, Konstantinos Arfanakis, Alejandro Arias Vásquez, Nicola J. Armstrong, Lavinia Athanasiu, Mark E. Bastin, Alexa Beiser, David A. Bennett, Joshua C. Bis, Marco P. Boks, Dorret I. Boomsma, Henry Brodaty, Rachel M. Brouwer, Jan K. Buitelaar, Ralph Burkhardt, Wiepke Cahn, Vince D. Calhoun, Owen Carmichael, M. Mallar Chakravarty, Qiang Chen, Christopher R. K. Ching, Sven Cichon, Benedicto Crespo‐Facorro, Fabrice Crivello, Anders M. Dale, George Davey Smith, Eco J. C. de Geus, Philip L. De Jager, Greig I. de Zubicaray, Stéphanie Debette, Charles DeCarli, Chantal Depondt, Sylvane Desrivières, Srdjan Djurovic, Stefan Ehrlich, Susanne Erk, Thomas Espeseth, Guillén Fernández, Irina Filippi, Simon E. Fisher, Debra Fleischman, Evan Fletcher, Myriam Fornage, Andreas J. Forstner, Clyde Francks, Barbara Franke, Tian Ge, Aaron L. Goldman, Hans J. Grabe, Robert C. Green, O. Grimm, Nynke A. Groenewold, Oliver Gruber, Vilmundur Guðnason, Asta K. Håberg, Unn K. Haukvik, Andreas Heinz, Derrek P. Hibar, Saima Hilal, Jayandra J. Himali, Beng‐Choon Ho, David Hoehn, Pieter J. Hoekstra, Edith Hofer, Wolfgang Hoffmann, Avram J. Holmes, Georg Homuth, Norbert Hosten, M. Kamran Ikram, Jonathan Ipser, Clifford R. Jack, Neda Jahanshad, Erik G. Jönsson, René S. Kahn, Ryota Kanai, Marieke Klein, Maria J. Knol, Lenore J. Launer, Stephen M. Lawrie, Stéphanie Le Hellard, Phil Hyu Lee, Hervé Lemaître, Shuo Li, David C. Liewald, Honghuang Lin, W.T. Longstreth, Oscar L. López, Michelle Luciano, Pauline Maillard, André F. Marquand, Nicholas G. Martin, Jean‐Luc Martinot, Karen A. Mather, Venkata S. Mattay, Katie L. McMahon, Patrizia Mecocci, Ingrid Melle, Andreas Meyer‐Lindenberg, Nazanin Mirza‐Schreiber, Yuri Milaneschi, Thomas H. Mosley, Thomas W. Mühleisen, Bertram Müller‐Myhsok, Susana Muñoz Maniega, Matthias Nauck, Kwangsik Nho, Wiro J. Niessen, Markus M. Nöthen, Paul Nyquist, Jaap Oosterlaan, Massimo Pandolfo, Tomáš Paus, Zdenka Pausová, Brenda W.J.H. Penninx, G. Bruce Pike, Bruce M. Psaty, Benno Pütz, Simone Reppermund, Marcella Rietschel, Shannon L. Risacher, Nina Romanczuk‐Seiferth, Rafael Romero-García, Gennady V. Roshchupkin, Jerome I. Rotter, Perminder S. Sachdev, Philipp G. Sämann, Arvin Saremi, Muralidharan Sargurupremraj, Andrew J. Saykin, Lianne Schmaal, Helena Schmidt, Reinhold Schmidt, Peter R. Schofield, Markus Scholz, Günter Schumann, Emanuel Schwarz, Li Shen, Jean Shin, Sanjay M. Sisodiya, Albert V. Smith, Jordan W. Smoller, Hilkka Soininen, Vidar M. Steen, Dan J. Stein, Jason L. Stein, Sophia I. Thomopoulos, Arthur W. Toga, Diana Tordesillas‐Gutiérrez, Julian N. Trollor, María Valdés Hernández, Dennis van ‘t Ent, Hans van Bokhoven, Dennis van der Meer, Nic J.A. van der Wee, Javier Vázquez-Bourgón, Dick J. Veltman, Meike W. Vernooij, Arno Villringer, Louis Vinke, Henry Völzke, Henrik Walter, Joanna M. Wardlaw, Daniel R. Weinberger, Michael Weiner, Wei Wen, Lars T. Westlye, Eric Westman, Tonya White, A. Veronica Witte, Christiane Wolf, Jingyun Yang, Marcel P. Zwiers, M. Arfan Ikram, Sudha Seshadri, Paul M. Thompson, Claudia L. Satizábal, Sarah E. Medland, Miguel E. Rentería
Bibliographic record
VenueNature Genetics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsHospital for Sick ChildrenSickKids FoundationUniversity of TorontoUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineUniversity of CalgaryMcGill UniversityDouglas College
FundersCilagNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Institute on AgingAvid RadiopharmaceuticalsUniversity of California, San DiegoGenentechHector Stiftung IINeuraxpharmEuropean College of NeuropsychopharmacologyFresenius Medical Care North AmericaEisaiNational Institute of Biomedical Imaging and BioengineeringDaimler und Benz StiftungHector StiftungServierEli Lilly and CompanyLundbeckfondenEuropean CommissionSunovionH. Lundbeck A/SNorthwell HealthNational Institutes of HealthNational Heart, Lung, and Blood InstituteFondation FondaMentalÉcole Polytechnique Fédérale de LausannePfizerBiogenSiemens Medical Solutions USA
KeywordsBiologyPutamenGenome-wide association studyNeuroscienceBrain sizeThalamusGlobus pallidusBrain morphometryNucleus accumbensCaudate nucleusMultifactorial InheritanceNeuroanatomyGeneticsGeneBasal gangliaSingle-nucleotide polymorphismCentral nervous systemGenotypeMedicine
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)0.001
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 score0.007
Threshold uncertainty score0.014
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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 head0.008
GPT teacher head0.299
Teacher spread0.291 · 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
Citations19
Published2024
Admission routes1
Has abstractno