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Record W4383999393 · doi:10.1176/appi.ajp.20220304

Subcortical Brain Alterations in Carriers of Genomic Copy Number Variants

2023· article· en· W4383999393 on OpenAlexafffund
Kuldeep Kumar, Claudia Modenato, Clara Moreau, Christopher R. K. Ching, Annabelle Harvey, Sandra Martin‐Brevet, Guillaume Huguet, Martineau Jean‐Louis, Élise Douard, Charles-Olivier Martin, Nadine Younis, Petra Tamer, Anne Maillard, Borja Rodríguez‐Herreros, Aurélie Pain, Leila Kushan, Dmitry Isaev, Kathryn Alpert, Anjani Ragothaman, Jessica A. Turner, Lei Wang, Tiffany C. Ho, Lianne Schmaal, Ana Isabel Silva, Marianne B. M. van den Bree, David E.J. Linden, Michael J. Owen, Jérémy Hall, Sarah Lippé, Guillaume Dumas, Bogdan Draganski, Boris A. Gutman, Ida E. Sønderby, Ole A. Andreassen, Laura M. Schultz, Laura Almasy, David C. Glahn, Carrie E. Bearden, Paul M. Thompson, Sébastien Jacquemont

Bibliographic record

VenueAmerican Journal of Psychiatry · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsUniversité de Montréal
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Mental HealthInstitut de Valorisation des DonnéesCentre Hospitalier Universitaire VaudoisNational Institutes of HealthCanada First Research Excellence FundSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNorges ForskningsrådSimons Foundation Autism Research InitiativeCanadian Institutes of Health ResearchNational Science FoundationCompute CanadaWellcome TrustEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentEuropean CommissionFondation Brain CanadaHelse Sør-Øst RHFBiogenHealth and Care Research Wales
KeywordsBiologyGeneticsCopy-number variationComputational biologyNeuroscienceGenomeGene

Abstract

fetched live from OpenAlex

OBJECTIVE: Copy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disorders (NPDs), including autism (ASD) and schizophrenia. Little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, the authors investigated gross volume, vertex-level thickness, and surface maps of subcortical structures in 11 CNVs and six NPDs. METHODS: Subcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (CNVs at 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2; age range, 6-80 years; 340 males) and 782 control subjects (age range, 6-80 years; 387 males) as well as ENIGMA summary statistics for ASD, schizophrenia, attention deficit hyperactivity disorder, obsessive-compulsive disorder, bipolar disorder, and major depression. RESULTS: All CNVs showed alterations in at least one subcortical measure. Each structure was affected by at least two CNVs, and the hippocampus and amygdala were affected by five. Shape analyses detected subregional alterations that were averaged out in volume analyses. A common latent dimension was identified, characterized by opposing effects on the hippocampus/amygdala and putamen/pallidum, across CNVs and across NPDs. Effect sizes of CNVs on subcortical volume, thickness, and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and schizophrenia. CONCLUSIONS: The findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions, as well distinct effects, with some CNVs clustering with adult-onset conditions and others with ASD. These findings provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD and why a single CNV increases the risk for a diverse set of NPDs.

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 imitation

Not 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.253
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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

Citations25
Published2023
Admission routes2
Has abstractyes

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