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Record W3012868777 · doi:10.1101/2020.03.18.20038505

The general impact of haploinsufficiency on brain connectivity underlies the pleiotropic effect of neuropsychiatric CNVs

2020· preprint· en· W3012868777 on OpenAlexafffund
Clara Moreau, Guillaume Huguet, Sebastian Urchs, Élise Douard, Hanad Sharmarke, Pierre Orban, Aurélie Labbe, Claudia Modenato, Sandra Martin‐Brevet, Charles-Olivier Martin, Khadijé Jizi, Nadine Younis, Petra Tamer, Martineau Jean‐Louis, Ana Isabel Silva, Aia Elise Jønch, Amy Lin, Jeremy Hall, Marianne B. M. van den Bree, Michael J. Owen, David E.J. Linden, Anne Maillard, Sarah Lippé, Celia M.T. Greenwood, Carrie E. Bearden, Paul M. Thompson, Pierre Bellec, Sébastien Jacquemont

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsJewish General HospitalHEC MontréalMcGill UniversityMontreal Neurological Institute and HospitalInstitut Universitaire en Santé Mentale de QuébecUniversité de MontréalInstitut Universitaire de Gériatrie de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health ResearchCanada First Research Excellence FundCompute CanadaCentre Hospitalier Universitaire VaudoisSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungHealth and Care Research WalesWellcome TrustFondation Brain Canada
KeywordsHaploinsufficiencyCopy-number variationSchizophrenia (object-oriented programming)AutismAutism spectrum disorderEndophenotypePsychologyPsychiatryNeuroscienceCognitionGeneticsBiologyGenePhenotypeGenome

Abstract

fetched live from OpenAlex

Abstract Copy number variants (CNVs) are among the most highly penetrant genetic risk factors for neuropsychiatric disorders. Their impact on brain connectivity remains mostly unstudied. Because they confer risk for overlapping conditions, we hypothesized that they may converge on shared connectivity patterns. We performed connectome-wide analyses using resting-state functional MRI data from 436 carriers of neuropsychiatric CNVs at the 1q21.1, 15q11.2, 16p11.2, 22q11.2 loci, 4 “neutral effect” CNVs, 66 carriers of scarcer neuropsychiatric CNVs, 756 individuals with idiopathic autism spectrum disorder (ASD), schizophrenia, attention deficit hyperactivity disorder, and 5,377 controls. Neuropsychiatric CNVs showed global shifts of mean connectivity. The effect size of CNVs on relative connectivity (adjusted for the mean) was correlated with the known level of neuropsychiatric risk conferred by CNVs. Individuals with idiopathic schizophrenia and ASD had similarities in connectivity with neuropsychiatric CNVs. We reported a linear relationship between connectivity and intolerance to haploinsufficiency measured for all genes encompassed by CNVs across 18 loci. This profile involved the thalamus, the basal ganglia, somatomotor and frontoparietal networks and was correlated with lower general intelligence and higher autism severity scores. An exploratory factor analysis confirmed the contribution of these regions to three latent components shared across CNVs and neuropsychiatric disorders. We posit that deleting genes intolerant to haploinsufficiency reorganize connectivity along general dimensions irrespective of where deletions occur in the genome. This haploinsufficiency brain signature opens new avenues to understand polygenicity in psychiatric conditions and the pleiotropic effect of CNVs on cognition and risk for neuropsychiatric disorders. One sentence summary Neuropsychiatric CNVs across the genome reorganize brain connectivity architecture along dominant patterns contributing to complex idiopathic conditions.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.271
Teacher spread0.260 · 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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