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Record W4414845913 · doi:10.1038/s41398-025-03579-x

The interplay between genomic copy number variants, sleep, and cognition in the general population

2025· article· en· W4414845913 on OpenAlexaff
Cécile Poulain, Rackeb Tesfaye, Élise Douard, Jean-Louis Martineau, Zohra Saci, Aurélie Labbe, David C. Glahn, Laura Almasy, Mayada Elsabbagh, Guillaume Huguet, Sébastien Jacquemont

Bibliographic record

VenueTranslational Psychiatry · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsHEC MontréalMontreal Neurological Institute and HospitalUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersNational Institute of Mental Health
KeywordsCognitionSleep (system call)Copy-number variationPopulationAffect (linguistics)Schizophrenia (object-oriented programming)Association (psychology)Effects of sleep deprivation on cognitive performance

Abstract

fetched live from OpenAlex

Genomic Copy Number variants (CNVs) increase risk for neurodevelopmental disorders (NDDs) and affect cognition, but their impact on sleep remains understudied despite the well-established link between sleep disturbances, NDDs, and cognition. We investigated the relationship between CNVs, sleep traits, cognitive ability, and executive function in 498,852 individuals from an unselected population in the UK Biobank. We replicated the U-shape relationship between measures of cognitive ability and sleep duration. The effects of CNVs on sleep duration were evident at the genome-wide level; CNV-burden analyses showed that overall, CNVs with an increasing number of intolerant genes were associated with increased or decreased sleep duration in a U-shape pattern (p < 2e −16 ), but did not increase risk of insomnia. Sleep duration only marginally mediated the robust association between CNVs and poorer cognitive performance, suggesting that sleep and cognitive phenotypes may result from pleiotropic effects of CNVs with minimal causal relationship.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.277
Teacher spread0.271 · 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 teacher head, 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

Citations1
Published2025
Admission routes1
Has abstractyes

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