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Record W4375861477 · doi:10.1093/hmg/ddad074

Gene copy number variation and pediatric mental health/neurodevelopment in a general population

2023· article· en· W4375861477 on OpenAlexafffund
Mehdi Zarrei, Christie L. Burton, Worrawat Engchuan, Edward J. Higginbotham, John Wei, Sabah Shaikh, Nicole M. Roslin, Jeffrey R. MacDonald, Giovanna Pellecchia, Thomas Nalpathamkalam, Sylvia Lamoureux, Roozbeh Manshaei, Jennifer Howe, Brett Trost, Bhooma Thiruvahindrapuram, Christian R. Marshall, Ryan K. C. Yuen, Richard F. Wintle, Lisa J. Strug, Dimitri J. Stavropoulos, Jacob Vorstman, Paul Arnold, Daniele Merico, Marc Woodbury‐Smith, Jennifer Crosbie, Russell Schachar, Stephen W. Scherer

Bibliographic record

VenueHuman Molecular Genetics · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsSickKids FoundationUniversity of TorontoUniversity of CalgaryTed Rogers Centre for Heart ResearchOntario GenomicsGenome CanadaHospital for Sick Children
FundersMedical Research CouncilUniversity of TorontoCanada Foundation for InnovationGovernment of OntarioCanadian Institutes of Health ResearchGenome CanadaSick Kids FoundationHospital for Sick ChildrenWellcome Trust
KeywordsCopy-number variationMental healthAutism spectrum disorderAutismAnxietyAttention deficit hyperactivity disorderOdds ratioPopulationCognitionOddsPsychiatryClinical psychologyBiologyPsychologyGeneGeneticsMedicineGenomeInternal medicine

Abstract

fetched live from OpenAlex

We assessed the relationship of gene copy number variation (CNV) in mental health/neurodevelopmental traits and diagnoses, physical health and cognition in a community sample of 7100 unrelated children and youth of European or East Asian ancestry (Spit for Science). Clinically significant or susceptibility CNVs were present in 3.9% of participants and were associated with elevated scores on a continuous measure of attention-deficit/hyperactivity disorder (ADHD) traits (P = 5.0 × 10-3), longer response inhibition (a cognitive deficit found in several mental health and neurodevelopmental disorders; P = 1.0 × 10-2) and increased prevalence of mental health diagnoses (P = 1.9 × 10-6, odds ratio: 3.09), specifically ADHD, autism spectrum disorder anxiety and learning problems/learning disorder (P's < 0.01). There was an increased burden of rare deletions in gene-sets related to brain function or expression in brain associated with more ADHD traits. With the current mental health crisis, our data established a baseline for delineating genetic contributors in pediatric-onset 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 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 score0.405
Threshold uncertainty score0.734

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 head0.010
GPT teacher head0.257
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 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

Citations21
Published2023
Admission routes2
Has abstractyes

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