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Record W4386492967 · doi:10.7554/elife.88117.2.sa3

Author Response: Gene-Environment Pathways to Cognitive Intelligence and Psychotic-Like Experiences in Children

2023· peer-review· en· W4386492967 on OpenAlexaff
Junghoon Park, Eunji Lee, Gyeongcheol Cho, Heungsun Hwang, Bo‐Gyeom Kim, Gakyung Kim, Yoonjung Yoonie Joo, Jiook Cha

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsMcGill University
Fundersnot available
KeywordsCognitionConfoundingSchizophrenia (object-oriented programming)PsychosisPsychologySocioeconomic statusStructural equation modelingEffects of sleep deprivation on cognitive performanceClinical psychologyDevelopmental psychologyCognitive skillIntelligence quotientMedicinePsychiatryEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning is a well-known risk factor and early marker for psychosis, schizophrenia, and other mental disorders. Since cognitive functioning is linked to various genetic and environmental factors during development, we hypothesize that it mediates the effects of those factors on childhood PLEs. Using large longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children.To estimate associations against potential confounding bias, we leveraged large-scale multimodal data of 6,602 children (aged 9-10 years old; 47.15% females; 5,211 European-ancestry) from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling).We identified that lower cognitive intelligence and higher PLEs correlated significantly with several genetic and environmental variables: i.e., lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, lower supportive parenting behavior, and lower positive school environment. In SEM, lower cognitive intelligence significantly mediated the genetic and environmental influences on higher PLEs (Indirect effects of PGS: β range=-0.0355∼ -0.0274; Family SES: β range=-0.0429∼ -0.0331; Neighborhood SES: β range=0.0126∼ 0.0164; Positive Environment: β range=-0.0039∼ -0.003). Supportive parenting and a positive school environment had the largest total impact on PLEs (β range=-0.152∼ -0.1316) than any other genetic or environmental factors.Our results reveal the role of genetic and environmental factors on children’s PLEs via its negative impact on cognitive intelligence. Our findings have policy implications in that improving the school and family environment and promoting local economic development might be a way to enhance cognitive and mental health in children.

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)0.002
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.248
Threshold uncertainty score0.829

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.2480.055

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 head0.051
GPT teacher head0.339
Teacher spread0.288 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2023
Admission routes1
Has abstractyes

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