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

Junghoon Park
Eunji Lee
Gyeongcheol Cho
Heungsun Hwang
Bogyeom Kim
Gakyung Kim
Yoonjung Yoonie Joo author has email address
Jiook Cha author has email address

Interdisciplinary Program in Artificial Intelligence, College of Engineering, Seoul National University, Seoul, South Korea
Department of Psychology, College of Social Sciences, Seoul National University, Seoul, South Korea
Department of Psychology, McGill University, Montréal, Canada
Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, South Korea
Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
Samsung Medical Center, Seoul, South Korea

https://doi.org/10.7554/eLife.88117.3

Open access
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Figures and data

Study diagram for longitudinal trajectories of genetic and environmental influences on PLEs through cognitive intelligence.
This study examines the mediating role of cognitive intelligence within the effects of cognitive phenotype PGSs, high family SES, low neighborhood SES, and positive family and school environments on PLEs observed at baseline, 1-year follow-up, and 2-year follow-up in children aged 9-10 years. Both direct and indirect effects, as well as total effects, were evaluated for statistical significance.

Demographic characteristics of the study participants.
Of the initial 11,878 ABCD samples, we obtained data for the variables of interest for 6,602 multiethnic children. For multiethnic subjects (main analyses, n=6,602), 47.15% were female, and the parents of 70.21% were married. In European ancestry samples (sensitivity analyses, n=5,211), 46.71% were female, and the parents of 77.47% were married. Children of European ancestry had significantly different marital status (p<0.0001), lower BMI (p<0.0001) and higher family history of psychiatric disorders (p<0.0001) than children of multiethnic ancestries. There were no significant differences in other characteristics between the two ancestry groups.
The 6,602 multiethnic participants consisted of 890 African-ancestry (13.48%), 229 Native American ancestry (3.47%), 91 East Asian ancestry (1.38%), 181 not specified (2.74%), and 5,211 European ancestry (78.93%) children. Differences between genetic ancestry groups were calculated using χ² tests for categorical variables and t tests for continuous variables.

Demographic characteristics of the study participants.
Of the initial 11,878 ABCD samples, we obtained data for the variables of interest for 6,602 multiethnic children. For multiethnic subjects (main analyses, n=6,602), 47.15% were female, and the parents of 70.21% were married. In European ancestry samples (sensitivity analyses, n=5,211), 46.71% were female, and the parents of 77.47% were married. Children of European ancestry had significantly different marital status (p<0.0001), lower BMI (p<0.0001) and higher family history of psychiatric disorders (p<0.0001) than children of multiethnic ancestries. There were no significant differences in other characteristics between the two ancestry groups.
The 6,602 multiethnic participants consisted of 890 African-ancestry (13.48%), 229 Native American ancestry (3.47%), 91 East Asian ancestry (1.38%), 181 not specified (2.74%), and 5,211 European ancestry (78.93%) children. Differences between genetic ancestry groups were calculated using χ² tests for categorical variables and t tests for continuous variables.

Linear models testing genetic, socioeconomic, and environmental factors associated with cognitive intelligence and PLEs.
Standardized coefficients of a linear mixed model with CP PGS (A, B) and EA PGS (C, D). The analyses included 6,602 samples of multiethnicity. Cognitive intelligence and PLEs correlated with the PGSs, residential disadvantage, positive environment, and family SES in the opposite directions. Error bars indicate 95% bootstrapped confidence intervals with 5,000 iterations. CP and EA denote cognitive performance and education attainment, respectively; PGS, polygenic scores; SES, socioeconomic status; PLEs, psychotic-like experiences; ADI, Area Deprivation Index; Poverty, percentage of individuals below −125% of the poverty level; Years, years of residence. (*p-FDR<0.05)

Linear models testing genetic, socioeconomic, and environmental factors associated with cognitive intelligence and PLEs.
Standardized coefficients of a linear mixed model with CP PGS (A, B) and EA PGS (C, D). The analyses included 6,602 samples of multiethnicity. Cognitive intelligence and PLEs correlated with the PGSs, residential disadvantage, positive environment, and family SES in the opposite directions. Error bars indicate 95% bootstrapped confidence intervals with 5,000 iterations. CP and EA denote cognitive performance and education attainment, respectively; PGS, polygenic scores; SES, socioeconomic status; PLEs, psychotic-like experiences; ADI, Area Deprivation Index; Poverty, percentage of individuals below −125% of the poverty level; Years, years of residence. (*p-FDR<0.05)

Direct/Indirect effects of gene-environment factors to cognitive and PLEs outcomes
A. Direct pathways from PGS, high family SES, low neighborhood SES, and positive environment to cognitive intelligence and PLEs. Standardized path coefficients are indicated on each path as direct effect estimates (significance level *p<0.05). B. Indirect pathways to PLEs via intelligence were significant for polygenic scores, high family SES, low neighborhood SES, and positive environment, indicating the significant mediating role of intelligence. C. Relative effect sizes of direct and indirect pathways within the total effects on PLEs. The standardized effect sizes of direct pathways are colored within each bar. In A-B, child sex, genetic ancestry, BMI, marital status, family history of psychiatric disorders, and ABCD research sites were included as covariates. CP PGS and EA PGS denote polygenic scores of cognitive performance and education attainment, respectively; SES, socioeconomic status; PLEs, psychotic-like experiences; Crystallized and Fluid, crystallized and fluid intelligence; ADI, Area Deprivation Index; Poverty, percentage of individuals below −125% of the poverty level; Years, years of residence.
Note: * indicates a statistically significant parameter estimate at α=0.05.

Direct/Indirect effects of gene-environment factors to cognitive and PLEs outcomes
A. Direct pathways from PGS, high family SES, low neighborhood SES, and positive environment to cognitive intelligence and PLEs. Standardized path coefficients are indicated on each path as direct effect estimates (significance level *p<0.05). B. Indirect pathways to PLEs via intelligence were significant for polygenic scores, high family SES, low neighborhood SES, and positive environment, indicating the significant mediating role of intelligence. C. Relative effect sizes of direct and indirect pathways within the total effects on PLEs. The standardized effect sizes of direct pathways are colored within each bar. In A-B, child sex, genetic ancestry, BMI, marital status, family history of psychiatric disorders, and ABCD research sites were included as covariates. CP PGS and EA PGS denote polygenic scores of cognitive performance and education attainment, respectively; SES, socioeconomic status; PLEs, psychotic-like experiences; Crystallized and Fluid, crystallized and fluid intelligence; ADI, Area Deprivation Index; Poverty, percentage of individuals below −125% of the poverty level; Years, years of residence.
Note: * indicates a statistically significant parameter estimate at α=0.05.

IGSCA analysis of multiethnic samples.
Sex, age, genetic ancestry, BMI, parental education, marital status of the caregiver, household income, and family’s financial adversity based on parents’ self-report, family history of psychiatric disorders, and ABCD research sites were included as covariates. Family socioeconomic status was included to confirm that the aassociations of PGS, neighborhood disadvantage, and positive environment are meaningful. SE and CI represent standard error and confidence intervals, respectively. Significant effects are marked with a star (*).

IGSCA analysis of multiethnic samples.
Sex, age, genetic ancestry, BMI, parental education, marital status of the caregiver, household income, and family’s financial adversity based on parents’ self-report, family history of psychiatric disorders, and ABCD research sites were included as covariates. Family socioeconomic status was included to confirm that the aassociations of PGS, neighborhood disadvantage, and positive environment are meaningful. SE and CI represent standard error and confidence intervals, respectively. Significant effects are marked with a star (*).

Component correlation matrix of IGSCA analysis.
A. Correlation between all component / factor variables of the IGSCA model. B. Correlation between all observed variables used to construct the relevant component / factor variables in the IGSCA model. CP PGS and EA PGS denote polygenic scores of cognitive performance and education attainment, respectively; PLEs, psychotic-like experiences; Crystallized and Fluid, crystallized and fluid intelligence; ADI, Area Deprivation Index; Poverty, percentage of individuals below −125% of the poverty level; Years, years of residence.

Component correlation matrix of IGSCA analysis.
A. Correlation between all component / factor variables of the IGSCA model. B. Correlation between all observed variables used to construct the relevant component / factor variables in the IGSCA model. CP PGS and EA PGS denote polygenic scores of cognitive performance and education attainment, respectively; PLEs, psychotic-like experiences; Crystallized and Fluid, crystallized and fluid intelligence; ADI, Area Deprivation Index; Poverty, percentage of individuals below −125% of the poverty level; Years, years of residence.

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