Introduction

Childhood is the critical developmental period in human life. Cognitive development and mental health in this period significantly impact key life outcomes at later ages, including academic performance, economic productivity, physical health, intelligence, and psychopathology (Shonkoff, 2012; Walker et al., 2021). Literature shows the significant impact of social adversities on cognitive development and mental health in early childhood. Lower family socioeconomic status (SES), particularly household income, is linked to lower or delayed neurocognitive development and higher risk of psychopathology in childhood (Hair, Hanson, Wolfe, & Pollak, 2015; Noble et al., 2015; Peverill et al., 2021; Tomasi & Volkow, 2021; Weissman, Conger, Robins, Hastings, & Guyer, 2018).

Additional to family SES, the importance of neighborhood social environment on children’s neurocognitive development has been also highlighted (Gard et al., 2021; Tooley et al., 2020). Adverse neighborhood environment, such as the percent of families below poverty line, education level, and violence exposure, was associated with lower cognitive performance and greater risk for psychosis in children (Butler, Yang, Laube, Kühn, & Immordino-Yang, 2018; Karcher, Schiffman, & Barch, 2021; Rakesh, Seguin, Zalesky, Cropley, & Whittle, 2021; Taylor, Cooper, Jackson, & Barch, 2020). Conversely, as a protective factor against familial and neighborhood socioeconomic challenges, supportive parenting (Brody et al., 2017; Brody et al., 2019; Holmes et al., 2018; J. L. Luby et al., 2012; Joan L. Luby, Belden, Harms, Tillman, & Barch, 2016) and positive school environment (Gard et al., 2021; Piccolo, Merz, & Noble, 2019; Rakesh et al., 2021) have been highlighted to improve child cognition and mental health.

Psychotic-like experiences (PLEs) are prevalent in childhood and indicate the risk of psychosis and schizophrenia (van der Steen et al., 2019; Van Os & Reininghaus, 2016) and other psychopathologies including mood, anxiety, and substance disorders (Poulton et al., 2000; van der Steen et al., 2019). PLEs are linked to a lower cognitive intelligence (M. Cannon et al., 2002; Kelleher & Cannon, 2011). In childhood, PLEs also have a stronger association with environmental risk factors than any other internalizing/externalizing symptoms (Karcher, Schiffman, et al., 2021).

Maladaptive cognitive intelligence may precede the manifestation of psychotic symptoms (T. D. Cannon et al., 2000; Keefe et al., 2006; Reichenberg et al., 2005). Family studies show a decline in cognitive intelligence preceding psychotic symptoms is related to genetic risk (Cosway et al., 2000; Curtis, Calkins, Grove, Feil, & Iacono, 2001). In more recent studies these associations lead to the model that cognitive intelligence mediates the genetic risk for psychopathology and PLEs (Karcher, Paul, et al., 2021; Pat et al., 2021). Moreover, genetic overlap between psychotic disorders and cognitive capacity has been shown (Escott-Price et al., 2020; Le Hellard et al., 2016).

To obtain unbiased estimates of environmental effects, it is crucial to adjust for genetic confounding (Sariaslan et al., 2016) considering the large genetic effects on intelligence (Bouchard & McGue, 1981; Deary, Spinath, & Bates, 2006; Plomin & Spinath, 2004) and psychosis (Hilker et al., 2018; Kendler, 1983; Sullivan, Kendler, & Neale, 2003). Recent advances in genetics have led to the development of the polygenic score (PGS) approach: a computational method to estimate the genetic loading for a complex trait using statistical associations of each single-nucleotide polymorphism (SNP) identified by genome-wide association studies (GWAS) (Choi, Mak, & O’Reilly, 2020). In particular, PGS of cognitive capacity holds particular importance. Cognitive capacity PGS is positively correlated with cognitive intelligence and education attainment (Judd et al., 2020; Okbay et al., 2022; Plomin & von Stumm, 2018), brain morphometry, and negatively correlated with psychopathologies (e.g. psychosis, autism, depression, Alzheimer’s disease) and cognitive decline (Harden & Koellinger, 2020; Judd et al., 2020; Karcher, Paul, et al., 2021; Plomin & von Stumm, 2018). An important gap in the literature is the lack of integrated assessment of the effects of genetic and environmental factors at multiple levels (e.g., familial vs neighborhood) to dissect the genetic and environmental effects underlying abnormal cognitive intelligence and the PLEs. Addressing this with large and representative data will allow for a more complete understanding of the factors related to the development of psychosis and schizophrenia.

In this study, we firstly aim to assess the associations of cognitive PGSs, family and neighborhood SES, and supportive environment with children’s cognitive intelligence and PLEs using statistical and computational approaches to carefully consider potential confounding. We then aim to test whether intelligence has a mediating effect on the relationship between genetic and environmental factors and PLEs.

Materials and Methods

Study Participants

We used the multi-modal genetic and environmental data of 11,878 preadolescent children aged 9-10 years old collected from 21 research sites of the Adolescent Brain Cognitive Development (ABCD) Study, one of the largest longitudinal studies for children’s neurodevelopment in the United States. We analyzed the baseline, first year, and second year follow-up datasets included in ABCD Release 4.0, downloaded on January 25, 2022. After k-nearest neighbor imputation of missing values of covariates (4.67% of total observations), we removed participants with missing data on study variables. The final samples included 6,602 multiethnic children, which comprised 890 of African ancestry (13.48%), 91 of East Asian ancestry (1.38%), 5,211 of European ancestry (78.93%), 229 of Native American ancestry (3.47%), and 181 not specified (2.74%).

Data

NIH Toolbox Cognitive Performance

To assess children’s neurocognitive abilities, we used baseline observations of uncorrected composite scores of the total, fluid, and crystallized intelligence from the NIH Toolbox Cognitive Battery, which has seven cognitive instruments for examining executive function, episodic memory, language abilities, processing speed, working memory, and attention (Thompson et al., 2019).

Psychotic-Like Experiences (PLEs)

Baseline, first year, and second year follow-up of PLEs were measured using the children’s responses to the Prodromal Questionnaire-Brief Child Version. In line with previous research (Karcher et al., 2018; Karcher, Perino, & Barch, 2020; Karcher, Schiffman, et al., 2021), we computed Total Score and Distress Score, each indicating the number of psychotic symptoms and levels of total distress. Considering self-reports and parent-reports of psychopathology may differ (Achenbach, 2006), we additionally used parent-rated PLEs derived from four items of the Child Behavior Checklist according to previous studies (Karcher et al., 2018; Karcher et al., 2020; Karcher, Schiffman, et al., 2021).

Polygenic Scores (PGS)

To investigate the aggregated effect of genetic components, we estimated PGS of two representative cognitive capacity traits for each participant: education attainment (EA) and cognitive performance (CP) (Choi et al., 2020). We used the summary statistics released from the largest GWAS (Lee et al., 2018) of European-descent individuals for educational attainment (EA, n=1,131,881) and cognitive performance (CP, n=257,841). EA was measured as the years of schooling; CP was measured as the respondent’s score on cognitive ability assessments. To construct PGS of schizophrenia for sensitivity analyses, we used the summary statistics from the multiple GWAS of European sample (n=65,967; Ruderfer et al. (2018)) and East Asian sample (n=58,140; Lam et al. (2019)). We applied PRS-CSx, a high-dimensional Bayesian regression framework that improves cross-population prediction via continuous shrinkage prior to SNP effect sizes (Ge, Chen, Ni, Feng, & Smoller, 2019) (For details, see Appendix S1).

Family-, Neighborhood-, and School-level Environment

We assessed children’s family-level SES with family income, parental education, and family’s financial adversity based on parent self-reporting (Karcher et al., 2020; Taylor et al., 2020; Tomasi & Volkow, 2021). Higher family income and parental education and lower family’s financial adversity denote a higher family SES.

Neighborhood-level SES was assessed using the Area Deprivation Index (ADI), the percentage of individuals below -125% of the poverty level (henceforth “poverty”), and years of residence, which were associated with PLEs in prior research (Karcher, Schiffman, et al., 2021). Higher values of ADI and poverty and fewer years of residence indicate a lower neighborhood SES.

Based on existing literature (Karcher, Schiffman, et al., 2021; Rakesh et al., 2021), we measured the level of supportive parenting behavior (henceforth “good parenting”) and benign school environment (henceforth “good schooling”) to assess the effect of positive family and school environment on each individual.

Statistical Modeling

Linear Mixed Models

We used linear mixed models to minimize potential population stratification from different household environments and geographical locations (Choi et al., 2020). To avoid multicollinearity, CP and EA PGSs were used separately in two other models. Key variables of interest were PGS, family SES, neighborhood SES, and positive environment. As a random factor, the variable indicating ABCD research sites was used. Variables within each model had no signs of multicollinearity (Fox & Monette, 1992) (generalized variance inflation factor < 2.0 for every variable in all models). The child’s sex, age, genetic ethnicity, BMI, marital status of the caregiver, and family history of psychiatric disorders were included as covariates in the model (Karcher, Schiffman, et al., 2021). All continuous variables were standardized (z-scaled) beforehand to get standardized estimates, and the analyses were conducted with the lme4 package in R version 4.1.2. Throughout this paper, threshold for statistical significance was set at p<0.05, with correction for multiple comparisons using the false discovery rate. 95% bootstrapped confidence intervals were obtained with 5,000 iterations.

Path Modeling

To test the plausibility of whether cognitive intelligence may mediate the association between genetic and environmental factors and PLEs, we used an up-to-date SEM method, integrated generalized structured component analysis (IGSCA) (Hwang, Cho, Jung, et al., 2021). This approach is suited to our study using the multi-modal genetic and environmental variables in that it estimates models with both factors and components as statistical proxies for the constructs. We considered path-analytic relationships among the six key constructs: the PGS, family SES, neighborhood SES, positive family and school environment, general intelligence, and PLEs. As the PGSs, family SES, neighborhood SES, positive family and school environment, and PLEs may be seen as aggregations of their relevant observed variables, indicating that they are determined by their observed variables, we statistically represented these constructs as the weighted sums of their observed variables or components (Cho, Sarstedt, & Hwang, 2021). On the other hand, we represented general intelligence as a common factor that determines the underlying covariance pattern of fluid and crystallized intelligence, based on the classical g theory of intelligence (Jensen, 1998; Spearman, 1904).

The IGSCA model included the same covariates used in the linear mixed model as well as the ABCD research site as an additional covariate. We applied GSCA Pro 1.1 (Hwang, Cho, & Choo, 2021) to fit the IGSCA model to the data and checked the model’s goodness-of-fit index (GFI) (Jöreskog & Sörbom, 1986), standardized root mean square residual (SRMR), and total variance of all indicators and components explained (FIT) to assess its overall goodness-of-fit. A larger FIT value indicates more variance of all variables is explained by the specified model. The rules-of-thumb cutoff criteria in IGSCA is GFI≥0.93 and SRMR≤0.08 for an acceptable fit (Cho, Hwang, Sarstedt, & Ringle, 2020). Finally, we conducted conditional process analyses to investigate further the indirect and total effects of the constructs in the model. We obtained 95% confidence intervals based on 5,000 bootstrap samples to test the statistical significance of parameter estimates.

Sensitivity Analyses

To ensure robustness of the main analyses results, we conducted multiple sensitivity analyses. As the European-descent-based GWAS was used for constructing PGS, we reran the main analyses using participants of European ancestry (n=5,211) to adjust for ethnic confounding. Next, we tested effects of gene x environment interactions on cognitive intelligence and PLEs, respectively. We also assessed whether the effects of cognitive capacity PGS in the linear mixed model are significant after adjusting for schizophrenia PGS, a more direct genetic predictor of PLEs. Lastly, we adjusted for unobserved confounding bias in the linear mixed model, using a recently developed framework for causal inference based on null treatments assumption (Miao, Hu, Ogburn, & Zhou, 2022). As this method allows dependence among treatments, it can be utilized in examining the effects of multiple treatment variables having shared variances due to unobserved confounders (Okbay et al., 2022) (e.g., cognitive capacity PGS, family and neighborhood SES, positive family and school environment).

Results

Demographics

Table 1 presents the demographic characteristics of the final samples. 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 showed significantly different marital status (p<0.0001), lower BMI (p<0.0001) and family history of psychiatric disorders (p<0.0001) compared to children of other genetic ancestries.

Table 1.

Genetic influence on cognitive capacity correlates positively with intelligence and negatively with PLEs

As shown in Figure 1, higher PGSs of cognitive capacity phenotypes correlated significantly with higher intelligence (CP PGS: β=0.1113∼0.1793; EA PGS: β=0.0699∼0.1567). While CP PGS was associated only with lower baseline year Distress Score PLEs (β=-0.0323), EA PGS was associated with lower baseline year and follow-up PLEs of all measures (baseline: β=-0.0518∼-0.0519; 1-year: β=-0.0423∼-0.043; 2-year: β=- 0.036∼ -0.0463). No significant correlations were found between CP PGS and follow-up PLEs (p>0.05). The effects of EA PGS were larger on baseline year PLEs than follow-up PLEs. The effect sizes of EA PGS on children’s PLEs were twice as large as those of CP PGS (EA PGS: β=-0.036∼-0.0519; CP PGS: β=-0.0323). (Table S1).

Figure 1:

Family and neighborhood SES correlates positively with intelligence and negatively with PLEs

Parental education associated positively with all types of intelligence (β=0.0699∼0.1745) and negatively with baseline year Total and Distress Score PLEs (β=- 0.0528∼-0.043), 1-year follow-up PLEs (β=-0.0538∼-0.0449), and 2-year follow-up PLEs (β=-0.0459∼-0.0389). Family income correlated positively with intelligence (β=0.0723∼0.1365) and negatively with 2-year follow-up parent-rated PLEs (β=-0.0503∼ - 0.0502). Family’s financial disadvantage correlated negatively with baseline year parent-rated PLEs (β=0.0726∼0.0728), 1-year follow-up PLEs of all types (β=0.0307∼0.0577), and 2-year follow-up PLEs of all types (β=0.0461∼0.0581).

The ADI correlated significantly negatively with all types of intelligence (β=- 0.0684∼-0.054). Additionally, a higher ADI correlated significantly with higher baseline year PLEs (β=0.0587∼0.0914), 1-year follow-up PLEs (β=0.0523∼0.0613), and 2-year follow-up PLEs (β=0.0397∼0.0449).

We found no significant associations of poverty with any of the target variables (p> 0.05). Years of residence correlated significantly with crystallized intelligence (β=0.035∼0.0372) and baseline year Total Score PLEs (β=-0.029∼-0.0273). (Table S1).

Positive family and school environments correlate positively with intelligence and negatively with the influence of PLEs

Good parenting behaviors showed significant negative correlations with baseline year PLEs (β=-0.0702∼-0.0419), 1-year follow-up PLEs (β=-0.0588∼-0.0397), and 2-year follow-up PLEs (β=-0.0623∼-0.0356) (Figure 1). Good school environment was associated positively with total intelligence (β=0.0353∼0.0397) and fluid intelligence (β=0.0514∼0.0545) and negatively with all three measures of baseline year PLEs (β=- 0.1193∼-0.0468), 1-year follow-up PLEs (β=-0.1078∼-0.04), and 2-year follow-up PLEs (β=- 0.1068∼-0.0586) (Table S1).

Structural Equation Modeling-Integrated Generalized Structured Component Analysis

The IGSCA model showed that intelligence mediated the effects of genes and environments on the risk for psychosis (PLEs) (Figure 2 and Table 2). The model showed a good model fit with a GFI of 0.9735, SRMR of 0.0359, and FIT value of 0.4912 (Cho et al., 2020). Intelligence was under significant direct influences of the cognitive capacity PGS (β=0.2427), family SES (β=0.2932), neighborhood SES (β=-0.1121), and positive environment (β=0.0268). Family SES and positive environment had significant negative direct effects on PLEs of all years. Cognitive capacity PGS and neighborhood SES showed no significant direct effects on any of the PLEs (p>0.05). Intelligence significantly mediated the effects of the PGS, family and neighborhood SES, and positive environment on PLEs of all years: the PGS (baseline year: β=-0.035; 1-year: β=-0.0355; 2-year: β=-0.0274), family SES (baseline year: β=-0.0423; 1-year: β=--0.0429; 2-year: β=-0.0331), neighborhood SES (baseline year: β=0.0162; 1-year: β=0.0164; 2-year: β=0.0126), and positive environment (baseline year: β=-0.0039; 1-year: β=-0.0039; 2-year: β=-0.003).

Figure 2:

Table 2.

For all observed years, positive environment had largest total effects on PLEs (baseline year: β=-0.152; 1-year: β=-0.1316; 2-year: β=-0.1364), followed by family SES (baseline year: β=-0.1216; 1-year: β=-0.1119; 2-year: β=-0.1164), neighborhood SES (baseline year: β=0.0504; 1-year: β=0.0329; 2-year: β=0.0192), and PGS (baseline year: β=- 0.0498; 1-year: β=-0.036; 2-year: β=-0.0365). The total effects of each indicator on PLEs were significant except for those of neighborhood SES (p>0.05).

Sensitivity Analyses

As sensitivity analyses, we reran our main analyses with adjustment for ethnic confounding, schizophrenia PGS, and unobserved confounding, respectively. Results of linear mixed model and IGSCA analyses were consistent with European ancestry participants (Table S3, S4). Interactions between PGS and good parenting had significant positive effects on 2-year follow-up Total and Distress Score PLEs (β=0.0343∼0.053) (Table S5). Unlike CP PGS and EA PGS, schizophrenia PGS did not have significant associations with any of the PLEs (p>0.05). Associations of cognitive capacitive PGS, family and neighborhood SES, and positive environment were similar after adjusting for schizophrenia PGS (See Table S6 for details). Lastly, when adjusting for unobserved confounders, CP and EA PGS, good parenting, and good schooling remained significantly correlated with intelligence and PLEs of all years (Table S7).

Discussion

This study investigated the relationships of the genetic and environmental influences on the development of intelligence and the PLEs in youth, leveraging genetic data from the large epidemiological samples and a multi-level environmental (socioeconomic) data. Our results support the model that genetic factors (PGS for cognitive capacity), socioeconomic conditions, and family and school environments may influence cognitive intelligence in children, and this impact may lead to the individual variability of the current and future PLEs in children. Our analysis with integrated data shows the contributions of genetic and environmental factors, respectively, to cognitive and mental wellness in children, and further provides policy implications to improve them.

Our SEM shows that cognitive intelligence mediates the environmental and genetic influence on the current and future PLEs. The environmental factors (family SES, neighborhood SES, and positive parenting and schooling) and PGS of cognitive capacity exhibit significant indirect effects via cognitive intelligence on PLEs. Prior studies identifying the mediation of cognitive intelligence focused on either genetic or environmental factors alone (Karcher, Paul, et al., 2021; Lewis et al., 2020). Our study advances this by demonstrating the specific and unbiased effects of genetic and environmental factors while controlling for each other. Considering the directions and magnitudes of the effects, though the effects of PGS remain significant, aggregated effects of environmental factors account for much greater degrees on PLEs (potential policy implications is discussed below). The indirect pathway of PGS & environment-intelligence-PLEs aligns with the studies with older clinical samples showing cognitive deficits as a precursor for the onset of psychosis and schizophrenia and their genetic and environmental factors (Eastvold, Heaton, & Cadenhead, 2007; Fett et al., 2020; Lewis et al., 2020; Vorstman et al., 2015).

Our results of cognitive intelligence mediating the genetic and environmental effects on PLEs may be related to several potential mechanisms. Children raised in higher family SES may have sufficient nutrition and cognitive stimulants, whereas children living in deprived neighborhoods may be exposed to higher rates of crime, air pollution, and substance abuse (Lewis et al., 2020; Marshall et al., 2020; Taylor et al., 2020; Tomasi & Volkow, 2021). Environmental enrichment may be associated with longer periods of neural plasticity (e.g. myelination, maturation of brain circuitry), leading to higher cognitive development and lower risk of mental disorders like psychosis (Tooley, Bassett, & Mackey, 2021). This may be further linked to the cognitive reserve theory. The theory suggests that genetic influence for cognitive capacity and environmental enrichment promotes more efficient, flexible brain networks, which may lead to greater resilience against psychopathology (Stern, 2009). Indeed, prior clinical studies show the linkage between cognitive reserve and psychosis (Amoretti et al., 2018; Leeson et al., 2011).

Our results indicate that genetic influences on cognitive capacity phenotypes are significantly linked to the risk of psychosis. While schizophrenia PGS showed no significant associations with PLEs, PGSs for CP and EA were strongly correlated with PLEs (baseline year, 1-year follow-up, and 2-year follow-up). These associations were robust after adjustment for ethnic confounding and unobserved confounders. Cognitive capacity PGS generally show higher predictive performance than PGS of any other traits (Lee et al., 2018; Okbay et al., 2022; Plomin & von Stumm, 2018). Genetic variants associated with CP and EA are related to complex traits across the life span, including neuroticism, depressive symptoms, smoking in adulthood, cognitive decline at a later age (Joo, Cha, Freese, & Hayes, 2022), risk for Alzheimer’s disease (Harden & Koellinger, 2020; Lee et al., 2018), brain volume, area, and thickness, as well as psychotic disorders (Karcher, Paul, et al., 2021). Prior gene expression studies suggest that polygenic signals for schizophrenia, bipolar disorder, and EA are significantly enriched in the central nervous system, particularly the cerebellum (Finucane et al., 2018). Our findings emphasize the importance of cognitive capacity PGS as a biomarker which not only implicates cognitive traits but also exhibits genetic overlap with the risk for psychosis.

The differing magnitudes of the PGS impact between EA and CP warrant attention. The effects of the EA PGS on the PLEs of all years were 160.68%∼371.67% larger than those of CP PGS. This discrepancy may result from that the larger sample size of EA GWAS than that of CP GWAS. Alternatively, the discrepancies in effect sizes may suggest different genetic compositions between EA and CP. Recent literature documents that more than half of the polygenic signal for EA is related to noncognitive and social skills required for successful educational attainment (Demange et al., 2021), whereas CP may rather be linked to cognitive skills. This observation also supports the well-established relationships of the EA PGS with socioeconomic and life-course outcomes (e.g., social mobility (Belsky et al., 2018), voter turnout (Aarøe et al., 2020), and wealth inequality (Barth, Papageorge, & Thom, 2020)), which may be influenced by unobserved environmental factors (Young, Benonisdottir, Przeworski, & Kong, 2019). In our analysis, using the two PGSs resulted in a more thorough assessment, leading to a more unbiased estimation of the genetic and environmental factors.

Furthermore, the significant effects of cognitive capacity PGS on cognitive intelligence (β=0.0699∼0.1793) remained robust and similar in magnitude after adjusting for ethnicity (β=0.0754∼0.1866) and other (unobserved) confounding (β=0.0546∼0.1776). As we controlled for family-, neighborhood-, and school-level environmental factors and unobserved confounders, our results may be interpreted as significant genetic influences on individual’s cognitive intelligence. This interpretation is supported by a recent study (Isungset et al., 2022): Despite of the socioeconomic differences in Norway (a typical social democratic welfare state) and the US (a typical liberal welfare state), the magnitudes of genetic influence on cognitive intelligence were similar (Norway: β=0.18; US: β=0.17). Cognitive capacity PGS is an important genetic factor across the nations and societies. Therefore, analyses omitting the genetic influence may be subject to overestimation of the socioeconomic impact (Plomin & von Stumm, 2018; Sariaslan et al., 2016).

This study shows that a high SES and positive environment, particularly good parenting and schooling, is associated with higher intelligence and a lower risk for PLEs in children. While prior research has emphasized the dominant role of family SES (e.g. family income) (Tomasi & Volkow, 2021), our SEM analyses(IGSCA) showed that positive environmental factors such as supportive parenting and schooling have a greater impact on children’s PLEs. Specifically, the effect sizes were the highest in supportive family and school environment, followed by family and neighborhood SES. Even after adjusting for ethnicity and unobserved confounders, the strong associations of good parenting and schooling with higher intelligence and fewer PLEs remained significant. These findings suggest that interventions that target positive family and school environments may be particularly effective. Recent research supports this notion, showing that interventions that promote supportive parenting and inclusive school environments can improve neurocognitive development, academic performance, and decrease risk behaviors such as drinking and emotional eating (Brody et al., 2017; Brody et al., 2019; Holmes et al., 2018).

Moreover, our results showed that positive parenting and schooling in baseline year were associated not only with baseline year PLEs but also with PLEs 1-2 years later. This is in line with prior research showing that intervention focused on parenting behavior and school environment have long-lasting positive effects that extend into adulthood and even across generations (Cunha & Heckman, 2007; Hill et al., 2020).

Our findings highlight the potential for preventative strategies that target residential environment, family SES, parenting, and schooling—a comprehensive approach that considers the entire ecosystem of children’s lives—to enhance children’s cognitive development and mental health. A holistic and quantitative approach that takes into account the comprehensive ecosystem of family, school, and residential environments may ensure treatment effects and efficient use of resources (Cree et al., 2018; Garner & Yogman, 2021; Shonkoff, 2012). For example, the Health Impact in 5 Years Initiative of the US Centers for Disease Control and Prevention (CDC, 2018) includes 14 evidence-based interventions, such as providing school-based prevention programs, public transportation, home improvement loans, and earned income tax credits, to tackle the social determinants of public health. Our study strengthens the idea that an interdisciplinary science-driven, coordinated approach to intervening in the select environmental factors may allow practical improvements in child development, particularly in those who are at a disadvantage.

Our study has some limitations. Firstly, due to data availability constraints in the ABCD study, we only utilized baseline observations for NIH Toolbox cognitive intelligence, and we could not test whether PLEs might be a mediator of intelligence. Secondly, the generalizability of our findings may be limited since most of the participants included in our analysis are from European ancestry. Finally, we did not include all important environmental variables, such as air pollution (Marshall et al., 2020) and social capital (Krabbendam, 2005) (not collected in the ABCD study).

In conclusion, our study provides unbiased estimation of the pathways of genetic factors of cognitive capacity and environmental factors of family, school, and neighborhood to cognitive and mental wellness in children. Our findings underscore the importance of a comprehensive approach that considers both biological and socioeconomic features in promoting young children’s cognitive development and mental health. Given the importance of child development, it requires joint efforts from multiple disciplines.

Data Availability

All data produced in the present study are available upon reasonable request to the authors.

Funding and Disclosure

This work was supported by the New Faculty Startup Fund from Seoul National University, a Research grant from Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, South Korea (2021R1I1A1A01054995), and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) [NO.2021-0-01343, Artificial Intelligence Graduate School Program (Seoul National University)]. The authors have nothing to disclose.

Author Contributions

JP, EL, and GC conducted the main analyses. HH, YYJ, and JC supervised the study. JP, EL preprocessed data. JP, EL, GC, HH performed statistical analyses. EL and YYJ constructed PGS. JP, EL, GC, YYJ, and JC drafted and critically reviewed the manuscript. All authors read and approved the final draft.

Supporting Information

Table S1.

Table S2.

Table S3.

Table S4.

Table S5.

Table S6.

Table S7.

Figure S1.

Appendix S1. Supplementary Methods

Genotype data

The ABCD study collected saliva samples of study participants at the baseline visit and shipped the samples from the collection site to the Rutgers University Cell and DNA Repository. Genomic DNA was extracted and genotyped using the Affymetrix NIDA Smokescreen array (733,293 SNPs). We removed any inferior SNPs with (i) genotype call rate<95%, (ii) sample call rate<95%, and (iii) rare variants with minor allele frequency (MAF)<0.01. Variant imputation was performed with the Michigan Imputation Server (Das et al., 2016) using the 1000 Genome phase 3 version 5 multiethnic Grch37/hg19 reference panel with Eagle ver2.4 phased output (Loh et al., 2016). For the imputed 12,046,090 SNPs, we only retained data from any individuals with <5% missing genotypes; without extreme heterozygosity (F coefficient < 3 standard deviations from the population mean); and SNPs with >0.4 imputation quality INFO score, <5% missingness rate, >0.01 MAF and Hardy-Weinberg equilibrium (p>10⁻⁶). The genetic ancestry of each participant was determined with the fastSTRUCTURE algorithm (Raj, Stephens, & Pritchard, 2014), available from ABCD Release 4.0. Considering the diverse ethnic and racial backgrounds of the study participants, we estimated both kinship coefficients (K.C.s) and ancestrally informative principal components (P.C.s) to additionally control familial relatedness and ancestry admixture using PC-Air (M. P. Conomos, Miller, & Thornton, 2015) and PC-Relate (Matthew P. Conomos, Reiner, Weir, & Thornton, 2016). We selected unrelated participants that were inferred to be more distant than 4^th-degree relatives (K.C.>0.022) and removed any outliers that fell significantly outside (>6 S.D. limits) the center in P.C. space. In the rest of this paper, we used final genotype data (11,301,999 variants) of 10,199 unrelated multiethnic samples, including 7,893 European-ancestry participants, after Q.C.

Polygenic Scores (PGS)

Hyperparameters of PGSs were optimized in the held-out validation set of 1,579 unrelated participants, consisting of 88 of African ancestry (5.57%), 25 of East Asian ancestry (1.58%), 1,365 of European ancestry (86.45%), 88 of Native American ancestry (2.91%), and 55 not specified (3.48%). The validation set was created during the quality control process of the study genotype data when we selected unrelated study participants of the original ABCD samples with pairwise kinship coefficients of less than 0.022 among them. To select the optimal hyperparameter, we fitted linear regression models for intelligence composite scores within the validation set and evaluated the model performance in terms of the highest R² and effect size (beta). The models were adjusted for age, sex, and the first ten principal components of genotype data. For the PGS of multiethnic participants, genetic ancestry determined by ADMIXTURE (Alexander, Novembre, & Lange, 2009) was additionally included as a covariate. The validation samples were only used for hyperparameter tuning for PGS optimization and were excluded from any further analyses. The PGSs were residualized against the first ten ancestrally informative P.C.s to adjust for population stratification.

Parent-rated Psychotic-like Experiences

As self-reports and parent-reports of psychopathology often differ, parent-rated PLEs derived from four items of the Child Behavior Checklist:

1. “Hears sounds or voices that other people think aren’t there.”

2. “Sees things that other people think aren’t there.”

3. “Does things that other people think are strange.”

4. “Has thoughts that other people would think are strange.”

Each question was scored from 0=not true, 1=somewhat or sometimes true, and 2=very true or often true.

Family and Neighborhood Socioeconomic Status (SES)

We assessed children’s family SES based on family Income, parental Education, and family’s financial adversity. All three variables were based on self-reported responses of children’s primary caregiver. For family income, the caregivers were asked “What is your TOTAL COMBINED FAMILY INCOME for the past 12 months? This should include income (before taxes and deductions) from all sources, wages, rent from properties, social security, disability and/or veteran’s benefits, unemployment benefits, workman’s compensation, help from relative (include child payments and alimony), and so on. If Separated/Divorced, please average the two household incomes.” Answer choices are shown below:

1. Less than $5,000

2. $5,000 through $11,999

3. $12,000 through $15,999

4. $16,000 through $24,999

5. $25,000 through $34,999

6. $35,000 through $49,999

7. $50,000 through $74,999

8. $75,000 through $99,999

9. $100,000 through $199,999

10. $200,000 and greater

Parental education was measured as the highest grade or level of school completed or highest degree received by the primary caregiver. (“What is the highest grade or level of school you have completed or the highest degree you have received?”). Answer choices were:

• 0) Never attended/Kindergarten only

• 1) 1st grade^st grade

• 2) 2nd grade^nd grade

• 3) 3rd grade^rd grade

• 4) 4th grade^th grade

• 5) 5th grade^th grade

• 6) 6th grade^th grade

• 7) 7th grade^th grade

• 8) 8th grade^th grade

• 9) 9th grade^th grade

• 10) 10th grade^th grade

• 11) 11th grade^th grade

• 12) 12th grade^th grade

• 13) High school graduate

• 14) GED or equivalent Diploma

• 15) Some college

• 16) Associate degree: Occupational

• 17) Associate degree: Academic Program

• 18) Bachelor’s degree (ex. BA)

• 19) Master’s degree (ex. MA)

• 20) Professional School degree (ex. MD)

• 21) Doctoral degree (ex. PhD)

Family’s financial adversity is measured with Parent-Reported Financial Adversity Questionnaire, reflecting family’s financial ability to pay for basic life expenses (Diemer et al., 2013). The questionnaire asked whether the child’s caregiver and family experienced any of the following difficulties within the past 12 months:

1. “Needed food but couldn’t afford to buy it or couldn’t afford to go out to get it?”

2. “Were without telephone service because you could not afford it?”

3. “Didn’t pay the full amount of the rent or mortgage because you could not afford it?”

4. “Were evicted from your home for not paying the rent or mortgage?”

5. “Had services turned off by the gas or electric company, or the oil company wouldn’t deliver oil because payments were not made?”

6. “Had someone who needed to see a doctor or go to the hospital but didn’t go because you could not afford it?”

7. “Had someone who needed a dentist but couldn’t go because you could not afford it?” Each of the seven items was scored with 0=no or 1=yes.

Neighborhood SES was measured by Residential History Derived Scores based on the census tracts of each respondent’s primary address. The national percentile score of Area Deprivation Index (ADI) (Karcher, Schiffman, et al., 2021; Rakesh et al., 2021) was calculated from the 2011–2015 American Community Survey 5-year summary. Components used for deriving ADI includes:

1. Percentage of population aged >=25 y with <9 y of education

2. Percentage of population aged >=25 y with at least a high school diploma

3. Percentage of employed persons aged >=16 y in white collar occupations

4. Median family income

5. Income disparity defined by Singh as the log of 100 x ratio of the number of households with <10000 annual income to the number of households with >50000 annual income.

6. Median home value

7. Median gross rent

8. Median monthly mortgage

9. Percentage of owner

10. Percentage of occupied housing units with >1 person per room (crowding)

11. Percentage of civilian labor force population aged >=16 y unemployed (unemployment rate)

12. Percentage of families below the poverty level

13. Percentage of population below 138% of the poverty threshold

14. Percentage of single

15. Percentage of occupied housing units without a motor vehicle

16. Percentage of occupied housing units without a telephone

17. Percentage of occupied housing units without complete plumbing (log)

Positive Family and School Environment

Good parenting was assessed using the ABCD Children’s Report of Parental Behavioral Inventory. We used the average values of mean summary scores of five questionnaire items about first and second caregivers. Single values were used for respondents lacking a second caregiver. Good schooling was assessed as the sum of children’s responses to twelve items in the ABCD School Risk and Protective Factors Survey.

Prior work used response items about the first caregiver for good parenting and the first six items about the school environment for good schooling (Rakesh et al., 2021). However, to obtain a comprehensive and accurate assessment, we also included items about the second caregiver and six additional questions about the school environment.

Children were asked to choose between 1=Not like him/her, 2=Somewhat like him/her, or 3=A lot like him/her about the five questions regarding the first and second caregiver:

1. Smiles at me very often.

2. Is able to make me feel better when I am upset.

3. Believes in showing his/her love for me.

4. Is easy to talk to.

5. Makes me feel better after talking over my worries with him/her

The following questions were asked to assess positive school environment:

1. In my school, students have lots of chances to help decide things like class activities and rules.

2. I get along with my teachers.

3. My teacher(s) notices when I am doing a good job and lets me know about it.

4. There are lots of chances for students in my school to get involved in sports, clubs, or other school activities outside of class.

5. I feel safe at my school.

6. The school lets my parents know when I have done something well.

7. I like school because I do well in class.

8. I feel I’m just as smart as other kids my age.

9. There are lots of chances to be part of class discussions or activities.

10. In general, I like school a lot.

11. Usually, school bores me.

12. Getting good grades is not so important to me.

Each of the 12 items was scored with 1=NO!, 2=no, 3=yes, 4=YES!. Item 11 and 12 were reverse coded when obtaining summary scores.

Appendix S2. Supplementary Results

Results of linear mixed models with European samples

As the European-descent-based GWAS was used for constructing PGS, we reran the main analyses using participants of European ancestry (n=5,211) to adjust for ethnic confounding (females 46.71%, mean age 118.99 [SD 7.46]).

The results of linear mixed models were similar to those of main analyses (Table S3). Higher intelligence was significantly associated with higher CP and EA PGS (βs> 0.0754, p<0.0001), lower ADI (βs<-0.0503, p=0.0253), more years of residence (βs> 0.0268, p=0.0311), positive school environment (βs> 0.0365, p=0.004), higher parental education (βs>0.1067, p<0.0001), more family income (βs>0.0561, p=0.0166), and less family’s financial disadvantages (βs<-0.0427, p=0.011). No significant association of poverty and supportive parenting behavior with intelligence was found (p>0.05).

More Total and Distress Score PLEs were significantly associated with lower CP and EA PGS (baseline: βs<-0.0316, p=0.0212; 1-year: βs<-0.0422, p=0.0016; 2-year: βs <-0.0489, p=0.0002), higher ADI (baseline: βs>0.0554, p=0.0421), less supportive parenting (baseline: βs<-0.0722, p<0.0001; 1-year: βs<-0.0473, p=0.0013; 2-year: βs<-0.0693, p<0.0001), less positive school environment (baseline: βs<- 0.1076, p<0.0001; 1-year: βs<-0.0896, p=0.0013; 2-year: βs<-0.0847, p<0.0001), lower parental education (baseline: βs<-0.0632, p=0.0011; 1-year: βs<-0.0567, p=0.004; 2-year: βs<-0.0463, p=0.0198), more family’s financial disadvantage (1-year: βs>0.0605, p=0.005; 2-year: βs>0.0646, p<0.0001). No significant association of years of residence, poverty, and family income with Total and Distress Score PLEs was found (p>0.05). Parent-rated PLEs was positively associated with ADI (baseline: β=0.0486, p=0.0378; 1-year: βs>0.0527, p=0.0225) and negatively with good schooling (baseline: βs<-0.0522, p=0.0018; 2-year: βs<-0.0601, p=0.0009) and family’s financial disadvantage (2-year: βs>0.0501, p=0.0216).

Results of IGSCA with European samples

The results of IGSCA in European ancestry samples were similar to those in multiethnic participants (Table S4). It showed a good model fit with a GFI of 0.9695, SRMR of 0.0397, and FIT value of 0.4854.

Intelligence was under a significant direct influence of the cognitive capacity PGS (β=0.2987 [95% CI=0.2673∼0.3281]), neighborhood SES (β=-0.0931 [95% CI=-0.1303∼-0.0586]), family SES (β=0.3034 [95% CI=0.2683∼0.3413]), and positive environment (β=0.0396 [95% CI=0.0104∼0.0698]). Neighborhood SES (β=0.0411 [95% CI=0.0037∼0.0784]), family SES (β=-0.0531 [95% CI=-0.0956∼- 0.0101]), and positive environment (β=-0.1473 [95% CI=-0.1779∼-0.1163]) showed significant direct influence on baseline year PLEs, but cognitive capacity PGS did not. Constructs that had significant direct effects on PLEs of 1-year and 2-year follow-up were family SES (βs<-0.0850) and positive environment (βs<-0.1222).

Intelligence had a significant mediating effect on the pathways of the PGS (β=-0.0555 [95% CI=- 0.0754∼-0.0392]), family SES (β=-0.0563 [95% CI=-0.07749∼-0.0392]), neighborhood SES (β=0.0173 [95% CI=0.0098∼0.0271]), and positive environment (β=-0.0074 [95% CI=-0.0142∼-0.0019]) to baseline year PLEs. For PLEs of 1-year and 2-year follow-up, the mediation effects of intelligence were also significant with all four constructs: PGS (1-year: β=-0.0397 [95% CI=-0.0586∼-0.0235]; 2-year: β=- 0.0204 [95% CI=-0.0389∼-0.0036]), family SES (1-year: β=-0.0404 [95% CI=-0.0601∼-0.0238]; 2-year: β=-0.0207 [95% CI=-0.0399∼-0.0036]), neighborhood SES (1-year: β=0.0124 [95% CI=0.0064∼0.0208]; 2-year: β=0.0064 [95% CI=0.0011∼0.0132]), and positive environment (1-year: β=-0.0053 [95% CI=- 0.0108∼-0.0012]; 2-year: β=-0.0027 [95% CI=-0.0067∼-0.0002]). Positive environment had the largest total effects on PLEs among the constructs (baseline year: β=-0.1547; 1-year: β =-0.1274; 2-year: β=- 0.1386).

Results of linear mixed models adjusted for schizophrenia PGS with multiethnic samples

We also assessed whether the effects of cognitive capacity PGS in the linear mixed model are significant including schizophrenia PGS, a more direct genetic predictor of PLEs. The results showed that inclusion of schizophrenia PGS in the models did not change much of the main results (Table S5). Schizophrenia PGS was negatively associated with total and fluid intelligence in EA PGS model (Total intelligence β=-0.0293, 95% CI=-0.0488∼-0.01, p=0.0048; Fluid intelligence β=-0.0317, 95% CI=- 0.0522∼-0.0097, p=0.0062). No significant association was found between schizophrenia PGS and PLEs of all three time points (p>0.05).

Results of linear mixed models adjusted for unobserved confounders with multiethnic samples

Unobserved confounding variables may bias linear regression estimates. In particular, linear estimates may be subject to collider bias when those unobserved confounders and genetic factors are jointly associated with environmental factors and target traits (Akimova, Breen, Brazel, & Mills, 2021). Thus, bias from unobserved confounding variables were adjusted using the null treatments approach (Miao et al., 2022). This approach can identify the causal effects of multiple treatment variables in presence of unobserved confounders. Assuming that fewer than half of the treatments have causal effects on the outcome, it first estimates the joint distribution of treatments-confounders to obtain asymptotic bias using a factor model. Second, it estimates the relationship between treatments, confounders, and outcome using standard density estimation methods (e.g., least squares regression). Finally, by eliminating the asymptotic bias from the estimated treatments-confounders-outcome relationship, it can adjust for unobserved confounding even without specifying which treatments are null.

The significance of effects of PGSs was mostly preserved after adjusting for unobserved confounders (Table S6). Higher cognitive capacity PGSs correlated significantly with higher intelligence (CP PGS: βs>0.1111, p<0.0001; EA PGS: βs>0.0546, p=0.0002]). CP PGS was associated with lower baseline year Distress Score PLEs (β= -0.0348, p=0.021) and EA PGS was associated with lower Total and Distress Score PLEs of baseline year and follow-up years (baseline: βs<-0.0534, p=0.0002; 1-year: β=-0.0375, p=0.0149; 2-year: βs<-0.0391, p=0.017). We found no significant associations of neighborhood SES variables with any of the target variables (p>0.05).

While good parenting behaviors did not have significant association with intelligence, good schooling was significantly associated with higher total and fluid intelligence (βs>0.0328, p=0.0242). Good parenting behaviors had significant negative correlations with all types of PLEs of baseline year and follow-up years (baseline: βs<-0.0412, p=0.036; 1-year: βs<-0.0435, p=0.0316; 2-year: βs<-0.0558, p=0.0017). Good schooling was also negatively associated with all types of PLEs of baseline year and follow-up years (baseline: βs<-0.0464, p=0.036; 1-year: βs<-0.0952, p<0.0001; 2-year: βs<-0.0583, p=0.0095).

Parental education and family income were not significantly associated with all types of intelligence and PLEs of all three time points. Family’s financial disadvantage did not show significant association with intelligence and baseline year PLEs, but it had positive associations with 1-year follow-up Total Score (β=0.0604, p=0.0113) and Distress Score PLEs (β=0.0532, p=0.0197) and 2-year follow up Total Score (β>0.0544, p=0.0284) and Distress Score PLEs (β=0.0584, p=0.0224).