Interaction between Neighborhood Exposome and Genetic Risk in Child Psychotic-like Experiences

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However, the gene-environment interaction to persistent distressing PLE is unknown. The study included 6,449 participants from the Adolescent Brain and Cognitive Development Study. Genetic risk was measured by a multi-ancestry schizophrenia polygenic risk score (SCZ-PRS). Multi-dimensional neighborhood-level exposures were used to form a neighborhood exposome (NE) score. SCZ-PRS was not statistically significantly associated with odds of persistent distressing PLE (OR = 1.04, 95% CI: 0.97, 1.13, P = 0.280), whereas NE score was (OR = 1.15, 95% CI: 1.05, 1.26, P = 0.003). The association between NE score and persistent distressing PLE was statistically significantly attenuated as SCZ-PRS increased (OR for interaction = 0.92, 95% CI: 0.86, 1.00, P = 0.039). The findings indicate that persistent distressing PLE may be driven by detrimental neighborhood exposures, particularly among children with low genetic risks. Health sciences/Medical research/Epidemiology Health sciences/Diseases/Psychiatric disorders/Psychosis Figures Figure 1 Figure 2 Figure 3 Figure 3 Figure 4 Figure 5 Introduction Psychotic-like experiences (PLEs), also known as subclinical psychotic symptoms or psychotic experiences, encompass unusual or unreal perceptions, thoughts, or beliefs. 1 PLEs are one of the earliest signs of psychotic disorders and are common in children, 2, 3 with a prevalence of 17% among those aged 9-12. 4 Although PLEs do not guarantee a future psychosis diagnosis, those that persist and cause significant distress may be more strongly associated with future psychopathology, including greater functional and cognitive impairments, as well as increased reliance on mental health services. 5, 6 Both nature (i.e., genetic inheritance) and nurture (i.e., non-genetic exposures) may play a role in the onset of persistent distressing PLE. Schizophrenia polygenic risk scores (SCZ-PRS), representing the individual’s genetic risk of schizophrenia, have been shown to be associated with a higher risk of persistent distressing PLE among children of European ancestry. 7, 8 Evidence also shows that non-genetic factors, such as household adversity, sociocultural values, and neighborhood environment, can also affect psychosis symptoms. 9-12 Our previous work reveals that the neighborhood exposome (NE), the totality of exposure to multi-dimensional neighborhood environmental factors, 13 is associated with an increased likelihood of persistent distressing PLE. 8 However, it remains unknown whether the nature of persistent distressing PLE can still be explained by the genetic risk of schizophrenia among children from multiple ancestries. Persistent distressing PLE could be precursors of multiple psychiatric disorders, which may have different susceptibility across ancestries. 14, 15 Moreover, the interaction between genes and NE contributing to persistent distressing PLE is underexplored. Studies on gene-environment interaction in psychosis and other psychiatric disorders produce mixed results, indicating complex etiology. 16, 17 Investigating the interaction pattern between genetic risk and NE could help elucidate the complex etiology of persistent distressing PLE and identify vulnerable populations, from which the evidence can be used to precisely inform prevention strategies for early manifestations of psychosis and other disorders. To address the above gaps, we examined the association between genetic risk, NE, and persistent distressing PLE, as well as the gene-environment interaction in a nationwide cohort with children from multiple ancestry backgrounds in the US. We used a multi-ancestral SCZ-PRS as the genetic risk of persistent distressing PLE. We also used a mixture method, weighted quantile sum (WQS) regression, for a holistic assessment of the NE in relation to persistent distressing PLE. Results Study population This population-based study used data from the Adolescent Brain and Cognitive Development (ABCD) Study. Participants were recruited from 22 US sites between September 2016 and January 2022. Data from baseline and three annual follow-ups were included. The selection process for the analytic sample is shown in Fig. 1 and described in detail in Methods. Briefly, 9,781 participants in the ABCD cohort had both genomic and phenotype (i.e., PLE) data. They were split into training (N = 1,955) and test samples (N = 7,826) for calculating the multi-ancestral PRS using SBayesRC (and PRScsx in sensitivity analysis; see details in Methods). The analytic sample, which is the subset with complete sociodemographic and clinical characteristics of the test sample, included 6,449 participants (Table 1 ). The mean (SD) baseline age of the participants was 9.9 (0.6) years, and 48% were female. The participants had a mean (SD) income-to-need ratio of 4.4 (3.3), and 66% had at least one parent with a bachelor’s degree or greater. The prevalence of having a family history of psychosis was 8%. Most of the participants reported that their race and ethnicity group was non-Hispanic White (58%), followed by Hispanic (18%) and non-Hispanic Black (11%). Participants were mostly from EUR (77%), followed by AFR (11%) and mixed ancestry (9%). Of all, 1,272 (20%) participants experienced persistent distressing PLE. Due to the random splitting process, the study characteristics were similar between the training sample and the analytic sample (Supplementary Table 1). The analytic sample had more non-Hispanic White participants, higher parental education levels, income-to-need ratio, and more endorsement of persistent distressing PLE compared with participants excluded from the analysis (Supplementary Table 2). Table 1 Characteristics of participants in analytic sample from the Adolescent Brain Cognitive Development (ABCD) cohort Characteristic N = 6,449 n (%) Age, mean (SD) 9.9 (0.6) Sex Female 3,069 (47.6) Income-to-needs ratio, mean (SD) 4.3 (3.3) Family history of psychosis Yes 517 (8.0) Parental education At least one parent has obtained a bachelor's degree or greater 4,264 (66.1) Self-reported race and ethnicity Non-Hispanic White 3,718 (57.7) Non-Hispanic Black 730 (11.3) Hispanic 1,174 (18.2) Non-Hispanic Asian 133 (2.1) Other 694 (10.8) Genetic ancestry EUR 4,985 (77.3) AFR 706 (10.9) EAS 168 (2.6) ADMIX 590 (9.1) Persistent distressing psychotic-like experiences Yes 1,272 (19.7) SCZ-PRS and Persistent Distressing PLE The distribution of the multi-ancestral SCZ-PRS varied across ancestry groups (Supplementary Fig. 1A). In the total test sample (N = 7,826), SCZ-PRS predicted persistent distressing PLE, with an area under the ROC curve (AUC) of 0.56 (95% CI: 0.55, 0.58) and a Nagelkerke R 2 of 1.1%. It also predicted persistent distressing PLE in EUR and EAS ancestry groups but did not in AFR and admixed ancestry groups (Supplementary Fig. 1B). After adjusting for selected covariates, the SCZ-PRS was not statistically significantly associated with increased odds of persistent distressing PLE (OR = 1.04, 95% CI: 0.97, 1.13, P = 0.280) among the total analytic sample (N = 6,449). No statistically significant association between SCZ-PRS and persistent distressing PLE was observed among any of the ancestry groups (Fig. 2 ). NE and Persistent Distressing PLE Of the 29 neighborhood-level exposure variables in the NE score, the top five exposures with the greatest weight were poor plumbing, lack of walkability, greater minority concentration, greater total crime, and lower percentage of those working in white-collar occupations (Fig. 3 ). After adjusting for selected covariates, a one-decile increase in NE score was associated with a 15% (OR = 1.15, 95% CI: 1.05, 1.26, P = 0.003) increased odds of persistent distressing PLE among the total analytic sample. Interaction between SCZ-PRS and NE SCZ-PRS modified the association between the NE score and persistent distressing PLE on the multiplicative scale. Among the total analytic sample, the association between NE score and persistent distressing PLE statistically significantly attenuated (OR for interaction = 0.92, 95% CI: 0.86, 1.00, P = 0.039) as SCZ-PRS increased (Fig. 4 A). The multiplicative interaction between SCZ-PRS and NE score was mainly contributed by neighborhood variables measured by ADI (Fig. 4 B). A statistically significant interaction was also observed among the EUR ancestry group (OR for interaction = 0.89, 95% CI: 0.81, 0.98, P = 0.017). No evidence of multiplicative interaction was found among other ancestries (Fig. 4 C). When following the same approach as the multiplicative interaction, there was no statistically significant additive interaction between SCZ-PRS and the NE score among the total analytic sample (RERI = -0.06, 95% CI, -0.15, 0.03, P = 0.189) or any of the individual ancestry groups (Supplementary Fig. 2). Sensitivity Analyses The association between SCZ-PRS calculated by PRScsx and persistent distressing PLE and the interaction with NE score followed the same trend as the primary analysis using SCZ-PRS calculated by SBayesRC, although it was statistically insignificant (Supplementary Fig. 3, 4). Similarly, results from additionally adjusting for self-reported race and ethnicity and excluding AFR ancestry followed the same trend as the primary analysis (Supplementary Table 3, 4). Discussion In this study, we examined the association between genetic risk, NE, and persistent distressing PLE, as well as the interaction between genetic risk and NE, among US children from multiple ancestry backgrounds in the ABCD cohort. Genetic risk was measured by a multi-ancestral SCZ-PRS, and an NE score was calculated using WQS regression incorporating multi-dimensional neighborhood factors. We found that the NE score was statistically significantly associated with increased odds of persistent distressing PLE. A statistically significant interaction was observed between SCZ-PRS and NE score, in which the association between NE score and persistent distressing PLE was attenuated as SCZ-PRS increased. We found little evidence that the multi-ancestral SCZ-PRS was associated with persistent distressing PLE. The multi-ancestral SCZ-PRS still had acceptable prediction properties in the EUR ancestry group, consistent with previous studies showing the positive association between SCZ-PRS and persistent distressing PLE among the EUR children. 7, 8 However, although we used the latest developed multi-ancestry PRS approach, SBayesRC, it performed poorly among children from AFR and admixed ancestries, representing 20% of the total analytic sample. Therefore, the noise from those of AFR and mixed ancestries could have prevented us from detecting the genetic risk of persistent distressing PLE when using the multi-ancestral SCZ-PRS. This could be due to several reasons. First, among the GWAS summary statistics of schizophrenia used to train the multi-ancestral SCZ-PRS, those of AFR ancestry had the smallest sample size, inherently encompassing a considerable variance. 18 However, AFR is the second-largest ancestry group following EUR in the study population, which also means the admixed population comprises a significant proportion of AFR background. The under-representation of multi-ancestral SCZ-PRS for AFR and admixed populations could result in a poor performance in predicting persistent distressing PLE in the study population. Meanwhile, persistent distressing PLE is not exclusively a predictor of schizophrenia but also of broader psychiatric disorders, 19 of which the genetic risk could vary across ancestries. 14, 15 The genetic risk of other psychiatric disorders may more accurately represent that of persistent distressing PLE among AFR and admixed populations. Consistent with our previous work in the ABCD cohort, 20 we found that NE was associated with increased odds of persistent distressing PLE. Extending the evidence, we found the association is stronger among individuals with a low genetic risk according to the observed negative interaction between NE and SCZ-PRS. The same gene-environment interaction pattern has been observed in twins from the UK and Sweden, in which the variance of psychotic experiences explained by environmental exposures decreased when genetic heritability increased. 21 In contrast, some previous studies found adverse socio-environmental exposures synergistically interacted with genetic liability in psychotic symptoms and diagnosis of schizophrenia among the European population. 22, 23 Besides differences in the study population, measures of genetic risk, and types of environmental factors that could contribute to these inconsistent results, the authors of these studies measured environmental factors based on retrospective self-report records as opposed to our objective measures. Individuals with psychotic symptoms may report exposure to adverse environmental factors differentially compared to those without. Recall bias could then arise and affect the validity of interaction estimates. Our findings suggest the predisposition for the development of persistent distressing PLE may vary depending on population characteristics, in which the population with low genetic risk may be more susceptible to neighborhood environmental exposures in developing psychotic outcomes. The reason for the negative interaction remains unclear. A high genetic risk may establish a stable trajectory for persistent distressing PLE that limits the impact of some neighborhood factors (e.g., factors from the ADI domain), resulting in the attenuated association of NE with persistent distressing PLE. However, imprecise additive interaction results caution against interpreting observed statistical interactions as biological effects. 24 The study has several limitations. First, as mentioned above, the SCZ-PRS performed poorly in AFR and admixed populations in predicting persistent distressing PLE. Although we applied the latest multi-ancestral PRS approach, it did not sufficiently overcome this limitation. Second, participants in the ABCD cohort with incomplete records of all included variables were excluded from the analytic sample. The difference in characteristics between included and excluded participants may indicate a risk of selection bias. Third, PLE was measured by a self-reported questionnaire, which may introduce outcome misclassification. Fourth, we only had data on baseline neighborhood exposures and could not examine the sensitive period in which environmental factors may have a more pronounced effect on long-term psychosis risk. Finally, residual confounding may remain due to unmeasured confounders and misclassification/measurement error of included covariates. Conclusion NE was associated with increased odds of persistent distressing PLE, particularly among children with a low genetic risk for schizophrenia. Future studies should consider finding a more robust indicator of the genetic risk of persistent distressing PLE for multi-ancestral populations. Additional research into time-varying neighborhood exposures and their interaction with genetic risk could provide insight into sensitive developmental periods and inform prevention strategies. Methods Study Population This study analyzed data from the Adolescent Brain and Cognitive Development (ABCD) Study's 5.0 release (N = 11,868), collected between September 2016 and January 2022. 25 The ABCD Study is a nationwide longitudinal investigation of brain and behavioral development across 22 U.S. research sites. Children aged 9 to 10 were recruited at baseline to represent national demographics as closely as possible. 26 Participating schools (public, private, and charter) were randomly selected within a 50-mile radius of each research site. 26 All participants and their parents provided written informed consent, with institutional review board approval obtained at each site. Persistent distressing psychotic-like experiences Psychotic-like experiences (PLEs) over the past month were assessed using the Prodromal Questionnaire-Brief Child Version (PQBC). 27, 28 This screening tool was administered at baseline and three annual follow-ups. For each endorsed PLE item, participants rated their level of distress using a cartoon pictorial 5-point Likert scale, ranging from neutral (1) to extreme distress (5). 27 Participants were classified as having distressing PLEs if they reported at least one PLE with an associated distress rating ≥ 3. 7 This threshold was selected to identify clinically meaningful levels of distress that warranted further attention. Among participants with all four years of PLE data (N = 9,912), the persistent distressing PLE group was operationalized as the presence of distressing PLEs at two or more assessment time points. 8 This definition of persistence was based on previous research on persistent PLEs. 5, 29 Genetic Data and Polygenic Risk Score Genotyping was conducted using the Affymetrix NIDA SmokeScreen Array (733,329 single nucleotide polymorphisms [SNPs]) in 11,666 participants. The genetic dataset was imputed using the TOPMed (Version R2 on GRCh38; https://imputation.biodatacatalyst.nhlbi.nih.gov ) reference panel through MiniMac 4, as facilitated by the TOPMed Imputation Server. 30 The imputed variants, initially represented as fractional dosages, were converted into integer allele counts by applying a best-guess threshold of 0.9. This process yielded a total of 280,985,564 imputed variants, aligned to the GRCh38 genome build. Quality control (QC) was performed in the imputed genetic dataset by removing SNPs with minor allele frequency (MAF) < 1% and Hardy-Weinberg Equilibrium P-value < 1e-6. Population stratification was assessed by conducting the genetic principal component analysis (PCA) using the plink --pca function in PLINK 1.9 beta ( https://www.cog-genomics.org/plink/1.9/ ). Genetic ancestry was determined using SNPweights software 31 with HapMap 3 reference panels. 32 Participants were grouped based on whether they had at least 50% genetic similarity to a continental reference panel (African [AFR], East Asian (EAS), or European (EUR) ancestry) or were classified as admixed if no single ancestry exceeded the 50% threshold, indicating a mixture of two or more continental ancestries. Based on participants’ PCs and assigned genetic ancestry, we found the first four PCs adequately distinguished population stratification and were used to adjust for population stratification in the following analyses (Supplementary Fig. 5). Given that the participant recruitment was nested within families (N = 5,678), we calculated the genetic relatedness matrix and used it to account for the dependency between participants using the plink --make-rel function. 33 We used SBayesRC 34 to calculate a multi-ancestral SCZ-PRS. The summary statistics of schizophrenia from EUR, EAS, and AFR subpopulations were extracted from the latest schizophrenia genome-wide association study (GWAS). 18 Each individual SCZ-PRS was calculated by incorporating genomic functional annotations and the corresponding UK Biobank (UKB) ancestry-specific LD reference panel with imputed common SNPs (> 7 millions) ( https://github.com/zhilizheng/SBayesRC ). To construct the multi-ancestral SCZ-PRS, participants with both genetic and PLE phenotype data (N = 9,781) were first randomly split into a training (N = 1,955) and test sample (N = 7,826) in a 20/80 ratio within each ancestry group. We then applied logistic regression to regress persistent distressing PLE on all ancestry-specific SCZ-PRSs in the training sample. Using the coefficients of ancestry-specific SCZ-PRSs as their weights, the multi-ancestral SCZ-PRS was calculated by weighted-summing all ancestry-specific SCZ-PRSs in the test sample (Supplementary Table 5). The final multi-ancestral SCZ-PRS was transformed to the standard normal distribution. To evaluate the robustness of SBayesRC, we also applied another multi-ancestral PRS approach, PRScsx, 35 to calculate the SCZ-PRS. The ancestry-specific SCZ-PRS were calculated using the ancestry-specific LD-reference panel using the UKB data before building the multi-ancestral PRS using the same process described above ( https://github.com/getian107/PRScsx ). Neighborhood-level Exposures Neighborhood-level characteristics were derived from participants’ primary home addresses at baseline. These geospatial location data were geocoded into census tracts and linked to external environmental constructs as part of the ABCD 5.0 release. 25 We included 29 neighborhood-level characteristics derived from five domains and based on prior literature: 20, 25 the Area Deprivation Index (ADI), 36, 37 Child Opportunity Index 2.0 (COI), 38 Crime, 39 Environmental Quality, 40, 41 and the Social Vulnerability Index (SVI). 42 All characteristics were standardized and coded such that greater values indicated worse conditions. A detailed list of neighborhood-level exposure variables is described in Supplementary Table 6. Sociodemographic and Clinical characteristics At baseline, sociodemographic and clinical characteristics, including age, sex, self-reported race and ethnicity, parental education, income-to-needs ratio, and family history of psychosis, were collected through parent reports and interviews. Race and ethnicity were categorized into non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, non-Hispanic other races, and Hispanic. 43 Parental education was dichotomized into high (having at least one parent or caregiver who obtained at least a bachelor’s degree) and low (neither parent or caregiver obtained at least a bachelor’s degree). The income-to-needs ratio was calculated by dividing the median value of the income band by the federal poverty line for the respective household size. 44 A value greater or less than one would denote above or below the poverty threshold. Family history of psychosis in first-degree and second-degree relatives was assessed using the parent-rated Family History Assessment Module Screener. 45 Statistical Analysis To quantify the levels of NE and the relative importance of each neighborhood-level exposure, we used WQS regression 46 to calculate an NE score for each participant with complete information on neighborhood factors and sociodemographic and clinical characteristics (N = 8,145) using the gWQS R package. 47 Consistent with our previous work, 20 all neighborhood factors were split into deciles before entering into the WQS regression to calculate the NE score and weights corresponding to the contribution of each exposure to the mixture effect. We adjusted for sex, age, income-to-need ratio, parental education levels, family history of psychosis, and four genetic PCs in WQS regression with constraint to positive unidirectionality. Using a complete case analysis, 6,449 participants with complete sociodemographic and clinical characteristics in the test sample were included in the final analytic sample. A detailed selection process is provided in Fig. 1 . To account for the relatedness of participants in the ABCD cohort by families (N = 5,678) and study sites (N = 22), we used the generalized linear mixed model approach of Chen et al. The model’s formula can have a simplified notation: $$\:g\left(E\left(y\right)\right)=X\beta\:+{b}_{1i}+{b}_{2j}$$ $$\:{b}_{1i}\sim\mathcal{N}(0,{V}_{1})\:\text{a}\text{n}\text{d}\:{b}_{2j}\sim\mathcal{N}(0,{V}_{2}),$$ where g() is the link function ( “logit” here), E(y) the expectation value of the outcome, X the covariate matrix, \(\:\beta\:\) the vector of fixed effects, \(\:{b}_{1i}\) the random intercept for the i th participant assuming to follow the normal distribution with mean 0 and covariance proportional to the genetic relatedness matrix \(\:{V}_{1}\) , and \(\:{b}_{2j}\) the random intercept for the j th study site assuming to follow the normal distribution with mean 0 and covariance proportional to the block diagonal matrix \(\:{V}_{2}\) . The analysis was performed using the GAMMT R package ( https://github.com/hanchenphd/GMMAT) . 48 We first separately estimated the association of SCZ-PRS calculated by SBayesRC and NE score with persistent distressing PLE, adjusting for sex, age, income-to-need ratio, parental education levels, family history of psychosis, and four genetic PCs. We then conducted a GxE interaction analysis by including SCZ-PRS, NE score, and their interaction term in the model, adjusting for the same covariates. We also assessed the interaction between each individual neighborhood-level exposure and SCZ-PRS to determine which exposures mainly contribute to the GxE interaction. Additive interaction was calculated using the relative excess risk due to interaction (RERI). 49 Ancestry-stratified analyses were conducted when assessing the association between SCZ-PRS and persistent distressing PLE, as well as the GxE interaction analysis. We report adjusted odds ratios (ORs), 95% confidence intervals (CIs), and two-sided P -values ( P ). Sensitivity analyses included (1) replicating all above analyses using SCZ-PRS calculated by PRScsx, (2) additionally adjusting for self-reported race and ethnicity, and (3) removing participants from the AFR ancestry group in the analysis due to its smallest sample size in GWAS summary statistics used to train multi-ancestral SCZ-PRS (N = 9,824). A two-sided P < 0.05 was considered statistically significant. All analyses were performed using R 4.4.0. Abbreviations ADMIX Admixed ancestry,AFR,African ancestry EAS East Asian ancestry EUR European ancestry Declarations Author Contributions: Drs Ku and Huels are considered co-corresponding authors. Acknowledgments This work was supported by the National Institute of Mental Health (NIMH K23MH129684; Dr. Ku), the National Institute on Aging (NIA R01AG079170; Huels/Wingo), the National Institute of Mental Health (NIMH R01MH129855; Risk), the Emory Constructive Collision Grant (Ku/Risk/Huels), and Emory HERCULES Pilot Award (Ku/Risk/Huels). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the National Institute of Mental Health, or Emory University. Data availability The ABCD study anonymized data are released annually and are publicly available via the NIMH Data Archive (NHA). All data from the Adolescent Brain Cognitive Development (ABCD) Study (https://nda.nih.gov/abcd/request-access) are made available to researchers from universities and other institutions with research inquiries following institutional review board and National Institute of Mental Health Data Archive approval. 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Estimating Additive Interaction in Two-Stage Individual Participant Data Meta-Analysis. American Journal of Epidemiology . 2024;doi:10.1093/aje/kwae325 Fan CC, Marshall A, Smolker H, et al. Adolescent Brain Cognitive Development (ABCD) study Linked External Data (LED): Protocol and practices for geocoding and assignment of environmental data. Developmental cognitive neuroscience . Dec 2021;52:101030. doi:10.1016/j.dcn.2021.101030 Garavan H, Bartsch H, Conway K, et al. Recruiting the ABCD sample: Design considerations and procedures. Dev Cogn Neurosci . Aug 2018;32:16-22. doi:10.1016/j.dcn.2018.04.004 Karcher NR, Barch DM, Avenevoli S, et al. Assessment of the Prodromal Questionnaire-Brief Child Version for Measurement of Self-reported Psychoticlike Experiences in Childhood. JAMA Psychiatry . Aug 1 2018;75(8):853-861. doi:10.1001/jamapsychiatry.2018.1334 Loewy RL, Pearson R, Vinogradov S, Bearden CE, Cannon TD. Psychosis risk screening with the Prodromal Questionnaire--brief version (PQ-B). Schizophr Res . Jun 2011;129(1):42-6. doi:10.1016/j.schres.2011.03.029 Dominguez MD, Wichers M, Lieb R, Wittchen HU, van Os J. Evidence that onset of clinical psychosis is an outcome of progressively more persistent subclinical psychotic experiences: an 8-year cohort study. Schizophrenia Bulletin . Jan 2011;37(1):84-93. doi:10.1093/schbul/sbp022 Das S, Forer L, Schönherr S, et al. Next-generation genotype imputation service and methods. Nature Genetics . 2016/10/01 2016;48(10):1284-1287. doi:10.1038/ng.3656 Chen C-Y, Pollack S, Hunter DJ, Hirschhorn JN, Kraft P, Price AL. Improved ancestry inference using weights from external reference panels. Bioinformatics . 2013;29(11):1399-1406. doi:10.1093/bioinformatics/btt144 Altshuler DM, Gibbs RA, Peltonen L, et al. Integrating common and rare genetic variation in diverse human populations. Nature . 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Journal of Urban Health . 2013;90:388-405. Acevedo-Garcia D, McArdle N, Hardy EF, et al. The child opportunity index: improving collaboration between community development and public health. Health Aff (Millwood) . Nov 2014;33(11):1948-57. doi:10.1377/hlthaff.2014.0679 Investigation USDoJOoJPFBo. Data from: Uniform Crime Reporting Program Data: County-Level Detailed Arrest and Offense Data, United States, 2010. 2014. doi:10.3886/ICPSR33523.v2 Di Q, Amini H, Shi L, et al. Assessing NO(2) Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging. Environ Sci Technol . Feb 4 2020;54(3):1372-1384. doi:10.1021/acs.est.9b03358 Requia WJ, Di Q, Silvern R, et al. An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States. Environ Sci Technol . Sep 15 2020;54(18):11037-11047. doi:10.1021/acs.est.0c01791 Fatemi F, Ardalan A, Aguirre B, Mansouri N, Mohammadfam I. Social vulnerability indicators in disasters: Findings from a systematic review. International journal of disaster risk reduction . 2017;22:219-227. Anglin DM, Espinosa A, Addington J, et al. Association of Childhood Area-Level Ethnic Density and Psychosis Risk Among Ethnoracial Minoritized Individuals in the US. JAMA Psychiatry . Dec 1 2023;80(12):1226-1234. doi:10.1001/jamapsychiatry.2023.2841 Rakesh D, Zalesky A, Whittle S. Assessment of parent income and education, neighborhood disadvantage, and child brain structure. JAMA Network Open . 2022;5(8):e2226208-e2226208. Van Dijk MT, Murphy E, Posner JE, Talati A, Weissman MM. Association of multigenerational family history of depression with lifetime depressive and other psychiatric disorders in children: results from the Adolescent Brain Cognitive Development (ABCD) Study. JAMA psychiatry . 2021;78(7):778-787. Renzetti S, Gennings C, Calza S. A weighted quantile sum regression with penalized weights and two indices. Front Public Health . 2023;11:1151821. doi:10.3389/fpubh.2023.1151821 Renzetti S, Curtin P, Allan C, Bello G, Gennings C. gWQS: generalized weighted quantile sum regression. 2016; Chen H, Wang C, Conomos MP, et al. Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models. Am J Hum Genet . Apr 7 2016;98(4):653-66. doi:10.1016/j.ajhg.2016.02.012 Richardson DB, Kaufman JS. Estimation of the relative excess risk due to interaction and associated confidence bounds. Am J Epidemiol . Mar 15 2009;169(6):756-60. doi:10.1093/aje/kwn411 Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5830171","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":415275944,"identity":"ed539edb-f57e-4926-8493-1d473b155094","order_by":0,"name":"Yinxian Chen","email":"","orcid":"https://orcid.org/0000-0002-9835-2121","institution":"Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA","correspondingAuthor":false,"prefix":"","firstName":"Yinxian","middleName":"","lastName":"Chen","suffix":""},{"id":415275945,"identity":"50600a22-50be-48c4-b081-3c90a88a9f8d","order_by":1,"name":"Qingyue Yuan","email":"","orcid":"","institution":"Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA","correspondingAuthor":false,"prefix":"","firstName":"Qingyue","middleName":"","lastName":"Yuan","suffix":""},{"id":415275946,"identity":"d287b2f6-13b8-45a6-8eaf-83cd75fbce3d","order_by":2,"name":"Lina Dimitrov","email":"","orcid":"","institution":"Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Dimitrov","suffix":""},{"id":415275947,"identity":"5bcf1656-b14f-4eb6-baaa-7bc65c94839a","order_by":3,"name":"Benjamin Risk","email":"","orcid":"","institution":"Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Risk","suffix":""},{"id":415275948,"identity":"389f87f2-1f03-4929-a37d-e045b381398a","order_by":4,"name":"Benson Ku","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYBACAzR+AgM/nH2AgBYemBbJBpK1GMBV4tBizt57+MPHHQz29uw9ho8LGNISNx/vTnxc2cYgx3cjAasWy55zaZIzzzAk9vCcMTaewZCTuO3M2c2GZ9sYjCVxaDG4kWPGzNvGkMAjkZYmzcNQkbjtRu42ycY2hsQNuLTcf2P8GajFnkf+WfpvkJbN89+CtdTj1HKDx0AaqIWxR4L5GDMP0GEbJHjBWhIMcPolx0xyZptEYs+Z5MPSPAZpxjPO5G42bDgnYTjzzAMcIXbG+MPHNht79vaDjZ95KpJl+9vPbnzYUGYjz3ccuy1QIAFzJ5RmZJPApRQn+EOyjlEwCkbBKBi+AABkuF1cIdaIIQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA","correspondingAuthor":true,"prefix":"","firstName":"Benson","middleName":"","lastName":"Ku","suffix":""},{"id":415275943,"identity":"11923efd-1ab3-42ec-986c-3872ec86435f","order_by":5,"name":"Anke Huels","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYFACNgYGxgYGBj4G5gMwIQPitLAxsCWQrIUHrhK/Fv7ZbYkPGHcclmPjP/Px0Y2Ke4kN7M3bJPBpkbhz7LAB45nDxmwSuZuNc84UJzbwHCvDq4XhRnqbBGPb4cQ2Cd5t0rltCYkNEjlmeLXI30hv/wHUUt/Gf+b5b7AW+Tf4tRjcSDvGANSSwMaQw8YMsYUHvxbDG2nJEolt6YZtEmnG0jlnEozbeNKKLfBpkbuRZvjhY5u1PD//4YefcyoSZPvZD2+8gU8LGCQgsR3bCCpHB/Yk6xgFo2AUjIJhDwAWEEfsMz/1rAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA; Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA; Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA","correspondingAuthor":true,"prefix":"","firstName":"Anke","middleName":"","lastName":"Huels","suffix":""}],"badges":[],"createdAt":"2025-01-15 00:25:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5830171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5830171/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44220-025-00563-8","type":"published","date":"2026-01-02T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76573711,"identity":"974b363a-80fc-4830-ace6-9eedb0b7ae13","added_by":"auto","created_at":"2025-02-18 13:59:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":206179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participant selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviation: ADMIX, Admixed ancestry, AFR, African ancestry; EAS, East Asian ancestry; EUR, European ancestry\u003c/p\u003e","description":"","filename":"1Flowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-5830171/v1/6164e8b345c226ee279415a7.png"},{"id":76573710,"identity":"03690b28-1576-4482-aec3-2791cc8476ed","added_by":"auto","created_at":"2025-02-18 13:59:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79156,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe association between schizophrenia polygenic risk score (calculated by SBayesRC) and persistent distressing psychotic-like experiences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviation: ADMIX, Admixed ancestry, AFR, African ancestry; EAS, East Asian ancestry; EUR, European ancestry; PLE, persistent distressing psychotic-like experiences\u003c/p\u003e\n\u003cp\u003eNote: Models were adjusted for age, sex, income-to-needs ratio (INR), family history of psychosis, parental education levels, and four genetic principal components (PCs)\u003c/p\u003e","description":"","filename":"2p.correct.png","url":"https://assets-eu.researchsquare.com/files/rs-5830171/v1/f51a74695e49201b07217b2d.png"},{"id":76577059,"identity":"0b84bb1d-ba6a-4242-a1d4-ec4975992006","added_by":"auto","created_at":"2025-02-18 14:23:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relative importance of neighborhood factors in neighborhood exposome score estimated by weighted-quantile sum regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The model was adjusted for age, sex, income-to-needs ratio (INR), family history of psychosis, parental education levels, and four genetic principal components (PCs).\u003c/p\u003e","description":"","filename":"3weight.plot.png","url":"https://assets-eu.researchsquare.com/files/rs-5830171/v1/b8d5faec28c438946cbfbbc9.png"},{"id":76573722,"identity":"19b43a70-0003-4ba2-8b11-d11975b1dc60","added_by":"auto","created_at":"2025-02-18 13:59:15","extension":"pdf","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":429360,"visible":true,"origin":"","legend":"","description":"","filename":"p.correct.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5830171/v1/0e11d3e6927d5f05ab6509a2.pdf"},{"id":76573718,"identity":"ab34f52b-b36d-494e-945a-e2b474b38af1","added_by":"auto","created_at":"2025-02-18 13:59:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":283820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultiplicative interaction between schizophrenia polygenic risk score (calculated by SBayesRC) and neighborhood exposome score on persistent distressing psychotic-like experiences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviation: ADMIX, Admixed ancestry, AFR, African ancestry; EAS, East Asian ancestry; EUR, European ancestry; NE, neighborhood exposome; PLE, persistent distressing psychotic-like experience; SCZ-PRS, schizophrenia polygenic risk score\u003c/p\u003e\n\u003cp\u003e(A) The association between neighborhood exposome score and persistent distressing psychotic-like experiences across polygenic risk score for schizophrenia; (B) Interaction between polygenic risk score and individual neighborhood factors; (C) Interaction between polygenic risk score and neighborhood exposome score among different ancestries. The interpretation for each term would be (1) OR of persistent distressing PLE for a one-decile increase in NE among those with a population mean SCZ-PRS (NE score); OR of persistent distressing PLE for a one-SD increase in SCZ-PRS among those in the first decile of NE score (SCZ-PRS); OR of the interaction between NE score and SCZ-PRS (NE score*SCZ-PRS).\u003c/p\u003e\n\u003cp\u003eNote: Models were adjusted for age, sex, income-to-needs ratio (INR), family history of psychosis, parental education levels, and four genetic principal components (PCs).\u003c/p\u003e","description":"","filename":"4p.int.ind.correct.png","url":"https://assets-eu.researchsquare.com/files/rs-5830171/v1/7ea32bc4872d473dd371092a.png"},{"id":76574580,"identity":"6ee6e252-121b-47ac-bea4-95c8ccbb34c8","added_by":"auto","created_at":"2025-02-18 14:07:15","extension":"pdf","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":593118,"visible":true,"origin":"","legend":"","description":"","filename":"weight.plot.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5830171/v1/5db1a66274e1c96d4b7172f5.pdf"},{"id":99419029,"identity":"022e71cc-4995-490e-b6be-b42a26048d64","added_by":"auto","created_at":"2026-01-03 08:07:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1449055,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5830171/v1/52c820e8-4147-4097-b6ee-a169123b8d5e.pdf"},{"id":76573714,"identity":"56b37ae5-dd73-4318-b44a-fb9ac997a8ba","added_by":"auto","created_at":"2025-02-18 13:59:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":873030,"visible":true,"origin":"","legend":"Supplemental material","description":"","filename":"supplementalmaterialNEPRSPLENature.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5830171/v1/c085e13b17e6e320eee158ce.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Interaction between Neighborhood Exposome and Genetic Risk in Child Psychotic-like Experiences","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsychotic-like experiences (PLEs), also known as subclinical psychotic symptoms or psychotic experiences, encompass unusual or unreal perceptions, thoughts, or beliefs.\u003csup\u003e1\u003c/sup\u003e PLEs are one of the earliest signs of psychotic disorders and are common in children,\u003csup\u003e2, 3\u003c/sup\u003e with a prevalence of 17% among those aged 9-12.\u003csup\u003e4\u003c/sup\u003e Although PLEs do not guarantee a future psychosis diagnosis, those that persist and cause significant distress may be more strongly associated with future psychopathology, including greater functional and cognitive impairments, as well as increased reliance on mental health services.\u003csup\u003e5, 6\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eBoth nature (i.e., genetic inheritance) and nurture (i.e., non-genetic exposures) may play a role in the onset of persistent distressing PLE. Schizophrenia polygenic risk scores (SCZ-PRS), representing the individual’s genetic risk of schizophrenia, have been shown to be associated with a higher risk of persistent distressing PLE among children of European ancestry.\u003csup\u003e7, 8\u003c/sup\u003e Evidence also shows that non-genetic factors, such as household adversity, sociocultural values, and neighborhood environment, can also affect psychosis symptoms.\u003csup\u003e9-12\u003c/sup\u003e Our previous work reveals that the neighborhood exposome (NE), the totality of exposure to multi-dimensional neighborhood environmental factors,\u003csup\u003e13\u003c/sup\u003e is associated with an increased likelihood of persistent distressing PLE.\u003csup\u003e8\u003c/sup\u003e However, it remains unknown whether the nature of persistent distressing PLE can still be explained by the genetic risk of schizophrenia among children from multiple ancestries. Persistent distressing PLE could be precursors of multiple psychiatric disorders, which may have different susceptibility across ancestries.\u003csup\u003e14, 15\u003c/sup\u003e Moreover, the interaction between genes and NE contributing to persistent distressing PLE is underexplored. Studies on gene-environment interaction in psychosis and other psychiatric disorders produce mixed results, indicating complex etiology.\u003csup\u003e16, 17\u003c/sup\u003e Investigating the interaction pattern between genetic risk and NE could help elucidate the complex etiology of persistent distressing PLE and identify vulnerable populations, from which the evidence can be used to precisely inform prevention strategies for early manifestations of psychosis and other disorders.\u003c/p\u003e\n\u003cp\u003eTo address the above gaps, we examined the association between genetic risk, NE, and persistent distressing PLE, as well as the gene-environment interaction in a nationwide cohort with children from multiple ancestry backgrounds in the US. We used a multi-ancestral SCZ-PRS as the genetic risk of persistent distressing PLE. We also used a mixture method, weighted quantile sum (WQS) regression, for a holistic assessment of the NE in relation to persistent distressing PLE.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eThis population-based study used data from the Adolescent Brain and Cognitive Development (ABCD) Study. Participants were recruited from 22 US sites between September 2016 and January 2022. Data from baseline and three annual follow-ups were included. The selection process for the analytic sample is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and described in detail in Methods. Briefly, 9,781 participants in the ABCD cohort had both genomic and phenotype (i.e., PLE) data. They were split into training (N\u0026thinsp;=\u0026thinsp;1,955) and test samples (N\u0026thinsp;=\u0026thinsp;7,826) for calculating the multi-ancestral PRS using SBayesRC (and PRScsx in sensitivity analysis; see details in Methods). The analytic sample, which is the subset with complete sociodemographic and clinical characteristics of the test sample, included 6,449 participants (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean (SD) baseline age of the participants was 9.9 (0.6) years, and 48% were female. The participants had a mean (SD) income-to-need ratio of 4.4 (3.3), and 66% had at least one parent with a bachelor\u0026rsquo;s degree or greater. The prevalence of having a family history of psychosis was 8%. Most of the participants reported that their race and ethnicity group was non-Hispanic White (58%), followed by Hispanic (18%) and non-Hispanic Black (11%). Participants were mostly from EUR (77%), followed by AFR (11%) and mixed ancestry (9%). Of all, 1,272 (20%) participants experienced persistent distressing PLE. Due to the random splitting process, the study characteristics were similar between the training sample and the analytic sample (Supplementary Table 1). The analytic sample had more non-Hispanic White participants, higher parental education levels, income-to-need ratio, and more endorsement of persistent distressing PLE compared with participants excluded from the analysis (Supplementary Table 2).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of participants in analytic sample from the Adolescent Brain Cognitive Development (ABCD) cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;6,449\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.9 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,069 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome-to-needs ratio, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history of psychosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e517 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParental education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt least one parent has obtained a bachelor\u0026apos;s degree or greater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,264 (66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-reported race and ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,718 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e730 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,174 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e694 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenetic ancestry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,985 (77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e706 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADMIX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e590 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersistent distressing psychotic-like experiences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,272 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eSCZ-PRS and Persistent Distressing PLE\u003c/h2\u003e\n \u003cp\u003eThe distribution of the multi-ancestral SCZ-PRS varied across ancestry groups (Supplementary Fig.\u0026nbsp;1A). In the total test sample (N\u0026thinsp;=\u0026thinsp;7,826), SCZ-PRS predicted persistent distressing PLE, with an area under the ROC curve (AUC) of 0.56 (95% CI: 0.55, 0.58) and a Nagelkerke R\u003csup\u003e2\u003c/sup\u003e of 1.1%. It also predicted persistent distressing PLE in EUR and EAS ancestry groups but did not in AFR and admixed ancestry groups (Supplementary Fig. 1B).\u003c/p\u003e\n \u003cp\u003eAfter adjusting for selected covariates, the SCZ-PRS was not statistically significantly associated with increased odds of persistent distressing PLE (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 0.97, 1.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.280) among the total analytic sample (N\u0026thinsp;=\u0026thinsp;6,449). No statistically significant association between SCZ-PRS and persistent distressing PLE was observed among any of the ancestry groups (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eNE and Persistent Distressing PLE\u003c/h3\u003e\n\u003cp\u003eOf the 29 neighborhood-level exposure variables in the NE score, the top five exposures with the greatest weight were poor plumbing, lack of walkability, greater minority concentration, greater total crime, and lower percentage of those working in white-collar occupations (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). After adjusting for selected covariates, a one-decile increase in NE score was associated with a 15% (OR\u0026thinsp;=\u0026thinsp;1.15, 95% CI: 1.05, 1.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) increased odds of persistent distressing PLE among the total analytic sample.\u003c/p\u003e\n\u003ch3\u003eInteraction between SCZ-PRS and NE\u003c/h3\u003e\n\u003cp\u003eSCZ-PRS modified the association between the NE score and persistent distressing PLE on the multiplicative scale. Among the total analytic sample, the association between NE score and persistent distressing PLE statistically significantly attenuated (OR for interaction\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.86, 1.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) as SCZ-PRS increased (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The multiplicative interaction between SCZ-PRS and NE score was mainly contributed by neighborhood variables measured by ADI (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). A statistically significant interaction was also observed among the EUR ancestry group (OR for interaction\u0026thinsp;=\u0026thinsp;0.89, 95% CI: 0.81, 0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). No evidence of multiplicative interaction was found among other ancestries (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). When following the same approach as the multiplicative interaction, there was no statistically significant additive interaction between SCZ-PRS and the NE score among the total analytic sample (RERI = -0.06, 95% CI, -0.15, 0.03, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.189) or any of the individual ancestry groups (Supplementary Fig. 2).\u003c/p\u003e\n\u003ch3\u003eSensitivity Analyses\u003c/h3\u003e\n\u003cp\u003eThe association between SCZ-PRS calculated by PRScsx and persistent distressing PLE and the interaction with NE score followed the same trend as the primary analysis using SCZ-PRS calculated by SBayesRC, although it was statistically insignificant (Supplementary Fig.\u0026nbsp;3, 4). Similarly, results from additionally adjusting for self-reported race and ethnicity and excluding AFR ancestry followed the same trend as the primary analysis (Supplementary Table\u0026nbsp;3, 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we examined the association between genetic risk, NE, and persistent distressing PLE, as well as the interaction between genetic risk and NE, among US children from multiple ancestry backgrounds in the ABCD cohort. Genetic risk was measured by a multi-ancestral SCZ-PRS, and an NE score was calculated using WQS regression incorporating multi-dimensional neighborhood factors. We found that the NE score was statistically significantly associated with increased odds of persistent distressing PLE. A statistically significant interaction was observed between SCZ-PRS and NE score, in which the association between NE score and persistent distressing PLE was attenuated as SCZ-PRS increased.\u003c/p\u003e \u003cp\u003eWe found little evidence that the multi-ancestral SCZ-PRS was associated with persistent distressing PLE. The multi-ancestral SCZ-PRS still had acceptable prediction properties in the EUR ancestry group, consistent with previous studies showing the positive association between SCZ-PRS and persistent distressing PLE among the EUR children.\u003csup\u003e7, 8\u003c/sup\u003e However, although we used the latest developed multi-ancestry PRS approach, SBayesRC, it performed poorly among children from AFR and admixed ancestries, representing 20% of the total analytic sample. Therefore, the noise from those of AFR and mixed ancestries could have prevented us from detecting the genetic risk of persistent distressing PLE when using the multi-ancestral SCZ-PRS. This could be due to several reasons. First, among the GWAS summary statistics of schizophrenia used to train the multi-ancestral SCZ-PRS, those of AFR ancestry had the smallest sample size, inherently encompassing a considerable variance.\u003csup\u003e18\u003c/sup\u003e However, AFR is the second-largest ancestry group following EUR in the study population, which also means the admixed population comprises a significant proportion of AFR background. The under-representation of multi-ancestral SCZ-PRS for AFR and admixed populations could result in a poor performance in predicting persistent distressing PLE in the study population. Meanwhile, persistent distressing PLE is not exclusively a predictor of schizophrenia but also of broader psychiatric disorders,\u003csup\u003e19\u003c/sup\u003e of which the genetic risk could vary across ancestries.\u003csup\u003e14, 15\u003c/sup\u003e The genetic risk of other psychiatric disorders may more accurately represent that of persistent distressing PLE among AFR and admixed populations.\u003c/p\u003e \u003cp\u003eConsistent with our previous work in the ABCD cohort,\u003csup\u003e20\u003c/sup\u003e we found that NE was associated with increased odds of persistent distressing PLE. Extending the evidence, we found the association is stronger among individuals with a low genetic risk according to the observed negative interaction between NE and SCZ-PRS. The same gene-environment interaction pattern has been observed in twins from the UK and Sweden, in which the variance of psychotic experiences explained by environmental exposures decreased when genetic heritability increased.\u003csup\u003e21\u003c/sup\u003e In contrast, some previous studies found adverse socio-environmental exposures synergistically interacted with genetic liability in psychotic symptoms and diagnosis of schizophrenia among the European population.\u003csup\u003e22, 23\u003c/sup\u003e Besides differences in the study population, measures of genetic risk, and types of environmental factors that could contribute to these inconsistent results, the authors of these studies measured environmental factors based on retrospective self-report records as opposed to our objective measures. Individuals with psychotic symptoms may report exposure to adverse environmental factors differentially compared to those without. Recall bias could then arise and affect the validity of interaction estimates. Our findings suggest the predisposition for the development of persistent distressing PLE may vary depending on population characteristics, in which the population with low genetic risk may be more susceptible to neighborhood environmental exposures in developing psychotic outcomes. The reason for the negative interaction remains unclear. A high genetic risk may establish a stable trajectory for persistent distressing PLE that limits the impact of some neighborhood factors (e.g., factors from the ADI domain), resulting in the attenuated association of NE with persistent distressing PLE. However, imprecise additive interaction results caution against interpreting observed statistical interactions as biological effects.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe study has several limitations. First, as mentioned above, the SCZ-PRS performed poorly in AFR and admixed populations in predicting persistent distressing PLE. Although we applied the latest multi-ancestral PRS approach, it did not sufficiently overcome this limitation. Second, participants in the ABCD cohort with incomplete records of all included variables were excluded from the analytic sample. The difference in characteristics between included and excluded participants may indicate a risk of selection bias. Third, PLE was measured by a self-reported questionnaire, which may introduce outcome misclassification. Fourth, we only had data on baseline neighborhood exposures and could not examine the sensitive period in which environmental factors may have a more pronounced effect on long-term psychosis risk. Finally, residual confounding may remain due to unmeasured confounders and misclassification/measurement error of included covariates.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNE was associated with increased odds of persistent distressing PLE, particularly among children with a low genetic risk for schizophrenia. Future studies should consider finding a more robust indicator of the genetic risk of persistent distressing PLE for multi-ancestral populations. Additional research into time-varying neighborhood exposures and their interaction with genetic risk could provide insight into sensitive developmental periods and inform prevention strategies.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis study analyzed data from the Adolescent Brain and Cognitive Development (ABCD) Study's 5.0 release (N\u0026thinsp;=\u0026thinsp;11,868), collected between September 2016 and January 2022.\u003csup\u003e25\u003c/sup\u003e The ABCD Study is a nationwide longitudinal investigation of brain and behavioral development across 22 U.S. research sites. Children aged 9 to 10 were recruited at baseline to represent national demographics as closely as possible.\u003csup\u003e26\u003c/sup\u003e Participating schools (public, private, and charter) were randomly selected within a 50-mile radius of each research site.\u003csup\u003e26\u003c/sup\u003e All participants and their parents provided written informed consent, with institutional review board approval obtained at each site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePersistent distressing psychotic-like experiences\u003c/h2\u003e \u003cp\u003ePsychotic-like experiences (PLEs) over the past month were assessed using the Prodromal Questionnaire-Brief Child Version (PQBC).\u003csup\u003e27, 28\u003c/sup\u003e This screening tool was administered at baseline and three annual follow-ups. For each endorsed PLE item, participants rated their level of distress using a cartoon pictorial 5-point Likert scale, ranging from neutral (1) to extreme distress (5).\u003csup\u003e27\u003c/sup\u003e Participants were classified as having distressing PLEs if they reported at least one PLE with an associated distress rating\u0026thinsp;\u0026ge;\u0026thinsp;3.\u003csup\u003e7\u003c/sup\u003e This threshold was selected to identify clinically meaningful levels of distress that warranted further attention. Among participants with all four years of PLE data (N\u0026thinsp;=\u0026thinsp;9,912), the persistent distressing PLE group was operationalized as the presence of distressing PLEs at two or more assessment time points.\u003csup\u003e8\u003c/sup\u003e This definition of persistence was based on previous research on persistent PLEs.\u003csup\u003e5, 29\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenetic Data and Polygenic Risk Score\u003c/h2\u003e \u003cp\u003eGenotyping was conducted using the Affymetrix NIDA SmokeScreen Array (733,329 single nucleotide polymorphisms [SNPs]) in 11,666 participants. The genetic dataset was imputed using the TOPMed (Version R2 on GRCh38; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imputation.biodatacatalyst.nhlbi.nih.gov\u003c/span\u003e\u003cspan address=\"https://imputation.biodatacatalyst.nhlbi.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) reference panel through MiniMac 4, as facilitated by the TOPMed Imputation Server.\u003csup\u003e30\u003c/sup\u003e The imputed variants, initially represented as fractional dosages, were converted into integer allele counts by applying a best-guess threshold of 0.9. This process yielded a total of 280,985,564 imputed variants, aligned to the GRCh38 genome build. Quality control (QC) was performed in the imputed genetic dataset by removing SNPs with minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;1% and Hardy-Weinberg Equilibrium P-value\u0026thinsp;\u0026lt;\u0026thinsp;1e-6. Population stratification was assessed by conducting the genetic principal component analysis (PCA) using the \u003cem\u003eplink --pca\u003c/em\u003e function in PLINK 1.9 beta (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cog-genomics.org/plink/1.9/\u003c/span\u003e\u003cspan address=\"https://www.cog-genomics.org/plink/1.9/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Genetic ancestry was determined using \u003cem\u003eSNPweights\u003c/em\u003e software\u003csup\u003e31\u003c/sup\u003e with HapMap 3 reference panels.\u003csup\u003e32\u003c/sup\u003e Participants were grouped based on whether they had at least 50% genetic similarity to a continental reference panel (African [AFR], East Asian (EAS), or European (EUR) ancestry) or were classified as admixed if no single ancestry exceeded the 50% threshold, indicating a mixture of two or more continental ancestries. Based on participants\u0026rsquo; PCs and assigned genetic ancestry, we found the first four PCs adequately distinguished population stratification and were used to adjust for population stratification in the following analyses (Supplementary Fig.\u0026nbsp;5). Given that the participant recruitment was nested within families (N\u0026thinsp;=\u0026thinsp;5,678), we calculated the genetic relatedness matrix and used it to account for the dependency between participants using the \u003cem\u003eplink --make-rel\u003c/em\u003e function.\u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe used SBayesRC\u003csup\u003e34\u003c/sup\u003e to calculate a multi-ancestral SCZ-PRS. The summary statistics of schizophrenia from EUR, EAS, and AFR subpopulations were extracted from the latest schizophrenia genome-wide association study (GWAS).\u003csup\u003e18\u003c/sup\u003e Each individual SCZ-PRS was calculated by incorporating genomic functional annotations and the corresponding UK Biobank (UKB) ancestry-specific LD reference panel with imputed common SNPs (\u0026gt;\u0026thinsp;7 millions) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/zhilizheng/SBayesRC\u003c/span\u003e\u003cspan address=\"https://github.com/zhilizheng/SBayesRC\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To construct the multi-ancestral SCZ-PRS, participants with both genetic and PLE phenotype data (N\u0026thinsp;=\u0026thinsp;9,781) were first randomly split into a training (N\u0026thinsp;=\u0026thinsp;1,955) and test sample (N\u0026thinsp;=\u0026thinsp;7,826) in a 20/80 ratio within each ancestry group. We then applied logistic regression to regress persistent distressing PLE on all ancestry-specific SCZ-PRSs in the training sample. Using the coefficients of ancestry-specific SCZ-PRSs as their weights, the multi-ancestral SCZ-PRS was calculated by weighted-summing all ancestry-specific SCZ-PRSs in the test sample (Supplementary Table\u0026nbsp;5). The final multi-ancestral SCZ-PRS was transformed to the standard normal distribution.\u003c/p\u003e \u003cp\u003eTo evaluate the robustness of SBayesRC, we also applied another multi-ancestral PRS approach, PRScsx,\u003csup\u003e35\u003c/sup\u003e to calculate the SCZ-PRS. The ancestry-specific SCZ-PRS were calculated using the ancestry-specific LD-reference panel using the UKB data before building the multi-ancestral PRS using the same process described above (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/getian107/PRScsx\u003c/span\u003e\u003cspan address=\"https://github.com/getian107/PRScsx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNeighborhood-level Exposures\u003c/h2\u003e \u003cp\u003e Neighborhood-level characteristics were derived from participants\u0026rsquo; primary home addresses at baseline. These geospatial location data were geocoded into census tracts and linked to external environmental constructs as part of the ABCD 5.0 release.\u003csup\u003e25\u003c/sup\u003e We included 29 neighborhood-level characteristics derived from five domains and based on prior literature:\u003csup\u003e20, 25\u003c/sup\u003e the Area Deprivation Index (ADI),\u003csup\u003e36, 37\u003c/sup\u003e Child Opportunity Index 2.0 (COI),\u003csup\u003e38\u003c/sup\u003e Crime,\u003csup\u003e39\u003c/sup\u003e Environmental Quality,\u003csup\u003e40, 41\u003c/sup\u003e and the Social Vulnerability Index (SVI).\u003csup\u003e42\u003c/sup\u003e All characteristics were standardized and coded such that greater values indicated worse conditions. A detailed list of neighborhood-level exposure variables is described in Supplementary Table\u0026nbsp;6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic and Clinical characteristics\u003c/h2\u003e \u003cp\u003eAt baseline, sociodemographic and clinical characteristics, including age, sex, self-reported race and ethnicity, parental education, income-to-needs ratio, and family history of psychosis, were collected through parent reports and interviews. Race and ethnicity were categorized into non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, non-Hispanic other races, and Hispanic.\u003csup\u003e43\u003c/sup\u003e Parental education was dichotomized into high (having at least one parent or caregiver who obtained at least a bachelor\u0026rsquo;s degree) and low (neither parent or caregiver obtained at least a bachelor\u0026rsquo;s degree). The income-to-needs ratio was calculated by dividing the median value of the income band by the federal poverty line for the respective household size.\u003csup\u003e44\u003c/sup\u003e A value greater or less than one would denote above or below the poverty threshold. Family history of psychosis in first-degree and second-degree relatives was assessed using the parent-rated Family History Assessment Module Screener.\u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eTo quantify the levels of NE and the relative importance of each neighborhood-level exposure, we used WQS regression\u003csup\u003e46\u003c/sup\u003e to calculate an NE score for each participant with complete information on neighborhood factors and sociodemographic and clinical characteristics (N\u0026thinsp;=\u0026thinsp;8,145) using the \u003cem\u003egWQS\u003c/em\u003e R package.\u003csup\u003e47\u003c/sup\u003e Consistent with our previous work,\u003csup\u003e20\u003c/sup\u003e all neighborhood factors were split into deciles before entering into the WQS regression to calculate the NE score and weights corresponding to the contribution of each exposure to the mixture effect. We adjusted for sex, age, income-to-need ratio, parental education levels, family history of psychosis, and four genetic PCs in WQS regression with constraint to positive unidirectionality.\u003c/p\u003e \u003cp\u003eUsing a complete case analysis, 6,449 participants with complete sociodemographic and clinical characteristics in the test sample were included in the final analytic sample. A detailed selection process is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To account for the relatedness of participants in the ABCD cohort by families (N\u0026thinsp;=\u0026thinsp;5,678) and study sites (N\u0026thinsp;=\u0026thinsp;22), we used the generalized linear mixed model approach of Chen et al. The model\u0026rsquo;s formula can have a simplified notation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:g\\left(E\\left(y\\right)\\right)=X\\beta\\:+{b}_{1i}+{b}_{2j}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{b}_{1i}\\sim\\mathcal{N}(0,{V}_{1})\\:\\text{a}\\text{n}\\text{d}\\:{b}_{2j}\\sim\\mathcal{N}(0,{V}_{2}),$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eg()\u003c/em\u003e is the link function (\u003cem\u003e\u0026ldquo;logit\u0026rdquo;\u003c/em\u003e here), \u003cem\u003eE(y)\u003c/em\u003e the expectation value of the outcome, \u003cem\u003eX\u003c/em\u003e the covariate matrix, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e the vector of fixed effects, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{b}_{1i}\\)\u003c/span\u003e\u003c/span\u003e the random intercept for the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e participant assuming to follow the normal distribution with mean 0 and covariance proportional to the genetic relatedness matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{1}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{b}_{2j}\\)\u003c/span\u003e\u003c/span\u003e the random intercept for the \u003cem\u003ej\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e study site assuming to follow the normal distribution with mean 0 and covariance proportional to the block diagonal matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{2}\\)\u003c/span\u003e\u003c/span\u003e. The analysis was performed using the \u003cem\u003eGAMMT\u003c/em\u003e R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/hanchenphd/GMMAT)\u003c/span\u003e\u003cspan address=\"https://github.com/hanchenphd/GMMAT)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003csup\u003e48\u003c/sup\u003e We first separately estimated the association of SCZ-PRS calculated by SBayesRC and NE score with persistent distressing PLE, adjusting for sex, age, income-to-need ratio, parental education levels, family history of psychosis, and four genetic PCs. We then conducted a GxE interaction analysis by including SCZ-PRS, NE score, and their interaction term in the model, adjusting for the same covariates. We also assessed the interaction between each individual neighborhood-level exposure and SCZ-PRS to determine which exposures mainly contribute to the GxE interaction. Additive interaction was calculated using the relative excess risk due to interaction (RERI).\u003csup\u003e49\u003c/sup\u003e Ancestry-stratified analyses were conducted when assessing the association between SCZ-PRS and persistent distressing PLE, as well as the GxE interaction analysis. We report adjusted odds ratios (ORs), 95% confidence intervals (CIs), and two-sided \u003cem\u003eP\u003c/em\u003e-values (\u003cem\u003eP\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eSensitivity analyses included (1) replicating all above analyses using SCZ-PRS calculated by PRScsx, (2) additionally adjusting for self-reported race and ethnicity, and (3) removing participants from the AFR ancestry group in the analysis due to its smallest sample size in GWAS summary statistics used to train multi-ancestral SCZ-PRS (N\u0026thinsp;=\u0026thinsp;9,824). A two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were performed using R 4.4.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADMIX\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdmixed ancestry,AFR,African ancestry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEast Asian ancestry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEUR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean ancestry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Drs Ku and Huels are considered co-corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Institute of Mental Health (NIMH K23MH129684; Dr. Ku), the National Institute on Aging (NIA R01AG079170; Huels/Wingo), the National Institute of Mental Health (NIMH R01MH129855; Risk), the Emory Constructive Collision Grant (Ku/Risk/Huels), and Emory HERCULES Pilot Award (Ku/Risk/Huels). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the National Institute of Mental Health, or Emory University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ABCD study anonymized data are released annually and are publicly available via the NIMH Data Archive (NHA). All data from the Adolescent Brain Cognitive Development (ABCD) Study (https://nda.nih.gov/abcd/request-access) are made available to researchers from universities and other institutions with research inquiries following institutional review board and National Institute of Mental Health Data Archive approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts of interest for any authors concerning the data or the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHinterbuchinger B, Mossaheb N. Psychotic-Like Experiences: A Challenge in Definition and Assessment. Mini Review. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e. 2021-March-29 2021;12doi:10.3389/fpsyt.2021.582392\u003c/li\u003e\n \u003cli\u003eLinscott RJ, van Os J. 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Mar 15 2009;169(6):756-60. doi:10.1093/aje/kwn411\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5830171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5830171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePersistent distressing psychotic-like experiences (PLE) among children may be driven by genetics and neighborhood environmental exposures. However, the gene-environment interaction to persistent distressing PLE is unknown. The study included 6,449 participants from the Adolescent Brain and Cognitive Development Study. Genetic risk was measured by a multi-ancestry schizophrenia polygenic risk score (SCZ-PRS). Multi-dimensional neighborhood-level exposures were used to form a neighborhood exposome (NE) score. SCZ-PRS was not statistically significantly associated with odds of persistent distressing PLE (OR = 1.04, 95% CI: 0.97, 1.13, \u003cem\u003eP \u003c/em\u003e= 0.280), whereas NE score was (OR = 1.15, 95% CI: 1.05, 1.26, \u003cem\u003eP \u003c/em\u003e= 0.003). The association between NE score and persistent distressing PLE was statistically significantly attenuated as SCZ-PRS increased (OR for interaction = 0.92, 95% CI: 0.86, 1.00, \u003cem\u003eP \u003c/em\u003e= 0.039). The findings indicate that persistent distressing PLE may be driven by detrimental neighborhood exposures, particularly among children with low genetic risks.\u003c/p\u003e","manuscriptTitle":"Interaction between Neighborhood Exposome and Genetic Risk in Child Psychotic-like Experiences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-18 13:59:10","doi":"10.21203/rs.3.rs-5830171/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-mental-health","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natmentalhealth","sideBox":"Learn more about [Nature Mental Health](https://www.nature.com/natmentalhealth/)","snPcode":"44220","submissionUrl":"https://mts-natmentalhealth.nature.com/cgi-bin/main.plex","title":"Nature Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4f063127-4813-4582-b289-75420683207f","owner":[],"postedDate":"February 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44282292,"name":"Health sciences/Medical research/Epidemiology"},{"id":44282293,"name":"Health sciences/Diseases/Psychiatric disorders/Psychosis"}],"tags":[],"updatedAt":"2026-01-03T08:07:47+00:00","versionOfRecord":{"articleIdentity":"rs-5830171","link":"https://doi.org/10.1038/s44220-025-00563-8","journal":{"identity":"nature-mental-health","isVorOnly":false,"title":"Nature Mental Health"},"publishedOn":"2026-01-02 05:00:00","publishedOnDateReadable":"January 2nd, 2026"},"versionCreatedAt":"2025-02-18 13:59:10","video":"","vorDoi":"10.1038/s44220-025-00563-8","vorDoiUrl":"https://doi.org/10.1038/s44220-025-00563-8","workflowStages":[]},"version":"v1","identity":"rs-5830171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5830171","identity":"rs-5830171","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}