IMPORTANCE Suicide is the second leading cause of death among youths worldwide, but no available means exist to identify the risk of suicide in this population.OBJECTIVE To assess whether genome-wide polygenic scores for psychiatric and common traits are associated with the risk of suicide among preadolescent children and to investigate whether and to what extent the interaction between early life stress (a major environmental risk factor) and polygenic factors is associated with suicidal thoughts and behaviors among youths. DESIGN, SETTING, AND PARTICIPANTSThis cohort study analyzed the genotype-phenotype data of 11 869 preadolescent children aged 9 to 10 years from the Adolescent Brain and Cognitive

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9-10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC-AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (p fdr < .001, η 2 p = .011-.034; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (p fdr < .001, η 2 p < .005). Structural equation models revealed that the GPS-intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006-.009; p = .002-.022; sex-stratified analysis). The finding of the sex modulatory effect on the gene-brain-cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.

Early life stress (ELS), such as abuse, neglect, and maltreatment, is a well-known risk factor for mental illness. However, it is unclear how ELS affects the brain and cognitive development. Identifying specific relationships of ELS with the genetic and brain-related underpinnings of cognitive development may reveal biological mechanisms responsible for the negative impact of ELS and those that lead to individual differences in sensitivity (or resilience) to ELS. In this study, to investigate the interlinked processes of cognitive development, we analyzed the multimodal data of DNA genotypes, brain imaging (MRI), and neuropsychological assessment (NIH Toolbox) outcomes of 4,276 children (ages 9 to 10 years, European ancestry) from the Adolescent Brain Cognitive Development (ABCD) study. We estimated the genetic influence on cognitive capacity using genome-wide polygenic scores (GPSs). Our regression and mediation analyses revealed significant causal relationships for the gene-brain-cognition pathway: Brain structural development significantly mediated the genetic influence on cognitive development (partial mediation effect = 0.016, PFWE<0.001). Interestingly, within the triangular relationship, we found a significant moderation effect of abuse only on the gene-to-brain pathway (Index of Moderated Mediation = -0.007; 95% CI= -0.012 ~ -0.002; PFWE<0.05). These findings indicate the negative modulatory effects of ELS on the genetic influence on brain structural development that lead to disadvantageous neurocognitive development in prepubertal children.

No abstract

No abstract

Suicide is a leading cause of death in youth worldwide, but identifying which youth are at high risk for suicide remains challenging. We constructed genome-wide polygenic scores (GPSs) from 24 psychiatric disorders and common traits from 8,212 US preadolescent children ages 9 to 10 and investigated their associations and predictive utility with suicidality (suicidal ideation and attempt). We identified three GPSs significantly associated with youth suicidality: ADHD (P=2.83x10-4; odds ratio=1.12), general happiness with a belief that life is meaningful (P=1.30x10-3; odds ratio=0.89) and autism spectrum disorder (ASD) (P=1.81x10-3; odds ratio=1.14). We also found a significant gene-by-environment interaction such that the GPS of ASD in the context of early life stress substantially increased suicidal ideation (P=1.39x10-2, odds ratio=1.11). Machine learning models showed, in predicting suicidal ideation, a receiver operators characteristics-area under the curve (ROC-AUC) of 0.72, and, in suicidal attempts, a ROC-AUC of 0.765. By providing the first quantitative account of the polygenic and environmental factors of suicidality in a large, representative population of preadolescent youth, this study shows the potential utility of the GPSs in investigating youth suicidality for early screening, intervention, and prevention.

BackgroundSuicide is the leading cause of death in youth worldwide.1 Identifying children with high risk for suicide remains challenging.2 Here we test the extents to which genome-wide polygenic scores (GPS) for common traits and psychiatric disorders are linked to the risk for suicide in young children.MethodsWe constructed GPSs of 24 traits and psychiatric disorders broadly related to suicidality from 8,212 US children with ages of 9 to 10 from the Adolescent Brain Cognitive Development study. We performed multiple logistic regression to test the association between childhood suicidality, defined as suicidal ideation or suicidal attempt, and the GPSs. Machine learning techniques were used to test the predictive utility of the GPSs and other phenotypic outcomes on suicide and suicidal behaviors in the youth.OutcomesWe identified three GPSs significantly associated with childhood suicidality: Attention deficit hyperactivity disorder (ADHD) (P = 2.83×10−4; odds ratio (OR) = 1.12, FDR correction), general happiness with belief that own life is meaningful (P = 1.30×10−3; OR = 0.89) and autism spectrum disorder (ASD) (P = 1.81×10−3; OR = 1.14). Furthermore, the ASD GPS showed significant interaction with ELS such that a greater polygenic score in the presence of a greater ELS has even greater likelihood of suicidality (with active suicidal ideation, P = 1.39×10−2, OR = 1.11). In machine learning predictions, the cross validated and optimized model showed an ROC-AUC of 0.72 and accuracy of 0.756 for the hold-out set of overall suicidal ideation prediction, and showed an ROC-AUC of 0.765 and accuracy of 0.750 for the hold-out set of suicidal attempts.InterpretationOur results show that childhood suicidality is linked to the GPSs for psychiatric disorders, ADHD and ASD, and for a common trait, general happiness, respectively; and that GPSs for ASD and insomnia, respectively, have synergistic effects on suicidality via an interaction with early life stress. By providing the quantitative account of the polygenic and environmental factors of childhood suicidality in a large, representative population, this study shows the potential utility of the GPS in investigation of childhood suicidality for early screening, intervention, and prevention.

Importance. Suicide is the second leading cause of death in children worldwide but no available means exist to identify the risk in youth. Objective. To predict the risk of suicide in children and to investigate whether and to what extents genetic factors and a major environmental risk factor, early life stress(ELS), influence youth suicide. Design, Setting and Participants. We analyzed the genotype-phenotype data of 11,869 preadolescent children ages 9- to 10-year-old from the Adolescent Brain and Cognitive Development (ABCD) study. We estimated genome-wide polygenic scores (GPSs) of 25 complex traits to investigate their phenome-wide associations and predictive utility with suicidality (suicidal ideation and attempt) with machine learning approaches. Predictors. GPSs of 25 traits including psychiatric disorders, personality, cognitive capacity, and psychological traits. Parent Child Behavior Checklist to measure ELS in youth and Youth Family Environment Scale to assess family environment. Main outcomes and Measures. Records of suicidal ideation and attempt of the participants were derived from the computerized version of Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS). Results. We identified three GPSs associated with youth suicidality in multiethnic (n = 7,206) and European-ancestry (n = 5,749) participants: ADHD (P = 3.48x10− 4; odds ratio = 1.13 in multiethnic participants, P = 5.60x10− 5, OR = 1.25 in European-ancestry participants), general happiness (P = 1.43x10− 3; OR = 0.89 in multiethnic, P = 8.61x10− 4, OR = 0.89 in European) and autism spectrum disorder(ASD) (P = 1.81x10− 3; OR = 1.15 in multiethnic, P = 1.26x10− 3, OR = 1.18 in European). We also found a significant GPS-by-environment interaction between the effects of genetic risk factors for ASD and the level of ELS in increasing the risk for suicidal ideation (P = 1.36x10− 2, OR = 1.12 in multiethnic, P = 1.39x10− 3, OR = 1.19 in European). A machine learning model trained on the same data showed moderately accurate prediction of children with overall suicidal ideation with a test ROC-AUC of 0.727 (0.746 in European), and with suicidal attempts with a test ROC-AUC of 0.641 (0.975 in European) in held-out samples. Conclusions and Relevance. This study provides the first quantitative account of polygenic and environmental factors of suicidality in a large, representative population of preadolescent youth. It thus shows the potential utility of the GPSs in identifying a child with high risk for suicidality for early screening, intervention, and prevention.