Early life stress modulates the genetic influence on brain structure and cognitive function in children.

The enduring influence of early life stress (ELS) on brain and cognitive development has been widely acknowledged, yet the precise mechanisms underlying this association remain elusive. We hypothesize that ELS might disrupt the genome-wide influence on brain morphology and connectivity development, consequently exerting a detrimental impact on children's cognitive ability. We analyzed the multimodal data of DNA genotypes, brain imaging (structural and diffusion MRI), and neurocognitive battery (NIH Toolbox) of 4276 children (ages 9-10 years, European ancestry) from the Adolescent Brain Cognitive Development (ABCD) study. The genome-wide influence on cognitive function was estimated using the polygenic score (GPS). By using brain morphometry and tractography, we identified the brain correlates of the cognition GPSs. Statistical analyses revealed relationships for the gene-brain-cognition pathway. The brain structural variance significantly mediated the genetic influence on cognition (indirect effect = 0.016, PFDR < 0.001). Of note, this gene-brain relationship was significantly modulated by abuse, resulting in diminished cognitive capacity (Index of Moderated Mediation = -0.007; 95 % CI = -0.012 âˆ¼ -0.002). Our results support a novel gene-brain-cognition model likely elucidating the long-lasting negative impact of ELS on children's cognitive development.

The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children.

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 (pfdr < .001, ηp2  = .011-.034; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (pfdr < .001, ηp2 < .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.

Association of Genome-Wide Polygenic Scores for Multiple Psychiatric and Common Traits in Preadolescent Youths at Risk of Suicide.

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 Participants: This cohort study analyzed the genotype-phenotype data of 11 869 preadolescent children aged 9 to 10 years from the Adolescent Brain and Cognitive Development study. Data were collected from September 1, 2016, to October 21, 2018, and analyzed from August 1, 2020, to January 3, 2021. Using machine learning approaches, genome-wide polygenic scores of 24 complex traits were estimated to investigate their phenome-wide associations and utility for assessing risk of suicidal thoughts and behaviors (suicidal ideation [active, passive, and overall] and suicide attempt). Main Outcomes and Measures: Genome-wide polygenic scores were used to measure 24 traits, including psychiatric disorders, cognitive capacity, and personality and psychological characteristics. The Child Behavior Checklist was used to measure early life stress, and the Family Environment Scale was used to assess family environment. Suicidal ideation and suicide attempts were derived from the computerized version of the Kiddie Schedule for Affective Disorders and Schizophrenia. Results: Among 11 869 preadolescent children in the US, complete data for phenotypic outcomes, genotypes, and covariates were available for 7140 participants in the multiethnic cohort (mean [SD] age, 9.9 [0.6] years; 3588 girls [50.3%]), including 925 participants with suicidal ideation and 63 participants with suicide attempts. Among those 7140 participants, 729 had African ancestry (self-reported race or ethnicity: 569 Black, 71 Hispanic, and 89 other), 276 had admixed American ancestry (self-reported race or ethnicity: 265 Hispanic, 3 White, and 8 other), 150 had East Asian ancestry (self-reported race or ethnicity: 67 Asian, 18 Hispanic, and 65 other), 5718 had European ancestry (self-reported race or ethnicity: 7 Asian, 39 Black, 1142 Hispanic, 3934 White, and 596 other), and 267 had other ancestries (self-reported race or ethnicity: 70 Asian, 13 Black, 126 Hispanic, 48 White, and 10 other). Three genome-wide polygenic scores were significantly associated (false discovery rate P < .05) with suicidal thoughts and behaviors among all participants: attention-deficit/hyperactivity disorder (odds ratio [OR], 1.12; 95% CI, 1.05-1.21; P = .001), schizophrenia (OR, 1.50; 95% CI, 1.17-1.93; P = .002), and general happiness (OR, 0.89; 95% CI, 0.83-0.96; P = .002). In the analysis including only children with European ancestry, 3 additional genome-wide polygenic scores with false discovery rate significance were associated with suicidal thoughts and behaviors: autism spectrum disorder (OR, 1.18; 95% CI, 1.06-1.31; P = .002), major depressive disorder (OR, 1.12; 95% CI, 1.04-1.21; P = .003), and posttraumatic stress disorder (OR, 1.12; 95% CI, 1.04-1.21; P = .004). A significant interaction between genome-wide polygenic scores and environment was found, with genetic risk factors for autism spectrum disorder and the level of early life stress associated with increases in the risk of overall suicidal ideation and overall suicidal thoughts and behaviors (OR, 1.20; 95% CI, 1.07-1.35; P = .002). A machine learning model using multitrait genome-wide polygenic scores and additional self-reported questionnaire data (Child Behavior Checklist and Family Environment Scale) produced a moderately accurate estimate of overall suicidal thoughts and behaviors (area under the receiver operating characteristic curve [AUROC], 0.77; 95% CI, 0.73-0.81; accuracy, 0.67) and suicidal ideation (AUROC, 0.76; 95% CI, 0.72-0.80; accuracy, 0.66) among children with European ancestry only. Among all children in the multiethnic cohort, the integrated model also outperformed the baseline model in estimating the risk of overall suicidal thoughts and behaviors (AUROC, 0.71; 95% CI, 0.67-0.75; accuracy, 0.68) and suicidal ideation (AUROC, 0.75; 95% CI, 0.71-0.78; accuracy, 0.67). Conclusions and Relevance: In this cohort study of preadolescent youths in the US, higher genome-wide polygenic scores for psychiatric disorders, such as attention-deficit/hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, and schizophrenia, were significantly associated with a greater risk of suicidal ideation and suicide attempt. The findings and quantitative models from this study may help to identify children with a high risk of suicide, potentially assisting with early screening, intervention, and prevention.