Decoupling sleep and brain size in childhood: An investigation of genetic covariation in the ABCD study

Abstract

Childhood sleep problems are common and frequently comorbid with neurodevelopmental, psychiatric disorders. However, little is known about the genetic contributions to sleep-related traits in childhood, their potential relationship with brain development and psychiatric outcomes, or their association with adult sleep disturbance. Using data from the Adolescent Brain and Cognitive Development study, we performed a comprehensive characterization of the genetic and phenotypic relationships between childhood sleep disturbances (SDs; insomnia, arousal, breathing, somnolence, hyperhidrosis, sleep-wake transitions), global and regional measures of brain structure, and multiple dimensions of psychiatric symptomology in 9-10-year-old youth (discovery/replication N=4,428). Among the six SDs assessed, only insomnia showed significant SNP-based heritability (h2=0.15) and had replicable associations with smaller brain surface area (SA). Furthermore, insomnia showed significant positive phenotypic and genetic correlations with externalizing disorders (e.g., attention-deficit/hyperactivity disorder [ADHD]). Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of ADHD predicted insomnia and externalizing symptoms longitudinally, as well as decreased SA at baseline. In contrast, PRS trained using the largest adult insomnia GWAS did not predict childhood insomnia. Together, these findings demonstrate a distinct genetic architecture between childhood and adult SD, and indicate that childhood insomnia should be considered along the dimensional axis of ADHD and externalizing traits. These results highlight the importance of developmental context when interpreting gene-brain-behavior relationships and underscore the need for further large-scale genetic investigations of psychiatric phenotypes in pediatric populations.

White matter microstructure shows sex differences in late childhood: Evidence from 6,797 children

“…Specifically, we statistically compared the effect size of the most sensitive DTI metric with that of the most sensitive RSI metric for each fiber tract ROI. In line with all other analyses, results were only deemed significant and replicable if they survived FDR correction in the discovery cohort (q<0.05) and demonstrated p<0.05 in the replication cohort (Hernandez et al, 2020).…”

“…All regressions were completed in R 3.6.1 using the lme4 package and included the following nuisance covariates as fixed effects: age, household income, parental education, race, and ethnicity; MRI scanner was modeled as a random effect in all analyses. Results were considered significant and replicable if they survived a 5% false discovery rate (FDR) applied across the number of ROIs in the discovery cohort (q<0.05) and demonstrated p<0.05 in the replication cohort (Benjamini & Hochberg, 1995;Hernandez et al, 2020). Effect sizes are reported for all analyses as the magnitude of the standardized regression coefficients (standardized betas; bs), reflecting that group comparisons were completed as regressions to allow for the inclusion of nuisance covariates.…”

The role of sleep in the human brain and body: insights from multi-organ imaging genetics

“…The sleep-associated genetic variants are enriched for genes expressed in the brain and for metabolic and psychiatric pathways 44 . Genetic correlations between sleep traits and brain-related disorders (such as depression and schizophrenia) have been discovered, suggesting their shared neurogenetic basis 46 . On the other hand, both brain and cardiac MRI traits are also heritable and hundreds of associated genetic loci have been identified in recent GWAS [47][48][49][50][51][52][53][54][55][56][57][58] .…”