Search for: All records

Abstract Adolescence is often associated with an increase in psychopathology. Although previous studies have examined how family environments and neural reward sensitivity separately play a role in youth’s emotional development, it remains unknown how they interact with each other in predicting youth’s internalizing symptoms. Therefore, the current research took a biopsychosocial approach to examine this question using two-wave longitudinal data of 9353 preadolescents (mean age = 9.93 years at T1; 51% boys) from the Adolescent Brain Cognitive Development study. Using mixed-effects models, results showed that higher family conflict predicted youth’s increased internalizing symptoms 1 year later, whereas greater ventral striatum (VS) activity during reward receipt predicted reduced internalizing symptoms over time. Importantly, there was an interaction effect between family conflict and VS activity. For youth who showed greater VS activation during reward receipt, high family conflict was more likely to predict increased internalizing symptoms. In contrast, youth with low VS activation during reward receipt showed high levels of internalizing symptoms regardless of family conflict. The findings suggest that youth’s neural reward sensitivity is a marker of susceptibility to adverse family environments and highlight the importance of cultivating supportive family environments where youth experience less general conflict within the family. 

Fewer than half of individuals with a suspected Mendelian or monogenic condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control data sets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project (1KGP) Oxford Nanopore Technologies Sequencing Consortium aims to generate LRS data from at least 800 of the 1KGP samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37× and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs.