ABSTRACT
Studying the genetic basis of gene expression and chromatin organization is key to characterizing the effect of genetic variability on the function and structure of the human genome. Here we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression, and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and interchromosomal levels within 12,583 cis-regulatory domains and 30 trans-regulatory hubs that highly reflect the local (that is, topologically associating domains) and global (that is, open and closed chromatin compartments) nuclear chromatin organization. These structures delimit cell type-specific regulatory networks that control gene expression and coexpression and mediate the genetic effects of cis- and trans-acting regulatory variants on genes.
Subject(s)
Chromatin/metabolism , Gene Expression Regulation , Chromatin/chemistry , Genetic Variation , Genome, Human , Humans , Quantitative Trait Loci , Regulatory Elements, TranscriptionalABSTRACT
Copy number variants (CNVs) are major contributors to genomic imbalance disorders. Phenotyping of 137 unrelated deletion and reciprocal duplication carriers of the distal 16p11.2 220 kb BP2-BP3 interval showed that these rearrangements are associated with autism spectrum disorders and mirror phenotypes of obesity/underweight and macrocephaly/microcephaly. Such phenotypes were previously associated with rearrangements of the non-overlapping proximal 16p11.2 600 kb BP4-BP5 interval. These two CNV-prone regions at 16p11.2 are reciprocally engaged in complex chromatin looping, as successfully confirmed by 4C-seq, fluorescence in situ hybridization and Hi-C, as well as coordinated expression and regulation of encompassed genes. We observed that genes differentially expressed in 16p11.2 BP4-BP5 CNV carriers are concomitantly modified in their chromatin interactions, suggesting that disruption of chromatin interplays could participate in the observed phenotypes. We also identified cis- and trans-acting chromatin contacts to other genomic regions previously associated with analogous phenotypes. For example, we uncovered that individuals with reciprocal rearrangements of the trans-contacted 2p15 locus similarly display mirror phenotypes on head circumference and weight. Our results indicate that chromosomal contacts' maps could uncover functionally and clinically related genes.
Subject(s)
Autistic Disorder/genetics , Chromosome Mapping/methods , Chromosomes, Human, Pair 16/physiology , Obesity/genetics , Adolescent , Adult , Aged , Autism Spectrum Disorder/genetics , Body Mass Index , Child , Child, Preschool , Chromatin/metabolism , Chromatin/physiology , Chromosome Deletion , Chromosome Duplication , Chromosomes, Human, Pair 16/genetics , DNA Copy Number Variations/genetics , Female , Humans , In Situ Hybridization, Fluorescence , Infant , Intellectual Disability/genetics , Male , Megalencephaly/genetics , Microcephaly/genetics , Middle Aged , PhenotypeABSTRACT
Anatomical structures and mechanisms linking genes to neuropsychiatric disorders are not deciphered. Reciprocal copy number variants at the 16p11.2 BP4-BP5 locus offer a unique opportunity to study the intermediate phenotypes in carriers at high risk for autism spectrum disorder (ASD) or schizophrenia (SZ). We investigated the variation in brain anatomy in 16p11.2 deletion and duplication carriers. Beyond gene dosage effects on global brain metrics, we show that the number of genomic copies negatively correlated to the gray matter volume and white matter tissue properties in cortico-subcortical regions implicated in reward, language and social cognition. Despite the near absence of ASD or SZ diagnoses in our 16p11.2 cohort, the pattern of brain anatomy changes in carriers spatially overlaps with the well-established structural abnormalities in ASD and SZ. Using measures of peripheral mRNA levels, we confirm our genomic copy number findings. This combined molecular, neuroimaging and clinical approach, applied to larger datasets, will help interpret the relative contributions of genes to neuropsychiatric conditions by measuring their effect on local brain anatomy.
