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Schizophrenia is a polygenic psychiatric disorder with limited understanding about the mechanistic changes in gene expression regulation. To elucidate on this, we integrate interindividual variability of regulatory activity (ChIP-sequencing for H3K27ac histone mark) with gene expression and genotype data captured from the prefrontal cortex of 272 cases and controls. By measuring interindividual correlation among proximal chromatin peaks, we show that regulatory element activity is structured into 10,936 and 10,376 cis-regulatory domains in cases and controls, respectively. The schizophrenia-specific cis-regulatory domains are enriched for fetal-specific (p= 0.0014, OR = 1.52) and depleted of adult-specific regulatory activity (p= 3.04 × 10−50, OR = 0.57) and are enriched for SCZ heritability (p= 0.001). By studying the interplay among genetic variants, gene expression, and cis-regulatory domains, we ascertain that changes in coordinated regulatory activity tag alterations in gene expression levels (p= 3.43 × 10−5, OR = 1.65), unveil case-specific QTL effects, and identify regulatory machinery changes for genes affecting synaptic function and dendritic spine morphology in schizophrenia. Altogether, we show that accounting for coordinated regulatory activity provides a novel mechanistic approach to reduce the search space for unveiling genetically perturbed regulation of gene expression in schizophrenia.

We present the software Condition-specific Regulatory Units Prediction (CRUP) to infer from epigenetic marks a list of regulatory units consisting of dynamically changing enhancers with their target genes. The workflow consists of a novel pre-trained enhancer predictor that can be reliably applied across cell types and species, solely based on histone modification ChIP-seq data. Enhancers are subsequently assigned to different conditions and correlated with gene expression to derive regulatory units. We thoroughly test and then apply CRUP to a rheumatoid arthritis model, identifying enhancer-gene pairs comprising known disease genes as well as new candidate genes.