Background: As large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. Results: We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation based on a genome-wide summary, and identify genes that deviate from the genome-wide trend. Using a linear mixed model, variancePartition quantifies variation in each expression trait attributable to differences in disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large-scale transcriptome profiling datasets illustrates that variancePartition recovers striking patterns of biological and technical variation that are reproducible across multiple datasets. Conclusions: Our open source software, variancePartition, enables rapid interpretation of complex gene expression studies as well as other high-throughput genomics assays. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition.

Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet, current methods for differential expression are inadequate for cross-individual testing for these repeated measures designs. Most problematic, we observe across multiple datasets that current methods can give reproducible false-positive findings that are driven by genetic regulation of gene expression, yet are unrelated to the trait of interest. Here, we introduce a statistical software package, dream, that increases power, controls the false positive rate, enables multiple types of hypothesis tests, and integrates with standard workflows. In 12 analyses in 6 independent datasets, dream yields biological insight not found with existing software while addressing the issue of reproducible false-positive findings. Dream is available within the variancePartition Bioconductor package at http://bioconductor.org/packages/variancePartition. gabriel.hoffman@mssm.edu Supplementary data are available at Bioinformatics online.

Schizophrenia and bipolar disorder are serious mental illnesses that affect more than 2% of adults. While large-scale genetics studies have identified genomic regions associated with disease risk, less is known about the molecular mechanisms by which risk alleles with small effects lead to schizophrenia and bipolar disorder. In order to fill this gap between genetics and disease phenotype, we have undertaken a multi-cohort genomics study of postmortem brains from controls, individuals with schizophrenia and bipolar disorder. Here we present a public resource of functional genomic data from the dorsolateral prefrontal cortex (DLPFC; Brodmann areas 9 and 46) of 986 individuals from 4 separate brain banks, including 353 diagnosed with schizophrenia and 120 with bipolar disorder. The genomic data include RNA-seq and SNP genotypes on 980 individuals, and ATAC-seq on 269 individuals, of which 264 are a subset of individuals with RNA-seq. We have performed extensive preprocessing and quality control on these data so that the research community can take advantage of this public resource available on the Synapse platform at http://CommonMind.org.

INTRODUCTION: Chromosomal conformations, topologically associated chromatin domains (TADs) assembling in nested fashion across hundreds of kilobases, and other “three-dimensional genome” (3DG) structures bypass the linear genome on a kilo- or megabase scale and play an important role in transcriptional regulation. Most of the genetic variants associated with risk for schizophrenia (SZ) are common and could be located in enhancers, repressors, and other regulatory elements that influence gene expression; however, the role of the brain’s 3DG for SZ genetic risk architecture, including developmental and cell type–specific regulation, remains poorly understood. RATIONALE: We monitored changes in 3DG after isogenic differentiation of human induced pluripotent stem cell–derived neural progenitor cells (NPCs) into neurons or astrocyte-like glial cells on a genome-wide scale using Hi-C. With this in vitro model of brain development, we mapped cell type–specific chromosomal conformations associated with SZ risk loci and defined a risk-associated expanded genome space. RESULTS: Neural differentiation was associated with genome-wide 3DG remodeling, including pruning and de novo formations of chromosomal loopings. The NPC-to-neuron transition was defined by the pruning of loops involving regulators of cell proliferation, morphogenesis, and neurogenesis, which is consistent with a departure from a precursor stage toward postmitotic neuronal identity. Loops lost during NPC-to-glia transition included many genes associated with neuron-specific functions, which is consistent with non-neuronal lineage commitment. However, neurons together with NPCs, as compared with glia, harbored a much larger number of chromosomal interactions anchored in common variant sequences associated with SZ risk. Because spatial 3DG proximity of genes is an indicator for potential coregulation, we tested whether the neural cell type–specific SZ-related “chromosomal connectome” showed evidence of coordinated transcriptional regulation and proteomic interaction of the participating genes. To this end, we generated lists of genes anchored in cell type–specific SZ risk-associated interactions. Thus, for the NPC-specific interactions, we counted 386 genes, including 146 within the risk loci and another 240 genes positioned elsewhere in the linear genome but connected via intrachromosomal contacts to risk locus sequences. Similarly, for the neuron-specific interactions, we identified 385 genes: 158 within risk loci and 227 outside of risk loci. Last, for glia-specific interactions, we identified 201 genes: 88 within and 113 outside of risk loci. We labeled the genes located outside of schizophrenia risk loci as “risk locus–connect,” which we define as a collection of genes identified only through Hi-C interaction data, expanding—depending on cell type—by 50 to 150% the current network of known genes overlapping risk sequences that is informed only by genome-wide association studies. This disease-related chromosomal connectome was associated with “clusters” of coordinated gene expression and protein interactions, with at least one cluster strongly enriched for regulators of neuronal connectivity and synaptic plasticity and another cluster for chromatin-associated proteins, including transcriptional regulators. CONCLUSION: Our study shows that neural differentiation is associated with highly cell type–specific 3DG remodeling. This process is paralleled by an expansion of 3DG space associated with SZ risk. Specifically, developmentally regulated chromosomal conformation changes at SZ-relevant sequences disproportionally occurred in neurons, highlighting the existence of cell type–specific disease risk vulnerabilities in spatial genome organization. 3DG remodeling across neuronal differentiation with parallel expansion of SZ risk space.: (Left) Chromatin conformation assays reveal pruning of short-range loops in neurons along with widening of TADs upon differentiation from NPCs. (Right) Cell type–specific chromatin interactions, functionally validated with CRISPR assays, expand the network of known risk-associated genes (blue circle), which show evidence for coregulation at the transcriptomic and proteomic levels.