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<jats:sec> <jats:title>INTRODUCTION</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>RATIONALE</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p>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.</jats:p> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSION</jats:title> <jats:p>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.</jats:p> <jats:fig fig-type="figure" orientation="portrait" position="float"> <jats:caption> <jats:title>3DG remodeling across neuronal differentiation with parallel expansion of SZ risk space.</jats:title> <jats:p>(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.</jats:p> </jats:caption> <jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" orientation="portrait" position="float" xlink:href="362_aat4311_fa.jpeg" /> </jats:fig> </jats:sec>

<jats:sec> <jats:title>INTRODUCTION</jats:title> <jats:p>The human cerebral cortex has undergone an extraordinary increase in size and complexity during mammalian evolution. Cortical cell lineages are specified in the embryo, and genetic and epidemiological evidence implicates early cortical development in the etiology of neuropsychiatric disorders such as autism spectrum disorder (ASD), intellectual disabilities, and schizophrenia. Most of the disease-implicated genomic variants are located outside of genes, and the interpretation of noncoding mutations is lagging behind owing to limited annotation of functional elements in the noncoding genome.</jats:p> </jats:sec> <jats:sec> <jats:title>RATIONALE</jats:title> <jats:p>We set out to discover gene-regulatory elements and chart their dynamic activity during prenatal human cortical development, focusing on enhancers, which carry most of the weight upon regulation of gene expression. We longitudinally modeled human brain development using human induced pluripotent stem cell (hiPSC)–derived cortical organoids and compared organoids to isogenic fetal brain tissue.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p>Fetal fibroblast–derived hiPSC lines were used to generate cortically patterned organoids and to compare oganoids’ epigenome and transcriptome to that of isogenic fetal brains and external datasets. Organoids model cortical development between 5 and 16 postconception weeks, thus enabling us to study transitions from cortical stem cells to progenitors to early neurons. The greatest changes occur at the transition from stem cells to progenitors. The regulatory landscape encompasses a total set of 96,375 enhancers linked to target genes, with 49,640 enhancers being active in organoids but not in mid-fetal brain, suggesting major roles in cortical neuron specification. Enhancers that gained activity in the human lineage are active in the earliest stages of organoid development, when they target genes that regulate the growth of radial glial cells.</jats:p> <jats:p>Parallel weighted gene coexpression network analysis (WGCNA) of transcriptome and enhancer activities defined a number of modules of coexpressed genes and coactive enhancers, following just six and four global temporal patterns that we refer to as supermodules, likely reflecting fundamental programs in embryonic and fetal brain. Correlations between gene expression and enhancer activity allowed stratifying enhancers into two categories: activating regulators (A-regs) and repressive regulators (R-regs). Several enhancer modules converged with gene modules, suggesting that coexpressed genes are regulated by enhancers with correlated patterns of activity. Furthermore, enhancers active in organoids and fetal brains were enriched for ASD de novo variants that disrupt binding sites of homeodomain, Hes1, NR4A2, Sox3, and NFIX transcription factors.</jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSION</jats:title> <jats:p>We validated hiPSC-derived cortical organoids as a suitable model system for studying gene regulation in human embryonic brain development, evolution, and disease. Our results suggest that organoids may reveal how noncoding mutations contribute to ASD etiology.</jats:p> <jats:fig fig-type="figure" orientation="portrait" position="float"> <jats:caption> <jats:title>Summary of the study, analyses, and main results.</jats:title> <jats:p>Data were generated for iPSC-derived human telencephalic organoids and isogenic fetal cortex. Organoids modeled embryonic and early fetal cortex and show a larger repertoire of enhancers. Enhancers could be divided into activators and repressors of gene expression. We derived networks of modules and supermodules with correlated gene and enhancer activities, some of which were implicated in autism spectrum disorders (ASD).</jats:p> </jats:caption> <jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" orientation="portrait" position="float" xlink:href="362_aat6720_fa.jpeg" /> </jats:fig> </jats:sec>

<jats:sec> <jats:title>INTRODUCTION</jats:title> <jats:p>Strong genetic associations have been found for a number of psychiatric disorders. However, understanding the underlying molecular mechanisms remains challenging.</jats:p> </jats:sec> <jats:sec> <jats:title>RATIONALE</jats:title> <jats:p>To address this challenge, the PsychENCODE Consortium has developed a comprehensive online resource and integrative models for the functional genomics of the human brain.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p>The base of the pyramidal resource is the datasets generated by PsychENCODE, including bulk transcriptome, chromatin, genotype, and Hi-C datasets and single-cell transcriptomic data from ~32,000 cells for major brain regions. We have merged these with data from Genotype-Tissue Expression (GTEx), ENCODE, Roadmap Epigenomics, and single-cell analyses. Via uniform processing, we created a harmonized resource, allowing us to survey functional genomics data on the brain over a sample size of 1866 individuals.</jats:p> <jats:p>From this uniformly processed dataset, we created derived data products. These include lists of brain-expressed genes, coexpression modules, and single-cell expression profiles for many brain cell types; ~79,000 brain-active enhancers with associated Hi-C loops and topologically associating domains; and ~2.5 million expression quantitative-trait loci (QTLs) comprising ~238,000 linkage-disequilibrium–independent single-nucleotide polymorphisms and of other types of QTLs associated with splice isoforms, cell fractions, and chromatin activity. By using these, we found that &gt;88% of the cross-population variation in brain gene expression can be accounted for by cell fraction changes. Furthermore, a number of disorders and aging are associated with changes in cell-type proportions. The derived data also enable comparison between the brain and other tissues. In particular, by using spectral analyses, we found that the brain has distinct expression and epigenetic patterns, including a greater extent of noncoding transcription than other tissues.</jats:p> <jats:p>The top level of the resource consists of integrative networks for regulation and machine-learning models for disease prediction. The networks include a full gene regulatory network (GRN) for the brain, linking transcription factors, enhancers, and target genes from merging of the QTLs, generalized element-activity correlations, and Hi-C data. By using this network, we link disease genes to genome-wide association study (GWAS) variants for psychiatric disorders. For schizophrenia, we linked 321 genes to the 142 reported GWAS loci. We then embedded the regulatory network into a deep-learning model to predict psychiatric phenotypes from genotype and expression. Our model gives a ~6-fold improvement in prediction over additive polygenic risk scores. Moreover, it achieves a ~3-fold improvement over additive models, even when the gene expression data are imputed, highlighting the value of having just a small amount of transcriptome data for disease prediction. Lastly, it highlights key genes and pathways associated with disorder prediction, including immunological, synaptic, and metabolic pathways, recapitulating de novo results from more targeted analyses.</jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSION</jats:title> <jats:p>Our resource and integrative analyses have uncovered genomic elements and networks in the brain, which in turn have provided insight into the molecular mechanisms underlying psychiatric disorders. Our deep-learning model improves disease risk prediction over traditional approaches and can be extended with additional data types (e.g., microRNA and neuroimaging).</jats:p> <jats:fig fig-type="figure" orientation="portrait" position="float"> <jats:caption> <jats:title>A comprehensive functional genomic resource for the adult human brain.</jats:title> <jats:p>The resource forms a three-layer pyramid. The bottom layer includes sequencing datasets for traits, such as schizophrenia. The middle layer represents derived datasets, including functional genomic elements and QTLs. The top layer contains integrated models, which link genotypes to phenotypes. DSPN, Deep Structured Phenotype Network; PC1 and PC2, principal components 1 and 2; ref, reference; alt, alternate; H3K27ac, histone H3 acetylation at lysine 27.</jats:p> </jats:caption> <jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" orientation="portrait" position="float" xlink:href="362_aat8464_fa.jpeg" /> </jats:fig> </jats:sec>

<jats:sec> <jats:title>INTRODUCTION</jats:title> <jats:p>The brain is responsible for cognition, behavior, and much of what makes us uniquely human. The development of the brain is a highly complex process, and this process is reliant on precise regulation of molecular and cellular events grounded in the spatiotemporal regulation of the transcriptome. Disruption of this regulation can lead to neuropsychiatric disorders.</jats:p> </jats:sec> <jats:sec> <jats:title>RATIONALE</jats:title> <jats:p>The regulatory, epigenomic, and transcriptomic features of the human brain have not been comprehensively compiled across time, regions, or cell types. Understanding the etiology of neuropsychiatric disorders requires knowledge not just of endpoint differences between healthy and diseased brains but also of the developmental and cellular contexts in which these differences arise. Moreover, an emerging body of research indicates that many aspects of the development and physiology of the human brain are not well recapitulated in model organisms, and therefore it is necessary that neuropsychiatric disorders be understood in the broader context of the developing and adult human brain.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p> Here we describe the generation and analysis of a variety of genomic data modalities at the tissue and single-cell levels, including transcriptome, DNA methylation, and histone modifications across multiple brain regions ranging in age from embryonic development through adulthood. We observed a widespread transcriptomic transition beginning during late fetal development and consisting of sharply decreased regional differences. This reduction coincided with increases in the transcriptional signatures of mature neurons and the expression of genes associated with dendrite development, synapse development, and neuronal activity, all of which were temporally synchronous across neocortical areas, as well as myelination and oligodendrocytes, which were asynchronous. Moreover, genes including <jats:italic>MEF2C</jats:italic> , <jats:italic>SATB2</jats:italic> , and <jats:italic>TCF4</jats:italic> , <jats:italic /> with genetic associations to multiple brain-related traits and disorders, converged in a small number of modules exhibiting spatial or spatiotemporal specificity. </jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSION</jats:title> <jats:p>We generated and applied our dataset to document transcriptomic and epigenetic changes across human development and then related those changes to major neuropsychiatric disorders. These data allowed us to identify genes, cell types, gene coexpression modules, and spatiotemporal loci where disease risk might converge, demonstrating the utility of the dataset and providing new insights into human development and disease.</jats:p> <jats:fig fig-type="figure" orientation="portrait" position="float"> <jats:caption> <jats:title>Spatiotemporal dynamics of human brain development and neuropsychiatric risks.</jats:title> <jats:p>Human brain development begins during embryonic development and continues through adulthood (top). Integrating data modalities (bottom left) revealed age- and cell type–specific properties and global patterns of transcriptional dynamics, including a late fetal transition (bottom middle). We related the variation in gene expression (brown, high; purple, low) to regulatory elements in the fetal and adult brains, cell type–specific signatures, and genetic loci associated with neuropsychiatric disorders (bottom right; gray circles indicate enrichment for corresponding features among module genes). Relationships depicted in this panel do not correspond to specific observations. CBC, cerebellar cortex; STR, striatum; HIP, hippocampus; MD, mediodorsal nucleus of thalamus; AMY, amygdala.</jats:p> </jats:caption> <jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" orientation="portrait" position="float" xlink:href="362_aat7615_fa.jpeg" /> </jats:fig> </jats:sec>

<jats:sec> <jats:title>INTRODUCTION</jats:title> <jats:p>The DNA of protein-coding genes is transcribed into mRNA, which is translated into proteins. The “coding genome” describes the DNA that contains the information to make these proteins and represents ~1.5% of the human genome. Newly arising de novo mutations (variants observed in a child but not in either parent) in the coding genome contribute to numerous childhood developmental disorders, including autism spectrum disorder (ASD). Discovery of these effects is aided by the triplet code that enables the functional impact of many mutations to be readily deciphered. In contrast, the “noncoding genome” covers the remaining ~98.5% and includes elements that regulate when, where, and to what degree protein-coding genes are transcribed. Understanding this noncoding sequence could provide insights into human disorders and refined control of emerging genetic therapies. Yet little is known about the role of mutations in noncoding regions, including whether they contribute to childhood developmental disorders, which noncoding elements are most vulnerable to disruption, and the manner in which information is encoded in the noncoding genome.</jats:p> </jats:sec> <jats:sec> <jats:title>RATIONALE</jats:title> <jats:p>Whole-genome sequencing (WGS) provides the opportunity to identify the majority of genetic variation in each individual. By performing WGS on 1902 quartet families including a child affected with ASD, one unaffected sibling control, and their parents, we identified ~67 de novo mutations across each child’s genome. To characterize the functional role of these mutations, we integrated multiple datasets relating to gene function, genes implicated in neurodevelopmental disorders, conservation across species, and epigenetic markers, thereby combinatorially defining 55,143 categories. The scope of the problem—testing for an excess of de novo mutations in cases relative to controls for each category—is challenging because there are more categories than families.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p>Comparing cases to controls, we observed an excess of de novo mutations in cases in individual categories in the coding genome but not in the noncoding genome. To overcome the challenge of detecting noncoding association, we used machine learning tools to develop a de novo risk score to look for an excess of de novo mutations across multiple categories. This score demonstrated a contribution to ASD risk from coding mutations and a weaker, but significant, contribution from noncoding mutations. This noncoding signal was driven by mutations in the promoter region, defined as the 2000 nucleotides upstream of the transcription start site (TSS) where mRNA synthesis starts. The strongest promoter signals were defined by conservation across species and transcription factor binding sites. Well-defined promoter elements (e.g., TATA-box) are usually observed within 80 nucleotides of the TSS; however, the strongest ASD association was observed distally, 750 to 2000 nucleotides upstream of the TSS.</jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSION</jats:title> <jats:p>We conclude that de novo mutations in the noncoding genome contribute to ASD. The clearest evidence of noncoding ASD association came from mutations at evolutionarily conserved nucleotides in the promoter region. The enrichment for transcription factor binding sites, primarily in the distal promoter, suggests that these mutations may disrupt gene transcription via their interaction with enhancer elements in the promoter region, rather than interfering with transcriptional initiation directly.</jats:p> <jats:fig fig-type="figure" orientation="portrait" position="float"> <jats:caption> <jats:title>Promoter regions in autism.</jats:title> <jats:p>De novo mutations from 1902 quartet families are assigned to 55,143 annotation categories, which are each assessed for autism spectrum disorder (ASD) association by comparing mutation counts in cases and sibling controls. A de novo risk score demonstrated a noncoding contribution to ASD driven by promoter mutations, especially at sites conserved across species, in the distal promoter or targeted by transcription factors.</jats:p> </jats:caption> <jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" orientation="portrait" position="float" xlink:href="362_aat6576_fa.jpeg" /> </jats:fig> </jats:sec>

<jats:sec> <jats:title>INTRODUCTION</jats:title> <jats:p>Improved understanding of how the developing human nervous system differs from that of closely related nonhuman primates is fundamental for teasing out human-specific aspects of behavior, cognition, and disorders.</jats:p> </jats:sec> <jats:sec> <jats:title>RATIONALE</jats:title> <jats:p>The shared and unique functional properties of the human nervous system are rooted in the complex transcriptional programs governing the development of distinct cell types, neural circuits, and regions. However, the precise molecular mechanisms underlying shared and unique features of the developing human nervous system have been only minimally characterized.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p> We generated complementary tissue-level and single-cell transcriptomic datasets from up to 16 brain regions covering prenatal and postnatal development in humans and rhesus macaques ( <jats:italic>Macaca mulatta</jats:italic> ), a closely related species and the most commonly studied nonhuman primate. We created and applied TranscriptomeAge and TempShift algorithms to age-match developing specimens between the species and to more rigorously identify temporal differences in gene expression within and across the species. By analyzing regional and temporal patterns of gene expression in both the developing human and macaque brain, and comparing these patterns to a complementary dataset that included transcriptomic information from the adult chimpanzee, we identified shared and divergent transcriptomic features of human brain development. Furthermore, integration with single-cell and single-nucleus transcriptomic data covering prenatal and adult periods of both species revealed that the developmental divergence between humans and macaques can be traced to distinct cell types enriched in different developmental times and brain regions, including the prefrontal cortex, a region of the brain associated with distinctly human aspects of cognition and behavior. </jats:p> <jats:p> We found two phases of prominent species differences: embryonic to late midfetal development and adolescence/young adulthood. This evolutionary cup-shaped or hourglass-like pattern, with high divergence in prenatal development and adolescence/young adulthood and lower divergence in early postnatal development, resembles the developmental cup-shaped pattern described in the accompanying study by Li <jats:italic>et al</jats:italic> . Even though the developmental (ontogenetic) and evolutionary (phylogenetic) patterns have similar profiles, the overlap of genes driving these two patterns is not substantial, indicating the existence of different molecular mechanisms and constraints for regional specification and species divergence. </jats:p> <jats:p>Notably, we also identified numerous genes and gene coexpression modules exhibiting human-distinct patterns in either temporal (heterochronic) or spatial (heterotopic) gene expression, as well as genes with human-distinct developmental expression, linked to autism spectrum disorder, schizophrenia, and other neurological or psychiatric diseases. This finding potentially suggests mechanistic underpinnings of these disorders.</jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSION</jats:title> <jats:p>Our study provides insights into the evolution of gene expression in the developing human brain and may shed some light on potentially human-specific underpinnings of certain neuropsychiatric disorders.</jats:p> <jats:fig fig-type="figure" orientation="portrait" position="float"> <jats:caption> <jats:title>Concerted ontogenetic and phylogenetic transcriptomic divergence in human and macaque brain.</jats:title> <jats:p>Left: Human and macaque brain regions spanning both prenatal and postnatal development were age-matched using TranscriptomeAge. Right: Phylogenetic transcriptomic divergence between humans and macaques resembles the developmental (ontogenetic) cup-shaped pattern of each species, with high divergence in prenatal development and adolescence/young adulthood and lower divergence during the early postnatal period (from perinatal to adolescence). Single-cell transcriptomics revealed shared and divergent transcriptomic features of distinct cell types.</jats:p> </jats:caption> <jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" orientation="portrait" position="float" xlink:href="362_aat8077_fa.jpeg" /> </jats:fig> </jats:sec>

<jats:sec> <jats:title>INTRODUCTION</jats:title> <jats:p>Our understanding of the pathophysiology of psychiatric disorders, including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD), lags behind other fields of medicine. The diagnosis and study of these disorders currently depend on behavioral, symptomatic characterization. Defining genetic contributions to disease risk allows for biological, mechanistic understanding but is challenged by genetic complexity, polygenicity, and the lack of a cohesive neurobiological model to interpret findings.</jats:p> </jats:sec> <jats:sec> <jats:title>RATIONALE</jats:title> <jats:p>The transcriptome represents a quantitative phenotype that provides biological context for understanding the molecular pathways disrupted in major psychiatric disorders. RNA sequencing (RNA-seq) in a large cohort of cases and controls can advance our knowledge of the biology disrupted in each disorder and provide a foundational resource for integration with genomic and genetic data.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p>Analysis across multiple levels of transcriptomic organization—gene expression, local splicing, transcript isoform expression, and coexpression networks for both protein-coding and noncoding genes—provides an in-depth view of ASD, SCZ, and BD molecular pathology. More than 25% of the transcriptome exhibits differential splicing or expression in at least one disorder, including hundreds of noncoding RNAs (ncRNAs), most of which have unexplored functions but collectively exhibit patterns of selective constraint. Changes at the isoform level, as opposed to the gene level, show the largest effect sizes and genetic enrichment and the greatest disease specificity. We identified coexpression modules associated with each disorder, many with enrichment for cell type–specific markers, and several modules significantly dysregulated across all three disorders. These enabled parsing of down-regulated neuronal and synaptic components into a variety of cell type– and disease-specific signals, including multiple excitatory neuron and distinct interneuron modules with differential patterns of disease association, as well as common and rare genetic risk variant enrichment. The glial-immune signal demonstrates shared disruption of the blood-brain barrier and up-regulation of NFkB-associated genes, as well as disease-specific alterations in microglial-, astrocyte-, and interferon-response modules. A coexpression module associated with psychiatric medication exposure in SCZ and BD was enriched for activity-dependent immediate early gene pathways. To identify causal drivers, we integrated polygenic risk scores and performed a transcriptome-wide association study and summary-data–based Mendelian randomization. Candidate risk genes—5 in ASD, 11 in BD, and 64 in SCZ, including shared genes between SCZ and BD—are supported by multiple methods. These analyses begin to define a mechanistic basis for the composite activity of genetic risk variants.</jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSION</jats:title> <jats:p>Integration of RNA-seq and genetic data from ASD, SCZ, and BD provides a quantitative, genome-wide resource for mechanistic insight and therapeutic development at Resource.PsychENCODE.org. These data inform the molecular pathways and cell types involved, emphasizing the importance of splicing and isoform-level gene regulatory mechanisms in defining cell type and disease specificity, and, when integrated with genome-wide association studies, permit the discovery of candidate risk genes.</jats:p> <jats:fig fig-type="figure" orientation="portrait" position="float"> <jats:caption> <jats:title>The PsychENCODE cross-disorder transcriptomic resource.</jats:title> <jats:p>Human brain RNA-seq was integrated with genotypes across individuals with ASD, SCZ, BD, and controls, identifying pervasive dysregulation, including protein-coding, noncoding, splicing, and isoform-level changes. Systems-level and integrative genomic analyses prioritize previously unknown neurogenetic mechanisms and provide insight into the molecular neuropathology of these disorders.</jats:p> </jats:caption> <jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" orientation="portrait" position="float" xlink:href="362_aat8127_fa.jpeg" /> </jats:fig> </jats:sec>