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.

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.

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.

To explore the developmental reorganization of the three-dimensional genome of the brain in the context of neuropsychiatric disease, we monitored chromosomal conformations in differentiating neural progenitor cells. Neuronal and glial differentiation was associated with widespread developmental remodeling of the chromosomal contact map and included interactions anchored in common variant sequences that confer heritable risk for schizophrenia. We describe cell type–specific chromosomal connectomes composed of schizophrenia risk variants and their distal targets, which altogether show enrichment for genes that regulate neuronal connectivity and chromatin remodeling, and evidence for coordinated transcriptional regulation and proteomic interaction of the participating genes. Developmentally regulated chromosomal conformation changes at schizophrenia-relevant sequences disproportionally occurred in neurons, highlighting the existence of cell type–specific disease risk vulnerabilities in spatial genome organization.

Spatial genome organization is highly regulated and critically important for normal brain development and function (1). Many of the risk variants contributing to the heritability of complex genetic psychiatric disorders are located in noncoding sequences (2), presumably embedded in “three-dimensional genome” (3DG) structures important for transcriptional regulation, such as chromosomal loop formations that bypass linear genome on a kilobase (or megabase) scale and topologically associated domains (TADs) (3) that assemble in nested fashion across hundreds of kilobases (4–7). By linking noncoding schizophrenia-associated genetic variants with distal gene targets, 3DG mapping with Hi-C (3, 8) and other genome-scale approaches could inform how higher-order chromatin organization affects genetic risk for psychiatric disease. To date, only a very limited number of Hi-C datasets exist for the human brain: two generated from bulk tissue of developing forebrain structures (7) and adult brain (9) and one from neural stem cells (10). Although such datasets have advanced our understanding of the genetic risk architecture of psychiatric disease (7, 11), 3DG mapping from postmortem tissue lacks cell type–specific resolution and may not capture higher-order chromatin structures sensitive to the autolytic process (12). We monitored developmentally regulated changes in chromosomal conformations during the course of isogenic neuronal and glial differentiation, describing large-scale pruning of chromosomal contacts during the transition from neural progenitor cells (NPCs) to neurons. Furthermore, we uncovered an expanded 3DG risk space for schizophrenia—with a functional network of disease-relevant regulators of neuronal connectivity, synaptic signaling, and chromatin remodeling—and demonstrate neural cell type–specific coordination at the level of the chromosomal connectome, transcriptome, and proteome.

Results

Neural progenitor differentiation is associated with dynamic 3DG remodeling We applied in situ Hi-C to map the 3DG of two male human induced pluripotent stem cell (hiPSC)–derived neural progenitor cells (NPCs) (13), together with isogenic populations of induced excitatory neurons (“neuron”) generated through viral overexpression of the transcription factor NGN2 (14) and differentiations of astrocyte-like glial cells (“glia”) (Fig. 1, A and B, and table S1) (15). Transcriptome RNA sequencing (RNA-seq) comparison with published datasets (16) confirmed that the NPCs, but not glia, from subjects S1 and S2 clustered together with NPCs from independent donors, whereas S1 and S2 NGN2 neurons closely aligned with directed differentiation forebrain neurons (17) and prenatal brain datasets (fig. S1, A and B). As with our transcriptomic datasets, hierarchical clustering of our Hi-C datasets after initial processing (fig. S2A) also showed clear separation by cell type (Fig. 1A and fig. S2B). Genome-scale interaction matrices were enriched for intrachromosomal conformations (fig. S2C), with the exception of the negative control (“No Ligase”) NPC library, in which we omitted the ligase step (Materials and methods) and observed an interaction map with no signal due to the loss of chimeric fragments (fig. S2D). Given the observed correlation between technical replicates of Hi-C assays from the same donor and cell type, and the correlation between cell type–specific Hi-C from the two donors (Pearson correlation of PC1, R technical replicates , range = 0.970 to 0.979; R subject1-subject 2 by cell type , range = 0.962 to 0.970), we pooled by cell type for subsequent analyses (fig. S2E). Fig. 1 Neural differentiation is associated with large-scale remodeling of the 3D genome. (A) (Top) Derivation scheme of isogenic cell types from two male control cell lines. Pink oval, donor hiPSC; orange, NPC; green, neuron; purple, glia. (Bottom) Hierarchical clustering of intrachromosomal interactions (Materials and methods) from six in situ Hi-C libraries. a and b are technical replicates of the same library; height corresponds to the distance between libraries (Materials and methods) (fig. S2B). (B) Immunofluorescent staining of characteristic cell markers for NPCs (Nestin and SOX2), neurons (TUJ1 and MAP2), and glia (Vimentin and S100β). (C) Venn diagram of loop calls specific to and shared by different subsets of cells, including previously published GM12878 lymphoblastoid Hi-C data. (D) Gene ontology (GO) enrichment (significant terms only) of genes overlapping anchors of loops shared by NPCs, neurons, and glia but absent in GM12878. (E) (Left) Cell-type pooled whole-genome heatmaps at 500-kb resolution (fig. S2C). (Right) “Arc map” showing intrachromosomal interactions at 40-kb resolution of the q-arm of chr17 for isogenic neurons, NPCs, and glia, as indicated, from subject 2. RNA-seq tracks for each cell type shown on top of arc maps. Green, neuron; orange, NPC; purple, glia. (F) FPKM gene expression of CUX2 across three cell types with heatmap zoomed in on CUX2 loop (black arrow) (fig. S3). (G) Number of loops specific to each cell type (not shared with other cell types) with one anchor in an A compartment and another in a B compartment (pink), both in B compartments (red), or both in A compartments (blue). (H) (Left) Box-and-whisker distribution plot of TAD size across four cell types. (Right) Median TAD length for each of the four cell types. (I) Heatmaps at 40-kb resolution for a 3-Mb window at the CDH2 locus on chr18. (Bottom) Nested TAD landscape in glia with multiple subTADs (black arrows) called, which (top) is absent from neuronal Hi-C. RNA-seq tracks: green, neuron; purple, glia (figs. S1 to S5). We first focused on intrachromosomal loop formations, which are conservatively defined as distinct contacts between two loci in the absence of similar interactions in the surrounding sequences (3). Our comparative analyses included published (3) in situ Hi-C data from the B lymphocyte–derived cell line GM12878 (table S1). When analyzed with the HiCCUPS pipeline (5- and 10-kb loop resolutions combined, subsampled to 372 million valid-intrachromosomal read pairs to reflect the library with the fewest reads after filtration) (3), 17,767 distinct loops were called: n = 3118 (17.5%) were shared among all four cell types, whereas n = 5068 (28.5%) were specific to only one of the four cell types (Fig. 1C). Biologically relevant terms such as “central nervous system development,” “forebrain development,” and “neuron differentiation” were among the top gene ontology (GO) enrichments from genes overlapping loops shared between NPCs, glia, and neurons (brain-specific) but not identified in lymphocytes (Fig. 1D and table S2), indicating strong tissue-specific loop signatures that were also confirmed in individual cell types (fig. S3A and tables S3 to S6). Unexpectedly, there was a reduction (~40 to 50% decrease) in the total number of chromosomal loops in neurons relative to isogenic glia and NPCs (fig. S3, B and C). Reduced densities of chromosomal conformations were also evident in genome browser visualization of chromosomal arms, including chr17q (Fig. 1E). Although both glia and NPCs harbored ~13,000 loop formations, only 7206 were identified in neurons (Fig. 1C; fig. S3, B and C; and table S1), including 442 neuron-specific loop formations. One such neuron-specific loop was at CUX2, a transcription factor whose expression marks a subset of cortical projection neurons (18) and that is highly expressed in our NGN2-induced neurons (Fig. 1F and fig. S3, D and E). Examples of loops lost in neurons include one spanning the Ca2+ channel and dystonia-risk gene, ANO3 (fig. S3F) (19). Furthermore, NPCs, neurons, and glia had similar proportions of loops anchored in solely active (A) compartments, solely inactive (B) compartments, or in both, indicating no preferential loss of either active or inactive loops in neurons (Fig. 1G). However, among the genes overlapping anchors of loops that underwent pruning during the course of the NPC-to-neuron transition, regulators of cell proliferation, morphogenesis, and neurogenesis ranked prominently in the top 25 GO terms with significant enrichment (Benjamini-Hochberg corrected P < 10−6 – 10−12) (fig. S3G and table S4B), which is consistent with a departure from precursor stage toward postmitotic neuronal identity (20). Likewise, loops lost during NPC-to-glia transition were significantly enriched (Benjamini-Hochberg corrected P < 10−3 – 10−6) for neuron-specific functions, including “transmission across chemical synapse,” “γ-aminobutyric acid (GABA) receptor activation,” and “postsynapse” (fig. S3G and table S4C), which is consistent with non-neuronal lineage commitment. We defined “loop genes” as genes that either have gene body or transcription start site (TSS) overlap with a loop anchor (5- or 10-kb bins forming the points of contact in a chromatin loop). Genes with loop-bound gene bodies (one-tailed Z test, Z range = 42.1 to 59.2, P < 10−324 for all) or loop-bound TSS (one-tail Z-test, Z range = 15.2 to 28.8, P range < 2.32 × 10−52 to 4.40 × 10−182) both showed significantly greater expression [mean log 10 (FPKM + 1); FPKM, fragments per kilobase of exon per million fragments mapped] than that of background (all genes for all brain cell types) (fig. S4A), suggesting that looping architecture was associated with increased gene expression. Furthermore, 3% of loops shared by NPCs, neurons, and glia (brain-specific loops) interconnected a brain expression quantitative trait locus (eQTL) single-nucleotide polymorphism (SNP) with its destined target gene(s), representing significant enrichment over background as determined with 1000 random distance- and functional annotation–matched loop samplings, (random sampling, one-sided empirical P = 0.012) (Materials and methods) (fig. S4B). We aimed to confirm that the observed net loss of loop formations during the NPC-to-neuron transition could be replicated across a variety of independent cell culture and in vivo approaches and was not specific to our methodological choice of NGN2-induction. We conducted an additional Hi-C experiment on cells differentiated from hiPSC-NPCs by means of a non-NGN2 protocol that used only differentiation medium and yielded a heterogeneous population of hiPSC-forebrain-neurons in addition to a small subset of glia (17). In addition, we reanalyzed Hi-C datasets generated from a mouse model of neural differentiation, consisting of mouse embryonic stem cell (mESCs), mESC-derived NPCs (mNPC), and cortical neurons (mCN) differentiated from the mNPCs via inhibition of the Sonic Hedgehog (SHH) pathway (21). To examine whether such genome-wide chromosomal loop remodeling also occurred in the developing brain in vivo, we reanalyzed Hi-C data from human fetal cortical plate (CP), mostly composed of young neurons, and forebrain germinal zone (GZ), primarily harboring dividing neural precursor cells in addition to a smaller subset of newly generated neurons (7). Across both the hiPSC-NPC-to-forebrain neuron and mESC-mNPC-mCN differentiation, in vitro neurons showed a 20% decrease in loops compared with their neural progenitors (fig. S4, C and D). Consistent with this, in vivo CP (neuron) compared with GZ (progenitor) showed a 13% decrease in loops genome-wide (fig. S4E). The highly replicative cell types included here, mouse ESCs and human lymphoblastoid GM12878 cells, exhibited loop numbers very similar to their neuronal counterparts (fig. S4, D and E), suggesting that the changes in 3DG architecture from NPC to neurons do not simply reflect a generalized effect explained by mitotic potential. Along with having fewer total loops, neurons exhibited a greater proportion of longer-range (>100 kb) loops than did NPCs or glia (two-sample two-tailed Kolmogorov-Smirnov test, KS range = 0.1269 to 0.2317, P < 2.2 × 10−16 for three comparisons: Neu versus NPC/Glia/GM) (fig. S5A). Likewise, in each of the alternative in vitro and in vivo analyses considered above, neurons exhibited a greater proportion of longer-range (>100 kb) loops than did NPCs or glia [two-sample two-tailed Kolmogorov-Smirnov test, KS = 0.0427, P = 1.5 × 10−5 for hiPSC-NPC versus forebrain neuron; KS = 0.0936, P = 1.1 × 10−16 for mESC-NPC versus mCN; KS = 0.0663, P = 2.04 × 10−8 for fetal CP (neuron) compared with GZ (progenitor)] (fig. S5, B, C, D, and E). Therefore, multiple in vitro and in vivo approaches comparing, in human and mouse, neural precursors to young neurons consistently show a reduced number of loops in neuron-enriched cultures and tissues, primarily affecting shorter-range loops. Consistent with studies in peripheral tissues reporting conservation of the overall loop-independent TAD landscape across developmental stages, tissues, and species (when considering syntenic loci) (10, 22), overall TAD landscapes (3) remained similar between neurons, glia, and NPCs. Nonetheless, TADs also showed a subtle (~10%) increase in average size in neurons compared with isogenic NPCs, independent of the differentiation protocol applied (Wilcoxon-Mann-Whitney test, P < 5.3 × 10−6) (Fig. 1H and fig. S5, F and G), as highlighted here at a 3.4-Mb TAD at the CDH2 cell adhesion gene locus (Fig. 1I). TAD remodeling may therefore reflect restructuring of nested subdomains within larger neuronal TADs (tables S7 and S8). To examine whether such developmental reorganization of the brain’s spatial genomes was associated with a generalized shift in chromatin structure, we applied the assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq) to map open chromatin sequences before and after NGN2-neuronal induction (table S1). Genome-wide distribution profiles for transposase-accessible chromatin were only minimally different between NPCs and neurons (fig. S5H) and further revealed that both NPCs and neurons showed low to moderate chromatin accessibility [–2.5 < log 2 (ATAC signal) < 1] for ≥89% of the anchor sequences comprising cell type–specific and shared “brain” loops in our cell culture system (fig. S5I). These findings, taken together, point to widespread 3DG changes during the NPC-to-neuron transition and NPC-to-glia transition in human and mouse brain that are unlikely attributable to global chromatin accessibility differences. This includes highly cell type–specific signatures in gene ontologies of differentiation-induced loop prunings, reflecting neuronal and glial (non-neuronal) lineage commitment (fig. S3, A and G, and table S4, B and C), and a subtle widening of average loop and TAD length in young neurons (Fig. 1H and fig. S5, A to G).

Chromosomal contacts associated with schizophrenia risk sequences Because many schizophrenia risk variants lie in noncoding regions in proximity to several genes, we predicted that chromosomal contact mapping could resolve putative regulatory elements capable of conferring schizophrenia risk via their physical proximity (bypassing linear genome) to the target gene, as has been demonstrated in tissue in vivo (7, 11). We overlaid our cell type–specific interactions onto the 145 risk loci associated with schizophrenia risk (2, 23). Because only very few loops (defined as distinct pixels with greater contact frequency than neighboring pixels on a contact map) (3) were associated with schizophrenia risk loci (n = 212, 81, and 17 loops in NPC, glia, and neurons, respectively) (table S9), we applied an established alternative approach to more comprehensively explore the 3DG in context of disease-relevant sequences (7). This approach defines interactions as those filtered contacts that stand out over the global background and applies binomial statistics to identify chromosomal contacts anchored at disease-relevant loci (7). To begin, we examined the 40 loci with strongest statistical evidence for colocalization of an adult postmortem brain eQTL and schizophrenia genome-wide association study (GWAS) signal (24). Chromosomal contacts were called for 29 of the 46 eQTLs present in the 40 loci, with 8 of 29 (28%) of the loci showing significant interactions (binomial test, –log q value range = 1.33 to 11.0) between the eQTL-SNPs (eSNPs) in the one contact anchor and the transcription start site of the associated gene(s) in the other anchor (table S10). We conclude that ~30% of risk locus–associated eQTLs with strong evidence for colocalization with GWAS signal bypass the linear genome and are in physical proximity to the proximal promoter and transcription start site of the target gene, resonating with previous findings in fetal brain tissue that used a similar contact mapping strategy (7). Cell type–specific contact maps with 10-kb-wide bins, queried for the schizophrenia-associated loci, frequently revealed differential chromosomal conformations in NPCs, glia, and neurons. For example, the risk locus upstream of the PROTOCADHERIN cell adhesion molecule gene clusters (chromosome 5), which is critically relevant for neuronal connectivity in developing and adult brain (fig. S6A) (25, 26), showed through both observed/expected interaction matrix (27) and global background-filtered contact mapping (7) a bifurcated bundle of interactions in NPCs, with one bundle emanating to sequences 5′ and the other bundle to sequences 3′ from the locus. In neurons, the 3′ bundle was maintained, but the 5′ bundle was “pruned,” whereas glia showed the opposite pattern; these differences between the three cell types were highly significant (observed/expected Wilcoxon rank sum P < 10−9 to 10−15) (Fig. 2, A to C). Dosage of the noncoding schizophrenia risk-SNP (rs111896713) at the PCDH locus significantly increased the expression of multiple PROTOCADHERIN genes (PCDHA2, PCDHA4, PCDHA7, PCDHA8, PCDHA9, PCDHA10, and PCDHA13) in adult frontal cortex of a large cohort of 579 individuals, including cases with schizophrenia and controls (fig. S6B and table S11) (28). The affected genes were interconnected to the disease-relevant noncoding sequence in neurons and NPCs but not in glia (fig. S6C). Therefore, cell type–specific Hi-C identified chromosomal contacts anchored in schizophrenia-associated risk sequences that affected expression of the target gene(s). On the basis of earlier chromosome conformation capture assays at the site of candidate genes, the underlying mechanisms may include alterations in transcription factor and other nucleoprotein binding at loop-bound cis-regulatory elements (5) or even local disruption of chromosomal conformations (6). Fig. 2 Cell type–specific chromosomal contact maps at schizophrenia risk loci. (A) Juicebox observed/expected interaction heatmaps at 10-kb resolution for the risk-associated clustered PCDH locus chr5:140023665−140222664 for NPC, glia, and neurons as indicated. (Far right) Grayscale heatmap depicts areas of highly cell-specific contact enrichments: upstream genes including ANKHD1 (dotted rectangle “A” and arrowhead) and downstream PCDH gene clusters (dotted rectangle “B” and arrows). Clustered PCDH gene expression patterns are available in fig. S6A. (B) Violin plots of observed/expected interaction values in the regions A and B highlighted in (A). (C) Map of contacts identified by binomial statistics. Red box with dashed black line represents the schizophrenia risk locus, dotted boxes regions “A” and “B” in heatmaps. (D) Cell-type resolved contact map of 10-kb bins (bold, black vertical lines) within risk sequences on chr12 (left), chrX (middle), and chr5 (right); NPC (orange), neuron (green), glia (purple); –log q value, significance of contact between schizophrenia risk locus and each 10-kb bin; gene models (“Genes”) below with SNP-loop target gene highlighted in red. (E) Epigenomic editing (CRISPRa with nuclease-deficient dCas9 in NPCs) for three risk SNP-target gene pairs and their respective control sequences (top), measured with quantitative reverse transcription polymerase chain reaction (RT-PCR) (fold change from baseline) for VP64 (middle) and VPR (bottom) transcriptional activators. (F) Quantitative PCR gene expression changes upon directing catalytically active Cas9 to schizophrenia risk-associated credible SNPs (vertical red dashes with rsIDs) interacting via chromosomal contacts with promoters of ASCL1, EFNB1, and MATR3 in NPCs. Targeting strategy and contact distances depicted above; *P < 0.05, **P < 0.01, ***P < 0.0001 (figs. S6 and S7). Transcriptional profiles of hiPSC-derived NPCs and neurons most closely resemble those of the human fetus in the first trimester (29); moreover, a portion of the genetic risk architecture of schizophrenia matches to regulatory elements that are highly active during prenatal development (30). We surveyed in our Hi-C datasets seven loci encompassing 36 “credible” (potentially causal) schizophrenia-risk SNPs with known chromosomal interactions in fetal brain to genes important for neuron development and function (7). We found that risk-associated chromosomal contacts were conserved between our hiPSC-NPCs and the published human fetal CP and germinal zone Hi-C datasets (7) for five of the seven loci (71%) tested (CHRNA2, EFNB1, MATR3, PCDH, and SOX2, but not ASCL1 or DRD2) (table S12). To test the regulatory function of these conserved risk sequence-bound conformations, we performed single-guide RNA (sgRNA)–based epigenomic editing experiments on isogenic antibiotic-selected NPCs that stably express nuclease-deficient dCas9-VP64 (31, 32) or dCas9-VPR (33, 34) transactivators (table S13). Previous studies in peripheral cell lines succeeded in inducing gene expression changes by placing dCas9-repressor fusion proteins at the site of chromosomal contacts separated by up to 2 Mb of linear genome from the promoter target (35). We tested ASCL1-, EFNB1-, MATR-3, and SOX2-bound chromosomal contacts separated by 200- to 700-kb interspersed sequences (Fig. 2, D and E; fig. S7A; and table S14). Pools of five individual sgRNAs directed against a risk-associated noncoding sequence bypassing 225 and 355 kb of genome consistently resulted in significantly decreased expression of ASCL1 [one-way analysis of variance (ANOVA), F VP64 (2, 15) = 22.20, P < 0.0001; Dunnett’s P VP64 = 0.023] and EFNB1 target genes [one-way ANOVA, F VP64 (2, 6) = 14.47, P = 0.0051, Dunnett’s P VP64 = 0.0356; F VPR (2, 6) = 1.46, P = 0.0111, Dunnett’s P VPR = 0.0088], in comparison with positive (promoter-bound) and negative (linear genome) control sgRNAs. Epigenomic editing of risk sequence 500 to 600 kb distant from the SOX2 and MATR3 loci did not alter target gene expression (Fig. 2, D and E, and fig. S7, A and B), which could reflect practical limitations in nonintegrative transfection-based (as opposed to viral) methods, impact of epigenetic landscape, or suboptimal guide RNA positioning (34), further limited by the 10-kb contact map resolution. Because portions of the MATR3-bound risk sequences are embedded in repressive chromatin, we directed five sgRNAs for Cas9 nuclease mutagenesis toward a 138–base pair (bp) sequence within a MATR3 long-range contact that was enriched with trimethyl-histone H3K27me3, commonly associated with Polycomb repressive chromatin remodeling, in order to disrupt it (fig. S7, C to E). This strategy produced a significant increase in MATR3 expression upon ablation of the putative repressor sequence, whereas targeting MATR3 (linear genome) control sequence remained ineffective (fig. S7, D and E). We conducted additional genomic mutagenesis assays, with sgRNAs directly overlapping credible SNPs participating in chromatin contacts with ASCL1, EFNB1, EP300, MATR3, PCDHA7, PCDHA8, and PCDHA10 (table S10). Cas9 nuclease deletion of interacting credible SNPs significantly increased gene expression of ASCL1, EFNB1, and EP300 (P range = 0.0053 to 0.04, t range = 2.449 to 4.265) (Fig. 2F and fig. S7F). Similar targeting of four credible SNPs upstream of the clustered PCDH locus significantly decreased levels, by ~50 to 60%, of PCDHA8 and PCDHA10 (P range = 0.0122 to 0.0124, t range = 4.326 to 4.343), two of the genes whose expression increased with dosage of the risk SNP rs111896713 in adult postmortem brain (figs. S6C and S7G). Taken together, our (epi)genomic editing assays (fig. S7H) demonstrate that chromosomal contacts anchored in schizophrenia risk loci potentially affect target gene expression across hundreds of kilobases, which is consistent with predictions from chromosomal conformation maps from hiPSC-derived brain cells described here, and from developing (7, 11) and adult (5) human brain tissue.