Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in disease brain are limited. Here, we integrate genotype and RNA-sequencing in brain samples from 1695 subjects with autism, schizophrenia, bipolar disorder and controls. Over 25% of the transcriptome exhibits differential splicing or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. co-expression networks isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon response modules defining novel neural-immune mechanisms. We prioritize disease loci likely mediated by cis-effects on brain expression via transcriptome-wide association analysis. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for mechanistic insight and therapeutic development.

Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Heritability and polygenic predictionIn the EUR sample, the SNP-based heritability (h 2 SNP ) (that is, the proportion of variance in liability attributable to all measured SNPs)

Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.

We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).

We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).

Highlights d Dorsolateral prefrontal cortex and hippocampus gene expression across development d Novel region-specific schizophrenia genetic risk features d Decreased regional functional coherence in schizophrenia d Public brain gene expression and eQTL resource at http:// eqtl.brainseq.org/phase2

Autism spectrum disorder (ASD) is genetically heterogeneous with convergent symptomatology, suggesting common dysregulated pathways. We analyzed brain transcriptional changes in five mouse models of Pitt-Hopkins Syndrome (PTHS), a syndromic form of ASD caused by mutations in TCF4 (transcription factor 4, not TCF7L2 / T-Cell Factor 4). Analyses of differentially expressed genes (DEGs) highlighted oligodendrocyte (OL) dysregulation, which we confirmed in two additional mouse models of syndromic ASD ( Pten m3m4/m3m4 and Mecp2 tm1.1Bird ). The PTHS mouse models showed cell-autonomous reductions in OL numbers and myelination, functionally confirming OL transcriptional signatures. Next, we integrated PTHS mouse model DEGs with human idiopathic ASD postmortem brain RNA-seq data, and found significant enrichment of overlapping DEGs and common myelination-associated pathways. Importantly, DEGs from syndromic ASD mouse models, and reduced deconvoluted OL numbers, distinguished human idiopathic ASD cases from controls across three postmortem brain datasets. These results implicate disruptions in OL biology as a cellular mechanism in ASD pathology.

44Most genetic variants implicated in complex diseases by genome-wide association 45 studies (GWAS) are non-coding, making it challenging to understand the causative 46 genes involved in disease. Integrating external information such as quantitative trait 47 locus (QTL) mapping of molecular traits (e.g., expression, methylation) is a powerful 48 approach to identify the subset of GWAS signals explained by regulatory effects. In 49 particular, expression QTLs (eQTLs) help pinpoint the responsible gene among the 50 GWAS regions that harbor many genes, while methylation QTLs (mQTLs) help identify 51 the epigenetic mechanisms that impact gene expression which in turn affect disease 52 risk. In this work we propose multiple-trait-coloc (moloc), a Bayesian statistical 53 framework that integrates GWAS summary data with multiple molecular QTL data to 54 identify regulatory effects at GWAS risk loci. We applied moloc to schizophrenia (SCZ) 55 and eQTL/mQTL data derived from human brain tissue and identified 52 candidate 56 genes that influence SCZ through methylation. Our method can be applied to any 57 GWAS and relevant functional data to help prioritize disease associated genes. 58 59 AUTHOR SUMMARY 60In this paper, we propose multiple-trait-coloc (moloc), a statistical method that can use 61 single variant summary statistics from multiple studies to test for colocalization. Using 62 moloc, we integrate risk variants identified through genome-wide association studies 63 (GWAS) in schizophrenia with quantitative trait loci that affect gene expression (eQTL) 64 and DNA methylation (mQTL). Our method links non-coding risk variants with 65