Over 100 genetic loci harbor schizophrenia associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of schizophrenia cases (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ~20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3, or SNAP91. Altering expression of FURIN, TSNARE1, or CNTN4 changes neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yields abnormal migration. Of 693 genes showing significant case/control differential expression, their fold changes are ≤ 1.33, and an independent cohort yields similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.

73Over 100 genetic loci harbor schizophrenia associated variants, yet how these common 74 variants confer risk is uncertain. The CommonMind Consortium has sequenced dorsolateral 75 prefrontal cortex RNA from schizophrenia cases (n=258) and control subjects (n=279), creating 76 the largest publicly available resource to date of gene expression and its genetic regulation; ~5 77 times larger than the latest release of GTEx. Using this resource, we find that ~20% of the 78 schizophrenia risk loci have common variants that could explain regulation of brain gene 79 expression. In five loci, these variants modulate expression of a single gene: FURIN, TSNARE1, 80 CNTN4, CLCN3 or SNAP91. Experimentally altered expression of three of them, FURIN, 81 TSNARE1, and CNTN4, perturbs the proliferation and apoptotic index of neural progenitors and 82 leads to neuroanatomical deficits in zebrafish. Furthermore, shRNA mediated knock-down of 83 FURIN in neural progenitor cells derived from human induced pluripotent stem cells produces 84 abnormal neural migration. Although 4.2% of genes (N = 693) display significant differential 85 expression between cases and controls, 44% show some evidence for differential expression. 86All fold changes are ≤ 1.33, and an independent cohort yields similar differential expression for 87 these 693 genes (r = 0.58). These findings are consistent with schizophrenia being highly 88 polygenic, as has been reported in investigations of common and rare genetic variation. Co-89 expression analyses identify a gene module that shows enrichment for genetic associations and 90 is thus relevant for schizophrenia. Taken together, these results pave the way for mechanistic 91 interpretations of genetic liability for schizophrenia and other brain diseases. 4The human brain is complicated and not well understood. Seemingly straightforward 93 fundamental information such as which genes are expressed therein and what functions they 94 perform are only partially characterized. To overcome these obstacles, we established the 95 CommonMind Consortium (CMC; www.synpase.org/CMC), a public-private partnership to 96 generate functional genomic data in brain samples obtained from autopsies of cases with and 97 without severe psychiatric disorders. The CMC is the largest existing collection of collaborating 98 brain banks and includes over 1,150 samples. A wide spectrum of data is being generated on 99 these samples including regional gene expression, epigenomics (cell-type specific histone 100 modifications and open chromatin), whole genome sequencing, and somatic mosaicism. 101 102 Schizophrenia (SCZ), affecting roughly 0.7% of adults, is a severe psychiatric disorder 103 characterized by abnormalities in thought and cognition (1). Despite a century of evidence 104 establishing its genetic basis, only recently have specific genetic risk factors been conclusively 105identified, including rare copy number variants (2) and >100 common variants (3). However, 106 there is not a one-to-one Mendelian mapping between these SCZ ris...

Worldwide, hundreds of thousands of humans have had their genomes or exomes sequenced, and access to the resulting data sets can provide valuable information for variant interpretation and understanding gene function. Here, we present a lightweight, flexible browser framework to display large population datasets of genetic variation. We demonstrate its use for exome sequence data from 60 706 individuals in the Exome Aggregation Consortium (ExAC). The ExAC browser provides gene- and transcript-centric displays of variation, a critical view for clinical applications. Additionally, we provide a variant display, which includes population frequency and functional annotation data as well as short read support for the called variant. This browser is open-source, freely available at http://exac.broadinstitute.org, and has already been used extensively by clinical laboratories worldwide.

Copy number variation (CNV) impacting protein-coding genes contributes significantly to human diversity and disease. Here we characterized the rates and properties of rare genic CNV (<0.5% frequency) in exome-sequencing data from nearly 60,000 individuals in the Exome Aggregation Consortium (ExAC). On average, individuals possessed 0.81 deleted and 1.75 duplicated genes, and most (70%) carried at least one rare genic CNV. For every gene, we empirically estimated an index of relative intolerance to CNVs that demonstrated moderate correlation with measures of genic constraint based on single-nucleotide variation (SNV) and was independently correlated with measures of evolutionary conservation. For individuals with schizophrenia, genes impacted by CNVs were more intolerant than in controls. ExAC CNV data constitutes a critical component of an integrated database spanning the spectrum of human genetic variation, aiding the interpretation of personal genomes as well as population-based disease studies. These data are freely available for download and visualization online.

Motivation Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above. Results In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data. Availability The inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/). Supplementary information Supplementary data are available at Bioinformatics online.