Summary Inherited alleles account for most of the genetic risk for schizophrenia. However, new (de novo) mutations, in the form of large chromosomal copy number changes, occur in a small fraction of cases and disproportionally disrupt genes encoding postsynaptic proteins. Here, we show that small de novo mutations, affecting one or a few nucleotides, are overrepresented among glutamatergic postsynaptic proteins comprising activity-regulated cytoskeleton-associated protein (ARC) and N-methyl-D-aspartate receptor (NMDAR) complexes. Mutations are additionally enriched in proteins that interact with these complexes to modulate synaptic strength, namely proteins regulating actin filament dynamics and those whose mRNAs are targets of fragile X mental retardation protein (FMRP). Genes affected by mutations in schizophrenia overlap those mutated in autism and intellectual disability, as do mutation-enriched synaptic pathways. Aligning our findings with a parallel case-control study, we demonstrate reproducible insights into aetiological mechanisms for schizophrenia and reveal pathophysiology shared with other neurodevelopmental disorders.

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (OR=1.11, P=5.7×10−15), which persisted after excluding loci implicated in previous studies (OR=1.07, P=1.7 ×10−6). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 ×10−11) and neurobehavioral phenotypes in mouse (OR = 1.18, P= 7.3 ×10−5). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by non-allelic homologous recombination.

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.

Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.

Recent research on disparate psychiatric disorders has implicated rare variants in genes involved in global gene regulation and chromatin modification, as well as many common variants located primarily in regulatory regions of the genome. Understanding precisely how these variants contribute to disease will require a deeper appreciation for the mechanisms of gene regulation in the developing and adult human brain. The PsychENCODE project aims to produce a public resource of multidimensional genomic data using tissue- and cell type–specific samples from approximately 1,000 phenotypically well-characterized, high-quality healthy and disease-affected human post-mortem brains, as well as functionally characterize disease-associated regulatory elements and variants in model systems. We are beginning with a focus on autism spectrum disorder, bipolar disorder and schizophrenia, and expect that this knowledge will apply to a wide variety of psychiatric disorders. This paper outlines the motivation and design of PsychENCODE.