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.