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Exome sequence analysis of more than 5,000 schizophrenia cases and controls identifies a polygenic burden primarily arising from rare, disruptive mutations distributed across many genes, among which are those encoding voltage-gated calcium ion channels and the signalling complex formed by the ARC protein of the postsynaptic density; as in autism, mutations were also found in homologues of known targets of the fragile X mental retardation protein.

This study uses gene expression predictors for the dorsolateral prefrontal cortex and other brain regions to perform a transcriptomic imputation analysis of schizophrenia, identifying 413 genic associations across 13 brain regions and 36 significantly enriched pathways.

A genome-wide association study including over 76,000 individuals with schizophrenia and over 243,000 control individuals identifies common variant associations at 287 genomic loci, and further fine-mapping analyses highlight the importance of genes involved in synaptic processes.

The CNV analysis group of the Psychiatric Genomic Consortium analyzes a large schizophrenia cohort to examine genomic copy number variants (CNVs) and disease risk. They find an enrichment of CNV burden in cases versus controls and identify 8 genome-wide significant loci as well as novel suggestive loci conferring either risk or protection to schizophrenia.

LINE-1 activity was quantified in a large, pan-cancer dataset, finding locus-specific heterogeneity and new associations using a computational pipeline. A mathematical mediation model of p53 and L1 interactions was inferred. Somatic retrotransposition was seen in Li-Fraumeni Syndrome with heritable TP53 mutations.

The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of subjects with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, they found that ∼20% of schizophrenia loci have variants that may contribute to altered gene expression and liability.

In this study of protein-coding de novo mutations in schizophrenia, researchers found only a small contribution toward overall risk, coming predominantly from genes under negative selection and highly expressed in the brain.

The authors report the largest family-trio exome sequencing study of schizophrenia to date; mutations are overrepresented in genes for glutamatergic synaptic proteins and also genes mutated in autism and intellectual disability, providing insights into aetiological mechanisms and pathopshyisology shared with other neurodevelopmental disorders.

Whole-exome sequencing in a large autism study identifies over 100 autosomal genes that are likely to affect risk for the disorder; these genes, which show unusual evolutionary constraint against mutations, carry de novo loss-of-function mutations in over 5% of autistic subjects and many function in synaptic, transcriptional and chromatin-remodelling pathways.

Douglas Ruderfer, Shaun Purcell and colleagues characterized the rates and properties of rare genic copy number variants in exome sequencing data from nearly 60,000 individuals in the Exome Aggregation Consortium. These data are available through an integrated database that spans the spectrum of human genetic variation, aiding in the interpretation of personal genomes and population-based disease studies.

This study analyzes allelic expression bias in post-mortem brains of healthy individuals and those diagnosed with schizophrenia or bipolar disorder. The study shows that the number of imprinted genes is consistent with low estimates, and that allelic bias is independent of psychiatric disease status.

Exome sequencing data from 60,706 people of diverse geographic ancestry is presented, providing insight into genetic variation across populations, and illuminating the relationship between DNA variants and human disease.

Patrick Sullivan and colleagues report a multi-stage genome-wide association study for schizophrenia in a Swedish population. They identify 13 loci newly associated with schizophrenia.

Using whole-exome sequencing, the authors identified 244,246 coding-sequence and splice-site ultra-rare variants (URVs) and found that gene-disruptive and putatively protein-damaging URVs were significantly more abundant in schizophrenia cases than in controls. The excess of protein-compromising URVs was concentrated in brain-specific genes, particularly in neuronally expressed genes whose proteins are located at the synapse.

Survey of postzygotic mosaic mutations (PZMs) in 5,947 trios with autism spectrum disorders (ASD) discovers differences in mutational properties between germline mutations and PZMs. Spatiotemporal analyses of the PZMs also revealed the association of the amygdala with ASD and implicated risk genes, including recurrent potential gain-of-function mutations in SMARCA4.

Human disease genetics is extended to the identification of individuals who remain healthy despite carrying highly penetrant disease-causing mutations.

Stratified medicine promises to tailor treatment for individual patients, however it remains a major challenge to leverage genetic risk data to aid patient stratification. Here the authors introduce an approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue-specific gene expression levels, and highlight its ability to identify biologically meaningful and clinically actionable patient subgroups, supporting the notion of different patient ‘biotypes’ characterized by partially distinct disease mechanisms.

Schizophrenia is a highly heritable genetic disorder, however, identification of specific genetic risk variants has proven difficult because of its complex polygenic nature—a large multi-stage genome-wide association study identifies 128 independent associations in over 100 loci (83 of which are new); key findings include identification of genes involved in glutamergic neurotransmission and support for a link between the immune system and schizophrenia.

Sexual dimorphism in genetic vulnerability to schizophrenia, systemic lupus erythematosus and Sjögren’s syndrome is linked to differential protein abundance from alleles of complement component 4.

Relatives of patients with amyotrophic lateral sclerosis have an unexpectedly high incidence of schizophrenia. Here, the authors show a genetic link between the two conditions, suggesting shared neurobiological mechanisms.

The authors defined a roadmap for investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders. Their proof-of-concept study using the largest available common variant data sets for schizophrenia and volumes of several (mainly subcortical) brain structures did not find evidence of genetic overlap.

Exome sequencing of 175 autism spectrum disorder parent–child trios reveals that few de novo point mutations have a role in autism spectrum disorder and those that do are distributed across many genes and are incompletely penetrant, further supporting extreme genetic heterogeneity of this spectrum disorder.

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.