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Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease. Samples of different body regions from hundreds of human donors are used to study how genetic variation influences gene expression levels in 44 disease-relevant tissues. Genetic effects on gene expression across human tissues The GTEx (Genotype-Tissue Expression) Consortium has established a reference catalogue and associated tissue biobank for gene-expression levels across individuals for diverse tissues of the human body, with a broad sampling of normal, non-diseased human tissues from postmortem donors. The consortium now presents the deepest survey of gene expression across multiple tissues and individuals to date, encompassing 7,051 samples from 449 donors across 44 human tissues. Barbara Engelhardt and colleagues characterize the relationship between genetic variation and gene expression, and find that most genes are regulated by genetic variation near to the affected gene. In accompanying GTEx studies, Alexis Battle, Stephen Montgomery and colleagues examine the effect of rare genetic variation on gene expression across human tissues, Daniel MacArthur and colleagues systematically survey the landscape of X chromosome inactivation in human tissues, and Jin Billy Li and colleagues provide a comprehensive cross-species analysis of adenosine-to-inosine RNA editing in mammals. In an accompanying News & Views, Michelle Ward and Yoav Gilad put the latest results in context and discuss how these findings are helping to crack the regulatory code of the human genome.

Humans have systematic sex differences in brain-related behavior, cognition, and pattern of mental illness risk. Many of these differences emerge during adolescence, a developmental period of intense neurostructural and endocrine change. Here, by creating \"movies\" of sexually dimorphic brain development using longitudinal in vivo structural neuroimaging, we show regionally specific sex differences in development of the cerebral cortex during adolescence. Within cortical subsystems known to underpin domains of cognitive behavioral sex difference, structural change is faster in the sex that tends to perform less well within the domain in question. By stratifying participants through molecular analysis of the androgen receptor gene, we show that possession of an allele conferring more efficient functioning of this sex steroid receptor is associated with \"masculinization\" of adolescent cortical maturation. Our findings extend models first established in rodents, and suggest that in humans too, sex and sex steroids shape brain development in a spatiotemporally specific manner, within neural systems known to underpin sexually dimorphic behaviors.

Jonathan Sebat and colleagues report the association of microduplication on chromosome 16p11.2 with schizophrenia, while the reciprocal microdeletion was associated with autism and developmental disorders. Recurrent microdeletions and microduplications of a 600-kb genomic region of chromosome 16p11.2 have been implicated in childhood-onset developmental disorders 1 , 2 , 3 . We report the association of 16p11.2 microduplications with schizophrenia in two large cohorts. The microduplication was detected in 12/1,906 (0.63%) cases and 1/3,971 (0.03%) controls ( P = 1.2 × 10 −5 , OR = 25.8) from the initial cohort, and in 9/2,645 (0.34%) cases and 1/2,420 (0.04%) controls ( P = 0.022, OR = 8.3) of the replication cohort. The 16p11.2 microduplication was associated with a 14.5-fold increased risk of schizophrenia (95% CI (3.3, 62)) in the combined sample. A meta-analysis of datasets for multiple psychiatric disorders showed a significant association of the microduplication with schizophrenia ( P = 4.8 × 10 −7 ), bipolar disorder ( P = 0.017) and autism ( P = 1.9 × 10 −7 ). In contrast, the reciprocal microdeletion was associated only with autism and developmental disorders ( P = 2.3 × 10 −13 ). Head circumference was larger in patients with the microdeletion than in patients with the microduplication ( P = 0.0007).

As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome through pilot WGS projects will be critical to determining which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The Whole Genome Sequencing for Psychiatric Disorders Consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

The heterogeneity of symptoms associated with autism spectrum disorders (ASDs) has presented a significant challenge to genetic analyses. Even when associations with genetic variants have been identified, it has been difficult to associate them with a specific trait or characteristic of autism. Here, we report that quantitative trait analyses of ASD symptoms combined with case-control association analyses using distinct ASD subphenotypes identified on the basis of symptomatic profiles result in the identification of highly significant associations with 18 novel single nucleotide polymorphisms (SNPs). The symptom categories included deficits in language usage, non-verbal communication, social development, and play skills, as well as insistence on sameness or ritualistic behaviors. Ten of the trait-associated SNPs, or quantitative trait loci (QTL), were associated with more than one subtype, providing partial replication of the identified QTL. Notably, none of the novel SNPs is located within an exonic region, suggesting that these hereditary components of ASDs are more likely related to gene regulatory processes (or gene expression) than to structural or functional changes in gene products. Seven of the QTL reside within intergenic chromosomal regions associated with rare copy number variants that have been previously reported in autistic samples. Pathway analyses of the genes associated with the QTL identified in this study implicate neurological functions and disorders associated with autism pathophysiology. This study underscores the advantage of incorporating both quantitative traits as well as subphenotypes into large-scale genome-wide analyses of complex disorders.

As compared with X-linked mental retardation, little is known about the genetic causes of autosomal nonsyndromic mental retardation. This study implicates variants of SYNGAP1 as a cause of this disorder in approximately 3% of patients who were tested. Little is known about the genetic causes of autosomal nonsyndromic mental retardation. This study implicates variants of SYNGAP1 as a cause of this disorder in approximately 3% of patients who were tested. Mental retardation is the most prevalent severe handicap of children, affecting 1 to 3% of the population. 1 Most patients have the nonsyndromic form of the disorder, which is characterized by the absence of associated morphologic, radiologic, and metabolic features. 2 The genetic factors involved in nonsyndromic mental retardation remain poorly understood. Linkage and cytogenetic analyses have led to the identification of 29 X-linked and 5 autosomal recessive genes associated with nonsyndromic mental retardation, which together account for less than 10% of cases. 1 – 6 Moreover, autosomal dominant genes have yet to be identified, mainly because mental retardation results in lower reproductive fitness, . . .

Schizophrenia is a devastating neurodevelopmental disorder whose genetic influences remain elusive. We hypothesize that individually rare structural variants contribute to the illness. Microdeletions and microduplications >100 kilobases were identified by microarray comparative genomic hybridization of genomic DNA from 150 individuals with schizophrenia and 268 ancestry-matched controls. All variants were validated by high-resolution platforms. Novel deletions and duplications of genes were present in 5% of controls versus 15% of cases and 20% of young-onset cases, both highly significant differences. The association was independently replicated in patients with childhood-onset schizophrenia as compared with their parents. Mutations in cases disrupted genes disproportionately from signaling networks controlling neurodevelopment, including neuregulin and glutamate pathways. These results suggest that multiple, individually rare mutations altering genes in neurodevelopmental pathways contribute to schizophrenia.

In the version of this article initially published, the consortium authorship and corresponding authors were not presented correctly. In the PDF and print versions, the Whole Genome Sequencing for Psychiatric Disorders (WGSPD) consortium was missing from the author list at the beginning of the paper, where it should have appeared as the seventh author; it was present in the author list at the end of the paper, but the footnote directing readers to the Supplementary Note for a list of members was missing. In the HTML version, the consortium was listed as the last author instead of as the seventh, and the line directing readers to the Supplementary Note for a list of members appeared at the end of the paper under Author Information but not in association with the consortium name itself. Also, this line stated that both member names and affiliations could be found in the Supplementary Note; in fact, only names are given. In all versions of the paper, the corresponding author symbols were attached to A. Jeremy Willsey, Steven E. Hyman, Anjene M. Addington and Thomas Lehner; they should have been attached, respectively, to Steven E. Hyman, Anjene M. Addington, Thomas Lehner and Nelson B. Freimer. As a result of this shift, the respective contact links in the HTML version did not lead to the indicated individuals. The errors have been corrected in the HTML and PDF versions of the article.

Schizophrenia likely results from poorly understood genetic and environmental factors. We studied the gene encoding the synaptic protein SHANK3 in 285 controls and 185 schizophrenia patients with unaffected parents. Two de novo mutations (R1117X and R536W) were identified in two families, one being found in three affected brothers, suggesting germline mosaicism. Zebrafish and rat hippocampal neuron assays revealed behavior and differentiation defects resulting from the R1117X mutant. As mutations in SHANK3 were previously reported in autism, the occurrence of SHANK3 mutations in subjects with a schizophrenia phenotype suggests a molecular genetic link between these two neurodevelopmental disorders.

The Genomics Workgroup of the National Advisory Mental Health Council (NAMHC) recently issued a set of recommendations for advancing the NIMH psychiatric genetics research program and prioritizing subsequent follow-up studies. The report emphasized the primacy of rigorous statistical support from properly designed, well-powered studies for pursuing genetic variants robustly associated with disease. Here we discuss the major points NIMH program staff consider when assessing research applications based on common and rare variants, as well as genetic syndromes, associated with psychiatric disorders. These are broad guiding principles for investigators to consider prior to submission of their applications. NIMH staff weigh these points in the context of reviewer comments, the existing literature, and current investments in related projects. Following the recommendations of the NAMHC, statistical strength and robustness of the underlying genetic discovery weighs heavily in our funding considerations as does the suitability of the proposed experimental approach. We specifically address our evaluation of applications motivated in whole, or in part, by an association between human DNA sequence variation and a disease or trait relevant to the mission of the NIMH.