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Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10−4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10−2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10−5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34–0.81) as well as several psychiatric disorders (rg = 0.26–0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

Poor sleep health is associated with increased all-cause mortality and incidence of many chronic conditions. Previous studies have relied on cross-sectional and self-reported survey data or polysomnograms, which have limitations with respect to data granularity, sample size and longitudinal information. Here, using objectively measured, longitudinal sleep data from commercial wearable devices linked to electronic health record data from the All of Us Research Program, we show that sleep patterns, including sleep stages, duration and regularity, are associated with chronic disease incidence. Of the 6,785 participants included in this study, 71% were female, 84% self-identified as white and 71% had a college degree; the median age was 50.2 years (interquartile range = 35.7, 61.5) and the median sleep monitoring period was 4.5 years (2.5, 6.5). We found that rapid eye movement sleep and deep sleep were inversely associated with the odds of incident atrial fibrillation and that increased sleep irregularity was associated with increased odds of incident obesity, hyperlipidemia, hypertension, major depressive disorder and generalized anxiety disorder. Moreover, J-shaped associations were observed between average daily sleep duration and hypertension, major depressive disorder and generalized anxiety disorder. These findings show that sleep stages, duration and regularity are all important factors associated with chronic disease development and may inform evidence-based recommendations on healthy sleeping habits. In a cohort of 6,785 participants from the All of Us Research Program whose sleep was monitored by a Fitbit over a median of 4.5 years, sleep duration, stages and irregularity were associated with the incidence of obesity and a number of cardiovascular and psychological disorders.

Of yeast and man Baker's yeast, Saccharomyces cerevisiae , is one of the best studied model organisms, and has been associated with human activity for thousands of years. Two papers published in the 19 March 2009 issue of Nature provide a picture of its population structure and its relationship with other yeasts. Liti et al . compare genome variation in S. cerevisiae isolates with its closest wild cousin, S. paradoxus , which has never been associated with human activity. They find that variation in S. paradoxus closely follows geographic borders; S. cerevisiae shows less differentiation, consistent with opportunities for cross-breeding, rather than a few distinct domestication events, as the main human influence. Schacherer et al . compare 63 S. cerevisiae isolates from different ecological niches and geographic locations. They find evidence for genetic differentiation of three distinct subgroups based on where the strains were isolated: from vineyards, sake and related fermentations and lab strains. Their data support the hypothesis that these three groups represent separate domestication events, and that S. cerevisiae as a whole is not domesticated. This study provides a nucleotide-level survey of genome variation in 63 Saccharomyces cerevisiae strains sampled from different ecological niches and geographical locations. The analysis of genome-wide patterns of the nucleotide polymorphism and deletion variants discovered lays the foundation for genome-wide association studies in yeast. Comprehensive identification of polymorphisms among individuals within a species is essential both for studying the genetic basis of phenotypic differences and for elucidating the evolutionary history of the species. Large-scale polymorphism surveys have recently been reported for human 1 , mouse 2 and Arabidopsis thaliana 3 . Here we report a nucleotide-level survey of genomic variation in a diverse collection of 63 Saccharomyces cerevisiae strains sampled from different ecological niches (beer, bread, vineyards, immunocompromised individuals, various fermentations and nature) and from locations on different continents. We hybridized genomic DNA from each strain to whole-genome tiling microarrays and detected 1.89 million single nucleotide polymorphisms, which were grouped into 101,343 distinct segregating sites. We also identified 3,985 deletion events of length >200 base pairs among the surveyed strains. We analysed the genome-wide patterns of nucleotide polymorphism and deletion variants, and measured the extent of linkage disequilibrium in S. cerevisiae . These results and the polymorphism resource we have generated lay the foundation for genome-wide association studies in yeast. We also examined the population structure of S. cerevisiae , providing support for multiple domestication events as well as insight into the origins of pathogenic strains.

The power of human induced pluripotent stem cell (hiPSC)-based studies to resolve the smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain. We identified and accounted for a variety of technical and biological sources of variation in a large case/control schizophrenia (SZ) hiPSC-derived cohort of neural progenitor cells and neurons. Reducing the stochastic effects of the differentiation process by correcting for cell type composition boosted the SZ signal and increased the concordance with post-mortem data sets. We predict a growing convergence between hiPSC and post-mortem studies as both approaches expand to larger cohort sizes. For studies of complex genetic disorders, to maximize the power of hiPSC cohorts currently feasible, in most cases and whenever possible, we recommend expanding the number of individuals even at the expense of the number of replicate hiPSC clones. Induced pluripotent stem cell (hiPSC)-based models have inherent variations in their cellular and molecular output and readouts. Here, Hoffman and colleagues devise a method to account for gene expression variations in hiPSC-derived neurons from patients with childhood-onset 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. By analyzing the exomes of 12,332 unrelated Swedish individuals, including 4,877 individuals affected with schizophrenia, in ways informed by exome sequences from 45,376 other individuals, we identified 244,246 coding-sequence and splice-site ultra-rare variants (URVs) that were unique to individual Swedes. We found that gene-disruptive and putatively protein-damaging URVs (but not synonymous URVs) were more abundant among individuals with schizophrenia than among controls ( P = 1.3 × 10 −10 ). This elevation of protein-compromising URVs was several times larger than an analogously elevated rate for de novo mutations, suggesting that most rare-variant effects on schizophrenia risk are inherited. Among individuals with schizophrenia, the elevated frequency of protein-compromising URVs was concentrated in brain-expressed genes, particularly in neuronally expressed genes; most of this elevation arose from large sets of genes whose RNAs have been found to interact with synaptically localized proteins. Our results suggest that synaptic dysfunction may mediate a large fraction of strong, individually rare genetic influences on schizophrenia risk.

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. Copy number variation (CNV) affecting protein-coding genes contributes substantially to human diversity and disease. Here we characterized the rates and properties of rare genic CNVs (<0.5% frequency) in exome sequencing data from nearly 60,000 individuals in the Exome Aggregation Consortium (ExAC) database. 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 affected by CNVs were more intolerant than in controls. The ExAC CNV data constitute a critical component of an integrated database spanning the spectrum of human genetic variation, aiding in the interpretation of personal genomes as well as population-based disease studies. These data are freely available for download and visualization online.

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 messenger RNAs 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. 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. Pathogenic mechanisms in schizophrenia Two major sequencing studies of the exome — the protein-coding portion of the genome — in schizophrenia sufferers and their relatives are published in this issue of Nature . Together they provide strong pointers to specific pathogenic mechanisms that disrupt the glutamatergic synapses in schizophrenia. In particular, mutations that influence the action of the scaffold protein ARC (activity-regulated cytoskeleton-associated protein) are prominently involved, as are mutations in targets of the fragile X mental retardation protein (FMRP). Defects in FMRP have previously been shown to be associated with autism spectrum disorders.

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. Genetic prediction of complex traits so far has limited accuracy because of insufficient understanding of the genetic risk. Here, Maier et al. develop an improved method for trait prediction that makes use of genetic correlations between traits and apply it to summary statistics of psychiatric diseases.

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. 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 people with schizophrenia ( 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 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.