Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide insights. We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci. We also show for the first time that the common variant association signal is highly enriched among genes that are under strong selective pressures. These findings provide new insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation-intolerant genes and suggest a mechanism by which common risk variants persist in the population.

To broaden our understanding of human neurodevelopment, we profiled transcriptomic and epigenomic landscapes across brain regions and/or cell types for the entire span of prenatal and postnatal development. Integrative analysis revealed temporal, regional, sex, and cell type–specific dynamics. We observed a global transcriptomic cup-shaped pattern, characterized by a late fetal transition associated with sharply decreased regional differences and changes in cellular composition and maturation, followed by a reversal in childhood-adolescence, and accompanied by epigenomic reorganizations. Analysis of gene coexpression modules revealed relationships with epigenomic regulation and neurodevelopmental processes. Genes with genetic associations to brain-based traits and neuropsychiatric disorders (including MEF2C, SATB2, SOX5, TCF4, and TSHZ3) converged in a small number of modules and distinct cell types, revealing insights into neurodevelopment and the genomic basis of neuropsychiatric risks.

With few exceptions, the marked advances in knowledge about the genetic basis of schizophrenia have not converged on findings that can be confidently used for precise experimental modeling. By applying knowledge of the cellular taxonomy of the brain from single-cell RNA sequencing, we evaluated whether the genomic loci implicated in schizophrenia map onto specific brain cell types. We found that the common-variant genomic results consistently mapped to pyramidal cells, medium spiny neurons (MSNs) and certain interneurons, but far less consistently to embryonic, progenitor or glial cells. These enrichments were due to sets of genes that were specifically expressed in each of these cell types. We also found that many of the diverse gene sets previously associated with schizophrenia (genes involved in synaptic function, those encoding mRNAs that interact with FMRP, antipsychotic targets, etc.) generally implicated the same brain cell types. Our results suggest a parsimonious explanation: the common-variant genetic results for schizophrenia point at a limited set of neurons, and the gene sets point to the same cells. The genetic risk associated with MSNs did not overlap with that of glutamatergic pyramidal cells and interneurons, suggesting that different cell types have biologically distinct roles in schizophrenia.

Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Heritability and polygenic predictionIn the EUR sample, the SNP-based heritability (h 2 SNP ) (that is, the proportion of variance in liability attributable to all measured SNPs)

Bipolar disorder (BD) is a heritable mental illness with complex etiology. We performed a genome-wide association study (GWAS) of 41,917 BD cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. BD risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics, and anesthetics. Integrating eQTL data implicated 15 genes robustly linked to BD via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of BD subtypes indicated high but imperfect genetic correlation between BD type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of BD, identify novel therapeutic leads, and prioritize genes for functional follow-up studies.

BackgroundHuntington's disease (HD) is a fatal inherited neurodegenerative disease, caused by a

Abstract Inferring changes in effective population size (Ne) in the recent past is of special interest for conservation of endangered species and for human history research. Current methods for estimating the very recent historical Ne are unable to detect complex demographic trajectories involving multiple episodes of bottlenecks, drops and expansions. We develop a theoretical and computational framework to infer the demographic history of a population within the past 100 generations from the observed spectrum of linkage disequilibrium (LD) of pairs of loci over a wide range of recombination rates in a sample of contemporary individuals. The cumulative contributions of all of the previous generations to the observed LD are included in our model, and a genetic algorithm is used to search for the sequence of historical Ne values that best explains the observed LD spectrum. The method can be applied from large samples to samples of fewer than 10 individuals using a variety of genotyping and DNA sequencing data: haploid, diploid with phased or unphased genotypes and pseudo-haploid data from low-coverage sequencing. The method was tested by computer simulation for sensitivity to genotyping errors, temporal heterogeneity of samples, population admixture and structural division into subpopulations, showing high tolerance to deviations from the assumptions of the model. Computer simulations also show that the proposed method outperforms other leading approaches when the inference concerns recent timeframes. Analysis of data from a variety of human and animal populations gave results in agreement with previous estimations by other methods or with records of historical events.

Importance At least 11 rare copy number variants (CNVs) have been shown to be major risk factors for schizophrenia (SZ). These CNVs also increase the risk for other neurodevelopmental disorders, such as intellectual disability. It is possible that additional intellectual disability–associated CNVs increase the risk for SZ but have not yet been implicated in SZ because of previous studies being underpowered. Objective To examine whether additional CNVs implicated in intellectual disability represent novel SZ risk loci. Design, Setting, and Participants We used single-nucleotide polymorphism (SNP) array data to evaluate a set of 51 CNVs implicated in intellectual disability (excluding the known SZ loci) in a large data set of patients with SZ and healthy persons serving as controls recruited in a variety of settings. We analyzed a new sample of 6934 individuals with SZ and 8751 controls and combined those data with previously published large data sets for a total of 20 403 cases of SZ and 26 628 controls. Main Outcomes and Measures Burden analysis of CNVs implicated in intellectual disability (excluding known SZ CNVs) for association with SZ. Association of individual intellectual disability CNV loci with SZ. Results Of data on the 20 403 cases (6151 [30.15%] female) and 26 628 controls (14 252 [53.52%] female), 51 intellectual disability CNVs were analyzed. Collectively, intellectual disability CNVs were significantly enriched for SZ (P = 1.0 × 10−6; odds ratio [OR], 1.9 [95% CI, 1.46-2.49]). Of the 51 CNVs tested, 19 (37%) were more common in SZ cases; only 4 (8%) were more common in controls (no observations were made for the remaining 28 [55%] loci). One novel locus, deletion at 16p12.1, was significantly associated with SZ after correction for multiple testing (rate in SZ, 33 [0.16%]; rate in controls, 12 [0.05%]; corrected P = .017; OR, 3.3; 95% CI, 1.61-7.05), and 2 loci reached nominal levels of significance (deletions at 2q11.2: 6 [0.03%] vs 1 [0.004%]; OR, 9.3; 95% CI, 1.03-447.76; corrected P > .99; and duplications at 10q11.21q11.23: 5 [0.2%] vs 0 [0.03%]; OR, infinity; 95% CI, 1.26-infinity; corrected P = .71). Our new data set also provided independent support for the 11 SZ risk loci previously reported to be associated with the disorder and for the protective effect of 22q11.2 duplication. Conclusions and Relevance A large proportion of CNV loci implicated in intellectual disability are risk factors for SZ, but the available sample size precludes statistical confirmation for additional individual loci.