Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association (GWA) meta-analysis based in 135,458 cases and 344,901 control, We identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression, and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relations of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine and define the basis of major depression and imply a continuous measure of risk underlies the clinical phenotype.

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture, we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD.

Highlights d Three groups of highly genetically-related disorders among 8 psychiatric disorders d Identified 109 pleiotropic loci affecting more than one disorder d Pleiotropic genes show heightened expression beginning in 2 nd prenatal trimester d Pleiotropic genes play prominent roles in neurodevelopmental processes Authors Cross-Disorder Group of the Psychiatric Genomics Consortium

Protein misfolding is a common event in living cells. In young and healthy cells, the misfolded protein load is disposed of by protein quality control (PQC) systems. In aging cells and in cells from certain individuals with genetic diseases, the load may overwhelm the PQC capacity, resulting in accumulation of misfolded proteins. Dependent on the properties of the protein and the efficiency of the PQC systems, the accumulated protein may be degraded or assembled into toxic oligomers and aggregates. To illustrate this concept, we discuss a number of very different protein misfolding diseases including phenylketonuria, Parkinson's disease, alpha-1-antitrypsin deficiency, familial neurohypophyseal diabetes insipidus, and short-chain acyl-CoA dehydrogenase deficiency. Despite the differences, an emerging paradigm suggests that the cellular effects of protein misfolding provide a common framework that may contribute to the elucidation of the cell pathology and guide intervention and treatment strategies of many genetic and age-dependent diseases.

BackgroundMassively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples.ResultsFalse-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive predictive values.ConclusionsOur results highlighted the need for combined use of more sensitive DEG analysis methods and high-throughput validation of identified DEGs in future RNA-seq experiments. They indicated limited utility of sample pooling strategies for RNA-seq in similar setups and supported increasing the number of biological replicate samples.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1767-y) contains supplementary material, which is available to authorized users.

Type 2 diabetes is characterized by insulin resistance and mitochondrial dysfunction in classical target tissues such as muscle, fat, and liver. Using a murine model of type 2 diabetes, we show that there is hypothalamic insulin resistance and mitochondrial dysfunction due to downregulation of the mitochondrial chaperone HSP60. HSP60 reduction in obese, diabetic mice was due to a lack of proper leptin signaling and was restored by leptin treatment. Knockdown of Hsp60 in a mouse hypothalamic cell line mimicked the mitochondrial dysfunction observed in diabetic mice and resulted in increased ROS production and insulin resistance, a phenotype that was reversed with antioxidant treatment. Mice with a heterozygous deletion of Hsp60 exhibited mitochondrial dysfunction and hypothalamic insulin resistance. Targeted acute downregulation of Hsp60 in the hypothalamus also induced insulin resistance, indicating that mitochondrial dysfunction can cause insulin resistance in the hypothalamus. Importantly, type 2 diabetic patients exhibited decreased expression of HSP60 in the brain, indicating that this mechanism is relevant to human disease. These data indicate that leptin plays an important role in mitochondrial function and insulin sensitivity in the hypothalamus by regulating HSP60. Moreover, leptin/insulin crosstalk in the hypothalamus impacts energy homeostasis in obesity and insulin-resistant states.

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Our study bridges the gap between genetic association and pathogenic effects and yields novel insights into the unfolding molecular changes in the brain of a new schizophrenia model that incorporates genetic risk at three levels: allelic, chromatin interactomic, and brain transcriptomic.