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Large rare copy number variants (CNVs) have been recognized as significant genetic risk factors for the development of schizophrenia (SCZ). However, due to their low frequency (1∶150 to 1∶1000) among patients, large sample sizes are needed to detect an association between specific CNVs and SCZ. So far, the majority of genome-wide CNV analyses have focused on reporting only CNVs that reached a significant P-value within the study cohort and merely confirmed the frequency of already-established risk-carrying CNVs. As a result, CNVs with a very low frequency that might be relevant for SCZ susceptibility are lost for secondary analyses. In this study, we provide a concise collection of high-quality CNVs in a large German sample consisting of 1,637 patients with SCZ or schizoaffective disorder and 1,627 controls. All individuals were genotyped on Illumina's BeadChips and putative CNVs were identified using QuantiSNP and PennCNV. Only those CNVs that were detected by both programs and spanned ≥30 consecutive SNPs were included in the data collection and downstream analyses (2,366 CNVs, 0.73 CNVs per individual). The genome-wide analysis did not reveal a specific association between a previously unknown CNV and SCZ. However, the group of CNVs previously reported to be associated with SCZ was more frequent in our patients than in the controls. The publication of our dataset will serve as a unique, easily accessible, high-quality CNV data collection for other research groups. The dataset could be useful for the identification of new disease-relevant CNVs that are currently overlooked due to their very low frequency and lack of power for their detection in individual studies. [ABSTRACT FROM AUTHOR]

Abstract: In the past decade, imaging genetics has evolved into a highly successful neuroimaging discipline with a variety of sophisticated research tools. To date, several neural systems mechanisms have been identified that mediate genetic risk for mental disorders linked to common candidate and genome-wide-supported variants. In particular, the examination of intermediate connectivity phenotypes has recently gained increasing popularity. This paper gives an overview of the scientific methods and evidence that link indices of neural network organization to the genetic susceptibility for mental illness with a focus on the effects of candidate genes and genome-wide supported risk variants on brain structure and function. [Copyright &y& Elsevier]

The rs1625579 variant near the microRNA-137 (MIR137) gene is one of the best-supported schizophrenia variants in genome-wide association studies (GWAS), and microRNA-137 functionally regulates other GWAS identified schizophrenia risk variants. Schizophrenia patients with the MIR137 rs1625579 risk genotype (homozygous for the schizophrenia risk variant) also have aberrant brain structure. It is unclear if the effect of MIR137 among schizophrenia patients is due to potential epistasis with genetic risk for schizophrenia or other factors of the disorder. Here, we investigated the effect of MIR137 genotype on white matter fractional anisotropy (FA), cortical thickness (CT), and surface area (SA) in a sample comprising healthy control subjects, and individuals with familial risk for psychosis (first-degree relatives of patients with schizophrenia or bipolar disorder; N=426). In voxel-wise analyses of FA, we observed a significant genotype-by-group interaction (PFWE<0.05). The familial risk group with risk genotype had lower FA (PFWE<0.05), but there was no genetic association in controls. In vertex-wise analyses of SA, we also observed a significant genotype-by-group interaction (PFWE<0.05). Relatives with MIR137 risk genotype had lower SA, however the risk genotype was associated with higher SA in the controls (all PFWE<0.05). These results show that MIR137 risk genotype is associated with lower FA in psychosis relatives that is similar to previous imaging-genetics findings in patients with schizophrenia. Furthermore, MIR137 genotype may also be a risk factor in a subclinical population with wide reductions in white matter FA and cortical SA. [ABSTRACT FROM AUTHOR]

Recent large-scale genetic studies have provided robust evidence implicating several novel susceptibility genes for schizophrenia. These include ZNF804A, TCF4 and NRGN, which contain common variants that weakly increase schizophrenia susceptibility, and NRXN1, in which rare copy number variants have a greater impact on schizophrenia risk. Investigation of these and other substantiated susceptibility genes are providing valuable insight into the primary neurobiological mechanisms underlying schizophrenia, which may lead to novel therapeutic interventions for the disorder. In the meantime, several novel pharmacological strategies, including activation of mGluRs, elevation of synaptic glycine and inhibition of phosphodiesterase 10A, have recently shown promise for the treatment of schizophrenia in clinical trials. [ABSTRACT FROM AUTHOR]