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Abstract Background Sequencing studies have pointed to the involvement in schizophrenia of rare coding variants in neuronally expressed genes, including activity-regulated cytoskeleton-associated protein (ARC) and N -methyl-D-aspartate receptor (NMDAR) complexes; however, larger samples are required to reveal novel genes and specific biological mechanisms. Methods We sequenced 187 genes, selected for prior evidence of association with schizophrenia, in a new dataset of 5207 cases and 4991 controls. Included among these genes were members of ARC and NMDAR postsynaptic protein complexes, as well as voltage-gated sodium and calcium channels. We performed a rare variant meta-analysis with published sequencing data for a total of 11,319 cases, 15,854 controls, and 1136 trios. Results While no individual gene was significantly associated with schizophrenia after genome-wide correction for multiple testing, we strengthen the evidence that rare exonic variants in the ARC (p = 4.0 × 10–4) and NMDAR (p = 1.7 × 10–5) synaptic complexes are risk factors for schizophrenia. In addition, we found that loss-of-function variants and missense variants at paralog-conserved sites were enriched in voltage-gated sodium channels, particularly the alpha subunits (p = 8.6 × 10–4). Conclusions In one of the largest sequencing studies of schizophrenia to date, we provide novel evidence that multiple voltage-gated sodium channels are involved in schizophrenia pathogenesis and confirm the involvement of ARC and NMDAR postsynaptic complexes. [ABSTRACT FROM AUTHOR]

Several recurrent copy number variants (CNVs) increasing risk to neuropsychiatric diseases have been identified in recent years. They show variable clinical expressivity, being associated with different disorders, and incomplete penetrance. However, due to its very low frequency, the full variety of clinical outcomes associated with each one of these CNVs is unknown. Current methods for detection of CNVs are labor intensive, expensive or not suitable for high throughput analysis. Quantitative interspecies competitive PCR linked to variant minisequencing and detection by mass-spectrometry may overcome these limitations. Here, we present two multiplex assays based on this method to screen for eleven psychiatric risk CNVs, such as 1q21, 16p11.2, 3q29, or 16p13.11 regions, among others. The assays were tested in our collection of 514 schizophrenia patients. Results were compared with MLPA at two CNVs. Additional positive results were confirmed by exome sequencing. A total of fourteen patients were CNV carriers. The method presents high sensitivity and specificity, showing its utility as a cheap, accurate, high throughput screening tool for recurrent CNVs. The method may be very useful for management of psychiatric patients as well as screening of different collections of samples to better identify the full spectrum of clinical variability. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies using several hundred thousand anonymous markers present limited statistical power. Alternatively, association studies restricted to common nonsynonymous single nucleotide polymorphisms (nsSNPs) have the advantage of strongly reducing the multiple testing problem, while increasing the probability of testing functional single nucleotide polymorphisms (SNPs). Methods: We performed a case-control association study of common nsSNPs in Galician (northwest Spain) samples using the Affymetrix GeneChip Human 20k cSNP Kit, followed by a replication study of the more promising results. After quality control procedures, the discovery sample consisted of 5100 nsSNPs at minor allele frequency >5% analyzed in 476 schizophrenia patients and 447 control subjects. The replication sample consisted of 4069 cases and 15,128 control subjects of European origin. We also performed multilocus analysis, using aggregated scores of nsSNPs at liberal significance thresholds and cross-validation procedures. Results: The 5 independent nsSNPs with false discovery rate q ≤ .25, as well as 13 additional nsSNPs at p < .01 and located in functional candidate genes, were genotyped in the replication samples. One SNP, rs13107325, located at the metal ions transporter gene SLC39A8, reached significance in the combined sample after Bonferroni correction (trend test, p = 2.7 × 10−6, allelic odds ratio = 1.32). This SNP presents minor allele frequency of 5% to 10% in many European populations but is rare outside Europe. We also confirmed the polygenic component of susceptibility. Conclusions: Taking into account that another metal ions transporter gene, SLC39A3, is associated to bipolar disorder, our findings reveal a role for brain metal homeostasis in psychosis. [Copyright &y& Elsevier]

Abstract: Schizophrenia is a common disease associated with reduced fertility. Therefore, the existence of common susceptibility alleles not removed by natural selection may be considered an evolutionary paradox. The antagonistic pleiotropy model, proposed to explain this paradox, states that an allele may be common because of its overall selective advantage, in spite of deleterious effects on specific traits. Recent work on DAOA, PPP1R1B, and APOL1 suggests that these genes present common alleles associated to increase risk of schizophrenia but conferring an overall selective advantage, related to better cognitive performance (DAOA and PPP1R1B) or protection against pathogens (APOL1). To test if these genes fit the antagonistic pleiotropy model, we searched for recent natural selection at these loci applying the long-range haplotype test on data from the HapMap Project; and performed case-control association analysis in a well-powered sample, including 301 schizophrenic patients and 604 controls from Spain. For DAOA and PPP1R1B, we genotyped the Single-nucleotide polymorphisms (SNPs) needed to replicate previous associations, while for APOL1, we genotyped 15 tagSNPs, and seven putative functional SNPs. We did not detect evidence of recent natural selection. Furthermore, we did not find significant associations. Thus, these genes do not fit the antagonistic pleiotropy model. [ABSTRACT FROM AUTHOR]