Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci

Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA.
Nature Genetics (Impact Factor: 29.35). 09/2011; 43(10):969-76. DOI: 10.1038/ng.940
Source: PubMed


We examined the role of common genetic variation in schizophrenia in a genome-wide association study of substantial size: a stage 1 discovery sample of 21,856 individuals of European ancestry and a stage 2 replication sample of 29,839 independent subjects. The combined stage 1 and 2 analysis yielded genome-wide significant associations with schizophrenia for seven loci, five of which are new (1p21.3, 2q32.3, 8p23.2, 8q21.3 and 10q24.32-q24.33) and two of which have been previously implicated (6p21.32-p22.1 and 18q21.2). The strongest new finding (P = 1.6 × 10(-11)) was with rs1625579 within an intron of a putative primary transcript for MIR137 (microRNA 137), a known regulator of neuronal development. Four other schizophrenia loci achieving genome-wide significance contain predicted targets of MIR137, suggesting MIR137-mediated dysregulation as a previously unknown etiologic mechanism in schizophrenia. In a joint analysis with a bipolar disorder sample (16,374 affected individuals and 14,044 controls), three loci reached genome-wide significance: CACNA1C (rs4765905, P = 7.0 × 10(-9)), ANK3 (rs10994359, P = 2.5 × 10(-8)) and the ITIH3-ITIH4 region (rs2239547, P = 7.8 × 10(-9)).

Download full-text


Available from: Stephan Ripke,
  • Source
    • "In this perspective, it has to be noted that, among our more significant results in the multivariate analyses, another CpGs (cg14035771) near a miRNA (miR137) was associated with severity of childhood maltreatment (see Table 4). miR137 is mainly found in hippocampus and amygdala and has been strongly associated with schizophrenia, but also with autism spectrum disorders (Pinto et al. 2014; Ripke et al. 2011; Yin et al. 2014). As for miR124, miR137 has also been associated to amygdala functionality and emotion processing (Mothersill et al. 2014). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Early life adversity plays a critical role in the emergence of borderline personality disorder (BPD) and this could occur through epigenetic programming. In this perspective, we aimed to determine whether childhood maltreatment could durably modify epigenetic processes by the means of a whole-genome methylation scan of BPD subjects.Using the Illumina Infinium® HumanMethylation450 BeadChip, global methylation status of DNA extracted from peripheral blood leucocytes was correlated to the severity of childhood maltreatment in 96 BPD subjects suffering from a high level of child adversity and 93 subjects suffering from major depressive disorder (MDD) and reporting a low rate of child maltreatment.Several CpGs within or near the following genes (IL17RA, miR124-3, KCNQ2, EFNB1, OCA2, MFAP2, RPH3AL, WDR60, CST9L, EP400, A2ML1, NT5DC2, FAM163A, SPSB2) were found to be differently methylated, either in BPD compared to MDD or in relation to the severity of childhood maltreatment. A highly relevant biological result was observed for cg04927004 close to miR124-3 that was significantly associated with BPD and severity of childhood maltreatment. miR124-3 codes for a microRNA (miRNA) targeting several genes previously found to be associated with BPD such as NR3C1.Our results highlight the potentially important role played by miRNAs in the etiology of neuropsychiatric disorders such as BPD and the usefulness of using methylome-wide association studies to uncover such candidate genes. Moreover, they offer new understanding of the impact of maltreatments on biological processes leading to diseases and may ultimately result in the identification of relevant biomarkers.
    Genes Brain and Behavior 01/2015; 14(2). DOI:10.1111/gbb.12197 · 3.66 Impact Factor
  • Source
    • "Lambda values did not show evidence of major population stratification factors (lambda = 1.04 ± 0.05). The observed small inflation factor in the total sample was interpreted as indicative of a large number of weakly associated SNPs consistent with the disease's polygenic inheritance, as yet observed in larger mega-analyses (Ripke et al., 2011). "
    [Show abstract] [Hide abstract]
    ABSTRACT: schizophrenia is a complex mental disorder marked by severely impaired thinking, delusional thoughts, hallucinations and poor emotional responsiveness. The biological mechanisms that lead to schizophrenia may be related to the genetic background of patients. Thus, a genetic perspective may help to unravel the molecular pathways disrupted in schizophrenia. In the present work, we used a molecular pathway analysis to identify the molecular pathways associated with schizophrenia. We collected data of genetic loci previously associated with schizophrenia, identified the genes located in those positions and created the metabolic pathways that are related to those genes' products. These pathways were tested for enrichment (a number of SNPs associated with the phenotype significantly higher than expected by chance) in a sample of schizophrenic patients and controls (4,486 and 4,477, respectively). The molecular pathway that resulted from the identification of all the genes located in loci previously found to be associated with schizophrenia was found to enriched, as expected (permutated p(10(6))=9.9999e-06).We found 60 SNPs amongst 30 different genes with a strong association with schizophrenia. The genes are related to the pathways related to neurodevelopment, apoptosis, vesicle traffic, immune response and MAPk cascade. The pathway related to the toll-like receptor family seemed to play a central role in the modulation/connection of various pathways whose disruption leads to schizophrenia. This pathway is related to the innate immune system, further stressing the role of immunological-related events in increasing the risk to schizophrenia. Copyright © 2014. Published by Elsevier Inc.
    Progress in Neuro-Psychopharmacology and Biological Psychiatry 12/2014; 59. DOI:10.1016/j.pnpbp.2014.12.009 · 3.69 Impact Factor
  • Source
    • "VEGAS uses as a test statistic the sum of univariate v 2 of SNPs within a gene and assesses its statistical significance using an empirical H 0 distribution simulated from a multivariate normal distribution with LD matrix of the SNPs as a covariance matrix . We applied the earlier methods to four real summary datasets: (i) PGC1 bipolar disorder (BD) (Sklar et al., 2011), (ii) schizophrenia (SCZ) (Ripke et al., 2011), (iii) major depressive disorder (MDD) (Sullivan et al., 2013) cohorts and (iv) anorexia nervosa cohort from Genetic Consortium For Anorexia Nervosa (GCAN) (Boraska et al., 2014). Before the applied analyses, we converted all four summary datasets to National Center for Biotechnology Information (NCBI) build 37 (hg19) using liftOver (Hinrichs et al., 2006). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Gene expression is influenced by variants commonly known as expression quantitative trait loci (eQTL). Based on this fact, researchers proposed to use eQTL/functional information univariately for prioritizing Single Nucleotide Polymorphisms (SNPs) signals from genome-wide association studies (GWAS). However, most genes are influenced by multiple eQTLs which, thus, jointly affect any downstream phenotype. Therefore, when compared to the univariate prioritization approach, a joint modeling of eQTL action on phenotypes has the potential to substantially increase signal detection power. Nonetheless, a joint eQTL analysis is impeded by i) not measuring all eQTLs in a gene and/or ii) lack of access to individual genotypes. We propose JEPEG, a novel software tool which uses only GWAS summary statistics to i) impute the summary statistics at unmeasured eQTLs and ii) test for the joint effect of all measured and imputed eQTLs in a gene. We illustrate the behavior/performance of the developed tool by analyzing the GWAS meta-analysis summary statistics from the Psychiatric Genomics Consortium stage 1 (PGC1) and the Genetic Consortium for Anorexia Nervosa (GCAN). Applied analyses results suggest that JEPEG complements commonly used univariate GWAS tools by: i) increasing signal detection power via uncovering a) novel genes or b) known associated genes in smaller cohorts, and ii) assisting in fine-mapping of challenging regions, e.g. Major Histocompatibility Complex (MHC) for schizophrenia. Availability and implementation: JEPEG, its associated database of eQTL SNPs and usage examples are publicly available at SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. © The Author(s) 2014. Published by Oxford University Press.
    Bioinformatics 12/2014; 31(8). DOI:10.1093/bioinformatics/btu816 · 4.98 Impact Factor
Show more