Project description:Background: Schizophrenia is a severe, highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have focussed attention on the role of non-sequence-based genomic variation in health and disease. Although a better understanding of the molecular mechanisms underlying disease phenotypes is best achieved using an integrated functional genomics strategy, few studies have attempted to systematically integrate genetic and epigenetic epidemiological approaches. Results: We performed a multi-stage epigenome-wide association study (EWAS), quantifying genome-wide patterns of DNA methylation in a total of 1,801 individuals from three independent sample cohorts. We identified multiple differentially methylated positions (DMPs) and region (DMRs) associated with schizophrenia, independently of important confounders such as smoking, with consistent effects across the three independent cohorts. We also show that polygenic burden for schizophrenia is associated with epigenetic variation at multiple loci across the genome, independently of loci implicated in the analysis of diagnosed schizophrenia. Finally, we show how DNA methylation quantitative trait loci (mQTL) analyses can be used to annotate the extended genomic regions nominated by genetic studies of schizophrenia, with Bayesian co-localization analyses highlighting potential regulatory variation causally involved in disease. Conclusion: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological pipeline that can be used to inform EWAS analyses of other complex traits and diseases. We demonstrate the utility of using polygenic risk score (PRS) for identifying molecular variation associated with etiological variation, and mQTLs for refining the functional/regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation. 675 whole blood derived DNA samples (353 schizophrenia cases and 322 controls) representing phase 1 of our meta-analysis. Bisulfite converted DNA from these samples were hybridized to the Illumina Infinium 450k Human Methylation Beadchip v1.0.

Project description:Abstract Background: Schizophrenia is a severe, highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have focussed attention on the role of non-sequence-based genomic variation in health and disease. Although a better understanding of the molecular mechanisms underlying disease phenotypes is best achieved using an integrated functional genomics strategy, few studies have attempted to systematically integrate genetic and epigenetic epidemiological approaches. Results: We performed a multi-stage epigenome-wide association study (EWAS), quantifying genome-wide patterns of DNA methylation in a total of 1,801 individuals from three independent sample cohorts. We identified multiple differentially methylated positions (DMPs) and region (DMRs) associated with schizophrenia, independently of important confounders such as smoking, with consistent effects across the three independent cohorts. We also show that polygenic burden for schizophrenia is associated with epigenetic variation at multiple loci across the genome, independently of loci implicated in the analysis of diagnosed schizophrenia. Finally, we show how DNA methylation quantitative trait loci (mQTL) analyses can be used to annotate the extended genomic regions nominated by genetic studies of schizophrenia, with Bayesian co-localization analyses highlighting potential regulatory variation causally involved in disease. Conclusion: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological pipeline that can be used to inform EWAS analyses of other complex traits and diseases. We demonstrate the utility of using polygenic risk score (PRS) for identifying molecular variation associated with etiological variation, and mQTLs for refining the functional/regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation. 847 whole blood derived DNA samples (414 schizophrenia cases and 433 controls) representing phase 2 of our meta-analysis. Bisulfite converted DNA from these samples were hybridised to the Illumina Infinium 450k Human Methylation Beadchip v1.0.

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