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      Integrated Pathway-Based Approach Identifies Association between Genomic Regions at CTCF and CACNB2 and Schizophrenia

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      1 , * , 2 , 3 , 4 , 5 , 6 , 7 , GROUP Investigators, PSYCH-GEMS SCZ working group, 7 , 3 , 8 , 9 , 7 , 7 , 7 , 10 , 3 , 8 , 11 , 12 , 13 , 14 , 15 , 2 , 16 , 17 , 18 , 19 , 20 , 20 , 11 , 12 , 19 ,   21 , 22 , 23 , 24 , 25 , 3 , 8 , 9 , 26 , 3 , 8 , 7 , 8 , 16 , 17 , 21 , 22 , 1
      PLoS Genetics
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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In the present study, an integrated hierarchical approach was applied to: (1) identify pathways associated with susceptibility to schizophrenia; (2) detect genes that may be potentially affected in these pathways since they contain an associated polymorphism; and (3) annotate the functional consequences of such single-nucleotide polymorphisms (SNPs) in the affected genes or their regulatory regions. The Global Test was applied to detect schizophrenia-associated pathways using discovery and replication datasets comprising 5,040 and 5,082 individuals of European ancestry, respectively. Information concerning functional gene-sets was retrieved from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and the Molecular Signatures Database. Fourteen of the gene-sets or pathways identified in the discovery dataset were confirmed in the replication dataset. These include functional processes involved in transcriptional regulation and gene expression, synapse organization, cell adhesion, and apoptosis. For two genes, i.e. CTCF and CACNB2, evidence for association with schizophrenia was available (at the gene-level) in both the discovery study and published data from the Psychiatric Genomics Consortium schizophrenia study. Furthermore, these genes mapped to four of the 14 presently identified pathways. Several of the SNPs assigned to CTCF and CACNB2 have potential functional consequences, and a gene in close proximity to CACNB2, i.e. ARL5B, was identified as a potential gene of interest. Application of the present hierarchical approach thus allowed: (1) identification of novel biological gene-sets or pathways with potential involvement in the etiology of schizophrenia, as well as replication of these findings in an independent cohort; (2) detection of genes of interest for future follow-up studies; and (3) the highlighting of novel genes in previously reported candidate regions for schizophrenia.

          Author Summary

          Large-scale genetic studies of complex diseases such as schizophrenia have identified a variety of susceptibility loci. Since many of the respective variants have only a weak influence on disease risk, pathophysiological interpretation of the results is problematic. Investigation of the joint effects of multiple functionally related genes or pathways increases the power to detect disease related genes, and provides insights into the etiology of the disease in question. In the present study, an integrated hierarchical approach was applied to: (i) identify pathways associated with complex neuropsychiatric disease schizophrenia (ii) detect potentially affected genes in these pathways; and (iii) annotate the functional consequences of genetic markers in the affected genes or their regulatory regions. Two samples comprising >10,000 individuals of European ancestry as well as data from the Psychiatric Genomics Consortium schizophrenia study were examined. Pathways representing transcriptional regulation and gene expression, cell adhesion, apoptosis, and synapse organization showed significant association with schizophrenia. In particular, CTCF, CACNB2, and ARL5B, i.e. genes involved in chromatin modulation, calcium channel signaling and membrane transport, respectively, were highlighted as candidate genes for schizophrenia risk.

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          Most cited references28

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          Pathway analysis of seven common diseases assessed by genome-wide association.

          Recent genome-wide association studies (GWAS) have identified DNA sequence variations that exhibit unequivocal statistical associations with many common chronic diseases. However, the vast majority of these studies identified variations that explain only a very small fraction of disease burden in the population at large, suggesting that other factors, such as multiple rare or low-penetrance variations and interacting environmental factors, are major contributors to disease susceptibility. Identifying multiple low-penetrance variations (or "polygenes") contributing to disease susceptibility will be difficult. We present a pathway analysis approach to characterizing the likely polygenic basis of seven common diseases using the Wellcome Trust Case Control Consortium (WTCCC) GWAS results. We identify numerous pathways implicated in disease predisposition that would have not been revealed using standard single-locus GWAS statistical analysis criteria. Many of these pathways have long been assumed to contain polymorphic genes that lead to disease predisposition. Additionally, we analyze the genetic relationships between the seven diseases, and based upon similarities with respect to the associated genes and pathways affected in each, propose a new way of categorizing the diseases.
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            CTCF/cohesin-mediated DNA looping is required for protocadherin α promoter choice.

            The closely linked human protocadherin (Pcdh) α, β, and γ gene clusters encode 53 distinct protein isoforms, which are expressed in a combinatorial manner to generate enormous diversity on the surface of individual neurons. This diversity is a consequence of stochastic promoter choice and alternative pre-mRNA processing. Here, we show that Pcdhα promoter choice is achieved by DNA looping between two downstream transcriptional enhancers and individual promoters driving the expression of alternate Pcdhα isoforms. In addition, we show that this DNA looping requires specific binding of the CTCF/cohesin complex to two symmetrically aligned binding sites in both the transcriptionally active promoters and in one of the enhancers. These findings have important implications regarding enhancer/promoter interactions in the generation of complex Pcdh cell surface codes for the establishment of neuronal identity and self-avoidance in individual neurons.
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              Pathway analysis of genomic data: concepts, methods, and prospects for future development.

              Genome-wide data sets are increasingly being used to identify biological pathways and networks underlying complex diseases. In particular, analyzing genomic data through sets defined by functional pathways offers the potential of greater power for discovery and natural connections to biological mechanisms. With the burgeoning availability of next-generation sequencing, this is an opportune moment to revisit strategies for pathway-based analysis of genomic data. Here, we synthesize relevant concepts and extant methodologies to guide investigators in study design and execution. We also highlight ongoing challenges and proposed solutions. As relevant analytical strategies mature, pathways and networks will be ideally placed to integrate data from diverse -omics sources to harness the extensive, rich information related to disease and treatment mechanisms. Copyright © 2012 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                June 2014
                5 June 2014
                : 10
                : 6
                : e1004345
                Affiliations
                [1 ]Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
                [2 ]German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
                [3 ]Institute of Human Genetics, University of Bonn, Bonn, Germany
                [4 ]Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
                [5 ]Department of Psychiatry, University of Bonn, Bonn, Germany
                [6 ]Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
                [7 ]Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
                [8 ]Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
                [9 ]Institute for Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany
                [10 ]National Centre for Integrated Register-based Research (NCRR), Department of Economics and Business, Aarhus University, Aarhus, Denmark
                [11 ]Division of Molecular and Clinical Neurobiology, Department of Psychiatry, Ludwig-Maximilians-University, Munich, Germany
                [12 ]Department of Psychiatry, University of Halle-Wittenberg, Halle, Germany
                [13 ]Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [14 ]Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
                [15 ]Institute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, Bonn, Germany
                [16 ]Department of Genomic Mathematics, University of Bonn, Bonn, Germany
                [17 ]Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [18 ]Department of Psychiatry and Psychotherapy, University Medical Center Georg-August-Universität, Göttingen, Germany
                [19 ]Department of Psychiatry, University of Bonn, Bonn, Germany
                [20 ]Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
                [21 ]Department of Biomedicine, Aarhus University, Aarhus C, Denmark and Center for Integrated Sequencing, iSEQ, Aarhus, Denmark
                [22 ]Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus and Copenhagen, Denmark
                [23 ]Centre for Psychiatric Research, Aarhus University Hospital, Risskov, Denmark
                [24 ]UCLA Center for Neurobehavioral Genetics, Los Angeles, California, United States of America
                [25 ]Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
                [26 ]Department of Medical Genetics, University Hospital Basel, Basel, Switzerland
                Cardiff University, United Kingdom
                Author notes

                ¶ MR, MM, and BB also contributed equally.

                ‡ Membership for GROUP Investigators and iPSYCH-GEMS SCZ working group is provided in the Acknowledgments.

                The authors declare no conflict of interest.

                Conceived and designed the experiments: DJ BH MZ SC MMN MR MM BB. Performed the experiments: DJ ML CL SR MZ MM. Analyzed the data: DJ BH SC MMN MR MM BB. Contributed reagents/materials/analysis tools: MZ JF SHW TWM JT JS SM FD IG TGS RM IN HS DR WM AB RO SC MMN MR BB. Wrote the paper: DJ BH SC MMN MR MM BB.

                Article
                PGENETICS-D-13-02113
                10.1371/journal.pgen.1004345
                4046913
                24901509
                d8c53705-876c-418d-b316-3184f6960b66
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 August 2013
                : 20 March 2014
                Page count
                Pages: 11
                Funding
                This study was supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Genome Research Network (IG) MooDS (Systematic Investigation of the Molecular Causes of Major Mood Disorders and Schizophrenia; grant 01GS08144 to MMN and SC, grant 01GS08147 to MR, grant 01GS08149 to BB), under the auspices of the National Genome Research Network plus (NGFNplus). The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007–2013) under grant agreement n° 279227 (CRESTAR). Further funding came from the European Union Seventh Framework Programme (FP7/2007–2011) under grant agreement no. 242257 (ADAMS). The Heinz Nixdorf Recall cohort was established with the support of the Heinz Nixdorf Foundation (Dr G Schmidt, Chairman). MMN is a member of the DFG-funded Excellence Cluster ImmunoSensation. IN was supported by a Junior Scientist Grant (Rotationsstelle) of IZKF, Jena University Hospital. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Genetics
                Neuroscience
                Psychology
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Social Sciences

                Genetics
                Genetics

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