5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Increased burden of ultra-rare structural variants localizing to boundaries of topologically associated domains in schizophrenia

      research-article

      Read this article at

      Bookmark
          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

          Despite considerable progress in schizophrenia genetics, most findings have been for large rare structural variants and common variants in well-imputed regions with few genes implicated from exome sequencing. Whole genome sequencing (WGS) can potentially provide a more complete enumeration of etiological genetic variation apart from the exome and regions of high linkage disequilibrium. We analyze high-coverage WGS data from 1162 Swedish schizophrenia cases and 936 ancestry-matched population controls. Our main objective is to evaluate the contribution to schizophrenia etiology from a variety of genetic variants accessible to WGS but not by previous technologies. Our results suggest that ultra-rare structural variants that affect the boundaries of topologically associated domains (TADs) increase risk for schizophrenia. Alterations in TAD boundaries may lead to dysregulation of gene expression. Future mechanistic studies will be needed to determine the precise functional effects of these variants on biology.

          Abstract

          Common variants identified by large-scale genomewide association studies cannot account fully account for the heritability of schizophrenia (SCZ). Here, the authors report high-coverage whole-genome sequencing of 1162 SCZ cases and 936 controls and explore the contribution of different types of variants to SCZ.

          Related collections

          Most cited references17

          • Record: found
          • Abstract: found
          • Article: not found

          Genome-wide association studies for complex traits: consensus, uncertainty and challenges.

          The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Rare-variant association analysis: study designs and statistical tests.

            Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions. Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Fragile X mental retardation protein targets G quartet mRNAs important for neuronal function.

              Loss of fragile X mental retardation protein (FMRP) function causes the fragile X mental retardation syndrome. FMRP harbors three RNA binding domains, associates with polysomes, and is thought to regulate mRNA translation and/or localization, but the RNAs to which it binds are unknown. We have used RNA selection to demonstrate that the FMRP RGG box binds intramolecular G quartets. This data allowed us to identify mRNAs encoding proteins involved in synaptic or developmental neurobiology that harbor FMRP binding elements. The majority of these mRNAs have an altered polysome association in fragile X patient cells. These data demonstrate that G quartets serve as physiologically relevant targets for FMRP and identify mRNAs whose dysregulation may underlie human mental retardation.
                Bookmark

                Author and article information

                Contributors
                patrick.sullivan@ki.se
                jin_szatkiewicz@med.unc.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                15 April 2020
                15 April 2020
                2020
                : 11
                : 1842
                Affiliations
                [1 ]ISNI 0000 0001 1034 1720, GRID grid.410711.2, Department of Genetics, , University of North Carolina, ; Chapel Hill, NC 27599 USA
                [2 ]ISNI 0000 0001 1034 1720, GRID grid.410711.2, Department of Biostatistics, , University of North Carolina, ; Chapel Hill, NC 27599 USA
                [3 ]ISNI 0000 0001 0930 2361, GRID grid.4514.4, Department of Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, , Lund University, ; 22362 Lund, Sweden
                [4 ]ISNI 0000 0001 0775 6028, GRID grid.5371.0, Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, , Chalmers University of Technology, ; 41258 Göteborg, Sweden
                [5 ]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Department of Medical Epidemiology and Biostatistics, , Karolinska Institutet, ; 17177 Stockholm, Sweden
                [6 ]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, , Uppsala University, ; 75237 Uppsala, Sweden
                [7 ]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, , Uppsala University, ; 75185 Uppsala, Sweden
                [8 ]ISNI 0000 0001 1034 1720, GRID grid.410711.2, Department of Psychiatry, , University of North Carolina, ; Chapel Hill, NC 27599 USA
                [9 ]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Department of Clinical Neuroscience, , Karolinska Institutet, ; 17177 Stockholm, Sweden
                Author information
                http://orcid.org/0000-0001-5326-8893
                http://orcid.org/0000-0003-3273-7704
                http://orcid.org/0000-0002-1921-1305
                http://orcid.org/0000-0002-8949-2587
                http://orcid.org/0000-0001-7809-7664
                http://orcid.org/0000-0001-6085-6749
                http://orcid.org/0000-0002-9520-6209
                http://orcid.org/0000-0002-7315-7899
                http://orcid.org/0000-0002-4898-7401
                Article
                15707
                10.1038/s41467-020-15707-w
                7160146
                32296054
                145eea2e-38b9-469c-a083-124bc142bab1
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 February 2020
                : 24 March 2020
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                dna sequencing,next-generation sequencing,genetics of the nervous system,schizophrenia

                Comments

                Comment on this article