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      Evidence-Based Assessment of Genes in Dilated Cardiomyopathy

      research-article
      , MS, CGC 1 , , MS, CGC 1 , , MD, PhD 1 , , MD, PhD 3 , , PhD 4 , 5 , , MGC, CGC 6 , , PhD 7 , , MSc, DipRCPath 8 , , MS, CGC 9 , , PhD, MPH 10 , , ScM, PhD, CGC 6 , , PhD 4 , 5 , , PhD, MGC 11 , 12 , , MD 13 , , MD 14 , , PhD 15 , , PhD 16 , 17 , 18 , , PhD 19 , 20 , 21 , 22 , , MD, PhD 23 , , MS, CGC 24 , , MS, CGC 25 , , MS, CGC 6 , , MBBS 26 , , PhD 7 , , MD 27 , , MBBS, PhD, MPH 28 , , MD, MS 24 , , PhD 27 , , PhD 29 , , MD, PhD 30 , , PhD 14 , , MD, PhD 9 , , MRCP, PhD 19 , 20 , 31 , , MD 1 , 2
      Circulation
      Lippincott Williams & Wilkins
      cardiomyopathy, genetics

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          Abstract

          Supplemental Digital Content is available in the text.

          Background:

          Each of the cardiomyopathies, classically categorized as hypertrophic cardiomyopathy, dilated cardiomyopathy (DCM), and arrhythmogenic right ventricular cardiomyopathy, has a signature genetic theme. Hypertrophic cardiomyopathy and arrhythmogenic right ventricular cardiomyopathy are largely understood as genetic diseases of sarcomere or desmosome proteins, respectively. In contrast, >250 genes spanning >10 gene ontologies have been implicated in DCM, representing a complex and diverse genetic architecture. To clarify this, a systematic curation of evidence to establish the relationship of genes with DCM was conducted.

          Methods:

          An international panel with clinical and scientific expertise in DCM genetics evaluated evidence supporting monogenic relationships of genes with idiopathic DCM. The panel used the Clinical Genome Resource semiquantitative gene-disease clinical validity classification framework with modifications for DCM genetics to classify genes into categories on the basis of the strength of currently available evidence. Representation of DCM genes on clinically available genetic testing panels was evaluated.

          Results:

          Fifty-one genes with human genetic evidence were curated. Twelve genes (23%) from 8 gene ontologies were classified as having definitive ( BAG3, DES, FLNC, LMNA, MYH7, PLN, RBM20, SCN5A, TNNC1, TNNT2, TTN) or strong ( DSP) evidence. Seven genes (14%; ACTC1, ACTN2, JPH2, NEXN, TNNI3, TPM1, VCL) including 2 additional ontologies were classified as moderate evidence; these genes are likely to emerge as strong or definitive with additional evidence. Of these 19 genes, 6 were similarly classified for hypertrophic cardiomyopathy and 3 for arrhythmogenic right ventricular cardiomyopathy. Of the remaining 32 genes (63%), 25 (49%) had limited evidence, 4 (8%) were disputed, 2 (4%) had no disease relationship, and 1 (2%) was supported by animal model data only. Of the 16 evaluated clinical genetic testing panels, most definitive genes were included, but panels also included numerous genes with minimal human evidence.

          Conclusions:

          In the curation of 51 genes, 19 had high evidence (12 definitive/strong, 7 moderate). It is notable that these 19 genes explain only a minority of cases, leaving the remainder of DCM genetic architecture incompletely addressed. Clinical genetic testing panels include most high-evidence genes; however, genes lacking robust evidence are also commonly included. We recommend that high-evidence DCM genes be used for clinical practice and that caution be exercised in the interpretation of variants in variable-evidence DCM genes.

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          Most cited references47

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          Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

          The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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            The mutational constraint spectrum quantified from variation in 141,456 humans

            Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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              ClinGen--the Clinical Genome Resource.

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                Author and article information

                Contributors
                Journal
                Circulation
                Circulation
                CIR
                Circulation
                Lippincott Williams & Wilkins (Hagerstown, MD )
                0009-7322
                1524-4539
                05 May 2021
                06 July 2021
                : 144
                : 1
                : 7-19
                Affiliations
                [1 ]Division of Human Genetics (E.J., L.P., T.A., R.E.H.), Department of Internal Medicine, Wexner Medical Center, The Ohio State University, Columbus.
                [2 ]Division of Cardiovascular Medicine (R.E.H.), Department of Internal Medicine, Wexner Medical Center, The Ohio State University, Columbus.
                [3 ]Department for Cardiology, Inselspital, Bern University Hospital, University of Bern, Switzerland (B.A.).
                [4 ]Department of Genetics, Children’s Hospital of Eastern Ontario, Ottawa, Canada (L.B., O.J.).
                [5 ]Department of Laboratory and Pathology Medicine, University of Ottawa, Ontario, Canada (L.B., O.J.).
                [6 ]Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD (E.B., C.A.J., B.M.).
                [7 ]Department of Cardiac-Thoracic-Vascular Sciences and Public Health, University of Padua, Italy (R.C., K.P.).
                [8 ]Clinical Genetics and Genomics Laboratory, Royal Brompton and Harefield NHS Foundation Trust, London, United Kingdom (M.E.).
                [9 ]Department of Medicine, University of California, Los Angeles (J.F., J. Wang).
                [10 ]Cardio Genomics Program at Centenary Institute, University of Sydney, Australia (J.I.).
                [11 ]Victor Chang Cardiac Research Institute, Sydney, Australia (R.J.).
                [12 ]Department of Medicine, University of New South Wales, Sydney, Australia (R.J.).
                [13 ]Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston (D.P.J.).
                [14 ]Department of Clinical and Experimental Cardiology, Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam Universitair Medische Centra, University of Amsterdam, the Netherlands (N.L., R.W.).
                [15 ]Department of Clinical Genetics, Amsterdam University Medical Center location Academic Medical Center, the Netherlands (R.H.L.D.).
                [16 ]Institute of Health Informatics, University College London, London, UK (R.T.L.).
                [17 ]Health Data Research UK London, University College London, UK (R.T.L.).
                [18 ]University College London British Heart Foundation Research Accelerator, London, United Kingdom (R.T.L.).
                [19 ]Cardiovascular Research Center, Royal Brompton and Harefield Hospitals, National Health Service Foundation Trust, London, United Kingdom (F.M., J. Ware).
                [20 ]National Heart and Lung Institute, Imperial College London, United Kingdom (F.M., J. Ware).
                [21 ]Department of Clinical and Experimental Medicine, University of Florence, Italy (F.M.).
                [22 ]Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy (F.M.).
                [23 ]Swiss DNAlysis Cardiogenetics, Dübendorf, Switzerland (A.M.D.).
                [24 ]Cardiovascular Genomics Center, Inova Heart and Vascular Institute, Falls Church, VA (R.L.M., P. Shah).
                [25 ]Invitae Corp, San Francisco, CA (A.M.).
                [26 ]Department of Cardiology and Genomic Medicine, Royal Melbourne Hospital, Australia (S.P.).
                [27 ]Centre for Heart Muscle Disease, Institute of Cardiovascular Science, University College London, London, United Kingdom (A.P., P. Syrris).
                [28 ]Agnes Ginges Centre for Molecular Cardiology at Centenary Institute, University of Sydney, Australia (C.S.).
                [29 ]Department of Genetics, University of North Carolina, Chapel Hill (C.T.).
                [30 ]Department of Genetics, University Medical Center Utrecht, University of Utrecht, The Netherlands (J.P.v.T.).
                [31 ]Medical Research Council London Institute for Medical Sciences, Imperial College London, United Kingdom (J. Ware).
                Author notes
                Correspondence to: Elizabeth Jordan, MS, CGC, The Ohio State University, Wexner Medical Center, Biomedical Research Tower Room 306, 460 West 12th Avenue, Columbus, OH 43210; Email elizabeth.jordan@ 123456osumc.edu
                Ray E. Hershberger, MD, The Ohio State University, Wexner Medical Center, Biomedical Research Tower Room 304, 460 West 12th Avenue, Columbus, OH 43210. Email ray.hershberger@ 123456osumc.edu
                Article
                00003
                10.1161/CIRCULATIONAHA.120.053033
                8247549
                33947203
                ffa61c37-5022-44f2-be2e-240b0602dff5
                © 2021 The Authors.

                Circulation is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited.

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                : 20 December 2020
                : 13 March 2020
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