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      Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect

      research-article
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          Abstract

          The heart muscle diseases hypertrophic (HCM) and dilated (DCM) cardiomyopathies are leading causes of sudden death and heart failure in young otherwise healthy individuals. We conducted genome-wide association studies (GWAS) and multi-trait analyses in HCM (1,733 cases), DCM (5,521 cases), and nine left ventricular (LV) traits in 19,260 UK Biobank participants with structurally normal hearts. We identified 16 loci associated with HCM, 13 with DCM, and 23 with LV traits. We show strong genetic correlations between LV traits and cardiomyopathies, with opposing effects in HCM and DCM. Two-sample Mendelian randomization supports a causal association linking increased contractility with HCM risk. A polygenic risk score (PRS) explains a significant portion of phenotypic variability in carriers of HCM-causing rare variants. Our findings thus provide evidence that PRS may account for variability in Mendelian diseases. More broadly, we provide insights into how genetic pathways may lead to distinct disorders through opposing genetic effects.

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          Most cited references51

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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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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              Is Open Access

              Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

              Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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                Author and article information

                Journal
                9216904
                Nat Genet
                Nat Genet
                Nature genetics
                1061-4036
                1546-1718
                26 January 2021
                01 February 2021
                25 January 2021
                25 July 2021
                : 53
                : 2
                : 128-134
                Affiliations
                [1 ]Cardiovascular Genetics Center, Montreal Heart Institute and Faculty of Medicine, University of Montreal, Montreal, QC, Canada
                [2 ]Amsterdam UMC, University of Amsterdam, Department of Clinical and Experimental Cardiology, Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
                [3 ]Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK
                [4 ]National Heart & Lung Institute, Imperial College London, London, UK
                [5 ]MRC London Institute of Medical Sciences, Imperial College London, London, UK
                [6 ]Amsterdam UMC, University of Amsterdam, Department of Clinical Genetics, Amsterdam, the Netherlands
                [7 ]European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARDHEART; http://guardheart.ern-net.eu)
                [8 ]Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
                [9 ]Wellcome Centre for Human Genetics, Oxford, UK
                [10 ]Department of Cardiology, Thoraxcentre, Erasmus Medical Centre, Rotterdam, the Netherlands
                [11 ]Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands
                [12 ]Netherlands Heart Institute, Utrecht, the Netherlands
                [13 ]Erasmus University Medical Center Rotterdam, Department of Clinical Genetics, Thorax Centre, Rotterdam, the Netherlands
                [14 ]Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands
                [15 ]Data Science Institute, Imperial College London, London, UK
                [16 ]Department of Brain Sciences and UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, Imperial College, London, UK
                [17 ]University Medical Center Groningen, University of Groningen, Department of Cardiology, Groningen, the Netherlands
                [18 ]Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
                [19 ]Institute for Cardiomyopathies, Heidelberg Heart Center, University of Heidelberg, Heidelberg, Germany
                [20 ]DZHK (German Centre for Cardiovascular Research), Berlin, Germany
                [21 ]Department of Experimental and Clinical Medicine, University of Florence, Italy
                [22 ]Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy
                [23 ]Department of Cardiovascular Medicine, Tohoku University Hospital, Seiryo, Aoba, Sendai, Japan
                [24 ]Tohoku Medical Megabank Organization, Tohoku University, Seiryo, Aoba, Sendai, Japan
                [25 ]The Francis Crick Institute, London, UK
                [26 ]Department of Epidemiology and Biostatistics, Imperial College London, London, UK
                [27 ]APHP, Service de biochimie métabolique, UF de cardiogénétique et myogénétique moléculaire et cellulaire, Hôpital Pitié-Salpêtrière, Paris, France
                [28 ]Sorbonne Université, INSERM, UMR_S 1166 and ICAN Institute for Cardiometabolism and Nutrition, Faculté de Médecine, Paris, France
                [29 ]Université de Paris, Faculté de Pharmacie, Paris, France
                [30 ]Helmholtz Zentrum Muenchen, Institute of Human Genetics, Neuherberg, Germany
                [31 ]Klinikum rechts der Isar der TU Muenchen, School of Medicine, Institute of Human Genetics, Munich, Germany
                [32 ]DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
                [33 ]Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam Public Health (APH), Amsterdam, the Netherlands
                [34 ]Department of Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
                [35 ]Department of Medicine and Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
                [36 ]Montreal Heart Institute and Faculty of Medicine, University of Montreal, Montreal, QC, Canada
                [37 ]Amsterdam UMC, University of Amsterdam, Department of Cardiology, Heartcenter, Amsterdam, the Netherlands
                [38 ]Institute of Health Informatics, University College London, London, UK
                [39 ]Health Data Research UK, Gibbs Building, London, UK
                [40 ]Barts Heart Centre, Saint Bartholomew’s Hospital, London, UK
                [41 ]Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
                [42 ]Institute of Cardiovascular Science and Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK
                [43 ]National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore
                [44 ]Cardiovascular and Metabolic Disorders Program, Duke‐National University of Singapore Medical School, Singapore
                [45 ]Department of Physiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
                [46 ]APHP, Département de Génétique, Centre de référence des maladies cardiaques héréditaires ou rares, Hôpital Pitié-Salpêtrière, Paris, France
                Author notes
                Corresponding authors: Rafik Tadros, rafik.tadros@ 123456umontreal.ca , James S. Ware, j.ware@ 123456imperial.ac.uk , Connie R. Bezzina, c.r.bezzina@ 123456amsterdamumc.nl
                Article
                EMS114661
                10.1038/s41588-020-00762-2
                7611259
                33495596
                9e644861-e6d0-42ad-b9d7-b2d483f07b38

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