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

Tadros, Rafik; Francis, Catherine Y.; Xu, Xiao; Vermeer, Alexa M C; Harper, Andrew R.; Huurman, Roy; Bisabu, Ken Kelu; Walsh, Roddy; Hoorntje, Edgar T; te Rijdt, Wouter P.; Buchan, Rachel J; van Velzen, Hannah G.; van Slegtenhorst, Marjon A; Vermeulen, Jentien M; Offerhaus, Joost Allard; Bai, Wenjia; de Marvao, Antonio; Lahrouchi, Najim; Beekman, Leander; Karper, Jacco C.; Veldink, Jan H.; Kayvanpour, Elham; Pantazis, Antonis; Baksi, A. John; Whiffin, Nicola; Mazzarotto, Francesco; Sloane, Geraldine; Suzuki, Hideaki; Schneider-Luftman, Deborah; Elliott, Paul; Richard, Pascale; Ader, Flavie; Villard, Eric; Lichtner, Peter; Meitinger, Thomas; Tanck, Michael W. T.; van Tintelen, J Peter; Thain, Andrew P; McCarty, David; Hegele, Robert A; Roberts, Jason D; Amyot, Julie; Dubé, Marie-Pierre; Cadrin-Tourigny, Julia; Giraldeau, Genevieve; L’Allier, Philippe L.; Garceau, Patrick; Tardif, Jean-Claude; Boekholdt, S Matthijs; Lumbers, R. Thomas; Asselbergs, Folkert W.; Barton, Paul J. R.; Cook, Stuart A; Prasad, Sanjay K; O’Regan, Declan P; van der Velden, Jolanda; Verweij, Karin J H; Talajic, Mario; Lettre, Guillaume; Pinto, Yigal M.; Meder, Benjamin; Charron, Philippe; de Boer, Rudolf A; Christiaans, Imke; Michels, Michelle; Wilde, Arthur A. M.; Watkins, Hugh; Matthews, Paul M; Ware, James S.; Bezzina, Connie R.

doi:10.1038/s41588-020-00762-2

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

Author(s): Rafik Tadros , Catherine Francis , Xiao Xu , Alexa M. C. Vermeer , Andrew R. Harper , Roy Huurman , Ken Kelu Bisabu , Roddy Walsh , Edgar T. Hoorntje , Wouter P. te Rijdt , Rachel J. Buchan , Hannah G. van Velzen , Marjon A. van Slegtenhorst , Jentien M. Vermeulen , Joost Allard Offerhaus , Wenjia Bai , Antonio de Marvao , Najim Lahrouchi , Leander Beekman , Jacco C. Karper , Jan H. Veldink , Elham Kayvanpour , Antonis Pantazis , A. John Baksi , Nicola Whiffin , Francesco Mazzarotto , Geraldine Sloane , Hideaki Suzuki , Deborah Schneider-Luftman , Paul Elliott , Pascale Richard , Flavie Ader , Eric Villard , Peter Lichtner , Thomas Meitinger , Michael W. T. Tanck , J. Peter van Tintelen , Andrew Thain , David McCarty , Robert A. Hegele , Jason D. Roberts , Julie Amyot , Marie-Pierre Dubé , Julia Cadrin-Tourigny , Geneviève Giraldeau , Philippe L. L’Allier , Patrick Garceau , Jean-Claude Tardif , S. Matthijs Boekholdt , R. Thomas Lumbers , Folkert W. Asselbergs , Paul J. R. Barton , Stuart A. Cook , Sanjay K. Prasad , Declan P. O’Regan , Jolanda van der Velden , Karin J. H. Verweij , Mario Talajic , Guillaume Lettre , Yigal M. Pinto , Benjamin Meder , Philippe Charron , Rudolf A. de Boer , Imke Christiaans , Michelle Michels , Arthur A. M. Wilde , Hugh Watkins , Paul M. Matthews , James S. Ware , Connie R. Bezzina

Publication date (Electronic): January 25 2021

Journal: Nature Genetics

Publisher: Springer Science and Business Media LLC

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Abstract

<p class="first" id="P1">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. </p>

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Most cited references 51

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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

Sue Richards, Nazneen Aziz, Sherri Bale … (2015)

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

Jack Bowden, George Davey Smith, Stephen Burgess (2016)

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.