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

doi:10.1038/s41588-020-00762-2

Meta-Analysis

. 2021 Feb;53(2):128-134.

doi: 10.1038/s41588-020-00762-2. Epub 2021 Jan 25.

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

Rafik Tadros^#^{1

2}, Catherine Francis^#^{3

4}, Xiao Xu^#⁵, Alexa M C Vermeer^#^{6

7

8}, Andrew R Harper^{9

10}, Roy Huurman¹¹, Ken Kelu Bisabu¹², Roddy Walsh⁶, Edgar T Hoorntje^{13

14}, Wouter P Te Rijdt^{13

14}, Rachel J Buchan^{3

4}, Hannah G van Velzen¹¹, Marjon A van Slegtenhorst¹⁵, Jentien M Vermeulen¹⁶, Joost Allard Offerhaus⁶, Wenjia Bai^{17

18}, Antonio de Marvao⁵, Najim Lahrouchi⁶, Leander Beekman⁶, Jacco C Karper¹⁹, Jan H Veldink²⁰, Elham Kayvanpour^{21

22}, Antonis Pantazis³, A John Baksi^{3

4}, Nicola Whiffin^{3

4

5}, Francesco Mazzarotto^{3

4

23

24}, Geraldine Sloane^{3

4}, Hideaki Suzuki^{18

25

26}, Deborah Schneider-Luftman^{27

28}, Paul Elliott²⁸, Pascale Richard^{29

30}, Flavie Ader^{29

30

31}, Eric Villard³⁰, Peter Lichtner³², Thomas Meitinger^{32

33

34}, Michael W T Tanck³⁵, J Peter van Tintelen^{7

36}, Andrew Thain³⁷, David McCarty³⁷, Robert A Hegele³⁷, Jason D Roberts³⁷, Julie Amyot¹², Marie-Pierre Dubé³⁸, Julia Cadrin-Tourigny¹², Geneviève Giraldeau¹², Philippe L L'Allier¹², Patrick Garceau¹², Jean-Claude Tardif³⁸, S Matthijs Boekholdt³⁹, R Thomas Lumbers^{40

41

42}, Folkert W Asselbergs^{43

44}, Paul J R Barton^{3

4}, Stuart A Cook^{4

5

45

46}, Sanjay K Prasad^{3

4}, Declan P O'Regan⁵, Jolanda van der Velden⁴⁷, Karin J H Verweij¹⁶, Mario Talajic¹², Guillaume Lettre³⁸, Yigal M Pinto^{6

8}, Benjamin Meder²¹, Philippe Charron^{8

30

48}, Rudolf A de Boer¹⁹, Imke Christiaans¹³, Michelle Michels¹¹, Arthur A M Wilde^{6

8}, Hugh Watkins^#^{9

10}, Paul M Matthews^#¹⁸, James S Ware^#^{49

50

51}, Connie R Bezzina^#^{52

53}

Affiliations

¹ Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada. rafik.tadros@umontreal.ca.
² Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands. rafik.tadros@umontreal.ca.
³ Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK.
⁴ National Heart and Lung Institute, Imperial College London, London, UK.
⁵ MRC London Institute of Medical Sciences, Imperial College London, London, UK.
⁶ Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
⁷ Department of Clinical Genetics, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
⁸ European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART).
⁹ Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
¹⁰ Wellcome Centre for Human Genetics, Oxford, UK.
¹¹ Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands.
¹² Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada.
¹³ Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
¹⁴ Netherlands Heart Institute, Utrecht, the Netherlands.
¹⁵ Department of Clinical Genetics, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands.
¹⁶ Department of Psychiatry, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
¹⁷ Data Science Institute, Imperial College London, London, UK.
¹⁸ Department of Brain Sciences and UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, Imperial College London, London, UK.
¹⁹ Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
²⁰ Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
²¹ Institute for Cardiomyopathies, Heidelberg Heart Center, University of Heidelberg, Heidelberg, Germany.
²² DZHK (German Center for Cardiovascular Research), Berlin, Germany.
²³ Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
²⁴ Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy.
²⁵ Department of Cardiovascular Medicine, Tohoku University Hospital, Seiryo, Aoba, Sendai, Japan.
²⁶ Tohoku Medical Megabank Organization, Tohoku University, Seiryo, Aoba, Sendai, Japan.
²⁷ The Francis Crick Institute, London, UK.
²⁸ Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
²⁹ Service de biochimie métabolique, UF de cardiogénétique et myogénétique moléculaire et cellulaire, APHP, Hôpital Pitié-Salpêtrière, Paris, France.
³⁰ INSERM, UMR_S 1166 and ICAN Institute for Cardiometabolism and Nutrition, Faculté de Médecine, Sorbonne Université, Paris, France.
³¹ Faculté de Pharmacie, Université de Paris, Paris, France.
³² Institute of Human Genetics, Helmholtz Zentrum Muenchen, Neuherberg, Germany.
³³ Klinikum rechts der Isar der TU Muenchen School of Medicine, Institute of Human Genetics, Munich, Germany.
³⁴ DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.
³⁵ Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam Public Health (APH), Amsterdam UMC, Amsterdam, the Netherlands.
³⁶ Department of Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
³⁷ Department of Medicine and Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
³⁸ Montreal Heart Institute Research Center, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada.
³⁹ Department of Cardiology, University of Amsterdam, Heartcenter, Amsterdam UMC, Amsterdam, the Netherlands.
⁴⁰ Institute of Health Informatics, University College London, London, UK.
⁴¹ Health Data Research UK, Gibbs Building, London, UK.
⁴² Barts Heart Centre, Saint Bartholomew's Hospital, London, UK.
⁴³ Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
⁴⁴ Institute of Cardiovascular Science and Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK.
⁴⁵ National Heart Research Institute Singapore, National Heart Center Singapore, Singapore, Singapore.
⁴⁶ Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore.
⁴⁷ Department of Physiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Amsterdam, the Netherlands.
⁴⁸ Département de Génétique, Centre de référence des maladies cardiaques héréditaires ou rares, APHP, Hôpital Pitié-Salpêtrière, Paris, France.
⁴⁹ Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK. j.ware@imperial.ac.uk.
⁵⁰ National Heart and Lung Institute, Imperial College London, London, UK. j.ware@imperial.ac.uk.
⁵¹ MRC London Institute of Medical Sciences, Imperial College London, London, UK. j.ware@imperial.ac.uk.
⁵² Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands. c.r.bezzina@amsterdamumc.nl.
⁵³ European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART), . c.r.bezzina@amsterdamumc.nl.

^# Contributed equally.

PMID: 33495596
PMCID: PMC7611259
DOI: 10.1038/s41588-020-00762-2

Meta-Analysis

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

Rafik Tadros et al. Nat Genet. 2021 Feb.

. 2021 Feb;53(2):128-134.

doi: 10.1038/s41588-020-00762-2. Epub 2021 Jan 25.

Authors

Affiliations

¹ Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada. rafik.tadros@umontreal.ca.
² Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands. rafik.tadros@umontreal.ca.
³ Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK.
⁴ National Heart and Lung Institute, Imperial College London, London, UK.
⁵ MRC London Institute of Medical Sciences, Imperial College London, London, UK.
⁶ Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
⁷ Department of Clinical Genetics, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
⁸ European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART).
⁹ Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
¹⁰ Wellcome Centre for Human Genetics, Oxford, UK.
¹¹ Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands.
¹² Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada.
¹³ Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
¹⁴ Netherlands Heart Institute, Utrecht, the Netherlands.
¹⁵ Department of Clinical Genetics, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands.
¹⁶ Department of Psychiatry, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
¹⁷ Data Science Institute, Imperial College London, London, UK.
¹⁸ Department of Brain Sciences and UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, Imperial College London, London, UK.
¹⁹ Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
²⁰ Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
²¹ Institute for Cardiomyopathies, Heidelberg Heart Center, University of Heidelberg, Heidelberg, Germany.
²² DZHK (German Center for Cardiovascular Research), Berlin, Germany.
²³ Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
²⁴ Cardiomyopathy Unit, Careggi University Hospital, Florence, Italy.
²⁵ Department of Cardiovascular Medicine, Tohoku University Hospital, Seiryo, Aoba, Sendai, Japan.
²⁶ Tohoku Medical Megabank Organization, Tohoku University, Seiryo, Aoba, Sendai, Japan.
²⁷ The Francis Crick Institute, London, UK.
²⁸ Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
²⁹ Service de biochimie métabolique, UF de cardiogénétique et myogénétique moléculaire et cellulaire, APHP, Hôpital Pitié-Salpêtrière, Paris, France.
³⁰ INSERM, UMR_S 1166 and ICAN Institute for Cardiometabolism and Nutrition, Faculté de Médecine, Sorbonne Université, Paris, France.
³¹ Faculté de Pharmacie, Université de Paris, Paris, France.
³² Institute of Human Genetics, Helmholtz Zentrum Muenchen, Neuherberg, Germany.
³³ Klinikum rechts der Isar der TU Muenchen School of Medicine, Institute of Human Genetics, Munich, Germany.
³⁴ DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.
³⁵ Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam Public Health (APH), Amsterdam UMC, Amsterdam, the Netherlands.
³⁶ Department of Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
³⁷ Department of Medicine and Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
³⁸ Montreal Heart Institute Research Center, Faculty of Medicine, Université de Montréal, Montreal, Québec, Canada.
³⁹ Department of Cardiology, University of Amsterdam, Heartcenter, Amsterdam UMC, Amsterdam, the Netherlands.
⁴⁰ Institute of Health Informatics, University College London, London, UK.
⁴¹ Health Data Research UK, Gibbs Building, London, UK.
⁴² Barts Heart Centre, Saint Bartholomew's Hospital, London, UK.
⁴³ Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
⁴⁴ Institute of Cardiovascular Science and Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK.
⁴⁵ National Heart Research Institute Singapore, National Heart Center Singapore, Singapore, Singapore.
⁴⁶ Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore.
⁴⁷ Department of Physiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Amsterdam, the Netherlands.
⁴⁸ Département de Génétique, Centre de référence des maladies cardiaques héréditaires ou rares, APHP, Hôpital Pitié-Salpêtrière, Paris, France.
⁴⁹ Cardiovascular Research Centre, Royal Brompton and Harefield National Health Service Foundation Trust, London, UK. j.ware@imperial.ac.uk.
⁵⁰ National Heart and Lung Institute, Imperial College London, London, UK. j.ware@imperial.ac.uk.
⁵¹ MRC London Institute of Medical Sciences, Imperial College London, London, UK. j.ware@imperial.ac.uk.
⁵² Department of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands. c.r.bezzina@amsterdamumc.nl.
⁵³ European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART), . c.r.bezzina@amsterdamumc.nl.

^# Contributed equally.

PMID: 33495596
PMCID: PMC7611259
DOI: 10.1038/s41588-020-00762-2

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 and multi-trait analyses in HCM (1,733 cases), DCM (5,521 cases) and nine left ventricular (LV) traits (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 LV contractility with HCM risk. A polygenic risk score explains a significant portion of phenotypic variability in carriers of HCM-causing rare variants. Our findings thus provide evidence that polygenic risk score 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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Conflict of interest statement

Competing interests statement

M.-P.D. is author on a patent pertaining to pharmacogenomics-guided CETP inhibition (US20170233812A1), has a minor equity interest in DalCor and has received honoraria from Dalcor and Servier and research support (access to samples and data) from AstraZeneca, Pfizer, Servier, Sanofi and GlaxoSmithKline. J.-C.T. has received research grants from Amarin, AstraZeneca, DalCor, Esperion, Ionis, Sanofi and Servier; honoraria from AstraZeneca, DalCor, HLS, Sanofi and Servier; holds minor equity interest in DalCor; and is an author of a patent on pharmacogenomics-guided CETP inhibition (US20170233812A1). B.M. has received Research Funding from Siemens Healtheneers, Daiichi Sankyo. The UMCG, which employs R.A.d.B., has received research grants and/or fees from AstraZeneca, Abbott, Bristol-Myers Squibb, Novartis, Novo Nordisk, and Roche. R.A.d.B. received speaker fees from Abbott, AstraZeneca, Novartis, and Roche. HW is a consultant for Cytokinetics. P.M.M. receives an honorarium as Chair of the UKRI Medical Research Council Neuroscience and Mental Health Board. He acknowledges consultancy fees from Adelphi Communications, MedScape, Neurodiem, Nodthera, Biogen, Celgene and Roche. He has received speakers’ honoraria from Celgene, Biogen, Novartis and Roche, and has received research or educational funds from Biogen, GlaxoSmithKline and Novartis. He is paid as a member of the Scientific Advisory Board for Ipsen Pharmaceuticals. J.S.W. has received research support and consultancy fees from Myokardia, Inc.

Figures

**Extended Data Fig. 1. Manhattan and QQ plots of DCM GWAS and MTAG**
a,b, Summary results of the dilated cardiomyopathy (DCM) GWAS meta-analysis of 5,521 cases and 397,323 controls shown as Manhattan plots for the single trait (a) and the multi-trait analyses (MTAG; b). Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using summary statistics of three previously published DCM GWAS, and multi-trait analysis results (b) were obtained using MTAG for DCM, including GWAS for hypertrophic cardiomyopathy (HCM) and nine left ventricular (LV) traits. Red dashed line shows the significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots shown as inserts in corresponding panels. Genomic inflation (λ) = 1.028 (single-trait) and 1.049 (MTAG). Numbering of signals as shown in Supplementary Table 7. Black numbers refer to loci reaching the statistical significance threshold in single trait analysis, while red numbers refer to loci only reaching statistical significance in the multi-trait analysis. The low density of association signals in the single trait analysis (a) is attributable to the inclusion of a large sample size study that used a low density array (Illumina Infinium HumanExome BeadChip; Supplementary Table 5).

**Extended Data Fig. 2. Manhattan and QQ plots of LV ejection fraction GWAS and MTAG**
a,b, Summary results of the left ventricular ejection fraction (LVEF) GWAS in the UK Biobank (n = 19,260) shown as Manhattan plots for the single trait (a) and the multi-trait analyses (MTAG; b). Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using a linear mixed model (BOLT-LMM), and multi-trait analysis results (b) were obtained using MTAG including summary statistics for all nine left ventricular (LV) traits. Red dashed line shows the significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots shown as inserts in corresponding panels. Genomic inflation (λ) = 1.041 (single-trait) and 1.049 (MTAG). Numbering of loci as shown in Supplementary Table 8. Black numbers refer to loci reaching the statistical significance threshold in any single trait analysis, while red numbers refer to loci only reaching statistical significance in the multi-trait analysis.

**Extended Data Fig. 3. Manhattan and QQ plots of LV concentricity GWAS and MTAG**
a,b, Summary results of the left ventricular concentricity index (LVconc) GWAS in the UK Biobank (n = 19,260) shown as Manhattan plots for the single trait (a) and the multi-trait analyses (MTAG; b). LVconc is defined as the ratio of left ventricular mass to the left ventricular end-diastolic volume. Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using a linear mixed model (BOLT-LMM), and multi-trait analysis results (b) were obtained using MTAG including summary statistics for all nine left ventricular (LV) traits. Red dashed line shows the significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots shown as inserts in corresponding panels. Genomic inflation (λ) = 1.06 (single-trait) and 1.084 (MTAG). Numbering of signals as shown in Supplementary Table 8. Black numbers refer to loci reaching the statistical significance threshold in any single trait analysis, while red numbers refer to loci only reaching statistical significance in the multi-trait analysis.

**Extended Data Fig. 4. Manhattan and QQ plots of LV mass GWAS and MTAG**
a,b, Summary results of the left ventricular mass (LVM) GWAS in the UK Biobank (n = 19,260) shown as Manhattan plots for the single trait (a) and the multi-trait analyses (MTAG; b). Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using a linear mixed model (BOLT-LMM), and multi-trait analysis results (b) were obtained using MTAG including summary statistics for all nine left ventricular (LV) traits. Red dashed line shows the significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots shown as inserts in corresponding panels. Genomic inflation (λ) = 1.081 (single-trait) and 1.071 (MTAG). Numbering of signals as shown in Supplementary Table 8.

**Extended Data Fig. 5. Manhattan and QQ plots of LV end-diastolic volume GWAS and MTAG**
a,b, Summary results of the left ventricular end-diastolic volume (LVEDV) GWAS in the UK Biobank (N=19,260) shown as Manhattan plots for the single trait (a) and the multi-trait analyses (MTAG; b). Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using a linear mixed model (BOLT-LMM), and multi-trait analysis results (b) were obtained using MTAG including summary statistics for all nine left ventricular (LV) traits. Red dashed line shows the significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots shown as inserts in corresponding panels. Genomic inflation (λ) = 1.076 (single-trait) and 1.078 (MTAG). Numbering of signals as shown in Supplementary Table 8. Black numbers refer to loci reaching the statistical significance threshold in any single trait analysis, while red numbers refer to loci only reaching statistical significance in the multi-trait analysis.

**Extended Data Fig. 6. Manhattan and QQ plots of LV end-systolic volume GWAS and MTAG**
Summary results of the left ventricular end-systolic volume (LVESV) GWAS in the UK Biobank (n= 19,260) shown as Manhattan plots for the single trait (a) and the multi-trait analyses (MTAG; b). Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using a linear mixed model (BOLT-LMM), and multi-trait analysis results (b) were obtained using MTAG including summary statistics for all nine left ventricular (LV) traits. Red dashed line shows the significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots shown as inserts in corresponding panels. Genomic inflation (λ) = 1.069 (single-trait) and 1.081 (MTAG). Numbering of signals as shown in Supplementary Table 8.

**Extended Data Fig. 7. Manhattan and QQ plots of LV global circumferential strain GWAS and MTAG**
a,b, Summary results of the left ventricular global circumferential strain (strain^circ) GWAS in the UK Biobank (N=19,260) shown as Manhattan plots for the single trait (a) and the multi-trait analyses (MTAG; b). Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using a linear mixed model (BOLT-LMM), and multi-trait analysis results (b) were obtained using MTAG including summary statistics for all nine left ventricular (LV) traits. Red dashed line shows the significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots shown as inserts in corresponding panels. Genomic inflation (λ) = 1.046 (single-trait) and 1.061 (MTAG). Numbering of signals as shown in Supplementary Table 8.

**Extended Data Fig. 8. Manhattan and QQ plots of LV global radial strain GWAS and MTAG**
a,b, Summary results of the left ventricular global radial strain (strain^rad) GWAS in the UK Biobank (n = 19,260) shown as Manhattan plots for the single trait (a) and the multi-trait analyses (MTAG; b). Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using a linear mixed model (BOLT-LMM), and multi-trait analysis results (b) were obtained using MTAG including summary statistics for all nine left ventricular (LV) traits. Red dashed line shows the significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots shown as inserts in corresponding panels. Genomic inflation (λ) = 1.049 (single-trait) and 1.057 (MTAG). Numbering of signals as shown in Supplementary Table 8. Black numbers refer to loci reaching the statistical significance threshold in any single trait analysis, while red numbers refer to loci only reaching statistical significance in the multi-trait analysis.

**Extended Data Fig. 9. Manhattan and QQ plots of LV global longitudinal strain GWAS and MTAG**
a,b, Summary results of the left ventricular global longitudinal strain (strain^long) GWAS in the UK Biobank (n = 19,260) shown as Manhattan plots for the single trait (a) and the multi-trait analyses (MTAG; b). Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using a linear mixed model (BOLT-LMM), and multi-trait analysis results (b) were obtained using MTAG including summary statistics for all nine left ventricular (LV) traits. Red dashed line shows the significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots shown as inserts in corresponding panels. Genomic inflation (λ) = 1.040 (single-trait) and 1.059 (MTAG). Numbering of signals as shown in Supplementary Table 8.

**Extended Data Fig. 10. Manhattan and QQ plots of LV mean wall thickness GWAS and MTAG**
a,b, Summary results of the mean left ventricular wall thickness (meanWT) GWAS in the UK Biobank (n = 19,260) shown as Manhattan plots for the single trait (a) and the multi-trait analyses (MTAG; b). Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using a linear mixed model (BOLT-LMM), and multi-trait analysis results (b) were obtained using MTAG including summary statistics for all nine left ventricular (LV) traits. Red dashed line shows the significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots shown as inserts in corresponding panels. Genomic inflation (λ) = 1.065 (single-trait) and 1.072 (MTAG). Numbering of signals as shown in Supplementary Table 8.

**Figure 1. Study flowchart.**
CMR, cardiac magnetic resonance; DCM, dilated cardiomyopathy; HCM, hypertrophic cardiomyopathy; LV, left ventricle/ventricular; LDSC, LD score correlation; MTAG, multi-trait analysis of GWAS.

**Figure 2. Summary results of the hypertrophic cardiomyopathy (HCM) single trait GWAS and multi-trait analysis.**
**a,b,** Single trait analysis (a) consisted of a fixed effects meta-analysis of case-control GWAS using a frequentist test, and multi-trait analysis results (b) were obtained using MTAG for HCM, including GWAS for dilated cardiomyopathy (DCM) and nine left ventricular (LV) traits. Summary statistics shown as Manhattan plots with red dashed line showing the genome-wide significance threshold of P = 1 × 10^-8. Quantile-quantile (QQ) plots are shown as inserts in corresponding panels. Genomic inflation (λ) = 1.081 (single-trait) and 1.082 (MTAG). Six association signals were identified in single trait analysis (a), and an additional 10 signals were identified in multi-trait analysis (b). The wide signal on chromosome 11 tags founder *MYBPC3* pathogenic variants. Locus #4 was only significant in the single-trait analysis and did not replicate in an independent HCM GWAS. Numbering of signals is as shown in Table 1 and Supplementary Table 4, where red numbers refer to signals reaching genome-wide significance only in the multi-trait analysis.

**Figure 3. Genetic correlation between left ventricular traits, hypertrophic cardiomyopathy, and dilated cardiomyopathy.**
Hypertrophic cardiomyopathy (HCM, red bars) and dilated cardiomyopathy (DCM, blue bars) show strong genetic correlations with quantitative cardiac left ventricular (LV) traits measured in the general population, but with opposite effects. Center values are the estimated genetic correlation (r _g), and error bars indicate 95% confidence intervals. Samples sizes for included GWAS are as follows: 1,733 cases and 6,628 controls for HCM; 5,521 cases and 397,323 controls for DCM; and 19,260 for LV traits. Asterisks identify significant genetic correlations with a Benjamini–Hochberg false discovery rate (FDR) < 0.05. Data shown correspond to that in Supplementary Table 9. DCM, dilated cardiomyopathy; strain^circ, strain^long and strain^rad, global circumferential, longitudinal and radial strain, respectively (measures of contractility based on myocardial deformation); HCM, hypertrophic cardiomyopathy; LV, left ventricular; LVconc, LV concentricity (defined as LVM/LVEDV); LVEDV, LV end-diastolic volume; LVEF, LV ejection fraction (a volumetric measure of contractility); LVESV, LV end-systolic volume; LVM, LV mass; meanWT; mean LV wall thickness. Since strain^circ and strain^long are always negative values, -strain^circ and -strain^long are plotted to facilitate interpretation of effect direction.

**Figure 4. Cross-trait associations of hypertrophic and dilated cardiomyopathy loci.**
Heatmap of cross-trait associations of the 16 hypertrophic cardiomyopathy (HCM, left side) and 13 dilated cardiomyopathy (DCM, right) risk variants in HCM, DCM and nine LV traits in the general population. The dbSNP ID and risk alleles are shown on the x-axis, with the corresponding locus number in parenthesis (corresponding to numbering in Fig. 2, Table 1 and Supplementary Table 4 for HCM, and Extended Data Fig. 1 and Supplementary Table 7 for DCM). Variants sorted along the x-axis using Euclidean distance and complete hierarchical clustering (dendrogram on top). Effect of the HCM or DCM risk alleles shown as a colormap of Z-scores (legend), where positive values (concordant effect) are in shades of blue, and negative values (discordant effect) are in shades of red. Only associations with FDR < 0.05 are shown. HCM and DCM loci show many and reciprocal cross-trait associations. Since strain^circ and strain^long are negative values, we show -strain^circ and -strain^long to facilitate interpretation of effect direction. Lookup in DCM was performed using SNP proxies to maximize sample size, as shown in Supplementary Table 4. Note that the DCM risk allele rs2042995-T also increases risk of HCM, potentially through pleiotropic effects (decreased contractility and increased LV wall thickness). LV traits are as defined in the legend of Figure 3.

**Figure 5. A polygenic risk score for HCM stratifies event-free survival in carriers of disease-causing variants in sarcomere-encoding genes.**
Kaplan-Meier curves showing survival free from adverse clinical events (composite of septal reduction therapy, cardiac transplantation, sustained ventricular arrhythmia, sudden cardiac death, appropriate implantable cardioverter defibrillator [ICD] therapy or atrial fibrillation/flutter) in sarcomeric (likely) pathogenic variant carriers stratified by polygenic score (PRS_HCM) below (dark orange) vs. above (dark red) the median. Numbers at risk in each group along the time scale shown at the bottom of the plot. Ticks along the survival curves represent subject censoring. Two-sided log-rank test P = 0.032 (Cox proportional hazard analysis P = 9 × 10^-3; see Supplementary Table 21).

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New insights into the genetics of cardiomyopathies.
Robson A. Robson A. Nat Rev Cardiol. 2021 Apr;18(4):229. doi: 10.1038/s41569-021-00526-3. Nat Rev Cardiol. 2021. PMID: 33564142 No abstract available.

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References

1. Semsarian C, Ingles J, Maron MS, Maron BJ. New perspectives on the prevalence of hypertrophic cardiomyopathy. J Am Coll Cardiol. 2015;65:1249–1254. - PubMed
1. Walsh R, et al. Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: the case of hypertrophic cardiomyopathy. Genome Med. 2019;11:5. - PMC - PubMed
1. Elliott PM, et al. 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC) Eur Heart J. 2014;35:2733–2779. - PubMed
1. Yang J, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–569. - PMC - PubMed
1. Yang J, et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet. 2015;47:1114–1120. - PMC - PubMed

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[1] Semsarian C, Ingles J, Maron MS, Maron BJ. New perspectives on the prevalence of hypertrophic cardiomyopathy. J Am Coll Cardiol. 2015;65:1249–1254. - PubMed

[2] Semsarian C, Ingles J, Maron MS, Maron BJ. New perspectives on the prevalence of hypertrophic cardiomyopathy. J Am Coll Cardiol. 2015;65:1249–1254. - PubMed

[3] Walsh R, et al. Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: the case of hypertrophic cardiomyopathy. Genome Med. 2019;11:5. - PMC - PubMed

[4] Walsh R, et al. Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: the case of hypertrophic cardiomyopathy. Genome Med. 2019;11:5. - PMC - PubMed

[5] Elliott PM, et al. 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC) Eur Heart J. 2014;35:2733–2779. - PubMed

[6] Elliott PM, et al. 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC) Eur Heart J. 2014;35:2733–2779. - PubMed

[7] Yang J, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–569. - PMC - PubMed

[8] Yang J, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–569. - PMC - PubMed

[9] Yang J, et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet. 2015;47:1114–1120. - PMC - PubMed

[10] Yang J, et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet. 2015;47:1114–1120. - PMC - PubMed

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

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

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