Combinatorial interactions of genetic variants in human cardiomyopathy


Dilated cardiomyopathy (DCM) is a leading cause of morbidity and mortality worldwide; yet how genetic variation and environmental factors impact DCM heritability remains unclear. Here, we report that compound genetic interactions between DNA sequence variants contribute to the complex heritability of DCM. By using genetic data from a large family with a history of DCM, we discovered that heterozygous sequence variants in the TROPOMYOSIN 1 (TPM1) and VINCULIN (VCL) genes cosegregate in individuals affected by DCM. In vitro studies of patient-derived and isogenic human-pluripotent-stem-cell-derived cardiomyocytes that were genome-edited via CRISPR to create an allelic series of TPM1 and VCL variants revealed that cardiomyocytes with both TPM1 and VCL variants display reduced contractility and sarcomeres that are less organized. Analyses of mice genetically engineered to harbour these human TPM1 and VCL variants show that stress on the heart may also influence the variable penetrance and expressivity of DCM-associated genetic variants in vivo. We conclude that compound genetic variants can interact combinatorially to induce DCM, particularly when influenced by other disease-provoking stressors.

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Fig. 1: Novel TPM1 and VCL variants together cosegregate with family members exhibiting cardiomyopathy.
Fig. 2: hPSC-derived cardiomyocytes harbouring TEK; VCL genetic variants exhibit functional and sarcomeric organization defects.
Fig. 3: ECM and muscle contraction genes are coordinately upregulated in TV-Dhet hiPSC cardiomyocytes.
Fig. 4: TV-Dhet mouse hearts exhibit reduced contractility and respond worse than WT control mouse hearts to TAC.

Code availability

All custom code used in this study can be found at or

Data availability

The authors declare that all data supporting the findings of this study are available within the paper and its Supplementary Information. The materials and data of this study are available from the corresponding author on reasonable request, with the exception of patient DNA and tissue, which is limited and protected by local and federal privacy regulations. Microarray study and RNA-seq data are available via dbGaP with GEO accession numbers GSE121844 and GSE121559.


  1. 1.

    Hershberger, R. E., Hedges, D. J. & Morales, A. Dilated cardiomyopathy: the complexity of a diverse genetic architecture. Nat. Rev. Cardiol. 10, 531–547 (2013).

    CAS  Article  Google Scholar 

  2. 2.

    Benjamin, E. J. et al. Heart disease and stroke statistics—2017 update: a report from the American Heart Association. Circulation 135, e146–e603 (2017).

    Article  Google Scholar 

  3. 3.

    Stenson, P. D. et al. The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum. Genet. 136, 665–677 (2017).

  4. 4.

    Villard, E. et al. A genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy. Eur. Heart J. 32, 1065–1076 (2011).

    CAS  Article  Google Scholar 

  5. 5.

    Meder, B. et al. A genome-wide association study identifies 6p21 as novel risk locus for dilated cardiomyopathy. Eur. Heart J. 35, 1069–1077 (2014).

    CAS  Article  Google Scholar 

  6. 6.

    Li, L., Bainbridge, M. N., Tan, Y., Willerson, J. T. & Marian, A. J. A potential oligogenic etiology of hypertrophic cardiomyopathy: a classic single gene disorder. Circ. Res. 126, 1084–1090 (2017).

    Article  Google Scholar 

  7. 7.

    Roncarati, R. et al. Doubly heterozygous LMNA and TTN mutations revealed by exome sequencing in a severe form of dilated cardiomyopathy. Eur. J. Hum. Genet. 21, 1105–1111 (2013).

    CAS  Article  Google Scholar 

  8. 8.

    Maron, B. J., Maron, M. S. & Semsarian, C. Double or compound sarcomere mutations in hypertrophic cardiomyopathy: a potential link to sudden death in the absence of conventional risk factors. Heart Rhythm 9, 57–63 (2012).

    Article  Google Scholar 

  9. 9.

    Petropoulou, E. et al. Digenic inheritance of mutations in the cardiac troponin (TNNT2) and cardiac beta myosin heavy chain (MYH7) as the cause of severe dilated cardiomyopathy. Eur. J. Med. Genet. 60, 485–488 (2017).

    Article  Google Scholar 

  10. 10.

    Haas, J. et al. Atlas of the clinical genetics of human dilated cardiomyopathy. Eur. Heart J. 36, 1123–1135 (2014).

    Article  Google Scholar 

  11. 11.

    Kimura, A. Molecular genetics and pathogenesis of cardiomyopathy. J. Hum. Genet. 61, 41–50 (2016).

    CAS  Article  Google Scholar 

  12. 12.

    Olson, T. M., Kishimoto, N. Y., Whitby, F. G. & Michels, V. V. Mutations that alter the surface charge of alpha-tropomyosin are associated with dilated cardiomyopathy. J. Mol. Cell. Cardiol. 33, 723–732 (2001).

    CAS  Article  Google Scholar 

  13. 13.

    Whitby, F. G. & Phillips, G. N. Crystal structure of tropomyosin at 7 Angstroms resolution. Proteins 38, 49–59 (2000).

    CAS  Article  Google Scholar 

  14. 14.

    Panopoulos, A. D. et al. iPSCORE: a resource of 222 iPSC lines enabling functional characterization of genetic variation across a variety of cell types. Stem Cell Rep. 8, 1086–1100 (2017).

    CAS  Article  Google Scholar 

  15. 15.

    Hsu, P. D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827–832 (2013).

    CAS  Article  Google Scholar 

  16. 16.

    Lian, X. et al. Directed cardiomyocyte differentiation from human pluripotent stem cells by modulating Wnt/β-catenin signaling under fully defined conditions. Nat. Protoc. 8, 162–175 (2013).

    CAS  Article  Google Scholar 

  17. 17.

    Tohyama, S. et al. Distinct metabolic flow enables large-scale purification of mouse and human pluripotent stem cell-derived cardiomyocytes. Cell Stem Cell 12, 127–137 (2012).

    Article  Google Scholar 

  18. 18.

    Dhalla, A. K., Hill, M. F. & Singal, P. K. Role of oxidative stress in transition of hypertrophy to heart failure. J. Am. Coll. Cardiol. 28, 506–514 (1996).

    CAS  Article  Google Scholar 

  19. 19.

    Rosenkranz, S. et al. Alterations of β-adrenergic signaling and cardiac hypertrophy in transgenic mice overexpressing TGF-β1. Am. J. Physiol. Heart Circ. Physiol. 283, H1253–H1262 (2002).

    CAS  Article  Google Scholar 

  20. 20.

    Razeghi, P. et al. Metabolic gene expression in fetal and failing human heart. Circulation 104, 2923–2931 (2001).

    CAS  Article  Google Scholar 

  21. 21.

    Bristow, M. R. et al. β1- and β2-adrenergic-receptor subpopulations in nonfailing and failing human ventricular myocardium: coupling of both receptor subtypes to muscle contraction and selective β1-receptor down-regulation in heart failure. Circ. Res. 59, 297–309 (1986).

    CAS  Article  Google Scholar 

  22. 22.

    Hinson, J. T. et al. HEART DISEASE. Titin mutations in iPS cells define sarcomere insufficiency as a cause of dilated cardiomyopathy. Science 349, 982–986 (2015).

    CAS  Article  Google Scholar 

  23. 23.

    Szklarczyk, D. et al. STRINGv10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).

    CAS  Article  Google Scholar 

  24. 24.

    Xu, W., Baribault, H. & Adamson, E. D. Vinculin knockout results in heart and brain defects during embryonic development. Development 125, 327–337 (1998).

    CAS  PubMed  Google Scholar 

  25. 25.

    Rockman, H. A. et al. Segregation of atrial-specific and inducible expression of an atrial natriuretic factor transgene in an in vivo murine model of cardiac hypertrophy. Proc. Natl Acad. Sci. USA 88, 8277–8281 (1991).

    CAS  Article  Google Scholar 

  26. 26.

    Golbus, J. R. et al. Population-based variation in cardiomyopathy genes. Circ. Cardiovasc. Genet. 5, 391–399 (2012).

    Article  Google Scholar 

  27. 27.

    McNally, E. M. & Puckelwartz, M. J. Genetic variation in cardiomyopathy and cardiovascular disorders. Circ. J. 79, 1409–1415 (2015).

    CAS  Article  Google Scholar 

  28. 28.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  Article  Google Scholar 

  29. 29.

    Happe, C. L. & Engler, A. J. Mechanical forces reshape differentiation cues that guide cardiomyogenesis. Circ. Res. 118, 296–310 (2016).

    CAS  Article  Google Scholar 

  30. 30.

    Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).

    CAS  Article  Google Scholar 

  31. 31.

    Therneau, T. M. et al. kinship2: pedigree functions v.1.6.4. (CRAN, 2015);

  32. 32.

    Marschner, I.C. & Donoghoe, M. W. glm2: fitting generalized linear models v.1.2.1. (CRAN, 2018);

  33. 33.

    Abecasis, G. R., Cherny, S. S., Cookson, W. O. & Cardon, L. R. Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet. 30, 97–101 (2002).

    CAS  Article  Google Scholar 

  34. 34.

    McGregor, T. L. et al. Consanguinity mapping of congenital heart disease in a South Indian population. PLoS ONE 5, e10286 (2010).

    Article  Google Scholar 

  35. 35.

    Purcell, S., Cherny, S. S. & Sham, P. C. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19, 149–150 (2003).

    CAS  Article  Google Scholar 

  36. 36.

    Hashem, S. I. et al. Oxidative stress mediates cardiomyocyte apoptosis in a human model of Danon disease and heart failure. Stem Cells 33, 2343–2350 (2015).

    CAS  Article  Google Scholar 

  37. 37.

    Carter, M. S. et al. A regulatory mechanism that detects premature nonsense codons in T-cell receptor transcripts in vivo is reversed by protein synthesis inhibitors in vitro. J. Biol. Chem. 270, 28995–29003 (1995).

    CAS  Article  Google Scholar 

  38. 38.

    Byrne, S. M., Mali, P. & Church, G. M. in Methods in Enzymolology, Vol. 546 (eds Doudna, J.A. & Sontheimer, E.J.) 119–138 (Elsevier, Amsterdam, 2014).

  39. 39.

    Yang, L., Mali, P., Kim-Kiselak, C. & Church, G. CRISPR–Cas-mediated targeted genome editing in human cells. Methods Mol. Biol. 1114, 245–267 (2014).

    CAS  Article  Google Scholar 

  40. 40.

    Giacomelli, E. et al. Three-dimensional cardiac microtissues composed of cardiomyocytes and endothelial cells co-differentiated from human pluripotent stem cells. Development 144, 1008–1017 (2017).

    CAS  Article  Google Scholar 

  41. 41.

    Wu, H. et al. Epigenetic regulation of phosphodiesterases 2A and 3A underlies compromised β-adrenergic signaling in an iPSC model of dilated cardiomyopathy. Cell Stem Cell 17, 89–100 (2015).

    CAS  Article  Google Scholar 

  42. 42.

    Tse, J. R. & Engler, A. J. Preparation of hydrogel substrates with tunable mechanical properties. Curr. Protoc. Cell BioI. 47, 10.16.1–10.16.16 (2010).

  43. 43.

    del Alamo, J. C. et al. Three-dimensional quantification of cellular traction forces and mechanosensing of thin substrata by Fourier traction force microscopy. PLoS ONE 8, e69850 (2013).

    Article  Google Scholar 

  44. 44.

    Sayols, S., Scherzinger, D. & Klein, H. dupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data. BMC Bioinform. 17, 428 (2016).

    Article  Google Scholar 

  45. 45.

    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    CAS  Article  Google Scholar 

  46. 46.

    Harrow, J. et al. GENCODE: the reference human genome annotation for the ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    CAS  Article  Google Scholar 

  47. 47.

    Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4, 1521 (2015).

    Article  Google Scholar 

  48. 48.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  49. 49.

    Tripathi, S. et al. Meta- and orthogonal integration of influenza “OMICs” data defines a role for UBR4 in virus budding. Cell Host Microbe 18, 723–735 (2015).

    CAS  Article  Google Scholar 

  50. 50.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  51. 51.

    Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014).

    CAS  Article  Google Scholar 

  52. 52.

    Yang, H., Wang, H. & Jaenisch, R. Generating genetically modified mice using CRISPR/Cas-mediated genome engineering. Nat. Protoc. 9, 1956–1968 (2014).

    CAS  Article  Google Scholar 

  53. 53.

    Wang, H. et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910–918 (2013).

    CAS  Article  Google Scholar 

  54. 54.

    Li, R. et al. β1 integrin gene excision in the adult murine cardiac myocyte causes defective mechanical and signaling responses. Am. J. Pathol. 180, 952–962 (2012).

    CAS  Article  Google Scholar 

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We thank the patients who participated in this study. K. DeMali (Univ. Iowa) provided the truncated Gallus gallus VCL peptide. Various experiments were conducted with the assistance, expertise and support of the following UCSD core facilities: Institute for Genomic Medicine Core, Mouse Transgenic Core, Histology and Immunohistochemistry Core, Seaweed Canyon Cardiovascular Physiology Laboratory, Microscopy Core and Human Embryonic Stem Cell Core facilities. We also thank P. Mali and H. Taylor-Weiner for assistance with hPSC culture, members of the Bruce Hamilton laboratory for helpful discussions and experimental design and members of the Chi lab for comments on the manuscript. This work was supported in part by grants from the NIH to N.C.C., J.C., R.S.R., E.D.A. and grant no. R01AG045428 to A.J.E. D.C.D. was supported by a CIRM pre-doctoral fellowship (grant no. TG2-01154) and an NIH pre-doctoral training grant (grant no. T32 GM008666). C.L.H. was supported by post-doctoral fellowships from the American Heart Association (grant no. 15POST25720070) and NIH (grant no. F32HL131424). J.C. is an American Heart Association Endowed Chair. E.N.F. was supported by a NIH pre-doctoral training grant (grant no. 4T32HL007444-34).

Author information




D.C.D., C.L.H., C.C., A.J.E., R.S.R. and N.C.C. conceived the project. D.C.D., C.L.H., C.C., N.T., A.M.M., T.L., N.D.D., Q.P., E.N.F., Y.G., K.P.T., V.D.T., J.C. and K.L.P. planned the design of studies and conducted experiments. D.C.D. and E.D.A. recruited patients and generated human fibroblast lines. D.C.D. generated CRISPR-edited mouse lines and C.L.H. generated CRISPR-edited hESC lines. Q.P., J.C., K.L.P. and N.J.S. assisted in data interpretation and provided experimental advice. D.C.D., C.L.H., C.C., A.J.E., R.S.R. and N.C.C. prepared and wrote the manuscript.

Corresponding authors

Correspondence to Adam J. Engler or Robert S. Ross or Neil C. Chi.

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Supplementary information

Supplementary Information

Supplementary figures, tables, references and video captions.

Reporting summary

Supplementary Video 1

Representative parasternal short axis left ventricular views of mouse echocardiography pre-surgery for WT mice.

Supplementary Video 2

Representative parasternal short axis left ventricular views of mouse echocardiography post-TAC for WT mice.

Supplementary Video 3

Representative parasternal short axis left ventricular views of mouse echocardiography pre-surgery for VclVFS/+ mice.

Supplementary Video 4

Representative parasternal short axis left ventricular views of mouse echocardiography post-TAC for VclVFS/+ mice.

Supplementary Video 5

Representative parasternal short axis left ventricular views of mouse echocardiography pre-surgery for Tpm1TEK/+ mice.

Supplementary Video 6

Representative parasternal short axis left ventricular views of mouse echocardiography post-TAC for Tpm1TEK/+ mice.

Supplementary Video 7

Representative parasternal short axis left ventricular views of mouse echocardiography pre-surgery for TV-Dhet mice.

Supplementary Video 8

Representative parasternal short axis views of TV-Dhet mouse echocardiography reveals grossly decreased left ventricular function post-TAC.

Supplementary Table 2

Differentially expressed genes revealed by RNA-seq of TV-Dhet, and control hiPSC-cardiomyocytes.

Supplementary Table 8

Primers, guideRNA (gRNA), and single strand DNA oligonucleotide (ssDNA) sequences.

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Deacon, D.C., Happe, C.L., Chen, C. et al. Combinatorial interactions of genetic variants in human cardiomyopathy. Nat Biomed Eng 3, 147–157 (2019).

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