Skip to main content

Advertisement

SpringerLink
Log in

Linking Genes to Cardiovascular Diseases: Gene Action and Gene–Environment Interactions

  • Published:
Journal of Cardiovascular Translational Research Aims and scope Submit manuscript

Abstract

A unique myocardial characteristic is its ability to grow/remodel in order to adapt; this is determined partly by genes and partly by the environment and the milieu intérieur. In the “post-genomic” era, a need is emerging to elucidate the physiologic functions of myocardial genes, as well as potential adaptive and maladaptive modulations induced by environmental/epigenetic factors. Genome sequencing and analysis advances have become exponential lately, with escalation of our knowledge concerning sometimes controversial genetic underpinnings of cardiovascular diseases. Current technologies can identify candidate genes variously involved in diverse normal/abnormal morphomechanical phenotypes, and offer insights into multiple genetic factors implicated in complex cardiovascular syndromes. The expression profiles of thousands of genes are regularly ascertained under diverse conditions. Global analyses of gene expression levels are useful for cataloging genes and correlated phenotypes, and for elucidating the role of genes in maladies. Comparative expression of gene networks coupled to complex disorders can contribute insights as to how “modifier genes” influence the expressed phenotypes. Increasingly, a more comprehensive and detailed systematic understanding of genetic abnormalities underlying, for example, various genetic cardiomyopathies is emerging. Implementing genomic findings in cardiology practice may well lead directly to better diagnosing and therapeutics. There is currently evolving a strong appreciation for the value of studying gene anomalies, and doing so in a non-disjointed, cohesive manner. However, it is challenging for many—practitioners and investigators—to comprehend, interpret, and utilize the clinically increasingly accessible and affordable cardiovascular genomics studies. This survey addresses the need for fundamental understanding in this vital area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Wallace, B. (1992). The search for the gene. Ithaca: Cornell University Press.

    Google Scholar 

  2. Pasipoularides, A. (2010). Heart's vortex: intracardiac blood flow phenomena. Shelton: People's Medical Publishing House. 960 p.

    Google Scholar 

  3. Pasipoularides, A. (2012). Diastolic filling vortex forces and cardiac adaptations: probing the epigenetic nexus. Hellenic Journal of Cardiology, 53, 458–469.

    PubMed  Google Scholar 

  4. Pasipoularides, A. (2015). Mechanotransduction mechanisms for intraventricular diastolic vortex forces and myocardial deformations: Part 1. Journal of Cardiovascular Translational Research, 8, 76–87. doi:10.1007/s12265-015-9611-y.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Pasipoularides, A. (2015). Mechanotransduction mechanisms for intraventricular diastolic vortex forces and myocardial deformations: Part 2. Journal of Cardiovascular Translational Research, 8, 293–318. doi:10.1007/s12265-015-9630-8.

    Article  PubMed  Google Scholar 

  6. Lunkenheimer, P. P., Niederer, P., Sanchez-Quintana, D., Murillo, M., & Smerup, M. (2013). Models of ventricular structure and function reviewed for clinical cardiologists. Journal of Cardiovascular Translational Research, 6, 176–186.

    Article  PubMed  Google Scholar 

  7. Johannsen, W. (1909). Elemente der exakten Erblichkeitslehre. Jena: Gustav Fischer.

    Google Scholar 

  8. Larribe, F., & Fearnhead, P. (2011). On composite likelihoods in statistical genetics. Stat Sinica, 21, 43–69.

    Google Scholar 

  9. Venter, J. C., Adams, M. D., Myers, E. W., et al. (2001). The sequence of the human genome. Science, 291, 1304–1351.

    Article  CAS  PubMed  Google Scholar 

  10. International Human Genome Sequencing Consortium. (2001). Initial sequencing and analysis of the human genome. Nature, 409, 860–921.

    Article  Google Scholar 

  11. International Human Genome Mapping Consortium. (2001). A physical map of the human genome. Nature, 409, 934–941.

    Article  Google Scholar 

  12. International Human Genome Sequencing Consortium. (2004). Finishing the euchromatic sequence of the human genome. Nature, 431, 931–945.

    Article  CAS  Google Scholar 

  13. Glotov, A. S., Kazakov, S. V., Zhukova, E. A., et al. (2015). Targeted next-generation sequencing (NGS) of nine candidate genes with custom AmpliSeq in patients and a cardiomyopathy risk group. Clinica Chimica Acta, 446, 132–140.

    Article  CAS  Google Scholar 

  14. Stakos, D. A., & Boudoulas, H. (2002). Pharmacogenetics and pharmacogenomics in cardiology. Hellenic Journal of Cardiology, 43, 1–15.

    Google Scholar 

  15. Wheeler, M. T., Ho, M., Knowles, J. W., Pavlovic, A., & Ashley, E. A. (2008). Pharmacogenetics of heart failure: evidence, opportunities, and challenges for cardiovascular pharmacogenomics. Journal of Cardiovascular Translational Research, 1, 25–36.

    Article  PubMed  Google Scholar 

  16. Ware, J. S., John, S., Roberts, A. M., et al. (2013). Next generation diagnostics in inherited arrhythmia syndromes : a comparison of two approaches. Journal of Cardiovascular Translational Research, 6, 94–103.

    Article  PubMed Central  PubMed  Google Scholar 

  17. Matkovich, S. J., Van Booven, D. J., Hindes, A., et al. (2010). Cardiac signaling genes exhibit unexpected sequence diversity in sporadic cardiomyopathy, revealing HSPB7 polymorphisms associated with disease. Journal of Clinical Investigation, 120, 280–289.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Sakharkar, M. K., Chow, V. T., & Kangueane, P. (2004). Distributions of exons and introns in the human genome. In Silico Biology, 4(4), 387–393.

    CAS  PubMed  Google Scholar 

  19. Watson, J. D., Gilman, M., Witkowski, J., & Zoller, M. (1992). Recombinant DNA (2dth ed.). New York: WH Freeman and Company, Scientific American Books.

    Google Scholar 

  20. Kan, Z., States, D., & Gish, W. (2002). Selecting for functional alternative splices in ESTs. Genome Research, 12, 1837–1845.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. de Klerk, E., & 't Hoen, P. A. (2015). Alternative mRNA transcription, processing, and translation: insights from RNA sequencing. Trends in Genetics, 31, 128–139.

    Article  PubMed  CAS  Google Scholar 

  22. Pan, Q., Shai, O., Lee, L. J., Frey, B. J., & Blencowe, B. J. (2008). Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nature Genetics, 40, 1413–1415.

    Article  CAS  PubMed  Google Scholar 

  23. Luco, R. F., Allo, M., Schor, I. E., Kornblihtt, A. R., & Misteli, T. (2011). Epigenetics in alternative pre-mRNA splicing. Cell, 144, 16–26.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  24. Wang, E. T., Sandberg, R., Luo, S., et al. (2008). Alternative isoform regulation in human tissue transcriptomes. Nature, 456, 470–476.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Celotto, A. M., & Graveley, B. R. (2001). Alternative splicing of the Drosophila Dscam pre-mRNA is both temporally and spatially regulated. Genetics, 159, 599–608.

    PubMed Central  CAS  PubMed  Google Scholar 

  26. Boley, N., Stoiber, M. H., Booth, B. W., et al. (2014). Genome-guided transcript assembly by integrative analysis of RNA sequence data. Nature Biotechnology, 32(4), 341–346.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Nadal-Ginard, B. (1990). Muscle cell differentiation and alternative splicing. Current Opinion in Cell Biology, 2, 1058–1064.

    Article  CAS  PubMed  Google Scholar 

  28. Lara-Pezzi, E., Gómez-Salinero, J., Gatto, A., & García-Pavía, P. (2013). The alternative heart: impact of alternative splicing in heart disease. Journal of Cardiovascular Translational Research, 6, 945–955.

    Article  PubMed  Google Scholar 

  29. Weeland, C. J., van den Hoogenhof, M. M., Beqqali, A., & Creemers, E. E. (2015). Insights into alternative splicing of sarcomeric genes in the heart. Journal of Molecular and Cellular Cardiology, 81, 107–113.

    Article  CAS  PubMed  Google Scholar 

  30. Pennisi, E. (2012). ENCODE Project writes eulogy for junk DNA. Science, 337, 1159–1160.

    Article  CAS  PubMed  Google Scholar 

  31. Hudson, J. E., & Porrello, E. R. (2013). The non-coding road towards cardiac regeneration. Journal of Cardiovascular Translational Research, 6, 909–923.

    Article  PubMed  Google Scholar 

  32. Bernal, J. A. (2013). RNA-based tools for nuclear reprogramming and lineage-conversion: towards clinical applications. Journal of Cardiovascular Translational Research, 6, 956–968.

    Article  PubMed Central  PubMed  Google Scholar 

  33. Maurano, M. T., Humbert, R., Rynes, E., et al. (2012). Systematic localization of common disease-associated variation in regulatory DNA. Science, 337, 1190–1195.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  34. Thurman, R. E., Rynes, E., Humbert, R., et al. (2012). The accessible chromatin landscape of the human genome. Nature, 489, 75–82.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Neph, S., Stergachis, A. B., Reynolds, A., Sandstrom, R., Borenstein, E., & Stamatoyannopoulos, J. A. (2012). Circuitry and dynamics of human transcription factor regulatory networks. Cell, 150, 1274–1286.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Deddens, J. C., Colijn, J. M., Oerlemans, M. I., et al. (2013). Circulating microRNAs as novel biomarkers for the early diagnosis of acute coronary syndrome. Journal of Cardiovascular Translational Research, 6, 884–898.

    Article  CAS  PubMed  Google Scholar 

  37. Papait, R., Kunderfranco, P., Stirparo, G. G., Latronico, M. V., & Condorelli, G. (2013). Long noncoding RNA: a new player of heart failure? Journal of Cardiovascular Translational Research, 6, 876–883.

    Article  PubMed Central  PubMed  Google Scholar 

  38. Terwilliger, J. D., & Hiekkalinna, T. (2006). An utter refutation of the “Fundamental Theorem of the HapMap.”. European Journal of Human Genetics, 14, 426–437.

    Article  CAS  PubMed  Google Scholar 

  39. The International HapMap Consortium. (2005). A haplotype map of the human genome. Nature, 437, 1299–1320.

    Article  PubMed Central  CAS  Google Scholar 

  40. Cohen, J. C., Kiss, R. S., Pertsemlidis, A., Marcel, Y. L., McPherson, R., & Hobbs, H. H. (2004). Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science, 305, 869–872.

    Article  CAS  PubMed  Google Scholar 

  41. Glass, D. J. (2010). A critique of the hypothesis, and a defense of the question, as a framework for experimentation. Clinical Chemistry, 56, 1080–1085.

    Article  CAS  PubMed  Google Scholar 

  42. Harrington, E. D., Jensen, L. J., & Bork, P. (2008). Predicting biological networks from genomic data. FEBS Letters, 582, 1251–1258.

    Article  CAS  PubMed  Google Scholar 

  43. Diez, D., Wheelock, A. M., Goto, S., et al. (2010). The use of network analyses for elucidating mechanisms in cardiovascular disease. Molecular BioSystems, 6, 289–304.

    Article  CAS  PubMed  Google Scholar 

  44. Cordeddu, V., Di Schiavi, E., Pennacchio, L. A., Ma'ayan, A., et al. (2009). Mutation of SHOC2 promotes aberrant protein N-myristoylation and causes Noonan-like syndrome with loose anagen hair. Nature Genetics, 41, 1022–1026.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  45. Berger, S., Posner, J., & Ma'ayan, A. (2007). Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases. BMC Bioinformatics, 8(1), 372.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  46. Schmitt, T., Ogris, C., & Sonnhammer, E. L. (2013). FunCoup 3.0: database of genome-wide functional coupling networks. Nucleic Acids Research, 42(Database issue), D380–D388.

    PubMed Central  PubMed  Google Scholar 

  47. Shannon, P., Markiel, A., Ozier, O., et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13, 2498–2504.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  48. Moreno-Moral, A., Mancini, M., D'Amati, G., Camici, P., & Petretto, E. (2013). Transcriptional network analysis for the regulation of left ventricular hypertrophy and microvascular remodeling. Journal of Cardiovascular Translational Research, 6, 931–944.

    Article  PubMed  Google Scholar 

  49. Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12, 56–68.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  50. Fuxman Bass, J. I., Sahni, N., Shrestha, S., et al. (2015). Human gene-centered transcription factor networks for enhancers and disease variants. Cell, 161, 661–673.

    Article  CAS  PubMed  Google Scholar 

  51. Beyer, A., Bandyopadhyay, S., & Ideker, T. (2007). Integrating physical and genetic maps: from genomes to interaction networks. Nature Reviews Genetics, 8, 699–710.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  52. Celniker, S. E., Dillon, L. A. L., Gerstein, M. B., et al. (2009). Unlocking the secrets of the genome. Nature, 459, 927–930.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  53. Beadle, G. W., & Tatum, E. L. (1941). Genetic control of biochemical reactions in Neurospora. Proceedings of the National Academy of Sciences of the United States of America, 27, 499–506.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  54. Mason, P. H. (2010). Degeneracy at multiple levels of complexity. Biological Theory, 5, 277–288.

    Article  Google Scholar 

  55. Moffatt, J. D. (2005). What targets have knockouts revealed in asthma? Pharmacology and Therapeutics, 107, 343–357.

    Article  CAS  PubMed  Google Scholar 

  56. Baldwin, H. S. (1999). Advances in understanding the molecular regulation of cardiac development. Current Opinion in Pediatrics, 11, 413–418.

    Article  CAS  PubMed  Google Scholar 

  57. Goh, K. I., Cusick, M. E., Valle, D., et al. (2007). The human disease network. Proceedings of the National Academy of Sciences of the United States of America, 104, 8685–8690.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  58. Austin, E. D., & Loyd, J. E. (2014). The genetics of pulmonary arterial hypertension. Circulation Research, 115, 189–202.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  59. Pasipoularides, A. (2014). Galen, father of systematic medicine. An essay on the evolution of modern medicine and cardiology. International Journal of Cardiology, 172, 47–58.

    Article  PubMed  Google Scholar 

  60. Pasipoularides, A. (2014). Historical continuity in the methodology of modern medical science: Leonardo leads the way. International Journal of Cardiology, 171, 103–115.

    Article  PubMed  Google Scholar 

  61. Pasipoularides, A. (2013). Greek underpinnings to his methodology in unraveling De motu cordis and what Harvey has to teach us still today. International Journal of Cardiology, 168, 3173–3182.

    Article  PubMed  Google Scholar 

  62. Pasipoularides, A. (2013). Harvey's epoch-making discovery of the Circulation, its historical antecedents, and some initial consequences on medical practice. Journal of Applied Physiology, 114, 1493–1503.

    Article  PubMed  Google Scholar 

  63. Moss, L. (2003). What genes can't do. Cambridge: MIT Press/Bradford Books.

    Google Scholar 

  64. Teekakirikul, P., Kelly, M. A., Rehm, H. L., Lakdawala, N. K., & Funke, B. H. (2013). Inherited cardiomyopathies: molecular genetics and clinical genetic testing in the postgenomic era. Journal of Molecular Diagnostics, 15, 158–170.

    Article  PubMed  Google Scholar 

  65. Friede, K. A., Ginsburg, G. S., & Voora, D. (2015). Gene expression signatures and the spectrum of coronary artery disease. Journal of Cardiovascular Translational Research, 8, 339–352.

    Article  PubMed  Google Scholar 

  66. Su, Z., Ning, B., Fang, H., et al. (2011). Next-generation sequencing and its applications in molecular diagnostics. Expert Review of Molecular Diagnostics, 11, 333–343.

    PubMed  Google Scholar 

  67. Antonarakis, S. E., & Beckmann, J. S. (2006). Mendelian disorders deserve more attention. Nature Reviews Genetics, 7, 277–282.

    Article  CAS  PubMed  Google Scholar 

  68. Steinberg, M. H., Forget, P. G., Higgs, D. R., & Nagel, R. L. (Eds.). (2001). Disorders of hemoglobin : genetics, pathophysiology, and clinical management. Cambridge: Cambridge University Press.

    Google Scholar 

  69. Kitao, H., & Takata, M. (2011). Fanconi anemia: a disorder defective in the DNA damage response. International Journal of Hematology, 93, 417–424. doi:10.1007/s12185-011-0777-z.

    Article  CAS  PubMed  Google Scholar 

  70. The International HapMap Consortium. (2003). The International HapMap Project. Nature, 426, 789–796.

    Article  CAS  Google Scholar 

  71. Hall, J. L. (2008). Building a program in translational genomics. Journal of Cardiovascular Translational Research, 1, 283–287.

    Article  PubMed  Google Scholar 

  72. Dudley, J. T., Kim, Y., Liu, L., et al. (2012). Human genomic disease variants: a neutral evolutionary explanation. Genome Research, 22, 1383–1394.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  73. Rosendaal, F. R. (1993). Venous thrombosis: a multicausal disease. Lancet, 353, 1167–1173.

    Article  Google Scholar 

  74. Van Berlo, J. H., Maillet, M., & Molkentin, J. D. (2013). Signaling effectors underlying pathologic growth and remodeling of the heart. Journal of Clinical Investigation, 123, 37–45.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  75. Kimura, A. (2010). Molecular basis of hereditary cardiomyopathy: abnormalities in calcium sensitivity, stretch response, stress response and beyond. Journal of Human Genetics, 55, 81–90.

    Article  CAS  PubMed  Google Scholar 

  76. Hershberger, R. E., Lindenfeld, J., Mestroni, L., Seidman, C. E., Taylor, M. R., & Towbin, J. A. (2009). Genetic evaluation of cardiomyopathy--a Heart Failure Society of America practice guideline. Journal of Cardiac Failure, 15, 83–97.

    Article  PubMed  Google Scholar 

  77. Gelb, B. D., & Chung, W. K. (2014). Complex genetics and the etiology of human congenital heart disease. Cold Spring Harbor Perspectives in Medicine, 4(7), a013953.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  78. Sturm, A. C. (2013). Genetic testing in the contemporary diagnosis of cardiomyopathy. Current Heart Failure Reports, 10, 63–72.

    Article  PubMed  Google Scholar 

  79. Stearns, F. W. (2010). One hundred years of pleiotropy: a retrospective. Genetics, 186, 767–773.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  80. Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M., & Smoller, J. W. (2013). Pleiotropy in complex traits: challenges and strategies. Nature Reviews Genetics, 14, 483–495.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  81. Kamisago, M., Sharma, S. D., DePalma, S. R., et al. (2000). Mutations in sarcomere protein genes as a cause of dilated cardiomyopathy. New England Journal of Medicine, 343, 1688–1696.

    Article  CAS  PubMed  Google Scholar 

  82. Pasipoularides, A. (1990). Clinical assessment of ventricular ejection dynamics with and without outflow obstruction. Journal of the American College of Cardiology, 15, 859–882.

    Article  CAS  PubMed  Google Scholar 

  83. Georgiadis, J., Wang, M., & Pasipoularides, A. (1992). Computational fluid dynamics of ventricular ejection with and without outflow stenosis. Annals of Biomedical Engineering, 20, 81–97.

    Article  CAS  PubMed  Google Scholar 

  84. Pasipoularides, A. (2007). Complementarity and competitiveness of the intrinsic and extrinsic components of the total ventricular load: demonstration after valve replacement in aortic stenosis. American Heart Journal, 153, 4–6.

    Article  PubMed  Google Scholar 

  85. Pasipoularides, A., Shu, M., Shah, A., Womack, M. S., & Glower, D. D. (2003). Diastolic right ventricular filling vortex in normal and volume overload states. American Journal of Physiology - Heart and Circulatory Physiology, 284, H1064–H1072.

    Article  CAS  PubMed  Google Scholar 

  86. Pasipoularides, A., Shu, M., Shah, A., Tucconi, A., & Glower, D. D. (2003). RV instantaneous intraventricular diastolic pressure and velocity distributions in normal and volume overload awake dog disease models. American Journal of Physiology - Heart and Circulatory Physiology, 285, H1956–H1965.

    Article  CAS  PubMed  Google Scholar 

  87. Pasipoularides A. Analysis of vortex flow imaging in normal and dysfunctional RV’s. American Society of Echocardiography 22nd Annual Scientific Sessions, Montreal, 2011. EE02d - Flow Vortex Imaging; PROLibraries.com. http://www.aseuniversity.org/ase/sessions/search/pasipoularides

  88. Pasipoularides, A. (2013). RV/LV diastolic flow field: why are measured intraventricular pressure gradients small? Revista Española de Cardiología, 66, 337–341.

    Article  PubMed  Google Scholar 

  89. Pasipoularides, A. (2013). Evaluation of right and left ventricular diastolic filling. Journal of Cardiovascular Translational Research, 6, 623–639.

    Article  PubMed Central  PubMed  Google Scholar 

  90. Pasipoularides, A. (2015). Fluid dynamics of ventricular filling in heart failure: overlooked problems of RV/LV chamber dilatation. Hellenic Journal of Cardiology, 56, 85–95.

    PubMed  PubMed Central  Google Scholar 

  91. Pasipoularides, A., Murgo, J. P., Miller, J. W., & Craig, W. E. (1987). Nonobstructive left ventricular ejection pressure gradients in man. Circulation Research, 61, 220–227.

    Article  CAS  PubMed  Google Scholar 

  92. Shim, Y., Hampton, T. G., Straley, C. A., Harrison, J. K., Spero, L. A., Bashore, T. M., & Pasipoularides, A. D. (1992). Ejection load changes in aortic stenosis: observations made following balloon aortic valvuloplasty. Circulation Research, 71, 1174–1184.

    Article  CAS  PubMed  Google Scholar 

  93. Isaaz, K., & Pasipoularides, A. (1991). Noninvasive assessment of intrinsic ventricular load dynamics in dilated cardiomyopathy. Journal of the American College of Cardiology, 17, 112–121.

    Article  CAS  PubMed  Google Scholar 

  94. Bird, J. J., Murgo, J. P., & Pasipoularides, A. (1982). Fluid dynamics of aortic stenosis: subvalvular gradients without subvalvular obstruction. Circulation, 66, 835–840.

    Article  CAS  PubMed  Google Scholar 

  95. Pasipoularides, A., Murgo, J. P., Bird, J. J., & Craig, W. E. (1984). Fluid dynamics of aortic stenosis: mechanisms for the presence of subvalvular pressure gradients. American Journal of Physiology, 246, H542–H550.

    CAS  PubMed  Google Scholar 

  96. Pasipoularides, A. (1992). Cardiac Mechanics: basic and clinical contemporary research. Annals of Biomedical Engineering, 20, 3–17.

    Article  CAS  PubMed  Google Scholar 

  97. Kassem, H. S., Azer, R. S., Saber-Ayad, M., et al. (2013). Early results of sarcomeric gene screening from the Egyptian National BA-HCM Program. Journal of Cardiovascular Translational Research, 6, 65–80.

    Article  PubMed Central  PubMed  Google Scholar 

  98. Pasipoularides, A. (2011). Fluid dynamic aspects of ejection in hypertrophic cardiomyopathy. Hellenic Journal of Cardiology, 52, 416–426.

    PubMed  Google Scholar 

  99. Bateman, M. G., Quill, J. L., Hill, A. J., & Iaizzo, P. A. (2013). The clinical anatomy and pathology of the human atrioventricular valves: implications for repair or replacement. Journal of Cardiovascular Translational Research, 6, 155–165.

    Article  PubMed  Google Scholar 

  100. Pasipoularides, A. (2011). LV twisting-and-untwisting in HCM: ejection begets filling. Diastolic functional aspects of HCM. [Progress in Cardiology]. American Heart Journal, 162, 798–810.

    Article  PubMed  Google Scholar 

  101. Craig, W. E., Murgo, J. P., & Pasipoularides, A. (1987). Evaluation of time course of left ventricular isovolumic relaxation in humans. In W. Grossman & B. Lorell (Eds.), Diastolic relaxation of the heart (pp. 125–132). The Hague, Boston: Martinus Nijhoff.

    Chapter  Google Scholar 

  102. Mirsky, I., & Pasipoularides, A. (1990). Clinical assessment of diastolic function. Progress Cardiovascular Diseases, 32, 291–318.

    Article  CAS  Google Scholar 

  103. Weiner, R. B., & Baggish, A. L. (2014). Acute versus chronic exercise-induced left-ventricular remodeling. Expert Review of Cardiovascular Therapy, 12, 1243–1246.

    Article  CAS  PubMed  Google Scholar 

  104. Mirsky, I., & Pasipoularides, A. (1980). Elastic properties of normal and hypertrophied cardiac muscle. Federation Proceedings, 39, 156–161.

    CAS  PubMed  Google Scholar 

  105. Pasipoularides, A. (2013). Right and left ventricular diastolic pressure–volume relations: a comprehensive review. Journal of Cardiovascular Translational Research, 6, 239–252.

    Article  PubMed Central  PubMed  Google Scholar 

  106. Pasipoularides, A., Mirsky, I., Hess, O. M., Grimm, J., & Krayenbuehl, H. P. (1986). Myocardial relaxation and passive diastolic properties in man. Circulation, 74, 991–1001.

    Article  CAS  PubMed  Google Scholar 

  107. Hershberger, R. E., Norton, N., Morales, A., Li, D., Siegfried, J. D., & Gonzalez-Quintana, J. (2010). Coding sequence rare variants identified in MYBPC3, MYH6, TPM1, TNNC1, and TNNI3 from 312 patients with familial or idiopathic dilated cardiomyopathy. Circulation Cardiovascular Genetics, 3, 155–161.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  108. Moller, D. V., Andersen, P. S., Hedley, P., et al. (2009). The role of sarcomere gene mutations in patients with idiopathic dilated cardiomyopathy. European Journal of Human Genetics, 17, 1241–1249.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  109. Marston, S. B. (2011). How do mutations in contractile proteins cause the primary familial cardiomyopathies? Journal of Cardiovascular Translational Research, 4, 245–255.

    Article  PubMed  Google Scholar 

  110. Robinson, P., Griffiths, P. J., Watkins, H., & Redwood, C. S. (2007). Dilated and hypertrophic cardiomyopathy mutations in troponin and a-tropomyosin have opposing effects on the calcium affinity of cardiac thin filaments. Circulation Research, 101, 1266–1273.

    Article  CAS  PubMed  Google Scholar 

  111. Haldane, J. (1941). The relative importance of principal and modifying genes in determining some human diseases. Journal of Genetics, 41, 149–157.

    Article  Google Scholar 

  112. Chen, J., & Chien, K. R. (1999). Complexity in simplicity: monogenic disorders and complex cardiomyopathies. Journal of Clinical Investigation, 103, 1483–1485.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  113. Cooper, D. N., Krawczak, M., Polychronakos, C., Tyler-Smith, C., & Kehrer-Sawatzki, H. (2013). Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease. Human Genetics, 132, 1077–1130.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  114. Génin, E., Feingold, J., & Clerget-darpoux, F. (2008). Identifying modifier genes of monogenic disease: strategies and difficulties. Human Genetics, 124, 357–368.

    Article  PubMed Central  PubMed  Google Scholar 

  115. Risch, N. J. (2000). Searching for genetic determinants in the new millennium. Nature, 405, 847–856.

    Article  CAS  PubMed  Google Scholar 

  116. Rao, D. C. (2008). An overview of the genetic dissection of complex traits. Advances in Genetics, 60, 3–34.

    Article  CAS  PubMed  Google Scholar 

  117. Phillips, P. C. (2008). Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems. Nature Reviews Genetics, 9, 855–867.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  118. Marian, A. J., & Roberts, R. (2001). The molecular genetic basis for hypertrophic cardiomyopathy. Journal of Molecular and Cellular Cardiology, 33, 655–670.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  119. Ooi, C. H., & Tan, P. (2003). Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics, 19, 37–44.

    Article  CAS  PubMed  Google Scholar 

  120. Draghici, S. (2003). Data analysis tools for DNA microarrays. Boca Raton: Chapman & Hall.

    Book  Google Scholar 

  121. Bandyopadhyay, S., Maulik, U., & Wang, J. T. L. (Eds.). (2007). Analysis of biological data: a soft computing approach. Singapore/Hackensack: World Scientific.

    Google Scholar 

  122. Wakabayashi I, Groschner K (editors). Interdisciplinary concepts in cardiovascular health Volume I: Primary risk factors. Wien/ New York: Springer-Verlag, 2013.

  123. Bevilacqua, V., Mastronardi, G., Menolascina, F., Paradiso, A., & Tommasi, S. (2006). Genetic algorithms and artificial neural networks in microarray data analysis: a distributed approach. Engineering Letters, 13, 335–343.

    Google Scholar 

  124. Lawrence, J. (1994). Introduction to neural networks: design, theory, and applications (6th ed.). Nevada City: California Scientific Software.

    Google Scholar 

  125. Tarasov, K. V., Brugh, S. A., Tarasova, Y. S., & Boheler, K. R. (2007). Serial analysis of gene expression (SAGE): a useful tool to analyze the cardiac transcriptome. Methods in Molecular Biology, 366, 41–59.

    Article  CAS  PubMed  Google Scholar 

  126. Kraus, W. E., Granger, C. B., Sketch, M. H., Jr., et al. (2015). A guide for a cardiovascular genomics biorepository: the CATHGEN experience. Journal of Cardiovascular Translational Research. doi:10.1007/s12265-015-9648-y.

    PubMed  Google Scholar 

  127. Douglas, P. S., & Ginsburg, G. S. (2008). Clinical genomic testing: getting it right. Journal of Cardiovascular Translational Research, 1, 17–20.

    Article  PubMed  Google Scholar 

  128. Bodi, V., Marrachelli, V. G., Husser, O., Chorro, F. J., Viña, J. R., & Monleon, D. (2013). Metabolomics in the diagnosis of acute myocardial ischemia. Journal of Cardiovascular Translational Research, 6, 808–815.

    Article  PubMed  Google Scholar 

  129. Krishnamoorthy, P., Gupta, D., Chatterjee, S., Huston, J., & Ryan, J. J. (2014). A review of the role of electronic health record in genomic research. Journal of Cardiovascular Translational Research, 7, 692–700.

    Article  PubMed  Google Scholar 

  130. Rasmussen, L. V. (2014). The electronic health record for translational research. Journal of Cardiovascular Translational Research, 7, 607–614.

    Article  PubMed Central  PubMed  Google Scholar 

  131. McKernan, K. J., Peckham, H. E., Costa, G. L., et al. (2009). Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Research, 19, 1527–1541.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  132. Dalton, L., Ballarin, V., & Brun, M. (2009). Clustering algorithms: on learning, validation, performance, and applications to genomics. Current Genomics, 10, 430–445.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  133. Bittner, M., Meltzer, P., & Trent, J. (1999). Data analysis and integration: of steps and arrows. Nature Genetics, 22, 213–215.

    Article  CAS  PubMed  Google Scholar 

  134. Madeira, S. C., & Oliveira, A. L. (2004). Biclustering algorithms for biological data analysis: a survey. IEEE Transactions on Computational Biology and Bioinformatics, 1, 24–45.

    Article  CAS  PubMed  Google Scholar 

  135. Baldi, P., & Hatfield, G. W. (2002). DNA microarrays and gene expression: from experiments to data analysis and modelling. Cambridge: Cambridge Univ. Press.

    Book  Google Scholar 

  136. Klugar, Y., Basri, R., Chang, J. T., & Gerstein, M. (2003). Spectral biclustering of microarray data: coclustering genes and conditions,”. Genome Research, 13, 703–716.

    Article  CAS  Google Scholar 

  137. Nührenberg, T. G., Langwieser, N., Binder, H., et al. (2013). Transcriptome analysis in patients with progressive coronary artery disease: identification of differential gene expression in peripheral blood. Journal of Cardiovascular Translational Research, 6, 81–93.

    Article  PubMed  Google Scholar 

  138. Pontes, B., Girldez, R., & Aguilar-Ruiz, J. S. (2015). Quality measures for gene expression biclusters. PLoS One, 10(3), e0115497.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  139. Pontes B, Giráldez R, Aguilar-Ruiz JS. Biclustering on expression data: A review. J Biomed Inform 2015. In press. pii: S1532-0464(15)00138-0. doi:10.1016/j.jbi.2015.06.028

  140. Jacob, F., & Monod, J. (1961). Genetic regulatory mechanisms in the synthesis of proteins. Journal of Molecular Biology, 3, 318–356.

    Article  CAS  PubMed  Google Scholar 

  141. Kauffman, S. (1969). Homeostasis and differentiation in random genetic control networks. Nature, 224, 177–178.

    Article  CAS  PubMed  Google Scholar 

  142. Kitano, H. (2001). Foundations of systems biology. Cambridge: MIT Press.

    Google Scholar 

  143. Evans, G. A. (2000). Designer science and the ‘omic’ revolution. Nature Biotechnology, 18, 127.

    Article  CAS  PubMed  Google Scholar 

  144. Noble, D. (2013). A biological relativity view of the relationships between genomes and phenotypes. Progress in Biophysics and Molecular Biology, 111, 59–65.

    Article  CAS  PubMed  Google Scholar 

  145. King, M. C., & Wilson, A. C. (1975). Evolution at two levels in humans and chimpanzees. Science, 188, 107–116.

    Article  CAS  PubMed  Google Scholar 

  146. Enard, W., Khaitovich, P., Klose, J., et al. (2002). Intra- and interspecific variation in primate gene expression patterns. Science, 296, 340–343.

    Article  CAS  PubMed  Google Scholar 

  147. Cheung, V. G., & Spielman, R. S. (2002). The genetics of variation in gene expression. Nature Genetics, 32(Suppl), 522–525.

    Article  CAS  PubMed  Google Scholar 

  148. Arnaudo, A. M., & Garcia, B. A. (2013). Proteomic characterization of novel histone post-translational modifications. Epigenetics and Chromatin, 6, 24.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  149. Jones, M. J., Fejes, A. P., & Kobor, M. S. (2013). DNA methylation, genotype and gene expression: who is driving and who is along for the ride? Genome Biology, 14(7), 126.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  150. Bartel, D. P. (2009). MicroRNAs: target recognition and regulatory functions. Cell, 136, 215–233.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Duke University School of Medicine, Durham, NC, USA

    Ares Pasipoularides

  2. Duke/NSF Research Center for Emerging Cardiovascular Technologies, Durham, NC, USA

    Ares Pasipoularides

  3. Department of Surgery, Duke University School of Medicine, Durham, NC, 27710, USA

    Ares Pasipoularides

Authors
  1. Ares Pasipoularides
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ares Pasipoularides.

Ethics declarations

Sources of Funding

Research support, for work from my Laboratory surveyed here, was provided by National Heart, Lung, and Blood Institute, Grant R01 HL 050446; National Science Foundation, Grant CDR 8622201; and North Carolina Supercomputing Center and Cray Research.

Conflict of interest

I declare that I have no conflict of interest, whatsoever.

Ethical approval

All procedures performed in studies involving human participants that are reviewed here were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments. All applicable international, national, and/or institutional guidelines for the care and use of animals in studies involving animals that are reviewed here were followed.

Ares Pasipoularides

Additional information

Associate Editor Jennifer L. Hall oversaw the review of this article

Electronic supplementary material

Below is the link to the electronic supplementary material.

Glossary, ESM

(DOCX 21 kb)

Supplementary Table 1

(DOCX 12 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pasipoularides, A. Linking Genes to Cardiovascular Diseases: Gene Action and Gene–Environment Interactions. J. of Cardiovasc. Trans. Res. 8, 506–527 (2015). https://doi.org/10.1007/s12265-015-9658-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12265-015-9658-9

Keywords

Search

Navigation