Journal of Cardiovascular Translational Research

, Volume 8, Issue 9, pp 506–527 | Cite as

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

  • Ares PasipoularidesEmail author


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.


Genotype and expressed phenotypes Exons, introns, and alternative splicing Monogenic and polygenic traits and gene networks Major gene, “modifier genes,” and pleiotropy Regulatory DNA “switches” and regulation of gene expression Gene interactions and epistasis Genetic cardiomyopathies, HCM, DCM Environmental influences and epigenetics Mutations and haplotypes 


Compliance with Ethical Standards

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

Supplementary material

12265_2015_9658_MOESM1_ESM.docx (22 kb)
Glossary, ESM (DOCX 21 kb)
12265_2015_9658_MOESM2_ESM.docx (12 kb)
Supplementary Table 1 (DOCX 12 kb)


  1. 1.
    Wallace, B. (1992). The search for the gene. Ithaca: Cornell University Press.Google Scholar
  2. 2.
    Pasipoularides, A. (2010). Heart's vortex: intracardiac blood flow phenomena. Shelton: People's Medical Publishing House. 960 p.Google Scholar
  3. 3.
    Pasipoularides, A. (2012). Diastolic filling vortex forces and cardiac adaptations: probing the epigenetic nexus. Hellenic Journal of Cardiology, 53, 458–469.PubMedGoogle Scholar
  4. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  5. 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.PubMedCrossRefGoogle Scholar
  6. 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.PubMedCrossRefGoogle Scholar
  7. 7.
    Johannsen, W. (1909). Elemente der exakten Erblichkeitslehre. Jena: Gustav Fischer.Google Scholar
  8. 8.
    Larribe, F., & Fearnhead, P. (2011). On composite likelihoods in statistical genetics. Stat Sinica, 21, 43–69.Google Scholar
  9. 9.
    Venter, J. C., Adams, M. D., Myers, E. W., et al. (2001). The sequence of the human genome. Science, 291, 1304–1351.PubMedCrossRefGoogle Scholar
  10. 10.
    International Human Genome Sequencing Consortium. (2001). Initial sequencing and analysis of the human genome. Nature, 409, 860–921.CrossRefGoogle Scholar
  11. 11.
    International Human Genome Mapping Consortium. (2001). A physical map of the human genome. Nature, 409, 934–941.CrossRefGoogle Scholar
  12. 12.
    International Human Genome Sequencing Consortium. (2004). Finishing the euchromatic sequence of the human genome. Nature, 431, 931–945.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 14.
    Stakos, D. A., & Boudoulas, H. (2002). Pharmacogenetics and pharmacogenomics in cardiology. Hellenic Journal of Cardiology, 43, 1–15.Google Scholar
  15. 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.PubMedCrossRefGoogle Scholar
  16. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  17. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  18. 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.PubMedGoogle Scholar
  19. 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. 20.
    Kan, Z., States, D., & Gish, W. (2002). Selecting for functional alternative splices in ESTs. Genome Research, 12, 1837–1845.PubMedCentralPubMedCrossRefGoogle Scholar
  21. 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.PubMedCrossRefGoogle Scholar
  22. 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.PubMedCrossRefGoogle Scholar
  23. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  24. 24.
    Wang, E. T., Sandberg, R., Luo, S., et al. (2008). Alternative isoform regulation in human tissue transcriptomes. Nature, 456, 470–476.PubMedCentralPubMedCrossRefGoogle Scholar
  25. 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.PubMedCentralPubMedGoogle Scholar
  26. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Nadal-Ginard, B. (1990). Muscle cell differentiation and alternative splicing. Current Opinion in Cell Biology, 2, 1058–1064.PubMedCrossRefGoogle Scholar
  28. 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.PubMedCrossRefGoogle Scholar
  29. 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.PubMedCrossRefGoogle Scholar
  30. 30.
    Pennisi, E. (2012). ENCODE Project writes eulogy for junk DNA. Science, 337, 1159–1160.PubMedCrossRefGoogle Scholar
  31. 31.
    Hudson, J. E., & Porrello, E. R. (2013). The non-coding road towards cardiac regeneration. Journal of Cardiovascular Translational Research, 6, 909–923.PubMedCrossRefGoogle Scholar
  32. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  33. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  34. 34.
    Thurman, R. E., Rynes, E., Humbert, R., et al. (2012). The accessible chromatin landscape of the human genome. Nature, 489, 75–82.PubMedCentralPubMedCrossRefGoogle Scholar
  35. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  36. 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.PubMedCrossRefGoogle Scholar
  37. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  38. 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.PubMedCrossRefGoogle Scholar
  39. 39.
    The International HapMap Consortium. (2005). A haplotype map of the human genome. Nature, 437, 1299–1320.PubMedCentralCrossRefGoogle Scholar
  40. 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.PubMedCrossRefGoogle Scholar
  41. 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.PubMedCrossRefGoogle Scholar
  42. 42.
    Harrington, E. D., Jensen, L. J., & Bork, P. (2008). Predicting biological networks from genomic data. FEBS Letters, 582, 1251–1258.PubMedCrossRefGoogle Scholar
  43. 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.PubMedCrossRefGoogle Scholar
  44. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  45. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  46. 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.PubMedCentralPubMedGoogle Scholar
  47. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  48. 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.PubMedCrossRefGoogle Scholar
  49. 49.
    Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12, 56–68.PubMedCentralPubMedCrossRefGoogle Scholar
  50. 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.PubMedCrossRefGoogle Scholar
  51. 51.
    Beyer, A., Bandyopadhyay, S., & Ideker, T. (2007). Integrating physical and genetic maps: from genomes to interaction networks. Nature Reviews Genetics, 8, 699–710.PubMedCentralPubMedCrossRefGoogle Scholar
  52. 52.
    Celniker, S. E., Dillon, L. A. L., Gerstein, M. B., et al. (2009). Unlocking the secrets of the genome. Nature, 459, 927–930.PubMedCentralPubMedCrossRefGoogle Scholar
  53. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  54. 54.
    Mason, P. H. (2010). Degeneracy at multiple levels of complexity. Biological Theory, 5, 277–288.CrossRefGoogle Scholar
  55. 55.
    Moffatt, J. D. (2005). What targets have knockouts revealed in asthma? Pharmacology and Therapeutics, 107, 343–357.PubMedCrossRefGoogle Scholar
  56. 56.
    Baldwin, H. S. (1999). Advances in understanding the molecular regulation of cardiac development. Current Opinion in Pediatrics, 11, 413–418.PubMedCrossRefGoogle Scholar
  57. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  58. 58.
    Austin, E. D., & Loyd, J. E. (2014). The genetics of pulmonary arterial hypertension. Circulation Research, 115, 189–202.PubMedCentralPubMedCrossRefGoogle Scholar
  59. 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.PubMedCrossRefGoogle Scholar
  60. 60.
    Pasipoularides, A. (2014). Historical continuity in the methodology of modern medical science: Leonardo leads the way. International Journal of Cardiology, 171, 103–115.PubMedCrossRefGoogle Scholar
  61. 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.PubMedCrossRefGoogle Scholar
  62. 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.PubMedCrossRefGoogle Scholar
  63. 63.
    Moss, L. (2003). What genes can't do. Cambridge: MIT Press/Bradford Books.Google Scholar
  64. 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.PubMedCrossRefGoogle Scholar
  65. 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.PubMedCrossRefGoogle Scholar
  66. 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.PubMedGoogle Scholar
  67. 67.
    Antonarakis, S. E., & Beckmann, J. S. (2006). Mendelian disorders deserve more attention. Nature Reviews Genetics, 7, 277–282.PubMedCrossRefGoogle Scholar
  68. 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. 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.PubMedCrossRefGoogle Scholar
  70. 70.
    The International HapMap Consortium. (2003). The International HapMap Project. Nature, 426, 789–796.CrossRefGoogle Scholar
  71. 71.
    Hall, J. L. (2008). Building a program in translational genomics. Journal of Cardiovascular Translational Research, 1, 283–287.PubMedCrossRefGoogle Scholar
  72. 72.
    Dudley, J. T., Kim, Y., Liu, L., et al. (2012). Human genomic disease variants: a neutral evolutionary explanation. Genome Research, 22, 1383–1394.PubMedCentralPubMedCrossRefGoogle Scholar
  73. 73.
    Rosendaal, F. R. (1993). Venous thrombosis: a multicausal disease. Lancet, 353, 1167–1173.CrossRefGoogle Scholar
  74. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  75. 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.PubMedCrossRefGoogle Scholar
  76. 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.PubMedCrossRefGoogle Scholar
  77. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  78. 78.
    Sturm, A. C. (2013). Genetic testing in the contemporary diagnosis of cardiomyopathy. Current Heart Failure Reports, 10, 63–72.PubMedCrossRefGoogle Scholar
  79. 79.
    Stearns, F. W. (2010). One hundred years of pleiotropy: a retrospective. Genetics, 186, 767–773.PubMedCentralPubMedCrossRefGoogle Scholar
  80. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  81. 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.PubMedCrossRefGoogle Scholar
  82. 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.PubMedCrossRefGoogle Scholar
  83. 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.PubMedCrossRefGoogle Scholar
  84. 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.PubMedCrossRefGoogle Scholar
  85. 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.PubMedCrossRefGoogle Scholar
  86. 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.PubMedCrossRefGoogle Scholar
  87. 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;
  88. 88.
    Pasipoularides, A. (2013). RV/LV diastolic flow field: why are measured intraventricular pressure gradients small? Revista Española de Cardiología, 66, 337–341.PubMedCrossRefGoogle Scholar
  89. 89.
    Pasipoularides, A. (2013). Evaluation of right and left ventricular diastolic filling. Journal of Cardiovascular Translational Research, 6, 623–639.PubMedCentralPubMedCrossRefGoogle Scholar
  90. 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.PubMedPubMedCentralGoogle Scholar
  91. 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.PubMedCrossRefGoogle Scholar
  92. 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.PubMedCrossRefGoogle Scholar
  93. 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.PubMedCrossRefGoogle Scholar
  94. 94.
    Bird, J. J., Murgo, J. P., & Pasipoularides, A. (1982). Fluid dynamics of aortic stenosis: subvalvular gradients without subvalvular obstruction. Circulation, 66, 835–840.PubMedCrossRefGoogle Scholar
  95. 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.PubMedGoogle Scholar
  96. 96.
    Pasipoularides, A. (1992). Cardiac Mechanics: basic and clinical contemporary research. Annals of Biomedical Engineering, 20, 3–17.PubMedCrossRefGoogle Scholar
  97. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  98. 98.
    Pasipoularides, A. (2011). Fluid dynamic aspects of ejection in hypertrophic cardiomyopathy. Hellenic Journal of Cardiology, 52, 416–426.PubMedGoogle Scholar
  99. 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.PubMedCrossRefGoogle Scholar
  100. 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.PubMedCrossRefGoogle Scholar
  101. 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.CrossRefGoogle Scholar
  102. 102.
    Mirsky, I., & Pasipoularides, A. (1990). Clinical assessment of diastolic function. Progress Cardiovascular Diseases, 32, 291–318.CrossRefGoogle Scholar
  103. 103.
    Weiner, R. B., & Baggish, A. L. (2014). Acute versus chronic exercise-induced left-ventricular remodeling. Expert Review of Cardiovascular Therapy, 12, 1243–1246.PubMedCrossRefGoogle Scholar
  104. 104.
    Mirsky, I., & Pasipoularides, A. (1980). Elastic properties of normal and hypertrophied cardiac muscle. Federation Proceedings, 39, 156–161.PubMedGoogle Scholar
  105. 105.
    Pasipoularides, A. (2013). Right and left ventricular diastolic pressure–volume relations: a comprehensive review. Journal of Cardiovascular Translational Research, 6, 239–252.PubMedCentralPubMedCrossRefGoogle Scholar
  106. 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.PubMedCrossRefGoogle Scholar
  107. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  108. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  109. 109.
    Marston, S. B. (2011). How do mutations in contractile proteins cause the primary familial cardiomyopathies? Journal of Cardiovascular Translational Research, 4, 245–255.PubMedCrossRefGoogle Scholar
  110. 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.PubMedCrossRefGoogle Scholar
  111. 111.
    Haldane, J. (1941). The relative importance of principal and modifying genes in determining some human diseases. Journal of Genetics, 41, 149–157.CrossRefGoogle Scholar
  112. 112.
    Chen, J., & Chien, K. R. (1999). Complexity in simplicity: monogenic disorders and complex cardiomyopathies. Journal of Clinical Investigation, 103, 1483–1485.PubMedCentralPubMedCrossRefGoogle Scholar
  113. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  114. 114.
    Génin, E., Feingold, J., & Clerget-darpoux, F. (2008). Identifying modifier genes of monogenic disease: strategies and difficulties. Human Genetics, 124, 357–368.PubMedCentralPubMedCrossRefGoogle Scholar
  115. 115.
    Risch, N. J. (2000). Searching for genetic determinants in the new millennium. Nature, 405, 847–856.PubMedCrossRefGoogle Scholar
  116. 116.
    Rao, D. C. (2008). An overview of the genetic dissection of complex traits. Advances in Genetics, 60, 3–34.PubMedCrossRefGoogle Scholar
  117. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  118. 118.
    Marian, A. J., & Roberts, R. (2001). The molecular genetic basis for hypertrophic cardiomyopathy. Journal of Molecular and Cellular Cardiology, 33, 655–670.PubMedCentralPubMedCrossRefGoogle Scholar
  119. 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.PubMedCrossRefGoogle Scholar
  120. 120.
    Draghici, S. (2003). Data analysis tools for DNA microarrays. Boca Raton: Chapman & Hall.CrossRefGoogle Scholar
  121. 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. 122.
    Wakabayashi I, Groschner K (editors). Interdisciplinary concepts in cardiovascular health Volume I: Primary risk factors. Wien/ New York: Springer-Verlag, 2013.Google Scholar
  123. 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. 124.
    Lawrence, J. (1994). Introduction to neural networks: design, theory, and applications (6th ed.). Nevada City: California Scientific Software.Google Scholar
  125. 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.PubMedCrossRefGoogle Scholar
  126. 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.PubMedGoogle Scholar
  127. 127.
    Douglas, P. S., & Ginsburg, G. S. (2008). Clinical genomic testing: getting it right. Journal of Cardiovascular Translational Research, 1, 17–20.PubMedCrossRefGoogle Scholar
  128. 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.PubMedCrossRefGoogle Scholar
  129. 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.PubMedCrossRefGoogle Scholar
  130. 130.
    Rasmussen, L. V. (2014). The electronic health record for translational research. Journal of Cardiovascular Translational Research, 7, 607–614.PubMedCentralPubMedCrossRefGoogle Scholar
  131. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  132. 132.
    Dalton, L., Ballarin, V., & Brun, M. (2009). Clustering algorithms: on learning, validation, performance, and applications to genomics. Current Genomics, 10, 430–445.PubMedCentralPubMedCrossRefGoogle Scholar
  133. 133.
    Bittner, M., Meltzer, P., & Trent, J. (1999). Data analysis and integration: of steps and arrows. Nature Genetics, 22, 213–215.PubMedCrossRefGoogle Scholar
  134. 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.PubMedCrossRefGoogle Scholar
  135. 135.
    Baldi, P., & Hatfield, G. W. (2002). DNA microarrays and gene expression: from experiments to data analysis and modelling. Cambridge: Cambridge Univ. Press.CrossRefGoogle Scholar
  136. 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.CrossRefGoogle Scholar
  137. 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.PubMedCrossRefGoogle Scholar
  138. 138.
    Pontes, B., Girldez, R., & Aguilar-Ruiz, J. S. (2015). Quality measures for gene expression biclusters. PLoS One, 10(3), e0115497.PubMedCentralPubMedCrossRefGoogle Scholar
  139. 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. 140.
    Jacob, F., & Monod, J. (1961). Genetic regulatory mechanisms in the synthesis of proteins. Journal of Molecular Biology, 3, 318–356.PubMedCrossRefGoogle Scholar
  141. 141.
    Kauffman, S. (1969). Homeostasis and differentiation in random genetic control networks. Nature, 224, 177–178.PubMedCrossRefGoogle Scholar
  142. 142.
    Kitano, H. (2001). Foundations of systems biology. Cambridge: MIT Press.Google Scholar
  143. 143.
    Evans, G. A. (2000). Designer science and the ‘omic’ revolution. Nature Biotechnology, 18, 127.PubMedCrossRefGoogle Scholar
  144. 144.
    Noble, D. (2013). A biological relativity view of the relationships between genomes and phenotypes. Progress in Biophysics and Molecular Biology, 111, 59–65.PubMedCrossRefGoogle Scholar
  145. 145.
    King, M. C., & Wilson, A. C. (1975). Evolution at two levels in humans and chimpanzees. Science, 188, 107–116.PubMedCrossRefGoogle Scholar
  146. 146.
    Enard, W., Khaitovich, P., Klose, J., et al. (2002). Intra- and interspecific variation in primate gene expression patterns. Science, 296, 340–343.PubMedCrossRefGoogle Scholar
  147. 147.
    Cheung, V. G., & Spielman, R. S. (2002). The genetics of variation in gene expression. Nature Genetics, 32(Suppl), 522–525.PubMedCrossRefGoogle Scholar
  148. 148.
    Arnaudo, A. M., & Garcia, B. A. (2013). Proteomic characterization of novel histone post-translational modifications. Epigenetics and Chromatin, 6, 24.PubMedCentralPubMedCrossRefGoogle Scholar
  149. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  150. 150.
    Bartel, D. P. (2009). MicroRNAs: target recognition and regulatory functions. Cell, 136, 215–233.PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Duke University School of MedicineDurhamUSA
  2. 2.Duke/NSF Research Center for Emerging Cardiovascular TechnologiesDurhamUSA
  3. 3.Department of SurgeryDuke University School of MedicineDurhamUSA

Personalised recommendations