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Genetics and Genomics of Coronary Artery Disease

  • Cardiovascular Genomics (TL Assimes, Section Editor)
  • Published:
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Abstract

Coronary artery disease (or coronary heart disease), is the leading cause of mortality in many of the developing as well as the developed countries of the world. Cholesterol-enriched plaques in the heart’s blood vessels combined with inflammation lead to the lesion expansion, narrowing of blood vessels, reduced blood flow, and may subsequently cause lesion rupture and a heart attack. Even though several environmental risk factors have been established, such as high LDL-cholesterol, diabetes, and high blood pressure, the underlying genetic composition may substantially modify the disease risk; hence, genome composition and gene-environment interactions may be critical for disease progression. Ongoing scientific efforts have seen substantial advancements related to the fields of genetics and genomics, with the major breakthroughs yet to come. As genomics is the most rapidly advancing field in the life sciences, it is important to present a comprehensive overview of current efforts. Here, we present a summary of various genetic and genomics assays and approaches applied to coronary artery disease research.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Kuska B. Beer, Bethesda, and biology: how “genomics” came into being. J Natl Cancer Inst. 1998;90(2):93.

    Article  CAS  PubMed  Google Scholar 

  2. Consortium, C.A.D. A comprehensive 1000 genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47(10):1121–30. This study provides a largest GWAS meta-analysis for the coronary artery disease including 60,801 cases and 123,504 controls from 48 individual GWAS studies.

    Article  CAS  Google Scholar 

  3. Consortium CAD et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45(1):25–33.

    Google Scholar 

  4. Park DS et al. Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses. Bioinformatics. 2015;31(12):i181–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Gagliano SA et al. A Bayesian method to incorporate hundreds of functional characteristics with association evidence to improve variant prioritization. PLoS One. 2014;9(5):e98122.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Manolio TA et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zuk O et al. The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci U S A. 2012;109(4):1193–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Farh KK et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature. 2015;518(7539):337–43.

    Article  CAS  PubMed  Google Scholar 

  9. Kichaev G et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 2014;10(10):e1004722.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Trynka G et al. Disentangling the effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex-trait loci. Am J Hum Genet. 2015;97(1):139–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet. 2009;10(6):392–404.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wang IM et al. Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers. Mol Syst Biol. 2012;8:594.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Makinen VP et al. Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease. PLoS Genet. 2014;10(7):e1004502.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Huan T et al. A systems biology framework identifies molecular underpinnings of coronary heart disease. Arterioscler Thromb Vasc Biol. 2013;33(6):1427–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Miller CL, Pjanic M, Quertermous T. From locus association to mechanism of gene causality: the devil is in the details. Arterioscler Thromb Vasc Biol. 2015;35(10):2079–80. This editorial provides a good overview of methods to identify causal variation and causal genes, and reviews a recent paper in the field.

    Article  CAS  PubMed  Google Scholar 

  16. Kwon SM et al. Perspectives of integrative cancer genomics in next generation sequencing era. Genome Inform. 2012;10(2):69–73.

    Article  Google Scholar 

  17. Hawkins RD, Hon GC, Ren B. Next-generation genomics: an integrative approach. Nat Rev Genet. 2010;11(7):476–86.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Trapnell C et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28(5):511–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhao S et al. Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One. 2014;9(1):e78644.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Wang C et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat Biotechnol. 2014;32(9):926–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. McPherson A et al. deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data. PLoS Comput Biol. 2011;7(5):e1001138.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. van de Geijn B et al. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat Methods. 2015;12(11):1061–3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Mayba O et al. MBASED: allele-specific expression detection in cancer tissues and cell lines. Genome Biol. 2014;15(8):405.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Pirinen M et al. Assessing allele-specific expression across multiple tissues from RNA-seq read data. Bioinformatics. 2015;31(15):2497–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sen SK et al. Integrative DNA, RNA, and protein evidence connects TREML4 to coronary artery calcification. Am J Hum Genet. 2014;95(1):66–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Liu Y et al. RNA-Seq identifies novel myocardial gene expression signatures of heart failure. Genomics. 2015;105(2):83–9.

    Article  CAS  PubMed  Google Scholar 

  28. Ali SR et al. Developmental heterogeneity of cardiac fibroblasts does not predict pathological proliferation and activation. Circ Res. 2014;115(7):625–35.

    Article  CAS  PubMed  Google Scholar 

  29. Chu M et al. A novel role of CDX1 in embryonic epicardial development. PLoS One. 2014;9(7):e103271.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Nurnberg ST et al. Coronary artery disease associated transcription factor TCF21 regulates smooth muscle precursor cells that contribute to the fibrous cap. PLoS Genet. 2015;11(5):e1005155. This study using multiple functional and in vivo assays demonstrates that the TCF21 gene, one of the lead CAD GWAS hits, is indeed causal for CAD.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Bell RD et al. Identification and initial functional characterization of a human vascular cell-enriched long noncoding RNA. Arterioscler Thromb Vasc Biol. 2014;34(6):1249–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Gutierrez-Arcelus M et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. Elife. 2013;2:e00523.

    PubMed  PubMed Central  Google Scholar 

  33. Feinberg AP. Epigenomics reveals a functional genome anatomy and a new approach to common disease. Nat Biotechnol. 2010;28(10):1049–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Esteller M. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future. Oncogene. 2002;21(35):5427–40.

    Article  CAS  PubMed  Google Scholar 

  35. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Kim M et al. DNA methylation as a biomarker for cardiovascular disease risk. PLoS One. 2010;5(3):e9692.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Sharma P et al. Detection of altered global DNA methylation in coronary artery disease patients. DNA Cell Biol. 2008;27(7):357–65.

    Article  CAS  PubMed  Google Scholar 

  38. Dick KJ et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014;383(9933):1990–8.

    Article  CAS  PubMed  Google Scholar 

  39. Lamon-Fava S, Wilson PW, Schaefer EJ. Impact of body mass index on coronary heart disease risk factors in men and women. The Framingham Offspring Study. Arterioscler Thromb Vasc Biol. 1996;16(12):1509–15.

    Article  CAS  PubMed  Google Scholar 

  40. Putku M et al. CDH13 promoter SNPs with pleiotropic effect on cardiometabolic parameters represent methylation QTLs. Hum Genet. 2015;134(3):291–303.

    Article  CAS  PubMed  Google Scholar 

  41. Banovich NE et al. Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet. 2014;10(9):e1004663.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57–74.

    Article  CAS  Google Scholar 

  43. Kundaje A et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518(7539):317–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ward LD, Kellis M. Interpreting noncoding genetic variation in complex traits and human disease. Nat Biotechnol. 2012;30(11):1095–106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Boyle AP et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22(9):1790–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. del Rosario RC et al. Sensitive detection of chromatin-altering polymorphisms reveals autoimmune disease mechanisms. Nat Methods. 2015;12(5):458–64.

    Article  PubMed  CAS  Google Scholar 

  47. Hazelett DJ et al. Comprehensive functional annotation of 77 prostate cancer risk loci. PLoS Genet. 2014;10(1):e1004102.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Yao L et al. Functional annotation of colon cancer risk SNPs. Nat Commun. 2014;5:5114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Miller CL et al. Disease-related growth factor and embryonic signaling pathways modulate an enhancer of TCF21 expression at the 6q23.2 coronary heart disease locus. PLoS Genet. 2013;9(7):e1003652.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Reschen ME et al. Lipid-induced epigenomic changes in human macrophages identify a coronary artery disease-associated variant that regulates PPAP2B Expression through Altered C/EBP-beta binding. PLoS Genet. 2015;11(4):e1005061.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Creyghton MP et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc Natl Acad Sci U S A. 2010;107(50):21931–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Mikkelsen TS et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature. 2007;448(7153):553–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Gomez D et al. Detection of histone modifications at specific gene loci in single cells in histological sections. Nat Methods. 2013;10(2):171–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Crawford GE et al. Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome Res. 2006;16(1):123–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Schones DE et al. Dynamic regulation of nucleosome positioning in the human genome. Cell. 2008;132(5):887–98.

    Article  CAS  PubMed  Google Scholar 

  56. Hogan GJ, Lee CK, Lieb JD. Cell cycle-specified fluctuation of nucleosome occupancy at gene promoters. PLoS Genet. 2006;2(9):e158.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Buenrostro JD et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10(12):1213–8. This study demonstrates the application of ATAC-Seq, a method for probing open chomatin regions, that dramatically reduces the number of cells needed for the experiment while preserving the resolution.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Weintraub H, Groudine M. Chromosomal subunits in active genes have an altered conformation. Science. 1976;193(4256):848–56.

    Article  CAS  PubMed  Google Scholar 

  59. Enver T, Brewer AC, Patient RK. Simian virus 40-mediated cis induction of the Xenopus beta-globin DNase I hypersensitive site. Nature. 1985;318(6047):680–3.

    Article  CAS  PubMed  Google Scholar 

  60. Thurman RE et al. The accessible chromatin landscape of the human genome. Nature. 2012;489(7414):75–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Bell O et al. Determinants and dynamics of genome accessibility. Nat Rev Genet. 2011;12(8):554–64.

    Article  CAS  PubMed  Google Scholar 

  62. Degner JF et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature. 2012;482(7385):390–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Sazonova O et al. Characterization of TCF21 downstream target regions identifies a transcriptional network linking multiple independent coronary artery disease loci. PLoS Genet. 2015;11(5):e1005202.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Winter DR et al. DNase-seq predicts regions of rotational nucleosome stability across diverse human cell types. Genome Res. 2013;23(7):1118–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Schep AN et al. Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions. Genome Res. 2015;25(11):1757–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Dekker J et al. Capturing chromosome conformation. Science. 2002;295(5558):1306–11.

    Article  CAS  PubMed  Google Scholar 

  67. Harismendy O et al. 9p21 DNA variants associated with coronary artery disease impair interferon-gamma signalling response. Nature. 2011;470(7333):264–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Davison LJ et al. Long-range DNA looping and gene expression analyses identify DEXI as an autoimmune disease candidate gene. Hum Mol Genet. 2012;21(2):322–33.

    Article  CAS  PubMed  Google Scholar 

  69. Smemo S et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature. 2014;507(7492):371–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Belton JM et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods. 2012;58(3):268–76.

    Article  CAS  PubMed  Google Scholar 

  71. Fullwood MJ et al. An oestrogen-receptor-alpha-bound human chromatin interactome. Nature. 2009;462(7269):58–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Buenrostro JD et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523(7561):486–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Getz GS, Reardon CA. Animal models of atherosclerosis. Arterioscler Thromb Vasc Biol. 2012;32(5):1104–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Welcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–78.

    Article  CAS  Google Scholar 

  75. Helgadottir A et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. 2007;316(5830):1491–3.

    Article  CAS  PubMed  Google Scholar 

  76. McPherson R et al. A common allele on chromosome 9 associated with coronary heart disease. Science. 2007;316(5830):1488–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Samani NJ et al. Genomewide association analysis of coronary artery disease. N Engl J Med. 2007;357(5):443–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Helgadottir A et al. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet. 2008;40(2):217–24.

    Article  CAS  PubMed  Google Scholar 

  79. Congrains A et al. Genetic variants at the 9p21 locus contribute to atherosclerosis through modulation of ANRIL and CDKN2A/B. Atherosclerosis. 2012;220(2):449–55.

    Article  CAS  PubMed  Google Scholar 

  80. Cunnington MS, Keavney B. Genetic mechanisms mediating atherosclerosis susceptibility at the chromosome 9p21 locus. Curr Atheroscler Rep. 2011;13(3):193–201.

    Article  CAS  PubMed  Google Scholar 

  81. Folkersen L et al. Relationship between CAD risk genotype in the chromosome 9p21 locus and gene expression. Identification of eight new ANRIL splice variants. PLoS One. 2009;4(11):e7677.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Cunnington MS et al. Chromosome 9p21 SNPs associated with multiple disease phenotypes correlate with ANRIL expression. PLoS Genet. 2010;6(4):e1000899.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Liu Y et al. INK4/ARF transcript expression is associated with chromosome 9p21 variants linked to atherosclerosis. PLoS One. 2009;4(4):e5027.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Motterle A et al. Functional analyses of coronary artery disease associated variation on chromosome 9p21 in vascular smooth muscle cells. Hum Mol Genet. 2012;21(18):4021–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Holdt LM et al. Expression of Chr9p21 genes CDKN2B (p15(INK4b)), CDKN2A (p16(INK4a), p14(ARF)) and MTAP in human atherosclerotic plaque. Atherosclerosis. 2011;214(2):264–70.

    Article  CAS  PubMed  Google Scholar 

  86. Jarinova O et al. Functional analysis of the chromosome 9p21.3 coronary artery disease risk locus. Arterioscler Thromb Vasc Biol. 2009;29(10):1671–7.

    Article  CAS  PubMed  Google Scholar 

  87. Pilbrow AP et al. The chromosome 9p21.3 coronary heart disease risk allele is associated with altered gene expression in normal heart and vascular tissues. PLoS One. 2012;7(6):e39574.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Visel A et al. Targeted deletion of the 9p21 non-coding coronary artery disease risk interval in mice. Nature. 2010;464(7287):409–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Gonzalez-Navarro H et al. p19(ARF) deficiency reduces macrophage and vascular smooth muscle cell apoptosis and aggravates atherosclerosis. J Am Coll Cardiol. 2010;55(20):2258–68.

    Article  CAS  PubMed  Google Scholar 

  90. Gizard F et al. PPAR alpha inhibits vascular smooth muscle cell proliferation underlying intimal hyperplasia by inducing the tumor suppressor p16INK4a. J Clin Invest. 2005;115(11):3228–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Fuster JJ et al. Increased gene dosage of the Ink4/Arf locus does not attenuate atherosclerosis development in hypercholesterolaemic mice. Atherosclerosis. 2012;221(1):98–105.

    Article  CAS  PubMed  Google Scholar 

  92. Wouters K et al. Bone marrow p16INK4a-deficiency does not modulate obesity, glucose homeostasis or atherosclerosis development. PLoS One. 2012;7(3):e32440.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Kuo CL et al. Cdkn2a is an atherosclerosis modifier locus that regulates monocyte/macrophage proliferation. Arterioscler Thromb Vasc Biol. 2011;31(11):2483–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Leeper NJ et al. Loss of CDKN2B promotes p53-dependent smooth muscle cell apoptosis and aneurysm formation. Arterioscler Thromb Vasc Biol. 2013;33(1):e1–10.

    Article  CAS  PubMed  Google Scholar 

  95. Kojima Y et al. Cyclin-dependent kinase inhibitor 2B regulates efferocytosis and atherosclerosis. J Clin Invest. 2014;124(3):1083–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Kim JB et al. Effect of 9p21.3 coronary artery disease locus neighboring genes on atherosclerosis in mice. Circulation. 2012;126(15):1896–906.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Schunkert H et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet. 2011;43:333–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Miller CL et al. Coronary heart disease-associated variation in TCF21 disrupts a miR-224 binding site and miRNA-mediated regulation. PLoS Genet. 2014;10(3):e1004263.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Lu J, Richardson JA, Olson EN. Capsulin: a novel bHLH transcription factor expressed in epicardial progenitors and mesenchyme of visceral organs. Mech Dev. 1998;73(1):23–32.

    Article  CAS  PubMed  Google Scholar 

  100. Acharya A et al. Efficient inducible Cre-mediated recombination in Tcf21 cell lineages in the heart and kidney. Genesis. 2011;49(11):870–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Acharya A et al. The bHLH transcription factor Tcf21 is required for lineage-specific EMT of cardiac fibroblast progenitors. Development. 2012;139(12):2139–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Braitsch CM et al. Pod1/Tcf21 is regulated by retinoic acid signaling and inhibits differentiation of epicardium-derived cells into smooth muscle in the developing heart. Dev Biol. 2012;368(2):345–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Mali P, Esvelt KM, Church GM. Cas9 as a versatile tool for engineering biology. Nat Methods. 2013;10(10):957–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Gupta RM, Musunuru K. Expanding the genetic editing tool kit: ZFNs, TALENs, and CRISPR-Cas9. J Clin Invest. 2014;124(10):4154–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Miller CL et al. Dissecting the causal genetic mechanisms of coronary heart disease. Curr Atheroscler Rep. 2014;16(5):406.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Nagiec MM et al. Modulators of hepatic lipoprotein metabolism identified in a search for small-molecule inducers of tribbles pseudokinase 1 expression. PLoS One. 2015;10(3):e0120295.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Beaudoin M et al. Myocardial infarction-associated SNP at 6p24 interferes with MEF2 binding and associates with PHACTR1 expression levels in human coronary arteries. Arterioscler Thromb Vasc Biol. 2015;35(6):1472–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Ding Q et al. Permanent alteration of PCSK9 with in vivo CRISPR-Cas9 genome editing. Circ Res. 2014;115(5):488–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Bauer DE et al. An erythroid enhancer of BCL11A subject to genetic variation determines fetal hemoglobin level. Science. 2013;342(6155):253–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Canver MC et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature. 2015;527(7577):192–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

Robert Wirka receives grant support from the National Institute of Health (NIH) (F32HL129670-01). Clint L. Miller receives grant support from NIH (HL125912). Thomas Quertermous receives grant support from NIH (U01HL107388, HL109512, R21HL120757) and from the LeDucq Foundation.

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Correspondence to Thomas Quertermous.

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Milos Pjanic, Clint L. Miller, Robert Wirka, Juyong B. Kim, Daniel M. DiRenzo, and Thomas Quertermous declare that they have no conflict of interest.

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This article is part of the Topical Collection on Cardiovascular Genomics

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Pjanic, M., Miller, C.L., Wirka, R. et al. Genetics and Genomics of Coronary Artery Disease. Curr Cardiol Rep 18, 102 (2016). https://doi.org/10.1007/s11886-016-0777-y

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