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The Genome-Wide Association Study—A New Era for Common Polygenic Disorders

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Abstract

This review covers the advances made in the last decade utilizing the high-density single-nucleotide microarrays to screen the entire human genome for genetic risk variants and outlines future strategies to draw deeper into the human genetic front. The sequence of the human genome provides the blueprint for life, while its variation provides the spice of life. Approximately 99.5% of the human genome DNA sequence is identical among humans with 0.5% of the genome sequence (15 million bps) accounting for all individual differences including susceptibility for disease. The new technology of the computerized chip array containing up to millions of SNPs as DNA markers makes possible genome-wide association studies to detect genetic predisposition to common polygenic disorders such as coronary artery disease (CAD). The sample sizes required for these studies are massive and large; worldwide consortiums such as CARDIoGRAM have been formed to accommodate this requirement. The progress has been remarkable with the identification of 9p21 followed by several others within the past 2 years. It is expected that most of the common variants (minor allele frequency, MAF >5%) will be identified for CAD within the next 2 to 3 years. Rare variants (MAF <5%) will require direct sequencing which will be delayed somewhat. The ultimate objective for the future is the sequencing and functional analysis of the causative polymorphisms. This will require a new approach involving several disciplines, namely, bioinformatics, high-throughput cell expression, and animal models.

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References

  1. Redon, R., Ishikawa, S., Fitch, L., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.

    Article  CAS  PubMed  Google Scholar 

  2. McCarroll, S. (2008). Extending genome-wide association studies to copy-number variation. Human Molecular Genetics, 17(R2), R135–R142.

    Article  CAS  PubMed  Google Scholar 

  3. The International HapMap Consortium. (2003). The international HapMap project. Science, 426, 789–796.

    Google Scholar 

  4. Tregouet, D. A., et al. (2009). Genome-wide haplotype association study identifies the SLC22A3–LPAL2–LPA gene cluster as a risk locus for Coronaroy Artery Disease. Nature Genetics, 41, 283–285.

    Article  CAS  PubMed  Google Scholar 

  5. Collins, F. S., & McKusick, V. A. (2001). Implications of the Human Genome Project for medical science. JAMA, 285(5), 540–544.

    Article  CAS  PubMed  Google Scholar 

  6. Pollex, R. L., & Hegele, R. A. (2007). Copy number variation in the human genome and its implications for cardiovascular disease. Circulation, 115(24), 3130–3138.

    Article  PubMed  Google Scholar 

  7. Roberts, R. (2007). New gains in understanding coronary artery disease, interview with Dr. Robert Roberts. Affymetrix Microarray Bulletin, 3(2), 1–4.

    Google Scholar 

  8. Marian, A. J., & Roberts, R. (2001). The molecular genetic basis for hypertrophic cardiomyopathy. Journal of Molecular and Cellular Cardiology, 33(4), 655–670.

    Article  CAS  PubMed  Google Scholar 

  9. Geisterfer-Lowrance, A. A., Christe, M., Conner, D. A., Ingwall, J. S., Schoen, F. J., Seidman, C. E., et al. (1996). A mouse model of familial hypertrophic cardiomyopathy. Science, 272(5262), 731–734.

    Article  CAS  PubMed  Google Scholar 

  10. Geisterfer-Lowrance, A., Kass, S., Tanigawa, G., et al. (1990). A molecular basis for familial hypertrophic cardiomyopathy: a beta cardiac myosium heavy chain missense mutation. Cell, 62, 999–1006.

    Article  CAS  PubMed  Google Scholar 

  11. Roberts, R. (2000). The genetics of hypertrophic cardiomyopathy. In C. I. Berul & J. Towbin (Eds.), The molecular genetics of cardiac electrophysiology. Norwell: Kluwer.

    Google Scholar 

  12. Keating, M., Dunn, C., Atkinson, D., Timothy, K., Vincent, G. M., & Leppert, M. (1991). Linkage of a cardiac arrhythmia, the long QT syndrome, and the Harvey ras-1 gene. Science, 252, 704–706.

    Article  CAS  PubMed  Google Scholar 

  13. Gollob, M. H., Green, M. S., Tang, A., Ahmad, F., Hassan, A., Gollob, T., et al. (2001). Identification of a gene responsible for familial Wolff–Parkinson-White syndrome. New England Journal of Medicine, 344(24), 1823–1864.

    Article  CAS  PubMed  Google Scholar 

  14. Roberts, R. (2006). Genomics and cardiac arrhythmias. Journal of the American College of Cardiology, 47(1), 9–21.

    Article  CAS  PubMed  Google Scholar 

  15. Brugada, R., & Roberts, R. (2001). Brugada syndrome: why are there multiple questions to a simple answer? Circulation, 104, 3017–3019.

    CAS  PubMed  Google Scholar 

  16. Gollob, M. H., Jones, D. L., Krahn, A. D., Danis, L., Gong, X. Q., Shao, Q., et al. (2006). Somatic mutations in the Connexin 40 Gene (GJA5) in atrial fibrillation. New England Journal of Medicine, 354(25), 2677–2688.

    Article  CAS  PubMed  Google Scholar 

  17. Brugada, R., Tapscott, T., Czernuszewicz, G. Z., Marian, A. J., Iglesias, A., Mont, L., et al. (1997). Identification of a genetic locus for familial atrial fibrillation. New England Journal of Medicine, 336, 905–911.

    Article  CAS  PubMed  Google Scholar 

  18. Roberts, R. (2006). Mechanisms of disease: genetic mechanisms of atrial fibrillation. Nature Clinical Practice, 3(4), 276–282.

    Article  CAS  PubMed  Google Scholar 

  19. Roberts, R., & Gollob, M. H. (2006). Molecular cardiology and genetics in the 21st century—a primer. Current Problems in Cardiology, 31(10), 637–701.

    Article  PubMed  Google Scholar 

  20. Wang, D. G., Fan, J. B., Siao, C. J., Berno, A., Young, P., Sapolsky, R., et al. (1998). Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science, 280(5366), 1077–1082.

    Article  CAS  PubMed  Google Scholar 

  21. Petretto, E., Liu, E. T., & Aitman, T. (2007). A gene harvest revealing the archeology and complexity of human disease. Nature Genetics, 39(11), 1299–1301.

    Article  CAS  PubMed  Google Scholar 

  22. CARDIoGRAM consortium. (2009). Design of the Coronary Artery Disease Genome-wide Replication and Meta Analysis (CARDIoGRAM) Consortium—a prospective meta-analysis of 13 genome-wide association studies. Circulation: Cardiovascular Genetics, in press.

  23. NCINHGRI Working Group on Replication in Association Studies, Chanock, S. J., Manolio, T. A., Boehke, M., et al. (2007). Replicating genotype–phenotype associations. Nature, 447(7145), 655–660.

    Article  CAS  PubMed  Google Scholar 

  24. Devlin, B., & Roeder, K. (2010). Genomic control for association studies. Biometrics, 55, 997–1004.

    Article  Google Scholar 

  25. Price, A. L., Patterson, N. J., Plenge, R. M., Weinblat, M. E., Shadick, N. A., & Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38, 904–909.

    Article  CAS  PubMed  Google Scholar 

  26. Freidlin, B., Zheng, G., Li, Z. H., & Gastwirth, J. L. (2010). Trend tests for case–control studies of genetic markers: power, sample size and robustness. Human Heredity, 53, 146–152.

    Article  Google Scholar 

  27. Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genome-wide studies. Proceedings of the National Academy of Sciences of the United States of America, 100, 9440–9445.

    Article  CAS  PubMed  Google Scholar 

  28. Wellcome Trust Case Consortium. (2007). Genome-wide association study of 14, 000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678.

    Article  Google Scholar 

  29. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.

    Article  Google Scholar 

  30. Chan, K., & Loh, W. (2004). LOTUS: an algorithm for building accurate and comprehensible logistc regression trees. Journal of Computational and Graphial Statistics, 13, 826–852.

    Article  Google Scholar 

  31. Marchini, J., Howie, S., Myers, G., McVean, G., & Donnelly, P. (2007). New multipoint method for genome-wide association studies by imputation of genotypes. Nature Genetics, 39, 906–916.

    Article  CAS  PubMed  Google Scholar 

  32. Li, Y., & Abecasis, G. R. (2010). Mach 1.0: rapid haplotype reconstruction and missing genotype inference. American Journal of Human Genetics, S79, 2290.

    Google Scholar 

  33. Lin, D. Y., & Zeng, D. (2006). Likelihood-based inference on haplotype effects in genetic association studies. Journal of the American Statistical Association, 101, 89–104.

    Article  CAS  Google Scholar 

  34. Hahn, L. W., Ritchie, M. D., & Moore, J. H. (2003). Multifactor dimensionality reduction software for detecting gene–gene and gene-environment interactions. Bioinformatics, 19(3), 376–382.

    Article  CAS  PubMed  Google Scholar 

  35. LaFramboise, T. (2009). Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances. Nucleic Acids Research, 37(13), 4181–4193.

    Article  CAS  PubMed  Google Scholar 

  36. Helgadottir, A., et al. (2007). A common variant on Chromosome 9p21 affects the risk of myocardial infarction. Science, 316(5830), 1491–1493.

    Article  CAS  PubMed  Google Scholar 

  37. McPherson, R., Pertsemlidis, A., Kavaslar, N., Stewart, A. F. R., Cohen, J. C., Roberts, R., et al. (2007). A common allele on Chromosome 9 associated with coronary heart disease. Science, 316, 1488–1491.

    Article  CAS  PubMed  Google Scholar 

  38. Dandona, S., Chen, L., Assogba, O., Belanger, M., Ewart, G., LaRose, R., et al. (2009). The transcription factor GATA-2 does not associate with angiographic coronary artery disease in the Ottawa Heart Genomics and Cleveland Clinic GeneBank Studies. Human Genetics, 127(1), 101–105.

    Article  PubMed  Google Scholar 

  39. Stewart, A. F. R., Dandona, S., Chen, L., Assogba, O., Belanger, M., Ewart, G., et al. (2009). Kinesin family member 6 variant Trp719Arg does not associate with angiographically defined coronary artery disease in the Ottawa Heart Genomics Study. Journal of the American College of Cardiology, 53(16), 1471–1472.

    Article  CAS  PubMed  Google Scholar 

  40. Childers, D. K., Kang, G., Liu, N., Gao, G., & Zhang, K. (2009). Application of imputation methods to the analysis of rheumatoid arthritis data in genome-wide association studies. BMC Proceedings, 3(Suppl 7), S24.

    Article  PubMed  Google Scholar 

  41. Hadley, D., & Strachan, D. P. (2009). Inference of disease associations with unmeasured genetic variants by combining results from genome-wide association studies with linkage disequilibrium patterns in a reference data set. BMC Proceedings, 3(Suppl 7), S55.

    Article  PubMed  Google Scholar 

  42. Huang, L., Wang, C., & Rosenberg, N. A. (2009). The relationship between imputation error and statistical power in genetic association studies in diverse populations. American Journal of Human Genetics, 85(5), 692–698.

    Article  CAS  PubMed  Google Scholar 

  43. Li, Y., Willer, C., & Sanna, S. (2009). Genotype imputation. Annual Review of Genomics and Human Genetics, 10, 387–406.

    Article  CAS  PubMed  Google Scholar 

  44. Wallace, C., Smyth, D. J., Maisuria-Armer, M., Walker, N. M., Tood, J. A., & Clayton, D. G. (2010). The imprinted DLK1-MEG3 gene region on Chromosome 14q32.2 alters susceptibility to type 1 diabetes. Nature Genetics, 42(1), 68–71.

    Article  CAS  PubMed  Google Scholar 

  45. Samani, N. J., Erdmann, J., Hall, A. S., Hengstenberg, C., Mangino, M., Mayer, B., et al. (2007). Genome-wide association analysis of coronary artery disease. New England Journal of Medicine, 357(5), 443–453.

    Article  CAS  PubMed  Google Scholar 

  46. Hinohara, K., et al. (2008). Replication of the association between a Chromosome 9p21 polymorphism and coronary artery disease in Japanese and Korean populations. Journal of Human Genetics, 53(4), 357–359.

    Article  PubMed  Google Scholar 

  47. Chen, Z., Qian, Q., Ma, G., Wang, J., Zhang, X., Feng, Y., et al. (2008). A common variant on Chromosome 9p21 affects the risk of early-onset coronary artery disease. Molecular Biology Reports, 36, 889–893.

    Article  PubMed  Google Scholar 

  48. Maitra, A., Dash, D., John, S., Sannappa, P. R., Das, A. P., Shanker, J., et al. (2009). A common variant in Chromosome 9p21 associated with coronary artery disease in Asian Indians. Journal of Genetics, 88(1), 113–118.

    Article  CAS  PubMed  Google Scholar 

  49. Helgadottir, A., et al. (2008). The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nature Genetics, 40(2), 217–224.

    Article  CAS  PubMed  Google Scholar 

  50. Dandona, S., Stewart, A. F. R., Chen, L., Williams, K., So, D., O’Brien, E., et al. (2010). Gene Dosage of the common variant 9p21 predicts severity of coronary artery disease. JACC, in press.

  51. Jarinova, O., Stewart, A. F. R., Roberts, R., Wells, G., Lau, P., Naing, T., et al. (2009). Functional analysis of the Chromosome 9p21.3 coronary artery disease risk locus. Arteriosclerosis, Thrombosis, and Vascular Biology, 29(10), 1671–1677.

    Article  CAS  PubMed  Google Scholar 

  52. Ye, S., Willeit, J., Viao, Q., et al. (2010). Single nucleotide polymorphism on Chromosome 9p21 and endothelial progenitor cells in a general population cohort. Atherosclerosis, 208(2), 451–455.

    Google Scholar 

  53. Paynter, N. P., et al. (2009). Cardiovascular disease risk prediction with and without knowledge of genetic variation at Chromosome 9p21.3: the Women’s Genome Health Study. Annals of Internal Medicine, 150(2), 65–72.

    PubMed  Google Scholar 

  54. Newton-Cheh, C., Johnson, T., Gateva, V., Tobin, M. D., Bochud, M., Coin, L., et al. (2009). Genome-wide association study identifies eight loci associated with blood pressure. Nature Genetics, 41(6), 666–676.

    Article  CAS  Google Scholar 

  55. Erdmann, J., et al. (2009). Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nature Genetics, 41(3), 334–341.

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  57. Cohen, J. C., Boerwinkle, E., Mosley, T. H., Jr., & Hobbs, H. (2006). Sequence variations in PCSK9, low LDL and protection against coronary heart disease. New England Journal of Medicine, 354(12), 1264–1272.

    Article  CAS  PubMed  Google Scholar 

  58. Erdmann, J., Groszhennig, A., Braund, P. S., Konig, I. R., Hengstenberg, C., Hall, A. S., et al. (2009). New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nature Genetics, 41(3), 280–282.

    Article  CAS  PubMed  Google Scholar 

  59. Tregouet, D. A., Konig, I. R., Erdmann, J., Munteanu, A., Braund, P. S., Hall, A. S., et al. (2009). Genome-wide haplotype association study identifies the SLC22A3-LPAL2-LPA gene cluster as a risk locus for coronary artery disease. Nature Genetics, 41(3), 283–285.

    Article  CAS  PubMed  Google Scholar 

  60. Clarke, R., Peden, J. F., Hopewell, J. C., Kyriakou, T., Goel, A., Heath, S. C., et al. (2009). Genetic variants associated with Lp(a) lipoprotein level and coronary disease. New England Journal of Medicine, 361(26), 2518–2528.

    Article  CAS  PubMed  Google Scholar 

  61. Smyth, D. J., Plagnol, V., Walker, N. M., Cooper, J. D., Downes, K., Yang, J. H. M., et al. (2008). Shared and distinct genetic variants in type 1 diabetes and celiac disease. New England Journal of Medicine, 359(26), 2767–2777.

    Article  CAS  PubMed  Google Scholar 

  62. Gudbjartsson, D. F., Bjornsdottir, U. S., Halapi, E., Helgadottir, A., Sulem, P., Jonsdottir, G. M., et al. (2009). Sequence variants affecting eosinophil numbers associate with asthma and myocardial infarction. Nature Genetics, 41(3), 342–347.

    Article  CAS  PubMed  Google Scholar 

  63. Soranzo, N., Spector, T. D., Mangino, M., Kuhnel, B., Rendon, A., Teumer, A., et al. (2009). A genome-wide meta analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nature Genetics, 41(11), 1182–1192.

    Article  CAS  PubMed  Google Scholar 

  64. Dandona, S., & Roberts, R. (2009). Creating a genetic risk score for coronary artery disease. Current Atherosclerosis Reports, 11(3), 175–181.

    Article  PubMed  Google Scholar 

  65. Dandona, S., & Roberts, R. (2010). Creating a risk score for coronary artery disease. Current Atherosclerosis Reports, 11, 175–181.

    Article  Google Scholar 

  66. Levy, D., Ehret, G. B., Rice, K., et al. (2009). Genome-wide association study of blood pressure and hypertension. Nature Genetics, 41(6), 677–687.

    Article  CAS  Google Scholar 

  67. Service, R. F. (2010). Gene sequencing. the race for the $1000 genome. Science, 311(5767), 1544–1546.

    Article  Google Scholar 

  68. Patel, R., Nagueh, S. F., Tsybouleva, N., Abdellatif, M., Lutucuta, S., Kopelen, H., et al. (2001). Simvastatin induces regression of cardiac hypertrophy and fibrosis and improves cardiac function in a transgenic rabbit model of human hypertrophic cardiomyopathy. Circulation, 104, r27–r34.

    Article  Google Scholar 

  69. Chalfie, M., Tu, Y., Euskirchen, G., Ward, W. W., & Prasher, D. C. (1994). Green fluorescent protein as a marker for gene expression. Science, 263, 802–805.

    Article  CAS  PubMed  Google Scholar 

  70. Fang, C., Frontiera, R. R., Tran, R., & Mathies, R. A. (2009). Mapping GFP structure evolution during proton transfer with femtosecond Raman spectroscopy. Nature, 462, 200–204.

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada

    George A. Wells

  2. Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada

    George A. Wells & Li Chen

  3. The Ruddy Canadian Cardiovascular Genetics Centre, Ottawa, Ontario, Canada

    Robert Roberts, Alexandre F. R. Stewart & Sonny Dandona

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  2. George A. Wells
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Correspondence to Robert Roberts.

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Roberts, R., Wells, G.A., Stewart, A.F.R. et al. The Genome-Wide Association Study—A New Era for Common Polygenic Disorders. J. of Cardiovasc. Trans. Res. 3, 173–182 (2010). https://doi.org/10.1007/s12265-010-9178-6

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