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Genome-based exome sequencing analysis identifies GYG1, DIS3L and DDRGK1 are associated with myocardial infarction in Koreans

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Abstract

Myocardial infarction (MI) is a complex disease caused by combination of genetic and environmental factors. Although genome-wide association studies (GWAS) identified more than 46 risk loci which are associated with coronary artery disease and MI, most of the genetic variability in MI still remains undefined. Here, we screened the susceptibility loci for MI using exome sequencing and validated candidate variants in replication sets. We identified that three genes (GYG1, DIS3L and DDRGK1) were associated with MI at the discovery and replication stages. Further research will be required to determine the functional association of these genes with MI risk, and these associations have to be confirmed in other ethnic populations.

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References

  • Altshuler D., Daly M. J. and Lander E. S. 2008 Genetic mapping in human disease. Science 322, 881–888.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Anderson J. L., Carlquist J. F., Horne B. D. and Hopkins P. N. 2007 Progress in unraveling the genetics of coronary artery disease and myocardial infarction. Curr. Atheroscler. Rep. 9, 179–186.

    Article  CAS  PubMed  Google Scholar 

  • Arad M., Maron B. J., Gorham J. M., Johnson Jr W. H., Saul J. P., Perez-Atayde A. R. et al. 2005 Glycogen storage diseases presenting as hypertrophic cardiomyopathy. N. Engl. J. Med. 352, 362–372.

  • Bansal V., Libiger O., Torkamani A. and Schork N. J. 2010 Statistical analysis strategies for association studies involving rare variants. Nat. Rev. Genet. 11, 773–785.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • CARDIoGRAMplusC4D Consortium, Deloukas P., Kanoni S., Willenborg C., Farrall M., Assimes T. L. et al. 2013 Large-scale association analysis identifies new risk loci for coronary artery disease. Nat. Genet. 45, 25–33.

  • Cho Y. S., Go M. J., Kim Y. J., Heo J. Y., Oh J. H., Ban H. J.et al. 2009 A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat. Genet. 41, 527–534.

    Article  CAS  PubMed  Google Scholar 

  • Do R., Stitziel N. O., Won H. H., Jorgensen A. B., Duga S., Angelica Merlini P. et al. 2015 Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518, 102–106.

    Article  CAS  PubMed  Google Scholar 

  • Kessler T., Erdmann J. and Schunkert H. 2013 Genetics of coronary artery disease and myocardial infarction–2013. Curr. Cardiol. Rep. 15, 368.

    Article  PubMed  Google Scholar 

  • Lee J. Y., Lee B. S., Shin D. J., Woo Park K., Shin Y. A., Joong Kim K. et al. 2013 A genome-wide association study of a coronary artery disease risk variant. J. Hum. Genet. 58, 120–126.

    Article  CAS  PubMed  Google Scholar 

  • Li B. and Leal S. M. 2008 Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Malmberg K., Bavenholm P. and Hamsten A. 1994 Clinical and biochemical factors associated with prognosis after myocardial infarction at a young age. JACC 24, 592–599.

    Article  CAS  PubMed  Google Scholar 

  • Manolio T. A., Collins F. S., Cox N. J., Goldstein D. B., Hindorff L. A., Hunter D. J. et al. 2009 Finding the missing heritability of complex diseases. Nature 461, 747–753.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • McKenna A., Hanna M., Banks E., Sivachenko A., Cibulskis K., Kernytsky A. et al. 2010 The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Moslemi A. R., Lindberg C., Nilsson J., Tajsharghi H., Andersson B. and Oldfors A. 2010 Glycogenin-1 deficiency and inactivated priming of glycogen synthesis. N. Engl. J. Med. 362, 1203–1210.

    Article  CAS  PubMed  Google Scholar 

  • Neale B. M., Rivas M. A., Voight B. F., Altshuler D., Devlin B., Orho-Melander M. et al. 2011 Testing for an unusual distribution of rare variants. PLoS Genet. 7, e1001322.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ochi H., Maekawa T., Abe H., Hayashida Y., Nakano R., Kubo M. et al. 2010 ITPA polymorphism affects ribavirin-induced anemia and outcomes of therapy–a genome-wide study of Japanese HCV virus patients. Gastroenterology 139, 1190–1197.

    Article  CAS  PubMed  Google Scholar 

  • Pan W. and Shen X. 2011 Adaptive tests for association analysis of rare variants. Genet. Epidemiol. 35, 381–388.

    Article  PubMed  PubMed Central  Google Scholar 

  • Peden J. F. and Farrall M. 2011 Thirty-five common variants for coronary artery disease: the fruits of much collaborative labour. Hum. Mol. Genet. 20, R198–R205.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schunkert H., Erdmann J. and Samani N. J. 2010 Genetics of myocardial infarction: a progress report. Eur. Heart J. 31, 918–925.

    Article  PubMed  Google Scholar 

  • Shah N., Kelly A. M., Cox N., Wong C. and Soon K. 2016 Myocardial infarction in the “Young”: risk factors, presentation, management and prognosis. Heart Lung Circ. 25, 955–960.

    Article  PubMed  Google Scholar 

  • Stitziel N. O., Stirrups K. E., Masca N. G. D., Erdmann J., Ferrario P. G., König I. R. et al. 2016 Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N. Engl. J. Med. 374, 1134–1144.

  • Tanaka Y., Kurosaki M., Nishida N., Sugiyama M., Matsuura K., Sakamoto N. et al. 2011 Genome-wide association study identified ITPA/DDRGK1 variants reflecting thrombocytopenia in pegylated interferon and ribavirin therapy for chronic hepatitis C. Hum. Mol. Genet. 20, 3507–3516.

    Article  CAS  PubMed  Google Scholar 

  • Yusuf S., Hawken S., Ounpuu S., Dans T., Avezum A., Lanas F. et al. 2004 Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 364, 937–952.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank all participants and investigators of the Korea Genome Epidemiology Study (KoGES). This work was supported by grants from the Korea Centers for Disease Control and Prevention (4845-301), an intramural grant from the Korea National Institute of Health (2012-N73003-00).

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Correspondence to Jeong Euy Park, Yangsoo Jang or Bok-Ghee Han.

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Corresponding editor: Kunal Ray

Ji-Young Lee and Sanghoon Moon contributed equally to this work.

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Lee, JY., Moon, S., Kim, Y.K. et al. Genome-based exome sequencing analysis identifies GYG1, DIS3L and DDRGK1 are associated with myocardial infarction in Koreans. J Genet 96, 1041–1046 (2017). https://doi.org/10.1007/s12041-017-0854-z

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