Genome-Wide Association Study of Coronary Artery DiseaseReport as inadecuate




Genome-Wide Association Study of Coronary Artery Disease - Download this document for free, or read online. Document in PDF available to download.

International Journal of HypertensionVolume 2010 2010, Article ID 790539, 8 pages

Review Article

Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8655, Japan

Division of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Japan

Department of Translational Research for Healthcare and Clinical Science, Graduate School of Medicine, The University of Tokyo, Japan

Received 31 May 2010; Accepted 25 June 2010

Academic Editor: Tomohiro Katsuya

Copyright © 2010 Naomi Ogawa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Coronary artery disease CAD is a multifactorial disease with environmental and genetic determinants. The genetic determinants of CAD have previously been explored by the candidate gene approach. Recently, the data from the International HapMap Project and the development of dense genotyping chips have enabled us to perform genome-wide association studies GWAS on a large number of subjects without bias towards any particular candidate genes. In 2007, three chip-based GWAS simultaneously revealed the significant association between common variants on chromosome 9p21 and CAD. This association was replicated among other ethnic groups and also in a meta-analysis. Further investigations have detected several other candidate loci associated with CAD. The chip-based GWAS approach has identified novel and unbiased genetic determinants of CAD and these insights provide the important direction to better understand the pathogenesis of CAD and to develop new and improved preventive measures and treatments for CAD.





Author: Naomi Ogawa, Yasushi Imai, Hiroyuki Morita, and Ryozo Nagai

Source: https://www.hindawi.com/



DOWNLOAD PDF




Related documents