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Advances in Genetics

Genome-Wide Association Studies for Atherosclerotic Vascular Disease and Its Risk Factors

Keyue Ding, PhD and Iftikhar J. Kullo, MD

From the Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minn.

Correspondence to Iftikhar J. Kullo, MD, Division of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW, Rochester, MN 55905. E-mail kullo.iftikhar@mayo.edu

Received August 26, 2008; accepted December 10, 2008.

Key Words: atherosclerosis • genetics • risk factors • genome-wide association study

An extract of the first 250 words of the full text is provided, because this article has no abstract.

	Introduction

 
Atherosclerotic vascular disease is a major health care burden, being the leading cause of morbidity and death worldwide.¹ A better understanding of the genetic basis of atherosclerotic vascular disease is urgently needed to provide new insights into the underlying pathophysiological mechanisms and facilitate development of novel diagnostic and therapeutic modalities. The advent of genome-wide association (GWA) studies (see supplementary Table 1 for glossary) is an important step in this direction, having led to the identification of susceptibility alleles for many of the common "complex" diseases. This is in contrast to genetic linkage studies, which had limited success in identifying genes for complex diseases or quantitative trait loci and candidate gene-based association studies, the results of which have been mostly irreproducible.

Editorial see p 1

GWA studies became possible with the completion of the Human Genome Project,² the discovery of millions of single-nucleotide polymorphisms (SNPs) in the human genome, and the International HapMap Project³ that characterized the patterns of linkage disequilibrium (LD) in the human genome, as well as the availability of high-throughput genotyping platforms and decreased costs of genotyping. In contrast to candidate gene studies in which genes are selected on the basis of known or suspected disease mechanisms, GWA studies permit a relatively comprehensive scan of the genome in an agnostic fashion, and thus have the potential to identify novel disease susceptibility or quantitative trait loci.

Although there are at least 7 million common SNPs (minor allele frequency >5%) in the human genome,⁴ neighboring SNPs are often strongly correlated . . . [Full Text of this Article]

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