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.
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 which 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 with each other (ie, in LD). LD is measured by the r2 statistic, which indicates the correlation of alleles at two sites, and ranges from 0 (no correlation) to 1 (perfect correlation). GWA studies take advantage of patterns of LD, such that genotyping 500k SNPs (in non-African samples) can achieve high coverage of ~ 90% of all known SNPs, despite directly testing less than one-tenth of the SNPs. The GWA design is based on the assumption that common variants with modest effects on a complex trait exist and explain substantial proportion of variation in the trait. GWA studies have 2 key advantages compared with the hitherto widely used family-based linkage approaches. GWA signals are localized to small (10~100 kb) regions of the chromosome, making fine mapping of the actual disease susceptibility/quantitative trait locus less-effort intensive. Additionally, these studies can identify alleles with modest effect size that are unlikely to be uncovered with linkage studies.
In a relatively short time, GWA studies have identified >130 susceptibility alleles for common ‘complex’ diseases (http://www.genome.gov/GWAstudies/)5 including atherosclerotic vascular disease (Table 1). In addition, this approach has also been used to identify genetic variants that influence quantitative traits related to vascular disease, such as body mass index,13,14 plasma lipid levels,10,15–17 and circulating markers of inflammation.18–21 At least 35 loci that influence atherosclerotic vascular disease and related intermediate traits have been identified. Many of these discoveries point to previously unknown etiologic pathways in disease pathogenesis, highlighting the potential of this research approach to provide new insights into pathophysiological mechanisms underlying atherosclerotic vascular disease.
Our review summarizes the methodological approaches to a GWA study, and provides an update on the results of GWA studies for atherosclerotic vascular diseases - coronary artery disease (CAD), peripheral arterial disease (PAD), and aneurysmal disease - and risk factors for atherosclerosis, including obesity, type 2 diabetes, hypertension, plasma lipid levels, and markers of inflammation. Finally, we discuss limitations of the GWA approach in identifying susceptibility genes for cardiovascular diseases. Recent reviews provide a genetic epidemiologic perspective on GWA studies5,22 and as well as an emerging consensus regarding the GWA approach and the challenges of such studies.23