The pathophysiology of coronary artery disease (CAD) is affected by environmental and genetic factors and their interactions. Pathogenic mechanisms contributing to plaque development and subsequent CAD can be affected both negatively and positively by environmental exposures and genes. Environmental exposures can be either discrete (presence or absence) or continuous. Typically, CAD associated mutations and polymorphisms are found in genes encoding proteins that have key roles in intermediate pathways. Neither the environmental nor genetic lists shown here are comprehensive.

Putative gene-environment interactions. For even the simplest case, a dichotomous genetic risk factor (for example, carriers versus non-carriers) and a dichotomous environmental risk factor (for example, present versus absent), several types of interactions are possible. If both the gene and environment have main effects (odds ratios > 1), and thus could be identified independently, a synergistic interaction would result in an effect size larger than a simple additive effect. A second possibility is that an environmental factor could have no main effect but could modify the effect of a genetic factor that does have a main effect, creating a larger than expected combined effect. The inverse is also possible, in which a modifier gene with no main effect of its own increases the effect size of an environmental risk factor. A fourth possibility is that neither the gene nor the environment has a detectable main effect, and interaction is required to produce a measurable effect. A fifth possibility is for a gene and an environmental factor to have redundant effects, in which case the combination of factors produces no increase in risk. These types of interactions can be extended to include different effect sizes or gene-gene interactions.

Statistical power is directly proportional to the number of study participants and to the size of the effect under study. Factors to be included in power calculations of all genetic investigations include the minor allele frequency, the degree of linkage disequilibrium between the queried marker and the hypothetical disease locus, the genotype error rate and the genetic or phenotypic heterogeneity (Box 2). Fortunately, high-throughput genotyping platforms have a negligible genotype error rate [37]. Correction for multiple comparisons and the measurement error of environmental exposures also influence study power [1,2]. As a result of the greater accuracy of genotyping compared with the measurement or report of environmental exposures, there is theoretically more power to detect a gene-gene interaction than a gene-environment interaction for the same sized sample. Studies with inaccurate or imprecise measurement of phenotype or environmental exposure may require up to 20 times larger samples to detect an association signal above background noise [36]. However, the power advantage of gene-gene investigations resulting from their higher measurement accuracy is diminished by the need to correct for multiple comparisons and by the potentially increased complexity of interactions compared with gene-environment investigations.

Recent advances in cost-effective, array-based, high-throughput genotyping platforms have led to a flood of investigations of common single nucleotide polymorphisms (SNPs) in various diseases. Genome-wide association studies (GWASs) have successfully identified genetic determinants of CAD and its component risk factors [3-16]. For instance, several investigations found a region of chromosome 9p21 that was associated with CAD independently of traditional risk factors [3-6]. Furthermore, multiple genetic associations for T2DM [7,17] and body mass index (BMI) [18] have been discovered. However, most associated loci from GWASs have been reported for lipoprotein traits, including over 30 loci associated with plasma concentrations of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglyceride [7-16]. The success in finding genetic associations with lipoprotein phenotypes was due to methodological standardization (accuracy and precision) in trait measurement and to evaluation of large sample sizes, allowing detection of relatively subtle effects. Meta-analyses and collaborative consortia with large sample sizes have allowed GWASs to detect risk variants with low minor allele frequencies (< 5%) and small effect sizes (odds ratio of about 1.1 to 1.7) (Box 1); SNP association studies may have already reached their limit to detect clinically or biologically relevant loci with such effect sizes [8,11,13,17,18].