Of course, GWAS only interrogate a portion of the types of variation that could underlie disease risk. Analysis of GWAS data has been mainly focused on single nucleotide polymorphisms (SNPs), but there are other types of genetic variation, such as structural variants, that have not been studied in depth. However, recent studies of common (MAF > 5%) copy number variants (CNVs) have shown that they seem unlikely to account for a substantial proportion of the ‘missing heritability’ (28). Similarly, the analysis of gene–environment and gene–gene interactions (epistasis) might improve the fraction of heritability explained by loci documented thus far. Several epistatic interactions have been indentified in humans [e.g. between the RET protooncogene and endothelin receptor type B genes in Hirschsprung disease (29), the interleukin 4 receptor variants and interleukin 13 promoter variants in asthma (30) and the alpha- and beta-adrenergic receptors in congestive heart failure (31)], although they have not been replicated. However, this phenomenon has not been thoroughly explored through large-scale analysis of genome-wide SNP interactions, first due to the fact that current sample sizes are underpowered to detect
