Most of GWAS to date have used a single-locus analysis strategy, in which each variant is tested individually for association with a specific phenotype, assuming independent effects, despite the axiomatic consideration that complex disorders are caused by the complex interplay between multiple genetic and environmental factors.25 A genetic variant that functions primarily through such a complex mechanism might be missed in a GWAS, if the variant is examined in isolation without allowing for its potential interactions with other unknown factors. Many SNPs have been shown to have small individual effect sizes, but their combined effect may be much larger.26 Unfortunately, these useful signals are embedded in a genome-wide sea of noise. Given the large multiple testing, very few signals exceed the genome-wide significance threshold and those that do not exceed this stringent statistical requirement are generally neglected. Exploration of interactions has been the exception rather than the rule in GWAS analyses, and only a few small-scale examples are available.27 A search for epistatic effects is computationally demanding, as several billions of SNP pairs exist for typical genotyping chips and gene-environment
