Genome-wide association studies (GWAS) have dominated the field of human genetics for the past 10 years. This study design allows for an unbiased, dense exploration of the genome and provides researchers with a vast array of SNPs to look for association with their trait or disease of interest. GWAS has been referred to as finding needles in a haystack and while many of these "needles," or SNPs associating with disease, have been identified, there is still a great deal of heritability yet to be explained. The missing or phantom heritability is due, at least in part, to epistasis or gene-gene interactions, which have not been extensively explored in GWAS. Part of the challenge for epistasis analysis in GWAS is the sheer magnitude of the search and the computational complexity associated with it. An exhaustive search for epistasis models is not computationally feasible; thus, alternate approaches must be considered. In this chapter, these approaches will be reviewed briefly, and the incorporation of biological knowledge to guide this process will be further expanded upon. Real biological data examples where this approach has yielded successful identification of epistasis will also be provided. Epistasis has been known to be important since the early 1900s; however, its prevalence in mainstream research has been somewhat overshadowed by molecular technology advances. Due to the increasing evidence of epistasis in complex traits, it continues to emerge as a likely explanation for missing heritability.