A more useful approach for prevention, intervention, and treatment genetic studies are those that jointly test multiple genetic predictors that are grouped in the same biological pathways. The basic motivation for pathway-based analyses is the high likelihood that genetic associations with an outcome will co-occur in SNPs grouped within the same biological pathway. There are two broad classes of pathway analysis: those that test whether an excess of statistically significant results occur in SNPs in a pathway and those that test whether the top signals are more closely related biologically than by chance. Commonly used approaches include DAPPLE (Rossin et al., 2011), Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005), ALIGATOR (Holmans et al., 2009), MAGENTA (Segrè et al., 2010), FORGE (Pedroso et al., 2012) and INRICH (Lee, O’Dushlaine, Thomas, & Purcell, 2012). Although varying in methodology, these approaches rely on gene sets or pathways defined in specific databases (e.g., KEGG, Gene Onotology) to organize SNPs for excess statistical significance within a pathway, while accounting for potential confounders like gene size and LD pattern within those genes. In addition
