Biernacka et al. (2013) used GWAS data from the EA subset of the SAGE sample (n=1,165 AD cases, 1,379 controls) to investigate genetic influences on AD risk. They grouped genes into sets based on all pathway annotations from KEGG, which generated 200 gene sets. They used a “two-step” association approach, wherein SNPs within a gene were first tested for association of that gene with AD, and then gene-level tests conducted to evaluate the association of a gene set with AD. They also used a “one-step” approach, in which all SNPs in a gene set were included in the analysis irrespective of individual gene-level associations with AD. Although neither method showed significant associations after correction for the number of pathways tested, several gene sets yielded suggestive evidence for association with AD. For the two-step analysis, the “synthesis and degradation of ketone bodies” gene set was the most significant (P=0.0009), while for the one-step analysis, the most significant gene set was the “neuroactivated ligand-receptor interaction” pathway (P=0.008). Interestingly, many of the strongly associated SNPs in this gene set were in glutamate receptor
