Genome-wide association studies (GWAS)s provide an unbiased means of exploring the landscape of complex genetic disease. As such, these studies have identified genetic variants that are robustly associated with a multitude of conditions. I hypothesize that these genetic variants serve as excellent tools for evaluation of the genetic interface between epidemiologically related conditions. Herein, I test the association between SNPs associated with either (i) plasma lipids, (ii) rheumatoid arthritis (RA) or (iii) diabetes mellitus (DM) and late-onset Alzheimer’s disease (AD) to identify shared genetic variants. Regarding the most significantly AD-associated variants, I have also attempted to elucidate their molecular function. Only cholesterol-associated SNPs, as a group, are significantly associated with AD. This association remains after excluding APOE SNPs and suggests that peripheral and or central cholesterol metabolism contribute to AD risk. The general lack of association between RA-associated SNPs and AD is also significant in that these data challenge the hypothesis that genetic variants that increase risk of RA confer protection against AD. Functional studies of variants exhibiting novel associations with AD reveal that the lipid-associated SNP rs3846662 modulates HMGCR exon 13 splicing differentially in different cell types. Although less clear, trends were also observed between the RA-associated rs2837960 and the expression of several BACE2 isoforms, and between the DM-associated rs7804356 and expression of a rare SKAP2 isoform, respectively. In conclusion, the overlap of lipid-, RA- or DM-associated SNPs with AD is modest but in several instances significant. Continued analysis of the interface between GWAS of separate conditions will likely facilitate novel associations missed by conventional GWAS. Furthermore, the identification of functional variants associated with multiple conditions should provide insight into novel mechanisms of disease and may lead to the identification of new therapeutic targets in an era of personalized genomic medicine.