Genome-wide association studies (GWAS) are effective in identifying highly significant susceptibility loci for complex diseases, but potentially missing other variants with weaker effects. Pathway analysis offers a unique approach to collectively evaluate the effect of multiple genetic variants, and thus highlight additional candidate genes and biological processes. We applied pathway analysis to GWAS data of breast and bladder cancers conducted at the National Cancer Institute (NCI). Pathway data were retrieved from five publically available resources (KEGG, BioCarta, PID, HumanCyc, and Reactome). Two complementary pathway-based methods (gene-set enrichment analysis [GSEA], and adapted rank-truncated product [ARTP]), were used to assess the overrepresentation of association signals within each pathway. In addition, we used hierarchical clustering to study groups of pathways with overlapping genes. Seven pathways (Syndecan-1-mediated signaling, Signaling of hepatocyte growth factor receptor, Growth hormone signaling, Insulin signaling, Basal cell carcinoma, Eicosanoid metabolism, and Signaling events mediated by stem cell factor receptor) were highly enriched with association signals in the breast cancer GWAS (P<0.01). Furthermore, our clustering analysis revealed that pathways containing key components of the RAS/RAF/MAPK canonical signaling cascade, had an excess of association signals (P= 0.0051). In the second study, seven other pathways (Aromatic amine metabolism, NAD biosynthesis, NAD salvage, Clathrin derived vesicle budding, Lysosome vesicle, Retrograde neurotrophin signaling, and Mitotic metaphase/anaphase transition) belonging to three fundamental cellular processes (metabolic detoxification, mitosis, and clathrin-mediated vesicles) were significantly associated with bladder cancer (P<0.01). Notably, in both studies, we identified pathways with established relevance to these diseases thus, providing support to the validity of this approach as a complementary method to the primary single marker analysis of GWAS.
