Han et al. (2013) used a network-based approach to identify potential pathways underlying AD risk, which they argued is more flexible in gene set definition, less biased, and better at detecting genes that work across multiple pathways than standard pathway analysis. They focused on human protein interaction networks (HPIN) to identify an HPIN network enriched for AD-associated genes using the 2,332 unrelated EA and 1,088 unrelated AA subjects from the SAGE and COGA datasets. Using the R package dmGWAS (Jia et al., 2011), they identified 429 HPIN modules significantly enriched for AD-associated genes in EAs, seven of which were also significantly enriched for AD-associated genes in AAs. Combining these seven modules yielded a set of 39 genes, which showed significant association with AD in EAs (P<0.0001) and AAs (P=0.0008), findings that were replicated in the European OZALC GWAS sample (P=0.006) and the EA and AA Yale-Penn samples (P=0.001 and 0.007, respectively). The subnetwork was not associated with other complex disorders, including bipolar disorder, major depressive disorder, and type 2 diabetes, supporting its specificity to AD.
