Abstract
The results of several genome-wide association studies (GWASs) in the field of Alzheimer's disease (AD) have recently been published. Although these studies reported in detail on single-nucleotide polymorphisms (SNPs) and the neighboring genes with the strongest evidence of association with AD, little attention was paid to the rest of the genome. However, complementary statistical and bio-informatics approaches now enable the extraction of pertinent information from other SNPs and/or genes which are only nominally associated with the disease risk. Two different tools (the ALIGATOR and GenGen/KEGG software packages) were used to analyze a large GWAS dataset containing 2,032 AD cases and 5,328 controls. Convergent outputs from the two gene set enrichment approaches suggested an immune system dysfunction in AD. Furthermore, although these statistical approaches did not adopt a priori hypotheses concerning a biological function's putative role in the disease process, genes associated with AD risk were overrepresented in the "Alzheimer's disease" KEGG pathway. In conclusion, a systematic search for biological pathways using GWAS data set seems to comfort the primary causes already suspected but may specifically highlight the importance of the immune system in AD.