Late-onset Alzheimer’s disease (LOAD) is a devastating neurodegenerative disease with no effective treatment or cure. In addition to APOE, recent large genome-wide association studies have identified variation in over 20 loci that contribute to disease risk: CR1, BIN1, INPP5D, MEF2C, TREM2, CD2AP, HLA-DRB1/HLA-DRB5, EPHA1, NME8, ZCWPW1, CLU, PTK2B, PICALM, SORL1, CELF1, MS4A4/MS4A6E, SLC24A4/RIN3,FERMT2, CD33, ABCA7, CASS4. In addition, rare variants associated with LOAD have also been identified in APP, TREM2 and PLD3 genes. Previous research has identified inflammatory response, lipid metabolism and homeostasis, and endocytosis as the likely modes through which these gene products participate in Alzheimer’s disease. Despite the clustering of these genes across a few common pathways, many of their roles in disease pathogenesis have yet to be determined. In this review, we examine both general and postulated disease functions of these genes and consider a comprehensive view of their potential roles in LOAD risk.

Late-onset Alzheimer's disease (LOAD) is a multifactorial disorder with over twenty loci associated with disease risk. Given the number of genome-wide significant variants that fall outside of coding regions, it is possible that some of these variants alter some function of gene expression rather than tagging coding variants that alter protein structure and/or function. RegulomeDB is a database that annotates regulatory functions of genetic variants. In this study, we utilized RegulomeDB to investigate potential regulatory functions of lead single nucleotide polymorphisms (SNPs) identified in five genome-wide association studies (GWAS) of risk and age-at onset (AAO) of LOAD, as well as SNPs in LD (r²≥0.80) with the lead GWAS SNPs. Of a total 614 SNPs examined, 394 returned RegulomeDB scores of 1–6. Of those 394 variants, 34 showed strong evidence of regulatory function (RegulomeDB score <3), and only 3 of them were genome-wide significant SNPs (ZCWPW1/rs1476679, CLU/rs1532278 and ABCA7/rs3764650). This study further supports the assumption that some of the non-coding GWAS SNPs are true associations rather than tagged associations and demonstrates the application of RegulomeDB to GWAS data.