a review on molecular genetics of alzheimer’s disease

alzheimer’s disease (ad) is the most common cause of dementia caused by complicated interactions between genetic and environmental factors. molecular genetic research has provided valuable information regarding the genetic etiology of the disease. identifying the genetics of ad not only could shed light on disease pathogenesis, but it may also provide potential targets for effective treatment, screening, and prevention. genomic studies have demonstrated that more than 200 genes might be involved in ad pathogenesis. in contrast to early-onset ad (eoad), which is associated with mutations in amyloid precursor protein, presenilin 1 and presenilin 2 genes, the more common late onset form of ad (load) is assumed to be multifactorial with both genetic and environmental components. so far, the only confirmed genetic risk factor for load is apolipoprotein e (apoe) gene, which encodes a protein with crucial roles in cholesterol metabolism. the presence of the apoe ε4 allele increases the risk of ad in a dose-related manner and is associated with about 50% of late-onset cases. apoe ε4 contributes to ad pathogenesis by modulating the metabolism and aggregation of amyloid-beta (aβ) peptide and by directly regulating brain lipid metabolism and synaptic functions through apoe receptors. however, it is neither sufficient nor necessary to explain all occurrences of the disease. therefore, the search continues for the discovery of additional genetic factors associated with increased risk of ad and numerous genetic variants have been reported. although most genes conferring susceptibility to ad are related to biological mechanisms that may be associated with ad pathogenesis such as aβ deposition, oxidative stress and neuroinflammation, they still await replication in large representative populations. the fact that load is a polygenic/multifactorial complex disorder with the contribution of multiple genes with small effects suggests that to date, genetic variants associated with load are better suited as risk biomarkers than diagnostic tools.