Alzheimer’s disease (AD) is the most common neurodegenerative disease in the world. It is classified as familial and sporadic. The dominant familial or autosomal presentation represents 1–5% of the total number of cases. It is categorized as early onset (EOAD; <65 years of age) and presents genetic mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or the Amyloid precursor protein (APP). Sporadic AD represents 95% of the cases and is categorized as late-onset (LOAD), occurring in patients older than 65 years of age. Several risk factors have been identified in sporadic AD; aging is the main one. Nonetheless, multiple genes have been associated with the different neuropathological events involved in LOAD, such as the pathological processing of Amyloid beta (Aβ) peptide and Tau protein, as well as synaptic and mitochondrial dysfunctions, neurovascular alterations, oxidative stress, and neuroinflammation, among others. Interestingly, using genome-wide association study (GWAS) technology, many polymorphisms associated with LOAD have been identified. This review aims to analyze the new genetic findings that are closely related to the pathophysiology of AD. Likewise, it analyzes the multiple mutations identified to date through GWAS that are associated with a high or low risk of developing this neurodegeneration. Understanding genetic variability will allow for the identification of early biomarkers and opportune therapeutic targets for AD.
Alzheimer’s disease (AD) is a neurodegenerative disease and represents the most common form of dementia (60–80% of all cases of dementia) . At present, an estimated 50 million people worldwide suffer from some form of dementia; however, as a result of the increase in life expectancy rates, it is expected that by 2050, 139 million people worldwide will suffer from some type of dementia [2,3], which will cause major socioeconomic and health system impacts .
AD is characterized by chronic and acquired memory impairment and cognitive deficits in domains, such as language, spatio-temporal orientation, and executive capacity, as well as behavior alterations, all of which lead to progressive loss of personal autonomy . Histopathologically, AD is characterized by two pathognomonic hallmarks (Figure 1) : (1) the intracellular deposition of abnormally phosphorylated Tau protein that promotes the formation of neurofibrillary tangles (NFTs) in the cerebral cortex and subcortical gray matter (Figure 1a); and (2) extracellular aggregates of Amyloid-beta peptide (Aβ) fibrils in the form of neuritic plaques (NPs; Figure 1b). In this context, it has been postulated that endogenous “damage signals”, such as Aβ oligomers, could cause the activation of microglial cells with the consequent release of proinflammatory cytokines, which would trigger signaling cascades in neurons causing hyperphosphorylation and the aggregation of Tau protein. This protein is released when neurons die, triggering microglial cell activation and, therefore, becomes a cyclic pathological process that culminates in neurodegeneration [7,8]. Therefore, both NPs and NFTs are involved in several neuronal processes and ultimately trigger neuronal death [9,10], synaptic alteration, oxidative stress, mitochondrial disturbance, neuroinflammation, alterations in the permeability of the blood–brain barrier (BBB), and neu
Impact of GWAS on Understanding Alzheimer’s Disease
rom 2005 to the present, multiple genetic studies have tracked most of the genes that conform to the human genome. These genetic studies are known as Genome-wide Association Studies (GWAS) and their objective is to associate certain genes with multiple pathologies or disorders [145,146]. Until 2007, only mutations in the APOE-ε4 allele were reliably associated with increased susceptibility to LOAD. Nonetheless, to date, multiple analyses have been performed with GWAS technology, demonstrating many possible genes associated with LOAD (Table 2). Targeted genetic approaches and next-generation sequencing studies have also identified several low-frequency genes that are associated with a relatively high risk of developing AD, therefore providing insight into the pathogenesis . GWAS have associated more than 40 risk alleles with AD, identifying variants that trigger neurodegeneration, such as lipid metabolism, inflammation, innate immunity, Aβ production and clearance, and endosomal vesicle recycling (as shown in Table 1 and Figure 2) [147,148]. In particular, GWAS have also allowed us to identify those genes related to the development of both EOAD and LOAD