Applications of Three Genomic Approaches in Alzheimer's Disease Research

Dementia is a group of diseases that affect brain function and lead to a decline in cognitive abilities. Some types involve the gradual loss of function in brain neurons, such as Alzheimer's disease, which is characterized by progressive memory loss and cognitive dysfunction and is one of the most common neurodegenerative dementias. The growing prevalence, particularly in aging populations, underscores the need for enhanced diagnostic and therapeutic stproportiongies. The application of cutting-edge genomic technologies, encompassing genome-wide association studies (GWAS) to identify genetic variants associated with Alzheimer's disease (AD), whole exome sequencing (WES) to explore protein-coding regions for functional mutations, and single-cell RNA sequencing (scRNA-seq) to dissect cellular heterogeneity at a granular level, has collectively enhanced our insight into the intricate genetic architecture of AD, shedding light on the complex interplay of genes and biological pathways implicated in the disease's pathogenesis. GWAS has identified several risk loci, including the APOE ε4 allele, which plays a crucial role in lipid metabolism and amyloid-β clearance. However, GWAS primarily detects common variants, limiting its ability to explain rare genetic mutations. WES, which focuses on protein-coding regions, has uncovered rare variants in genes such as PSEN1 and PSEN2, which are linked to early-onset familial AD. Despite its strengths, WES overlooks non-coding regions critical for gene regulation. scRNA-seq provides high-resolution insights into cellular heterogeneity, identifying cell subsets and molecular mechanisms involved in AD. This technique has revealed novel therapeutic targets by analyzing gene expression at the single-cell level. However, it faces challenges in data processing and cost. Integrating these approaches offers a more comprehensive view of AD’s pathology, enhancing early diagnosis and personalized treatment. As genomic technologies evolve, they hold great promise for improving our understanding and management of AD.