Genetic Risk Architecture of Late-Onset Alzheimer's Disease: Mapping Single-Nucleotide Polymorphisms to Disease-Defining Molecular Pathways.

Late-onset Alzheimer's disease (LOAD) is increasingly recognized as a complex, multifactorial disorder driven by the convergence of genetic susceptibility and interconnected molecular pathways, rather than a single linear pathogenic cascade. In this review, we integrate evidence from genome-wide association studies, sequencing analyses, and experimental research to map disease-associated single-nucleotide polymorphisms (SNPs) onto key biological processes underlying LOAD pathogenesis. Converging data consistently implicate a limited number of core domains, including amyloid-β metabolism, tau pathology, neuroinflammation, lipid homeostasis, endosomal-lysosomal trafficking, mitochondrial dysfunction, and synaptic impairment. Importantly, genetic risk variants are not randomly distributed, but cluster within these pathways, exerting coordinated effects across multiple biological systems and supporting a network-based organization of disease. We further examine how these pathways interact at a systems level, highlighting key interfaces such as the amyloid-tau, lipid-immune, inflammation-proteostasis, and mitochondria-synaptic dysfunction axes. These interconnected processes form self-reinforcing feedback loops that amplify neurodegeneration and contribute to disease progression. From a translational perspective, this integrated framework provides a mechanistic basis for the limited efficacy of single-target therapies and supports the development of multi-target and pathway-oriented strategies, as well as precision medicine approaches based on genetic and biomarker integration. Collectively, this review supports a unified model of LOAD as a polygenic network disorder and underscores the importance of pathway convergence in understanding disease heterogeneity and guiding future therapeutic development.