Despite concise, biomarker-driven definitions of Alzheimer's Disease (AD), there remains no clear characterization of potential subtypes, defined by disparate biomarker presentation and varied cognitive impacts. The long-term goal of this proposal is to establish flexible characterization of AD that will not only distinguish pathological AD from normal aging, but also allow for interventions that will targeting subtypes and avoid treatment failure in the general AD population. The objective of this proposal is to develop a classification algorithm capable of identifying individuals with, or at risk for, AD using widespread genomic information, rather than specifically proposed AD correlated genes. The central hypothesis is that late onset AD (LOAD) is the result of widespread genomic and epigenomic drift, leading to dysregulation for which AD pathology is a response, and that these alterations can be used to develop diagnostic biomarkers. The rationale guiding this proposal is that it will significantly advance the field of Alzheimer's Disease research by establishing a simple and effective framework for categorizing patients into disease subgroups. This will allow us to overcome the obfuscation of treating all neuropathologically affected individuals similarly, and thus may facilitate the development of successful personalized/targeted treatment paradigms for AD. The central hypothesis will be tested through investigation of 2 specific aims: (1) Identification of subtypes of Alzheimer's disease through clustering of epigenome-wide patterns of methylation, and (2) Assessment of the degree of somatic mutational burden acquired by AD afflicted brain regions, beyond baseline brain mutability. The aims will be addressed using innovative approaches to the field of AD research by capitalizing on multi-omic and personalized medicine breakthroughs in the study of epigenetic aging and cancer biology. This proposal is significant because it addresses recent failures to treat and understand AD etiology and progression through the explicit and direct inclusion of biological heterogeneity in patient populations, which will advance a trend towards AD treatment using precision medicine and targeted treatment. Addressing biological heterogeneity in complex diseases can be challenging, as there are enormous sources of variability that can be difficult to address using traditional approaches applied to the study of AD. However, this project leverages methods of analysis and modeling that have already shown immense promise in other diseases and aspects of health. The overall expected outcome of this project is the demonstration that AD to is a complex- input disease, in which the patient's categorization of genomic and epigenomic drift trajectory is essential to their diagnosis. Outcomes of this project will have immediate and outstanding positive impact, by both suggesting modified characterization of research subjects in future and ongoing research for more targe...