Alzheimer's disease (AD) is the most common neurodegenerative disorder, affecting about 6% of people 65 years and older worldwide. Currently, there is no effective treatment or prevention for the disease. With the rising elderly population in the US, AD has become a major public health problem and one of the most financially costly diseases. Despite recent progress in identifying genetic variants associated with AD, the biological mechanism underlying AD remains elusive. The vast majority of AD cases are sporadic (idiopathic), with disease likely resulting from a complicated interplay of genetic and environmental factors such as smoking, poor diet, and lack of exercise. Epigenetic studies investigate the mechanisms that modify the expression levels of selected genes without changes to the underlying DNA sequence. The study of these epigenetic patterns hold excellent promise for detecting new regulatory mechanisms that may be susceptible to modification by environmental factors, which in turn increase the risk of disease. Among epigenetic modifications, DNA methylation is the most widely studied. Alterations of DNA methylation levels are involved in many diseases including Alzheimer's Disease. Although a number of tools have been developed to identify Differentially Methylated Regions (DMRs) in Epigenome-Wide Association Studies, most of them only focus on the regions that contain highly significantly differentially methylated CpGs in the genome, i.e. the ?tip of the iceberg?, but lack information on regions that contain CpGs with real but modest associations in the rest of the genome. We hypothesize that in the majority of complex diseases such as Alzheimer's Disease, methylation at multiple genomic regions are causally implicated in the development and progression of the disease, and some of these regions might be undetected using the conventional ?most significant hits? approaches.
In Aim 1, we will develop an efficient analytical pipeline for identifying biologically meaningful DMRs as well as providing comprehensive significance assessment to regions across the genome, which will streamline downstream integrative analysis.
In Aim 2, we will apply the new method to brain samples in two Alzheimer disease datasets, to identify genes and pathways most likely controlled by epigenetic mechanism in AD. Successful completion of Aim 1 will provide critical tools for integrative analysis of epigenome-wide association studies (EWAS) and will help shift the current analysis paradigm of EWAS, which focuses only on regions contain the most significant differentially methylated CpGs, and largely ignores information in the rest of the genome. Successful completion of Aim 2 will provide important insights into understanding the epigenetic programs underlying Alzheimer's Disease.
In this proposal, we combine statistical, bioinformatics and genomic approaches to develop new analytical tools that will help improve understanding of epigenetic programs underlying complex diseases. The successful completion of this project will significantly enhance our understanding of epigenetic programs underlying Alzheimer's Disease, and will lead to better diagnosis and treatment strategies.
