New Statistical Strategies For Comprehensive Analysis Of Epigenomewide Methylation Data

Funding: R21AG060459, PI: Wang L

With the rising elderly population in the US, Alzheimer’s disease (AD) has become a major public health problem and one of the most financially costly diseases. 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.

Among epigenetic modifications, DNA methylation is the most widely studied. Alterations of DNA methylation levels are involved in many diseases including Alzheimer's Disease. 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.

We propose to 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. We will also apply the new method to brain samples of several Alzheimer disease datasets, to identify genes and pathways most likely controlled by epigenetic mechanism in AD.