ABSTRACT
In this chapter, the authors analyze epigenomic data in the form of DNA methylation. They identify sites where differences in methylation can help to explain phenotypic differences between individuals. The authors demonstrate the use of the package on the Genome Expression Omnibus study of Alzheimer’s disease GSE80970. They show how to perform different analyses within a single pipeline and how to extract, evaluate and plot results. Methylation is radically different between tissues, and therefore when having cell-type mixture is important to adjust by the cellular mixture ratios in association analyses. Methylomic data that is collected from complex tissues contains a mixture of methylation signals from different cell types. When methlylomic data is obtained from heterogeneous sources for different individuals, methylome-wide association analyses may then reflect the differences between the sources rather than phenotypic differences. Epigenetic instability or the loss of epigenetic control of important genomic domains can lead to increased methylation variability in some diseases.
