Novel Computational Methods to Understand Epigenetic Variation and the Analysis of DNA Methylation Subtypes in Elderly Acute Myeloid Leukemia

A key challenge in epigenetics is to determine the biological significance of high-dimensional genome-scale epigenetic data. In this dissertation, I present two methods that address this challenge and build on those methods to analyze DNA methylation subtypes in elderly acute myeloid leukemia (AML). The first method MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for independent region sets with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for each region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of open chromatin and protein binding regions to be leveraged for novel insight into the regulatory state of DNA methylation datasets. The second method COCOA is a computational framework that
uses covariation of epigenetic signals across individuals and a database
of region sets to annotate epigenetic heterogeneity. COCOA is the first
such tool for DNA methylation data and can also analyze any epigenetic
signal with genomic coordinates. I demonstrate COCOA's utility by
analyzing DNA methylation, ATAC-seq, and multi-omic data in supervised
and unsupervised analyses, showing that COCOA provides new understanding
of inter-sample epigenetic variation. The MIRA and COCOA methods are available as Bioconductor R packages. Finally, I characterize DNA methylation subtypes in elderly AML. In AML, there is frequently epigenetic dysregulation, including dysregulation of DNA methylation. DNA methylation subtypes of AML can have different survival outcomes and mechanisms of dysregulation. In this study, I identify DNA methylation variation in regions related to stemness, differentiation, and hematopoietic cell type. Through a clustering analysis, I show that there are non-exclusive epigenetic subtypes depending on regions examined, with differing gene regulatory implications.