Codes within codes: How genetic variation influences disease through regional changes in methylation (360G-Wellcome-203746_Z_16_A)

Inter-individual DNA methylation variation has been linked to a variety of traits and behaviours, such as BMI, smoking, and cancer, through epigenome-wide association studies (EWAS). However, these studies often do not incorporate gene expression and examine changes at single CpG sites. Often a regional change in methylation is required to actuate a downstream effect. I aim to produce new methods, to collapse DNA methylation data using multiple ‘omics datasets and histone marks as a guide. I will explore how collapsing DNA methylation data can be informed by gene expression, histone modifications and further regulatory information. I will use current methods to collapse DNA methylation data and develop new methods using large scale reference datasets, such as the ENCODE, Blueprint and Roadmap epigenomics projects. I will then apply these methods genome-wide and within larger datasets. I will then explore the association between regional methylation and a range of traits and explore potentially causal associations using Mendelian randomisation. These studies will give insights into the interplay between DNA methylation and other molecular traits, lay the foundations for future work collapsing DNA methylation in a biologically meaningful context, and could guide future EWAS.