A framework for genome-wide characterization of DNA methylation and variant effect prediction

DNA methylation plays a critical role in many essential biological processes, including transcriptional regulation, genomic imprinting, chromosomal maintenance, genomic stability and development, and has phenotypic and disease impacts. However, genome-wide characterization of methylation patterns across cell types and tissues remains a major challenge. Here, to address this, we present a framework that enables systematic genome-wide analysis of methylation and sequence-based prediction of context-specific methylation effects of variants. Our data-driven discovery of methylation classes reveals context-dependent roles for methylation across tissues, cell types and regions of the genome. The framework also includes Wreath, a deep learning-based sequence model specifically designed to accurately predict the context-dependent impact of any genetic variant on methylation. These developments leverage a compendium that harmonizes disparate methylation profiles from disparate studies including over 90 tissues and cell types. Using Wreath, we systematically characterized the impact of noncoding pathogenic variants on tissue-specific methylation, generating mechanistic hypotheses for individual regulatory variants. We make the full framework, including the compendium, the methylation classes and the model, available for use by the community.