DNA methylation is a crucial epigenetic marker used in various clocks to predict epigenetic age. However, many existing clocks fail to account for crucial information about CpG sites and their interrelationships, such as co-methylation patterns. We present a novel approach to represent methylation data as a graph, using methylation values and relevant information about CpG sites as nodes, and relationships like co-methylation, same gene, and same chromosome as edges. We then use a graph neural network (GNN) to predict age. Thus our model, GraphAge leverages both the structural and positional information for prediction as well as better interpretation. Although, we had to train in a constrained compute setting, GraphAge still showed competitive performance with a mean absolute error of 3.207 and a mean squared error of 25.277, substantially outperforming the existing models. Perhaps more importantly, we utilized GNN explainer for interpretation purposes and were able to unearth interesting insights (e.g. key CpG sites, pathways and their relationships through methylation regulated networks in the context of aging), which were not possible to "decode" without leveraging the unique capability of GraphAge to "encode" various structural relationships. GraphAge has the potential to consume and utilize all relevant information (if available) about an individual that relates to the complex process of aging. So, in that sense it is one of its kind and can be seen as the first benchmark for a multimodal model which can incorporate all these information in order to close the gap in our understanding of the true nature of aging.
Keywords: DNA methylation; co-methylation; computational epigenetics; epigenetic age; graph neural network (GNN).
© The Author(s) 2025. Published by Oxford University Press on behalf of National Academy of Sciences.