  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 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 (MAE) of 3.207 and a Mean Squared Error (MSE) of 25.277,
slightly outperforming the current state of the art. 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 that can
incorporate all this information in order to close the gap in our understanding
of the true nature of aging.
