GraphAge: Unleashing the power of Graph Neural Network to Decode Epigenetic Aging

TL;DR

GraphAge introduces a graph neural network approach to predict epigenetic age from DNA methylation data, capturing complex CpG site relationships for improved accuracy and interpretability.

Contribution

It presents the first graph-based model for epigenetic age prediction that integrates structural CpG relationships, outperforming existing methods.

Findings

Achieved MAE of 3.207, MSE of 25.277, slightly better than state-of-the-art.

Used GNN explainer to identify key CpG sites and pathways involved in aging.

Demonstrated the potential of multimodal, graph-based models in epigenetic research.

Abstract

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.