Bridging Sequence and Graph Structure for Epigenetic Age Prediction

TL;DR

This paper introduces a novel unified framework combining DNA sequence features and methylation graph structure for improved epigenetic age prediction, outperforming existing methods.

Contribution

It presents a new sequence-graph integration approach with a gated modulation mechanism, demonstrating superior accuracy and interpretability over prior models.

Findings

Achieves a test MAE of 3.149 years, 12.8% better than the best baseline.

Handcrafted sequence features outperform CNN-based encoding in this setting.

Identifies CpG density and adenine frequency as key age-related features.

Abstract

Epigenetic clocks based on DNA methylation have emerged as powerful tools for estimating biological age, with broad applications in aging research, age-related disease studies, and longevity science. Despite advances across machine learning approaches to epigenetic age prediction, spanning penalised linear regression, deep feedforward networks, residual architectures, and graph neural networks, no existing method jointly models co-methylation graph structure and site-specific DNA sequence context within a unified framework. We propose a unified sequence--graph integration framework for epigenetic age prediction that addresses this gap, integrating eight-dimensional DNA sequence statistical features through a lightweight gated modulation mechanism that adaptively scales each site's methylation signal according to its sequence-determined biological relevance prior to graph convolution. Evaluated on 3,707 blood methylation samples against a comprehensive set of baselines, our method achieves a test MAE of 3.149 years, a 12.8\% improvement over the strongest graph-based baseline. Biologically informed statistical features outperform CNN-based sequence encoding, demonstrating that handcrafted sequence features are more effective than end-to-end learned representations in this data regime. Post-hoc interpretability analysis identifies CpG density and local adenine frequency as features with age-dependent importance shifts, consistent with known mechanisms of age-related hypermethylation at CpG-dense promoter regions. Our code is at https://github.com/yaoli2022/graphage-seq.