Adapting Epigenetic Clocks for Cell-Free DNA High-Throughput Sequencing Data

Cell-free DNA (cfDNA) methylation sequencing holds promise for developing epigenetic aging clocks. However, current clocks—primarily trained on array-based data—do not readily generalize to high-throughput sequencing (HTS) cfDNA profiles. Using datasets with technical replicates encompassing HTS data from both cfDNA and gDNA, alongside gDNA methylation array data, we systematically assessed factors influencing clock accuracy and reproducibility. We identified key strategies to overcome HTS-specific challenges: maintaining ≥10× mean target depth, applying elastic net regression with strong L2 regularization, and imputing unreliable beta-values. Transfer learning further enhanced accuracy robustly across multiple independent cohorts. Our findings demonstrate that array-derived epigenetic clocks can be effectively adapted to cfDNA sequencing data. This work offers critical methodological insights and practical guidelines, advancing the feasibility of minimally invasive aging assessment using cfDNA.