A hands-on interactive workshop on DNA methylation-based biomarkers of aging
DNA methylation (DNAm)-based epigenetic clocks are machine-learning (ML)-trained predictors of biological age that have emerged as widely adopted biomarkers in aging research, population health, and clinical epidemiology. Despite their increased application, the analytical workflows and computational foundations underlying them remain largely inaccessible and inconsistently applied across the broader bioinformatics and computational biology community. This half-day, hands-on tutorial addresses that gap, providing researchers at any career stage with an integrated framework for epigenetic clock investigations, from biological foundations through clock computation, health outcome association analyses, and population-level application across ancestrally diverse cohorts. All hands-on exercises use open-source R/Bioconductor tools and require no prior epigenetics expertise. By the end, participants will be equipped to incorporate DNAm-derived biological aging metrics into their research pipelines, with analytical skills broadly transferable across omics disciplines.
By the end of this tutorial, participants will be able to:
- Navigate the generational arc of epigenetic clock development and apply this understanding to make principled choices among available tools.
- Execute the full analytical pipeline for epigenetic age computation, from DNAm beta value input through clock implementation to the calculation and visualization of epigenetic age difference and acceleration.
- Conduct and interpret association analyses by linking DNAm-derived biological aging metrics to health-relevant outcomes.
- Critically evaluate clock performance across ancestrally diverse populations.
- Engage critically with the ethical and translational frontier of the field.