Statistical Approaches to Addressing Data Science Challenges in Epigenetic Aging Research

Speaker: Peter Song, University of Michigan

Title: Statistical Approaches to Addressing Data Science Challenges in Epigenetic Aging Research

Abstract:  

DNA methylation (DNAm) has emerged as a key source of omics data for assessing biological age, offering a wealth of genetic markers that reflect cellular changes influenced by social and environmental factors. Epigenetic age can be estimated through predictive models known as epigenetic clocks, which rely on high-dimensional data analytics. However, current epigenetic age calculators face significant limitations as DNAm data collection technology rapidly advances. In this talk, I will present statistical approaches to tackle several critical challenges, including: (i) refining epigenetic clocks with higher-resolution DNAm data using convolutional neural networks, (ii) quantifying prediction uncertainty using conformal prediction techniques, (iii) leveraging transfer learning to shift from mean to quantile predictions, and (iv) identifying causal pathways through plausible mediators to explore mechanisms from exposure to aging for the screening of potential interventional targets. This presentation will integrate both statistical methodologies and algorithmic solutions, demonstrated through real-world data applications.