Developing computational methods for multi-omics integration to reveal mechanisms of healthy aging
Embargo Date
2026-08-27
OA Version
Citation
Abstract
This dissertation investigates complementary dimensions of biological aging through three integrated aims, each addressing a critical gap in our understanding of aging mechanisms and measurement. In Aim 1, we explored the role of somatic genomic instability by analyzing mosaic chromosomal alterations (mCAs) in two longevity-enriched cohorts: the New England Centenarian Study (NECS) and the Long-Life Family Study (LLFS). We found that while the prevalence of mCAs increases with age, it plateaus after 102 years, suggesting that high mCA burden may be incompatible with extreme longevity. Moreover, individuals with familial longevity and carriers of protective APOE alleles exhibited significantly fewer mCAs, reinforcing the hypothesis that genetic and familial factors may protect against somatic alterations.In Aim 2, we addressed the challenge of biological interpretability in aging clocks by developing a system-specific framework using pathway-based transcriptomic clocks. Instead of training a single global clock, we constructed 50 clocks based on Hallmark gene sets and identified distinct pathways — such as G2M checkpoint, inflammatory response, and hypoxia—as strong predictors of mortality. Replication in an independent cohort (ILO) and network analysis revealed a modular structure of transcriptomic aging, suggesting that aging is driven by interacting but semi-independent biological subsystems.
In Aim 3, we focus on multi-omic integration to develop models that explain some biological mechanisms of aging. To this end, we developed the Bayesian Community of Networks (BCoN), a novel framework that overcomes the limitations of traditional Bayesian network learning from incomplete data. BCoN leverages multiple imputation and ensemble structure learning to generate a community of networks that better capture uncertainty. This method enables more robust probabilistic inference, and we validated on both benchmark and synthetic omics datasets. We further implemented BCoN as part of the software pipeline, facilitating reproducible analysis and interactive network exploration.
Together, these three aims contribute to a more nuanced and mechanistic understanding of aging by linking somatic genomic alterations, system-specific biological clocks, and probabilistic modeling into a unified framework for studying human longevity.
Description
2025
License
Attribution 4.0 International