Aging is the primary risk factor for chronic diseases and mortality. Accelerated aging, where biological age exceeds chronological age, predicts multi-morbidity and premature death. While GWAS have identified genetic variants linked to aging, they explain limited variation. The exposome (environmental, lifestyle, and socioeconomic factors) and proteome are critical yet underexplored layers influencing aging. Proteins translate genetic and environmental inputs into physiological changes, but their integrated role remains unclear. UK Biobank’s multi-omic data provides a unique opportunity to investigate this.
This project aims to define the biological architecture of aging by integrating genomic, exposomic, and proteomic data. Objectives include: (1) Performing GWAS and TWAS for aging phenotypes (e.g., phenotypic age, frailty, mortality risk, and imaging-derived organ-specific age); (2) Conducting ExWAS to identify exposures associated with accelerated aging; (3) Leveraging proteomics to discover pQTLs, biomarkers, and proteomic mediators of genetic and environmental effects.
Methods include Mendelian randomization to infer causality, colocalization to pinpoint shared genetic mechanisms, mediation analysis to quantify pathways, and machine learning to integrate multi-omic data.
Expected outcomes: novel causal genetic variants and proteins; ranked modifiable risk factors; elucidated pathways linking exposures to proteomic changes and aging; validated multi-omic biomarkers for early risk detection; and quantified relationships between accelerated aging, chronic diseases, and mortality. This work will enable targeted interventions to promote healthy aging.