GENOMICS

Our genomics pipeline integrates experimental genomics with machine learning to prioritize the identification of genetic variants that contribute to age-related diseases. This approach focuses on alterations in telomere dynamics and senescence pathways, enabling the discovery of key drivers in the aging process.

We are working to extract genomic structural equation modeling data from summary-level genome-wide associations on healthspan to identify novel genetic variants that broadly impact healthy aging processes, with a focus on our pipeline of diseases. Our ultimate goal is to integrate our multi-omics datasets to draw comprehensive conclusions about the underlying biological mechanisms of age-related diseases.