Identifying genetically-supported targets for aging by integrating the clinical and multi-omics data

Project Summary

Aging refers to the process of a series of degenerative changes in the structure and function of organs, tissues, and cells that occur after the maturation period with increasing age. In this project, we will build a robust machine learning model of biological age utilizing clinical data. We define the age gap between biological age and chronological age as an aging digital phenotype. The methylation sites and metabolites associated with digital aging phenotype are identified and annotated to genes, respectively. These annotated genes could be considered as preliminary candidate targets for anti-aging. GWAS study of the digital aging phenotype is conducted to identify significant genome loci. By integrating public available eQTL and pQTL data, drug target Mendelian randomization is used to identify anti-aging targets with causal associations. Ultimately, candidate anti-aging targets that are collectively supported by genomics, epigenomics and metabolomics analysis will be refined.