Skip to navigation Skip to main content Skip to footer

Approved Research

Using phenome-wide mendelian randomization and machine leaning to identify novel drug targets for aging and aging-related disease

Principal Investigator: Professor Feifei Cheng
Approved Research ID: 91850
Approval date: September 8th 2022

Lay summary

Understanding the factors that drive the aging process in humans is developing rapidly, but we still don't know how the knowledge gained in recent years can be best translated into better patient care. The goal of this research is to make progress on identifying the cause of aging, if it exists, by determining the phenotypes to predict aging and age-related diseases, and search the phenomic, genomic and proteomic biomarkers associated with a large fraction of age-related diseases. Integrating genomic, proteomic, and phenomic data through Mendelian randomization (MR) and machine learning facilitates discovery of drug targets and their side-effects of aging. Epidemiological studies to identify phenotypes that correlate with aging and age-related diseases, thus representing potential therapeutic targets. Furthermore, recent technological advances in high-throughput protein quantification have enabled genome-wide association studies (GWAS) to uncover genetic determinants for thousands of biomarkers simultaneously. Accordingly, we sought to discover new and effective drug targets for aging by integrating genetic and proteomic data through MR analysis and machine learning. Beyond drug target prioritization, MR can also be applied to predict target-mediated side-effects to reveal unanticipated adverse effects and opportunities for drug repurposing. The research will improve understanding of how disease is caused and how it progresses, inform the development of new drugs and further personalized treatments of aging. Any phenotypes that are identified in this project to be helpful to predict differential treatment responses and help researchers design prevention strategies.