TargetMI is an EIC Pathfinder project at the forefront of research on Cardio genomics. In this project a high throughput multi-omic approach for the rapid discovery of novel drug targets, biomarkers and risk algorithms will be developed, and applied here to atherosclerosis, myocardial infarction (MI) and their risk factors. Cardiovascular disease is a major cause of death and morbidity worldwide. The causes of MI are highly complex and involve genetic, lifestyle and environmental factors.
Whilst much research effort has been invested in attempting to decipher these factors, clinical applications of findings are disappointingly few. We will harness four -omic datasets (whole genome, transcriptomic, metabolomic and proteomic data) on 1000 highly phenotype samples of the Maltese Acute Myocardial Infarction (MAMI) Study. These were collected from cases, controls and relatives of cases (including 80 families) with meticulous attention to preanalytical variables.
Intermediate phenotypes associated with the risk of MI and its associated risk factors will be identified. Using a combination of approaches, we will identify variants which robustly influence these intermediate phenotypes. The genes thus identified are potential drug targets that influence the risk of MI via an intermediate phenotype and are applicable across all populations. They will be validated through various approaches including computational analysis, (using Mendelian randomisation and 10-year follow-up data), and functional work that includes using zebrafish as an animal model.
Machine learning algorithms will be used to analyse the multi-layered data to identify novel biomarkers and risk algorithms, including polygenic risk scores, for early risk prediction in the clinic. Quantitative targeted proteomic assays will be developed for further validation in other cohorts facilitating clinical use.
Besides the increase in knowledge on the molecular aetiology of MI, this powerful integrated strategy is expected to bring rapid clinical translation of unprecedented multi-omic data.
More information on the project is available on CORDIS.