SECRET

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death worldwide. Aside from asymptomatic manifestations, the first sign of clinically significant ASCVD is often a severe clinical event, such as stroke or myocardial infarction. Thus, identification of people at high risk is central to battle the deadly consequences of ASCVD. The usefulness of current risk prediction models such as SCORE2 is unsatisfactory most likely since the score is built on prevalent risk factors rather than mechanistic changes occuring along the disease path. Especially, genetic risk factors acting already early in life and diverse longitudinal exposures accumulating during the lifetime of a person, lead to disturbance of gene regulatory networks which are not considered in the current risk models. In addition, the current models predict the combined risk of coronary and peripheral artery disease and ischemic stroke despite mounting evidence of ASCVD heterogeneity. To capture these missing aspects of ASCVD risk, we leverage the predictive ability of genetic variation provided to us by the world’s largest meta-analysis of GWAS for ASCVD and introduce a new disease mechanism-based stratification. In work package (WP) 1, we will map the transcriptomic and epigenetic effects of risk variants using single cell multiomics profiling of 500 human atherosclerotic tissue samples. In WP2, we infer disease associated genes, gene-gene interactions and gene regulatory networks using an innovative CRISPR-based experimental approach. In WP3, we will make use of the generated information to develop novel functionally informed polygenic risk models which are benchmarked against the conventional risk prediction models for predictive accuracy. Ultimately, this information will provide us with a mechanistic understanding of the genetic basis of disease while allowing construction of new gold standard polygenic risk prediction models for prevention of ASCVD events.

2024

2029