Single cEll guided polygeniC Risk prEdicTion of ASCVD

Introduction

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 battling the deadly consequences of ASCVD.

Limitations of Current Models

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 occurring along the disease path.

Genetic and Longitudinal Factors

Especially, genetic risk factors acting early in life and diverse longitudinal exposures accumulating during a person's lifetime lead to disturbances in gene regulatory networks, which are not considered in the current risk models.

Heterogeneity of ASCVD

In addition, the current models predict the combined risk of coronary and peripheral artery disease and ischemic stroke, despite mounting evidence of ASCVD heterogeneity.

Proposed Approach

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.

Work Packages

1. WP1: We will map the transcriptomic and epigenetic effects of risk variants using single-cell multiomics profiling of 500 human atherosclerotic tissue samples.

2. WP2: We will infer disease-associated genes, gene-gene interactions, and gene regulatory networks using an innovative CRISPR-based experimental approach.

3. WP3: We will utilize the generated information to develop novel functionally informed polygenic risk models, which are benchmarked against the conventional risk prediction models for predictive accuracy.

Conclusion

Ultimately, this information will provide us with a mechanistic understanding of the genetic basis of the disease while allowing the construction of new gold standard polygenic risk prediction models for the prevention of ASCVD events.