Genetic and polygenic factors in cardiovascular disease

Cardiovascular disease (CVD) is the leading cause of death and disease burden worldwide. The disease etiology of CVD is complex, involving the interaction of multiple factors such as genetics, epigenetics, environment, and lifestyles. Genetics plays an important role in the development of CVD. Rare mutation with substantial impact on the function of genes in the pathways critical to the disease phenotype is highly predictive of disease. However, common complex diseases like coronary artery disease are not only caused by single high-impact genetic variants but also by cumulative risk from the small-risk genetic variants across a whole genome. Polygenic risk score (PRS) combines this information across an entire genome into a single score that can be used to examine disease risk, potentially identifying high-risk people at the beginning of life with the largest window for prevention.

Although PRS recently gained prominence with the promise to revolutionize medicine in personalized prevention for complex diseases like CVD, there are still many challenges before it can be implemented as a robust clinical utility, including the poor generalizability across diverse cohorts and ancestries, considering the potential ethical and privacy challenges, calling for higher prediction accuracy by potential integrating risk estimates from a broader spectrum of factors like somatic mutation, telomere length, traditional and novel risk factors or even health metrics obtained from wearable smart devices. Therefore, this Research Collection aims to call for papers on the cutting-edge research surrounding genetic and polygenic factors and their interacting factors to realize the full and equitable potential of CVD disease risk assessments and clinical implementations.

We welcome original research, reviews, brief communication, and opinion articles covering, particularly, the following areas, but not limited to:

• Identifying novel risk factors associated with CVD, especially by multi-omics data or single-cell resolution data, and/or dissecting the causal role of known or novel risk factors.
• Further statistical or functional elucidation of novel/known genetic factors, which pathways involved in and the mechanisms contributing to the pathogenesis of cardiovascular disease.
• Developing methods for improving the portability and/or accuracy of genetic and polygenic factors for the diagnosis or prediction of CVD.
• Exploring the interactions of genetic and polygenic factors with other factors, such as but not limited to somatic mutation, telomere length, wearable smart device metrics, etc.
• Leveraging more advanced approaches like artificial intelligence (AI) models to integrate the risks from various factors, and innovative methods for improving the interpretability of disease risk prediction models, like converting relative disease risk to communicable absolute disease risk, explainable AI model for CVD risk prediction, etc.
• Endeavors for the innovative methods of CVD risk assessment and clinical implementation of CVD clinical decision support tools to facilitate personalized care.