Periodic Reporting for period 4 - EnDeCAD (Enhancers Decoding the Mechanisms Underlying CAD Risk)
Berichtszeitraum: 2023-07-01 bis 2024-06-30
The project addresses the critical challenge of understanding the genetic basis of coronary artery disease (CAD), a leading cause of death worldwide. Despite advances in genome-wide association studies (GWAS), much of the genetic risk for CAD remains unexplained, particularly regarding the functional impact of genetic variants. This gap in knowledge is significant because it limits our ability to predict, prevent, and treat CAD effectively.
Our research has uncovered important new insights into how genetic factors contribute to CAD disease mechanisms. First, we identified hundreds of key genetic variants that play a significant role in the development of coronary artery disease (CAD) and linked most of them to specific genes that cause the disease. We found that many of these risk variants operate within specific cell types. Contrary to the current understanding, we discovered that some risk areas in the genome contain more than one harmful variant and that these variants can influence the activity of multiple genes at the same time. One of our major findings was that while one third of GWAS risk loci are related to traditional risk factors acting through the liver, such as cholesterol, the large majority are involved in other pathways that current treatments don’t target. By using pioneering single-cell analysis, we identified that certain cells in the blood vessels, like smooth muscle cells and endothelial cells, are particularly affected by these genetic changes. We further discovered new disease-associated subtypes of these cells within atherosclerotic lesions, uncovering specific biological pathways that substantially contribute to CAD risk. Finally, we conducted a proof-of-concept study demonstrating that a hybrid genetic risk score that combines this functional genetic information could allow for more accurate prediction of those at high risk for CAD. This work opens the door to better, more personalized approaches to preventing and treating heart disease.
Our research has uncovered important new insights into how genetic factors contribute to CAD disease mechanisms. First, we identified hundreds of key genetic variants that play a significant role in the development of coronary artery disease (CAD) and linked most of them to specific genes that cause the disease. We found that many of these risk variants operate within specific cell types. Contrary to the current understanding, we discovered that some risk areas in the genome contain more than one harmful variant and that these variants can influence the activity of multiple genes at the same time. One of our major findings was that while one third of GWAS risk loci are related to traditional risk factors acting through the liver, such as cholesterol, the large majority are involved in other pathways that current treatments don’t target. By using pioneering single-cell analysis, we identified that certain cells in the blood vessels, like smooth muscle cells and endothelial cells, are particularly affected by these genetic changes. We further discovered new disease-associated subtypes of these cells within atherosclerotic lesions, uncovering specific biological pathways that substantially contribute to CAD risk. Finally, we conducted a proof-of-concept study demonstrating that a hybrid genetic risk score that combines this functional genetic information could allow for more accurate prediction of those at high risk for CAD. This work opens the door to better, more personalized approaches to preventing and treating heart disease.
Our research has led to the identification of hundreds of key genetic variants linked to CAD, many of which we successfully associated with causal genes. We found that most of these risk variants operate within specific cell types, and contrary to the current understanding, we identified several risk loci containing more than one functional variant, with the potential to influence the expression of multiple genes simultaneously.
A significant discovery was that while about one-third of the genetic risk factors for CAD are related to traditional risk factors, such as cholesterol metabolism in the liver, the majority function through alternative mechanisms involving the vascular wall—mechanisms that current treatments do not address. By pioneering the use of single-cell chromatin accessibility profiling (scATAC-Seq), we identified that specific cells within blood vessels, particularly smooth muscle cells (SMCs) and endothelial cells (ECs), are heavily impacted by these genetic changes. Our research further revealed twelve distinct disease-associated subtypes within atherosclerotic plaques, with a particular focus on the phenotypic changes in SMCs, which play a substantial role in CAD heritability.
In addition, we conducted a proof-of-concept study that demonstrated how a hybrid polygenic risk score (PRS), integrating this detailed functional genetic information, can significantly improve the accuracy of predicting individuals at high risk for CAD. This innovative approach not only advances our understanding of the genetic mechanisms driving CAD but also paves the way for more personalized and potentially more effective strategies for preventing and treating heart disease.
The results of this project have been extensively disseminated through nearly 30 conferences and multiple social media platforms, significantly enhancing the visibility of our findings within the scientific community and beyond. Additionally, we have expanded our collaboration networks, forming a robust research community focused on understanding the functional impact of each CAD locus and translating these findings into clinical practice. This collaborative effort is crucial in driving forward the application of our research to improve CAD diagnosis, prevention, and treatment on a global scale.
A significant discovery was that while about one-third of the genetic risk factors for CAD are related to traditional risk factors, such as cholesterol metabolism in the liver, the majority function through alternative mechanisms involving the vascular wall—mechanisms that current treatments do not address. By pioneering the use of single-cell chromatin accessibility profiling (scATAC-Seq), we identified that specific cells within blood vessels, particularly smooth muscle cells (SMCs) and endothelial cells (ECs), are heavily impacted by these genetic changes. Our research further revealed twelve distinct disease-associated subtypes within atherosclerotic plaques, with a particular focus on the phenotypic changes in SMCs, which play a substantial role in CAD heritability.
In addition, we conducted a proof-of-concept study that demonstrated how a hybrid polygenic risk score (PRS), integrating this detailed functional genetic information, can significantly improve the accuracy of predicting individuals at high risk for CAD. This innovative approach not only advances our understanding of the genetic mechanisms driving CAD but also paves the way for more personalized and potentially more effective strategies for preventing and treating heart disease.
The results of this project have been extensively disseminated through nearly 30 conferences and multiple social media platforms, significantly enhancing the visibility of our findings within the scientific community and beyond. Additionally, we have expanded our collaboration networks, forming a robust research community focused on understanding the functional impact of each CAD locus and translating these findings into clinical practice. This collaborative effort is crucial in driving forward the application of our research to improve CAD diagnosis, prevention, and treatment on a global scale.
The project has successfully achieved its goal of elucidating the mechanisms by which genetic variants contribute to CAD, marking significant advancements in the field of cardiovascular genetics. Adoption of several state-of-the-art techniques, such as single cell technologies, have pushed the boundaries of current research, allowing us to uncover novel insights into the genetic mechanisms underlying CAD that were previously inaccessible. This deeper understanding is expected to drive further research into the molecular underpinnings of CAD and related diseases, fostering innovation in the translation of GWAS findings into clinical practice. To this end, we have demonstrated proof of concept for a PRS that integrates detailed functional genetic information, significantly enhancing the accuracy of predicting individuals at high risk for CAD. Advancing this development in the future could enable earlier and more precise identification of people at risk, motivating more timely and targeted interventions. Finally, the project’s insights into how specific genetic variants function within different cell types and influence CAD progression could pave the way for more personalized treatment approaches. By tailoring interventions to an individual’s unique genetic profile, healthcare providers can deliver more effective therapies, ultimately improving patient outcomes and reducing the overall burden of CAD.