| Autor: |
Turner AW; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States., Wong D; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States.; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, United States., Dreisbach CN; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States.; Data Science Institute, University of Virginia, Charlottesville, VA, United States., Miller CL; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States.; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, United States.; Data Science Institute, University of Virginia, Charlottesville, VA, United States.; Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States. |
| Abstrakt: |
Coronary artery disease (CAD) is the leading cause of mortality worldwide and poses a considerable public health burden. Recent genome-wide association studies (GWAS) have revealed >100 genetic loci associated with CAD susceptibility in humans. While a number of these loci harbor gene targets of currently approved therapies, such as statins and PCSK9 inhibitors, the majority of the annotated genes at these loci encode for proteins involved in vessel wall function with no known drugs available. Importantly many of the associated genes linked to vascular (smooth muscle, endothelial, and macrophage) cell processes are now organized into distinct functional pathways, e.g., vasodilation, growth factor responses, extracellular matrix and plaque remodeling, and inflammation. In this mini-review, we highlight the most recently identified loci that have predicted roles in the vessel wall and provide genetic context for pre-existing therapies as well as new drug targets informed from GWAS. With the development of new modalities to target these pathways, (e.g., antisense oligonucleotides, CRISPR/Cas9, and RNA interference) as well as the computational frameworks to prioritize or reposition therapeutics, there is great opportunity to close the gap from initial genetic discovery to clinical translation for many patients affected by this common disease. |
