Coronary Artery Disease (CAD) is the most common form of heart disease. It affects nearly 13 million Americans and is the leading cause of death in the United States. The disease is intimately linked to the aging of the cardiovascular system, environmental assault, and genetic predisposition. This multifactorial disease is an excellent model to elucidate associated genetic variation and the impact of changes in transcription and epigenetic influences. A better understanding of the complex interplay of the molecular factors involved in CAD will help to define molecular markers that lead to disease predisposition, formation, and progression. This training proposal will identify molecular factors that lead to CAD using family based association analysis of early onset CAD linkage regions and through the application of high-resolution genomic microarray experiments to determine DNA methylation patterns and transcription factor binding associated with CAD. The array work focuses on two components, (1) defining GATA2 DNA binding sites across the human genome and (2) identifying DNA methylation patterns in proliferating aortic smooth muscle cells. ? ?
