Project 458558

Coronary artery disease (CAD) and late-onset Alzheimer's disease (LOAD) are prevalent disorders that collectively encompass the leading cause of morbidity and mortality worldwide while also having a strong genetic basis. Currently, the genetic causes of CAD and LOAD have been shown to be mediated by either rare genetic variants within single genes that result in high disease risk (i.e. monogenic inheritance) or the aggregate of common genetic variants located across several genes that individually result in modest disease risk (i.e. polygenic inheritance). However, individuals can still have a genetic predisposition to CAD and LOAD even when it's not due to these causes. We therefore propose a novel class of inheritance that is mediated by an estimate known as a rare variant genetic risk score (RVGRS), which combines features of both monogenic and polygenic inheritance. Given the substantial burden of disease caused by CAD and LOAD, it is essential to detect all forms of genetic predisposition at an early age to institute preventative measures that can mitigate disease onset later in life. To accomplish this, we will query all twenty-thousand genes in the human genome to identify those that are most strongly associated with CAD and LOAD through an enrichment of rare variants. We will then leverage these associations to generate a so called "functional RVGRS" (fRVGRS) that not only combines the risk imposed by each associated gene for a given individual, but also accounts whether these genes are biologically connected. We hypothesize that the fRVGRS will be a highly predictive for CAD and LOAD, thereby confirming a new model of genetic inheritance. Given the strong influence of genetics on CAD and LOAD, the fRVGRS offers an approach to help predict disease onset at the population-level and to identify individuals at high risk of disease who would not have identified using previous models of inheritance.