Leveraging the Genetics of carotid stenosis for identifying novel risk factors and therapeutic opportunities

Current guidelines for primary and secondary prevention of atherosclerotic cardiovascular disease (ASCVD), including coronary heart disease (CHD), peripheral artery disease (PAD), and cerebrovascular disease, focus on the uniform application of risk factor modification irrespective of CVD subtype, despite rising evidence that each of these diseases has specific underlying pathobiology. Large scale genetic studies have identified new biology, clarified the role of modifiable risk factors, improved risk prediction, and identified therapeutic targets for CHD and PAD. This work has demonstrated vascular territory specific effects of risk factors and therapies, motivating disease specific approaches to prevention and treatment of atherosclerotic cardiovascular disease. Unfortunately, genetic studies of cerebrovascular disease have lagged. Extant studies have tended to focus on either early-stage subclinical atherosclerosis (carotid intima to media thickness [cIMT]) or late-stage outcomes (ischemic stroke). Studies of actual atherosclerotic cerebrovascular disease, in the form of carotid stenosis, have been limited by small sample size. The VA Million Veteran Program (MVP) was initiated in 2011 to study how genes, lifestyle, and military exposure affect health and disease and offers a unique opportunity to advance the genetics of atherosclerotic cerebrovascular disease through the study of carotid stenosis. To do this we have developed a validated natural language processing (NLP) algorithm to extract quantitative measures of carotid stenosis severity from imaging reports in the VA electronic health record (EHR), facilitating the study of both disease susceptibility and progression. The overarching hypothesis of this proposal is that the development of carotid plaque, progression of stenosis, and resulting ischemic stroke represent distinct pathobiological states, each offering a separate opportunity for therapeutic intervention. To address this hypothesis, we will conduct genome-wide association studies of both disease susceptibility and progression. The results from these analyses will be then used for genetic causal inference experiments to: 1. Quantify the causal impact of traditional risk factors on the susceptibility to carotid stenosis; 2. Determine the causal risk factors for the progression of carotid stenosis; and 3. Identify the shared genetic architecture of carotid stenosis and ischemic stroke using genomic structural equation modeling. Successfully completion of this project will clarify the role of traditional risk factors with carotid stenosis, identify novel opportunities for prevention and treatment, and establish the basis for tailored, precision medicine approaches to the treatment of carotid stenosis and stroke prevention for Veterans.