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Precision medicine approaches have promise to improve the prevention and treatment of cardiovascular disease, which is the leading cause of death in the United States and globally. As the size and number of electronic health record (EHR)-linked biobanks with paired genetic information continue to increase globally, so too do the opportunities for clinical utility of genetic discoveries. My research focuses on the optimal use of rich genetic and phenotypic information from biobanks to translate genetic discoveries to clinical applications.
First, I utilize exome sequencing to identify thoracic aortic dissection patients within the Cardiac Health Improvement Project (CHIP) biobank that carry pathogenic genetic changes. Patients with monogenic causes of dissection fit a clinical profile of onset less than 50 years of age with no history of hypertension, and a family history of aortic disease. We conclude that aortic dissection patients in this demographic should be prioritized for clinical genetic testing followed by cascade screening of family members to guide clinical decision-making such as enhanced surveillance of aortic diameter and earlier surgical intervention.
Second, I illustrate the promises and challenges of family health history in the context of genetic research studies, with examples from the Trøndelag Health (HUNT) Study and UK Biobank. Individuals who report having a first-degree relative with heart disease have a genetic burden of disease risk alleles intermediate between cases and controls. Family history captures shared genetic and environmental factors, and self-reported family history ascertained in biobank questionnaires is a significant predictor of disease. Self-reported family history remains a significant predictor in the context of polygenic scores, which quantify the genetic risk for disease. Self-reported family history demonstrates some interesting time-varying effects that should be considered. Intuitively, young individuals who likely have younger family members report lower rates of family history of disease, whereas older individuals who have higher rates of positive family history benefit less from preventive interventions. This work motivates biobanks to survey for self-reported family history at multiple time points for a variety of complex diseases.
Finally, I examine how polygenic scores improve upon existing risk prediction models used in the clinic, such as the Pooled Cohorts Equation, to aid in earlier identification and treatment of people at high risk. By examining the HUNT Study and the UK Biobank, I systematically compare published polygenic scores for coronary artery disease (CAD) with and without conventional risk factors such as cholesterol, smoking, and hypertension. When the top performing polygenic score for CAD, a metaGRS (Inouye et al, 2018), is added to a model with conventional risk factors, it allows re-classification of 3% of individuals to the high-risk category recommended for therapeutic intervention. Over 10 years, 10.5% of the group re-classified into the high-risk category experienced a CAD event. These are patients who would benefit from implementing preventive lifestyle and medication changes if polygenic scores were added to existing clinical approaches for risk stratification.
This dissertation illustrates the use of genetic variation, polygenic scores, and self-reported family history in EHR-linked biobanks with deep phenotyping. I establish criteria for prioritized genetic screening in thoracic aortic dissection, explore the relationship between genetic risk and self-reported family history in complex disease association, and benchmark polygenic scores for better and earlier disease classification. In total, this research aims to harness extensive genetic data for precision medicine approaches that prevent and treat cardiovascular disease. |
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