My research focuses on developing approaches that integrate genetic data sources including electronic health record systems and large epidemiological studies, to identify genetically-modulated risk factors and biomarkers that impact health and disease.
I am a clinically trained scientist with a professional background in mathematics, information systems development and epidemiology and doctoral training in molecular and mouse models of mammary gland tumorigenesis. My primary interest is in leveraging data from electronic health record (EHR) data sources to improve health care delivery by reducing unnecessary health care utilization and improving risk stratification. In particular, my research focuses on the application of polygenic modelling approaches to enhance the utility and enable new discovery using EHR resources. Content areas that I focus on include metabolomics, proteomics and cardiac phenotypes and other diseases.
Virtual biomarkers. Many clinical diseases and putative biomarkers for these diseases have complex underlying genetic architectures. I am interested in study designs that leverage the shared genetics between diseases and biomarkers to identify new biomarker-disease associations.
Polygenic prediction. Benign genetic variation can cause healthy individuals to have outlying trait values that are misinterpreted as pathology in a clinical setting. These individuals may be subjected to unnecessary testing and evaluations. I am interested in using genetics to prospectively identify these individuals in order to prevent unnecessary clinical evaluations.
Precision epidemiology. I am interested in using genetics methods to identify risk mechanisms that contribute to cardiac disease in targeted EHR populations. Findings from these types of studies can enable precision health care delivery such that treatment and prevention strategies target the important disease mechanisms adversely affecting a population.