Estimate risk for common disease or health outcomes based on known genetic factors combined with novel environmental data types such as proximity to food sources and pollution and laboratory measures of pollutant exposure, for example, what is the relative risk of genetic factors for diabetes compared to dietary habits and proximity to grocery stores?
Characterize phenotypes of participants with rare deleterious variants of unknown significance discovered by deep sequencing through available surveys and EHR data or through recontact with additional studies.
The goal of this study is to ascertain clinical implications of rare genetic variants predicted to be deleterious discovered in individuals not carrying the diagnosis. Using existing phenotypes or potentially with recontact to participants physiological impact of variants on health and disease can be understood.
For example, does treatment failure of proton pump inhibitors in genetic hypermetabolizers cause increased surgical procedures and how do drugs interact in patients treated with multiple... more »
The goal of this study is to understand if mobile technologies can contribute clinically meaningful metrics. By analyzing mobile technology over time and comparing to available clinical health records, supervised and unsupervised machine learning can be applied to understand potential of this technology in clinical care and outcomes.
The goal of this study is to use existing data such as billing codes and laboratory values to predict the presence of a genetic condition before it is diagnosed by traditional medical practice.
The goal of this study is to research underlying causes of health disparities using existing data and novel data sources for example is diagnosis and treatment of diabetes different across ethnic groups, does a1c vary by G6PD genotype and does follow up and a1c target differ by diversity standards.
Of particular concern on the neighborhood scale are pesticides and hydrophobic organic chemicals -- especially those emitted... more »