BioVU® is a bio-bank of de-identified longitudinal medical records and DNA extracted from discarded blood samples collected during routine clinical care designed to enable exploration of the relationships among genetic variation, disease susceptibility and variable drug responses.
BioVU® was designed and created by a community of geneticists, informaticians, basic scientists and translational medicine experts at Vanderbilt University Medical Center and Vanderbilt University. Planning for BioVU® began in mid-2004, with the first samples collected in February 2007. In early 2010, Vanderbilt researchers began collecting samples for BioVU® from children for the first time. Widescale genotyping began shortly thereafter, and the first blood plasma samples were collected in 2012. Since then, BioVU® has grown to contain millions of longitudinal medical records with more than 250,000 matched genetic samples from the diverse VUMC patient community.
Better understanding the mechanisms of disease can help scientists discover new treatment approaches and preventative measures; successfully tackling this breadth of disease is only possible with a dataset like BioVU®.
Samples are donated via opt-in process and consented for translational research and scanned via a custom-developed sample acceptance program that includes automated exclusion based on specific criteria.
Once a sample passes the necessary criteria, it is accepted by the program. Acceptance of a sample triggers the encryption program to assign a unique research ID number to the sample. Genetic samples are linked to the corresponding EHRs in which identifying information has been deleted to protect patient privacy.
Drug development has traditionally been approached by identifying a disease of interest, attempting to identify the genes or targets underlying the disease, and then identifying a molecule that interacts with that target. With this approach, we don’t always understand the diseases process or how the drug target is implicated in the disease — leaving us with a best guess scenario at the expense of significant resources. The pursuit of new targets is also difficult.
The pheWAS™ approach increases the probability of clinical trial success by leveraging natural genetic ‘experiments’ within patient DNA. We use small mutations naturally present in each and every human genome, and correlate those mutations to the full disease landscape. This ‘target-first’ approach allows us to map out diseases that may be treated by a new drug, or potential sides effects than may occur.
PheWAS™ is only possible with a dataset like BioVU® that contains clinical information about the entire spectrum of human disease and prospectively analyzed DNA samples. This approach can be used to analyze both new and existing drug targets to understand what diseases they may be involved with and potential side effects that may occur.