Recent advances in genomics technologies are rapidly generating data related to the underlying genetic architecture contributing to complex human diseases. Indeed, there are currently thousands of genetic variants implicated in risk for complex human disease and the list is continually growing. In order to understand how genetic information can be useful to informing treatment, it is important to identify efficient ways to sort through the sea of association study results to determine clinically actionable genes. There are a number of excellent tools available that allow for identifying clinically useful ways to interpret information from genetic studies. Unfortunately, there is limited opportunity for many individuals who have the most opportunity to enact precision medicine approaches to healthcare (e.g., clinicians) to spend time training in the skill sets necessary to use these tools. This represents a pressing issue given the current push for clinicians to begin using evidence related to clinically actionable genetic variation to guide preventative interventions and clinical decision making. The study proposed within this K-Award is intended to be a first step in a larger program of research that will serve to build bridges between basic and clinical research by developing and applying innovative biomedical informatics methods to inform translational medicine for complex human diseases. Important databases that incorporate evidence from multiple sources will be used to automate a bioinformatics pipeline to: 1) identify genes expressed in tissues relevant to the disease of interest with evidence for convergent biological function related to the disease, 2) identify genes with evidence for functional consequences relevant to the disease, 3) identify genes with evidence of pathogenic genetic variants or predict potential genes via evidence for direct interactions with the protein products of known pathogenic genes, 4) identify genetic mechanisms targeted by approved pharmaceutical compounds with evidence for known genetic effects influencing individual treatment response. This career development award is designed to provide critical training in the fundamentals of biomedical informatics that are necessary for developing a tool to help translate results from genetic studies into clinically useful information. The primary goal is to develop a method for prioritizing results from genetic studies to help inform clinicians as to whether or not genetic testing will be beneficial toward optimizing treatment for a patient with a complex disease. Additionally, an easy-to-use tool (i.e., a mobile application) will be built to rapidly present this information in a manner that is conducive to clinical translation. The proposed project will build an important tool to help provide information that will be useful to the eventual goal of using genetics to tailor precision care for each individual patient.
This project will develop an automated method for prioritizing clinically actionable results from genetic studies of complex human disease. Furthermore, the validated method will be streamlined in an easy-to-use, highly accessible mobile application that presents complicated genetics results in a format useful to inform clinicians as to whether or not genetic testing will be beneficial toward optimizing treatment for a patient with a complex disease. The goal is to rapidly deliver important information in such a manner that the clinician can be more informed when making key decisions regarding the benefits of ordering genetic testing for a patient.
