In order to fully personalize healthcare delivery, clinical medicine must be informed by an individual's genetic make-up. One area of clinical practice in which incorporating genetic data can impact patient care is the interpretation of surrogate biomarkers. These biomarkers are quantitative surrogate measures of disease risk but are not direct mediators of disease. Misclassification of disease is inherent to these biomarkers and, consequently, healthy individuals undergo extensive and often repeated clinical evaluations that seek to identify a non-existent disease state. The underlying premise of our proposal is that benign common genetic variation predisposes some individuals to have clinically outlying biomarker levels without altering their disease risk. These individuals undergo clinical evaluations which are captured in the electronic health record (EHR). We hypothesize that we can reduce misclassification of healthy individuals related to the use of biomarkers for clinical diagnoses, by applying a genetic correction factor to adjust biomarker measurements based on common SNP variation that predicts biomarker levels but is not associated with disease risk. These analyses will integrate individual-level clinical and genetic data from EHR-linked DNA biobanks including Vanderbilt's biobank (n=~240,000 subjects) and from the eMERGE network (n=~60,000), as well as other epidemiological data sets. The primary aims of this proposal are to: 1) develop and validate genetic risk scores (GRS) for surrogate biomarkers that are not associated with disease risk; 2) test the hypothesis that common genetic variation modulating surrogate biomarker levels is associated with related measures of healthcare utilization; and 3) test the hypothesis that a GRS for a surrogate biomarker predicts benign outcomes among healthy individuals who have completed a clinical evaluation for pathologic causes of an outlying biomarker level. A key innovative feature of the study design is that these results will be developed and characterized in their native environment (i.e. the EHR), which will accelerate translation of the research findings to clinical practice. This will enable us to create and disseminate relevant clinical tools which reclassify healthy, low-risk individuals whose biomarker values exceed a clinical threshold so they avoid non-beneficial risks and costs of unnecessary medical testing. Ultimately, these studies will advance the field of personalized medicine by identifying real-world instances in which incorporating common genetic information into clinical practice will lead to improved patient outcomes.
Some individuals have clinical measurements that fall outside of the normal ranges, despite the fact that they do not have an underlying disease. As a consequence, these individuals undergo unnecessary testing that is designed to identify an underlying disease. We will investigate whether accounting for genetic factors known to influence these measurements can appropriately reclassify these individuals and avoid unnecessary testing.
