# Leveraging common genetic variation to reduce misclassification of non-diseased individuals and unnecessary health care utilization attributable to surrogate biomarkers

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $365,803

## Abstract

PROJECT SUMMARY / ABSTRACT
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.

## Key facts

- **NIH application ID:** 10436261
- **Project number:** 5R01GM130791-04
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Jonathan David Mosley
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $365,803
- **Award type:** 5
- **Project period:** 2019-09-17 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10436261

## Citation

> US National Institutes of Health, RePORTER application 10436261, Leveraging common genetic variation to reduce misclassification of non-diseased individuals and unnecessary health care utilization attributable to surrogate biomarkers (5R01GM130791-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10436261. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*