# Efficient Translation of Genetics Research for Clinical Decision Support

> **NIH NIH K01** · UNIVERSITY OF KANSAS MEDICAL CENTER · 2020 · $180,276

## Abstract

PROJECT SUMMARY
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.

## Key facts

- **NIH application ID:** 10227350
- **Project number:** 7K01LM012870-03
- **Recipient organization:** UNIVERSITY OF KANSAS MEDICAL CENTER
- **Principal Investigator:** Olivia J Veatch
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $180,276
- **Award type:** 7
- **Project period:** 2020-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10227350

## Citation

> US National Institutes of Health, RePORTER application 10227350, Efficient Translation of Genetics Research for Clinical Decision Support (7K01LM012870-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10227350. Licensed CC0.

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