# A Unified High Performance Web Service for Systems Genetics and Precision Medicine

> **NIH NIH R01** · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · 2021 · $453,683

## Abstract

High-throughput technologies have transformed biology by enabling deep phenotyping of living organisms.
Deep phenotyping in genetically randomized populations provides a powerful resource for understanding
cause and effect – this is the promise of systems genetics and precision medicine. Despite the advances in
data collection and the availability of genetic and genomic resources, generation of actionable knowledge from
these data presents computational and statistical challenges. The goal of the GeneNetwork project is to
provide a platform of innovation to make the best use of the data and computational tools for systems genetics.
The proposed project will focus on broadening the impact of the web service by broadening community
participation in the web service and strengthening computational engine powering the web service. We will
enhance our database by providing automated tools for data entry and curation (Aim 1); we will improve tools
for genetic analysis of high-throughput traits (Aim 2); enable bidirectional information sharing between animal
model and human genetic studies (Aim 3); and provide a platform for toolset prototying and testing by the
computational community (Aim 4).

## Key facts

- **NIH application ID:** 10210974
- **Project number:** 2R01GM123489-05
- **Recipient organization:** UNIVERSITY OF TENNESSEE HEALTH SCI CTR
- **Principal Investigator:** Pjotr Prins
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $453,683
- **Award type:** 2
- **Project period:** 2017-04-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10210974

## Citation

> US National Institutes of Health, RePORTER application 10210974, A Unified High Performance Web Service for Systems Genetics and Precision Medicine (2R01GM123489-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10210974. Licensed CC0.

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