# Network-based algorithms for target identification and drug repositioning from genetic associations

> **NIH NIH R01** · UNIVERSITY OF COLORADO DENVER · 2020 · $245,121

## Abstract

In the field of genetics, genome-wide association studies of common variants (GWAS) and exome sequencing-
based analyses are a common strategy to elucidate the relationship between genetic variants and a specific
phenotype. While these approaches have strengths, they also have significant limitations such as their inability
to identify complex biological interactions that lead to genetic predispositions, their inability to integrate distinct
but related phenotypes, and their inability to separate genetic variants effects by tissue. If a phenotype is
manifest only as a result of the complex interplay of multiple factors, it can be impossible to successfully isolate
individual parts by investigating genotype-phenotype associations for only one outcome trait or disease alone.
To affect a disease, drugs need to act on the right target and in the right tissue. Bioinformatics approaches that
integrate multiple key layers of information to reveal effective drugs will address a critical unmet need because
it is expected that a complex interplay of factors forms the basis for most human phenotypes and diseases.
The overall objective of this proposal is the development of algorithms that integrate gene and phenome-wide
association results with chromosome structure data and functional relationship networks to identify genes that
give rise to complex phenotypes and drugs that modify them. These algorithms will provide a new and unique
means to study the genetic etiology of complex traits and outcomes, increasing the interpretability of and
ultimately the insights generated from high throughput association testing. The proposal's rationale is that
robust tissue-specific methods will open the door for geneticists, researchers with biorepositories, and those
with access to other extensive phenotyping data to effectively reposition drugs and identify new targets.
Complementary algorithms to address distinct aspects of this challenge are proposed as specific aims: (AIM 1)
Development of algorithms that integrate exome sequencing results with biological networks to identify genes
and pathways associated with phenotypes in specific tissues; (AIM 2) Development of algorithms that integrate
3D genome structure with robust associations via biological networks to identify genes underlying phenotypes
in specific tissues; (AIM 3) Development of algorithms that identify drugs that specifically alter regions of gene-
gene networks associated with a complex phenotype. Methods will be applied to phenome-wide analysis of the
Geisinger Health System MyCode® biorepository and a subset of candidates will be validated via molecular
assays.
The outcomes of this grant, namely algorithms for tissue-specific network analysis of genes and drugs, are
expected to generate positive translational impact because such algorithms enable researchers to translate
existing data resources into causal genes and effective drugs.

## Key facts

- **NIH application ID:** 10427765
- **Project number:** 7R01HG010067-04
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Casey S Greene
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $245,121
- **Award type:** 7
- **Project period:** 2021-01-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10427765

## Citation

> US National Institutes of Health, RePORTER application 10427765, Network-based algorithms for target identification and drug repositioning from genetic associations (7R01HG010067-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10427765. Licensed CC0.

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