# The Monarch Initiative: Linking diseases to model organism resources

> **NIH NIH R24** · UNIVERSITY OF COLORADO DENVER · 2023 · $1,323,698

## Abstract

ABSTRACT
Biomedical researchers need to identify novel disease genes and understand disease mechanisms; clinicians
need to diagnose diseases and optimize treatments. An improved understanding of the genetic basis of
disease helps achieve both goals. The Monarch Initiative makes this possible by integrating the fragmented
data landscape into the most comprehensive open collection of genotype-phenotype data in the world. Our
Knowledge Graph (KG) links together clinical, biomedical, and basic science research data spanning multiple
organisms, and supports reasoning across a wide range of organisms, body systems, and diseases. Monarch
has achieved demonstrable clinical and translational success using model organism data to perform rare
disease diagnosis and gene-to-disease discovery, and our resources have become global standards. In Phase
II, we integrated the Human Phenotype Ontology (HPO) into the UMLS; enhanced our variant prioritization
algorithms and Exomiser tool, which was applied to 30,000 patients in the National Health Service (UK);
developed the Biolink Model/API; released new ontologies (Mondo unified disease ontology, Unified
Phenotype Ontology (uPheno), and the Environmental Conditions and Treatments Ontology (ECTO)), raised
the number of harmonized data sources in our KG to 34; and overhauled our web Portal. We will leverage this
foundation to make Monarch more intuitive for a diversity of users and contexts in phase III as follows:
Augment the Monarch Portal with new visualizations and tools. Guided by user requirements, iterative
user testing, and feedback, we propose to enhance the user experience and Portal functionality, focusing our
work on improvements to Navigation, Visualization, and Query.
Evaluate, optimize, and enhance algorithms for disease diagnostics, cross-species inference, and
gene-disease discovery. We will develop a comprehensive, modular evaluation framework, ‘PhEval,’ that will
allow us to monitor the diagnostic yield and performance of cross-species inference as our ontologies and data
graphs evolve. This will assist basic science researchers and clinicians to reveal cross-species mechanistic
evidence and evaluate potential precision disease modeling strategies.
Disseminate computational tools, data, services, and tutorials to a broad translational community. We
will expand access to our KG to enable users to process the KG for different domains and use cases, through
simplified downloads, APIs, software libraries, R packages, Jupyter notebooks, and Dockerized resources,
along with training materials. This will better support bioinformaticians and other researchers in leveraging our
KG and phenotype data in their analyses.
The Monarch Initiative aims to significantly improve the utilization, accessibility, and value of animal
models for disease diagnosis and discovery.

## Key facts

- **NIH application ID:** 10693346
- **Project number:** 5R24OD011883-12
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** MELISSA A HAENDEL
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1,323,698
- **Award type:** 5
- **Project period:** 2012-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10693346

## Citation

> US National Institutes of Health, RePORTER application 10693346, The Monarch Initiative: Linking diseases to model organism resources (5R24OD011883-12). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10693346. Licensed CC0.

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