# Enabling AI-based Mouse Genetic Discovery

> **NIH NIH R24** · STANFORD UNIVERSITY · 2023 · $779,707

## Abstract

Abstract
No model organism has contributed more than the laboratory mouse to improving human health. Many genetic
factors and therapies for human diseases were initially discovered or characterized in mice, before they were
transitioned to human use.
Large-scale efforts are underway to integrate recent advances in artificial
intelligence (AI) into human healthcare, but very few AI advances have been used for analysis of the data
produced using the model organism that has formed the foundation for many healthcare innovations. We
recently developed an AI-based computational pipeline that could identify causative genetic factors for murine
genetic models of human biomedical traits and diseases. After assessing the strength of allelic associations
with the phenotypic response pattern exhibited by the inbred strains; this AI pipeline uses a machine-learning
trained method to analyze 29M published papers and assess candidate gene-phenotype relationships; and the
information obtained from assessment of their protein-protein interaction network and protein sequence
features of the candidate genes are also incorporated into the graph neural network-based analysis.
This project will produce a markedly enhanced AI pipeline (AIv2) that will greatly accelerate the pace of genetic
discovery using murine genetic models. First, long read genomic sequencing (LRS) and computational tools
are used to produce a more complete map of the pattern of genetic variation among the inbred strains, which
also includes alleles for two major types of genetic variation (structural variants, tandem repeats), which are
poorly characterized using conventional sequencing methods. Second, we develop two additional
computational tools for the AI, which facilitate candidate gene prioritization through the evaluation of: (i) the
phenotypes exhibited by 8200 mouse lines with individual gene knockouts (KOs); and (ii) the results of 5700
human GWAS covering many biomedical phenotypes to determine if alleles within the human homologues of
candidate murine genes affect an analyzed trait. The ability of AIv2 to accelerate genetic discovery will be
demonstrated by using it to identify new genetic factors through analysis of a public database with >10,307
datasets, which measure biomedical or disease-related responses in panels of inbred strains. Since it is critical
to experimentally confirm some of the computational findings, genetic factors for two murine models of human
diseases that are major public health problems (cancer, diabetes/obesity), which were identified by the AI
pipeline, will be experimentally validated. CRISPR engineering is used to revert the causative mutation(s) to
wildtype on the genetic background of the strain exhibiting the disease phenotype, and the genome engineered
mice are analyzed to assess the contribution of the genetic factor to the disease phenotype.

## Key facts

- **NIH application ID:** 10724522
- **Project number:** 1R24OD035408-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** GARY A PELTZ
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $779,707
- **Award type:** 1
- **Project period:** 2023-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10724522

## Citation

> US National Institutes of Health, RePORTER application 10724522, Enabling AI-based Mouse Genetic Discovery (1R24OD035408-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10724522. Licensed CC0.

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