# AI-based genetic discovery for hearing loss

> **NIH NIH R01** · STANFORD UNIVERSITY · 2023 · $659,597

## Abstract

Abstract
Age-related hearing loss is one of the most common conditions in the elderly. Many genetic factors for hearing
loss have been identified, but many more remain to be identified; and our lack of knowledge about the
mechanisms by which they cause hearing loss is a barrier that must be overcome if we are to develop methods
for preventing (or reversing) age-related hearing loss. No model organism has contributed more than the
laboratory mouse to improving human health, and mouse models have shaped our understanding of the
mammalian auditory system. Mice with genetic mutations have been used to identify genes that are critical for
auditory function, and for characterizing human genetic factors that cause hearing loss.
A spontaneous hearing loss with an oligogenic basis develops in several well-studied inbred mouse strains
(A/J, DBA/2J, MA/My, NOD/LtJ, NOR/LtJ, C57BR/cdJ, C57L/J). Our recently developed AI-based
computational pipeline (GNNHap) identified four causative genetic factors for spontaneous hearing loss in
three strains (A/J, DBA/2, NOD/LtJ). However, to accelerate the pace of genetic discovery for hearing loss, this
project will enhance our AI by enabling it to analyze structural variant alleles present in the genomes of inbred
strains, and by adding three computational capabilities for prioritizing candidate genes. The enhanced AI will
be able to: (i) determine if alleles within the human homologues of identified mouse candidate genes were
associated with hearing loss in human GWAS; (ii) analyze a phenotypic database to determine if a mouse line
with a knockout of a candidate gene has impaired hearing; and (iii) analyze gene expression data in the Gene
Expression Analysis Resource (gEAR) to determine whether identified candidate murine genes (and their
human homologues) are expressed in the ear. The enhanced computational tool will then be used to identify
genetic factors for hearing loss in four strains (MA/My, NOR/LtJ, C57BR/cdJ, C57L/J). Since it is critical to
characterize genetic effector mechanisms, state of the art genome engineering is used to generate knockin
(KI) mice, which have a reversion of a causative genetic factor for hearing loss to wild type. A detailed
evaluation of these KI mice is performed to characterize the individual (and combined) effect of these
mutations on hearing loss and cochlear morphology. Characterization of their genetic effector mechanisms will
reveal how a set of interacting oligogenic factors produce a spontaneous hearing loss. As a stretch goal, we
will use some of these KI mice to determine if we can develop a novel gene x environment model for noise-
induced hearing loss.

## Key facts

- **NIH application ID:** 10708476
- **Project number:** 1R01DC021133-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** GARY A PELTZ
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $659,597
- **Award type:** 1
- **Project period:** 2023-06-16 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10708476

## Citation

> US National Institutes of Health, RePORTER application 10708476, AI-based genetic discovery for hearing loss (1R01DC021133-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10708476. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*