# Linking genetics to cellular behavior and disease via multimodal data integration

> **NIH NIH DP2** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2024 · $882,476

## Abstract

Project Summary / Abstract
Studies of mechanisms underlying genetic associations with disease have primarily focused on gene
regulatory mechanisms. In contrast, the impact of genetic variation on cellular phenotypes such as
morphology and behavior is poorly understood, despite their importance in disease progression. This poor
understanding is in part because measurement of some cellular phenotypes such as electrophysiological
response patterns require skilled manual labor and is performed cell by cell, and is thus low throughput.
Furthermore, some cellular phenotype assays require live cells, which for cell types such as neurons are
prohibitively challenging to obtain from humans.
The goal of this proposal is to develop deep learning-based frameworks for characterizing how molecular
and cellular phenotypes covary using multimodal datasets, then to predict how these cellular behaviors
mediate the effect of genetic variation on the risk of illnesses such as psychiatric and neurodevelopmental
disorders. We will achieve this overall goal through the development of three frameworks.
Multimodal models linking molecular and cellular phenotypes of cells. By linking gene regulation with
cellular phenotypes such as neuron electrophysiology and morphology, we can then understand how
changes at the molecular level propagate to cellular phenotypes and vice versa. Furthermore, we can use
these models to impute cellular phenotypes when they cannot be measured experimentally.
Identification of cellular phenotypes that mediate genetic risk of mental disorders. We will jointly
model genotype, gene expression, cellular phenotypes and disease risk to generate mechanistic
hypotheses about the mediation of genetic effects on disease risk through cellular phenotypes.
Prediction of cellular phenotypes associated with disease progression. We will develop a prediction
framework for exploring which cellular phenotypes change significantly with disease progression. By
applying our imputation framework developed above, we will predict changes in neuron electrophysiological
response and morphology associated with a range of psychiatric and neurodevelopmental disorders.
We expect completion of this project to yield generalizable computational frameworks for linking genetics
to molecular and cellular phenotypes for diverse cell types and organs.

## Key facts

- **NIH application ID:** 11062758
- **Project number:** 4DP2MH129987-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Gerald Quon
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $882,476
- **Award type:** 4N
- **Project period:** 2021-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11062758

## Citation

> US National Institutes of Health, RePORTER application 11062758, Linking genetics to cellular behavior and disease via multimodal data integration (4DP2MH129987-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11062758. Licensed CC0.

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