# Modeling the dynamicimpact of rare and common genetic variation on gene expression anddisease

> **NIH NIH R35** · JOHNS HOPKINS UNIVERSITY · 2024 · $627,783

## Abstract

Understanding the genetic basis of human disease will require a deep understanding of genetic effects on
gene expression. The vast majority of disease-risk loci are non-coding, so in order to link them to target
genes, cellular pathways, and cell types, we seek to identify which genes’ expression they disrupt and under
what conditions. Population studies of gene expression have now provided thousands of “expression
quantitative trait loci” (eQTLs) where individual genetic variants are associated with expression of a target
gene. While eQTL studies across tissues and populations have served as a valuable resource for querying
the likely gene targets of disease loci, key obstacles remain. First, eQTL studies simply do not address rare
genetic variation, thus excluding evaluation of tens of thousands of variants per individual whole genome
sequence, and many known pathogenic loci. Second, even among common variants, it is estimated that
over half of disease loci do not coincide with any known eQTL, even based on current multi-tissue data. The
remainder of disease loci and rare variants require new data and statistical methods in order to characterize
their mechanisms. Here, we propose a research agenda to decipher the complex impact of regulatory
genetic variation across the frequency spectrum. 1) First, we will pursue analysis of rare genetic variation
and statistical methods for personal whole genome interpretation. Current methods simply do not provide
confident predictions for the majority of the variants from whole genome sequencing, and the overall impact
of rare regulatory variation on human disease is unknown. We will investigate the use of personal RNA-seq
and other functional data to complement whole genome sequence (WGS) in the evaluation of rare variant
impact, personal genome interpretation for rare disease patients, and incorporation of rare variants into
population studies and genetic risk scores. 2) Second, we will consider common disease variants that are
not characterized by current eQTL studies, which almost all use static, adult tissue samples and bulk
RNA-seq data. Genetic effects on gene expression are not static, but rather vary over time, cell type, and
environment, complicating the identification of disease mechanism. Some disease loci may have only
transient effects on a proximal gene’s expression during development, for example. We will study temporally
dynamic and context-specific genetic effects. In a novel study, we will evaluate genetic effects on individual
cell types and states during cellular differentiation using time-series single-cell RNA-seq across individuals.
We will also evaluate dynamic genetic effects during disease progression based on patient longitudinal
data. Combined with novel statistical methods, these will provide a map of genetic effects over cell-type,
time, and context that may better explain disease loci. All data, methods, and software will be made publicly
available. Our work will provide a gr...

## Key facts

- **NIH application ID:** 10758232
- **Project number:** 5R35GM139580-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Alexis Battle
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $627,783
- **Award type:** 5
- **Project period:** 2021-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10758232

## Citation

> US National Institutes of Health, RePORTER application 10758232, Modeling the dynamicimpact of rare and common genetic variation on gene expression anddisease (5R35GM139580-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10758232. Licensed CC0.

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