# Mapping gene-by-environment interactions using multiplexed single cell RNA-sequencing

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $1,017,314

## Abstract

ABSTRACT
While common variants are known to additively contribute to gene expression variation, there has been limited
statistical evidence of gene-by-environment interactions (GxE) in humans. This is because even the largest
expression quantitative trait loci (eQTL) studies have little statistical power to detect GxE interactions given the
large number of segregating loci and extensive variability in environmental exposure. We hypothesize that the
integration of multiplexed perturbations and single-cell RNA-sequencing is an efficient strategy for mapping GxE
interactions in large population cohorts. However, current approaches are not scalable to sequencing 107 cells
across 104 samples (i.e. 103 donors by 10 conditions) needed for sufficiently powered perturbation screens in
human cohorts. In this proposal, we will first develop a cost-effective single-cell RNA-sequencing approach called
DIT-seq that reduces the cost of sequencing to $0.06/cell (Aim 1). We will then develop strategies for encoding
environmental perturbations using sample multiplexing to map and characterize GxE interactions in the human
immune response (Aim 2). Finally, we will develop a new statistical model and a computational pipeline for
efficient hypothesis testing using tens of millions of cells (Aim 3). The experimental and computational
technologies proposed have the potential to create fundamental new ways to study genotype-phenotype
relationships and the biological insights gained could shed light on the genetic architecture of gene expression
and facilitate the interpretation of disease-associations from large-scale genome and exome sequencing studies.

## Key facts

- **NIH application ID:** 10645108
- **Project number:** 5R01HG011239-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Chun Jimmie Ye
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1,017,314
- **Award type:** 5
- **Project period:** 2020-08-07 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10645108

## Citation

> US National Institutes of Health, RePORTER application 10645108, Mapping gene-by-environment interactions using multiplexed single cell RNA-sequencing (5R01HG011239-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10645108. Licensed CC0.

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