# Direct measurement of gene-environment interactions by high-throughput precision genome editing

> **NIH NIH F31** · STANFORD UNIVERSITY · 2021 · $30,036

## Abstract

Abstract
 Modern genetics has identified many genetic variants that affect traits such as height, but most
phenotypic variation still cannot be explained by these variants alone. Importantly, differences in environment
often result in individual variation of traits—including disease risk and drug response—for different genotypes.
These relationships are known as genotype by environment (GxE) interactions. For example, the sickle cell
allele of hemoglobin S causes sickle cell anemia, but also provides a fitness advantage in the presence of
malaria by conferring resistance to infection. However, there are few examples where the exact causal variants
are known. Therefore, we need to develop new methodology for identifying more of these GxE interactions, to
improve prediction of disease risk and treatment outcomes.
 In this study, I will fill in the gap of knowledge in GxE interactions by establishing an experimental
framework for identifying hundreds of causal GxE variants in parallel, providing the first comprehensive view of
GxE causal variant landscape. Specifically, I will study how thousands of genetic variants between a laboratory
yeast strain (BY) and a vineyard strain (RM) lead to their differences in growth upon stress and drug
treatments, as one form of GxE interaction. In Aim 1, I will use a novel gene-editing technology that can detect
the fitness effects of thousands of variants in one experiment, as shown in a pilot experiment. Using this
method, I will be able to map hundreds of casual variants that contribute to growth differences under various
conditions, such as carbon source, oxidative stress and drug treatment. In Aim 2, I will measure allele-specific
mRNA expression (ASE) from BYxRM F1 hybrids in above-mentioned conditions and associate the mapped
causal GxE variants, to identify GxE variants that influence growth rate through gene expression. Then, I will
apply a machine learning model to predict causal GxE genes using the molecular features found in this study.
By mapping causal GxE variants, linking them to gene expression and predicting causal genes through gene
expression, I will establish a complete framework for accelerating the discovery of GxE interactions.

## Key facts

- **NIH application ID:** 10216263
- **Project number:** 5F31ES030282-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Shi-An Anderson Chen
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $30,036
- **Award type:** 5
- **Project period:** 2019-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10216263

## Citation

> US National Institutes of Health, RePORTER application 10216263, Direct measurement of gene-environment interactions by high-throughput precision genome editing (5F31ES030282-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10216263. Licensed CC0.

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