Skip Navigation
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Internet Explorer is no longer a supported browser.

This website may not display properly with Internet Explorer. For the best experience, please use a more recent browser such as the latest versions of Google Chrome, Microsoft Edge, and/or Mozilla Firefox. Thank you.

Your Environment. Your Health.

DIRECT MEASUREMENT OF GENE-ENVIRONMENT INTERACTIONS BY HIGH-THROUGHPUT PRECISION GENOME EDITING

Export to Word (http://www.niehs.nih.gov//portfolio/index.cfm/portfolio/grantdetail/grant_number/F31ES030282/format/word)
Principal Investigator: Chen, Shi-An Anderson
Institute Receiving Award Stanford University
Location Stanford, CA
Grant Number F31ES030282
Funding Organization National Institute of Environmental Health Sciences
Award Funding Period 01 Aug 2019 to 29 Jul 2022
DESCRIPTION (provided by applicant): 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.
Science Code(s)/Area of Science(s) Primary: 07 - Human Genetics/Gene X Environment Interaction
Secondary: 03 - Carcinogenesis/Cell Transformation
Publications No publications associated with this grant
Program Officer Kimberly Mcallister
Back
to Top