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Gene-Environment Interactions

Gene-Environment Interactions

Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...

Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Behavioral Genetics and Its Designs

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...

Background and Environment Affect Phenotype

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...

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Related Experiment Video

Updated: May 21, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Powerful cocktail methods for detecting genome-wide gene-environment interaction.

Li Hsu¹, Shuo Jiao, James Y Dai

¹Biostatistics and Biomathematics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. lih@fhcrc.org

Genetic Epidemiology

|June 21, 2012

Summary

Discovering gene-environment interactions (G × E) is crucial for understanding complex diseases. This study introduces novel "cocktail" methods that improve the power of genome-wide association studies for detecting these interactions.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Related Experiment Videos

Last Updated: May 21, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

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Published on: July 27, 2021

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Identifying gene-environment interactions (G × E) is key to understanding complex diseases, but few have been discovered due to underpowered studies.
Existing methods for G × E analysis have varying performance depending on the specific scenario.

Purpose of the Study:

To develop a powerful and comprehensive tool for detecting gene-environment interactions in genome-wide association studies (GWAS).
To integrate various G × E detection methods into a modular approach to enhance statistical power and reliability.

Main Methods:

A module-based approach integrating screening, multiple comparison, and G × E testing modules.
Development of two novel "cocktail" methods combining these modules to leverage the strengths of individual approaches.
Utilizing common test statistics for conceptual straightforwardness and computational simplicity.

Main Results:

The proposed "cocktail" methods effectively control type I error rates.
The new methods demonstrate competitive statistical power, comparable to the best existing methods across various interaction models and scenarios.
The modular approach is computationally efficient and easily extensible for incorporating future methods.

Conclusions:

The developed "cocktail" methods offer a powerful and versatile strategy for genome-wide detection of gene-environment interactions.
This modular approach provides a robust tool for GWAS, particularly appealing when true interaction models are unknown.
The framework facilitates the discovery of novel genes and synergistic environmental factors, aiding in disease risk prediction.