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Related Concept Videos

Gene-Environment Interactions01:20

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...
Genome-wide Association Studies-GWAS01:11

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 Designs01:23

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 Phenotype02:27

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...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
Genetic Screens02:46

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...

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Related Experiment Video

Updated: May 20, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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

Published on: June 21, 2018

Simultaneously testing for marginal genetic association and gene-environment interaction.

James Y Dai1, Benjamin A Logsdon, Ying Huang

  • 1Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. jdai@fhcrc.org

American Journal of Epidemiology
|July 10, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical test to find genetic variants associated with diseases by examining both marginal genetic effects and gene-environment interactions. This method improves the discovery of single nucleotide polymorphisms involved in complex gene-environment or gene-treatment relationships.

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

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Last Updated: May 20, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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

Published on: June 21, 2018

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

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

Published on: July 27, 2021

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Identifying genetic factors influencing disease risk is crucial.
  • Understanding gene-environment interactions (GxE) is essential for personalized medicine.
  • Current methods may not efficiently leverage gene-environment independence.

Purpose of the Study:

  • To develop a novel statistical framework for simultaneously testing marginal genetic association and GxE.
  • To discover single nucleotide polymorphisms (SNPs) involved in GxE or gene-treatment interactions.
  • To offer a flexible approach that exploits gene-environment independence.

Main Methods:

  • Proposing a joint test combining marginal association and interaction tests.
  • Utilizing asymptotic independence between marginal and interaction estimators.
  • Employing case-only or empirical Bayes estimators for GxE, depending on study design (observational vs. randomized trial).

Main Results:

  • The proposed test offers a simple and flexible method for GxE analysis.
  • It differs from existing methods by focusing on marginal association and leveraging GxE independence.
  • Simulations and application to the Women's Health Initiative data demonstrate its utility.

Conclusions:

  • The developed joint test provides a powerful approach for genetic association studies.
  • It enhances the ability to detect SNPs involved in complex interactions.
  • This method is applicable to both observational and randomized trial settings for GxE and gene-treatment interaction studies.