Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
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...
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...
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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A High-Performance Si-Based Photocathode Enhanced by Spatial Confinement Strategy for Photoelectrochemical Hydrogen Production.

ACS applied materials & interfaces·2026
Same author

Polarity Inversion-Driven Band Structure Modulation, Strain Engineering, and Electrical Property Analysis on GaN/4H-SiC Heterojunctions.

ACS omega·2026
Same author

Early-life nutritional environment is associated with late-life cognition in the Health and Retirement Study, a pellagra epidemic natural experiment.

medRxiv : the preprint server for health sciences·2026
Same author

Gut bacterial metabolite imidazole propionate potentiates Alzheimer's disease pathology.

Nature communications·2026
Same author

Diagnostic Accuracy of MiRNA Panels for Endometrial Cancer: A Systematic Review and Meta-Analysis.

Journal of lower genital tract disease·2026
Same author

rSiglec-10(V set) armed oncolytic adenovirus improves the effects of virotherapy through enhancing oncolysis and antitumor immunity.

International immunopharmacology·2026

Related Experiment Video

Updated: Jun 18, 2026

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

Detecting gene-environment interactions in genome-wide association data.

Corinne D Engelman1, James W Baurley, Yen-Feng Chiu

  • 1Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53726-2397, USA. cengelman@wisc.edu

Genetic Epidemiology
|November 20, 2009
PubMed
Summary

Researchers developed new methods to detect gene-environment (GxE) interactions in genome-wide association studies for common diseases. The study highlights the significance of testing for GxE interactions, despite challenges like sample size and data analysis.

More Related Videos

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

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

Published on: August 21, 2016

Related Experiment Videos

Last Updated: Jun 18, 2026

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

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

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

Published on: August 21, 2016

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Common diseases often arise from complex gene-environment (GxE) interactions, yet methods for their detection in genome-wide association studies (GWAS) are underdeveloped.
  • Genetic Analysis Workshop 16 (GAW16) Group 10 focused on advancing GxE interaction detection methodologies.

Framework:

  • Introduced novel statistical methods for identifying GxE interactions in both case-control and family-based study designs.
  • Applied these methods to cross-sectional and longitudinal data, offering versatile analytical approaches.

Implementation:

  • Several contributions successfully detected statistically significant GxE interactions, suggesting the viability of the developed methods.
  • Acknowledged the need for further confirmation of these findings in independent studies.

Implications:

  • The study underscores the critical importance of incorporating GxE interaction analyses into GWAS for a comprehensive understanding of disease etiology.
  • Discussed key challenges including sample size, environmental exposure quantification, longitudinal and family-based data analysis, method selection, population stratification, and computational costs.