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

433
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...
433
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

6.7K
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...
6.7K
Multiple Allele Traits01:49

Multiple Allele Traits

34.8K
The Concept of Multiple Allelism
34.8K
Epistasis Analysis01:09

Epistasis Analysis

5.2K
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...
5.2K
Polygenic Traits01:18

Polygenic Traits

66.5K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
66.5K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

504
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...
504

You might also read

Related Articles

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

Sort by
Same author

A Bayesian method for estimating gene-level polygenicity under the framework of transcriptome-wide association study.

Statistics in medicine·2023
Same author

Testing Equality of Multiple Population Means under Contaminated Normal Model Using the Density Power Divergence.

Entropy (Basel, Switzerland)·2022
Same author

Leveraging eQTLs to identify individual-level tissue of interest for a complex trait.

PLoS computational biology·2021
Same author

Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies.

PLoS genetics·2021
Same author

A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes.

Bioinformatics (Oxford, England)·2021
Same author

Cis-eQTL-based trans-ethnic meta-analysis reveals novel genes associated with breast cancer risk.

PLoS genetics·2017

Related Experiment Video

Updated: Sep 8, 2025

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

10.2K

A Multi-Phenotype Approach to Joint Testing of Main Genetic and Gene-Environment Interaction Effects.

Saurabh Mishra1, Arunabha Majumdar1

  • 1Department of Mathematics, Indian Institute of Technology Hyderabad, Kandi, India.

Statistics in Medicine
|September 5, 2025
PubMed
Summary

This study introduces a powerful new method to find genetic links to complex diseases by analyzing multiple traits together. It improves gene-environment interaction discovery, especially for weak genetic effects.

Keywords:
cholesterol levelsgeneral linear hypothesisgeneralized estimating equationsgene‐environment interactionmultivariate regressionsleep duration

More Related Videos

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

23.0K
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

3.8K

Related Experiment Videos

Last Updated: Sep 8, 2025

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

10.2K
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

23.0K
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

3.8K

Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Gene-environment (GxE) interactions are key to complex traits, but detecting them is challenging due to small effect sizes.
  • Existing methods for GxE interactions often lack statistical power, limiting the discovery of associated genetic loci.
  • Univariate joint tests combining genetic and GxE effects improve power but do not leverage multi-phenotype information.

Purpose of the Study:

  • To develop a novel statistical approach for detecting combined genetic and GxE effects across multiple related phenotypes.
  • To enhance statistical power by integrating pleiotropy (shared genetic influences) in both main genetic and GxE effects.
  • To improve the discovery of genetic loci associated with complex traits influenced by environmental factors.

Main Methods:

  • Developed a multivariate regression framework based on general linear hypothesis testing for continuous phenotypes.
  • Utilized generalized estimating equations (GEE) within a seemingly unrelated regressions (SUR) framework for binary or mixed phenotypes.
  • Employed extensive simulations to compare the proposed multi-phenotype joint test against univariate and multi-phenotype GxE-only tests.

Main Results:

  • The proposed multi-phenotype joint test demonstrated superior power compared to univariate joint tests and multi-phenotype GxE-only tests when pleiotropy is present.
  • The method showed higher power than marginal genetic effect tests for weak genetic and substantial GxE effects.
  • Application to UK Biobank lipid data with sleep duration identified ten novel associated genetic loci, and two distinct loci for apolipoproteins (ApoA1, ApoB).

Conclusions:

  • The multi-phenotype joint approach offers a robust and powerful strategy for identifying genetic loci influenced by GxE interactions.
  • This method effectively leverages pleiotropy across related phenotypes to boost statistical power in genetic association studies.
  • The findings highlight the utility of integrated multi-phenotype analysis for uncovering complex genetic architectures and GxE effects.