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

287
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...
287
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

6.5K
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...
6.5K
Epistasis Analysis01:09

Epistasis Analysis

5.0K
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...
5.0K
Polygenic Traits01:18

Polygenic Traits

65.7K
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...
65.7K
Heritability01:06

Heritability

195
Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
195
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

352
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...
352

You might also read

Related Articles

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

Sort by
Same author

Epigenome-wide mediation analysis identified Cytosine-phosphate-Guanine sites linking environmental factors with diabetes indicators.

Epigenomics·2025
Same author

Three generations of epigenetic clocks in mediating the adverse effect of smoking on metabolic health.

Epigenomics·2025
Same author

Associations between lifestyle factors, physiological conditions, and epigenetic age acceleration in an Asian population.

Biogerontology·2025
Same author

Causal effects of cardiovascular health on five epigenetic clocks.

Clinical epigenetics·2024
Same author

Gene-Environment Interactions and Gene-Gene Interactions on Two Biological Age Measures: Evidence from Taiwan Biobank Participants.

Advanced biology·2024
Same author

Epigenetic age acceleration mediates the association between smoking and diabetes-related outcomes.

Clinical epigenetics·2023
Same journal

OmicsTransformer: Self-Supervised Masked Consistency and Uncertainty-Aware Fusion for Robust Multi-Omics Prediction.

Bioinformatics (Oxford, England)·2026
Same journal

Computational Tool Choice Impacts CRISPR Spacer-Proto spacer Detection.

Bioinformatics (Oxford, England)·2026
Same journal

ARISE: RNA-Anchored Shared-Edge Topology and Hierarchical Fusion for Spatial Multi-Omics Integration.

Bioinformatics (Oxford, England)·2026
Same journal

Interactive exploration of biobank-scale ancestral recombination graphs with Lorax.

Bioinformatics (Oxford, England)·2026
Same journal

PepMCP: A Graph-Based Membrane Contact Probability Predictor for Membrane-Lytic Antimicrobial Peptides.

Bioinformatics (Oxford, England)·2026
Same journal

ARGscape: A modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 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

10.1K

Detecting gene-environment interactions from multiple continuous traits.

Wan-Yu Lin1,2

  • 1Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei 100, Taiwan.

Bioinformatics (Oxford, England)
|June 25, 2024
PubMed
Summary
This summary is machine-generated.

A new multivariate scale test (MST) effectively detects gene-environment interactions (GxE) in complex diseases by analyzing multiple traits. This method offers greater power than traditional univariate tests for identifying genetic influences on health outcomes.

More Related Videos

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

3.6K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K

Related Experiment Videos

Last Updated: Jun 23, 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

10.1K
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

3.6K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Gene-environment interactions (GxE) influence disease risk, varying with environmental exposures.
  • Complex diseases often manifest with multiple continuous traits (e.g., obesity, diabetes).

Purpose of the Study:

  • To develop and evaluate a multivariate scale test (MST) for detecting GxE in diseases with multiple traits.
  • To identify specific traits and environmental factors contributing to significant GxE signals.

Main Methods:

  • Developed a multivariate scale test (MST) to analyze GxE across multiple continuous traits.
  • Compared the statistical power of MST against the univariate scale test (UST) via simulations.
  • Applied MST to large-scale genetic data from the Taiwan Biobank.

Main Results:

  • MST demonstrated potential for increased power compared to UST by integrating multiple traits and employing a less stringent multiple testing penalty.
  • Application to Taiwan Biobank data identified 18 independent variance quantitative trait loci and 41 significant GxE signals across eight trait domains.
  • The study analyzed over 2.5 million SNPs in nearly 119,000 individuals.

Conclusions:

  • MST is a powerful tool for uncovering complex gene-environment interactions in diseases characterized by multiple traits.
  • The identified GxE signals provide insights into the genetic architecture of complex diseases.
  • The developed methodology and findings are publicly available for further research.