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

676
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...
676
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

6.8K
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...
6.8K
Epistasis Analysis01:09

Epistasis Analysis

5.4K
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...
5.4K
Genetic Drift03:33

Genetic Drift

41.4K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
41.4K
Genetics of Speciation02:16

Genetics of Speciation

20.0K
Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
20.0K
Genetic Screens02:46

Genetic Screens

5.2K
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...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Predictive Value of Anxiety State During Acute Herpes Zoster for the Development of Postherpetic Neuralgia.

Actas espanolas de psiquiatria·2026
Same author

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.

Journal of machine learning research : JMLR·2026
Same author

[The impact of ultrasound-based diaphragmatic targeted functional exercise bundle strategy on clinical outcomes of patients with acute exacerbation of chronic obstructive pulmonary disease combined with type II respiratory failure receiving mechanical ventilation].

Zhonghua wei zhong bing ji jiu yi xue·2026
Same author

Medicare Insurance Type and Broad Genomic Profiling in Metastatic Cancer.

JAMA network open·2026
Same author

Doubly Robust Estimators of the Restricted Mean Time in Favor Estimands in Individual- and Cluster-Randomized Trials.

Statistics in medicine·2026
Same author

JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.

The annals of applied statistics·2026
Same journal

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

Related Experiment Video

Updated: Oct 14, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.3K

Gene-environment interaction identification via penalized robust divergence.

Mingyang Ren1,2, Sanguo Zhang1,2, Shuangge Ma3

  • 1School of Mathematics Sciences, University of Chinese Academy of Sciences, Beijing, P. R. China.

Biometrical Journal. Biometrische Zeitschrift
|November 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces robust statistical methods for identifying gene-environment interactions in cancer research, improving accuracy with contaminated data. These new approaches respect hierarchical structures, outperforming existing methods in simulations and real-world cancer datasets.

Keywords:
divergencegene-environment interactionhierarchical structurepenalized identificationrobustness

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K
Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
08:09

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

7.7K

Related Experiment Videos

Last Updated: Oct 14, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.3K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K
Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
08:09

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

7.7K

Area of Science:

  • Genomics and Bioinformatics
  • Cancer Research
  • Statistical Genetics

Background:

  • High-throughput cancer studies require accurate identification of gene-environment interactions for understanding disease outcomes.
  • Existing methods often fail to respect the hierarchical structure of main effects and interactions, and are sensitive to data contamination and long-tailed distributions.

Purpose of the Study:

  • To develop robust statistical methods for identifying gene-environment interactions that accommodate data contamination and long-tailed distributions.
  • To ensure methods respect the hierarchical structure of 'main effect, interaction' in genetic association studies.
  • To improve the accuracy of identifying significant gene-environment interactions in complex datasets.

Main Methods:

  • Proposed robust methods based on \(\phi\)-divergence and density power divergence to handle data contamination and long-tailed distributions.
  • Employed a hierarchical sparse group penalty for regularized estimation and selection, preserving the 'main effect, interaction' hierarchy.
  • Implemented the methods using an efficient group coordinate descent algorithm for practical application.

Main Results:

  • Simulations demonstrated that the proposed robust methods significantly outperform existing alternatives when data contamination is present, leading to more accurate identification of interactions.
  • The methods successfully identified important gene-environment interactions while respecting the specified hierarchical structure.

Conclusions:

  • The developed robust methods offer a significant advancement for gene-environment interaction analysis in high-throughput cancer studies, particularly in the presence of data challenges.
  • The approach was successfully applied to The Cancer Genome Atlas (TCGA) triple-negative breast cancer and Gene Environment Association Studies (GENEVA) Type 2 Diabetes datasets, demonstrating its real-world applicability.