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

769
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...
769
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

7.0K
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...
7.0K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

376
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
376
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

353
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
353
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

469
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
469
Interpreting R Charts01:22

Interpreting R Charts

174
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
174

You might also read

Related Articles

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

Sort by
Same author

DNA damage response inhibitors in pancreatic cancer: progress and challenges.

Frontiers in oncology·2026
Same author

ZFP91 restricts RSV replication by driving K48-linked ubiquitination and proteasomal degradation of M2-1.

Cellular and molecular life sciences : CMLS·2026
Same author

From diversity to stability: Acidification, antagonism, and resistance driven by Acetilactobacillus jinshanensis during jiang-flavor baijiu fermentation.

Food microbiology·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

Effect of Left Ventricular Ejection Fraction on Outcomes With Drug-coated Balloons vs Drug-Eluting Stents for De Novo Coronary Artery Disease.

The Canadian journal of cardiology·2026
Same author

CD74 deficiency protects against doxorubicin cardiotoxicity through RRM2-mediated regulation of ferroptosis.

Acta pharmaceutica Sinica. B·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

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

Related Experiment Video

Updated: Nov 6, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.8K

GEInter: an R package for robust gene-environment interaction analysis.

Mengyun Wu1, Xing Qin1, Shuangge Ma2

  • 1School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China.

Bioinformatics (Oxford, England)
|May 7, 2021
PubMed
Summary
This summary is machine-generated.

A new R package, GEInter, offers robust analysis for gene-environment (G-E) interactions, addressing limitations in current methods for complex diseases. This tool handles data contamination and missingness, improving G-E interaction research.

More Related Videos

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.6K
Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
10:40

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

10.7K

Related Experiment Videos

Last Updated: Nov 6, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.8K
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.6K
Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
10:40

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

10.7K

Area of Science:

  • Genetics
  • Environmental Health
  • Biostatistics

Background:

  • Gene-environment (G-E) interactions are crucial for understanding complex diseases, offering insights beyond individual gene or environmental effects.
  • Existing analytical methods and software often struggle with data contamination and long-tailed distributions common in biological data.

Purpose of the Study:

  • To develop a comprehensive and robust R package for analyzing gene-environment (G-E) interactions.
  • To address the limitations of current software in handling data contamination and complex distributions.

Main Methods:

  • Development of the GEInter R package, designed for robust G-E interaction analysis.
  • Implementation of methods for both marginal and joint G-E interaction analyses.
  • Capability to handle data with and without missing values, and for continuous and censored survival outcomes.

Main Results:

  • The GEInter package provides comprehensive identification, estimation, visualization, and prediction for G-E interactions.
  • Demonstrated utility through analysis of The Cancer Genome Atlas (TCGA) data.
  • The package is publicly available via CRAN.

Conclusions:

  • GEInter fills a significant gap in the available tools for G-E interaction analysis.
  • The package offers broad applicability for researchers studying complex diseases.
  • Robust analysis of G-E interactions is essential for advancing our understanding of disease etiology.