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

328
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...
328
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
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

376
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...
376
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
Heritability01:06

Heritability

205
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"...
205
Multiple Allele Traits01:49

Multiple Allele Traits

34.3K
The Concept of Multiple Allelism
34.3K

You might also read

Related Articles

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

Sort by
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

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

Statistics in medicine·2026
Same author

Subgroup Analysis of Differential Networks with Latent Variables.

Statistics and computing·2026
Same author

Robust Heterogeneity Adjustment for Gaussian Graphical Model With Latent Variables.

Statistics in medicine·2026
Same author

DNN-based semiparametric AFT model for integrating genomic and pathological imaging data in cancer prognosis.

Biometrics·2026
Same journal

Extracting Genetically-Imputed Causal Features From ECG Data.

Statistical analysis and data mining·2026
Same journal

Triangulation-Based Spatial Clustering for Adjacent Data With Heterogeneous Density.

Statistical analysis and data mining·2026
Same journal

Bayesian Posterior Interval Calibration to Improve the Interpretability of Observational Studies.

Statistical analysis and data mining·2025
Same journal

A treeless absolutely random forest with closed-form estimators of expected proximities.

Statistical analysis and data mining·2024
Same journal

Data-driven Stochastic Model for Quantifying the Interplay Between Amyloid-beta and Calcium Levels in Alzheimer's Disease.

Statistical analysis and data mining·2024
Same journal

Integrative Learning of Structured High-Dimensional Data from Multiple Datasets.

Statistical analysis and data mining·2023
See all related articles

Related Experiment Video

Updated: Jul 9, 2025

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

7.6K

A tree-based gene-environment interaction analysis with rare features.

Mengque Liu1, Qingzhao Zhang2, Shuangge Ma3

  • 1School of Journalism and New Media, Xi'an Jiaotong Universit0y, Shanxi Xi'an, China.

Statistical Analysis and Data Mining
|December 4, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel gene-environment interaction analysis method for rare genetic features. It improves the identification of complex disease associations by efficiently borrowing information from neighboring rare variants.

Keywords:
gene–environment interaction analysispenalized joint regressionrare featurestree-based aggregation

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

3.7K
Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

5.6K

Related Experiment Videos

Last Updated: Jul 9, 2025

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

7.6K
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

3.7K
Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

5.6K

Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Gene-environment (G-E) interaction analysis is crucial for understanding complex diseases.
  • Joint G-E analysis faces challenges with high dimensionality, weak signals, and variable selection hierarchies, especially for rare genetic features.
  • Existing methods for rare features are primarily for marginal analysis and not directly applicable to joint G-E interaction analysis.

Purpose of the Study:

  • To develop a new gene-environment interaction analysis approach specifically tailored for rare genetic features.
  • To address the limitations of existing methods in joint G-E interaction analysis concerning rare variants.
  • To improve the identification of significant main effects and interactions involving rare genetic factors.

Main Methods:

  • Developed a novel approach building upon a recent tree-based data aggregation technique for main-effect-only analysis.
  • The method incorporates efficient information borrowing from neighboring rare features.
  • Employs penalization for variable selection, regularized estimation, and respects the variable selection hierarchy.

Main Results:

  • Simulation studies demonstrated more accurate identification of important interactions and main effects compared to competing methods.
  • The proposed approach showed satisfactory prediction and stability performance in the NFBC1966 study analysis.
  • Findings from the NFBC1966 study differed from those obtained using alternative methods.

Conclusions:

  • The developed approach offers an effective strategy for joint gene-environment interaction analysis involving rare genetic features.
  • It enhances the ability to detect complex disease associations by leveraging information from rare variants.
  • The method provides a valuable tool for genetic epidemiology research, offering improved accuracy and performance.