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

1.0K
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...
1.0K
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

7.3K
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...
7.3K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

934
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...
934
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

945
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
945
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

507
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...
507
Frequency-dependent Selection01:21

Frequency-dependent Selection

23.0K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
23.0K

You might also read

Related Articles

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

Sort by
Same author

MixedBayes: An R Package for Longitudinal Gene-Environment Interaction Analysis Using Robust Sparse Bayesian Mixed Models.

Entropy (Basel, Switzerland)·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

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 author

Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.

Statistics in medicine·2026
Same author

Analysis of cross-platform health communication with a network approach.

Biometrics·2025
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jan 1, 2026

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

7.9K

Semiparametric Bayesian variable selection for gene-environment interactions.

Jie Ren1, Fei Zhou1, Xiaoxi Li1

  • 1Department of Statistics, Kansas State University, Manhattan, Kansas.

Statistics in Medicine
|December 22, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model for gene-environment (G×E) interactions, improving the identification of complex disease causes. The method efficiently detects both linear and nonlinear G×E effects in high-dimensional genetic data.

Keywords:
Bayesian variable selectionMCMCgene-environment interactionshigh-dimensional genomic datasemiparametric modeling

More Related Videos

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

8.5K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.2K

Related Experiment Videos

Last Updated: Jan 1, 2026

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

7.9K
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

8.5K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.2K

Area of Science:

  • Genetics and Bioinformatics
  • Statistical Modeling
  • Computational Biology

Background:

  • Complex diseases arise from interactions between genetic variants and environmental factors.
  • Studying gene-environment (G×E) interactions is crucial for understanding disease etiology.
  • Existing Bayesian methods face challenges with high-dimensional data and complex environmental influences in G×E studies.

Purpose of the Study:

  • To develop a novel semiparametric Bayesian variable selection model for G×E interactions.
  • To simultaneously investigate linear and nonlinear G×E interactions.
  • To enable structural identification distinguishing nonlinear interactions from main effects within a Bayesian framework.

Main Methods:

  • Proposed a novel semiparametric Bayesian variable selection model.
  • Incorporated spike-and-slab priors at individual and group levels for sparse effect identification.
  • Developed a method for distinguishing nonlinear interactions from main effects.

Main Results:

  • The proposed Bayesian method efficiently conducts variable selection for G×E interactions.
  • Simulations demonstrate superior performance in identification and prediction compared to existing methods.
  • The model successfully identifies significant main and interaction effects in high-dimensional SNP data.

Conclusions:

  • The novel Bayesian approach offers a powerful tool for analyzing complex G×E interactions.
  • This method has significant implications for high-throughput genetic profiling and disease research.
  • It enhances the ability to elucidate disease etiology by accurately identifying genetic and environmental contributions.