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

1.6K
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...
1.6K
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

8.1K
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...
8.1K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

1.0K
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
1.0K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

531
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
531
Epistasis Analysis01:09

Epistasis Analysis

6.2K
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...
6.2K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.6K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.6K

You might also read

Related Articles

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

Sort by
Same author

Development of a Biology-Informed Chemical Mixture Index for Oxidative Stress and Mortality in NHANES 2005-2010: A Survey-Weighted Quantile G-Computation Approach.

medRxiv : the preprint server for health sciences·2026
Same author

Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data.

Genetic epidemiology·2026
Same author

Maternal inflammation and oxidative stress during pregnancy and emotional-behavioral problems in children aged 1.5-3 years: A longitudinal repeated-measures study.

Journal of affective disorders·2026
Same author

Privacy-enhancing sequential learning under heterogeneous selection bias in multi-site electronic health records data.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Evaluation of integrated, multimedia biomarkers of prenatal metals exposure in association with child neurodevelopment in Puerto Rico.

Journal of exposure science & environmental epidemiology·2026
Same author

Prenatal phthalate exposure and emotional-behavioral problems in children aged 1.5 to 3 years from the PROTECT birth cohort.

Journal of exposure science & environmental epidemiology·2026

Related Experiment Video

Updated: Mar 27, 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

8.0K

Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification.

Philip S Boonstra, Bhramar Mukherjee, Stephen B Gruber

    American Journal of Epidemiology
    |January 13, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Exposure misclassification impacts genome-wide gene-environment interaction (G-E) testing. Modular and joint tests show robustness, offering insights into G-E interaction discovery methods.

    Keywords:
    case-controlgene discoverygene-environment independencegenome-wide associationmodular methodsmultiple testingscreening testweighted hypothesis test

    More Related Videos

    Using Caenorhabditis elegans for Studying Trans- and Multi-Generational Effects of Toxicants
    08:58

    Using Caenorhabditis elegans for Studying Trans- and Multi-Generational Effects of Toxicants

    Published on: July 29, 2019

    7.3K
    Long-term Behavioral and Reproductive Consequences of Embryonic Exposure to Low-dose Toxicants
    07:08

    Long-term Behavioral and Reproductive Consequences of Embryonic Exposure to Low-dose Toxicants

    Published on: March 6, 2018

    6.8K

    Related Experiment Videos

    Last Updated: Mar 27, 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

    8.0K
    Using Caenorhabditis elegans for Studying Trans- and Multi-Generational Effects of Toxicants
    08:58

    Using Caenorhabditis elegans for Studying Trans- and Multi-Generational Effects of Toxicants

    Published on: July 29, 2019

    7.3K
    Long-term Behavioral and Reproductive Consequences of Embryonic Exposure to Low-dose Toxicants
    07:08

    Long-term Behavioral and Reproductive Consequences of Embryonic Exposure to Low-dose Toxicants

    Published on: March 6, 2018

    6.8K

    Area of Science:

    • Genetics
    • Epidemiology
    • Biostatistics

    Background:

    • Genome-wide gene-environment (G-E) interaction studies aim to identify genetic risk factors and understand disease relationships.
    • Method performance relies on accurate G-E association estimates, which can be biased by exposure misclassification.

    Purpose of the Study:

    • To evaluate the impact of exposure misclassification on various genome-wide G-E interaction detection methods.
    • To compare the robustness of different statistical approaches under exposure misclassification scenarios.

    Main Methods:

    • Simulation study assessing 7 single-step/modular screening methods and 7 joint tests for G-E interaction.
    • Evaluation based on family-wise type I error rate and statistical power under exposure misclassification.

    Main Results:

    • Modular screening methods demonstrate greater robustness to exposure misclassification.
    • Joint tests incorporating marginal gene effects also exhibit similar robustness.
    • These findings confirm previous research on method performance.

    Conclusions:

    • Understanding the strengths and limitations of G-E interaction methods is crucial when dealing with exposure misclassification.
    • Modular and joint tests are more reliable for genome-wide G-E interaction searches in the presence of data inaccuracies.