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 Experiment Videos

Detecting interactions between gene, site, and environmental variables using GAP.

J Shin1, M Corey

  • 1Department of Public Health Sciences, University of Toronto, Ontario, Canada.

Genetic Epidemiology
|December 22, 1999
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Search for Light Pseudoscalar Bosons, Pair-Produced in Higgs Boson Decays in the Four-Electron Final State in Proton-Proton Collisions at sqrt[s]=13  TeV.

Physical review letters·2026
Same author

Observation of Suppressed Charged-Particle Production in Ultrarelativistic Oxygen-Oxygen Collisions.

Physical review letters·2026
Same author

Measurement of D^{0} Meson Photoproduction in Ultraperipheral Heavy Ion Collisions.

Physical review letters·2026
Same author

Development and application of a low-noise, high-speed optical detector module for carbon density fluctuation measurements on Wendelstein 7-X.

The Review of scientific instruments·2026
Same author

Observation of tWZ Production at the CMS Experiment.

Physical review letters·2026
Same author

First Exclusive Reconstruction of the B^{*+}, B^{*0}, and B_{s}^{*0} Mesons and Precise Measurement of Their Masses.

Physical review letters·2026
Same journal

Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.

Genetic epidemiology·2026
Same journal

Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease.

Genetic epidemiology·2026
Same journal

DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data.

Genetic epidemiology·2026
Same journal

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

Genetic epidemiology·2026
Same journal

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Genetic epidemiology·2026
Same journal

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

Genetic epidemiology·2026
See all related articles

Regressive models detected significant gene x environment x site interactions in genetic studies. These models can uncover complex genetic interactions in diverse populations, even with flawed data assumptions.

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Complex interactions between genes, environment, and research site can influence disease risk.
  • Understanding these multi-way interactions is crucial for accurate genetic studies.
  • Previous analyses may have been limited by assumptions about genetic transmission and population heterogeneity.

Purpose of the Study:

  • To detect gene x environment x site interactions using regressive models in GAW11 Problem 2.
  • To investigate gene x environment interactions within specific research sites.
  • To assess the robustness of regressive models in heterogeneous populations.

Main Methods:

  • Utilized regressive models incorporating measured variables and genetic parameters.

Related Experiment Videos

  • Analyzed replicates 1 to 5 for gene x environment x site interactions.
  • Performed site-specific analyses for gene x environment interactions, comparing segregation and linkage analyses.

    • Main Results:

    • Significant three-way gene x environment x site interactions were identified across all models.
    • A consistent gene x environment interaction pattern was observed at one research site.
    • No significant gene x environment interactions were detected at other sites.

    Conclusions:

    • Regressive modeling effectively identifies complex gene x environment x site interactions.
    • These methods are valuable for analyzing heterogeneous population data, even with violated ascertainment assumptions.
    • Site-specific analyses are essential for pinpointing localized gene-environment interactions.