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

Mixed modelling to characterize genotype-phenotype associations.

A S Foulkes1, M Reilly, L Zhou

  • 1Department of Biostatistics, University of Massachusetts, School of Public Health, 404 Arnold House, 715N. Pleasant Street, Amherst, MA 01003-9304, USA. foulkes@schoolph.umass.edu

Statistics in Medicine
|February 8, 2005
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

A catalog of associations between rare coding variants and COVID-19 outcomes.

medRxiv : the preprint server for health sciences·2021
Same author

Genome-wide analysis in 756,646 individuals provides first genetic evidence that <i>ACE2</i> expression influences COVID-19 risk and yields genetic risk scores predictive of severe disease.

medRxiv : the preprint server for health sciences·2021
Same author

Non-random sampling leads to biased estimates of transcriptome association.

Scientific reports·2020
Same author

Development and feasibility of quantitative dynamic cardiac imaging for mice using μSPECT.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology·2020
Same author

HIV-1-negative female sex workers sustain high cervical IFNɛ, low immune activation, and low expression of HIV-1-required host genes.

Mucosal immunology·2015
Same author

Deletion of murine Arv1 results in a lean phenotype with increased energy expenditure.

Nutrition & diabetes·2015
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
See all related articles

Mixed effects models effectively analyze gene-environment interactions for disease progression. This approach identifies genetic contributions and specific multi-locus genotypes interacting with environmental factors, improving cardiovascular disease risk prediction.

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Analyzing complex gene-gene and gene-environment interactions is challenging due to numerous candidate genes and unknown interaction patterns.
  • Existing methods for gene interaction analysis may fail to detect high-order interactions without main effects and lack confounder control.
  • Cardiovascular disease (CVD) risk is influenced by genetic and environmental factors, necessitating robust analytical methods.

Purpose of the Study:

  • To propose and apply mixed effects models for characterizing associations between multiple gene polymorphisms, environmental factors, and disease progression.
  • To overcome limitations of existing approaches in identifying high-order gene-environment interactions and controlling for confounders.
  • To identify specific multi-locus genotypes that interact with environmental factors in predicting disease outcomes.

Related Experiment Videos

Main Methods:

  • Utilized mixed effects models and associated testing procedures to analyze genetic and environmental influences on disease progression.
  • Applied the proposed modeling approach to a cohort of subjects at risk for cardiovascular disease.
  • Investigated four genetic polymorphisms in three related genes.

Main Results:

  • The mixed effects model approach successfully tested for significant genetic contributions to disease outcome variability.
  • Identified contributions through main effects of multi-locus genotypes and/or interactions between genotype and environmental factors.
  • Successfully identified specific multi-locus genotypes interacting with environmental factors in predicting disease outcome.

Conclusions:

  • Mixed effects models offer a flexible framework for controlling confounders in genetic association studies.
  • This approach effectively identifies interactions among multiple genes and environmental factors influencing disease progression.
  • The methodology enhances the understanding of genetic and environmental interplay in complex diseases like cardiovascular disease.