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

Modeling age x major gene interaction by a variance component approach.

S S Shete1, J Chen, X Zhou

  • 1Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd., Box 189, Houston, TX 77030, USA.

Genetic Epidemiology
|January 17, 2002
PubMed
Summary

This study introduces a mixed effects model to analyze how major genes and polygenes interact with age, improving genetic linkage analysis. The model successfully detected age-gene interactions for a quantitative trait in simulated data.

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

Genetic variants in the folate metabolic pathway genes predict cutaneous melanoma-specific survival.

The British journal of dermatology·2020
Same author

Innovations in risk-stratification and treatment of Veterans with oropharynx cancer; roadmap of the 2019 Field Based Meeting.

Oral oncology·2019
Same author

Genome-wide association study across European and African American ancestries identifies a SNP in DNMT3B contributing to nicotine dependence.

Molecular psychiatry·2017
Same author

Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence.

Translational psychiatry·2015
Same author

Genome-wide association study identifies HLA 8.1 ancestral haplotype alleles as major genetic risk factors for myositis phenotypes.

Genes and immunity·2015
Same author

Changes in screening behaviors and attitudes toward screening from pre-test genetic counseling to post-disclosure in Lynch syndrome families.

Clinical genetics·2013

Area of Science:

  • Genetics
  • Biostatistics
  • Quantitative Trait Analysis

Background:

  • Variance component methods are widely used for genetic linkage analysis due to efficiency.
  • Phenotypic variability is influenced by genetic factors and environmental interactions, such as age.

Purpose of the Study:

  • To develop and apply a mixed effects model to analyze phenotypic variability influenced by age-dependent gene interactions.
  • To assess the model's performance in detecting gene-age interactions in a quantitative trait.

Main Methods:

  • Modeling individual phenotypic variability using a mixed effects model.
  • Incorporating interactions between major gene effects, polygene effects, and age.
  • Applying the model to simulated quantitative trait data (Q4) from the Genetic Analysis Workshop 12.

Related Experiment Videos

Main Results:

  • The proposed mixed effects model effectively captured age-dependent genetic effects.
  • The model successfully detected significant interactions between major gene effects and age for the quantitative trait Q4.
  • Demonstrated the utility of the model in complex genetic analyses with age as a covariate.

Conclusions:

  • Mixed effects models provide a powerful framework for dissecting complex genetic architectures, including age-gene interactions.
  • The developed model enhances the ability to identify age-specific genetic influences on quantitative traits.
  • This approach offers improved insights into genetic linkage analysis by accounting for dynamic genetic effects.