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Related Experiment Videos

Methods for interaction analyses using family-based case-control data: conditional logistic regression versus

Dana B Hancock1, Eden R Martin, Yi-Ju Li

  • 1Center for Human Genetics, Duke University Medical Center, Durham, NC, USA.

Genetic Epidemiology
|June 15, 2007
PubMed
Summary
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Generalized estimating equations (GEE) offer greater power for detecting gene-gene and gene-environment interactions in family studies. However, caution is advised due to inflated type I error rates when association exists without linkage.

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Late-onset diseases often result from complex gene-gene and gene-environment interactions.
  • Accurate statistical modeling is crucial for dissecting these interactions in family-based studies, especially with missing parental data.

Purpose of the Study:

  • To compare the performance of conditional logistic regression (CLR) and generalized estimating equations (GEE) for modeling gene-gene and gene-environment interactions.
  • To evaluate these methods under various genetic and environmental scenarios, including linkage and association.

Main Methods:

  • Simulated family-based data sets using the SIMLA program, incorporating disease genes, environmental risk factors, and interaction effects.
  • Four scenarios were simulated: linkage and association, linkage without association, association without linkage, and absence of both.

Related Experiment Videos

  • Conditional logistic regression (CLR) and generalized estimating equations (GEE) with independence and exchangeable correlation matrices were applied.

    • Main Results:

    • Both CLR and GEE were valid for detecting association in the presence of linkage.
    • Type I error rates were inflated for association without linkage, particularly with GEE.
    • GEE demonstrated higher power than CLR for detecting main and interactive effects; GEE with an independence matrix showed lower type I error and less biased odds ratio (OR) estimates compared to the exchangeable matrix.

    Conclusions:

    • GEE is recommended for maximizing statistical power in detecting gene-gene and gene-environment interactions in family studies.
    • Users should exercise caution with GEE when population stratification (association without linkage) is possible, and carefully interpret OR estimates.