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Segregation analysis of case-control data using generalized estimating equations

A S Whittemore1, G Gong

  • 1Department of Health Research and Policy, Stanford University School of Medicine, California 94305.

Biometrics
|December 1, 1994
PubMed
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Generalized estimating equations (GEEs) provide a flexible semiparametric approach for analyzing family disease data in case-control studies. This method enhances genetic model fitting by accommodating complex correlations and covariates, proving computationally tractable.

Area of Science:

  • Biostatistics
  • Genetic Epidemiology
  • Statistical Genetics

Background:

  • Traditional genetic analysis methods often rely on strong, unverifiable assumptions.
  • Analyzing family data in case-control studies presents challenges due to correlated disease statuses within families.

Purpose of the Study:

  • To introduce and apply a semiparametric approach using Generalized Estimating Equations (GEEs) for fitting genetic models to binary disease data in family studies.
  • To offer a more computationally tractable and assumption-lean alternative to existing segregation analysis methods.

Main Methods:

  • Utilized Generalized Estimating Equations (GEEs) to model disease probabilities and intra-family correlation coefficients.
  • Modeled these quantities as nonlinear functions of unobserved genotypes, environmental covariates, and unknown parameters, accounting for family ascertainment.

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  • Employed goodness-of-fit tests by allowing flexible correlation coefficient forms, regressed against relevant covariates.

    • Main Results:

    • The GEE approach was successfully applied to both simulated and real case-control family data.
    • Demonstrated the method's ability to handle two-way and three-way correlations within families.
    • Showcased the semiparametric approach's robustness against unverifiable assumptions.

    Conclusions:

    • GEEs offer a powerful and flexible framework for genetic analysis in family-based case-control studies.
    • This method provides a computationally efficient and less assumption-dependent alternative for genetic modeling and segregation analysis.