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A log-linear model for binary pedigree data.

J L Hopper, P L Derrick

    Genetic Epidemiology. Supplement
    |January 1, 1986
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a pedigree model to assess familial aggregation of diseases like breast cancer. Accurate estimation of disease concordance requires population prevalence data and ascertainment correction for reliable results.

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    Area of Science:

    • Epidemiology
    • Statistical Genetics
    • Biostatistics

    Background:

    • Familial aggregation of diseases is a key area in epidemiology.
    • Log-linear models provide a framework for analyzing binary data in familial studies.
    • Understanding disease concordance is crucial for genetic and public health research.

    Purpose of the Study:

    • To develop and apply a pedigree model for binary data to examine familial aggregation of disease status.
    • To assess the impact of ascertainment and population prevalence on disease concordance estimation.
    • To explore the incorporation of ancillary risk factors into familial disease models.

    Main Methods:

    • Developed a pedigree model motivated by log-linear modeling for binary disease data.
    • Utilized odds ratios to express disease concordance between relatives.

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  • Applied the model to breast cancer data, considering ascertainment through an affected proband.
  • Incorporated population prevalence estimates and ascertainment correction for accurate concordance estimation.

    • Main Results:

    • Disease concordance estimation is critically dependent on accurate population prevalence and ascertainment correction.
    • The developed model demonstrated the multiplicative nature of relative risks in pedigrees.
    • The model's flexibility allows for the inclusion of ancillary risk factors, such as genetic markers.

    Conclusions:

    • The proposed pedigree model provides a robust method for assessing familial aggregation of diseases.
    • Accurate estimation of disease concordance necessitates careful consideration of ascertainment bias and population disease probabilities.
    • Future studies should collect comprehensive risk factor data, including genetic information, for all individuals in a pedigree.