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Family-based association studies.

W J Gauderman1, J S Witte, D C Thomas

  • 1Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90089, USA. jimg@rcf.usc.edu

Journal of the National Cancer Institute. Monographs
|June 16, 2000
PubMed
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Family-based case-control studies using relatives as controls effectively avoid population stratification. While sibling controls reduce efficiency, they can improve gene-environment interaction estimates for rare genes.

Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Case-control studies are crucial for investigating gene associations.
  • Population stratification can lead to spurious gene associations in traditional designs.
  • Family-based controls offer a solution to population stratification.

Purpose of the Study:

  • To review case-control designs utilizing relatives as controls.
  • To analyze the validity and efficiency of sibling, cousin, and pseudosibling controls.
  • To compare family-based designs with standard population-control designs.

Main Methods:

  • Review of case-control designs using sibling, cousin, and pseudosibling controls.
  • Application of a conditional likelihood framework for data analysis.

Related Experiment Videos

  • Efficiency comparisons between family-based and population-based designs.

    • Main Results:

    • Family-based designs effectively mitigate population stratification bias.
    • Sibling controls yield approximately 50% efficiency for genetic relative risk compared to population controls.
    • Cousin controls maintain approximately 90% efficiency.
    • Sibling controls can enhance efficiency for estimating gene-environment (G x E) interactions with rare genes.
    • Restricted designs, like case-sibling pairs with affected parents, improve efficiency.

    Conclusions:

    • Family-based case-control studies are a viable alternative to population-based designs.
    • These designs offer advantages in controlling for population stratification.
    • Specific family-based designs provide efficient estimation of genetic effects and interactions.