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Related Experiment Videos

Two-Stage sampling designs for gene association studies.

Duncan Thomas1, Rongrong Xie, Mulugeta Gebregziabher

  • 1Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089-9011, USA. dthomas@usc.edu

Genetic Epidemiology
|November 16, 2004
PubMed
Summary
This summary is machine-generated.

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Two-stage case-control designs improve cost-efficiency for testing single nucleotide polymorphism (SNP) associations with disease. These designs may become obsolete as genotyping costs decrease and public haplotype data expands.

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Case-control studies are essential for disease association research.
  • Two-stage designs utilize a subsample for initial SNP selection.
  • Tagging strategies aim to efficiently capture genetic variation.

Purpose of the Study:

  • To evaluate the cost-efficiency of two-stage case-control designs for SNP-disease association testing.
  • To compare SNP-tagging and haplotype-tagging approaches within these designs.
  • To assess the utility of two-stage designs in the context of falling genotyping costs and increasing public data.

Main Methods:

  • Development of a pseudolikelihood method combining data from main and substudies.
  • Comparison of SNP-tagging and haplotype-tagging strategies.

Related Experiment Videos

  • Analysis of cost-efficiency for relative risk estimation and variant location mapping.

    • Main Results:

    • Two-stage designs offer improved cost-efficiency over single-stage designs for estimating relative risk, even without the causal SNP in the tag set.
    • Optimal selection of cases and controls enhances efficiency.
    • The relative efficiency for mapping causal variants is also considered.

    Conclusions:

    • Two-stage case-control designs can be cost-effective for genetic association studies.
    • The necessity of these designs may diminish due to technological and data advancements.
    • Continued evaluation of design efficiency remains important in genetic epidemiology.