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Optimum two-stage designs in case-control association studies using false discovery rate.

Aya Kuchiba1, Noriko Y Tanaka2, Yasuo Ohashi3

  • 1Department of Biostatistics/Epidemiology and Preventive Health Sciences, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. kuchiba@epistat.m.u-tokyo.ac.jp.

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|September 28, 2006
PubMed
Summary

This study introduces a cost-effective two-stage genetic association study design. It reduces genotyping costs by 40-60% while maintaining high power for identifying disease susceptibility loci.

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Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Genetic association studies are crucial for identifying disease susceptibility loci.
  • Traditional case-control designs with numerous genetic markers are often cost-prohibitive.
  • Two-stage designs offer potential cost-effectiveness in genetic research.

Purpose of the Study:

  • To propose a two-stage genetic association study design that controls the false discovery rate.
  • To optimize sample sizes and marker selection criteria for cost reduction in genotyping.
  • To compare the power and cost-efficiency of the proposed two-stage design against one-stage designs.

Main Methods:

  • Implemented a false discovery rate control for multiple testing in a two-stage design.
  • Optimized sample sizes and marker selection criteria based on prior probabilities of marker-disease association.
  • Compared expected power and genotyping costs of one-stage versus the proposed two-stage designs with fixed total sample size and independent markers.

Main Results:

  • The proposed two-stage procedure significantly reduced genotyping costs, typically by 40-60%.
  • The two-stage design achieved power comparable to traditional one-stage designs.
  • Optimized parameters (sample size, selection criteria) were defined as a function of prior association probabilities.

Conclusions:

  • The developed two-stage design offers a cost-effective approach for genetic association studies.
  • This method maintains statistical power while substantially decreasing genotyping expenses.
  • The efficiency of the design is influenced by the accurate specification of prior probabilities for marker-disease associations.