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Statistical Screening Method for Genetic Factors Influencing Susceptibility to Common Diseases in a Two-Stage Genome-Wide Association Study

Author

Listed:
  • Sato Yasunori

    (Harvard School of Public Health)

  • Laird Nan

    (Harvard School of Public Health)

  • Suganami Hideki

    (Tokyo University of Science)

  • Hamada Chikuma

    (Tokyo University of Science)

  • Niki Naoto

    (Tokyo University of Science)

  • Yoshimura Isao

    (Tokyo University of Science)

  • Yoshida Teruhiko

    (National Cancer Center Research Institute)

Abstract

A genome-wide association study (GWAS) is a standard strategy for detecting disease susceptibility genes, despite unsettled controversies on many aspects, including optimal study design and statistical analysis. As for study design, a two-stage design has been applied to maximize cost-effectiveness. However, there has been little consensus on appropriate statistical analysis for two-stage design. Thereby perplexing the researchers as to which statistical measures should be applied at the first stage, and how to determine the significance level of the differences at the second stage. Here, using simulation studies, we compared statistical operating characteristics of the screening in a two-stage GWAS by taking into consideration the proper balance of false-positive and false-negative error. As a result, the lower bound of confidence interval for odds ratios is recommended as the first stage measure, and then the second stage criteria should primarily depend on the purpose of the genome screen or its role in the overall gene-hunting scheme. Based on the simulation study, we suggest rules of thumb about which statistics to use in a given situation. An application of all operating characteristics of the screening method to an actual GWAS for gastric cancer illustrates the practical relevance of our discussion.

Suggested Citation

  • Sato Yasunori & Laird Nan & Suganami Hideki & Hamada Chikuma & Niki Naoto & Yoshimura Isao & Yoshida Teruhiko, 2009. "Statistical Screening Method for Genetic Factors Influencing Susceptibility to Common Diseases in a Two-Stage Genome-Wide Association Study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-23, November.
    • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:46
    DOI: 10.2202/1544-6115.1490
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    References listed on IDEAS

    as
    1. Jaya M. Satagopan & E. S. Venkatraman & Colin B. Begg, 2004. "Two-Stage Designs for Gene–Disease Association Studies with Sample Size Constraints," Biometrics, The International Biometric Society, vol. 60(3), pages 589-597, September.
    2. Jaya M. Satagopan & David A. Verbel & E. S. Venkatraman & Kenneth E. Offit & Colin B. Begg, 2002. "Two-Stage Designs for Gene–Disease Association Studies," Biometrics, The International Biometric Society, vol. 58(1), pages 163-170, March.
    3. Robert Sladek & Ghislain Rocheleau & Johan Rung & Christian Dina & Lishuang Shen & David Serre & Philippe Boutin & Daniel Vincent & Alexandre Belisle & Samy Hadjadj & Beverley Balkau & Barbara Heude &, 2007. "A genome-wide association study identifies novel risk loci for type 2 diabetes," Nature, Nature, vol. 445(7130), pages 881-885, February.
    4. Christopher Genovese & Larry Wasserman, 2002. "Operating characteristics and extensions of the false discovery rate procedure," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 499-517, August.
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