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Multiple Testing in Candidate Gene Situations: A Comparison of Classical, Discrete, and Resampling-Based Procedures

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  • Elsäßer Amelie
  • Victor Anja
  • Hommel Gerhard

Abstract

In candidate gene association studies, usually several elementary hypotheses are tested simultaneously using one particular set of data. The data normally consist of partly correlated SNP information. Every SNP can be tested for association with the disease, e.g., using the Cochran-Armitage test for trend. To account for the multiplicity of the test situation, different types of multiple testing procedures have been proposed. The question arises whether procedures taking into account the discreteness of the situation show a benefit especially in case of correlated data. We empirically evaluate several different multiple testing procedures via simulation studies using simulated correlated SNP data. We analyze FDR and FWER controlling procedures, special procedures for discrete situations, and the minP-resampling-based procedure. Within the simulation study, we examine a broad range of different gene data scenarios. We show that the main difference in the varying performance of the procedures is due to sample size. In small sample size scenarios, the minP-resampling procedure though controlling the stricter FWER even had more power than the classical FDR controlling procedures. In contrast, FDR controlling procedures led to more rejections in higher sample size scenarios.

Suggested Citation

  • Elsäßer Amelie & Victor Anja & Hommel Gerhard, 2011. "Multiple Testing in Candidate Gene Situations: A Comparison of Classical, Discrete, and Resampling-Based Procedures," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-21, November.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:51
    DOI: 10.2202/1544-6115.1729
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    References listed on IDEAS

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    1. Neuhauser, Markus & Hothorn, Ludwig A., 1999. "An exact Cochran-Armitage test for trend when dose-response shapes are a priori unknown," Computational Statistics & Data Analysis, Elsevier, vol. 30(4), pages 403-412, June.
    2. Hothorn, Torsten & Hornik, Kurt & van de Wiel, Mark A. & Zeileis, Achim, 2006. "A Lego System for Conditional Inference," The American Statistician, American Statistical Association, vol. 60, pages 257-263, August.
    3. Peter B. Gilbert, 2005. "A modified false discovery rate multiple‐comparisons procedure for discrete data, applied to human immunodeficiency virus genetics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 143-158, January.
    4. Boulesteix Anne-Laure & Strobl Carolin & Weidinger Stefan & Wichmann H.-Erich & Wagenpfeil Stefan, 2007. "Multiple Testing for SNP-SNP Interactions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-24, December.
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