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Improved Minimum Cost and Maximum Power Two Stage Genome-Wide Association Study Designs

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  • Stephen A Stanhope
  • Andrew D Skol

Abstract

In a two stage genome-wide association study (2S-GWAS), a sample of cases and controls is allocated into two groups, and genetic markers are analyzed sequentially with respect to these groups. For such studies, experimental design considerations have primarily focused on minimizing study cost as a function of the allocation of cases and controls to stages, subject to a constraint on the power to detect an associated marker. However, most treatments of this problem implicitly restrict the set of feasible designs to only those that allocate the same proportions of cases and controls to each stage. In this paper, we demonstrate that removing this restriction can improve the cost advantages demonstrated by previous 2S-GWAS designs by up to 40%. Additionally, we consider designs that maximize study power with respect to a cost constraint, and show that recalculated power maximizing designs can recover a substantial amount of the planned study power that might otherwise be lost if study funding is reduced. We provide open source software for calculating cost minimizing or power maximizing 2S-GWAS designs.

Suggested Citation

  • Stephen A Stanhope & Andrew D Skol, 2012. "Improved Minimum Cost and Maximum Power Two Stage Genome-Wide Association Study Designs," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-10, September.
    • Handle: RePEc:plo:pone00:0042367
    DOI: 10.1371/journal.pone.0042367
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    References listed on IDEAS

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    1. André Scherag & Johannes Hebebrand & Helmut Schäfer & Hans-Helge Müller, 2009. "Flexible Designs for Genomewide Association Studies," Biometrics, The International Biometric Society, vol. 65(3), pages 815-821, September.
    2. Wang, Hansong & Stram, Daniel O., 2006. "Optimal two-stage genome-wide association designs based on false discovery rate," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 457-465, November.
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    Cited by:

    1. Yi-Hui Zhou & Paul Brooks & Xiaoshan Wang, 2018. "A Two-Stage Hidden Markov Model Design for Biomarker Detection, with Application to Microbiome Research," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 41-58, April.

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