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A new association test based on Chi-square partition for case-control GWA studies

Authors

  • Zhongxue Chen

    Corresponding author
    1. Biostatistics/Epidemiology/Research Design Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, Texas
    • Biostatistics/Epidemiology/Research Design Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030
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Abstract

In case-control genetic association studies, the robust procedure, Pearson's Chi-square test, is commonly used for testing association between disease status and genetic markers. However, this test does not take the possible trend of relative risks, which are due to genotype, into account. On the contrary, although Cochran-Armitage trend test with optimal scores is more powerful; it is usually difficult to assign the correct scores in advance since the true genetic model is rarely known in practice. If the unknown underlying genetic models are misspecified, the trend test may lose power dramatically. Therefore, it is desirable to find a powerful yet robust statistical test for genome-wide association studies. In this paper, we propose a new test based on the partition of Pearson's Chi-square test statistic. The new test utilizes the information of the monotonic (increasing or decreasing) trend of relative risks and therefore in general is more powerful than the Chi-square test; furthermore, it reserves the robustness. Using simulated and real single nucleotide polymorphism data, we compare the performance of the proposed test with existing methods. Genet. Epidemiol. 2011. © 2011 Wiley Periodicals, Inc. 35:658-663, 2011

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