Resampling-based multiple hypothesis testing procedures for genetic case-control association studies

Authors

  • Bingshu E. Chen,

    Corresponding author
    1. Biostatistics Branch, Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland
    • Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd, Executive Plaza South, Room 8089, Rockville, MD 20852-7244
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  • Lori C. Sakoda,

    1. Hormonal and Reproductive Epidemiology Branch, Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland
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  • Ann W. Hsing,

    1. Hormonal and Reproductive Epidemiology Branch, Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland
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  • Philip S. Rosenberg

    1. Biostatistics Branch, Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland
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  • This article is a US government work and, as such, is in the public domain in the United States of America.

Abstract

In case-control studies of unrelated subjects, gene-based hypothesis tests consider whether any tested feature in a candidate gene—single nucleotide polymorphisms (SNPs), haplotypes, or both—are associated with disease. Standard statistical tests are available that control the false-positive rate at the nominal level over all polymorphisms considered. However, more powerful tests can be constructed that use permutation resampling to account for correlations between polymorphisms and test statistics. A key question is whether the gain in power is large enough to justify the computational burden. We compared the computationally simple Simes Global Test to the min P test, which considers the permutation distribution of the minimum p-value from marginal tests of each SNP. In simulation studies incorporating empirical haplotype structures in 15 genes, the min P test controlled the type I error, and was modestly more powerful than the Simes test, by 2.1 percentage points on average. When disease susceptibility was conferred by a haplotype, the min P test sometimes, but not always, under-performed haplotype analysis. A resampling-based omnibus test combining the min P and haplotype frequency test controlled the type I error, and closely tracked the more powerful of the two component tests. This test achieved consistent gains in power (5.7 percentage points on average), compared to a simple Bonferroni test of Simes and haplotype analysis. Using data from the Shanghai Biliary Tract Cancer Study, the advantages of the newly proposed omnibus test were apparent in a population-based study of bile duct cancer and polymorphisms in the prostaglandin-endoperoxide synthase 2 (PTGS2) gene. Genet. Epidemiol. 2006. Published 2006 Wiley-Liss, Inc.

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