Correction for Multiplicity in Genetic Association Studies of Triads: The Permutational TDT

Authors

  • James F. Troendle,

    Corresponding author
    1. Biostatistics and Bioinformatics Branch of the Division of Epidemiology, Statistics, and Prevention Research of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH/DHHS, Bld 6100, Bethesda, MD 20892, USA
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  • James L. Mills

    1. Epidemiology Branch of the Division of Epidemiology, Statistics, and Prevention Research of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH/DHHS, Bld 6100, Bethesda, MD 20892, USA
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Corresponding author: James F. Troendle, Office of Biostatistics Research of the National Heart, Lung, and Blood Institute, NIH/DHHS, Bld Rockledge II, Room 9195, Bethesda, MD 20892, USA. Tel: 301-435-0421; Fax: 301-480-1862; E-mail: jt3t@nih.gov

Summary

New technology for large-scale genotyping has created new challenges for statistical analysis. Correcting for multiple comparison without discarding true positive results and extending methods to triad studies are among the important problems facing statisticians. We present a one-sample permutation test for testing transmission disequilibrium hypotheses in triad studies, and show how this test can be used for multiple single nucleotide polymorphism (SNP) testing. The resulting multiple comparison procedure is shown in the case of the transmission disequilibrium test to control the familywise error. Furthermore, this procedure can handle multiple possible modes of risk inheritance per SNP. The resulting permutational procedure is shown through simulation of SNP data to be more powerful than the Bonferroni procedure when the SNPs are in linkage disequilibrium. Moreover, permutations implicitly avoid any multiple comparison correction penalties when the SNP has a rare allele. The method is illustrated by analyzing a large candidate gene study of neural tube defects and an independent study of oral clefts, where the smallest adjusted p-values using the permutation procedure are approximately half those of the Bonferroni procedure. We conclude that permutation tests are more powerful for identifying disease-associated SNPs in candidate gene studies and are useful for analysis of triad studies.

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