title: How to analyze many contingency tables simultaneously in genetic association studies.
abstract: We study exact tests for (2 x 2) and (2 x 3) contingency tables, in particular exact chi-squared tests and exact tests of Fisher type. In practice, these tests are typically carried out without randomization, leading to reproducible results but not exhausting the significance level. We discuss that this can lead to methodological and practical issues in a multiple testing framework when many tables are simultaneously under consideration as in genetic association studies.Realized randomized p-values are proposed as a solution which is especially useful for data-adaptive (plug-in) procedures. These p-values allow to estimate the proportion of true null hypotheses much more accurately than their non-randomized counterparts. Moreover, we address the problem of positively correlated p-values for association by considering techniques to reduce multiplicity by estimating the "effective number of tests" from the correlation structure.An algorithm is provided that bundles all these aspects, efficient computer implementations are made available, a small-scale simulation study is presented and two real data examples are shown.
authors: Humboldt-University, Berlin.
authors: Thorsten
authors: T
authors: Dickhaus
authors: Klaus
authors: K
authors: Straßburger
authors: Daniel
authors: D
authors: Schunk
authors: Carlos
authors: C
authors: Morcillo-Suarez
authors: Thomas
authors: T
authors: Illig
authors: Arcadi
authors: A
authors: Navarro
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D016009:Chi-Square Distribution; D019295:Computational Biology / Q000379:methods / Q000592:standards; D003198:Computer Simulation; D020869:Gene Expression Profiling; D056726:Genetic Association Studies / Q000706:statistics & numerical data; D005819:Genetic Markers / Q000502:physiology; D057166:High-Throughput Screening Assays / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D011897:Random Allocation; D012107:Research Design
chemical_list: D005819:Genetic Markers