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

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.