title: Resampling-based multiple hypothesis testing procedures for genetic case-control association studies.
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.
authors.affiliation: Biostatistics Branch, Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland 20852-7244, USA. cheneric@mail.nih.gov
authors.forename: Bingshu E
authors.initials: BE
authors.lastname: Chen
authors.forename: Lori C
authors.initials: LC
authors.lastname: Sakoda
authors.forename: Ann W
authors.initials: AW
authors.lastname: Hsing
authors.forename: Philip S
authors.initials: PS
authors.lastname: Rosenberg
mesh_terms: D000465:Algorithms; D001650:Bile Duct Neoplasms / Q000235:genetics*; D016022:Case-Control Studies; D003198:Computer Simulation; D051546:Cyclooxygenase 2 / Q000235:genetics; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006239:Haplotypes; D006801:Humans; D015810:Linkage Disequilibrium; D008565:Membrane Proteins / Q000235:genetics; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012107:Research Design; D012494:Sampling Studies; D012641:Selection, Genetic
chemical_list: D005819:Genetic Markers; D008565:Membrane Proteins; D051546:Cyclooxygenase 2; C496540:PTGS2 protein, human