Multiple hypothesis testing strategies for genetic case-control association studies

• Published in: Statistics in medicine Vol. 25; no. 18; pp. 3134 - 3149
• Main Authors: Rosenberg, Philip S., Che, Anney, Chen, Bingshu E.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 30.09.2006; Wiley Subscription Services, Inc
• Subjects: Case-Control Studies; Computer Simulation; Data Interpretation, Statistical; Disease; epidemiology; Genetic Predisposition to Disease; Genetics; Genotype & phenotype; Haplotypes; human genome; Humans; Hypothesis testing; Logistic Models; Polymorphism; Polymorphism, Single Nucleotide; research design; single nucleotide; statistics and numerical data
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.2407

The genetic case–control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two‐stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p‐value that controls a familywise error rate. In the second stage, summary p‐values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature. Published in 2005 by John Wiley & Sons, Ltd.