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Multiple Hypothesis Testing Strategies for Genetic Case–control Association Studies by Philip S. Rosenberg; Anney Che; Bingshu E. Chen is a Mathematics article available to read on EtoBox.

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## Abstract 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 t

Who reads Multiple Hypothesis Testing Strategies for Genetic Case–control Association Studies?

Author

Philip S. Rosenberg; Anney Che; Bingshu E. Chen

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2005

Language

EN

Field

Mathematics (Physical Sciences)