title: Use of the gamma method for self-contained gene-set analysis of SNP data.
abstract: Gene-set analysis (GSA) evaluates the overall evidence of association between a phenotype and all genotyped single nucleotide polymorphisms (SNPs) in a set of genes, as opposed to testing for association between a phenotype and each SNP individually. We propose using the Gamma Method (GM) to combine gene-level P-values for assessing the significance of GS association. We performed simulations to compare the GM with several other self-contained GSA strategies, including both one-step and two-step GSA approaches, in a variety of scenarios. We denote a 'one-step' GSA approach to be one in which all SNPs in a GS are used to derive a test of GS association without consideration of gene-level effects, and a 'two-step' approach to be one in which all genotyped SNPs in a gene are first used to evaluate association of the phenotype with all measured variation in the gene and then the gene-level tests of association are aggregated to assess the GS association with the phenotype. The simulations suggest that, overall, two-step methods provide higher power than one-step approaches and that combining gene-level P-values using the GM with a soft truncation threshold between 0.05 and 0.20 is a powerful approach for conducting GSA, relative to the competing approaches assessed. We also applied all of the considered GSA methods to data from a pharmacogenomic study of cisplatin, and obtained evidence suggesting that the glutathione metabolism GS is associated with cisplatin drug response.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.
authors.forename: Joanna M
authors.initials: JM
authors.lastname: Biernacka
authors.forename: Gregory D
authors.initials: GD
authors.lastname: Jenkins
authors.forename: Liewei
authors.initials: L
authors.lastname: Wang
authors.forename: Ann M
authors.initials: AM
authors.lastname: Moyer
authors.forename: Brooke L
authors.initials: BL
authors.lastname: Fridley
mesh_terms: D003198:Computer Simulation; D016208:Databases, Factual; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide