Use of the gamma method for self-contained gene-set analysis of SNP data

Joanna M. Biernacka, Gregory D. Jenkins, Liewei Wang, Ann M. Moyer, Brooke L. Fridley

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)565-571
Number of pages7
JournalEuropean Journal of Human Genetics
Issue number5
StatePublished - May 2012


  • Fisher's method
  • gamma method
  • gene-level association
  • pathway
  • principal components
  • random effects model

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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