TY - JOUR
T1 - Use of the gamma method for self-contained gene-set analysis of SNP data
AU - Biernacka, Joanna M.
AU - Jenkins, Gregory D.
AU - Wang, Liewei
AU - Moyer, Ann M.
AU - Fridley, Brooke L.
N1 - Funding Information:
We thank Krishna (Rani) Kalari for the mapping of SNPs to genes within the glutathione metabolism gene set. The research was supported by the US National Institutes of Health (GM61388, CA140879, AA019570, CA130828, GM86689), a pilot project award from the Mayo Clinic SPORE in Ovarian Cancer (CA136393) and Minnesota Partnership for Biotechnology and Medical Genomics grant. The funders had no role in study design, data collection and analysis, decision to publish or in preparation of the manuscript.
PY - 2012/5
Y1 - 2012/5
N2 - 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.
AB - 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.
KW - Fisher's method
KW - gamma method
KW - gene-level association
KW - pathway
KW - principal components
KW - random effects model
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U2 - 10.1038/ejhg.2011.236
DO - 10.1038/ejhg.2011.236
M3 - Article
C2 - 22166939
AN - SCOPUS:84859909127
SN - 1018-4813
VL - 20
SP - 565
EP - 571
JO - European Journal of Human Genetics
JF - European Journal of Human Genetics
IS - 5
ER -