title: Jackknife-based gene-gene interactiontests for untyped SNPs.
abstract: BACKGROUND
  Testing gene-gene interaction in genome-wide association studies generally yields lower power than testing marginal association. Meta-analysis that combines different genotyping platforms is one method used to increase power when assessing gene-gene interactions, which requires a test for interaction on untyped SNPs. However, to date, formal statistical tests for gene-gene interaction on untyped SNPs have not been thoroughly addressed. The key concern for gene-gene interaction testing on untyped SNPs located on different chromosomes is that the pair of genes might not be independent and the current generation of imputation methods provides imputed genotypes at the marginal accuracy.
  
  
  RESULTS
  In this study we address this challenge and describe a novel method for testing gene-gene interaction on marginally imputed values of untyped SNPs. We show that our novel Wald-type test statistics for interactions with and without constraints in the interaction parameters follow the asymptotic distributions which are the same as those of the corresponding tests for typed SNPs. Through simulations, we show that the proposed tests properly control type I error and are more powerful than the extension of the classical dosage method to interaction tests. The increase in power results from a proper correction for the uncertainty in imputation through the variance estimator using the jackknife, one of resampling techniques. We apply the method to detect interactions between SNPs on chromosomes 5 and 15 on lung cancer data. The inclusion of the results at the untyped SNPs provides a much more detailed information at the regions of interest.
  
  
  CONCLUSIONS
  As demonstrated by the simulation studies and real data analysis, our approaches outperform the application of traditional dosage method to detection of gene-gene interaction in terms of power while providing control of the type I error.
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Rockville, MD, USA. songm4@mail.nih.gov.
authors.forename: Minsun
authors.initials: M
authors.lastname: Song
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004777:Environment; D004843:Epistasis, Genetic; D059647:Gene-Environment Interaction; D005820:Genetic Testing; D055106:Genome-Wide Association Study / Q000379:methods; D006801:Humans; D008175:Lung Neoplasms / Q000209:etiology; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide