These authors contributed to this study while working at the University of Southern California.
Efficient Two-Step Testing of Gene-Gene Interactions in Genome-Wide Association Studies
Article first published online: 30 APR 2013
© 2013 WILEY PERIODICALS, INC.
Volume 37, Issue 5, pages 440–451, July 2013
How to Cite
Lewinger, J. P., Morrison, J. L., Thomas, D. C., Murcray, C. E., Conti, D. V., Li, D. and Gauderman, W. J. (2013), Efficient Two-Step Testing of Gene-Gene Interactions in Genome-Wide Association Studies. Genet. Epidemiol., 37: 440–451. doi: 10.1002/gepi.21720
- Issue published online: 14 JUN 2013
- Article first published online: 30 APR 2013
- Manuscript Accepted: 6 FEB 2013
- Manuscript Revised: 12 DEC 2012
- Manuscript Received: 6 MAR 2012
- NCI. Grant Number: R41CA141852
- NIH/NIEHS. Grant Number: R01ES016813
- NIEHS. Grant Number: R01ES019876
- NICHD. Grant Number: U01HD061968
- missing heritability;
- case-control studies;
- marginal effects;
Exhaustive testing of all possible SNP pairs in a genome-wide association study (GWAS) generally yields low power to detect gene-gene (G × G) interactions because of small effect sizes and stringent requirements for multiple-testing correction. We introduce a new two-step procedure for testing G × G interactions in case-control GWAS to detect interacting single nucleotide polymorphisms (SNPs) regardless of their marginal effects. In an initial screening step, all SNP pairs are tested for gene-gene association in the combined sample of cases and controls. In the second step, the pairs that pass the screening are followed up with a traditional test for G × G interaction. We show that the two-step method is substantially more powerful to detect G × G interactions than the exhaustive testing approach. For example, with 2,000 cases and 2,000 controls, the two-step method can have more than 90% power to detect an interaction odds ratio of 2.0 compared to less than 50% power for the exhaustive testing approach. Moreover, we show that a hybrid two-step approach that combines our newly proposed two-step test and the two-step test that screens for marginal effects retains the best power properties of both. The two-step procedures we introduce have the potential to uncover genetic signals that have not been previously identified in an initial single-SNP GWAS. We demonstrate the computational feasibility of the two-step G × G procedure by performing a G × G scan in the asthma GWAS of the University of Southern California Children's Health Study.