Increasing the power of identifying gene x gene interactions in genome-wide association studies.
In this paper we investigate the power to identify gene x gene interactions in genome-wide association studies. In our analysis we focus on two-stage analyses: analyses in which we only test for interactions between single nucleotide polymorphisms that show some marginal effect. We give two algorithms to compute significance levels for such an analyses. One involves a Bonferoni correction on the number of interactions that are actually tested, and one is a resampling procedure similar to the one proposed by [Lin (2006) Am. J. Hum. Genet. 78:505-509]. We also give an algorithm to carry out approximate power calculations for studies that plan to use a two-stage analysis. We find that for most plausible interaction effects a two-stage analysis can dramatically increase the power to identify interactions compared to a single-stage analysis based on simulation studies using known genetic models and data from existing genome-wide association studies.
Kooperberg
Charles
C
Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, Washington 98109-1024, USA. clk@fhcrc.org
Leblanc
Michael
M
D000465:Algorithms; D003198:Computer Simulation; D030541:Databases, Genetic; D005787:Gene Frequency; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D015894:Genome, Human / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide