  Background: While the importance of gene-gene interactions in human diseases
has been well recognized, identifying them has been a great challenge,
especially through association studies with millions of genetic markers and
thousands of individuals. Computationally efficient and powerful tools are in
great need for the identification of new gene-gene interactions in
high-dimensional association studies. Result: We develop C++ software for
genome-wide gene-gene interaction analyses (GWGGI). GWGGI utilizes tree-based
algorithms to search a large number of genetic markers for a disease-associated
joint association with the consideration of high-order interactions, and then
uses non-parametric statistics to test the joint association. The package
includes two functions, likelihood ratio Mann-whitney (LRMW) and Tree
Assembling Mann-whitney (TAMW).We optimize the data storage and computational
efficiency of the software, making it feasible to run the genome-wide analysis
on a personal computer. The use of GWGGI was demonstrated by using two real
data-sets with nearly 500 k genetic markers. Conclusion: Through the empirical
study, we demonstrated that the genome-wide gene-gene interaction analysis
using GWGGI could be accomplished within a reasonable time on a personal
computer (i.e., ~3.5 hours for LRMW and ~10 hours for TAMW). We also showed
that LRMW was suitable to detect interaction among a small number of genetic
variants with moderate-to-strong marginal effect, while TAMW was useful to
detect interaction among a larger number of low-marginal-effect genetic
variants.
