GWGGI: software for genome-wide gene-gene interaction analysis

TL;DR

GWGGI is a new C++ software tool that efficiently identifies gene-gene interactions in genome-wide association studies, enabling analysis on personal computers with high accuracy and speed.

Contribution

The paper introduces GWGGI, a computationally optimized software package with novel algorithms for genome-wide gene-gene interaction analysis, capable of handling high-dimensional data efficiently.

Findings

GWGGI can analyze nearly 500,000 genetic markers within hours.

LRMW is effective for detecting interactions with moderate-to-strong effects.

TAMW is suitable for identifying interactions among many low-effect variants.

Abstract

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.