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GWGGI: software for genome-wide gene-gene interaction analysis
1Department of Epidemiology and Biostatistics, Michigan State University, East Lansing 48824, MI, USA. qlu@epi.msu.edu.
BMC Genetics
|October 17, 2014
Summary
Identifying gene-gene interactions for human diseases is challenging. We developed efficient C++ software (GWGGI) for genome-wide analyses, enabling personal computer feasibility for high-dimensional association studies.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Gene-gene interactions are crucial in human diseases but difficult to identify.
- High-dimensional association studies require efficient computational tools.
Purpose of the Study:
- To develop computationally efficient software for genome-wide gene-gene interaction analysis.
- To enable the identification of complex gene-gene interactions in large datasets.
Main Methods:
- Developed C++ software named genome-wide gene-gene interaction analyses (GWGGI).
- Utilized tree-based algorithms and non-parametric statistics (LRMW and TAMW) for joint association testing.
- Optimized for computational efficiency to run on personal computers.
Main Results:
- GWGGI software enables genome-wide gene-gene interaction analysis on a personal computer.
- Likelihood ratio Mann-Whitney (LRMW) and Tree Assembling Mann-Whitney (TAMW) functions were included.
- Demonstrated feasibility with real datasets containing nearly 500k genetic markers.
Conclusions:
- Genome-wide gene-gene interaction analysis is feasible on a personal computer within hours using GWGGI.
- LRMW is effective for detecting interactions with few, strong-effect variants.
- TAMW excels at finding interactions among many low-effect variants.
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