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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2011: Bioinformatics

Open Access Proceedings

A novel method to identify high order gene-gene interactions in genome-wide association studies: Gene-based MDR

Sohee Oh1, Jaehoon Lee1, Min-Seok Kwon2, Bruce Weir3, Kyooseob Ha4 and Taesung Park12*

Author Affiliations

1 Department of Statistics, Seoul National University, Seoul, South Korea

2 Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea

3 Department of Biostatistics, University of Washington, Seattle, Washington, USA

4 Department of Neuropsychiatry, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea

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BMC Bioinformatics 2012, 13(Suppl 9):S5  doi:10.1186/1471-2105-13-S9-S5

Published: 11 June 2012

Abstract

Background

Because common complex diseases are affected by multiple genes and environmental factors, it is essential to investigate gene-gene and/or gene-environment interactions to understand genetic architecture of complex diseases. After the great success of large scale genome-wide association (GWA) studies using the high density single nucleotide polymorphism (SNP) chips, the study of gene-gene interaction becomes a next challenge. Multifactor dimensionality reduction (MDR) analysis has been widely used for the gene-gene interaction analysis. In practice, however, it is not easy to perform high order gene-gene interaction analyses via MDR in genome-wide level because it requires exploring a huge search space and suffers from a computational burden due to high dimensionality.

Results

We propose dimensional reduction analysis, Gene-MDR analysis for the fast and efficient high order gene-gene interaction analysis. The proposed Gene-MDR method is composed of two-step applications of MDR: within- and between-gene MDR analyses. First, within-gene MDR analysis summarizes each gene effect via MDR analysis by combining multiple SNPs from the same gene. Second, between-gene MDR analysis then performs interaction analysis using the summarized gene effects from within-gene MDR analysis. We apply the Gene-MDR method to bipolar disorder (BD) GWA data from Wellcome Trust Case Control Consortium (WTCCC). The results demonstrate that Gene-MDR is capable of detecting high order gene-gene interactions associated with BD.

Conclusion

By reducing the dimension of genome-wide data from SNP level to gene level, Gene-MDR efficiently identifies high order gene-gene interactions. Therefore, Gene-MDR can provide the key to understand complex disease etiology.