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


CONCLUSIONS
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.
Department of Statistics, Seoul National University, Seoul, South Korea.
Sohee
S
Oh
Jaehoon
J
Lee
Min-Seok
MS
Kwon
Bruce
B
Weir
Kyooseob
K
Ha
Taesung
T
Park
D000465:Algorithms; D001714:Bipolar Disorder / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D059647:Gene-Environment Interaction; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D058011:Multifactor Dimensionality Reduction / Q000379:methods*; D020641:Polymorphism, Single Nucleotide