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BioMed Research International
Volume 2015, Article ID 671859, 7 pages
Research Article

A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype

1Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Republic of Korea
2Department of Statistics, Seoul National University, Seoul 151-747, Republic of Korea
3Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Republic of Korea

Received 21 November 2014; Revised 18 April 2015; Accepted 27 April 2015

Academic Editor: Xiang-Yang Lou

Copyright © 2015 Seungyeoun Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic variants left unexplained. This missing heritability problem might be due to the analytical strategy that limits analyses to only single SNPs. One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions. The multifactor dimensionality reduction method has been widely used to detect gene-gene interactions based on the constructive induction by classifying high-dimensional genotype combinations into one-dimensional variable with two attributes of high risk and low risk for the case-control study. Many modifications of MDR have been proposed and also extended to the survival phenotype. In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies.