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Bioinformatics Advance Access published online on November 8, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl557
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 27, 2006
Revised September 11, 2006
Accepted October 27, 2006

Article

Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions

Yujin Chung 1, Seung Yeoun Lee 2, Robert C. Elston 3, and Taesung Park 1 *

1 Department of Statistics, Seoul National University, San 56-1 Shillim-Dong, Kwanak-Gu, Seoul 151-747, Korea
2 Department of Applied Mathematics, Sejong University, 98 Gunja-Dong Kwangjin-Gu, Seoul 143-747, Korea
3 Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue Cleveland, Ohio 44106-7281, USA

* To whom correspondence should be addressed.
Taesung Park, E-mail: tspark{at}snu.ac.kr


  Abstract

Motivation: The identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases is a challenging task in genetic association studies. The multifactor dimensionality reduction (MDR) method has been proposed and implemented by Ritchie et al. (2001) to identify the combinations of multilocus genotypes and discrete environmental factors that are associated with a particular disease. However, the original MDR method classifies the combination of multilocus genotypes into highrisk and low-risk groups in an ad hoc manner based on a simple comparison of the ratios of the number of cases and controls. Hence, the MDR approach is prone to false positive and negative errors when the ratio of the number of cases and controls in a combination of genotypes is similar to that in the entire data, or when both the number of cases and controls is small. Hence, we propose the odds ratio based multifactor dimensionality reduction (OR MDR) method that uses the odds ratio as a new quantitative measure of disease risk.

Results: While the original MDR method provides a simple binary measures of risk, the OR MDR method provides not only the odds ratio as a quantitative measure of risk but also the ordering of the multilocus combinations from the highest risk to lowest risk groups. Furthermore, the OR MDR method provides a confidence interval for the odds ratio for each multilocus combination, which is extremely informative in judging its importance as a risk factor. The proposed OR MDR method is illustrated using the dataset obtained from the CDC Chronic Fatigue Syndrome Research Group (http://www.cdc.gov/ncidod/diseases/cfs/).

Availability: The program written in R is available.

Associate Editor: Keith A Crandall
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