Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR’s ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire.
© 2010 S. Karger AG, Basel
- Covariate adjustment
- Multifactor dimensionality reduction
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- Moore JH: Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics; in Zhu X, Davidson I (eds): Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data. Hershey: IGI Press, 2007, pp 17–30.
- Pattin KA, White BC, Barney N, Gui J, Nelson HH, Kelsey KR Andrew AS, Karagas MR, Moore JH: A computationally efficient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction. Genet Epidemiol 2008;33:87–94.
- Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH: Multifactor dimensionality reduction reveals high-order interactions among estrogen metabolism genes in sporadic breast cancer. Am J Hum Genet 2001;69:138–147.
- Ritchie MD, Hahn LW, Moore JH: Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 2003;24:150–157.
- Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy metric for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007;31:306–315.
Jason H. Moore, PhD
Computational Genetics Laboratory, Norris-Cotton Cancer Center
Dartmouth Medical School, 706 Rubin Bldg, HB7937, One Medical Center Dr.
Lebanon, NH 03756 (USA)
Tel. +1 603 653 9939, Fax +1 603 653 9900, E-Mail email@example.com
Received: February 1, 2010
Accepted after revision: July 13, 2010
Published online: October 1, 2010
Number of Print Pages : 7
Number of Figures : 1, Number of Tables : 2, Number of References : 17
Human Heredity (International Journal of Human and Medical Genetics)
Vol. 70, No. 3, Year 2010 (Cover Date: October 2010)
Journal Editor: Devoto M. (Philadelphia, Pa.)
ISSN: 0001-5652 (Print), eISSN: 1423-0062 (Online)
For additional information: http://www.karger.com/HHE
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