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A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of EpistasisGui J.c · Andrew A.S.c · Andrews P.a, b · Nelson H.M.i · Kelsey K.T.g · Karagas M.R.c · Moore J.H.a-f, h
aComputational Genetics Laboratory, and Departments of bGenetics and cCommunity and Family Medicine, Norris-Cotton Cancer Center, Dartmouth Medical School, Lebanon, N.H., dDepartment of Computer Science, University of New Hampshire, Durham, N.H., eDepartment of Computer Science, University of Vermont, Burlington, Vt., fDepartment of Psychiatry and Human Behavior, and gDepartment of Community Health, Brown University, Providence, R.I., hTranslational Genomics Research Institute, Phoenix, Ariz., and iDivision of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minn., USA Corresponding Author
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 firstname.lastname@example.org
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.
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