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Original Paper

Free Access

A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis

Gui 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

Author affiliations

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 jason.h.moore@dartmouth.edu

Related Articles for ""

Hum Hered 2010;70:219–225

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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

Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: February 01, 2010
Accepted: July 13, 2010
Published online: October 01, 2010
Issue release date: October 2010

Number of Print Pages: 7
Number of Figures: 1
Number of Tables: 2

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE

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