A simple and computationally efficient sampling approach to covariate adjustment for multifactor dimensionality reduction analysis of epistasis

Jiang Gui, Angeline S. Andrew, Peter Andrews, Heather M. Nelson, Karl T. Kelsey, Margaret R. Karagas, Jason H. Moore

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)219-225
Number of pages7
JournalHuman heredity
Volume70
Issue number3
DOIs
StatePublished - Oct 2010

Keywords

  • Covariate adjustment
  • Epistasis
  • Multifactor dimensionality reduction

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