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

Hum Hered. 2010;70(3):219-25. doi: 10.1159/000319175. Epub 2010 Oct 1.

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Case-Control Studies
  • Computer Simulation
  • Epistasis, Genetic*
  • Genetic Predisposition to Disease*
  • Genotype
  • Humans
  • Models, Genetic
  • Multifactor Dimensionality Reduction / methods*
  • New Hampshire
  • Polymorphism, Single Nucleotide
  • Sampling Studies
  • Urinary Bladder Neoplasms / genetics