A Generalized Combinatorial Approach for Detecting Gene-by-Gene and Gene-by-Environment Interactions with Application to Nicotine Dependence

We report a generalized multifactor dimensionality reduction method that permits adjustment for discrete and quantitative covariates and is applicable to both dichotomous and continuous phenotypes in various population-based study designs

Xiang-Yang Lou


Scholarcy highlights

  • The determination of gene-by-gene and gene-by-environment interactions has long been one of the greatest challenges in genetics
  • The score-based multifactor dimensionality reduction method proposed in this article uses the same data-reduction strategy as does the original MDR method14,15—that is, the possible cells classified by a set of factors are pooled into two distinct groups, effectively reducing the dimensionality from multidimensional to one-dimensional and thereby identifying, from all potential combinations, the specific combinations of factors that show the strongest association with the phenotype
  • To evaluate the ability of the generalized MDR method to detect factor interactions, we simulated a series of data sets on a sample consisting of 1,000 unrelated subjects for both continuous and dichotomous phenotypes under three different epistatic models that have been considered before— that is, digenic, trigenic, and tetragenic interaction models—but each with one extra risk factor that contributes to the phenotype
  • We simulated 500 cases and 500 controls on the basis of a logit model with a p Ϫ5.29, b p 3.09, and g p 1, where the genotypes of high risk have a penetrance of ∼0.1 and the others have a risk of ∼0.005 when the value of the covariate is 0
  • The results indicated that GMDR could identify the correct model irrespective of the presence of two or three underlying groups, demonstrating that GMDR is applicable to more-general cases, not to just discrete clinical endpoints or two risk groups of genotypes
  • To illustrate use of the method proposed here, we present an application to identify susceptibility genes for nicotine dependence, with a set of genotype data including 23 SNPs located in four candidate genes: brain-derived neurotrophic factor; neurotrophic tyrosine kinase, receptor, type 2; cholinergic receptor, nicotinic, alpha 4; and cholinergic receptor, nicotinic, beta 2
  • The results on simulations demonstrate that this new method can substantially increase the prediction accuracy when the phenotype is subject to the influence of covariate(s), even when applied to complex models that may or may not be common in the real world

Need more features? Save interactive summary cards to your Scholarcy Library.