title: Log-linear model-based multifactor dimensionality reduction method to detect gene gene interactions.
abstract: BACKGROUND
  The identification and characterization of susceptibility genes that influence the risk of common and complex diseases remains a statistical and computational challenge in genetic association studies. This is partly because the effect of any single genetic variant for a common and complex disease may be dependent on other genetic variants (gene-gene interaction) and environmental factors (gene-environment interaction). To address this problem, the multifactor dimensionality reduction (MDR) method has been proposed by Ritchie et al. to detect gene-gene interactions or gene-environment interactions. The MDR method identifies polymorphism combinations associated with the common and complex multifactorial diseases by collapsing high-dimensional genetic factors into a single dimension. That is, the MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups based on a comparison of the ratios of the numbers of cases and controls. When a high-order interaction model is considered with multi-dimensional factors, however, there may be many sparse or empty cells in the contingency tables. The MDR method cannot classify an empty cell as high risk or low risk and leaves it as undetermined.
  
  
  RESULTS
  In this article, we propose the log-linear model-based multifactor dimensionality reduction (LM MDR) method to improve the MDR in classifying sparse or empty cells. The LM MDR method estimates frequencies for empty cells from a parsimonious log-linear model so that they can be assigned to high-and low-risk groups. In addition, LM MDR includes MDR as a special case when the saturated log-linear model is fitted. Simulation studies show that the LM MDR method has greater power and smaller error rates than the MDR method. The LM MDR method is also compared with the MDR method using as an example sporadic Alzheimer's disease.
authors.affiliation: Department of Applied Mathematics, Sejong University, 98 Gunja-Dong Kwangjin-Gu, Seoul 143-747, Korea.
authors.forename: Seung Yeoun
authors.initials: SY
authors.lastname: Lee
authors.forename: Yujin
authors.initials: Y
authors.lastname: Chung
authors.forename: Robert C
authors.initials: RC
authors.lastname: Elston
authors.forename: Youngchul
authors.initials: Y
authors.lastname: Kim
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics*; D003198:Computer Simulation; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D005810:Multigene Family / Q000235:genetics*; D009419:Nerve Tissue Proteins / Q000235:genetics*; D025941:Protein Interaction Mapping / Q000379:methods*; D018570:Risk Assessment / Q000379:methods*; D013223:Statistics as Topic
chemical_list: D009419:Nerve Tissue Proteins