Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions

Bioinformatics. 2003 Feb 12;19(3):376-82. doi: 10.1093/bioinformatics/btf869.


Motivation: Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene-gene interaction or epistasis) and environmental factors (gene-environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package.

Results: We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2-15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects.

Availability: Information on obtaining the executable code, example data, example analysis, and documentation is available upon request.

Supplementary information: All supplementary information can be found at

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms*
  • DNA Mutational Analysis / methods
  • Environment
  • Epistasis, Genetic*
  • Genetic Linkage
  • Genetic Predisposition to Disease*
  • Genetic Variation
  • Genetics, Population
  • Genotype
  • Humans
  • Multifactorial Inheritance / genetics
  • Polymorphism, Genetic / genetics
  • Polymorphism, Single Nucleotide / genetics*
  • Sequence Alignment / methods
  • Sequence Analysis, DNA / methods*
  • Software
  • Statistics as Topic