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. 2001 Jul;69(1):138-47.
doi: 10.1086/321276. Epub 2001 Jun 11.

Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer

M D Ritchie  1 , L W Hahn, N Roodi, L R Bailey, W D Dupont, F F Parl, J H Moore
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Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer

M D Ritchie et al. Am J Hum Genet. .
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Abstract

One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common complex multifactorial human diseases. This challenge is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes and with environmental exposures. We introduce multifactor-dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus information, to improve the identification of polymorphism combinations associated with disease risk. The MDR method is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., it assumes no particular inheritance model), and is directly applicable to case-control and discordant-sib-pair studies. Using simulated case-control data, we demonstrate that MDR has reasonable power to identify interactions among two or more loci in relatively small samples. When it was applied to a sporadic breast cancer case-control data set, in the absence of any statistically significant independent main effects, MDR identified a statistically significant high-order interaction among four polymorphisms from three different estrogen-metabolism genes. To our knowledge, this is the first report of a four-locus interaction associated with a common complex multifactorial disease.

Figures

Figure 1
Figure 1
Summary of steps involved in implementation of the MDR method: a set of n genetic and/or discrete environmental factors is selected; the n factors and their possible multifactor classes or cells are represented in n-dimensional space; each multifactor cell in n-dimensional space is labeled as either “high-risk” or “low-risk”; and the prediction error of each model is estimated. For each multifactor combination, hypothetical distributions of cases (left bars in boxes) and of controls (right bars in boxes) are shown.
Figure 2
Figure 2
Summary of four-locus genotype combinations associated with high risk and with low risk for sporadic breast cancer, along with the corresponding distribution of cases (left bars in boxes) and of controls (right bars in boxes), for each multilocus-genotype combination. Note that the patterns of high-risk and low-risk cells differ across each of the different multilocus dimensions. This is evidence of epistasis, or gene-gene interaction.
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