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Table 1 Comparison of the different methods.

From: The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases

  Logistic regression Neural networks Set association CPM RPM MDR Random forests
   PDM GPNN      
Outcome variable dichotomous categorical continuous categorical continuous dichotomous continuous continuous dichotomous categorical
Dimensionality no no yes yes yes yes yes yes
Number of predictors few moderate many many moderate many* moderate† many
Power to detect important effects low no info high high high high high high
Detection of interactions no yes yes no yes yes yes yes‡
Correlated predictors no no yes n.i.** yes yes yes no
Genetic heterogeneity no yes yes no no no no yes
Software available
Open source
yes yes
no
no yes
yes
no at request and under development yes
yes
yes
yes
  1. For the problems of dimensionality, correlated predictors and genetic heterogeneity yes and no indicate respectively that a method is able or not able to handle the problem. For detection of interactions when main effects are absent yes and no indicate respectively that a method is able or not able to detect interactions while main effects of the loci involved in the interaction are small or absent.
  2. * RPM is subject to the multiple testing problem.
  3. † MDR can analyze a moderate number of factors, but filter methods that are part of the MDR software can be applied before using MDR, enabling the user of the MDR software to analyze large numbers of factors.
  4. ‡ Interactions contribute to the importance of predictors.
  5. ** n.i.: not implemented, adjustment of the test statistics for correlation between markers is not implemented in the software.