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Table 4 Main characteristics of each approach used to model SNP-SNP interactions

From: Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario

Approach Type of two-locus model detected Pattern of complex interactions Potential advantages Potential limitations Possible improvements
LRM Logical AND models – multiplicative models Can not be investigated Easy to fit Curse of dimensionality Logic regression
MARS*
CART Conditional recessive or dominant models Driven by SNP main effects and binary splits Deals with sparse data
Useful for risk characterization and prediction
Influence of main effects
Redundancy
Random forest
Boosting
MDR All types Diverse Deals with sparse data
Useful for risk characterization and prediction
Over-fitting
Difficult to find best models
Inefficient with large number of SNPs
Limit plausible genetic models
Use test statistic
  1. *Multivariate adaptive regression splines