Open Access Highly Accessed Research article

SNP interaction detection with Random Forests in high-dimensional genetic data

Stacey J Winham1*, Colin L Colby1, Robert R Freimuth1, Xin Wang1, Mariza de Andrade1, Marianne Huebner12 and Joanna M Biernacka13*

Author Affiliations

1 Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN, 55905, USA

2 Department of Statistics and Probability, Michigan State University, A413 Wells Hall, East Lansing, MI, 48824, USA

3 Department of Psychiatry and Psychology, Mayo Clinic, 200 First Street Southwest, Rochester, MN, 55905, USA

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BMC Bioinformatics 2012, 13:164  doi:10.1186/1471-2105-13-164

Published: 15 July 2012



Identifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional.


RF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions.


While RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.