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

BMC Bioinformatics

Research Article

Xin Wang¹  Mariza de Andrade¹  Stacey J Winham¹  Colin L Colby¹  Robert R Freimuth¹  Joanna M Biernacka²  Marianne Huebner³ 

[1] Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, 55905, Rochester, MN, USA;Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, 55905, Rochester, MN, USA;Department of Psychiatry and Psychology, Mayo Clinic, 200 First Street Southwest, 55905, Rochester, MN, USA;Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, 55905, Rochester, MN, USA;Department of Statistics and Probability, Michigan State University, A413 Wells Hall, 48824, East Lansing, MI, USA;

关键词: Linkage Disequilibrium;    Random Forest;    Detection Probability;    Strong Linkage Disequilibrium;    Multifactor Dimensionality Reduction;   

DOI  :  10.1186/1471-2105-13-164

 received in 2011-12-21, accepted in 2012-04-30,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundIdentifying 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.ResultsRF 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.ConclusionsWhile 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.

【 授权许可】

CC BY   
© Winham et al.; licensee BioMed Central Ltd. 2012