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BioData Min. 2016 Apr 6;9:14. doi: 10.1186/s13040-016-0093-5. eCollection 2016.

Detecting gene-gene interactions using a permutation-based random forest method.

Author information

1
Department of Genetics, Geisel School of Medicine, Dartmouth College, Hanover, NH USA.
2
Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, MD USA.
3
Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, NH USA.
4
Institute for Biomedical Informatics, University of Pennsylvania, Pennsylvania, PA USA ; Department of Biostatistics and Epidemiology, The Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA USA.

Abstract

BACKGROUND:

Identifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodology relying on a machine learning method, random forest (RF), to detect gene-gene interactions. Our approach called permuted random forest (pRF) which identified the top interacting single nucleotide polymorphism (SNP) pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions.

RESULTS:

We systematically tested our approach on a simulation study with datasets possessing various genetic constraints including heritability, number of SNPs, sample size, etc. Our methodology showed high success rates for detecting the interaction SNP pair. We also applied our approach to two bladder cancer datasets, which showed consistent results with well-studied methodologies, such as multifactor dimensionality reduction (MDR) and statistical epistasis network (SEN). Furthermore, we built permuted random forest networks (PRFN), in which we used nodes to represent SNPs and edges to indicate interactions.

CONCLUSIONS:

We successfully developed a scale-invariant methodology to detect pure gene-gene interactions based on permutation strategies and the machine learning method random forest. This methodology showed great potential to be used for detecting gene-gene interactions to study underlying genetic architectures in a scale-free way, which could be benefit to uncover the complex disease mechanisms.

KEYWORDS:

GWAS; Machine learning; Random forest; Scale invariant

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