Detecting gene-gene interactions using a permutation-based random forest method
By Jing Li, James D. Malley, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore. 2016
65 citations recorded by the source corpus.
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. 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. 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.
Subfields: Genetics, Molecular Biology, Pulmonary and Respiratory Medicine
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In Genetics
- Randal S. Olson
- Ryan J. Urbanowicz
- Jason H. Moore
- William La Cava
- Kai Puolamäki
- Marylyn DeRiggi Ritchie
In Molecular Biology
Citations in the corpus
- Interpreting Classifiers through Attribute Interactions in Datasets (2017, background): “Recently, machine learning methods have been applied to investigate drug-drug (Henelius et al., 2015) and gene-gene interactions (Li et al., 2016).”
- PMLB: a large benchmark suite for machine learning evaluation and comparison (2017, background): “classification of 2-way epistatic interactions [11, 12].”