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

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

SUBMITTER: Li J 

PROVIDER: S-EPMC4822295 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature