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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Detecting gene-gene interactions using a permutation-based random forest method.

Jing Li1, James D Malley2, Angeline S Andrew3

  • 1Department of Genetics, Geisel School of Medicine, Dartmouth College, Hanover, NH USA.

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|April 8, 2016
PubMed
Summary
This summary is machine-generated.

We developed a novel permuted random forest (pRF) method to identify gene-gene interactions, crucial for understanding complex diseases. This approach successfully detected interacting single nucleotide polymorphism (SNP) pairs in simulations and real-world bladder cancer data.

Keywords:
GWASMachine learningRandom forestScale invariant

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Area of Science:

  • Genetics
  • Bioinformatics
  • Machine Learning

Background:

  • Gene-gene interactions are key to understanding disease susceptibility and complex genetic architectures.
  • Current methods require new approaches for robust interaction detection.

Purpose of the Study:

  • To develop a permutation-based methodology using random forest (RF) for detecting gene-gene interactions.
  • To introduce the permuted random forest (pRF) approach for identifying interacting single nucleotide polymorphism (SNP) pairs.

Main Methods:

  • Utilized a random forest (RF) machine learning model.
  • Developed a permutation strategy (pRF) to assess the impact of pairwise interactions on model performance.
  • Constructed permuted random forest networks (PRFN) with SNPs as nodes and interactions as edges.

Main Results:

  • The pRF methodology demonstrated high success rates in detecting interaction SNP pairs across diverse simulation scenarios.
  • Applied to bladder cancer datasets, pRF yielded results consistent with established methods like multifactor dimensionality reduction (MDR) and statistical epistasis network (SEN).

Conclusions:

  • Successfully developed a scale-invariant methodology for detecting pure gene-gene interactions.
  • The pRF approach shows significant potential for analyzing genetic architectures in a scale-free manner, aiding in the elucidation of complex disease mechanisms.