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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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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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A novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction.

Hye-Young Jung1, Sangseob Leem2, Sungyoung Lee3

  • 1Faculty of Liberal Education, Seoul National University, Seoul, 08826, South Korea.

Computational Biology and Chemistry
|October 22, 2016
PubMed
Summary

This study introduces Fuzzy MDR, a new method for detecting gene-gene interactions that accounts for uncertain risk classifications. Fuzzy MDR demonstrates higher power than traditional MDR in identifying complex trait associations.

Keywords:
Fuzzy classifierFuzzy set theoryGene-gene interactionMultifactor dimensionality reductionUncertainty

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene-gene interaction (GGI) is crucial for understanding complex traits and missing heritability.
  • Multifactor Dimensionality Reduction (MDR) is a common method for detecting GGIs by classifying individuals into high (H) and low (L) risk groups.
  • Standard MDR's binary classification overlooks the inherent uncertainty in risk group assignment.

Purpose of the Study:

  • To develop a novel method, Fuzzy MDR, that incorporates fuzzy set theory to handle uncertainty in H/L risk classifications for GGI analysis.
  • To improve the accuracy of identifying GGI models associated with disease susceptibility by accounting for partial membership in risk groups.

Main Methods:

  • Fuzzy MDR treats H/L classification as a degree of membership using a membership function that maps uncertainty to a [0,1] scale.
  • It selects optimal genotype combinations by maximizing a new fuzzy set-based accuracy measure.
  • The method was evaluated through simulation studies and applied to the bipolar disorder (BD) dataset from the WTCCC.

Main Results:

  • Simulation studies revealed that Fuzzy MDR possesses higher statistical power compared to the standard MDR method.
  • The application to the WTCCC dataset successfully detected GGIs associated with bipolar disorder.

Conclusions:

  • Fuzzy MDR offers a more robust approach to GGI detection by addressing classification uncertainty.
  • The method shows improved power over traditional MDR and can be extended to analyze continuous phenotypes.
  • An R package for Fuzzy MDR is publicly available.