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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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A modified entropy-based approach for identifying gene-gene interactions in case-control study.

Jaeyong Yee1, Min-Seok Kwon, Taesung Park

  • 1Department of Physiology and Biophysics, Eulji University, Daejeon, Korea.

Plos One
|July 23, 2013
PubMed
Summary

This study introduces a novel entropy-based method for detecting gene-gene interactions in complex diseases. The method, using standardized relative information gain (RIG), outperforms multifactor dimensionality reduction (MDR) in identifying high-order interactions.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Complex diseases are influenced by gene-gene interactions.
  • Detecting these interactions is statistically challenging.
  • Existing methods have limitations in identifying high-order interactions.

Purpose of the Study:

  • To introduce a novel entropy-based statistical method for measuring gene-gene interactions.
  • To develop a graphical exploration procedure for interaction analysis.
  • To evaluate the proposed method's performance against existing approaches like MDR.

Main Methods:

  • Utilized entropy-based statistics and contingency tables for trait-genotype combinations.
  • Developed a standardized relative information gain (RIG) measure for single nucleotide polymorphism (SNP) combinations.
  • Employed permutation testing for RIG standardization and applied the method to simulated and real genetic data.

Main Results:

  • The standardized entropy-based method successfully identified genetic associations and gene-gene interactions in both simulated and real datasets.
  • The proposed method demonstrated superior performance compared to MDR in detecting high-order interactions.
  • The method effectively identified interactions with and without main genetic effects.

Conclusions:

  • The novel entropy-based method provides a robust approach for detecting gene-gene interactions, particularly high-order ones.
  • This method offers advantages over MDR, especially in complex genetic models.
  • The approach is suitable for a wide range of genetic association studies.