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Gene-Based Nonparametric Testing of Interactions Using Distance Correlation Coefficient in Case-Control Association

Yingjie Guo1,2, Chenxi Wu3, Maozu Guo4,5

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China. yjguo0625@gmail.com.

Genes
|December 20, 2018
PubMed
Summary

We introduce GBDcor, a novel gene-based method for detecting gene-gene interactions in genome-wide association studies (GWAS). This approach uses distance correlation to improve statistical power and biological interpretability in genetic research.

Keywords:
distance correlation coefficientgene–gene interactiongenome-wide association studiesqualitative trait

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Gene-gene interactions are crucial for understanding complex traits.
  • Existing gene-based methods for genome-wide association studies (GWAS) often rely on restrictive assumptions, limiting their statistical power.
  • There is a need for robust methods to identify gene-gene interactions with improved accuracy.

Purpose of the Study:

  • To propose a novel gene-based statistical method, GBDcor, for detecting gene-gene interactions in GWAS.
  • To leverage the distance correlation coefficient (dCor) for measuring gene dependencies.
  • To overcome the limitations of existing methods by relaxing assumptions about trait-SNP relationships.

Main Methods:

  • Developed the gene-based gene-gene interaction via distance correlation coefficient (GBDcor) method.
  • Utilized the difference in dCor between case and control datasets as an indicator of gene-gene interaction.
  • Implemented a permutation-based statistical test to assess the significance of identified interactions and provide p-values.

Main Results:

  • The GBDcor method demonstrated superior performance in detecting gene-gene interactions compared to existing approaches.
  • Experiments with simulated and real-world data validated the accuracy and effectiveness of GBDcor.
  • The method successfully identified significant gene-gene interactions with enhanced statistical power.

Conclusions:

  • GBDcor offers a powerful and biologically interpretable approach for identifying gene-gene interactions in GWAS.
  • The method's reliance on distance correlation provides a flexible framework for detecting complex genetic dependencies.
  • GBDcor represents a significant advancement in the statistical toolkit for genetic association studies.