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Measuring gene-gene interaction using Kullback-Leibler divergence.

Guanjie Chen1, Ao Yuan2, Tao Cai3

  • 1Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland.

Annals of Human Genetics
|June 18, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical method to analyze gene-gene interactions, potentially explaining missing heritability in complex traits like type 2 diabetes (T2D). The new approach shows promising results in simulations and real-world data analysis.

Keywords:
Kullback-Leibler statisticSNPcase-control studygene-gene interactionhypothesis testing

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify genetic variants for complex traits but leave heritability gaps.
  • Gene-gene interactions are hypothesized to contribute to this missing heritability.
  • Existing methods for gene-gene interaction analysis have limitations in high-dimensional data.

Purpose of the Study:

  • To propose a novel statistical method for analyzing gene-gene interactions between independent single-nucleotide polymorphisms (SNPs) in two genes.
  • To address limitations of existing methods using asymptotic chi-squared mixture distributions.
  • To provide a more tractable statistical approach for genetic interaction analysis.

Main Methods:

  • Development of a Kullback-Leibler-type statistic for gene-gene interaction analysis.
  • Evaluation of the proposed method through simulation studies.
  • Application of the method to analyze real-world data for type 2 diabetes (T2D) status.

Main Results:

  • The proposed Kullback-Leibler-type statistic follows an asymptotic positive normal distribution under the null hypothesis.
  • Under the alternative hypothesis, the statistic is normally distributed.
  • Simulation studies demonstrated promising performance of the new method.

Conclusions:

  • The novel statistical method effectively analyzes gene-gene interactions.
  • The method identified significant gene-gene interactions among RAB3A, MADD, and PTPRN in relation to type 2 diabetes (T2D) status.
  • This approach offers a valuable tool for dissecting complex genetic architectures and understanding missing heritability.