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Identifying and exploiting gene-pathway interactions from RNA-seq data for binary phenotype.

Fang Shao1, Yaqi Wang2, Yang Zhao1

  • 1Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu, People's Republic of China.

BMC Genetics
|March 21, 2019
PubMed
Summary
This summary is machine-generated.

We developed a new method, PEA, to identify gene-pathway interactions (GPIs) from RNA sequencing data, improving our understanding of complex diseases beyond just differentially expressed genes.

Keywords:
Binary phenotypeGarrote kernel machineGene-pathway interactionVariance component testing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • RNA sequencing (RNA-seq) identifies differentially expressed (DE) genes in complex diseases, but they explain limited variance.
  • The omnigenic model suggests indirectly relevant genes and functional pathways drive disease.
  • Identifying gene-pathway interactions (GPIs) is crucial for understanding complex disease etiology.

Purpose of the Study:

  • To propose an efficient analysis procedure for identifying gene-pathway interactions (GPIs) from RNA-seq data.
  • To develop a method that accounts for genes with indirect relevance to disease.
  • To enhance the understanding of complex disease mechanisms by integrating gene and pathway information.

Main Methods:

  • Developed Permutation based gEne-pAthway interaction identification (PEA), an efficient analysis procedure.
  • Utilized the relationship between the garrote kernel machine (GKM) and variance component test.
  • Employed permutations for empirical distributions of score statistics to determine significance.

Main Results:

  • PEA demonstrated well-calibrated type I error rates and higher statistical power compared to the traditional likelihood ratio test (LRT).
  • Applied PEA and gene set enrichment algorithms to pan-cancer data (GES68086) to identify GPIs.
  • Identified GPIs and associated genes that offer insights into cancer etiology, with some external genes and pathways aligning with prior research.

Conclusions:

  • PEA is an efficient tool for identifying GPIs from RNA-seq data.
  • The method can be extended to analyze interactions between omics data types for binary phenotypes.
  • PEA facilitates a deeper understanding of complex disease mechanisms by incorporating pathway-level information.