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A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models.

Jia Wen1, Colby T Ford2,3, Daniel Janies2

  • 1Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.

Bioinformatics (Oxford, England)
|April 1, 2020
PubMed
Summary
This summary is machine-generated.

We developed a faster method for epistasis analysis using parallel computing. This approach speeds up the identification of gene interactions influencing traits and diseases, overcoming computational barriers in large genomic datasets.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Epistasis, the combinatorial effect of genes on phenotypes, is crucial for understanding quantitative traits and complex human diseases.
  • Identifying epistasis in large genomic datasets is challenging due to model over-fitting and intensive computing requirements.

Purpose of the Study:

  • To develop efficient statistical and computational methods for scaling up epistasis analysis.
  • To overcome the computational barriers hindering the identification of gene-gene interactions in large-scale genomic data.

Main Methods:

  • Combined statistical and computational techniques, including Empirical Bayesian Elastic Net (EBEN) models.
  • Applied matrix manipulation for pre-computation and pre-filtering to reduce the search space.
  • Developed a parallelized approach to accelerate the epistasis modeling process.

Main Results:

  • Demonstrated tens of fold speed-up compared to sequential EBEN methods on synthetic and empirical genomic data.
  • Successfully identified main and epistatic effects of genetic variants associated with traits in a yeast dataset.
  • The parallelized approach significantly accelerates epistasis analysis.

Conclusions:

  • The developed parallelized EBEN method effectively addresses computational challenges in epistasis analysis.
  • This approach enhances the ability to identify genetic variants and their interactions influencing complex traits.
  • The software is publicly available for broader research application.