DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction

Hum Mutat. 2020 Mar;41(3):719-734. doi: 10.1002/humu.23951. Epub 2019 Nov 25.

Abstract

Detecting epistatic interaction is a typical way of identifying the genetic susceptibility of complex diseases. Multifactor dimensionality reduction (MDR) is a decent solution for epistasis detection. Existing MDR-based methods still suffer from high computational costs or poor performance. In this paper, we propose a new solution that integrates a dual screening strategy with MDR, termed as DualWMDR. Particularly, the first screening employs an adaptive clustering algorithm with part mutual information (PMI) to group single nucleotide polymorphisms (SNPs) and exclude noisy SNPs; the second screening takes into account both the single-locus effect and interaction effect to select dominant SNPs, which effectively alleviates the negative impact of main effects and provides a much smaller but accurate candidate set for MDR. After that, MDR uses the weighted classification evaluation to improve its performance in epistasis identification on the candidate set. The results on diverse simulation datasets show that DualWMDR outperforms existing competitive methods, and the results on three real genome-wide datasets: the age-related macular degeneration (AMD) dataset, breast cancer (BC), and celiac disease (CD) datasets from the Wellcome Trust Case Control Consortium, again corroborate the effectiveness of DualWMDR.

Keywords: dual screening; epistatic interaction; main effects; multifactor dimensionality reduction; part mutual information.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • Databases, Genetic
  • Epistasis, Genetic*
  • Genetic Loci
  • Genetic Predisposition to Disease
  • Humans
  • Macular Degeneration / genetics
  • Models, Genetic*
  • Multifactor Dimensionality Reduction / methods*
  • Polymorphism, Single Nucleotide