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DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction
Author(s) -
Cao Xia,
Yu Guoxian,
Ren Wei,
Guo Maozu,
Wang Jun
Publication year - 2020
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.23951
Subject(s) - multifactor dimensionality reduction , epistasis , single nucleotide polymorphism , cluster analysis , dimensionality reduction , biology , computer science , computational biology , locus (genetics) , set (abstract data type) , data mining , artificial intelligence , genotype , genetics , gene , programming language
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.