CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies

Yang, Cheng‐Hong; Chuang, Li‐Yeh; Lin, Yu‐Da

doi:10.1093/bioinformatics/btx163

Cited by 48 publications

(36 citation statements)

References 30 publications

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“…The prediction error rate of the listed epistatic interactions is all below 0.5 (Coffey et al, ) and the interaction p ‐values are all less than .0001, which indicates the significance of this epistasis (C.‐H. Yang et al, ). The results are analyzed by referring to the breast cancer pathway (hsa05224) in the KEGG database.…”

Section: Resultsmentioning

confidence: 98%

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

Cao

Ren

et al. 2019

Human Mutation

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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. K E Y W O R D Sdual screening, epistatic interaction, main effects, multifactor dimensionality reduction, part mutual information

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Section: Resultsmentioning

confidence: 98%

“…The empirical power of DualWMDR is compared with EDCF (Xie et al, ), DCHE (Guo et al, ), DECMDR (Yang et al, ), and MOMDR (Yang et al, ). In addition, to validate the effectiveness of PMI in epistasis detection, we introduce two variants of DualWMDR: DualWMDR_ave and DualWMDR_CMI.…”

Section: Resultsmentioning

confidence: 99%

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

Cao

Ren

et al. 2019

Human Mutation

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“…In the simulation data experiments, we used five three-locus disease models to compare ClusterMI with EDCF [25], DCHE [26], DECMDR [16] and HiSeeker [27]. ClusterMI had two variants: ClusterMI(A) and ClusterMI(E), where ClusterMI(A) utilized the improved ACO search strategy in the search stage to identify high-order SNP interactions, while ClusterMI(E) employed the exhaustive search strategy.…”

Section: Resultsmentioning

confidence: 99%

“…epiMODE [15] (epistatic module detection) extends BEAM; it uses Gibbs sampling and a reversible jump MCMC procedure to search for significant epistatic modules. DECMDR [16] (Differential Evolution algorithm combined with a Classification based Multifactor-Dimensionality Reduction) uses the classification-based MDR (CMDR) as a fitness measure to evaluate solutions in the differential evolution (DE) process to scan potential SNP interactions. Due to the high efficiency and simple implementation of DE, DECMDR can detect high-order SNP interactions in large genome-wide datasets.…”

Section: Introductionmentioning

confidence: 99%

“…For a large candidate set, ClusterMI employs an improved ant colony optimization (ACO) algorithm [28]. Extensive experiments on simulated datasets show that ClusterMI has better performance in detecting high-order interactions than four other recently-proposed approaches: EDCF [25], DCHE [26], DECMDR [16] and HiSeeker [27]. Experiments on real Wellcome Trust Case Control Consortium (WTCCC) datasets also demonstrate that ClusterMI is more capable of identifying high-order SNP interactions from genome-wide data than these compared methods.…”

Section: Introductionmentioning

confidence: 99%

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ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information

Cao

Liu

et al. 2018

IJMS

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Identifying single nucleotide polymorphism (SNP) interactions is considered as a popular and crucial way for explaining the missing heritability of complex diseases in genome-wide association studies (GWAS). Many approaches have been proposed to detect SNP interactions. However, existing approaches generally suffer from the high computational complexity resulting from the explosion of candidate high-order interactions. In this paper, we propose a two-stage approach (called ClusterMI) to detect high-order genome-wide SNP interactions based on significant pairwise SNP combinations. In the screening stage, to alleviate the huge computational burden, ClusterMI firstly applies a clustering algorithm combined with mutual information to divide SNPs into different clusters. Then, ClusterMI utilizes conditional mutual information to screen significant pairwise SNP combinations in each cluster. In this way, there is a higher probability of identifying significant two-locus combinations in each group, and the computational load for the follow-up search can be greatly reduced. In the search stage, two different search strategies (exhaustive search and improved ant colony optimization search) are provided to detect high-order SNP interactions based on the cardinality of significant two-locus combinations. Extensive simulation experiments show that ClusterMI has better performance than other related and competitive approaches. Experiments on two real case-control datasets from Wellcome Trust Case Control Consortium (WTCCC) also demonstrate that ClusterMI is more capable of identifying high-order SNP interactions from genome-wide data.

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A Belief Degree–Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection

Rahaman

Wong

2021

Methods in Molecular Biology

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CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 48 publications

References 30 publications

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

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

ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information

A Belief Degree–Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection

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