EFMDR-Fast: An Application of Empirical Fuzzy Multifactor Dimensionality Reduction for Fast Execution

Gene-gene interaction is a key factor for explaining missing heritability. Many methods have been proposed to identify gene-gene interactions. Multifactor dimensionality reduction (MDR) is a well-known method for the detection of gene-gene interactions by reduction from genotypes of single-nucleotide polymorphism combinations to a binary variable with a value of high risk or low risk. This method has been widely expanded to own a specific objective. Among those expansions, fuzzy-MDR uses the fuzzy set theory for the membership of high risk or low risk and increases the detection rates of gene-gene interactions. Fuzzy-MDR is expanded by a maximum likelihood estimator as a new membership function in empirical fuzzy MDR (EFMDR). However, EFMDR is relatively slow, because it is implemented by R script language. Therefore, in this study, we implemented EFMDR using RCPP (c++ package) for faster executions. Our implementation for faster EFMDR, called EMMDR-Fast, is about 800 times faster than EFMDR written by R script only.

[1]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[2]  Min-Seok Kwon,et al.  A Modified Entropy-Based Approach for Identifying Gene-Gene Interactions in Case-Control Study , 2013, PloS one.

[3]  Dirk Eddelbuettel,et al.  Seamless R and C++ Integration with Rcpp , 2013 .

[4]  Yongkang Kim,et al.  Robust Gene-Gene Interaction Analysis in Genome Wide Association Studies , 2015, PloS one.

[5]  Dirk Eddelbuettel,et al.  Rcpp: Seamless R and C++ Integration , 2011 .

[6]  Angeline S. Andrew,et al.  A novel survival multifactor dimensionality reduction method for detecting gene–gene interactions with application to bladder cancer prognosis , 2010, Human Genetics.

[7]  Taesung Park,et al.  An empirical fuzzy multifactor dimensionality reduction method for detecting gene-gene interactions , 2017, BMC Genomics.

[8]  Peggy Hall,et al.  The NHGRI GWAS Catalog, a curated resource of SNP-trait associations , 2013, Nucleic Acids Res..

[9]  Seungyeoun Lee,et al.  A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions , 2016, Bioinform..

[10]  Taesung Park,et al.  Odds ratio based multifactor-dimensionality reduction method for detecting gene – gene interactions , 2006 .

[11]  Taesung Park,et al.  A novel method to identify high order gene-gene interactions in genome-wide association studies: Gene-based MDR , 2012, BMC Bioinformatics.

[12]  Taesung Park,et al.  Multifactor dimensionality reduction analysis of multiple binary traits for gene-gene interaction , 2016, Int. J. Data Min. Bioinform..

[13]  J. H. Moore,et al.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. , 2001, American journal of human genetics.

[14]  Jun Zhu,et al.  A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. , 2007, American journal of human genetics.

[15]  Taesung Park,et al.  A novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction , 2016, Comput. Biol. Chem..

[16]  Scott M. Williams,et al.  A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction , 2007, Genetic epidemiology.

[17]  Taesung Park,et al.  Fuzzy set-based generalized multifactor dimensionality reduction analysis of gene-gene interactions , 2018, BMC Medical Genomics.

[18]  Seungyeoun Lee,et al.  Gene–gene interaction analysis for the survival phenotype based on the Cox model , 2012, Bioinform..

[19]  Taesung Park,et al.  Log-linear model-based multifactor dimensionality reduction method to detect gene-gene interactions , 2007, Bioinform..