Paper Info
Title
GWAS-GMDR: A program package for genome-wide scan of gene-gene interactions with covariate adjustment based on multifactor dimensionality reduction
Abstract
Multifactor dimensionality reduction (MDR) has been successfully applied to identification of gene-gene interactions for the complex traits. Generalized MDR (GMDR) was its extension that allows adjustment for covariates. The current GMDR software mainly focuses on candidate gene association studies with a relatively small number of genetic markers and has some limitations to be extended to genome-wide association studies (GWAS) with a large number of genetic markers. We develop GWAS-GMDR, an effective parallel computing program package with special features for GWAS with a large number of genetic markers by using distributed job scheduling method and/or CUDA-enabled high-performance graphic processing units (GPU). First, GWAS-GMDR implements an effective memory handling algorithm and efficient procedures for GMDR to make joint analysis of multiple genes feasible for GWAS. Second, a weighted version of cross-validation consistency based on `top-K selection' (WCVCK) is proposed to report multiple candidates for causal gene-gene interactions. Third, various performance measures are implemented to evaluate MDR classifiers, including balanced accuracy, tau-b, likelihood ratio and normalized mutual information. Fourth, some popular methods for handling missing genotypes are implemented. Finally, our applications support both CPU-based and GPU-based parallel computing system. We applied our applications using a real genome wide data set from WTCCC Crohn's disease dataset to identify two-way interaction models in genome-wide scale. The GWAS-GMDR package is a powerful tool for the gene-gene interaction analysis in a genome-wide scale. High-performance implementations are provided as native binaries for Linux, Mac OS X and Windows systems.
Year
DOI
Venue
2011
10.1109/BIBMW.2011.6112456
BIBM Workshops
Keywords
Field
DocType
graphic processing units,diseases,disease dataset,parallel programming,candidate gene association study,mdr,mdr classifiers,gwas,genetics,small number,genomics,genetic marker,graphics processing units,covariate adjustment,large number,biology computing,current gmdr software,parallel computing program package,linux,effective memory handling algorithm,cuda-enabled high-performance graphic processing,effective parallel computing program,gp-gpu,program package,mac os x,parallel computing,association study,gwas-gmdr,multifactor dimensionality reduction,gene-gene interaction,windows systems,genome-wide scan,gene-gene interactions,generalized mdr,genome-wide association studies,likelihood ratio,genome wide association study,job scheduling,parallel computer,candidate gene,cross validation
Small number,Data mining,Multifactor dimensionality reduction,Computer science,Genomics,Software,Artificial intelligence,Covariate,Candidate gene,Genome-wide association study,Job scheduler,Bioinformatics,Machine learning
Conference
ISSN
ISBN
Citations 
2163-6966
978-1-4577-1612-6
1
PageRank 
References 
Authors
0.36
3
7
Name
Order
Citations
PageRank
Minseok Kwon133928.71
Kyunga Kim2243.41
Sungyoung Lee32932279.41
Wonil Chung4131.34
Sung-Gon Yi513912.28
Junghyun Namkung6132.69
Taesung Park749064.41