Paper Info
Title
Detecting genome-wide epistases based on the clustering of relatively frequent items.
Abstract
In genome-wide association studies (GWAS), up to millions of single nucleotide polymorphisms (SNPs) are genotyped for thousands of individuals. However, conventional single locus-based approaches are usually unable to detect gene-gene interactions underlying complex diseases. Due to the huge search space for complicated high order interactions, many existing multi-locus approaches are slow and may suffer from low detection power for GWAS.In this article, we develop a simple, fast and effective algorithm to detect genome-wide multi-locus epistatic interactions based on the clustering of relatively frequent items. Extensive experiments on simulated data show that our algorithm is fast and more powerful in general than some recently proposed methods. On a real genome-wide case-control dataset for age-related macular degeneration (AMD), the algorithm has identified genotype combinations that are significantly enriched in the cases.http://www.cs.ucr.edu/~minzhux/EDCF.zipminzhux@cs.ucr.edu; jingli@cwru.eduSupplementary data are available at Bioinformatics online.
Year
DOI
Venue
2012
10.1093/bioinformatics/btr603
Bioinformatics
Keywords
Field
DocType
supplementary data,effective algorithm,single nucleotide polymorphism,simulated data,genome-wide multi-locus,conventional single locus-based approach,real genome-wide case,existing multi-locus approach,detecting genome-wide,frequent item,genome-wide association study,gene interaction,cluster analysis,algorithms,genome wide association study
Genome,Data mining,Epistasis,Computer science,Genome-wide association study,Genetic association,Single-nucleotide polymorphism,Bioinformatics,Locus (genetics),Cluster analysis
Journal
Volume
Issue
ISSN
28
1
1367-4811
Citations 
PageRank 
References 
16
0.77
10
Authors
3
Name
Order
Citations
PageRank
Minzhu Xie1918.16
Jing Li249850.65
Tao Jiang31809155.32