Minzhu Xie1, Jing Li, Tao Jiang. 1. Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.
Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY: http://www.cs.ucr.edu/~minzhux/EDCF.zip CONTACT: minzhux@cs.ucr.edu; jingli@cwru.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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. AVAILABILITY: http://www.cs.ucr.edu/~minzhux/EDCF.zip CONTACT: minzhux@cs.ucr.edu; jingli@cwru.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Robert J Klein; Caroline Zeiss; Emily Y Chew; Jen-Yue Tsai; Richard S Sackler; Chad Haynes; Alice K Henning; John Paul SanGiovanni; Shrikant M Mane; Susan T Mayne; Michael B Bracken; Frederick L Ferris; Jurg Ott; Colin Barnstable; Josephine Hoh Journal: Science Date: 2005-03-10 Impact factor: 47.728