Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/6446

SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies

Authors Yang, Can
He, Zengyou
Wan, Xiang
Yang, Qiang
Xue, Hong
Yu, Weichuan
Issue Date 2009
Source Bioinformatics, v. 25, (4), 2009, FEB 15, p. 504-511
Summary Motivation: Hundreds of thousands of single nucleotide polymorphisms (SNPs) are available for genome-wide association (GWA) studies nowadays. The epistatic interactions of SNPs are believed to be very important in determining individual susceptibility to complex diseases. However, existing methods for SNP interaction discovery either suffer from high computation complexity or perform poorly when marginal effects of disease loci are weak or absent. Hence, it is desirable to develop an effective method to search epistatic interactions in genome-wide scale. Results: We propose a new method SNPHarvester to detect SNP SNP interactions in GWA studies. SNPHarvester creates multiple paths in which the visited SNP groups tend to be statistically associated with diseases, and then harvests those significant SNP groups which pass the statistical tests. It greatly reduces the number of SNPs. Consequently, existing tools can be directly used to detect epistatic interactions. By using a wide range of simulated data and a real genome-wide data, we demonstrate that SNPHarvester outperforms its recent competitor significantly and is promising for practical disease prognosis.
Subjects
ISSN 1367-4803
Rights This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Bioinformatics, v. 25, no. 4, p. 504-511 is available online at : doi:10.1093/bioinformatics/btn652
Language English
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