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Bioinformatics Advance Access published online on October 30, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp622
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Predictive rule inference for epistatic interaction detection in genome-wide association studies

Xiang Wan 1, Can Yang 1, Qiang Yang 2, Hong Xue 3, Nelson L.S. Tang 4 and Weichuan Yu 1,*

1Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology.
2Department of Computer Science, The Hong Kong University of Science and Technology.
3Department of Biochemistry, The Hong Kong University of Science and Technology.
4Laboratory for Genetics of Disease Susceptibility, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong.

*To whom correspondence should be addressed. Weichuan Yu, E-mail: eeyu{at}ust.hk


  Abstract

Motivation: Under the current era of genome-wide association study (GWAS), finding epistatic interactions in the large volume of SNP data is a challenging and unsolved issue. Few of previous studies could handle genome-wide data due to the difficulties in searching the combinatorially explosive search space and statistically evaluating high-order epistatic interactions given the limited number of samples. In this work, we propose a novel learning approach (SNPRuler) based on the predictive rule inference to find disease-associated epistatic interactions.

Results: Our extensive experiments on both simulated data and real genome-wide data from Wellcome Trust Case Control Consortium (WTCCC) show that SNPRuler significantly outperforms its recent competitor. To our knowledge, SNPRuler is the first method that guarantees to find the epistatic interactions without exhaustive search. Our results indicate that finding epistatic interactions in GWAS is computationally attainable in practice.

Availability: http://bioinformatics.ust.hk/SNPRuler.zip

Contact: eexiangw{at}ust.hk, eeyu{at}ust.hk

Associate Editor: Prof. Dmitrij Frishman

Received on May 18, 2009; revised on October 15, 2009; accepted on October 28, 2009

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