Journal Article

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

Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang and Weichuan Yu

in Bioinformatics

Volume 26, issue 1, pages 30-37
Published in print January 2010 | ISSN: 1367-4803
Published online October 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp622
Predictive rule inference for epistatic interaction detection in genome-wide association studies

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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@ust.hk, eeyu@ust.hk

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5984 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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