SNPRuler finds epistatic interactions in GWASs. SNPRuler first uses the predictive rule learning to narrow down possible interactions among SNPs and then captures true interactions using χ2 statistic test. The rule-based strategy in our non-parametric learning approach enables our new method to search for interaction patterns more efficiently than existing methods. We conduct extensive experiments on both simulated data and real genome-wide data. The experimental results demonstrate that our new learning method is a powerful tool in handling large-scale SNP data both in terms of speed and detection of potential interactions that were not identified before.
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Xiang Wan et al.
Predictive rule inference for epistatic interaction detection in genome-wide association studies
Bioinformatics (2010) 26 (1): 30-37.