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

TLDR

Finding epistatic interactions in GWAS is a challenging, unsolved problem due to the combinatorial search space and limited sample sizes. We propose SNPRuler, a predictive rule inference approach to identify disease‑associated epistatic interactions. SNPRuler learns predictive rules from SNP data to detect epistatic interactions without exhaustive search. Experiments on simulated and WTCCC data show SNPRuler outperforms recent competitors, is the first method to guarantee discovery without exhaustive search, and demonstrates that epistatic interaction detection is computationally feasible. SNPRuler is available at http://bioinformatics.ust.hk/SNPRuler.zip; contact [e-mail], [e-mail]; supplementary data are online.

Abstract

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: [e-mail], [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.