Abstract
A significant challenge in epistasis detection is the huge amount of data, which leads to combinatorial explosion. This study focuses on a two-stage approach for detecting epistasis only among single nucleotide polymorphisms (SNPs) that show some marginal effect. We present this two-stage approach based on the fusion of two criteria (TwoFC) to detect epistatic interactions. We fuse the G 2 test and absolute probability difference function as a scoring function to measure the strength of association between SNPs and disease status. The fused scoring function is an excellent measure of the strength of such an association. The two-stage strategy greatly reduces the computation load on epistasis detection. We use both simulated data sets and a real disease data set to evaluate our method. The results of an experiment on the simulated data sets show that TwoFC exhibits high power and sample efficiency. The results of an experiment on the real disease data set show that our method performs well even with large-scale data sets.
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Acknowledgments
We thank Dr. Bing Han for providing the EpiBN code. This work is supported by the Program for New Century Excellent Talents in University (Grant NCET-10-0365), the National Nature Science Foundation of China (Grants 60973082, 11171369, 61272395, 61370171), the National Nature Science Foundation of Hunan Province (Grant 12JJ2041), and the Planned Science and Technology Project of Hunan Province (Grants 2009FJ3195, 2012FJ2012).
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Liao, Z., Zeng, Q., Liao, B. et al. A Novel Two-Stage Approach for Epistasis Detection in Genome-Wide Case–Control Studies. Biochem Genet 52, 403–414 (2014). https://doi.org/10.1007/s10528-014-9656-7
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DOI: https://doi.org/10.1007/s10528-014-9656-7