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The 1000 Genome project has made available a large number of Single Nucleotide Polymorphisms (SNPs) for Genome-Wide Association Studies (GWAS). However, the large number of SNPs has also rendered the discovery of epistatic interactions of SNPs computationally expensive. Parallelizing the computation offers a promising solution. This paper proposes a Cloud-based Epistasis Computing (CEO) model that examines all k-locus SNPs combinations to find statistically significant epistatic interactions.
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