CChi: An efficient cloud epistasis test model in human genome wide association studies

Due to the vast amounts of SNPs and huge search space, how to decrease the total computation costs is a challenge in genome wide association studies (GWAS). Triggered by this problem, we develop an effective and efficient algorithm for epistasis detection in GWAS. We propose a cloud-based algorithm using chi-square test, denoted as CChi. CChi adopts a pruning strategy by utilizing an upper bound to prune amounts of unnecessary SNP pairs, and is implemented under Google´s MapReduce framework. A best-fit model is proposed by us to distribute SNP pairs to each reducer. Extensive experimental results demonstrate that CChi is practically and computationally efficient.