Abstract:
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 ...Show MoreNotes: Please be advised that the paper you have accessed is a draft of the final paper that was presented at the conference. This draft will be replaced with the final paper shortly.
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Abstract:
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.
Notes: Please be advised that the paper you have accessed is a draft of the final paper that was presented at the conference. This draft will be replaced with the final paper shortly.
Date of Conference: 16-18 December 2013
Date Added to IEEE Xplore: 24 February 2014
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