CEO a cloud epistasis computing model in GWAS

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. In this paper, we propose a cloud-based epistasis computing (CEO) model that examines all k-locus SNPs combinations to find statistically significant epistatic interactions. Our CEO model uses the MapReduce framework which can be executed both on user's own clusters or on a cloud environment. Our cloud-based solution offers elastic computing resources to users, and more importantly, makes our approach affordable and available to all end-users. We evaluate CEO model on a cluster of more than 40 nodes. Our experiment results demonstrate that CEO model is computationally flexible, scalable and practical. Keywords-GWAS; Cloud computing; MapReduce; Hadoop

• Type: Journal
• Year: 2010
• Where: BIBM
• Authors: Zhengkui Wang, Yue Wang, Kian-Lee Tan, Limsoon Wong, Divyakant Agrawal
• Keywords: BIBM 2010, Bioinformatics, CEO Model, Epistatic Interactions, SNPs