eCEO: an efficient Cloud Epistasis cOmputing model in genome-wide association study | Zendy

Zhengkui Wang | Zendy; Yue Wang | Zendy; KianLee Tan | Zendy; Limsoon Wong | Zendy; Divyakant Agrawal | Zendy

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eCEO: an efficient Cloud Epistasis cOmputing model in genome-wide association study

Author(s) -

Zhengkui Wang,

Yue Wang,

KianLee Tan,

Limsoon Wong,

Divyakant Agrawal

Publication year - 2011

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btr091

Subject(s) - epistasis , cloud computing , computer science , scalability , single nucleotide polymorphism , genome wide association study , computational biology , data mining , biology , genetics , genotype , gene , database , operating system

Recent studies suggested that a combination of multiple single nucleotide polymorphisms (SNPs) could have more significant associations with a specific phenotype. However, to discover epistasis, the epistatic interactions of SNPs, in a large number of SNPs, is a computationally challenging task. We are, therefore, motivated to develop efficient and effective solutions for identifying epistatic interactions of SNPs.

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