Publication: An efficient process for enhancing genotype imputation in Genome-wide association studies using high performance computing
Issued Date
2016-02-08
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2-s2.0-84964318293
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Mahidol University
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SCOPUS
Bibliographic Citation
ICSEC 2015 - 19th International Computer Science and Engineering Conference: Hybrid Cloud Computing: A New Approach for Big Data Era. (2016)
Suggested Citation
Kasikrit Damkliang, Pichaya Tandayya, Unitsa Sangket, Surakameth Mahasirimongkol, Ekawat Pasomsab An efficient process for enhancing genotype imputation in Genome-wide association studies using high performance computing. ICSEC 2015 - 19th International Computer Science and Engineering Conference: Hybrid Cloud Computing: A New Approach for Big Data Era. (2016). doi:10.1109/ICSEC.2015.7401397 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/43541
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Title
An efficient process for enhancing genotype imputation in Genome-wide association studies using high performance computing
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Abstract
© 2015 IEEE. Genotype imputation based analysis usually consumes computational and data intensive. This paper presents a practical and efficient process for enhancing the genotype imputation based analysis on Single Nucleotide Polymorphism (SNP) using High Performance Computing (HPC). Our process is split into data quality control, haplotype estimation, and imputation. We validate and measure the process on a standard workstation and a server for pilot dataset of chromosome 22 from Genetic Analysis Workshop 16 (GAW16) provided by the North American Rheumatoid Arthritis Consortium (NARAC). The NARAC dataset consists of 2,062 individuals and 545,080 SNP variants. We use 1000 Genomes database as reference panels. Our process correctly and rapidly produces results more than ordinary steps of the genotype imputation based analysis.