CloudAssoc: A pipeline for imputation based genome wide association study on cloud

Author

Weidi Dai ; Qiuwen Wang ; Meng Gao ; Lu Zhang

Author_Institution

Sch. of Comput. Sci. & Technol., Tianjin Univ., Tianjin, China

fYear

2012

fDate

29-31 Dec. 2012

Firstpage

1435

Lastpage

1438

Abstract

Genome wide association study (GWAS) has been proved to be an efficient approach to identify susceptibility genes for complex diseases. In order to increase the power for detecting the disease causal variants, imputation has been used to predict genotype dosages of untyped variants on the basis of linkage disequilibrium evaluated by public data. However, as the volume of data grows, time-consuming of imputation based association study becomes extremely large. We developed a cloud based pipeline to implement data format conversion, imputation, quality control and association study based on Map/Reduce framework which can aid biologists to accelerate the identification and evaluation of susceptibility genes for complex diseases and make it easier to combine GWAS data from worldwide for meta analysis.

Keywords

cloud computing; genomics; medical computing; parallel algorithms; pipeline processing; public domain software; software packages; CloudAssoc software package; GWAS data; Hadoop; MapReduce framework; cloud based pipeline; cloud computing; data format conversion; disease causal variant detection; genotype dosage prediction; imputation based genome wide association study; linkage disequilibrium; meta analysis; public data; quality control; susceptibility gene identification; Cloud Computing; GWAS; Imputation;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Network Technology (ICCSNT), 2012 2nd International Conference on

Conference_Location

Changchun

Print_ISBN

978-1-4673-2963-7

Type

conf

DOI

10.1109/ICCSNT.2012.6526190

Filename

6526190