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GBOOST : A GPU-based tool for detecting gene-gene interactions in genome-wide case control studies

Ling Sing S. Yung, Can Yang, Xiang Wan, Weichuan Yu
Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
Bioinformatics (Oxford, England) (3 March 2011)

@article{yung2011gboost,

   title={GBOOST: A GPU-based tool for detecting gene-gene interactions in genome-wide case control studies},

   author={Yung, L.S. and Yang, C. and Wan, X. and Yu, W.},

   journal={Bioinformatics},

   issn={1367-4803},

   year={2011},

   publisher={Oxford Univ Press}

}

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MOTIVATION: Collecting millions of genetic variations is feasible with the advanced genotyping technology. With a huge amount of genetic variations data in hand, developing efficient algorithms to carry out the gene-gene interaction analysis in a timely manner has become one of the key problems in Genome-Wide Association Studies (GWAS). Boolean operation based screening and testing (BOOST), a recent work in GWAS, completes gene-gene interaction analysis in 2.5 days on a desktop computer. Compared to Central Processing Units (CPUs), Graphic Processing Units (GPUs) are highly parallel hardware and provide massive computing resources. We are therefore motivated to use GPUs to further speed up the analysis of gene-gene interactions. RESULTS: We implement the BOOST method based on a GPU framework and name it GBOOST. GBOOST achieves a 40 fold speedup compared to BOOST. It completes the analysis ofWellcome Trust Case Control ConsortiumType 2 Diabetes (WTCCCT2D) genome data within 1.34 hours on a desktop computer equipped with Nvidia GeForce GTX 285 display card. AVAILABILITY: GBOOST code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST. CONTACT: Ling Sing Yung(timyung@ust.hk),Weichuan Yu(eeyu@ust.hk) SUPPLEMENTARY INFORMATION: Supplementary documents are avaliable at Bioinformatics online.
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