GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies. 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 with 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 with BOOST. It completes the analysis of Wellcome Trust Case Control Consortium Type 2 Diabetes (WTCCC T2D) genome data within 1.34 h 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
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References in zbMATH (referenced in 2 articles )
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- Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter: Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting (2018)
- Yung, Ling Sing; Yang, Can; Wan, Xiang; Yu, Weichuan: GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies (2011) ioport