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. 2011 May 1;27(9):1309-10. doi: 10.1093/bioinformatics/btr114. Epub 2011 Mar 3.
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.
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.
GBOOST code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST.
随着先进的基因分型技术的发展，收集数百万个遗传变异是可行的。有了大量的遗传变异数据，开发有效的算法来及时进行基因-基因相互作用分析已成为全基因组关联研究 (GWAS) 的关键问题之一。基于布尔运算的筛选和测试 (BOOST) 是 GWAS 中的一项最新工作，它可以在台式计算机上在 2.5 天内完成基因-基因相互作用分析。与中央处理器 (CPU) 相比，图形处理单元 (GPU) 是高度并行的硬件，可以提供大量的计算资源。因此，我们有动力使用 GPU 进一步加快基因-基因相互作用的分析速度。
我们在 GPU 框架上实现了 BOOST 方法，并将其命名为 GBOOST。与 BOOST 相比，GBOOST 实现了 40 倍的加速。它在配备 Nvidia GeForce GTX 285 显示卡的台式计算机上，可在 1.34 小时内完成惠康信托基金会 2 型糖尿病 (WTCCC T2D) 基因组数据的分析。
GBOOST 代码可在 http://bioinformatics.ust.hk/BOOST.html#GBOOST 获得。
