Accelerating epistasis analysis in human genetics with consumer graphics hardware.
Accelerating epistasis analysis in human genetics with consumer graphics hardware.
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DOI：
10.1186/1756-0500-2-149
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发表时间：
2009-07-24
影响因子：
1.8
通讯作者：
Moore JH
中科院分区：
文献类型：
--
作者：
Sinnott-Armstrong NA;Greene CS;Cancare F;Moore JH
人类遗传学家现在能够测量人类基因组中超过100万个DNA序列变异。新的挑战是开发计算上可行的方法，能够分析这些数据与人类常见疾病的关联，特别是在上位性的背景下。上位性描述了多个基因以复杂的非线性方式相互作用以确定个体疾病风险的情况，并且被认为在常见疾病中普遍存在。多因子模糊性约简（MDR）是一种能够检测上位性的算法。一个详尽的分析与MDR通常是计算昂贵的，特别是高阶相互作用。这个挑战以前已经遇到了并行计算和昂贵的硬件。我们在这里研究的选项利用了为计算机图形设计的商品硬件。在现代计算机中，图形处理单元（GPU）比中央处理单元（CPU）具有更多的存储器带宽和计算能力，并且非常适合解决这个问题。视频游戏行业的进步导致了规模经济，创造了这些功能强大的组件可以以非常低的成本获得的情况。在这里，我们实现和评估性能的MDR算法的GPU上。主要关注的是上位性分析所需的时间和可用解决方案的性价比。我们发现，在GPU上使用MDR始终可以提高每台机器的性能，无论是功能丰富的Java软件包还是C++集群实现。与在CPU上运行Java实现的8核工作站相比，运行GPU实现的GPU工作站的性能将计算时间减少了160倍。该GPU工作站的性能类似于在Beowulf集群上运行优化的C++实现的150个核心。此外，该GPU系统提供了极具成本效益的性能，同时使CPU可用于其他任务。包含三个GPU的GPU工作站的成本为2000美元，而在Beowulf集群上获得类似的性能需要150个CPU核心，包括增加的基础设施和集群系统的支持成本，成本约为82，500美元。基于图形硬件的计算提供了一种成本有效的手段，在没有计算集群的基础设施的情况下，使用MDR在大型数据集上执行上位性的遗传分析。
Human geneticists are now capable of measuring more than one million DNA sequence variations from across the human genome. The new challenge is to develop computationally feasible methods capable of analyzing these data for associations with common human disease, particularly in the context of epistasis. Epistasis describes the situation where multiple genes interact in a complex non-linear manner to determine an individual's disease risk and is thought to be ubiquitous for common diseases. Multifactor Dimensionality Reduction (MDR) is an algorithm capable of detecting epistasis. An exhaustive analysis with MDR is often computationally expensive, particularly for high order interactions. This challenge has previously been met with parallel computation and expensive hardware. The option we examine here exploits commodity hardware designed for computer graphics. In modern computers Graphics Processing Units (GPUs) have more memory bandwidth and computational capability than Central Processing Units (CPUs) and are well suited to this problem. Advances in the video game industry have led to an economy of scale creating a situation where these powerful components are readily available at very low cost. Here we implement and evaluate the performance of the MDR algorithm on GPUs. Of primary interest are the time required for an epistasis analysis and the price to performance ratio of available solutions. We found that using MDR on GPUs consistently increased performance per machine over both a feature rich Java software package and a C++ cluster implementation. The performance of a GPU workstation running a GPU implementation reduces computation time by a factor of 160 compared to an 8-core workstation running the Java implementation on CPUs. This GPU workstation performs similarly to 150 cores running an optimized C++ implementation on a Beowulf cluster. Furthermore this GPU system provides extremely cost effective performance while leaving the CPU available for other tasks. The GPU workstation containing three GPUs costs $2000 while obtaining similar performance on a Beowulf cluster requires 150 CPU cores which, including the added infrastructure and support cost of the cluster system, cost approximately $82,500. Graphics hardware based computing provides a cost effective means to perform genetic analysis of epistasis using MDR on large datasets without the infrastructure of a computing cluster.