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Accelerating epistasis analysis in human genetics with consumer graphics hardware

机译:使用消费类图形硬件加速人类遗传学中的上位性分析

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摘要

BackgroundHuman 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.
机译:背景技术人类遗传学家现在能够测量整个人类基因组中超过一百万个DNA序列变异。新的挑战是开发在计算上可行的方法,该方法能够分析这些数据与常见人类疾病的关联,尤其是在上位情况下。上位性描述了一种情况,其中多个基因以复杂的非线性方式相互作用以确定个体的疾病风险,并且被认为对常见疾病无处不在。多因素降维(MDR)是一种能够检测上位性的算法。使用MDR进行详尽的分析通常在计算上昂贵,尤其是对于高阶交互而言。以前,并行计算和昂贵的硬件已解决了这一难题。我们在这里检查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)具有更多的内存带宽和计算能力,非常适合此问题。视频游戏产业的进步导致规模经济,从而形成了一种以非常低的成本容易获得这些强大组件的情况。在这里,我们实现并评估了MDR算法在GPU上的性能。首先要关注的是上位性分析所需的时间以及可用解决方案的性价比。

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