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多因子降维处理技术可用于图形处理单元,从而实现散发性肌萎缩侧索硬化症中上位性的全基因组检验。

Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS.

机构信息

Department of Genetics, Dartmouth Medical School, Lebanon, NH 03756, USA.

出版信息

Bioinformatics. 2010 Mar 1;26(5):694-5. doi: 10.1093/bioinformatics/btq009. Epub 2010 Jan 16.

DOI:10.1093/bioinformatics/btq009
PMID:20081222
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2828117/
Abstract

MOTIVATION

Epistasis, the presence of gene-gene interactions, has been hypothesized to be at the root of many common human diseases, but current genome-wide association studies largely ignore its role. Multifactor dimensionality reduction (MDR) is a powerful model-free method for detecting epistatic relationships between genes, but computational costs have made its application to genome-wide data difficult. Graphics processing units (GPUs), the hardware responsible for rendering computer games, are powerful parallel processors. Using GPUs to run MDR on a genome-wide dataset allows for statistically rigorous testing of epistasis.

RESULTS

The implementation of MDR for GPUs (MDRGPU) includes core features of the widely used Java software package, MDR. This GPU implementation allows for large-scale analysis of epistasis at a dramatically lower cost than the standard CPU-based implementations. As a proof-of-concept, we applied this software to a genome-wide study of sporadic amyotrophic lateral sclerosis (ALS). We discovered a statistically significant two-SNP classifier and subsequently replicated the significance of these two SNPs in an independent study of ALS. MDRGPU makes the large-scale analysis of epistasis tractable and opens the door to statistically rigorous testing of interactions in genome-wide datasets.

AVAILABILITY

MDRGPU is open source and available free of charge from http://www.sourceforge.net/projects/mdr.

摘要

动机

基因-基因相互作用的存在(上位性)被假设是许多常见人类疾病的根源,但目前的全基因组关联研究在很大程度上忽略了它的作用。多因子降维(MDR)是一种强大的无模型方法,可用于检测基因之间的上位性关系,但计算成本使其难以应用于全基因组数据。图形处理单元(GPU)是负责渲染计算机游戏的硬件,是强大的并行处理器。使用 GPU 在全基因组数据集上运行 MDR 可以进行统计学上严格的上位性测试。

结果

用于 GPU 的 MDR 实现(MDRGPU)包括广泛使用的 Java 软件包 MDR 的核心功能。这种 GPU 实现允许以比标准 CPU 实现低得多的成本进行大规模的上位性分析。作为概念验证,我们将该软件应用于散发性肌萎缩侧索硬化症(ALS)的全基因组研究。我们发现了一个统计学上显著的双 SNP 分类器,并随后在 ALS 的独立研究中复制了这两个 SNP 的显著性。MDRGPU 使大规模分析上位性变得可行,并为全基因组数据集中的交互作用提供了统计学上严格的测试方法。

可用性

MDRGPU 是开源的,可以从 http://www.sourceforge.net/projects/mdr 免费获得。

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