A high-performance computing toolset for relatedness and principal component analysis of SNP data
A high-performance computing toolset for relatedness and principal component analysis of SNP data
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DOI：
10.1093/bioinformatics/bts606
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发表时间：
2012-12-01
期刊：
影响因子：
5.8
通讯作者：
Weir, Bruce S.
中科院分区：
文献类型：
--
作者：
Zheng, Xiuwen;Levine, David;Weir, Bruce S.
摘要：全基因组关联研究被广泛用于研究疾病和性状的遗传基础，但它们构成了许多计算挑战。我们开发了gdsfmt和SNPRelate(用于多核对称多处理计算机体系结构的R包)来加速SNP数据上的两个关键计算：主成分分析(PCA)和基于同一性下降度量的关联性分析。我们的算法的内核是用C/C++编写的，并且经过了高度优化。基准测试表明，单处理器实现的PCA和Identity-by-Drosis分别比流行的EIGENSTRAT(v3.0)和plink(v1.07)程序中提供的实现快8-50倍，使用8个内核可以加速30-300倍。SNPRelate可以用数百万个SNPs分析数万个样本。例如，我们的程序包被用于对来自‘基因-环境关联研究’联盟研究的55324名受试者进行主成分分析。可用性和实施：Gdsfmt和SNPRelate可从R cran(http://cran.r-project.org)，，包括Vignette)获得。有关教程，请访问https://www.genevastudy.org/Accomplishments/software.。联系方式：zengx@u.washington.edu。
SUMMARY: Genome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed gdsfmt and SNPRelate (R packages for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations on SNP data: principal component analysis (PCA) and relatedness analysis using identity-by-descent measures. The kernels of our algorithms are written in C/C++ and highly optimized. Benchmarks show the uniprocessor implementations of PCA and identity-by-descent are 8-50 times faster than the implementations provided in the popular EIGENSTRAT (v3.0) and PLINK (v1.07) programs, respectively, and can be sped up to 30-300-fold by using eight cores. SNPRelate can analyse tens of thousands of samples with millions of SNPs. For example, our package was used to perform PCA on 55 324 subjects from the 'Gene-Environment Association Studies' consortium studies. Availability and implementation: gdsfmt and SNPRelate are available from R CRAN (http://cran.r-project.org), including a vignette. A tutorial can be found at https://www.genevastudy.org/Accomplishments/software. CONTACT: zhengx@u.washington.edu.