A high-performance computing toolset for relatedness and principal component analysis of SNP data

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 S...

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Vydáno v:Bioinformatics Ročník 28; číslo 24; s. 3326 - 3328
Hlavní autoři: Zheng, Xiuwen, Levine, David, Shen, Jess, Gogarten, Stephanie M., Laurie, Cathy, Weir, Bruce S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Oxford University Press 01.12.2012
Témata:
Algorithms
Applications Note
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
General aspects
Genome-Wide Association Study
Humans
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Polymorphism, Single Nucleotide
Principal Component Analysis
Software
High performance
Supercomputing
Data analysis
Data
Single nucleotide polymorphism
Computing
Component
Performance
Principal component analysis
ISSN:1367-4803, 1367-4811, 1367-4811, 1460-2059
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Shrnutí: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
Bibliografie:ObjectType-Article-1
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Associate Editor: Jeffrey Barrett
ISSN:1367-4803
1367-4811
1367-4811
1460-2059
DOI:10.1093/bioinformatics/bts606