Package: SNPRelate
Type: Package
Title: Parallel Computing Toolset for Relatedness and Principal
        Component Analysis of SNP Data
Version: 1.2.0
Date: 2015-03-26
Depends: R (>= 2.14), gdsfmt (>= 1.2.2)
LinkingTo: gdsfmt
Suggests: parallel, RUnit, knitr, MASS, BiocGenerics
Authors@R: c(person("Xiuwen", "Zheng", role=c("aut", "cre", "cph"),
        email="zhengx@u.washington.edu"),
        person("Stephanie", "Gogarten", role="ctb"),
        person("Cathy", "Laurie", role="ctb"),
        person("Bruce", "Weir", role=c("ctb", "ths"),
        email="bsweir@u.washington.edu"))
Description: Genome-wide association studies (GWAS) are widely used to
        investigate the genetic basis of diseases and traits, but they
        pose many computational challenges. We developed an R package
        SNPRelate to provide a binary format for single-nucleotide
        polymorphism (SNP) data in GWAS utilizing CoreArray Genomic
        Data Structure (GDS) data files. The GDS format offers the
        efficient operations specifically designed for integers with
        two bits, since a SNP could occupy only two bits. SNPRelate is
        also designed to accelerate two key computations on SNP data
        using parallel computing for multi-core symmetric
        multiprocessing computer architectures: Principal Component
        Analysis (PCA) and relatedness analysis using
        Identity-By-Descent measures. The SNP format in this package is
        also used by the GWASTools package with the support of S4
        classes and generic functions.
License: GPL-3
VignetteBuilder: knitr
URL: http://github.com/zhengxwen/SNPRelate,
        http://corearray.sourceforge.net/tutorials/SNPRelate/
BugReports: http://github.com/zhengxwen/SNPRelate/issues
biocViews: Infrastructure, Genetics, StatisticalMethod,
        PrincipalComponent
NeedsCompilation: yes
Packaged: 2015-04-17 05:29:38 UTC; biocbuild
Author: Xiuwen Zheng [aut, cre, cph],
  Stephanie Gogarten [ctb],
  Cathy Laurie [ctb],
  Bruce Weir [ctb, ths]
Maintainer: Xiuwen Zheng <zhengx@u.washington.edu>
Built: R 3.2.0; i386-w64-mingw32; 2015-04-17 13:24:47 UTC; windows
Archs: i386, x64
