SNPRelate-package {SNPRelate} | R Documentation

## Parallel Computing Toolset for Genome-Wide Association Studies

### Description

Genome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed SNPRelate (R package 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.

### Details

Package: | SNPRelate
Type: | Package
License: | GPL version 3
Depends: | gdsfmt (>= 1.0.4)

The genotypes stored in GDS format can be analyzed by the R functions in SNPRelate, which utilize the multi-core feature of machine for a single computer.

Webpage: http://github.com/zhengxwen/SNPRelate, http://corearray.sourceforge.net/

Tutorial: http://corearray.sourceforge.net/tutorials/SNPRelate/

### Author(s)

Xiuwen Zheng zhengxwen@gmail.com

### References

Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A High-performance Computing Toolset for Relatedness and Principal Component Analysis of SNP Data. Bioinformatics (2012); doi: 10.1093/bioinformatics/bts610

### Examples

    ####################################################################
    # Convert the PLINK BED file to the GDS file
    #

    # PLINK BED files
    bed.fn <- system.file("extdata", "plinkhapmap.bed.gz", package="SNPRelate")
    fam.fn <- system.file("extdata", "plinkhapmap.fam.gz", package="SNPRelate")
    bim.fn <- system.file("extdata", "plinkhapmap.bim.gz", package="SNPRelate")

    # convert
    snpgdsBED2GDS(bed.fn, fam.fn, bim.fn, "HapMap.gds")

    ####################################################################
    # Principal Component Analysis
    #

    # open
    genofile <- snpgdsOpen("HapMap.gds")

    RV <- snpgdsPCA(genofile)
    plot(RV$eigenvect[,2], RV$eigenvect[,1], xlab="PC 2", ylab="PC 1",
        col=rgb(0,0,150, 50, maxColorValue=255), pch=19)

    # close the file
    snpgdsClose(genofile)

    ####################################################################
    # Identity-By-Descent (IBD) Analysis
    #

    # open
    genofile <- snpgdsOpen(snpgdsExampleFileName())

    RV <- snpgdsIBDMoM(genofile)
    flag <- lower.tri(RV$k0)
    plot(RV$k0[flag], RV$k1[flag], xlab="k0", ylab="k1",
        col=rgb(0,0,150, 50, maxColorValue=255), pch=19)
    abline(1, -1, col="red", lty=4)

    # close the file
    snpgdsClose(genofile)

    ####################################################################
    # Identity-By-State (IBS) Analysis
    #

    # open
    genofile <- snpgdsOpen(snpgdsExampleFileName())

    RV <- snpgdsIBS(genofile)
    m <- 1 - RV$ibs
    colnames(m) <- rownames(m) <- RV$sample.id
    GeneticDistance <- as.dist(m[1:45, 1:45])
    HC <- hclust(GeneticDistance, "ave")
    plot(HC)

    # close the file
    snpgdsClose(genofile)

    ####################################################################
    # Linkage Disequilibrium (LD) Analysis
    #

    # open an example dataset (HapMap)
    genofile <- snpgdsOpen(snpgdsExampleFileName())

    snpset <- read.gdsn(index.gdsn(genofile, "snp.id"))[1:200]
    L1 <- snpgdsLDMat(genofile, snp.id=snpset, method="composite", slide=-1)

    # plot
    image(abs(L1$LD), col=terrain.colors(64))

    # close the file
    snpgdsClose(genofile)

[Package SNPRelate version 1.26.0 Index]