SNPRelate (version 1.6.4)

SNPRelate-package: 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/

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)