snpgdsLDMat: Linkage Disequilibrium (LD) analysis

In SNPRelate: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data

Description

Return a LD matrix for SNP pairs.

Usage

snpgdsLDMat(gdsobj, sample.id=NULL, snp.id=NULL, slide=250L,
    method=c("composite", "r", "dprime", "corr", "cov"), mat.trim=FALSE,
    num.thread=1L, with.id=TRUE, verbose=TRUE)

Arguments

gdsobj	an object of class SNPGDSFileClass, a SNP GDS file
sample.id	a vector of sample id specifying selected samples; if NULL, all samples are used
snp.id	a vector of snp id specifying selected SNPs; if NULL, all SNPs are used
slide	# of SNPs, the size of sliding window; if slide < 0, return a full LD matrix; see details
method	"composite", "r", "dprime", "corr", "cov", see details
mat.trim	if TRUE, trim the matrix when slide > 0: the function returns a "num_slide x (n_snp - slide)" matrix
num.thread	the number of (CPU) cores used; if NA, detect the number of cores automatically
with.id	if TRUE, the returned value with sample.id and sample.id
verbose	if TRUE, show information

Details

Four methods can be used to calculate linkage disequilibrium values: "composite" for LD composite measure, "r" for R coefficient (by EM algorithm assuming HWE, it could be negative), "dprime" for D', and "corr" for correlation coefficient. The method "corr" is equivalent to "composite", when SNP genotypes are coded as: 0 – BB, 1 – AB, 2 – AA.

If slide <= 0, the function returns a n-by-n LD matrix where the value of i row and j column is LD of i and j SNPs. If slide > 0, it returns a m-by-n LD matrix where n is the number of SNPs, m is the size of sliding window, and the value of i row and j column is LD of j and j+i SNPs.

Value

Return a list:

sample.id	the sample ids used in the analysis
snp.id	the SNP ids used in the analysis
LD	a matrix of LD values
slide	the size of sliding window

Author(s)

Xiuwen Zheng

References

Weir B: Inferences about linkage disequilibrium. Biometrics 1979; 35: 235-254.

Weir B: Genetic Data Analysis II. Sunderland, MA: Sinauer Associates, 1996.

Weir BS, Cockerham CC: Complete characterization of disequilibrium at two loci; in Feldman MW (ed): Mathematical Evolutionary Theory. Princeton, NJ: Princeton University Press, 1989.

See Also

snpgdsLDpair, snpgdsLDpruning

Examples

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

# missing proportion and MAF
ff <- snpgdsSNPRateFreq(genofile)

# chromosome 15
snpset <- read.gdsn(index.gdsn(genofile, "snp.id"))[
    ff$MissingRate==0 & ff$MinorFreq>0 &
    read.gdsn(index.gdsn(genofile, "snp.chromosome"))==15]
length(snpset)


# LD matrix without sliding window
ld.noslide <- snpgdsLDMat(genofile, snp.id=snpset, slide=-1, method="composite")
# plot
image(t(ld.noslide$LD^2), col=terrain.colors(16))

# LD matrix with a sliding window
ld.slide <- snpgdsLDMat(genofile, snp.id=snpset, method="composite")
# plot
image(t(ld.slide$LD^2), col=terrain.colors(16))


# close the genotype file
snpgdsClose(genofile)

Example output

Loading required package: gdsfmt
SNPRelate -- supported by Streaming SIMD Extensions 2 (SSE2)
[1] 203
Linkage Disequilibrium (LD) estimation on genotypes:
    # of samples: 279
    # of SNPs: 203
    using 1 thread
    method: composite
LD matrix:    the sum of all selected genotypes (0,1,2) = 56582
Linkage Disequilibrium (LD) estimation on genotypes:
    # of samples: 279
    # of SNPs: 203
    using 1 thread
    sliding window size: 203
    method: composite
LD matrix:    the sum of all selected genotypes (0,1,2) = 56582

SNPRelate documentation built on Nov. 8, 2020, 5:31 p.m.