getLD computes the value of D' and r^2 for each pair of SNPs in a matrix,
getLDlarge determines D' and r^2 between each SNP and a user-specified number of SNPs
closest to the SNP on the corresponding chromosome. Thus, getLDlarge can be applied
to much more SNPs than getLD.
getLD(x, which = c("both", "rSquare", "Dprime"), parentsOnly = FALSE, iter = 50, snp.in.col = TRUE, asMatrix = FALSE, addVarN = FALSE) getLDlarge(x, neighbors=25, which=c("both", "rSquare", "Dprime"), parentsOnly=FALSE, iter=50, snp.in.col=TRUE, addVarN=FALSE)snp.in.col = FALSE. It is assumed that the SNPs are ordered
by their position on the considered chromosome.
neighbors
columns of x), 2 * neighbors r^2 or D' values are computed.
"rSquare", or "Dprime",
or the values of "both" measures are computed. The latter is the default.
x, should be used in the computation of the LD measures when x
is in genotype format and contains case-parent trio data (see ped2geno and read.pedfile).
If FALSE (default), all rows are used in the determination of the pairwise LD measure.
x represents a SNP (and each row a
subject). If FALSE, each row represents a SNP (and each column a subject).
FALSE, the LD values are returned as a vector
of length $m * (m - 1) / 2$.
findLDblocks.
getLD or getLDlarge consisting (depending of the specification of which) the
D' (Dprime) or r^2 (rSquare) values for each SNP pair, and (depending of the specification
of addVarN) the variance estimates for D' (varDprime) and the numbers of non-missing values
(n). Furthermore, the names of the SNPs (rn) will be added (in getLD, if asMatrix = FALSE).plot.getLD, findLDblocks
# Load the simulated data.
data(trio.data)
# The values of Dprime and Rsquare for each pair of SNPs
# in LDdata can be computed by
ld.out <- getLD(LDdata)
# By default, the LD measures are returned as a vector.
# If they should be returned as a matrix, then use
ld.out2 <- getLD(LDdata, asMatrix = TRUE)
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