findLDblocks(x, alpha = 0.1, ciLD = c(0.7, 0.98), cuRecomb = 0.9, ratio = 9, alsoOthers = FALSE, parentsOnly = FALSE, iter = 50, snp.in.col = TRUE)
splitBlocks(blocks)getLD or getLDlarge, respectively,
or a numeric matrix
consisting of the integers 0, 1, and 2, where these integers are
assumed to be the number of minor alleles that the respective
SNPs shows at the respective subject. Missing values are allowed. By default,
each column of this matrix represents a SNP, and each row a subject
(for details, see snp.in.col). The SNPs must be ordered by their
position on the considered chromosome.
alpha)% confidence interval of D' is computed, and used
to specify pairs of SNPs that are either in strong LD, or show historical
evidence of recombination (see ciLD and cuRecomb). All SNP
pairs not falling into these two categories are specified as 'Others'.
ciLD and the upper bound is larger
than or equal to the second value, then this pair of SNP is considered to
be in strong LD.
cuRecomb, then this pair
of SNP is considered to show evidence of recombination.
ratio, then this block will
be identified as an LD-block. (Note that Gabriel et al. (2002) use ratio = 19
instead of ratio = 9.) Overlapping blocks are avoided by employing
the approach described in Wall and Pritchard (2003).
alsoOthers = FALSE),
this condition is used. If alsoOthers = TRUE, the endmarkers can also be categorized
as 'Others'.
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. Ignored
if x is the output of getLD or getLDlarge.
getLD). Ignored if x is the output of getLD.
x represents a SNP (and each row a subject).
If FALSE, each row represents a SNP (and each column a subject). Ignored if x is the
output of getLD or getLDlarge.
findLDblocks. See Details.
LDblocks consisting of
ldgetLD,blocksvec.blocksparamtrio.prepare the LD blocks are restricted to a maximum of 7 SNPs, splitBlocks
can be used to split LD blocks composed of more than 7 SNPs into smaller blocks, if the output of findLDblocks
should be used in trio.prepare to prepare a matrix for a trioLR or trioFS
analysis.
plot.LDblocks, getLD
# Load the simulated data.
data(trio.data)
# Estimate LD blocks.
blocks <- findLDblocks(LDdata)
# Alternatively, the LD blocks can be estimated by
ld.out <- getLD(LDdata, addVarN=TRUE)
blocks2 <- findLDblocks(ld.out)
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