findLDblocks {trio} | R Documentation |
Finds LD blocks using the procedure proposed by Gabriel et al. (2002).
findLDblocks(x, alpha = 0.1, ciLD = c(0.7, 0.98), cuRecomb = 0.9, ratio = 9, alsoOthers = FALSE, iter = 50, snp.in.col = TRUE)
x |
either the output of getLD 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 |
numeric value between 0 and 1. For each pair of SNPs, a two-sided
100 * (1 - 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 |
numeric vector consisting of two values between 0 and 1. If the
lower bound of the confidence interval of D' for a SNP pair is larger than
or equal to the first value in 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 |
numeric value between 0 and 1. If the upper bound of the confidence
interval of D' for a SNP pair is smaller than cuRecomb , then this pair
of SNP is considered to show evidence of recombination.
|
ratio |
numeric value larger than 1. If in a block of SNPs, the ratio of the
number of SNP pairs being in strong LD to the number of SNPs showing evidence
of recombination is larger than or equal to 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 |
logical value. Following the description of Wall and Pritchard (2003)
the endmarkers of a LD block must be in strong LD. By default (i.e.\ if alsoOthers = FALSE ),
this condition is used. If alsoOthers = TRUE , the endmarkers can also be categorized
as 'Others'.
|
iter |
integer specifying the number of iterations used in the computation of D (for details,
see getLD ). Ignored if x is the output of getLD .
|
snp.in.col |
logical specifying whether each column of 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 .
|
The LD-blocks are estimated using the method of Gabriel et al. (2002) as described in Wall and Pritchard (2003), where we use the approximate variance estimates of D' proposed by Zabaleta et al. (1997).
An object of class LDblocks
consisting of
|
the output of getLD , |
|
a vector specifying which SNP belongs to which LD-block, |
|
a list in which each entry contains the names of the SNPs belonging to a specific LD-block, |
|
a list of the input parameters. |
Holger Schwender, holger.schwender@udo.edu
Gabriel, S.B. et al. (2002). The Structure of Haplotype Blocks in the Human Genome. Science, 296, 2225-2229.
Wall, J.D. and Pritchard J.K. (2003). Assessing the Performance of the Haplotype Block Model of Linkage Disequilibrium. American Journal of Human Genetics, 73, 502-515.
Zapata, C., Alvarez, G., and Carollo, C. (1997). Approximate Variance of the Standardized Measure of Gametic Disequilibrium D'. American Journal of Human Genetics, 61, 771-774.
## Not run: # Andrea's data data(mat) # LD blocks can be identified either by out <- getLD(mat, addVarN=TRUE) out2 <- findLDblocks(out) out2 # or directly by out3 <- findLDblocks(mat) # Plot Gabriel's categories with LD blocks for all SNPs. plot(out3) # Plot Gabriel's categories starting with SNP 24 and # ending with SNP 60. plot(out3, start=24, end=60) # Note that the SNPs ABI.354, ABI.355, and ABI.357 # form a LD-block. But since ABI.354 is not part of # the plot, and thus the block is only shown partly # in the plot, the vertical block border is missing. # Now the same for D', but by using the SNP names plot(out3, "Dprime", start="ABI.355", end="CRC.45") ## End(Not run)