LD thinning
Select SNPs in LD below a given threshold.
LD.thin(x, threshold, max.dist = 250e3, beg = 1, end = ncol(x),
which.snps, dist.unit = c("bases", "indices", "cM"),
extract = TRUE, keep = c("left", "right", "random"))x |
|
threshold |
The maximum LD (measured by r^2) between SNPs |
max.dist |
The maximum distance for which the LD is computed |
beg |
The index of the first SNP to consider |
end |
The index of the last SNP to consider |
which.snps |
Logical vector, giving which SNPs are considerd. The default is to use all SNPs |
dist.unit |
Distance unit in |
extract |
A |
keep |
Which SNP is selected in a pair with LD above |
The SNPs to keep are selected by a greedy algorithm. The LD is computed only for SNP pairs for which distance is inferior to
max.dist, expressed in number of bases if dist.unit = "bases", in number of SNPs if dist.unit = "indices",
or in centiMorgan if dist.unit = "cM".
The argument which.snps allows to consider only a subset of SNPs.
The algorithm tries to keep the largest possible number of SNPs: it is not appropriate to select tag-SNPs.
If extract = TRUE, a bed.matrix extracted from x with SNPs in pairwise LD below the given threshold.
If extract = FALSE, a logical vector of length end - beg + 1, where TRUE indicates that
the corresponding SNPs is selected.
Hervé Perdry and Claire Dandine-Roulland
# Load data data(TTN) x <- as.bed.matrix(TTN.gen, TTN.fam, TTN.bim) # Select SNPs in LD r^2 < 0.4, max.dist = 500 kb y <- LD.thin(x, threshold = 0.4, max.dist = 500e3) y # Verifies that there is no SNP pair with LD r^2 > 0.4 # (note that the matrix ld.y has ones on the diagonal) ld.y <- LD( y, lim = c(1, ncol(y)) ) sum( ld.y > 0.4 )
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