LD.clump | R Documentation |
Construct group of SNPs in LD with 'top associated SNPs'
LD.clump(x, p, r2.threshold, p.threshold, max.dist = 500e3)
x |
A |
p |
A vector of p-values, or a data frame including p-values, such as sent back by |
r2.threshold |
The maximum LD (measured by |
p.threshold |
The threshold used to define associated SNPs |
max.dist |
The maximum distance for which the LD is computed |
The p-values provided through argument p
are assumed to correspond to the result of an association test with the SNPs of x
.
The aim of the function is to construct cluster of SNPs in strong LD with associated SNPs.
The algorithm first seeks the SNP with the lowest p-value (below p.threshold
) ; this SNP will be the 'index' of a cluster.
The corresponding cluster is constructed by aggregating SNPs that are in LD (above r2.threshold
) with the index. The cluster's name
is the position of the index SNP.
The processus is repeated on the SNPs which are not yet attributed to a cluster, until there is no associated SNP
(ie SNP with a p-value below threshold
) left.
The remaining SNPs are attributed to cluster 0.
The LD is computed only for SNP pairs for which distance is inferior to max.dist
, expressed in number of bases: above this
distance it is assumed to be null.
If p
was a data frame, then the function returns the same data frame with to extra columns, cluster
and is.index
.
If p
was a vector of p-values, it returns a data frame with columns chr
, id
, pos
, p
, cluster
and is.index
.
LD
, LD.thin
# Construct a bed matrix
x <- as.bed.matrix(TTN.gen, TTN.fam, TTN.bim)
standardize(x) <- "p"
# simulate quantitative phenotype with effect of SNPs #108 and #631
beta <- numeric(ncol(x))
beta[c(108,631)] <- 0.5
set.seed(1)
y <- x %*% beta + rnorm(nrow(x))
# association test with linear model
test <- association.test(x, y, method="lm", response = "quanti")
# LD clumping
test <- LD.clump(x, test, r2.threshold = 0.25, p.threshold = 1e-8)
# use as.factor for a quick-and-dirty cluster colouring on the manhattan plot
manhattan(test, col = as.factor(test$cluster), pch = 20)
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