Extracts one or more SNPs from each signal cluster
based on the posterior estimate of the effect size
for A (largest effect size in the positive direction).
After running this function, it is recommended to use
trimClusters to remove signal clusters
that are too highly correlated.
extractForSlope(
res,
niter = 0,
plot = TRUE,
label = "Effect size of",
a = "eQTL",
b = "GWAS"
)list with the following named elements:
beta_hat_a - list of point estimates of coefficients for A from colocalization
beta_hat_b - " " for B
sd_a - list of sampling SD for beta_hat_a (in practice original
SE are provided here)
sd_b - " " for beta_hat_b " "
alleles (optional) list of data.frame with allele information
number of iterations of EM to run for mclust, if set to 0, only the maximum variant (in terms of A effect size) per signal cluster is output. Default is to not run clustering, but to take the SNP with the largest effect size in A (in the positive direction)
logical, draw a before after of which variants will be included for slope estimation
what preceeds a and b in
the x- and y-axis labels
name of A experiment
name of B experiment
list of vectors of the first four arguments,
collapsed now across signal clusters, representing
variants with positive effect on A. So the null variants
have been removed (and any variants per cluster that
indicated a negative effect on A). If alleles
data.frames were included in the input, they will also be passed through as a single data.frame with the selected SNPs per signal cluster