Colocalise two datasets represented by Bayes factors
Usage
coloc.bf_bf(
bf1,
bf2,
p1 = 1e-04,
p2 = 1e-04,
p12 = 5e-06,
overlap.min = 0.5,
trim_by_posterior = TRUE,
prior_weights1 = NULL,
prior_weights2 = NULL
)Arguments
- bf1
named vector of log BF, or matrix of BF with colnames (cols=snps, rows=signals)
- bf2
named vector of log BF, or matrix of BF with colnames (cols=snps, rows=signals)
- p1
prior probability a SNP is associated with trait 1, default 1e-4
- p2
prior probability a SNP is associated with trait 2, default 1e-4
- p12
prior probability a SNP is associated with both traits, default 1e-5
- overlap.min
see trim_by_posterior
- trim_by_posterior
it is important that the signals to be colocalised are covered by adequate numbers of snps in both datasets. If TRUE, signals for which snps in common do not capture least overlap.min proportion of their posteriors support are dropped and colocalisation not attempted.
- prior_weights1
Non-negative weights for the prior probability a SNP is associated with trait 1
- prior_weights2
Non-negative weights for the prior probability a SNP is asscoiated with trait 2