colocalisation with multiple causal variants via SuSiE
coloc.susie(
dataset1,
dataset2,
back_calculate_lbf = FALSE,
susie.args = list(),
...
)either a coloc-style input dataset (see check_dataset), or the result of running runsusie on such a dataset
either a coloc-style input dataset (see check_dataset), or the result of running runsusie on such a dataset
by default, use the log Bayes factors returned by susie_rss. It is also possible to back-calculate these from the posterior probabilities. It is not advised to set this to TRUE, the option exists really for testing purposes only.
a named list of additional arguments to be passed to runsusie
other arguments passed to coloc.bf_bf, in particular prior values for causal association with one trait (p1, p2) or both (p12)
a list, containing elements * summary a data.table of posterior probabilities of each global hypothesis, one row per pairwise comparison of signals from the two traits * results a data.table of detailed results giving the posterior probability for each snp to be jointly causal for both traits assuming H4 is true. Please ignore this column if the corresponding posterior support for H4 is not high. * priors a vector of the priors used for the analysis