Run cophescan on multiple traits at once

cophe.multitrait(
  trait.dat,
  querysnpid,
  querytrait.names,
  LDmat = NULL,
  method = "single",
  simplify = FALSE,
  predict.hyp = TRUE,
  Hn.cutoff = 0.2,
  Hc.cutoff = 0.6,
  est.fdr.based.cutoff = FALSE,
  fdr = 0.05,
  ...
)

Arguments

trait.dat: Named(traits) list of coloc structured data for k traits (Total number of traits)
querysnpid: vector of query variant ids = length(trait.dat), if the same variant
querytrait.names: vector of names for the query traits, if the names of the multi.dat list contain the trait names please pass querytrait.names=names(multi.dat)
LDmat: LD matrix
method: either 'single' for cophe.single or 'susie' for cophe.susie
simplify: if TRUE removes intermediate results from output using 'multitrait.simplify'
predict.hyp: if TRUE predicts the hypothesis based on the provided thresholds for pp.Hc and pp.Hn (overrides simplify) using cophe.hyp.predict
Hn.cutoff: threshold for PP.Hc above which the associations are called Hc
Hc.cutoff: threshold for PP.Hc above which the associations are called Hn
est.fdr.based.cutoff: if True calculates the Hc.cutoff using 1-mean(PP.Hc)|PP.Hc > cutoff
fdr: fdr threshold to estimate Hc.cutoff
...: additional arguments of priors for cophe.susie or cophe.single

Value

if simplify is False returns multi-trait list of lists, each with:

a summary data.frame of the cophescan results
priors used
querysnp
querytrait

if simplify is TRUE only returns dataframe with posterior probabilties of Hn, Hc and Ha with no intermediate results if predict.hyp is TRUE returns a dataframe with output of simplify and the predicted hypotheses for all associations

Author

Ichcha Manipur