Check if a variant causally associated in one trait might be causal in another trait
cophe.susie(
dataset,
querysnpid,
querytrait,
pa = 3.82e-05,
pc = 0.00182,
p1 = NULL,
p2 = NULL,
p12 = NULL,
susie.args = list()
)
either a list with specifically named elements defining the dataset to be analysed. (see check_dataset)
Id of the query variant
Query trait name
prior probability that a non-query variant is causally associated with the query trait (cophescan prior), default 3.82e-5
prior probability that the query variant is causally associated with the query trait (cophescan prior), default 1.82e-3
prior probability a SNP is associated with trait 1, (coloc prior), pc derived by using \(pc = p12/p1+p12\); use p1, p2, p12 only when pa and pc are unavailable (See vignettes)
prior probability a SNP is associated with trait 2, (coloc prior), pa derived by using \(pa = p2\)
prior probability a SNP is associated with both traits, (coloc prior), pc derived by using \(pc = p12/p1+p12\)
a named list of additional arguments to be passed to runsusie
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 Hc 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
library(cophescan)
data(cophe_multi_trait_data)
query_trait_1 <- cophe_multi_trait_data$summ_stat[['Trait_1']]
querysnpid <- cophe_multi_trait_data$querysnpid
query_trait_1$LD <- cophe_multi_trait_data$LD
res.susie <- cophe.susie(query_trait_1, querysnpid = querysnpid, querytrait='Trait_1')
#> running max iterations: 100
#> converged: TRUE
#> SNP Priors
#> 0.96005643.82e-050.00182
#> Hypothesis Priors
#> 0.96005640.03812360.00182
#> Running cophe.susie...
#> Using 1000 and 999 available
#> 0.002120.3750.623
#> PP for causal query variant: 62.3%
summary(res.susie)
#> nsnps hit1 hit2 PP.Hn PP.Ha PP.Hc lBF.Ha
#> 1 1000 chr19-11182353 chr19-11182144 0.002115712 0.375218 0.6226663 15.31003
#> lBF.Hc querysnp typeBF idx1 idx2 querytrait
#> 1 11.95277 chr19-11182353 susieBF 1 1 Trait_1