CoPheScan: Example with Fixed Priors

Ichcha Manipur


Fixed priors are used only when the number of traits and variants to be tested are small.

The default cophescan fixed priors, pa=3.82e-5, pc=1.82e-3, provided for cophe.single and cophe.susie are those inferred from the disease-related variant dataset in the CoPheScan manscript [Supplementary table 12]. Additionally, priors from other datasets tested in the CoPheScan manscript that match your specific use case can be taken from Supplementary table 12.

Note: In the previous version (v1.3.2), the default cophescan fixed priors for : pn, pa and pc, were derived from default coloc priors by setting p1=1e-4, p2=1e-4 and p12=1e-5. This has now been changed as described above with priors pa=3.82e-5 and pc=1.82e-3 as default.

pa and pc should be carefully selected based on the dataset.

This example illustrates the use of cophescan for a small dataset using fixed priors where a hierarchical model cannot be applied.


Load test data

See the input data vignette for preparing the input for cophescan analysis.

#> [1] "summ_stat"  "LD"         "querysnpid" "covar_vec"

We will check for causal association between a single trait and a query variant using fixed priors.

querytrait <- cophe_multi_trait_data$summ_stat[['Trait_1']] 
querysnpid <- cophe_multi_trait_data$querysnpid
LD <- cophe_multi_trait_data$LD

Regional Manhattan plot showing the position of the query variant in the query trait.

# Additional  field named 'position' is required for the Manahattan plot. It is a numeric vector of chromosal positions
querytrait$position <- sapply(querytrait$snp, function(x) as.numeric(unlist(strsplit(x, "-"))[2]))
plot_trait_manhat(querytrait, querysnpid)

Cophescan with fixed priors under a single variant assumption (ABF)

CoPheScan can be run using a single variant assumption (which uses Approximate Bayes Factors) with the cophe.single function.


Case where nsnps in the queried region is very high and pa*(nsnps-1) + pc > 1: In this case please revaluate the supplied priors or run adjust_priors function (see help) which scales down the priors while maintaining the proportion of the supplied priors.

# Run cophescan under a single causal variant assumption by providing the snpid of the known causal variant for trait 1 = querysnpid
res.single <- cophe.single(querytrait, querysnpid = querysnpid, querytrait='Trait_1')
#> Running cophe.single...
#> SNP Priors
#> 0.96001823.82e-050.00182
#> Hypothesis Priors
#> 0.96001820.03816180.00182
#> 0.002090.3750.623
#> PP for causal query variant: 62.3%
#>   nsnps      PP.Hn     PP.Ha     PP.Hc   lBF.Ha   lBF.Hc       querysnp
#> 1  1000 0.00208927 0.3749638 0.6229469 15.32189 11.96576 chr19-11182353
#>   querytrait typeBF
#> 1    Trait_1    ABF

We observe that the posterior probability of causal association for the query variant is 0.969 which indicates that the query trait is causally associated with the query trait.

We can also use the cophe.hyp.predict function to predict the hypothesis given the posterior probabilities.

res.single.predict <- cophe.hyp.predict(res.single)
#> Hc.cutoff = 0.6
#> Hn.cutoff = 0.2
(paste0('The predicted hypothesis is: ', res.single.predict$, ' [PP.Hc =', round(res.single.predict$PP.Hc,3), ']' ))
#> [1] "The predicted hypothesis is: Hc [PP.Hc =0.623]"

Cophescan with fixed priors using SuSIE Bayes factors

# Run cophescan with susie (multiple variants) by providing the snpid of the known causal variant for trait 1 = querysnpid
querytrait$LD <- LD
res.susie <- cophe.susie(querytrait, querysnpid = querysnpid, querytrait='Trait_1')
#>   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

res.susie.predict <- cophe.hyp.predict(res.susie)
(paste0('The predicted hypothesis is: ', res.susie.predict$, ' [PP.Hc =', round(res.susie.predict$PP.Hc,3), ']' ))
#> [1] "The predicted hypothesis is: Hc [PP.Hc =0.623]"

We get similar results using SuSIE cophe.susie. We recommend the use of cophe.susie whenever LD information is available.

Note: When no credible sets are identified using cophe.susie cophescan reverts to cophe.single.