cophescan
Adaptation of the Coloc Method for PheWAS
A Bayesian method for Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and traits, while simultaneously addressing confounding due to linkage disequilibrium. For details see
Manipur et al (2024, Nature Communications)
CoPheScan
The cophescan package implements Coloc adapted Phenome-wide Scan (CoPheScan) [1], a Bayesian method to perform Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and phenotypes while simultaneously accounting for confounding due to linkage disequilibrium.
See the description vignette for background and references: Introduction to CoPheScan
Installation
Install cophescan from CRAN
install.packages("cophescan")
Install the developmental version from GitHub
if(!require("remotes"))
install.packages("remotes") # if necessary
remotes::install_github("ichcha-m/cophescan")
Vignettes
For a detailed walkthrough of cophescan browse through the vignettes in https://ichcha-m.github.io/cophescan/.
Vignette articles:
3. An example for running CoPheScan with fixed priors
4. An example for running CoPheScan with hierarchical priors
Quick start
Usage
library(cophescan)
## Load the simulated summary stats data of 24 traits
data("cophe_multi_trait_data")
names(cophe_multi_trait_data)
Single trait
query_trait_1 <- cophe_multi_trait_data$summ_stat[['Trait_1']]
querysnpid <- cophe_multi_trait_data$querysnpid
print(querysnpid)
query_trait_1$position <- sapply(query_trait_1$snp, function(x) as.numeric(unlist(strsplit(x, "-"))[2]))
plot_trait_manhat(query_trait_1, querysnpid)
# Run cophescan under a single causal variant assumption by providing the snpid of the query variant (querysnpid) for the query trait.
res.single <- cophe.single(query_trait_1, querysnpid = querysnpid, querytrait='Trait_1')
summary(res.single)
# Run cophescan with susie (multiple variants) by providing the snpid of the query variant (querysnpid) for the query trait
query_trait_1$LD <- cophe_multi_trait_data$LD
res.susie <- cophe.susie(query_trait_1, querysnpid = querysnpid, querytrait='Trait_1')
summary(res.susie)
Run multi-trait analysis
res.multi <- cophe.multitrait(cophe_multi_trait_data$summ_stat, querysnpid = querysnpid, querytrait.names = names(cophe_multi_trait_data$summ_stat), method = 'single')
Plot cophescan results
cophe.plots.res <- cophe_plot(res.multi, traits.dat = cophe_multi_trait_data$summ_stat, querysnpid = querysnpid)
ggpubr::ggarrange(cophe.plots.res$pval, cophe.plots.res$ppHa, cophe.plots.res$ppHc, nrow=1)
# cophe.plots.hmp <- cophe_heatmap(res.multi, traits.dat = cophe_multi_trait_data$summ_stat, querysnpid = querysnpid, color=colorRampPalette(rev(RColorBrewer::brewer.pal(n = 9, name ="Greens")))(100))
Run hierarchical model for priors
cophe.hier.res <- run_metrop_priors(res.multi, posterior=TRUE, avg_posterior=TRUE, pik=TRUE)
ll <- cophe.hier.res$ll
params <- cophe.hier.res$parameters
### store user parameters
old_par = par(no.readonly = TRUE)
## Plot mcmc diagnostics
par(mfrow=c(2,2))
plot(1:length(ll), ll, main="loglik",type="l", col="orange")
plot(1:ncol(params), params[1,], main="alpha",type="l", col="orange")
plot(1:ncol(params), params[2,], main="beta",type="l", col="orange")
### reset user parameters
par(old_par)
Predict
res.post.prob = cbind(cophe.hier.res$avg.posterior, cophe.hier.res$data)
res.hier.predict <- cophe.hyp.predict(as.data.frame(res.post.prob ))
tail(res.hier.predict, row.names = F)
NEWS: cophescan 1.4.2
CoPheScan Publication
Manipur, I., Reales, G., Sul, J.H., Shin, M.K., Longerich, S., Cortes, A. and Wallace, C., 2024. CoPheScan: phenome-wide association studies accounting for linkage disequilibrium. Nature Communications, 15(1), p.5862. https://doi.org/10.1038/s41467-024-49990-8