Genetic colocalisation

Here we’ll perform colocalisation analysis for a particular region, and plot the regions as well. We’ll do the same analysis two ways:

We’ll use the example of LDL cholesterol ieu-a-300 and coronary heart disease ieu-a-7.

Load libraries:

suppressPackageStartupMessages(suppressWarnings({
    library(gwasglue)
    library(dplyr)
    library(gassocplot)
    library(coloc)
}))

ieugwasr

First find a region that we know to be associated with LDL cholesterol.

top <- ieugwasr::tophits('ieu-a-300') %>% arrange(p)
top

Choose the best signal and create a range

chrpos <- paste0(top$chr[1], ":", top$position[1] - 90000, "-", top$position[1] + 90000)
chrpos

Extract, harmonise and format the data

out <- ieugwasr_to_coloc(id1='ieu-a-300', id2='ieu-a-7', chrompos=chrpos)

Run colocalisation analysis

res <- coloc::coloc.abf(out[[1]], out[[2]])

Plot

temp <- coloc_to_gassocplot(out)
gassocplot::stack_assoc_plot(temp$markers, temp$z, temp$corr, traits=temp$traits)

gwasvcf

Let’s do the same with the vcf files (and the indexes). Download from here:

Set a region to plot:

chrpos <- "19:11112306-11292306"

Extract region from vcf files and convert to coloc object

vout <- gwasvcf_to_coloc("ieu-a-300.vcf.gz", "ieu-a-7.vcf.gz", chrpos)

Run colocalisation analysis

vres <- coloc::coloc.abf(vout[[1]], vout[[2]])

Plot

library(gassocplot)
temp <- coloc_to_gassocplot(vout)
gassocplot::stack_assoc_plot(temp$markers, temp$z, temp$corr, traits=temp$traits)