library(catalogueR)
Here, we aim to more robustly test whether the genetic signals underlying two dataset (e.g. GWAS vs. eQTL in the same locus)are indeed the same, using a methodology called colocalization.
Specifically, we will use coloc
, which infers the probability that each SNP is causal in a given locus in each of the datasets. It then tests the hypothesis that those signals show substantially similar association distributions.
We have previously queried eQTL Catalogue using several Parkinson's disease
GWAS loci (with eQTLcatalogue_query()
).
Let's first gather those files saved as csv files.
gwas.qtl_paths <- catalogueR::eQTLcatalogue_example_queries(fnames = c( "BST1__Alasoo_2018.macrophage_IFNg+Salmonella.tsv.gz", "BST1__Alasoo_2018.macrophage_naive.tsv.gz", "BST1__Alasoo_2018.macrophage_Salmonella.tsv.gz" ))
coloc
has been updated so that it can now model multiple causal variants within a
given dataset (see the
paper),
whereas previously it could assume one causal variants.
Thus, it may be able to better estimate the colocalization probability
between two datasets. coloc_QTLs <- catalogueR::COLOC_run(gwas.qtl_paths = gwas.qtl_paths, top_snp_only = TRUE, split_by_group = FALSE, method = "abf")
First, let's plot only the results with >80% colocalization probability. This is a colocalization threshold commonly used in the field.
coloc_plot <- catalogueR::COLOC_heatmap(coloc_QTLs = coloc_QTLs, coloc_thresh = .8)
coloc_plot <- catalogueR::COLOC_heatmap(coloc_QTLs = coloc_QTLs, coloc_thresh = 0)
Too many results:
utils::sessionInfo()
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