In RajLabMSSM/echolocatoR: Automated genomic fine-mapping

library(echolocatoR)

Download data

Summarise

Using pre-merged data for vignette speed.

merged_DT <- echodata::get_Nalls2019_merged()

`get_SNPgroup_counts()`

Get the number of SNPs for each SNP group per locus.
It also prints the mean number of SNPs for each SNP group across all loci.
NOTE: You will need to make sure to set merge_finemapping_results(minimum_support=1) in the above step to get accurate counts for all SNP groups.

snp_groups <- echodata::get_SNPgroup_counts(merged_DT = merged_DT)

`get_CS_counts()`

County the number of tool-specific and UCS Credible Set SNPs per locus.

UCS_counts <- echodata::get_CS_counts(merged_DT = merged_DT)
knitr::kable(UCS_counts)

Plot

The following functions each return a list containing both the ...$plot and the ...$data used to make the plot.
Where available, snp_filter allows user to use any filtering argument (supplied as a string) to subset the data they want to use in the plot/data.

Colocalization results

If you ran colocalization tests with echolocatoR (via catalogueR) you can use those results to come up with a top QTL nominated gene for each locus (potentially implicating that gene in your phenotype).

coloc_res <- echodata::get_Nalls2019_coloc()

Super summary plot

super_plot <- echoannot::super_summary_plot(merged_DT = merged_DT, 
                                            coloc_results = coloc_res,
                                            plot_missense = FALSE)