In MRCIEU/gwasglue: GWAS summary data sources connected to analytical tools

knitr::opts_chunk$set(
  eval=FALSE,
  collapse = TRUE,
  comment = "#>"
)
library(gwasglue)

Trying other methods e.g. PAINTOR and MsCAVIAR

Seem really slow
They give an overall posterior probability, not clear how to extract number of distinct causal variants

library(tidyverse)
library(ieugwasr)
library(devtools)
library(glue)
load_all()

args <- list()
args$radius <- 50000
args$ids <- c("bbj-a-52", "ukb-b-20175")
args$bfiles <- c("/Users/gh13047/data/ld_files/ldmat/EAS", "/Users/gh13047/data/ld_files/ldmat/EUR")
args$plink <- "plink"
args$pops <- NULL
args$chr <- 4
args$position <- 54801228

regiondata <- do.call(organise_region, args=args)

pres <- run_paintor(regiondata)
dev.new()
plot_PAINTOR(pres)

n <- gwasinfo(args$ids)$sample_size
cres <- run_MsCAVIAR(regiondata, n)
plot_MsCAVIAR(cres)



alpha=0.05
mt_method="fdr"
ld_thresh=0.05
prune_bfile=NULL
prune_pop="EUR"


inst <- readRDS("~/repo/mr.trans/data/sbp_eas_eur.rds")
args <- list()
args$radius <- 100000
args$ids <- c("bbj-a-52", "ukb-b-20175")
args$bfiles <- c("/Users/gh13047/data/ld_files/ldmat/EAS", "/Users/gh13047/data/ld_files/ldmat/EUR")
args$plink <- "plink"


args$pops <- NULL
i <- 1

paintor_res <- list()
for(i in 1:nrow(inst$inst))
{
    args$chr <- inst$inst$chr.exposure[i]
    args$position <- as.numeric(inst$inst$pos.exposure[i])
    regiondata <- do.call(extract_regional_data, args=args)
    paintor_res[[i]] <- run_PAINTOR(regiondata)
}

o <- run_susie(regiondata)
plot_PAINTOR(paintor_res[[i]])