In variani/finemapr: R wrapper to fine-mappers

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", # NA
  dev = "png", dev.args = list(type = "cairo"), dpi = 92, fig.path = "figures/", 
  fig.width = 4, fig.height = 4,
  results = 'markup', tidy = F, message = T, warning = T, echo = T
)

About finemapr

finemapr is an R package that provides an interface to fine-mapping tools:

• FINEMAP http://www.christianbenner.com/
• CAVIAR https://github.com/fhormoz/caviar
• PAINTOR https://github.com/gkichaev/PAINTOR_V3.0

By using finemapr, your input files are automatically prepared for each tool, the analysis workflow is tool-independent; and exploration of fine-mapping results is powered by R in printing/plotting/data export.

Tool-independent scheme of analysis workflow

# set up
options(finemapr_<tool> = "<path to fine-mapping tool>")

# read input files
my_zscores <- read_zscores("<my_scores.tab>")
my_ld <- read_ld("<my_ld.tab>")

# run analysis
out <- run_<tool>(my_zscores, my_ld, args = "<custom arguments>")

# explore results
print(out)
head(out$snp) # main table of results
plot(out)

# export results
write.table(out$snp, "<my_results.tab>")

Installation

The user needs to download and install a fine-mapping tool before the analysis. An example of installation commands used in finemapr by default is given here.

After installing, for example, the FINEMAP tool, the user specify for finemapr where the tool is located:

options(finemapr_finemap = "~/apps/finemap/finemap")

Load packages

We load packages for the analysis conducted in this document.

library(devtools)
load_all("~/git/variani/finemapr")

library(magrittr)
library(dplyr)  
library(ggplot2)  
theme_set(theme_linedraw())

Example data

We load example data copied from the FINEMAP website (http://www.christianbenner.com/). This simulated dataset has two causal variants rs15 and rs47.

file1_z <- system.file("extdata/region1.z", package = "finemapr")
file1_ld <- system.file("extdata/region1.ld", package = "finemapr")

z1 <- read_zscore(file1_z)
ld1 <- read_ld(file1_ld, snps = z1$snp)
n1 <- 5363

Explore z-scores

Top 5 z-scores:

z1 %>% arrange(-abs(zscore)) %>% head(5) %>% kable(digits = 1)

ggplot(z1, aes(zscore)) + geom_histogram()

mutate(z1, pval = pchisq(zscore^2, df = 1, lower.tail = FALSE)) %>%
  ggplot(aes(pval)) + geom_histogram()

Run tools

Run FINEMAP

options(finemapr_finemap = "~/apps/finemap/finemap")

out_finemap <- run_finemap(z1, ld1, n1, args = "--n-causal-max 3")

print(out_finemap)

plot(out_finemap, label_size = 3, grid_ncol = 1)

Run CAVIAR

options(finemapr_caviar = "~/apps/caviar/CAVIAR")

out_caviar <- run_caviar(z1, ld1, args = "-c 3")

print(out_caviar)

plot(out_caviar, label_size = 3)

Run PAINTOR

options(finemapr_paintor = "~/apps/paintor/PAINTOR")

out_paintor <- run_paintor(z1, ld1, n1, args = "-enumerate 3")

print(out_paintor)

plot(out_paintor, label_size = 3)

Conclusions

All three fine-mapping tools estimated the poterior causal probabilities of the two variants, rs15 and rs47, very close to 1.

variani/finemapr documentation built on Dec. 12, 2020, 9:40 p.m.