Basic pQTL-MR analysis — pqtlMR • pQTLtools

This function takes exposure and outcome data as produced by format_data() and used to perform MR analysis against a list of outcomes; in the latter case it can be data from MR-Base, e.g., outcome <- extract_outcome_data(snps=with(exposure,SNP),outcomes=c("ieu-a-7","ebi-a-GCST007432")).

Usage

pqtlMR(
  exposure,
  outcome,
  mr_plot = FALSE,
  prefix = "pQTL-combined-",
  reverse = FALSE
)

Arguments

exposure: exposure data.
outcome: the counterpart for outcome.
mr_plot: to produce plots.
prefix: a prefix for output files.
reverse: if TRUE, perform reverse MR.

Value

No value is returned but several files.

Details

This function is adapted from Zheng et al. (2020) and in spirit similar to run_TwoSampleMR.

Note

Adapted from script by Jie Zheng.

References

Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, Gutteridge A, Erola P, Liu Y, Luo S, Robinson J, Richardson TG, Staley JR, Elsworth B, Burgess S, Sun BB, Danesh J, Runz H, Maranville JC, Martin HM, Yarmolinsky J, Laurin C, Holmes MV, Liu JZ, Estrada K, Santos R, McCarthy L, Waterworth D, Nelson MR, Smith GD, Butterworth AS, Hemani G, Scott RA, Gaunt TR (2020). “Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.” Nature Genetics, 52(10), 1122-1131. doi:10.1038/s41588-020-0682-6 .

Examples

library(TwoSampleMR)
#> TwoSampleMR version 0.5.11 
#> [>] New: Option to use non-European LD reference panels for clumping etc
#> [>] Some studies temporarily quarantined to verify effect allele
#> [>] See news(package = 'TwoSampleMR') and https://gwas.mrcieu.ac.uk for further details
library(pQTLtools)
fi <- file.path(find.package("pQTLtools",lib.loc=.libPaths()),"tests","Ins.csv")
exposure <- format_data(read.csv(fi),type="exposure")
fo <- file.path(find.package("pQTLtools",lib.loc=.libPaths()),"tests","Out.csv")
outcome <- format_data(read.csv(fo),type="outcome")
#> Warning: The following columns are not present but are helpful for harmonisation
#> eaf
pqtlMR(exposure, outcome, prefix="IL6R-")
#> Harmonising IL.6 (ImbABK) and Coronary artery disease (4nec1Z)
#> Harmonising IL.6 (ImbABK) and Atopic dermatitis (Ku3B84)
#> Harmonising IL.6 (ImbABK) and Rheumatoid arthritis (SGZG4k)
#> Analysing 'ImbABK' on '4nec1Z'
#> Analysing 'ImbABK' on 'Ku3B84'
#> Analysing 'ImbABK' on 'SGZG4k'
pqtlMR(exposure, outcome, prefix="IL6R_rev-",reverse=TRUE)
#> Harmonising IL.6 (ImbABK) and Coronary artery disease (4nec1Z)
#> Harmonising IL.6 (ImbABK) and Atopic dermatitis (Ku3B84)
#> Harmonising IL.6 (ImbABK) and Rheumatoid arthritis (SGZG4k)
#> Analysing '4nec1Z' on 'ImbABK'
#> Analysing 'Ku3B84' on 'ImbABK'
#> Analysing 'SGZG4k' on 'ImbABK'
unlink(c("IL6R*","pQTL-combined*"))
# Phenotype,SNP,effect_allele,other_allele,eaf,beta,se,pval
# ABO,rs505922,C,T,0.313,1.298,0.014,1.2e-1828
# LIFR,rs635634,T,C,0.180,-0.300,0.032,6.00E-21
# f <- file.path(find.package("pQTLtools",lib.loc=.libPaths()),"tests","ms.ins")
# exposure <- format_data(read.table(f, header=TRUE), samplesize_col="N")
# SNP Phenotype effect_allele other_allele eaf beta se pval N
# rs1800693 TNFB T C 0.6033 0.0282  0.0136 0.0389045   11787
# rs2364485 TNFB A C 0.1645 6514963 0.1759 1.62181e-20 11344
# https://raw.githubusercontent.com/MRCIEU/epigraphdb-pqtl/master/scripts/MR-pQTL-script.R