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Basic pQTL-MR analysis

Source: R/pqtlMR.R
pqtlMR.Rd

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

Adapted from Zheng et al. (2020) , this function is analogous 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

fi <- file.path(find.package("pQTLtools",lib.loc=.libPaths()),"tests","Ins.csv")
exposure <- TwoSampleMR::format_data(read.csv(fi),type="exposure")
fo <- file.path(find.package("pQTLtools",lib.loc=.libPaths()),"tests","Out.csv")
outcome <- TwoSampleMR::format_data(read.csv(fo),type="outcome")
#> Warning: The following columns are not present but are helpful for harmonisation
#> eaf
pQTLtools::pqtlMR(exposure, outcome, prefix="IL6R-")
#> Harmonising IL.6 (O9YIMd) and Rheumatoid arthritis (4WZ5NH)
#> Harmonising IL.6 (O9YIMd) and Coronary artery disease (aL6STL)
#> Harmonising IL.6 (O9YIMd) and Atopic dermatitis (cG94vA)
#> Analysing 'O9YIMd' on '4WZ5NH'
#> Analysing 'O9YIMd' on 'aL6STL'
#> Analysing 'O9YIMd' on 'cG94vA'
pQTLtools::pqtlMR(exposure, outcome, prefix="IL6R_rev-",reverse=TRUE)
#> Harmonising IL.6 (O9YIMd) and Rheumatoid arthritis (4WZ5NH)
#> Harmonising IL.6 (O9YIMd) and Coronary artery disease (aL6STL)
#> Harmonising IL.6 (O9YIMd) and Atopic dermatitis (cG94vA)
#> Analysing '4WZ5NH' on 'O9YIMd'
#> Analysing 'aL6STL' on 'O9YIMd'
#> Analysing 'cG94vA' on 'O9YIMd'
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