AhmedArslan / MRsimplify

TwosampleMR and MultivariableMR perform with simple commands without prior knowledge or having to go through lengthy boring protocols

MRsimplify

TwosampleMR and MultivariableMR, perform all steps with simple command(s) without prior knowledge or having to go through lengthy boring protocols

Internal steps in MRsimplify

A: exposure data: (i) read exposure data, (ii) perform SNP clumping and (iii) store data.

B: outcome data: (i) read outcome data, (ii) get proxy SNP(s)

C: harmonise

D: MR

E: sensitivity tests (heterogeneity, pleiotropy, singlesnp, leaveoneout, MR-PRESSO)

F: visualization (scatter plot, forest plot, leaveoneout and funnel plot)

G: compile all results into a file.

Step-1: installation..

Install required R library: TwoSampleMR, stringr, tidyverse, LDlinkR, ggplot2, ieugwasr, dplyr, gwasvcf.

Download and install:

• R codes (MRsimplify.r) (before running add the path to (i) plink executable (line 9), (ii) local LD reference panel on line-20 and line-38).
• The LD reference panel can be downloaded from here (currently supporting GRCh37/hg19 genome built).
• The LD reference panel contains information of 5 super-populations (EUR = European; EAS = East Asian; AMR = Admixed American; SAS = South Asian; AFR = African).

Download full gwas summary stat:

• From either GWASCatalog or individual publications with necessary information: SNP, CHR, POS, A1 (effect_allele), A2 (other_allele), BETA, SE, Phenotype, Pval, EAF (effect_allele Freq), samplesize. (NOTE: all data must have GRCh37 coordinates for smooth processing and reliable results.)
• In case genomic coordinates change required, MungeSumstats can be used.

Step-2: formate data..

• Filter exposure data with above mentioned columns by pval (recommended: p<5e-08) whereas outcome data should be full length summary stats files without pval threshold.
• Note: To save time, (it is recommended to) include data of different exposure(s) into one file, however in all TwosampleMR subsequent steps each exposure-outcome MR is computed separately.

Step-3: perform MR..

Rscript --vanilla MRsimplify.r exposure outcome

Step-4: MR analysis results..

a folder will be geneated with outcome name containing all the results including sensitivity tests plus all the visualizations

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Caution: things to consider to perform a successful MR analysis...

1. exposure and outcome variants have same (i) genomic positions and,(ii) A1 and A2 alleles

2. (i) LD reference penal must be upto date (1k_v3) and (ii) using same population as of used for the generation of exposure and outcome data

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additional readings: https://mrcieu.github.io/TwoSampleMR/index.html

citation: If you find repo useful please cite the link while manuscript is in preparation.

contact: [e-mail] or leave comments in issues page.