Mendelian Randomization

Mendelian Randomization (MR) is the process that refers to the random segregation and assortment of genes from ancestors to offspring that takes place during gamete formation and gives a method of using genetic variants to make casual inferences regarding the relationship between exposure and outcomes.

This package provides functionality for the following operations:

• Calculate the standard deviation from the effectsize or log odd score when it is not present. mr.calculate.se()
• Clumping, for pruning SNPs that are in linkage disequilibrium (LD). ALso is provided a method for finding proxy SNPs for replacing SNPs that are in LD. mr.clump()
• Cochran's Q test, for determining SNPs that overfit the model, or the ones that introduce pleiotropic effects. mr.cochran.Q.test()
• Mendelian Randomization Egger method (MR-egger method) for estimating causality, testing causality, and for testing the overall pleiotropy within the data set. mr.egger.method()
• When your data set misses allelic information, this can be queried by using a reference file. mr.find.missing.allelic.information()
• Forest plotting the MR analysis, for seeing the overall weight a SNP brings into the study. mr.forest.plot()
• Funnel plotting the methods used with the MR analysis to detect study bias. mr.funnel.plot()
• Get the chromosome number and position of SNPs. mr.get.chr.pos()
• Harmonization of the data set. Align SNPs, remove problematic SNPs, for example palindromic SNPs, mismatch SNPs, and SNPs that have a wrong reference. mr.harmonize()
• Inverse-Variance Weighted method for averaging the estimate ratios. mr.inverse.variance.weighted.method()
• A highly fasionable way of plotting MR results. mr.plot()
• The functionality to pre-process data, which test the data set on missing alleles, missing beta's, selects for genome-wide significance, removes duplicates, and removes alleles from which it is not possible to measure direction. mr.pre.process()
• qq-plot for the p-value distribution of a chosen method. mr.qq.p.distribution()
• A normal qq-plot for plotting the theoretical quantiles against the normal quantiles. mr.qq.plot()
• Remove a certain region within a chromosome. mr.remove.region()
• Perform wald-ratio for obtaining a causal estimate based on the exposure regression on genotype and the outcome regression of genotype. mr.wald.ratio()
• Test data for trying the package.