1. Introduction

MeRP is a free, open-source tool designed to streamline and automate large-scale Mendelian Randomization analysis.

1.1. What is Mendelian Randomization?

Mendelian Randomization (MR) is a method to examine causal associations of heritable, but modifiable, traits on disease using observational data sets. Traditional epidemiological studies are effective at discovering patterns and associations between potential risk factors and disease, but inherently cannot determine causality because of potential complications such as reverse causality or the effect confounding factors. Unlike traditional epidemiology, MR controls for interference from these factors by carefully selecting genetic variants that exclusively associate with trait of interest as an instrumental variable for analysis with disease data. With the ongoing rise in Genome Wide Association Studies (GWAS) linking genetic variants with traits and diseases, the utility of MR has been greater than ever.

1.2. Overview

There are three components of the MeRP pipeline.

  1. Pulling Genetic Instruments from Public Data
    • Pulls down and cleans most recent NHGRI Catalog of GWAS studies
    • Creates valid instrumental variable trait files (IVs) from catalog
  2. Filtering to satisfy MR conditions
    • Removes SNPs associated with potential confounding factors from IV file
    • Eliminates Linkage Disequilibrium (LD) within IV file.
  3. MR Calculator
    • Estimates causal effect of selected trait and preselected GWAS disease data

1.3. Download

Get the most up-to-date version at

1.4. Installation

Execute the following command in the py-merp directory

>>> sudo python setup.py install

1.5. Implementation

py-merp requires Python2.7 or higher.

py-merp dependencies are:

1.6. Citation

Please cite us when using py-MeRP in your work.

Yin, P., Voight, B. (2014) MeRP: A high-throughput pipeline for Mendelian Randomization Analysis. (In submission)

1.7. General information and Contact Info

Copyright 2014 by:
Peter Yin
Ben Voight


The latest Documentation was generated on: October 31, 2014