MeRP: a high-throughput pipeline for Mendelian randomization analysis

Area of Science:

• Genetics
• Epidemiology
• Bioinformatics

Background:

• Mendelian randomization (MR) is a powerful method for inferring causal relationships using genetic variants as instrumental variables.
• Automating the complex steps of MR analysis can accelerate discovery and improve reproducibility.
• Publicly available genetic and phenotypic data offer vast resources for large-scale causal inference studies.

Purpose of the Study:

• To present a novel, automated pipeline for Mendelian randomization analysis named MeRP (Mendelian randomization pipeline).
• To facilitate rapid and efficient causal inference by streamlining the development and analysis of genetic instruments.
• To apply the MeRP tool to identify potential causal associations between various traits and disease endpoints.

Main Methods:

• The MeRP pipeline utilizes the National Human Genome Research Institute (NHGRI) catalog of associations to generate instrumental variable trait files.
• It incorporates methods for filtering potential confounding associations and addressing linkage disequilibrium.
• Causal effect scores are estimated using MR-score analysis with publicly available summary-level data for disease endpoints.

Main Results:

• The pipeline was used to develop genetic instruments for seven traits.
• Potential causal relationships were evaluated for two disease endpoints.
• Two putatively causal associations were identified: one between blood pressure and type 2 diabetes, and another between bone-mineral density and type 2 diabetes.

Conclusions:

• The MeRP pipeline offers an efficient and systematic approach to Mendelian randomization analysis.
• The study highlights the utility of automated pipelines in leveraging large public datasets for genetic epidemiology.
• The findings suggest potential causal roles for blood pressure and bone-mineral density in the etiology of type 2 diabetes, warranting further investigation.