MeRP: a high-throughput pipeline for Mendelian randomization analysis

• Published in: Bioinformatics Vol. 31; no. 6; pp. 957 - 959
• Main Authors: Yin, Peter, Voight, Benjamin F
• Format: Journal Article
• Language: English
• Published: England 15-03-2015
• Subjects: Bioinformatics; Biomarkers - analysis; Blood Pressure; Bone Density; Computational Biology - methods; Density; Diabetes Mellitus, Type 2 - genetics; Diabetes Mellitus, Type 2 - physiopathology; Filtering; Genetics; High-Throughput Screening Assays; Humans; Linkage Disequilibrium; Mendelian Randomization Analysis - methods; Phenotype; Pipelines; Polymorphism, Single Nucleotide - genetics; Randomization; Software; Summaries
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btu742

We present a Mendelian randomization (MR) pipeline (MeRP) to facilitate rapid, causal inference analysis through automating key steps in developing and analyzing genetic instruments obtained from publicly available data. Our tool uses the National Human Genome Research Institute catalog of associations to generate instrumental variable trait files and provides methods for filtering of potential confounding associations as well as linkage disequilibrium. MeRP generates estimated causal effect scores via a MR-score analysis using summary data for disease endpoints typically found in the public domain. We utilize our pipeline to develop genetic instruments for seven traits and evaluate potential causal relationships with two disease endpoints, observing two putatively causal associations between blood pressure and bone-mineral density with type 2 diabetes. Our tool emphasizes the importance of careful but systematic screening of large datasets for discovery and systematic follow-up.