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Bioinformatics. 2015 Mar 15;31(6):957-9. doi: 10.1093/bioinformatics/btu742. Epub 2014 Nov 10.

MeRP: a high-throughput pipeline for Mendelian randomization analysis.

Author information

1
Department of Biology, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19143, USA, Department of Pharmacology and Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19143, USA.
2
Department of Biology, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19143, USA, Department of Pharmacology and Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19143, USA Department of Biology, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19143, USA, Department of Pharmacology and Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19143, USA.

Abstract

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.

PMID:
25388149
DOI:
10.1093/bioinformatics/btu742
[Indexed for MEDLINE]

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