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Mendelian Randomization

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Statistical Human Genetics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1666))

Abstract

Confounding and reverse causality have prevented us from drawing meaningful clinical interpretation even in well-powered observational studies. Confounding may be attributed to our inability to randomize the exposure variable in observational studies. Mendelian randomization (MR) is one approach to overcome confounding. It utilizes one or more genetic polymorphisms as a proxy for the exposure variable of interest. Polymorphisms are randomly distributed in a population, they are static throughout an individual’s lifetime, and may thus help in inferring directionality in exposure–outcome associations. Genome-wide association studies (GWAS) or meta-analyses of GWAS are characterized by large sample sizes and the availability of many single nucleotide polymorphisms (SNPs), making GWAS-based MR an attractive approach. GWAS-based MR comes with specific challenges, including multiple causality. Despite shortcomings, it still remains one of the most powerful techniques for inferring causality.

With MR still an evolving concept with complex statistical challenges, the literature is relatively scarce in terms of providing working examples incorporating real datasets. In this chapter, we provide a step-by-step guide for causal inference based on the principles of MR with a real dataset using both individual and summary data from unrelated individuals. We suggest best possible practices and give recommendations based on the current literature.

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Acknowledgment

S.G. greatly acknowledges the organizers of the recent workshop on Mendelian Randomization run through the University of Cambridge’s Department for Public Health and Primary Care. This work was supported by a grant from the German Research Foundation (Research Unit ProtectMove, FOR 2488).

Author information

Authors and Affiliations

  1. Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany

    Sandeep Grover & Andreas Ziegler

  2. Center for Biomedicine, EURAC Research, Via Galvani 31, Bolzano, Italy

    Fabiola Del Greco M.

  3. Case Western Reserve University, Cleveland, OH, USA

    Catherine M. Stein

Authors
  1. Sandeep Grover
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  2. Fabiola Del Greco M.
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  3. Catherine M. Stein
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  4. Andreas Ziegler
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Corresponding author

Correspondence to Andreas Ziegler .

Editor information

Editors and Affiliations

  1. Case Western Reserve University, Cleveland, OH, USA

    Robert C. Elston

1 Electronic Supplementary Materials

Dataset 1

Descriptive table of prioritized SNPs for estimating the causal estimates using individual and summary data (CSV 1 kb)

Dataset 2

Example dataset with quality-controlled individual data on phenotypic variables and prioritized genetic instruments (CSV 59 kb)

Dataset 3

Example dataset with summary data on genetic instrument–exposure and genetic instrument–outcome associations (CSV 1019 bytes)

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Grover, S., Del Greco M., F., Stein, C.M., Ziegler, A. (2017). Mendelian Randomization. In: Elston, R. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 1666. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7274-6_29

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  • DOI: https://doi.org/10.1007/978-1-4939-7274-6_29

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7273-9

  • Online ISBN: 978-1-4939-7274-6

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