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Mendelian Randomization

Sandeep Grover1, Fabiola Del Greco M2, Catherine M Stein3

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

Methods in Molecular Biology (Clifton, N.J.)
|October 6, 2017
PubMed

Insights

Mendelian randomization (MR) uses genetic variants to overcome confounding in observational studies, enabling causal inference. This guide provides practical examples for applying MR with real data, enhancing its clinical utility despite challenges.

Area of Science:

  • Epidemiology
  • Genetic Epidemiology
  • Biostatistics

Background:

  • Observational studies face challenges with confounding and reverse causality, limiting clinical interpretation.
  • Randomizing exposure variables is often not feasible in observational research.
  • Mendelian randomization (MR) offers a powerful approach to address these limitations using genetic variants.

Purpose of the Study:

  • To provide a practical, step-by-step guide for causal inference using Mendelian randomization.
  • To demonstrate the application of MR principles with real-world individual and summary data.
  • To offer best practices and recommendations for conducting GWAS-based MR.

Main Methods:

  • Utilizing genetic polymorphisms (SNPs) as instrumental variables for exposure variables.
  • Employing Genome-Wide Association Studies (GWAS) and meta-analyses of GWAS for large sample sizes.
  • Applying MR principles to both individual and summary-level data from unrelated individuals.

Main Results:

  • Demonstrates the feasibility of causal inference through MR with real datasets.
  • Highlights the utility of GWAS-derived SNPs in MR analyses.
  • Addresses challenges such as multiple causality in GWAS-based MR.

Conclusions:

  • Mendelian randomization is a robust technique for inferring causality despite statistical complexities.
  • Practical examples and best practices are crucial for advancing the application of MR.
  • This guide facilitates the use of MR for more reliable causal inference in research.
Keywords:
Causal inferenceGenome-wide association studyIndividual dataInstrumental variableMendelian randomizationObservational epidemiologyPleiotropyReverse causationSummary dataUnobserved confounding

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