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Genetic markers as instrumental variables.

Stephanie von Hinke1, George Davey Smith1, Debbie A Lawlor1

  • 1University of Bristol, Bristol, United Kingdom.

Journal of Health Economics
|November 29, 2015
PubMed
Summary

Genetic markers are increasingly used as instrumental variables (IV) in various fields. This study defines the conditions for using genetic variants as instruments, combining econometrics and genetic epidemiology for better application.

Keywords:
ALSPACGenetic variantsInstrumental variablesMendelian randomizationPotential outcomes

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Area of Science:

  • Econometrics
  • Genetic Epidemiology
  • Biomedical Data Analysis

Background:

  • Instrumental variables (IV) methods are gaining traction across multiple disciplines.
  • The application of genetic variants as IVs requires clearly defined conditions, especially with growing biomedical data.
  • Existing literature lacks comprehensive guidelines for using genotypes as instruments.

Purpose of the Study:

  • To define the appropriate conditions for utilizing genetic markers as instrumental variables.
  • To bridge the gap between econometric IV methods and genetic epidemiology.
  • To provide a framework for the successful application of genotypes as instruments in research.

Main Methods:

  • Integrating econometric IV literature with genetic epidemiology principles.
  • Utilizing the statistical potential outcomes framework.
  • Discussing biological conditions and IV assumptions relevant to genetic data.

Main Results:

  • The study outlines essential biological and statistical assumptions for employing genetic variants as IVs.
  • It clarifies the conditions under which genotypes can be reliably used as instruments.
  • Illustrative applications demonstrate the practical implementation of these principles.

Conclusions:

  • Understanding the specific conditions is crucial for the valid use of genetic markers as instrumental variables.
  • The combined approach offers a robust framework for genetic epidemiology and econometrics.
  • This work facilitates more accurate causal inference using genetic data.