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Factorial Mendelian randomization: using genetic variants to assess interactions.

Jessica M B Rees1,2, Christopher N Foley3, Stephen Burgess1,3

  • 1Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

International Journal of Epidemiology
|August 2, 2019
PubMed
Summary

Factorial Mendelian randomization (MR) methods can be improved for interaction analyses. Using all genetic variants and their interactions as instruments, rather than a 2x2 approach, significantly enhances efficiency in genetic studies.

Keywords:
Mendelian randomizationcausal inferencefactorial randomized trialinstrumental variablesinteraction

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Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Factorial Mendelian randomization (MR) uses genetic variants to study interactions.
  • Methodological guidance for factorial MR design and execution is limited.
  • Previous studies often used a 2x2 approach with dichotomized genetic scores.

Purpose of the Study:

  • To provide methodological advice for factorial MR analyses.
  • To propose improved methods for investigating interactions between risk factors or interventions.
  • To enhance the efficiency and power of factorial MR studies.

Main Methods:

  • Described two contexts: risk factor interactions and pharmacological intervention interactions.
  • Proposed two-stage least squares using all genetic variants and their interactions as instrumental variables.
  • Advocated for continuous genetic scores over dichotomized scores, using UK Biobank data for illustration.

Main Results:

  • Maximized efficiency using the full set of genetic variant interactions as instruments.
  • Demonstrated 4- to 10-fold efficiency improvement over the 2x2 approach in an applied example.
  • Continuous genetic scores were more efficient than dichotomized scores.

Conclusions:

  • Previous factorial MR analyses may have been underpowered.
  • Improved efficiency is achievable by utilizing all genetic variants and their interactions as instrumental variables.
  • The proposed methods offer a more powerful approach to interaction analysis in genetic epidemiology.