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Instrumental variable model average with applications in Mendelian randomization.

Loraine Liping Seng1,2, Ching-Ti Liu3,4, Jingli Wang5

  • 1Department of Statistics and Data Science, National University of Singapore, Singapore.

Statistics in Medicine
|July 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model average estimator for Mendelian randomization, improving causal inference with many genetic instruments. The method enhances precision and reduces bias in observational studies, particularly in high-dimensional settings.

Keywords:
causal inferencegeneticsgenome-wide association studyinstrument variablemodel averagepenalty functionsingle nucleotide polymorphism

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Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Mendelian randomization (MR) uses genetic variants to infer causal effects of exposures on traits in observational studies.
  • High-dimensional instrumental variables can improve precision but risk bias from weak instrument association.

Purpose of the Study:

  • To propose a novel model average estimator for Mendelian randomization to address high-dimensionality and weak instrument bias.
  • To develop a method that allows the number and size of submodels to scale with sample size.

Main Methods:

  • A two-stage model average estimator is proposed, utilizing subsets of single nucleotide polymorphisms (SNPs) as instruments.
  • Penalization methods (LASSO, SCAD, MCP) are used to weight submodel predictions for the genetically predicted exposure.
  • The model averaged prediction serves as the exposure in the second stage for causal effect estimation.

Main Results:

  • The proposed estimator demonstrates practical performance in numerical simulations.
  • The method effectively handles high-dimensional genetic data in Mendelian randomization analyses.
  • The estimator's ability to grow submodel complexity with sample size is a key feature.

Conclusions:

  • The novel model average estimator offers a robust approach to Mendelian randomization, particularly with numerous genetic instruments.
  • This method enhances causal inference accuracy in observational studies by mitigating bias associated with weak instruments.
  • The approach is validated through simulations and applied to investigate the stature-blood pressure relationship.