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Statistical methods for cis-Mendelian randomization with two-sample summary-level data.

Apostolos Gkatzionis1,2, Stephen Burgess1,3, Paul J Newcombe1

  • 1MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.

Genetic Epidemiology
|October 23, 2022
PubMed
Summary
This summary is machine-generated.

Mendelian randomization (MR) uses genetic variants to infer causality. For cis-MR with correlated variants, factor analysis and Bayesian methods offer more reliable estimates than pruning, especially with weak instruments.

Keywords:
Bayesian variable selectionMendelian randomizationcorrelated instrumentsfactor analysisprincipal components analysispruning

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Area of Science:

  • Statistical Genetics
  • Epidemiology
  • Bioinformatics

Background:

  • Mendelian randomization (MR) leverages genetic variants to establish causal relationships between risk factors and outcomes.
  • Two-sample summary-data MR, particularly cis-MR using protein expression, faces challenges with correlated variants within gene regions.
  • Using all correlated variants can lead to numerically unstable causal effect estimates due to ill-conditioned matrices.

Approach:

  • This study reviews variable selection and estimation methods for cis-MR with summary-level data.
  • Methods examined include stepwise pruning, conditional analysis, principal components analysis, factor analysis, and Bayesian variable selection.
  • A simulation study compares the performance of these methods under various conditions.

Key Points:

  • Methods show comparable performance with large sample sizes and strong genetic instruments.
  • Factor analysis and Bayesian variable selection provide more reliable inferences than pruning when weak instrument bias is suspected.
  • Case studies assess the causal effects of LDL-cholesterol and testosterone on coronary heart disease.

Conclusions:

  • The choice of variable selection method in cis-MR is crucial for reliable causal inference.
  • Factor analysis and Bayesian approaches are recommended for handling correlated variants and potential weak instrument bias.
  • Findings have implications for understanding genetic influences on complex diseases.