Falsification of the instrumental variable conditions in Mendelian randomization studies in the UK Biobank

Kelly Guo1, Elizabeth W Diemer2,3,4, Jeremy A Labrecque5

  • 1Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands. k.guo@erasmusmc.nl.

Insights

Instrumental inequalities can detect violations in Mendelian randomization (MR) causal effect estimates when using multiple genetic variants (SNPs). This method is less effective for individual SNPs but crucial for identifying bias in complex genetic analyses.

Area of Science:

  • Epidemiology
  • Statistical Genetics

Background:

  • Mendelian randomization (MR) is a widely used method for inferring causal relationships.
  • The core assumptions of MR cannot be directly tested but imply instrumental inequalities.
  • Instrumental inequalities offer a falsification approach to MR assumptions but are underutilized.

Purpose of the Study:

  • To evaluate the utility of instrumental inequalities in detecting violations of MR assumptions.
  • To assess the performance of instrumental inequalities in case studies of common exposures and coronary artery disease risk.

Main Methods:

  • Applied instrumental inequalities to MR models using 1077 SNPs in the UK Biobank.
  • Analyzed exposures including vitamin D, alcohol, CRP, triglycerides, HDL, and LDL cholesterol.
  • Tested MR models with individual SNPs and unweighted allele scores as instruments.

Main Results:

  • No MR assumption violations were detected when using individual SNPs as instruments.
  • Violations of MR assumptions were identified for 5 out of 6 exposures when using allele scores.
  • Demonstrated the potential of instrumental inequalities to detect bias in multi-SNP MR analyses.

Conclusions:

  • Instrumental inequalities are valuable for detecting MR assumption violations with multi-SNP instruments.
  • The method may be less effective for pinpointing specific invalid SNPs.
  • Incorporating instrumental inequalities can enhance the reliability of MR studies by identifying and mitigating bias.

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