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MVMRmode: Introducing an R package for plurality valid estimators for multivariable Mendelian randomisation.

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New multivariable modal estimators were developed for Mendelian randomisation (MR) studies. The multivariable contamination mixture (CM) method demonstrated precise and unbiased estimates, outperforming other methods in most scenarios.

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Area of Science:

  • Epidemiology
  • Statistical Genetics
  • Causal Inference

Background:

  • Mendelian randomisation (MR) employs genetic variants as instrumental variables.
  • Mode-based estimators (MBE) are common in univariable MR but underexplored in multivariable MR due to a lack of plurality valid regression estimators.
  • Pleiotropy, where genetic variants affect the outcome through multiple pathways, necessitates robust analytical methods.

Purpose of the Study:

  • To introduce and evaluate novel plurality valid estimators for multivariable MR.
  • To address the under-exploration of modal estimators in multivariable MR settings.
  • To assess the performance of new estimators against existing methods in simulations and real-world data.

Main Methods:

  • Developed two non-regression-based estimators for multivariable MR: multivariable-MBE and multivariable-CM, utilizing a residual framework.
  • Employed Monte-Carlo simulations to compare the performance of these novel estimators with established methods.
  • Re-analyzed existing data on the causal effects of intelligence, education, and household income on Alzheimer's disease.

Main Results:

  • Multivariable-MBE exhibited high variability, limiting its utility.
  • Multivariable-CM provided more precise estimates and generally outperformed MR-Egger and Weighted Median under balanced pleiotropy.
  • Multivariable-CM showed underperformance compared to Weighted Median in the presence of moderate directional pleiotropy.

Conclusions:

  • Introduced two plurality valid estimators for multivariable MR: multivariable-MBE and multivariable-CM.
  • Multivariable-CM demonstrated superior performance, offering precise and unbiased estimates, particularly with balanced pleiotropy and limited directional pleiotropy.
  • The study highlights the utility of multivariable-CM for causal inference in complex genetic epidemiology.