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A Linear Mixed Model With Measurement Error Correction (LMM-MEC): A Method for Summary-Data-Based Multivariable

Ming Ding1,2, Fei Zou3

  • 1Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

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|January 19, 2026
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A new linear mixed model with measurement error correction (LMM-MEC) improves causal inference in multivariable Mendelian randomization (MVMR) by accounting for summary statistic variances. The method identified high LDL-c levels causally linked to reduced longevity.

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Area of Science:

  • Genetics and Epidemiology
  • Statistical Genetics
  • Causal Inference

Background:

  • Multivariable Mendelian randomization (MVMR) methods assess causal effects of multiple risk factors on disease.
  • Existing MVMR methods face challenges in accounting for variances in risk factor summary statistics.
  • Accurate causal inference requires robust methods that handle uncertainty in genetic and phenotypic data.

Purpose of the Study:

  • To propose a novel linear mixed model with measurement error correction (LMM-MEC) for MVMR.
  • To address the challenge of accounting for variances in summary statistics for both disease outcomes and risk factors.
  • To evaluate the performance of LMM-MEC compared to existing MVMR methods under various conditions, including pleiotropy and linkage disequilibrium.

Main Methods:

  • Developed a linear mixed model (LMM) to account for variance in disease summary statistics (fixed- or random-effects).
  • Relaxed the NOME assumption and incorporated estimation error from risk factor summary statistics using regression calibration.
  • Validated the LMM-MEC method through simulation studies and an application to cholesterol biomarkers and longevity.

Main Results:

  • LMM-MEC demonstrated comparable performance to existing MVMR methods under no or balanced pleiotropy.
  • The method showed improved coverage rates and power under directional pleiotropy compared to some existing methods.
  • In an application study, LMM-MEC identified a causal association between high LDL-c levels and a lower likelihood of longevity, using 739 genetic variants with low linkage disequilibrium.

Conclusions:

  • The proposed LMM-MEC method effectively accounts for variances in summary statistics in MVMR analyses.
  • LMM-MEC offers improved performance, particularly under directional pleiotropy and specific linkage disequilibrium scenarios.
  • The study highlights a causal link between elevated LDL-c and reduced longevity, demonstrating the utility of LMM-MEC in genetic epidemiology.