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This paper proposes a new method, LMM-MEC, for summary-data-based multivariable Mendelian randomization that accounts for variance in both disease outcome and risk factor summary statistics. The method uses a linear mixed model to adjust for heterogeneity in effect sizes and incorporates measurement error correction through a regression calibration approach. The LMM-MEC method demonstrates comparable performance to existing MVMR methods in a simulation study.

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System
MEC

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Abstract

data-based multivariable Mendelian randomization (MVMR) methods, such as MVMR-Egger, MVMR-IVW, MVMR median-based, and MVMR-PRESSO, are used to assess the causal effects of multiple risk factors on disease. However, accounting for variances in the summary statistics of risk factors remains a challenge. We propose a linear mixed model with measurement error correction (LMM-MEC) that accounts for the variance in summary statistics for both disease outcomes and risk factors. First, under the NOME assumption, we apply a linear mixed model to account for variance in disease summary statistics by treating it as fixed- or random-effects, depending on whether there is heterogeneity in the effect sizes of the genetic variants on the disease outcome. Next, we relax the NOME assumption and further take the estimation error (or variance) in the summary statistics of risk factors into consideration by measurement models through a regression calibration approach. In a simulation study, using independent genetic variants as instrumental variables (IV), our method showed comparable performance to existing MVMR methods under conditions of no pleiotropy or with balanced pleiotropy on the disease outcome, and it achieved slightly improved coverage rates and power under directional pleiotropy. When genetic variants are in low to moderate linkage disequilibrium (LD) (0 < ρ $\rho $ 2 ≤ 0.3), our method showed comparable performance to MVMR-Egger, although both methods showed reduced coverage rates and power compared to situations where genetic variants as IVs are in LD. In the application study, we examined causal associations between correlated cholesterol biomarkers and longevity. By including 739 genetic variants selected based on p values < 5 × 10 -5 from GWAS and allowing for low LD ( ρ $\rho $ 2 ≤ 0.1), our method identified that large LDL-c levels were causally associated with a lower likelihood of achieving longevity.

Keywords

Mendelian randomizationStatisticsLinkage disequilibriumObservational errorPleiotropyLinear model

Identifiers

DOI
10.1002/gepi.70032
PubMed
41553776
Journal
Genetic epidemiology
Year
2026