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A Linear Mixed Model with Measurement Error Correction (LMM-MEC): A Method for Summary-data-based Multivariable

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    This summary is machine-generated.

    A new linear mixed model with measurement error correction (LMM-MEC) accurately assesses causal effects of multiple risk factors on disease, even with pleiotropy. This method identified large LDL-c as causally linked to lower longevity.

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    Area of Science:

    • Genetics
    • Epidemiology
    • Biostatistics

    Background:

    • Multivariable Mendelian randomization (MVMR) methods are crucial for assessing causal links between multiple risk factors and disease.
    • Existing MVMR methods face challenges in accurately accounting for variances in risk factor summary statistics.
    • Addressing these variances is essential for robust causal inference in genetic epidemiology.

    Purpose of the Study:

    • To propose a novel method, the linear mixed model with measurement error correction (LMM-MEC), to address the challenge of accounting for variances in summary statistics for both disease outcomes and risk factors in MVMR.
    • To evaluate the performance of LMM-MEC compared to existing MVMR methods through simulation studies.
    • To apply the LMM-MEC method to investigate the causal associations between cholesterol biomarkers and longevity.

    Main Methods:

    • Developed a two-step LMM-MEC approach: Step I uses a linear mixed model to handle disease summary statistic variance, treating heterogeneity as a random effect.
    • Step II employs regression calibration to correct for multiple measurement errors arising from risk factor summary statistic variances.
    • Simulations were conducted using independent genetic variants and variants with low to moderate linkage disequilibrium (LD) as instrumental variables (IVs).

    Main Results:

    • LMM-MEC demonstrated comparable performance to existing MVMR methods with independent IVs under no or balanced pleiotropy.
    • LMM-MEC exhibited higher coverage rates and statistical power under directional pleiotropy compared to other methods.
    • In an application study on longevity and cholesterol, LMM-MEC identified a causal association between high LDL-c and a lower likelihood of longevity, using 739 genetic variants with low LD.

    Conclusions:

    • LMM-MEC provides a robust framework for multivariable Mendelian randomization by effectively accounting for summary statistic variances in both outcomes and risk factors.
    • The method shows improved performance, particularly under directional pleiotropy, enhancing the reliability of causal inference.
    • The application highlights the utility of LMM-MEC in uncovering significant causal relationships, such as the detrimental effect of high LDL-c on longevity.