GENIUS-MAWII: for robust Mendelian randomization with many weak invalid instruments

A Modified Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization

Mendelian randomization uses genetic variants as instrumental variables to estimate the causal effect of exposure on outcome from observational data. A common challenge in Mendelian randomization is that many genetic variants are only modestly or even weakly associated with the exposure of interest, a setting known as many weak instruments. Conventional methods, such as the popular inverse-variance weighted (IVW) estimator, could be heavily biased toward zero when the instrument strength is weak. To address this issue, the debiased IVW (dIVW) estimator and the penalized IVW (pIVW) estimator, which are shown to be robust to many weak instruments, were recently proposed. However, we find that the dIVW estimator tends to produce an exaggerated estimate of the causal effect, especially when the effective sample size is small. Although the pIVW estimator has better statistical properties, it is slightly more complex, and the idea behind this method is also a bit less intuitive. Therefore, we propose a modified debiased IVW (mdIVW) estimator that directly multiplies a shrinkage factor with the original dIVW estimator. After this simple modification, we prove that the mdIVW estimator not only has second-order bias with respect to the effective sample size, but also has smaller variance and mean squared error than the preceding two estimators. We then extend the proposed method to account for the presence of instrumental variable selection and balanced horizontal pleiotropy. We demonstrate the improvement of the mdIVW estimator over the competing ones through extensive simulation studies and real data analysis.

Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics

Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.