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

ABSTRACT: 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 con

SUBMITTER: Hu X 

PROVIDER: S-EPMC11339627 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature