The integration of causal effect estimates from multiple Mendelian Randomization studies has become increasingly popular. However, the presence of overlapping databases compromises traditional meta-analysis, leading to inflated variance and reduced statistical power. Here, we propose JointMR, a joint likelihood-based approach designed to integrate multiple GWAS summary databases while explicitly accounting for the covariance matrix of the Wald ratio estimates. Specifically, to accommodate potential cross-study heterogeneity, JointMR incorporates both fixed-effect and random-effects models. Simulations demonstrated that JointMR provides unbiased estimates with higher statistical power and superior Type I error control compared to conventional meta-analysis methods of standard MR estimates (e.g., IVW), especially as database correlation increases. In a real-data application examining total cholesterol, HDL-C, LDL-C and triglycerides on type 2 diabetes, JointMR resolved contradictions seen in standard approaches, generating stable and biologically plausible estimates. In conclusion, JointMR overcomes critical limitations of existing methods, offering a more powerful and reliable tool for robust causal inference from the growing repository of GWAS summary statistics.
Wu et al. (Fri,) studied this question.