Summary
Inverse-variance weighted two-sample Mendelian randomization (IVW-MR) is the most widely used approach that utilizes genome-wide association studies (GWAS) summary statistics to infer the existence and the strength of the causal effect between an exposure and an outcome. Estimates from this approach can be subject to different biases due to the use of weak instruments and winner's curse, which can change as a function of the overlap between the exposure and outcome samples. We developed a method (MRlap) that simultaneously considers weak instrument bias and winner's curse while accounting for potential sample overlap. Assuming spike-and-slab genomic architecture and leveraging linkage disequilibrium score regression and other techniques, we could analytically derive, reliably estimate, and
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