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JointMR: A joint likelihood-based approach for causal effect estimation in overlapping Mendelian Randomization studies

2025-12-19 genetic and genomic medicine Title + abstract only
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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 potent...

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