Abstract
Recent advancements highlight the importance of large-scale causal inference in elucidating disease mechanisms and guiding public health strategies. Mendelian randomization (MR) has become a cornerstone method for identifying causal relationships by leveraging genetic variants as instrumental variables. However, existing tools lack flexibility for multivariable analyses and fail to integrate diverse datasets effectively. To address these challenges, we introduce MRdb, a comprehensive database designed for conducting both univariable and multivariable MR analyses. MRdb encompasses 12 distinct categories of exposure data, including but not limited to 19 126 expression quantitative trait loci genes, 4907 plasma proteins, and 1400 plasma metabolites. Additionally, it integrates 48 507 disease outcomes sourced from FinnGen R10 and the IEU Open GWAS Project. MRdb offers robust data preprocessing features, including handling missing statistics, harmonizing datasets, and selecting instrumental variables to ensure high-quality analyses. Collectively, MRdb bridges the gaps in existing tools by integrating diverse datasets with user-friendly functionalities, empowering researchers to explore complex causal mechanisms.