The MR-Base platform supports systematic causal inference across the human phenome

Area of Science:

• Genetics and Bioinformatics
• Statistical Genetics
• Computational Biology

Background:

• Genome-wide association studies (GWAS) enable causal inference between phenotypes via two-sample Mendelian randomization (2SMR).
• Current 2SMR implementation faces challenges due to rapidly evolving methods and insufficiently curated GWAS data.
• Efficiently leveraging large-scale genetic association data for causal inference remains a critical need.

Purpose of the Study:

• To develop a comprehensive platform, MR-Base, that integrates curated GWAS results with automated 2SMR tools.
• To facilitate rigorous hypothesis-driven causal inference by providing a robust and accessible resource.
• To enable large-scale phenome-wide association studies for exploring millions of potential causal relationships.

Main Methods:

• Development of MR-Base, a platform featuring a curated database of complete GWAS results.
• Integration of an application programming interface (API), web application, and R packages for automated 2SMR.
• Inclusion of multiple sensitivity analyses to assess violations of 2SMR assumptions, such as horizontal pleiotropy.

Main Results:

• MR-Base provides access to over 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS.
• The platform automates the 2SMR process, enhancing efficiency and rigor in genetic analyses.
• The integrated database and software facilitate the evaluation of millions of potential causal relationships.

Conclusions:

• MR-Base significantly enhances the implementation of two-sample Mendelian randomization by providing curated data and automated tools.
• The platform supports robust causal inference and facilitates large-scale phenome-wide association studies.
• MR-Base represents a valuable resource for researchers investigating genetic causality.