Motivation: The expansion of genetic association data from genome-wide association studies has increased the importance of methodologies like Polygenic Risk Scores (PRS) and Mendelian Randomization (MR) in genetic epidemiology. However, their application is often impeded by complex, multi-step workflows requiring specialized expertise and the use of disparate tools with varying data formatting requirements. Existing solutions are frequently standalone packages or command-line based-largely due to dependencies on tools like PLINK-limiting accessibility for researchers without computational experience. Given Python's popularity and ease of use, there is a need for an integrated, user-friendly Python toolkit to streamline PRS and MR analyses.
Results: We introduce Genal, a Python package that consolidates SNP-level data handling, cleaning, clumping, PRS computation, and MR analyses into a single, cohesive toolkit. By eliminating the need for multiple R packages and for command-line interaction by wrapping around PLINK, Genal lowers the barrier for medical scientists to perform complex genetic epidemiology studies. Genal draws on concepts from several well-established tools, ensuring that users have access to rigorous statistical techniques in the intuitive Python environment. Additionally, Genal leverages parallel processing for MR methods, including MR-PRESSO, significantly reducing the computational time required for these analyses.
Availability and implementation: The package is available on Pypi (https://pypi.org/project/genal-python/), the code is openly available on Github with a tutorial: https://github.com/CypRiv/genal, and the documentation can be found on readthedocs: https://genal.rtfd.io.
© The Author(s) 2024. Published by Oxford University Press.