Modeling and Analysis of Statistical Genetics data in Python (magenpy)

This site contains documentation, tutorials, and examples for using the magenpy package for the purposes of handling, harmonizing, and computing over genotype data to prepare them for downstream genetics analyses. The magenpy package provides tools for:

• Reading and processing genotype data in plink BED format.
• Efficient LD matrix construction and storage in Zarr array format.
• Data structures for harmonizing various GWAS data sources.
• Includes parsers for commonly used GWAS summary statistics formats.
• Simulating polygenic traits (continuous and binary) using complex genetic architectures.
  • Multi-cohort simulation scenarios (beta)
  • Simulations incorporating functional annotations in the genetic architecture (beta)
• Interfaces for performing association testing on simulated and real phenotypes.
• Preliminary support for processing and integrating genomic annotations with other data sources.

If you use magenpy in your research, please cite the following paper:

Zabad, S., Gravel, S., & Li, Y. (2023). Fast and accurate Bayesian polygenic risk modeling with variational inference. The American Journal of Human Genetics, 110(5), 741–761. https://doi.org/10.1016/j.ajhg.2023.03.009

Helpful links

• API Reference
• Installation
• Getting Started
• Features and Configurations
• Command Line Scripts
• Project homepage on GitHub
• Sister package viprs

Contact

If you have any questions or issues, please feel free to open an issue on the GitHub repository or contact us directly at:

• Shadi Zabad
• Yue Li
• Simon Gravel