Overview

Genomic Ensembling (GenomEn) is an ensemble framework for genotype-to-phenotype prediction that uses both linear and non-linear estimators to capture gene-gene interactions often overlooked by traditional polygenic risk score (PRS) methods. For more informations on the methods, please refer to our paper.

The package enables researchers to improve predictive performance beyond conventional linear PRS approaches by modeling complex genetic interactions. GenomEn also natively supports variants on the X sex chromosome, which are often neglected due to integration challenges with autosomes, further improving predictive performance and simplifying the study of X-linked traits. Finally, GenomEn allows for local and global variant-level interpretability via SHAP values, allowing to gain new insights into complex traits.

Installation

Install from PyPI:

pip install genomen

Install with optional dependency groups:

# Development dependencies (black, pytest, etc.)
pip install genomen[dev]

# GPU support (CUDA 12)
pip install genomen[gpu]

Quick Start

from genomen.data import DataSet, split
from genomen.model import GenomenModel

# Load and split data
dataset = DataSet()
train_set, test_set, val_set = split(dataset)

# Train model
model = GenomenModel()
model.fit(train_set, val_set)

# Make predictions
geno_preds, covar_preds, preds = model.predict(test_set)

Documentation

For detailed documentation, tutorials, and examples, please visit the official documentation site or browse the local documentation in the docs/ directory.

Citation

If you use GenomEn in your research, please cite:

@article{Thomassin2026,
  title   = {Biobank-scale Polygenic Risk Prediction with Nonlinear Estimators},
  author  = {Thomassin, Christophe and Franquesa Mon{\'e}s, Marc and Bonet, David and Gerlach, Peter A. and Comajoan Cara, Mar{\c{c}}al and Mas Montserrat, Daniel and Ioannidis, Alexander G.},
  year    = {2026},
  url     = {https://genomen-website.vercel.app/docs}
}

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For development setup, see the getting started guide.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Links

• Website: https://genomen-website.vercel.app/
• Documentation: https://genomen-website.vercel.app/docs
• Phenotype Browser: https://genomen-website.vercel.app/browser
• PyPI: https://pypi.org/project/genomen/
• GitHub: https://github.com/AI-sandbox/genomen