GELEX: GEnome LEX

Gelex is a high-performance C++ genomic analysis toolkit designed for large-scale genomic prediction and association studies. It integrates advanced Bayesian models (BayesAlphabet series), optimized for memory efficiency and computational speed on massive datasets.

Important

This project is under active development. APIs and features are subject to change.

🌟 Key Features

• Comprehensive Model Support:
  • Bayesian Series: BayesA, B, C, R, RR and their counterparts for dominance effects (d) and Pi estimation (pi) (14 prior strategies in total).
• Full-stack Analysis Workflow:
  • Model Fitting (fit): Efficient MCMC sampling.
  • Genomic Prediction (predict): Generate predictions for new samples based on trained effect sizes.
  • Association Testing (assoc): Mixed linear model GWAS with LOCO (Leave-One-Chromosome-Out) support.
  • GRM Computation (grm): Multiple algorithms (Yang, Zeng, Vitezica) for computing Genomic Relationship Matrices.
  • Phenotype Simulation (simulate): Simulate complex additive and dominance genetic architectures based on real genotypes.
• Exceptional Performance:
  • Multi-threaded Parallelism: OpenMP-based parallelization across all modules.
  • Memory Efficiency: Memory-mapped BED file reading via mio with chunk-based processing support.
  • Modern Backend: Powered by the Eigen linear algebra library with MKL or OpenBLAS support.

🚀 Installation

Install the latest version via pixi:

pixi global install -c conda-forge -c https://prefix.dev/gelex gelex

via Conda:

conda install -c conda-forge -c https://prefix.dev/gelex gelex

For detailed installation instructions, please visit the Installation Guide.

💡 Quick Start

1. Fit a Bayesian Model (BayesR)

gelex fit \
  --bfile data/genotypes \
  --pheno data/phenotypes.tsv \
  --method R \
  --iters 10000 \
  --burnin 2000 \
  --o result/my_analysis

For detailed tutorials on Genomic Prediction, GRM computation, and GWAS, please visit our Documentation.

🛠 Development & Testing

Run the full test suite:

pixi run test

For detailed development guidelines (coding style, architecture), please refer to CLAUDE.md.

📄 License

This project is licensed under the Apache-2.0 LICENSE.

📧 Contact & Citation

If you use Gelex in your research, please cite it using the following BibTeX entry:

@misc{gelex,
   author = {RuLei Chen},
   year = {2026},
   note = {https://github.com/r1cheu/gelex},
   title = {Gelex: A high-performance C++ genomic analysis toolkit}
}

🙏 Acknowledgements

Gelex utilizes several libraries:

• Eigen: Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.
• barkeep: Small, single C++ header to display async animations, counters, and progress bars.
• argparse: Argument parser for modern C++.
• mio: An easy to use header-only cross-platform C++11 memory mapping library with an MIT license.