POP-TOOLS (POst-Prediction TOOLS) is a Python3-based command line toolkit for conducting valid and powerful machine learning (ML)-assisted genetic association studies.
The POP-TOOLS toolkit can be used to conduct
- ML-Assisted Genome-Wide Association Studies (GWAS).
- ML-Assisted Rare-Variant Association Studies (RVAS).
The POP-TOOLS and its required modules can be installed via
git clone https://github.com/qlu-lab/POP-TOOLS
cd POP-TOOLS
pip install -r requirements.txt
Please see the TL;DR for ML-assisted GWAS using POP-GWAS
Please see the TL;DR for ML-assisted Rare-Variant Association Studies (single-variant and gene-level burden test) using POP-GWAS.
Please see the wiki for tutorials describing the basic function along with a detailed manual of POP-TOOLS.
Please see the FAQ for frequently asked questions related to POP-TOOLS.
We provide a web interface for the power and sample size calculation for ML-assisted GWAS.
[Version 1.2.0] (Nov 29, 2024): Added POP-GWAS for rare-variant association studies (single-variant and Burden test).
[Version 1.1.0] (May 1, 2024): Added quality control to remove SNPs with duplicate IDs; Added a version of the sample overlap correction; Modified scripts to accommodate the latest version of Polaris.
[Version 1.0.0] (Jan 2, 2024): Initial release.
If you use POP-GWAS, please cite
Miao, J., Wu, Y., Sun, Z. et al. Valid inference for machine learning-assisted genome-wide association studies. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01934-0
For questions and comments, please open a GitHub issue (preferred) or contact Jiacheng Miao at [email protected] or Qiongshi Lu at [email protected].