FiltpostQC-EU

Author: Etienne Kabongo Affiliation: Audrey Grant Lab – Computational Genomics, McGill University Contact: etienne.kabongo@mcgill.ca

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🧬 FiltpostQC-EU: Post-GWAS Filtering Tool for Regenie Results

FiltpostQC-EU is a robust and lightweight Python CLI tool developed to perform post-GWAS variant-level quality control on summary statistics generated by the REGENIE software. It filters variants based on an external European QC filter file and optionally formats the results for FUMA downstream annotation.

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✨ Key Features

• ✅ Input: Accepts Regenie merged summary statistics.
• 📊 Filtering: Retains only variants present in a QC-filter file (e.g., MAF, INFO, HWE).
• 🧪 Output: Supports filtered .txt output and/or FUMA-compatible files.
• 🧠 Bioinformatics-Ready: Designed for UK Biobank and large-scale GWAS datasets.
• ⚙️ CLI Interface: Fully command-line operable with detailed help menu.

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📦 Installation

pip install FiltpostQC-EU

Or from source:

git clone https://github.com/EtienneNtumba/FiltpostQC-EU.git
cd FiltpostQC-EU
pip install .

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🚀 Usage

filtpostqc-eu -i path/to/regenie_merged.txt \
              -f path/to/filter.tsv \
              -o filtered_output.txt \
              --fuma fuma_output.txt \
              --format both

Arguments

Argument	Description
-i, --input	Path to merged Regenie file (required)
-f, --filter	QC filter file (e.g., MAF/HWE/INFO based) (required)
-o, --output	Output for filtered Regenie results (default: filtered_output.txt)
--fuma	Output path for FUMA-formatted file (default: fuma_output.txt)
--format	Output format: filtered, fuma, or both (default: both)
--version	Print the tool version

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📂 Input Format

Merged Regenie File (P0_GWAS_qc_merged.txt)

Must contain columns like:

Name	Chr	Pos	Ref	Alt	Trait	Effect	Pval	Num_Cases	Num_Controls	Info

QC Filter File (EUR_QC_filter_regenie.tsv)

Must contain at least:

Name	Chr	Pos	Ref	Alt

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🧬 FUMA Output Format

The FUMA-compatible file will include:

• Chr, Pos, Name, Ref, Alt, Num_Cases, Num_Controls, Beta, Se, Pval

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👨‍🔬 About the Developer

This tool was created by Etienne Kabongo, computational biologist and research assistant in the Audrey Grant Lab at McGill University.

Our research focuses on large-scale genomic analyses, GWAS pipelines, and post-GWAS interpretation frameworks.

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📄 License

MIT License

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📬 Contact

For feedback, bug reports, or feature requests, feel free to contact:

• ✉️ Etienne Kabongo: etienne@example.com

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⭐ Acknowledgements

• Regenie: For efficient GWAS computation
• FUMA: For functional mapping and annotation

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"Reliable QC is the foundation of reproducible GWAS."