GWASPipe

GWASPipe is a Python-based computational pipeline that streamlines the management and processing of genome-wide association study (GWAS) summary statistics. It automates complex workflows for quality control, standardization, and visualization, making multi-study harmonization more reproducible, efficient, and less error-prone.

Key Features

• Modular Architecture: Organized into reusable components (order_alleles, utils)
• Automated Workflows: Handles normalization, allele harmonization, filtering, and QC metrics
• Flexible Configuration: Uses YAML configuration files for customizable processing
• Comprehensive Reporting: Generates QC reports and visualizations
• High Performance: Leverages parallel processing and optimized algorithms

Table of Contents

• Requirements
• Installation
• Quick Start
• Configuration
• Module Documentation
• Contributing
• License

Requirements

• Python: 3.11 or higher
• Dependencies: See pyproject.toml for complete list
• Key Packages:
  • gwaslab - Core GWAS processing library
  • pandas, numpy - Data manipulation
  • click, cloup - Command-line interface
  • loguru - Advanced logging
  • ruamel.yaml - YAML configuration

Installation

Via Conda/Mamba (Recommended)

# Clone the repository
git clone https://github.com/ht-diva/gwaspipe.git
cd gwaspipe

# Create and activate environment
conda env create -f environment_docker.yml
conda activate gwaspipe

# Install package
make install

Via Docker

# Pull the Docker image
docker pull ghcr.io/ht-diva/gwaspipe:latest

# Run container
docker run -v $(pwd):/data ghcr.io/ht-diva/gwaspipe gwaspipe --help

Quick Start

Process GWAS summary statistics with a single command:

gwaspipe \
  -c examples/config_sumstats_harmonization.yml \
  -i examples/input_data.tsv.gz \
  -f regenie \
  -o results/

Module Documentation

Order Alleles Module

The gwaspipe.order_alleles module provides comprehensive allele ordering functionality:

from gwaspipe.order_alleles import order_alleles
import pandas as pd
from gwaslab.info.g_Log import Log

# Basic usage
df = pd.DataFrame({
    'CHR': [1, 2, 3],
    'POS': [1000, 2000, 3000],
    'EA': ['A', 'T', 'C'],
    'NEA': ['T', 'A', 'G'],
    'STATUS': [9999999, 9999999, 9999999]
})

log = Log()
result = order_alleles(df, log=log)

Configuration

GWASPipe uses YAML configuration files to define processing pipelines. See Getting Started Guide for detailed configuration examples.

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-feature
3. Make changes and add tests
4. Commit changes: git commit -m "Add feature description"
5. Push branch: git push origin feature/your-feature
6. Open a Pull Request

License

GWASPipe is released under the MIT License.