audreylab-summarypostqc 0.1.2

GWAS summary QC plotting tool (QQ & Manhattan plots)

AudreyLab-SummaryPostQC

AudreyLab-SummaryPostQC is a robust and lightweight Python command-line utility for visualizing genome-wide association study (GWAS) summary statistics. It provides clear, publication-ready QQ plots and Manhattan plots, and computes the genomic inflation factor (λGC) as a key quality control metric.

Developed by Etienne Kabongo, member of the Audrey Grant Lab, McGill University.
Source code: github.com/EtienneNtumba/audreylab-summarypostqc

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🧬 Use Case

This tool is particularly useful for:

• Post-QC visualization of GWAS results (e.g., after using REGENIE or PLINK)
• Checking for population stratification or inflation via λGC
• Generating Manhattan plots for initial signal discovery

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🔧 Features

• ✅ Parses post-QC GWAS summary files (TSV/CSV)
• ✅ Filters out invalid or missing P-values
• ✅ Calculates the genomic inflation factor λGC
• ✅ Generates high-resolution QQ plots
• ✅ Generates Manhattan plots with chromosome separation
• ✅ Supports custom output filenames and minimal dependencies

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📦 Installation

Install via PyPI:

pip install audreylab-summarypostqc

🚀 Usage

After installation, you can call the tool from the command line using:

audreylab-summarypostqc --input <your_file.txt> --out <prefix>

This generates the following files:

• <prefix>_qqplot.png
• <prefix>_manhattan.png

Example 1: Basic usage

audreylab-summarypostqc --input results/gwas_summary.txt --out results/plots

📈 Input File Format

Your input file should be a tab-separated (.tsv or .txt) file with the following required columns:

Column	Description
Chr	Chromosome number (1–22)
Pos	Base pair position
Pval	P-value of association

⚠️ Missing or invalid values will be excluded from the plots.

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🧪 Output

• 📊 QQ Plot: Observed vs. expected -log10(P) values, includes calculated λGC
• 🗺️ Manhattan Plot: P-values across all chromosomes, with genome-wide significance threshold line

audreylab-summarypostqc