panGWAS

panGWAS is a pipeline for pangenome wide association studies. It reconstructs a pangenome from genomic assemblies, performs annotation and variant calling, estimates population structure, and models the association between genomic variants and variables of interest.

panGWAS is implemented as a python package and CLI tool, that can be run on any POSIX-based system (Linux, Mac). We additionally provide a nextflow pipeline for end-to-end analysis.

Please see the extended documentation at: https://phac-nml.github.io/pangwas/

Table of Contents

1. Why panGWAS?
2. Method
3. Install
4. Usage
5. Output
6. Credits

Why panGWAS?

panGWAS is distinct from other pangenome/GWAS workflows because it:

1. Provides end-to-end analysis, from genomic assemblies to GWAS results.
2. Includes both coding and non-coding sequences in the pangenome.
3. Ensures reproducible, deterministic results.
4. Offers both sensible defaults and extensive customization of underlying tools.
5. Keeps variants tightly linked to their annotations for easier interpretation at each stage.

Method

panGWAS performs the following analyses:

1. Annotate: Standardized annotation of genomes^* with bakta.
2. Cluster: Identify genomic regions with shared homology using MMseqs2.
3. Align: Concatenate and align clusters with mafft.
4. Variants: SNPs, presence absence, and structural variants.
5. Tree: Estimate a maximum-likelihood tree with IQ-TREE.
6. GWAS: Model the association between variants and traits with pyseer.
7. Plot: Manhattan plots, tree visualizations, heatmaps of signficant variants, QQ plots.

^* For non-bacterial genomes, you will need to bring your own gff annotations.

Install

Conda

❗ Pending release of the bioconda recipe.

conda create -n pangwas -c conda-forge -c bioconda pangwas

Docker

❗ Pending release of the bioconda recipe.

docker pull quay.io/biocontainers/pangwas:latest

Nextflow

nextflow pull phac-nml/pangwas

Source

Install pangwas from the github repository:

micromamba env create -f environment.yml -n pangwas
micromamba activate pangwas
pip install .

Build the Docker image from the github repository:

docker build -t phac-nml/pangwas:latest .

Usage

For more information, please see the Manual and Pipeline Documentation.

CLI

Individual commands can be run via the command-line interface:

pangwas extract --gff sample1.gff3
pangwas extract --gff sample2.gff3
pangwas collect --tsv sample1.tsv sample2.tsv
pangwas cluster --fasta sequences.fasta
...

For an end-to-end example using the CLI, please see the Command-Line Interface example.

Python

Individual commands can be run as python functions:

import pangwas

pangwas.extract(gff="sample1.gff3")
pangwas.extract(gff="sample2.gff3")
pangwas.collect(tsv=["sample1.tsv", "sample2.tsv"])
pangwas.cluster(fasta="sequences.fasta")
...

For an end-to-end example using python, please see the Python Package example.

Nextflow

An end-to-end pipeline is provided via nextflow:

nextflow run phac-nml/pangwas -profile test

For more examples, please see the tutorials. We recommend the Pyseer tutorial, which automates and reproduces the results from the penicillin resistance GWAS created by the pyseer authors:

Output

1. Plots: PNG and SVG files under the manhattan and heatmap directories.

   ❗Tip: Open the SVG in Edge or Firefox, to get hovertext!

   Manhattan	Heatmap	QQ Plot

2. GWAS Tables: Statistic results per variant.

   variant	af	filter-pvalue	lrt-pvalue	beta	beta-std-err	variant_h2	notes	-log10(p)	bonferroni	…
   pbpX|snp:G761A	3.78E-01	6.12E-94	3.01E-25	7.42E-01	6.82E-02	4.05E-01		24.521433504406158	1.180414561594032e-06	…
   pbpX|snp:T1077C	3.85E-01	1.11E-95	1.43E-24	7.23E-01	6.76E-02	4.00E-01		23.844663962534938	1.180414561594032e-06	…

3. Trees: We recommend Arborview for interactive visualization of the newick files!

   

   arborview

4. Pangenome: We recommend Bandage for interactive visualization of the pangenome graph!

   • GFA files can be found under summarize for both the full and linearized version of the pangenome.

   

And much more!

Credits

panGWAS is built and maintained by Katherine Eaton at the National Microbiology Laboratory (NML) of the Public Health Agency of Canada (PHAC).

If you have any questions, please email ktmeaton@gmail.com.

Katherine Eaton 💻 📖 🎨 🤔 🚇 🚧

Contributors

This project follows the all-contributors specification (emoji key). Contributions of any kind welcome!

Special thanks go to the developers of PPanGGOLiN. The Cluster and Align steps are heavily inspired by PPanGGOLiN, and in fact, panGWAS uses a modified version of PPanGGOLiN’s defragmentation algorithm.

Guillaume Gautreau 🎨 🤔

Jean Mainguy 🎨 🤔

Jérôme Arnoux 🎨 🤔

Jérôme Arnoux 🎨 🤔

Thanks go to the following people, who participated in the development of panGWAS:

Irene Martin 🤔 🔣	Alyssa Golden 🤔 🔣	Shelley Peterson 🤔 🔣	Natalie Knox 🤔	Andrea Tyler 🤔
Darian Hole 👀 ⚠️ 🛡️	Connor Chato 🔬 🤔	Amber Papineau 🔣 🔬	Molly Pratt 🎨	Kirsten Palmier 🔬
Adrian Azetner 🤔	Ana Duggan 🤔 👀 📆	Emily Haverhold 📆

License

Copyright 2025 Government of Canada

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this work except in compliance with the License. You may obtain a copy of the License at:

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.