GWASLab

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  • A handy Python-based toolkit for handling GWAS summary statistics (sumstats).
  • Each process is modularized and can be customized to your needs.
  • Sumstats-specific manipulations are designed as methods of a Python object, gwaslab.Sumstats.

Installation

install via pip

The latest version of GWASLab now supports Python 3.9, 3.10, 3.11, and 3.12.

pip install gwaslab

install in conda environment

Create a Python 3.9, 3.10, 3.11 or 3.12 environment and install gwaslab using pip:

conda env create -n gwaslab -c conda-forge python=3.12

conda activate gwaslab

pip install gwaslab

or create a new environment using yml file environment.yml

conda env create -n gwaslab -f environment.yml

install using docker (deprecated)

A docker file is available here for building local images.

Quick start

import gwaslab as gl

# load plink2 output
mysumstats = gl.Sumstats("sumstats.txt.gz", fmt="plink2")

# or load sumstats with auto mode (auto-detecting commonly used headers) 
# assuming ALT/A1 is EA, and frq is EAF
mysumstats = gl.Sumstats("sumstats.txt.gz", fmt="auto")

# or you can specify the columns:
mysumstats = gl.Sumstats("sumstats.txt.gz",
             snpid="SNP",
             chrom="CHR",
             pos="POS",
             ea="ALT",
             nea="REF",
             eaf="Frq",
             beta="BETA",
             se="SE",
             p="P",
             direction="Dir",
             n="N",
             build="19")

# manhattan and qq plot
mysumstats.plot_mqq()
...

Documentation and tutorials

Documentation and tutorials for GWASLab are avaiable at here.

Functions

Loading and Formatting

  • Loading sumstats by simply specifying the software name or format name, or specifying each column name.
  • Converting GWAS sumstats to specific formats:
    • LDSC / MAGMA / METAL / PLINK / SAIGE / REGENIE / MR-MEGA / GWAS-SSF / FUMA / GWAS-VCF / BED...
    • check available formats
  • Optional filtering of variants in commonly used genomic regions: Hapmap3 SNPs / High-LD regions / MHC region

Standardization & Normalization

  • Variant ID standardization
  • CHR and POS notation standardization
  • Variant POS and allele normalization
  • Genome build : Inference and Liftover

Quality control, Value conversion & Filtering

  • Statistics sanity check
  • Extreme value removal
  • Equivalent statistics conversion
    • BETA/SE , OR/OR_95L/OR_95U
    • P, Z, CHISQ, MLOG10P
  • Customizable value filtering

Harmonization

  • rsID assignment based on CHR, POS, and REF/ALT
  • CHR POS assignment based on rsID using a reference text file
  • Palindromic SNPs and indels strand inference using a reference VCF
  • Check allele frequency discrepancy using a reference VCF
  • Reference allele alignment using a reference genome sequence FASTA file

Visualization

  • Mqq plot: Manhattan plot, QQ plot or MQQ plot (with a bunch of customizable features including auto-annotate nearest gene names)
  • Miami plot: mirrored Manhattan plot
  • Brisbane plot: GWAS hits density plot
  • Regional plot: GWAS regional plot
  • Genetic correlation heatmap: ldsc-rg genetic correlation matrix
  • Scatter plot: variant effect size comparison
  • Scatter plot: allele frequency comparison
  • Scatter plot: trumpet plot (plot of MAF and effect size with power lines)

Visualization Examples

image image image image

Other Utilities

  • Read ldsc h2 or rg outputs directly as DataFrames (auto-parsing).
  • Extract lead variants given a sliding window size.
  • Extract novel loci given a list of known lead variants / or known loci obtained from GWAS Catalog.
  • Logging: keep a complete record of manipulations applied to the sumstats.
  • Sumstats summary: give you a quick overview of the sumstats.
  • ...

Issues

How to cite

  • GWASLab preprint: He, Y., Koido, M., Shimmori, Y., Kamatani, Y. (2023). GWASLab: a Python package for processing and visualizing GWAS summary statistics. Preprint at Jxiv, 2023-5. https://doi.org/10.51094/jxiv.370

Sample data used for tutorial

  • Sample GWAS data used in GWASLab is obtained from: http://jenger.riken.jp/ (Suzuki, Ken, et al. "Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population." Nature genetics 51.3 (2019): 379-386.).

Acknowledgement

Thanks to @sup3rgiu, @soumickmj and @gmauro for their contributions to the source codes.

Contacts

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for Yunye from gravatar.com Yunye

Credits

Author: Yunye He

License

GPL-3.0-only

Requires

Python >=3.9

Provides Extra

docs dev

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