/gwaslab

A Python package for handling and visualizing GWAS summary statistics. https://cloufield.github.io/gwaslab/ https://gwaslab.com/python-gwaslab/

Primary LanguagePythonMIT LicenseMIT

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gwaslab

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  • A handy python toolkit for handling GWAS 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.

Please check GWASLab document at https://cloufield.github.io/gwaslab/ Note: gwaslab is being updated very frequently for now. I will release the first stable version soon! Please stay tuned.

Install

pip install gwaslab==3.3.24

import gwaslab as gl
# load plink2 output
mysumstats = gl.Sumstats("t2d_bbj.txt.gz", fmt="plink2")

# or you can specify the columns:
mysumstats = gl.Sumstats("t2d_bbj.txt.gz",
             snpid="SNP",
             chrom="CHR",
             pos="POS",
             ea="ALT",
             nea="REF",
             neaf="Frq",
             beta="BETA",
             se="SE",
             p="P",
             direction="Dir",
             n="N",
             build="19")

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

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 : Manhattan plot
  • Brisbane plot: GWAS hits density plot
  • Regional plot : GWAS regional plot
  • Heatmap : ldsc-rg genetic correlation matrix
  • Scatter Plot : variant effect size comparison with sumstats
  • Scatter Plot : allele frequency comparison
  • Forest Plot : forest plots for meta-analysis of SNPs

Visualization Examples

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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 form GWAS Catalog.
  • Logging : keep a complete record of manipulations applied to the sumstats.
  • Sumstats summary: give you a quick overview of the sumstats.
  • ...

Requirements

Python >= 3.8
pySAM >0.18,<0.20
pyensembl >=2.2.3
scikit-allel
Biopython >= 1.79
liftover >= 1.1.13
pandas >= 1.3,<1.5
numpy >= 1.21.2
matplotlib >=3.5
seaborn >=0.11.1
scipy >=1.6.2
statsmodels > =0.13
adjustText

Citation

  • GWASLab manuscript is in preparation and will be released soon.
  • 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.).

Contacts