gwaslab 3.3.19

A collection of handy tools for GWAS SumStats

gwaslab

• 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 (within this year)! Please stay tuned.

Install

pip install gwaslab==3.3.18

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
• Customized 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

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

A manuscript is in preparation and will be released soon. Sample GWAS data used in gwaslab is obtained from: http://jenger.riken.jp/ Sample GWAS data citation : 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

• Github: https://github.com/Cloufield/gwaslab
• Blog (in Chinese): https://gwaslab.com/
• Email: [e-mail]
• Stats: https://pypistats.org/packages/gwaslab