/MungeSumstats

Rapid standardisation and quality control of summary statistics from GWAS

Primary LanguageR

MungeSumstats: Standardise the format of GWAS summary statistics

Authors: Alan Murphy, Brian Schilder and Nathan Skene
Updated: Mar-09-2022

R build status R build status License: Artistic-2.0

Introduction

The MungeSumstats package is designed to facilitate the standardisation of GWAS summary statistics as utilised in our Nature Genetics paper1.

Overview

The package is designed to handle the lack of standardisation of output files by the GWAS community. The MRC IEU Open GWAS team have provided full summary statistics for >10k GWAS, which are API-accessible via the ieugwasr and gwasvcf packages. But these GWAS are only standardised in the sense that they are VCF format, and can be fully standardised with MungeSumstats.

MungeSumstats provides a framework to standardise the format for any GWAS summary statistics, including those in VCF format, enabling downstream integration and analysis. It addresses the most common discrepancies across summary statistic files, and offers a range of adjustable Quality Control (QC) steps.

Citation

If you use MungeSumstats, please cite the original authors of the GWAS as well as:

Alan E Murphy, Brian M Schilder, Nathan G Skene (2021) MungeSumstats: A Bioconductor package for the standardisation and quality control of many GWAS summary statistics. Bioinformatics, btab665, https://doi.org/10.1093/bioinformatics/btab665

Installing MungeSumstats

MungeSumstats is available on Bioconductor (≥v3.13). To install MungeSumstats on Bioconductor run:

if (!require("BiocManager")) install.packages("BiocManager")

BiocManager::install("MungeSumstats")

You can then load the package and data package:

library(MungeSumstats)

Note that for a number of the checks implored by MungeSumstats a reference genome is used. If your GWAS summary statistics file of interest relates to GRCh38, you will need to install SNPlocs.Hsapiens.dbSNP144.GRCh38 and BSgenome.Hsapiens.NCBI.GRCh38 from Bioconductor as follows:

BiocManager::install("SNPlocs.Hsapiens.dbSNP144.GRCh38")
BiocManager::install("BSgenome.Hsapiens.NCBI.GRCh38")

If your GWAS summary statistics file of interest relates to GRCh37, you will need to install SNPlocs.Hsapiens.dbSNP144.GRCh37 and BSgenome.Hsapiens.1000genomes.hs37d5 from Bioconductor as follows:

BiocManager::install("SNPlocs.Hsapiens.dbSNP144.GRCh37")
BiocManager::install("BSgenome.Hsapiens.1000genomes.hs37d5")

These may take some time to install and are not included in the package as some users may only need one of GRCh37/GRCh38. If you are unsure of the genome build, MungeSumstats can also infer this information from your data.

Getting started

See the Getting started vignette website for up-to-date instructions on usage.

See the OpenGWAS vignette website for information on how to use MungeSumstats to access, standardise and perform quality control on GWAS Summary Statistics from the MRC IEU Open GWAS Project.

If you have any problems please do file an Issue here on GitHub.

Future Enhancements

The MungeSumstats package aims to be able to handle the most common summary statistic file formats including VCF. If your file can not be formatted by MungeSumstats feel free to report the Issue on GitHub along with your summary statistics file header.

We also encourage people to edit the code to resolve their particular issues too and are happy to incorporate these through pull requests on github. If your summary statistic file headers are not recognised by MungeSumstats but correspond to one of

SNP, BP, CHR, A1, A2, P, Z, OR, BETA, LOG_ODDS, SIGNED_SUMSTAT, N, N_CAS, N_CON, 
NSTUDY, INFO or FRQ, 

Feel free to update the data("sumstatsColHeaders") following the approach in the data.R file and add your mapping. Then use a Pull Request on GitHub and we will incorporate this change into the package.

Contributors

We would like to acknowledge all those who have contributed to MungeSumstats development:

References

1. Nathan G. Skene, T. E. B., Julien Bryois. Genetic identification of brain cell types underlying schizophrenia. Nature Genetics (2018). doi:10.1038/s41588-018-0129-5