Contents

library(MungeSumstats)

MungeSumstats now offers high throughput query and import functionality to data from the MRC IEU Open GWAS Project.

1 Find GWAS datasets

#### Search for datasets ####
metagwas <- MungeSumstats::find_sumstats(traits = c("parkinson","alzheimer"), 
                                         min_sample_size = 1000)
head(metagwas,3)
ids <- (dplyr::arrange(metagwas, nsnp))$id  
##          id               trait group_name year    author
## 1 ieu-a-298 Alzheimer's disease     public 2013   Lambert
## 2   ieu-b-2 Alzheimer's disease     public 2019 Kunkle BW
## 3 ieu-a-297 Alzheimer's disease     public 2013   Lambert
##                                                                                                                                                                                                                                                                                                                    consortium
## 1                                                                                                                                                                                                                                                                                                                        IGAP
## 2 Alzheimer Disease Genetics Consortium (ADGC), European Alzheimer's Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer's Disease Consortium (GERAD/PERADES),
## 3                                                                                                                                                                                                                                                                                                                        IGAP
##                 sex population     unit     nsnp sample_size       build
## 1 Males and Females   European log odds    11633       74046 HG19/GRCh37
## 2 Males and Females   European       NA 10528610       63926 HG19/GRCh37
## 3 Males and Females   European log odds  7055882       54162 HG19/GRCh37
##   category                subcategory ontology mr priority     pmid sd
## 1  Disease Psychiatric / neurological       NA  1        1 24162737 NA
## 2   Binary Psychiatric / neurological       NA  1        0 30820047 NA
## 3  Disease Psychiatric / neurological       NA  1        2 24162737 NA
##                                                                      note ncase
## 1 Exposure only; Effect allele frequencies are missing; forward(+) strand 25580
## 2                                                                      NA 21982
## 3                Effect allele frequencies are missing; forward(+) strand 17008
##   ncontrol     N
## 1    48466 74046
## 2    41944 63926
## 3    37154 54162

2 Import full results

You can supply import_sumstats() with a list of as many OpenGWAS IDs as you want, but we’ll just give one to save time.

datasets <- MungeSumstats::import_sumstats(ids = "ieu-a-298",
                                           ref_genome = "GRCH37")

2.1 Summarise results

By default, import_sumstats results a named list where the names are the Open GWAS dataset IDs and the items are the respective paths to the formatted summary statistics.

print(datasets)
## $`ieu-a-298`
## [1] "/tmp/Rtmpg70GRi/ieu-a-298.tsv.gz"

You can easily turn this into a data.frame as well.

results_df <- data.frame(id=names(datasets), 
                         path=unlist(datasets))
print(results_df)
##                  id                             path
## ieu-a-298 ieu-a-298 /tmp/Rtmpg70GRi/ieu-a-298.tsv.gz

3 Import full results (parallel)

Optional: Speed up with multi-threaded download via axel.

datasets <- MungeSumstats::import_sumstats(ids = ids, 
                                           vcf_download = TRUE, 
                                           download_method = "axel", 
                                           nThread = max(2,future::availableCores()-2))

4 Further functionality

See the Getting started vignette for more information on how to use MungeSumstats and its functionality.

5 Session Info

utils::sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] MungeSumstats_1.12.2 BiocStyle_2.32.1    
## 
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