# Import GWAS summary statistics from Open GWAS

Alan Murphy, Brian Schilder, and Nathan Skene

#### 2021-10-26

    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/Rtmp7CC56O/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/Rtmp7CC56O/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 4.1.1 (2021-08-10)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 20.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
    ## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
    ##
    ## 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
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] MungeSumstats_1.2.0 BiocStyle_2.22.0
    ##
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