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/RtmpAKpoKE/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/RtmpAKpoKE/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 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-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] XVector_0.42.0       GenomicRanges_1.54.0 S4Vectors_0.40.1    
## [4] IRanges_2.36.0       MungeSumstats_1.10.1 BiocStyle_2.30.0    
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.0                           
##  [2] dplyr_1.1.3                                
##  [3] blob_1.2.4                                 
##  [4] filelock_1.0.2                             
##  [5] R.utils_2.12.2                             
##  [6] Biostrings_2.70.1                          
##  [7] bitops_1.0-7                               
##  [8] fastmap_1.1.1                              
##  [9] RCurl_1.98-1.12                            
## [10] BiocFileCache_2.10.1                       
## [11] VariantAnnotation_1.48.0                   
## [12] GenomicAlignments_1.38.0                   
## [13] XML_3.99-0.14                              
## [14] digest_0.6.33                              
## [15] lifecycle_1.0.3                            
## [16] KEGGREST_1.42.0                            
## [17] RSQLite_2.3.1                              
## [18] googleAuthR_2.0.1                          
## [19] magrittr_2.0.3                             
## [20] compiler_4.3.1                             
## [21] rlang_1.1.1                                
## [22] sass_0.4.7                                 
## [23] progress_1.2.2                             
## [24] tools_4.3.1                                
## [25] utf8_1.2.4                                 
## [26] yaml_2.3.7                                 
## [27] data.table_1.14.8                          
## [28] rtracklayer_1.62.0                         
## [29] knitr_1.44                                 
## [30] prettyunits_1.2.0                          
## [31] S4Arrays_1.2.0                             
## [32] curl_5.1.0                                 
## [33] bit_4.0.5                                  
## [34] DelayedArray_0.28.0                        
## [35] xml2_1.3.5                                 
## [36] abind_1.4-5                                
## [37] BiocParallel_1.36.0                        
## [38] BiocGenerics_0.48.0                        
## [39] R.oo_1.25.0                                
## [40] grid_4.3.1                                 
## [41] stats4_4.3.1                               
## [42] fansi_1.0.5                                
## [43] biomaRt_2.58.0                             
## [44] SummarizedExperiment_1.32.0                
## [45] cli_3.6.1                                  
## [46] rmarkdown_2.25                             
## [47] crayon_1.5.2                               
## [48] generics_0.1.3                             
## [49] BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1
## [50] httr_1.4.7                                 
## [51] rjson_0.2.21                               
## [52] DBI_1.1.3                                  
## [53] cachem_1.0.8                               
## [54] stringr_1.5.0                              
## [55] zlibbioc_1.48.0                            
## [56] assertthat_0.2.1                           
## [57] parallel_4.3.1                             
## [58] AnnotationDbi_1.64.0                       
## [59] BiocManager_1.30.22                        
## [60] restfulr_0.0.15                            
## [61] matrixStats_1.0.0                          
## [62] vctrs_0.6.4                                
## [63] Matrix_1.6-1.1                             
## [64] jsonlite_1.8.7                             
## [65] bookdown_0.36                              
## [66] hms_1.1.3                                  
## [67] bit64_4.0.5                                
## [68] GenomicFiles_1.38.0                        
## [69] GenomicFeatures_1.54.0                     
## [70] jquerylib_0.1.4                            
## [71] glue_1.6.2                                 
## [72] codetools_0.2-19                           
## [73] stringi_1.7.12                             
## [74] GenomeInfoDb_1.38.0                        
## [75] BiocIO_1.12.0                              
## [76] tibble_3.2.1                               
## [77] pillar_1.9.0                               
## [78] SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.24   
## [79] rappdirs_0.3.3                             
## [80] htmltools_0.5.6.1                          
## [81] GenomeInfoDbData_1.2.11                    
## [82] BSgenome_1.70.0                            
## [83] R6_2.5.1                                   
## [84] dbplyr_2.4.0                               
## [85] evaluate_0.22                              
## [86] lattice_0.22-5                             
## [87] Biobase_2.62.0                             
## [88] R.methodsS3_1.8.2                          
## [89] png_0.1-8                                  
## [90] Rsamtools_2.18.0                           
## [91] gargle_1.5.2                               
## [92] memoise_2.0.1                              
## [93] bslib_0.5.1                                
## [94] SparseArray_1.2.0                          
## [95] xfun_0.40                                  
## [96] fs_1.6.3                                   
## [97] MatrixGenerics_1.14.0                      
## [98] pkgconfig_2.0.3