a snapshot of output of ieugwasr::gwasinfo()

gwidf_2021_01_30

Format

data.frame

Note

Includes 'batchcode' computed by code commented out in example

Examples

# batchcode = strsplit(gwidf_2021_01_30$id, "-")
# batchcode = sapply(batchcode,function(x) paste0(x[1], "-", x[2]))
head(gwidf_2021_01_30)
#>                       id                          trait     coverage
#> 1               ieu-b-73      Alcoholic drinks per week whole genome
#> 2               ieu-b-72 Urinary sodium-potassium ratio whole genome
#> 3 eqtl-a-ENSG00000267355                ENSG00000267355         <NA>
#> 4 eqtl-a-ENSG00000119707                ENSG00000119707         <NA>
#> 5 eqtl-a-ENSG00000145632                ENSG00000145632         <NA>
#> 6 eqtl-a-ENSG00000144791                ENSG00000144791         <NA>
#>   imputation_panel group_name year mr  author
#> 1              HRC     public 2019  1  Liu, M
#> 2             <NA>     public 2020  1 Zanetti
#> 3             <NA>     public 2018  1  Vosa U
#> 4             <NA>     public 2018  1  Vosa U
#> 5             <NA>     public 2018  1  Vosa U
#> 6             <NA>     public 2018  1  Vosa U
#>                                                   consortium               sex
#> 1 GWAS and Sequencing Consortium of Alcohol and Nicotine use Males and Females
#> 2                                                       <NA> Males and Females
#> 3                                                         NA Males and Females
#> 4                                                         NA Males and Females
#> 5                                                         NA Males and Females
#> 6                                                         NA Males and Females
#>                  qc_prior_to_upload     pmid population sample_size     nsnp
#> 1 mapped rsids to genomic position  30643251   European      335394 11887865
#> 2                              <NA> 32008434   European      326938 19420026
#> 3                              <NA>       NA   European        4677    18842
#> 4                              <NA>       NA   European       30935    19455
#> 5                              <NA>       NA   European       31684    19623
#> 6                              <NA>       NA   European       31470    18827
#>         build                                    study_design
#> 1 HG19/GRCh37 Meta-analysis of cohort/cross-sectional studies
#> 2 HG19/GRCh37                    Cohort/cross-sectional study
#> 3 HG19/GRCh37                                            <NA>
#> 4 HG19/GRCh37                                            <NA>
#> 5 HG19/GRCh37                                            <NA>
#> 6 HG19/GRCh37                                            <NA>
#>             covariates   category subcategory                               doi
#> 1         ancestry PCs Continuous Behavioural         10.1038/s41588-018-0307-5
#> 2 age, sex, batch, PCs Continuous   Biomarker 10.1161/hypertensionaha.119.14028
#> 3                 <NA>         NA          NA                              <NA>
#> 4                 <NA>         NA          NA                              <NA>
#> 5                 <NA>         NA          NA                              <NA>
#> 6                 <NA>         NA          NA                              <NA>
#>   note priority unit ontology sd ncase ncontrol batchcode
#> 1 <NA>       NA <NA>     <NA> NA    NA       NA     ieu-b
#> 2 <NA>       NA <NA>     <NA> NA    NA       NA     ieu-b
#> 3   NA        0   NA       NA NA    NA       NA    eqtl-a
#> 4   NA        0   NA       NA NA    NA       NA    eqtl-a
#> 5   NA        0   NA       NA NA    NA       NA    eqtl-a
#> 6   NA        0   NA       NA NA    NA       NA    eqtl-a