Import to CAMERA from local data

library(CAMeRa)

You can supply to CAMERA from text files or from OpenGWAS (currently not a mixture of both, though you can download the studies in OpenGWAS to be used as raw files).

Generating the data manually

You need to supply the following data:

  • instrument_raw = a data frame of pooled instruments across all ancestries, that has been extracted from each ancestry for the exposure traits. Optionally also provide the same for the outcome traits.
  • instrument_outcome = instruments in instrument_raw extracted from the outcome datasets
  • instrument_regions = named list of data frames of length number of unique instruments in instrument_raw. Names of each item are the instruments. Each item is a list of regional extracts around the instrument from each population exposure study.
  • instrument_outcome_regions = as above but for the outcome datasets.

Examples of these datasets can be seen in the following file:

load(system.file(package="CAMeRa", "extdata/example-local.rdata"))
instrument_raw
#> # A tibble: 45 × 14
#>      chr  position   eaf    beta      se        p ea    nea   rsid   trait pop  
#>    <int>     <dbl> <dbl>   <dbl>   <dbl>    <dbl> <chr> <chr> <chr>  <chr> <chr>
#>  1     3 122285218 0.224  0.0204 0.00583 4.69e- 4 C     CT    3:122… LDL   AFR  
#>  2     3 122285218 0.438  0.0198 0.00542 2.52e- 4 C     CT    3:122… LDL   EAS  
#>  3     3 122285218 0.302  0.0190 0.00166 2.66e-30 C     CT    3:122… LDL   EUR  
#>  4     3 122285218 0.237  0.0139 0.00808 8.45e- 2 C     CT    3:122… LDL   AMR  
#>  5     3 122285218 0.296  0.0154 0.00778 4.75e- 2 C     CT    3:122… LDL   SAS  
#>  6     5 139567696 0.244 -0.0127 0.0172  4.62e- 1 G     T     5:139… LDL   AFR  
#>  7     5 139567696 0.213 -0.0130 0.00192 1.10e-11 G     T     5:139… LDL   EUR  
#>  8     6  27067657 0.989  0.0172 0.0222  4.37e- 1 A     T     6:270… LDL   AFR  
#>  9     6  27067657 0.924  0.0325 0.00268 7.70e-34 A     T     6:270… LDL   EUR  
#> 10     6  27067657 0.978  0.0558 0.0242  2.12e- 2 A     T     6:270… LDL   AMR  
#> # ℹ 35 more rows
#> # ℹ 3 more variables: id <chr>, nstudies <int>, target_trait <chr>
instrument_outcome
#> # A tibble: 15 × 14
#>      chr  position    eaf    beta     se       p ea    nea   rsid    trait pop  
#>    <int>     <dbl>  <dbl>   <dbl>  <dbl>   <dbl> <chr> <chr> <chr>   <chr> <chr>
#>  1     5 139567696 0.230   0.0069 0.008  0.387   G     T     5:1395… Stro… EUR  
#>  2     5 139567696 0.247  -0.0483 0.0385 0.209   G     T     5:1395… Stro… AFR  
#>  3     5 139567696 0.262   0.0183 0.0831 0.826   G     T     5:1395… Stro… AMR  
#>  4     6  27067657 0.928  -0.0011 0.0132 0.934   A     T     6:2706… Stro… EUR  
#>  5     6  27067657 0.975   0.212  0.219  0.333   A     T     6:2706… Stro… AMR  
#>  6     6 161010118 0.936  -0.0427 0.0136 0.00177 A     G     6:1610… Stro… EUR  
#>  7     6 161010118 0.963  -0.0934 0.171  0.584   A     G     6:1610… Stro… AMR  
#>  8     7 137562744 0.0201  0.0259 0.0602 0.668   C     G     7:1375… Stro… EAS  
#>  9     7 137562744 0.132   0.0945 0.0417 0.0234  C     G     7:1375… Stro… AFR  
#> 10     7 143092269 0.0635  0.0158 0.014  0.257   A     G     7:1430… Stro… EUR  
#> 11     7 143092269 0.0781  0.025  0.0765 0.743   A     G     7:1430… Stro… SAS  
#> 12    16  72912880 0.957   0.0117 0.0194 0.547   A     G     16:729… Stro… EUR  
#> 13    16  72912880 0.982  -0.225  0.183  0.219   A     G     16:729… Stro… SAS  
#> 14    19  33864260 0.0307  0.0046 0.0192 0.813   A     G     19:338… Stro… EUR  
#> 15    19  33864260 0.0521 -0.0669 0.0924 0.469   A     G     19:338… Stro… SAS  
#> # ℹ 3 more variables: id <chr>, nstudies <int>, target_trait <chr>
instrument_regions[[1]]
#> $`LDL AFR`
#> # A tibble: 2,827 × 12
#>      chr  position    eaf     beta      se     p ea    nea   rsid    trait pop  
#>    <int>     <dbl>  <dbl>    <dbl>   <dbl> <dbl> <chr> <chr> <chr>   <chr> <chr>
#>  1     3 121981372 0.984   0.0232  0.0204  0.256 C     T     3:1219… LDL   AFR  
#>  2     3 121981609 0.0346  0.0189  0.0134  0.158 A     G     3:1219… LDL   AFR  
#>  3     3 121981619 0.146   0.00208 0.00690 0.763 G     T     3:1219… LDL   AFR  
#>  4     3 121981629 0.978  -0.00525 0.0167  0.754 A     G     3:1219… LDL   AFR  
#>  5     3 121981835 0.823  -0.00165 0.00633 0.794 C     G     3:1219… LDL   AFR  
#>  6     3 121981836 0.172   0.00212 0.00645 0.742 A     G     3:1219… LDL   AFR  
#>  7     3 121982002 0.987  -0.00833 0.0215  0.699 C     T     3:1219… LDL   AFR  
#>  8     3 121982338 0.178   0.00148 0.00632 0.814 G     T     3:1219… LDL   AFR  
#>  9     3 121983322 0.818  -0.00143 0.00626 0.819 C     T     3:1219… LDL   AFR  
#> 10     3 121983551 0.941  -0.00517 0.0108  0.631 C     T     3:1219… LDL   AFR  
#> # ℹ 2,817 more rows
#> # ℹ 1 more variable: id <chr>
#> 
#> $`LDL AMR`
#> # A tibble: 2,084 × 12
#>      chr  position    eaf     beta      se       p ea    nea   rsid  trait pop  
#>    <int>     <dbl>  <dbl>    <dbl>   <dbl>   <dbl> <chr> <chr> <chr> <chr> <chr>
#>  1     3 121981215 0.855   0.0303  0.0108  0.00521 A     G     3:12… LDL   AMR  
#>  2     3 121981609 0.106   0.0160  0.0111  0.15    A     G     3:12… LDL   AMR  
#>  3     3 121981619 0.2     0.0148  0.00988 0.135   G     T     3:12… LDL   AMR  
#>  4     3 121981835 0.969  -0.0206  0.0235  0.381   C     G     3:12… LDL   AMR  
#>  5     3 121981836 0.203   0.0119  0.00983 0.227   A     G     3:12… LDL   AMR  
#>  6     3 121982338 0.0316  0.0184  0.0234  0.432   G     T     3:12… LDL   AMR  
#>  7     3 121983322 0.968  -0.0192  0.0232  0.408   C     T     3:12… LDL   AMR  
#>  8     3 121983607 0.76    0.0192  0.00919 0.0371  C     T     3:12… LDL   AMR  
#>  9     3 121983805 0.795  -0.0150  0.00850 0.0775  C     T     3:12… LDL   AMR  
#> 10     3 121984020 0.977   0.00998 0.0233  0.669   C     T     3:12… LDL   AMR  
#> # ℹ 2,074 more rows
#> # ℹ 1 more variable: id <chr>
#> 
#> $`LDL EAS`
#> # A tibble: 1,646 × 12
#>      chr  position    eaf    beta      se       p ea    nea   rsid   trait pop  
#>    <int>     <dbl>  <dbl>   <dbl>   <dbl>   <dbl> <chr> <chr> <chr>  <chr> <chr>
#>  1     3 121981609 0.0257 -0.0237 0.0160  0.14    A     G     3:121… LDL   EAS  
#>  2     3 121981835 0.983  -0.0426 0.0195  0.0294  C     G     3:121… LDL   EAS  
#>  3     3 121982338 0.017   0.0426 0.0195  0.0289  G     T     3:121… LDL   EAS  
#>  4     3 121983322 0.983  -0.0425 0.0195  0.0288  C     T     3:121… LDL   EAS  
#>  5     3 121983607 0.975   0.0293 0.0163  0.0727  C     T     3:121… LDL   EAS  
#>  6     3 121984021 0.417   0.0154 0.00512 0.00261 A     G     3:121… LDL   EAS  
#>  7     3 121984792 0.0253 -0.0281 0.0158  0.0758  A     G     3:121… LDL   EAS  
#>  8     3 121985083 0.0253 -0.0282 0.0158  0.0748  A     AG    3:121… LDL   EAS  
#>  9     3 121985131 0.984  -0.0435 0.0200  0.0296  A     G     3:121… LDL   EAS  
#> 10     3 121985345 0.539  -0.0150 0.00503 0.0029  C     T     3:121… LDL   EAS  
#> # ℹ 1,636 more rows
#> # ℹ 1 more variable: id <chr>
#> 
#> $`LDL EUR`
#> # A tibble: 2,233 × 12
#>      chr  position    eaf     beta      se       p ea    nea   rsid  trait pop  
#>    <int>     <dbl>  <dbl>    <dbl>   <dbl>   <dbl> <chr> <chr> <chr> <chr> <chr>
#>  1     3 121981609 0.129   6.03e-3 0.00208 3.71e-3 A     G     3:12… LDL   EUR  
#>  2     3 121981619 0.356   6.02e-4 0.00146 6.81e-1 G     T     3:12… LDL   EUR  
#>  3     3 121981835 0.955   6.87e-3 0.00342 4.46e-2 C     G     3:12… LDL   EUR  
#>  4     3 121981836 0.356   6.09e-4 0.00146 6.78e-1 A     G     3:12… LDL   EUR  
#>  5     3 121982338 0.0448 -6.78e-3 0.00342 4.75e-2 G     T     3:12… LDL   EUR  
#>  6     3 121983322 0.955   6.80e-3 0.00342 4.65e-2 C     T     3:12… LDL   EUR  
#>  7     3 121983607 0.86    7.87e-3 0.00202 1   e-4 C     T     3:12… LDL   EUR  
#>  8     3 121983674 0.972  -1.62e-2 0.00440 2.38e-4 A     G     3:12… LDL   EUR  
#>  9     3 121983805 0.644  -6.39e-4 0.00146 6.62e-1 C     T     3:12… LDL   EUR  
#> 10     3 121984020 0.976  -1.46e-3 0.00464 7.53e-1 C     T     3:12… LDL   EUR  
#> # ℹ 2,223 more rows
#> # ℹ 1 more variable: id <chr>
#> 
#> $`LDL SAS`
#> # A tibble: 1,875 × 12
#>      chr  position    eaf      beta      se      p ea    nea   rsid  trait pop  
#>    <int>     <dbl>  <dbl>     <dbl>   <dbl>  <dbl> <chr> <chr> <chr> <chr> <chr>
#>  1     3 121981609 0.193  -0.000939 0.00906 0.917  A     G     3:12… LDL   SAS  
#>  2     3 121981619 0.234   0.00438  0.00837 0.601  G     T     3:12… LDL   SAS  
#>  3     3 121981835 0.952   0.0186   0.0166  0.26   C     G     3:12… LDL   SAS  
#>  4     3 121981836 0.234   0.00416  0.00836 0.619  A     G     3:12… LDL   SAS  
#>  5     3 121982338 0.0479 -0.0177   0.0164  0.28   G     T     3:12… LDL   SAS  
#>  6     3 121983322 0.952   0.0177   0.0164  0.281  C     T     3:12… LDL   SAS  
#>  7     3 121983607 0.85    0.00333  0.00985 0.735  C     T     3:12… LDL   SAS  
#>  8     3 121983805 0.766  -0.00409  0.00835 0.624  C     T     3:12… LDL   SAS  
#>  9     3 121984020 0.985   0.0239   0.0293  0.415  C     T     3:12… LDL   SAS  
#> 10     3 121984021 0.0591  0.0321   0.0150  0.0316 A     G     3:12… LDL   SAS  
#> # ℹ 1,865 more rows
#> # ℹ 1 more variable: id <chr>
instrument_outcome_regions[[1]]
#> $`Stroke African American or Afro-Caribbean`
#> # A tibble: 1,935 × 12
#>      chr  position    eaf    beta     se      p ea    nea   rsid     trait pop  
#>    <int>     <dbl>  <dbl>   <dbl>  <dbl>  <dbl> <chr> <chr> <chr>    <chr> <chr>
#>  1     3 122289921 0.387  -0.0073 0.0302 0.808  C     T     3:12228… Stro… AFR  
#>  2     3 122108718 0.345  -0.0224 0.0297 0.451  G     T     3:12210… Stro… AFR  
#>  3     3 122356077 0.153   0.0322 0.0422 0.445  G     T     3:12235… Stro… AFR  
#>  4     3 122125052 0.309   0.0319 0.0306 0.297  A     G     3:12212… Stro… AFR  
#>  5     3 122467637 0.0267  0.176  0.103  0.0864 C     T     3:12246… Stro… AFR  
#>  6     3 122297742 0.323  -0.0286 0.0303 0.344  A     G     3:12229… Stro… AFR  
#>  7     3 122365670 0.885  -0.0087 0.0479 0.855  C     G     3:12236… Stro… AFR  
#>  8     3 122214612 0.0976 -0.0716 0.0484 0.139  C     T     3:12221… Stro… AFR  
#>  9     3 122201610 0.0948 -0.0072 0.0483 0.882  C     G     3:12220… Stro… AFR  
#> 10     3 122376850 0.691   0.0424 0.0305 0.165  A     G     3:12237… Stro… AFR  
#> # ℹ 1,925 more rows
#> # ℹ 1 more variable: id <chr>
#> 
#> $`Stroke Hispanic or Latin American`
#> # A tibble: 1,566 × 12
#>      chr  position    eaf    beta     se      p ea    nea   rsid     trait pop  
#>    <int>     <dbl>  <dbl>   <dbl>  <dbl>  <dbl> <chr> <chr> <chr>    <chr> <chr>
#>  1     3 122289921 0.382  -0.101  0.0794 0.204  C     T     3:12228… Stro… AMR  
#>  2     3 122108718 0.174   0.0473 0.0737 0.521  G     T     3:12210… Stro… AMR  
#>  3     3 122125052 0.295  -0.0998 0.0586 0.0887 A     G     3:12212… Stro… AMR  
#>  4     3 122297742 0.316   0.125  0.0642 0.0522 A     G     3:12229… Stro… AMR  
#>  5     3 122365670 0.835   0.0308 0.0833 0.711  C     G     3:12236… Stro… AMR  
#>  6     3 122214612 0.0209  0.111  0.256  0.664  C     T     3:12221… Stro… AMR  
#>  7     3 122201610 0.132  -0.0104 0.0806 0.897  C     G     3:12220… Stro… AMR  
#>  8     3 122034854 0.426  -0.0195 0.0563 0.729  A     G     3:12203… Stro… AMR  
#>  9     3 122268506 0.118  -0.0509 0.0873 0.560  C     T     3:12226… Stro… AMR  
#> 10     3 122273816 0.516  -0.0404 0.0756 0.593  A     G     3:12227… Stro… AMR  
#> # ℹ 1,556 more rows
#> # ℹ 1 more variable: id <chr>
#> 
#> $`Stroke East Asian`
#> # A tibble: 1,351 × 12
#>      chr  position    eaf    beta     se       p ea    nea   rsid    trait pop  
#>    <int>     <dbl>  <dbl>   <dbl>  <dbl>   <dbl> <chr> <chr> <chr>   <chr> <chr>
#>  1     3 122289921 0.601  -0.0027 0.011  0.807   C     T     3:1222… Stro… EAS  
#>  2     3 122024691 0.0338 -0.0287 0.03   0.338   A     T     3:1220… Stro… EAS  
#>  3     3 122108718 0.0281 -0.0972 0.0327 0.00299 G     T     3:1221… Stro… EAS  
#>  4     3 122125052 0.588   0.0146 0.0096 0.130   A     G     3:1221… Stro… EAS  
#>  5     3 122297742 0.250  -0.0051 0.011  0.644   A     G     3:1222… Stro… EAS  
#>  6     3 122201610 0.0788 -0.0304 0.0205 0.138   C     G     3:1222… Stro… EAS  
#>  7     3 122376850 0.346   0.0151 0.0101 0.135   A     G     3:1223… Stro… EAS  
#>  8     3 122034854 0.099   0.0019 0.016  0.905   A     G     3:1220… Stro… EAS  
#>  9     3 122268506 0.439   0.0193 0.0097 0.0479  C     T     3:1222… Stro… EAS  
#> 10     3 122273816 0.353  -0.0072 0.0101 0.472   A     G     3:1222… Stro… EAS  
#> # ℹ 1,341 more rows
#> # ℹ 1 more variable: id <chr>
#> 
#> $`Stroke European`
#> # A tibble: 1,489 × 12
#>      chr  position   eaf    beta     se      p ea    nea   rsid      trait pop  
#>    <int>     <dbl> <dbl>   <dbl>  <dbl>  <dbl> <chr> <chr> <chr>     <chr> <chr>
#>  1     3 122289921 0.286  0.0039 0.0081 0.629  C     T     3:122289… Stro… EUR  
#>  2     3 122108718 0.143 -0.0035 0.0094 0.709  G     T     3:122108… Stro… EUR  
#>  3     3 122125052 0.437 -0.0122 0.0065 0.059  A     G     3:122125… Stro… EUR  
#>  4     3 122297742 0.24  -0.0084 0.0079 0.286  A     G     3:122297… Stro… EUR  
#>  5     3 122365670 0.757 -0.0168 0.0075 0.0264 C     G     3:122365… Stro… EUR  
#>  6     3 122201610 0.138 -0.0005 0.0096 0.960  C     G     3:122201… Stro… EUR  
#>  7     3 122376850 0.748  0.0103 0.0076 0.175  A     G     3:122376… Stro… EUR  
#>  8     3 122034854 0.281  0.0026 0.0077 0.739  A     G     3:122034… Stro… EUR  
#>  9     3 122268506 0.165 -0.0018 0.0086 0.837  C     T     3:122268… Stro… EUR  
#> 10     3 122273816 0.430  0.0097 0.0066 0.145  A     G     3:122273… Stro… EUR  
#> # ℹ 1,479 more rows
#> # ℹ 1 more variable: id <chr>
#> 
#> $`Stroke South Asian`
#> # A tibble: 561 × 12
#>      chr  position    eaf    beta     se      p ea    nea   rsid     trait pop  
#>    <int>     <dbl>  <dbl>   <dbl>  <dbl>  <dbl> <chr> <chr> <chr>    <chr> <chr>
#>  1     3 122289921 0.305  -0.066  0.0504 0.190  C     T     3:12228… Stro… SAS  
#>  2     3 122356077 0.0455  0.0271 0.109  0.804  G     T     3:12235… Stro… SAS  
#>  3     3 122297445 0.802  -0.0272 0.0512 0.595  A     T     3:12229… Stro… SAS  
#>  4     3 122292451 0.0652  0.135  0.102  0.185  A     G     3:12229… Stro… SAS  
#>  5     3 122321980 0.0313  0.256  0.120  0.0339 A     G     3:12232… Stro… SAS  
#>  6     3 122177260 0.953   0.0188 0.103  0.855  C     T     3:12217… Stro… SAS  
#>  7     3 122147542 0.0795  0.0231 0.0749 0.757  A     G     3:12214… Stro… SAS  
#>  8     3 122201061 0.0566 -0.0094 0.0958 0.922  A     C     3:12220… Stro… SAS  
#>  9     3 122089025 0.927  -0.019  0.0774 0.807  A     G     3:12208… Stro… SAS  
#> 10     3 122408098 0.0196  0.0389 0.147  0.791  C     T     3:12240… Stro… SAS  
#> # ℹ 551 more rows
#> # ℹ 1 more variable: id <chr>

For example scripts on how these data were generated see https://github.com/yoonsucho/CAMERA_analysis/tree/main/scripts/ldl_stroke_analysis

Generating the data using CAMERA_local

We have developed a separate set of functions to organise data from text files to generate the data above.

metadata <- readRDS(system.file(package="CAMeRa", "extdata/example-metadata.rds"))
metadata
#> # A tibble: 10 × 16
#>    what     pop   trait  id          n rsid_col chr_col pos_col eaf_col beta_col
#>    <chr>    <chr> <chr>  <chr>   <dbl>    <dbl>   <dbl>   <dbl>   <dbl>    <dbl>
#>  1 exposure AFR   LDL    LDL A… 9.11e4        1       2       3       8        9
#>  2 exposure EAS   LDL    LDL E… 7.98e4        1       2       3       8        9
#>  3 exposure EUR   LDL    LDL E… 9.00e5        1       2       3       8        9
#>  4 exposure AMR   LDL    LDL A… 4.48e4        1       2       3       8        9
#>  5 exposure SAS   LDL    LDL S… 3.04e4        1       2       3       8        9
#>  6 outcome  EUR   Stroke Strok… 1.31e6       NA       1       2       3        4
#>  7 outcome  EAS   Stroke Strok… 2.65e5       NA       1       2       3        4
#>  8 outcome  AFR   Stroke Strok… 2.40e4       NA       1       2       3        4
#>  9 outcome  AMR   Stroke Strok… 5.66e3       NA       1       2       3        4
#> 10 outcome  SAS   Stroke Strok… 1.13e4       NA       1       2       3        4
#> # ℹ 6 more variables: se_col <dbl>, pval_col <dbl>, ea_col <dbl>, oa_col <dbl>,
#> #   fn <chr>, units <chr>
ld_ref <- dplyr::tibble(
    pop = unique(metadata$pop),
    bfile = file.path("path/to/plink_files/", pop)
)
ld_ref
#> # A tibble: 5 × 2
#>   pop   bfile                   
#>   <chr> <chr>                   
#> 1 AFR   path/to/plink_files//AFR
#> 2 EAS   path/to/plink_files//EAS
#> 3 EUR   path/to/plink_files//EUR
#> 4 AMR   path/to/plink_files//AMR
#> 5 SAS   path/to/plink_files//SAS
localdata <- CAMERA_local$new(metadata = metadata, ld_ref = ld_ref, plink_bin = "path/to/plink")
localdata$organise()

This will read the files specified in the metadata and attempt to arrange the data as described above, generating localdata$instrument_raw, localdata$instrument_outcome etc.

You can then generate the CAMERA object e.g.

l <- CAMERA$new()
l$import_from_local(
  instrument_raw=instrument_raw, 
  instrument_outcome=instrument_outcome, 
  instrument_regions=instrument_regions, 
  instrument_outcome_regions=instrument_outcome_regions, 
  exposure_ids=unique(instrument_raw$id), 
  outcome_ids=unique(names(instrument_outcome_regions[[1]])),
  pops=c("AFR", "EAS", "EUR", "AMR", "SAS")
)
#> list()

l$instrument_heterogeneity()
#> # A tibble: 14 × 9
#>    Reference Replication  nsnp agreement     se     pval      Q   Q_pval
#>    <chr>     <chr>       <int>     <dbl>  <dbl>    <dbl>  <dbl>    <dbl>
#>  1 LDL AFR   LDL EAS         3     1.06  0.371  4.27e- 3 14.5   6.99e- 4
#>  2 LDL AFR   LDL EUR         3     0.942 0.0207 0         0.168 9.19e- 1
#>  3 LDL AFR   LDL AMR         4     0.901 0.138  7.04e-11  1.77  6.22e- 1
#>  4 LDL AFR   LDL SAS         2     1.06  0.261  4.93e- 5  1.10  2.94e- 1
#>  5 LDL EAS   LDL AFR         3     0.656 0.125  1.41e- 7  2.11  3.49e- 1
#>  6 LDL EAS   LDL EUR         3     0.692 0.151  4.65e- 6 41.3   1.10e- 9
#>  7 LDL EAS   LDL AMR         3     0.793 0.175  6.20e- 6  0.399 8.19e- 1
#>  8 LDL EAS   LDL SAS         3     0.726 0.156  3.47e- 6  0.216 8.98e- 1
#>  9 LDL EUR   LDL AFR         8     0.990 0.117  2.62e-17  2.27  9.43e- 1
#> 10 LDL EUR   LDL EAS         5     1.07  0.235  5.06e- 6 14.3   6.36e- 3
#> 11 LDL EUR   LDL AMR        10     0.883 0.146  1.63e- 9 11.6   2.40e- 1
#> 12 LDL EUR   LDL SAS         8     0.835 0.189  1.02e- 5  9.85  1.97e- 1
#> 13 LDL AMR   LDL AFR         2     0.931 0.198  2.68e- 6  2.18  1.39e- 1
#> 14 LDL AMR   LDL EUR         2     1.02  0.148  5.49e-12 40.1   2.47e-10
#> # ℹ 1 more variable: I2 <dbl>