Last updated: 2021-11-22

Checks: 7 0

Knit directory: proxyMR/

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    Modified:   analysis/AM_MR_summary.Rmd
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    Modified:   output/tables/define_Neale_categories.csv
    Modified:   output/tables/define_Neale_categories_filled.csv
    Modified:   renv.lock

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/update_meeting_11_11_2021.Rmd) and HTML (docs/update_meeting_11_11_2021.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	810aae7	Jenny Sjaarda	2021-11-22	wflow_publish("analysis/update_meeting_11_11_2021.Rmd")
Rmd	9845a87	jennysjaarda	2021-11-11	add update meeting 11-11-21
html	9845a87	jennysjaarda	2021-11-11	add update meeting 11-11-21

1 Progress update.

Last time, we ran a MVMR as follows: \(Y_p \sim X_i + Y_i + X_p\), using instruments for \({X_i, Y_i, X_p}\). Unfortunately, realized this won't work after all because there is too much co-linearity between \(X_i\) and \(X_p\). We also don't have additional instruments for \(X_p\), beyond those that are already instruments for \(X_i\). The only way around would be to run a GWAS on \(X_p\), but probably not worth that effort (unlikely to find any additional instrument anyways).
Updated pipeline to meta-analyze across sexes at the SNP level rather than the MR level (minimize weak-instrument bias).
In all two-trait MRs, I’ve filtered out SNPs that show evidence of reverse causation based on same-person statistics (i.e. from Neale, meta-analyzed across sexes as per update above) and then use those same, filtered IVs in the couple MRs. IVs were filtered based on the code below (for reference), essentially removing any SNP that is more strongly associated with the outcome than the expsoure at a threshold of p < # r reverse_MR_threshold, corresponding to the reverse_MR_threshold in the code below.

reverse_t_threshold  =  stats::qnorm(reverse_MR_threshold)

dat_filter <- dat %>%
  mutate(z_score.exposure = beta.exposure / se.exposure) %>%
  mutate(z_score.outcome = beta.outcome / se.outcome) %>%
  mutate(std_beta.exposure = z_score.exposure / sqrt(samplesize.exposure)) %>%
  mutate(std_beta.outcome = z_score.outcome / sqrt(samplesize.outcome)) %>%
  mutate(std_se.exposure = 1 / sqrt(samplesize.exposure)) %>%
  mutate(std_se.outcome = 1 / sqrt(samplesize.outcome)) %>%
  mutate(std_t_stat = (abs(std_beta.exposure) - abs(std_beta.outcome)) /
           sqrt(std_se.exposure^2 + std_se.outcome^2)) %>%
  filter(std_t_stat > reverse_t_threshold)

2 Results.

As a recap, the model is below.

Version	Author	Date
529020f	jennysjaarda	2021-09-24

A summary of the regressions between \(\omega\), \(\rho\) and \(\gamma\) is below.


Call:
lm(formula = rho_beta ~ omega_beta + 0, data = data)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.59720 -0.02468  0.00983  0.04595  0.36472 

Coefficients:
           Estimate Std. Error t value            Pr(>|t|)    
omega_beta 0.445129   0.009718    45.8 <0.0000000000000002 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.06695 on 929 degrees of freedom
Multiple R-squared:  0.6931,    Adjusted R-squared:  0.6928 
F-statistic:  2098 on 1 and 929 DF,  p-value: < 0.00000000000000022


Call:
lm(formula = gam_beta ~ omega_beta + 0, data = data)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.75402 -0.02179  0.01994  0.04937  1.10864 

Coefficients:
           Estimate Std. Error t value            Pr(>|t|)    
omega_beta  0.62689    0.01225   51.18 <0.0000000000000002 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.08438 on 929 degrees of freedom
Multiple R-squared:  0.7382,    Adjusted R-squared:  0.7379 
F-statistic:  2620 on 1 and 929 DF,  p-value: < 0.00000000000000022


Call:
lm(formula = gam_rho_beta ~ omega_beta + 0, data = data)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.18334 -0.02240  0.02567  0.07289  1.45454 

Coefficients:
           Estimate Std. Error t value            Pr(>|t|)    
omega_beta  1.07202    0.01664   64.42 <0.0000000000000002 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1147 on 929 degrees of freedom
Multiple R-squared:  0.8171,    Adjusted R-squared:  0.8169 
F-statistic:  4149 on 1 and 929 DF,  p-value: < 0.00000000000000022


Call:
lm(formula = gam_rho_resid ~ omega_beta + 0, data = data)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.76873 -0.02561  0.01144  0.04716  0.85106 

Coefficients:
           Estimate Std. Error t value            Pr(>|t|)    
omega_beta  0.72646    0.01171   62.02 <0.0000000000000002 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.0807 on 929 degrees of freedom
Multiple R-squared:  0.8054,    Adjusted R-squared:  0.8052 
F-statistic:  3846 on 1 and 929 DF,  p-value: < 0.00000000000000022

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /data/sgg2/jenny/bin/R-4.1.0/lib64/R/lib/libRblas.so
LAPACK: /data/sgg2/jenny/bin/R-4.1.0/lib64/R/lib/libRlapack.so

locale:
[1] en_CA.UTF-8

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] cowplot_1.1.1      kableExtra_1.3.4   knitr_1.33         DT_0.18.1         
 [5] forcats_0.5.1      stringr_1.4.0      dplyr_1.0.7        purrr_0.3.4       
 [9] readr_1.4.0        tidyr_1.1.3        tibble_3.1.2       ggplot2_3.3.4     
[13] tidyverse_1.3.1    targets_0.5.0.9001 workflowr_1.6.2   

loaded via a namespace (and not attached):
 [1] httr_1.4.2        sass_0.4.0        splines_4.1.0     jsonlite_1.7.2   
 [5] viridisLite_0.4.0 modelr_0.1.8      bslib_0.3.0       assertthat_0.2.1 
 [9] highr_0.9         renv_0.13.2-62    cellranger_1.1.0  yaml_2.2.1       
[13] lattice_0.20-44   pillar_1.6.1      backports_1.2.1   glue_1.4.2       
[17] digest_0.6.27     promises_1.2.0.1  rvest_1.0.0       colorspace_2.0-1 
[21] Matrix_1.3-3      htmltools_0.5.2   httpuv_1.6.1      pkgconfig_2.0.3  
[25] broom_0.7.7       haven_2.4.1       scales_1.1.1      webshot_0.5.2    
[29] processx_3.5.2    svglite_2.0.0     whisker_0.4       later_1.2.0      
[33] git2r_0.28.0      mgcv_1.8-35       farver_2.1.0      generics_0.1.0   
[37] ellipsis_0.3.2    withr_2.4.2       cli_2.5.0         magrittr_2.0.1   
[41] crayon_1.4.1      readxl_1.3.1      evaluate_0.14     ps_1.6.0         
[45] fs_1.5.0          fansi_0.5.0       nlme_3.1-152      xml2_1.3.2       
[49] tools_4.1.0       data.table_1.14.0 hms_1.1.0         lifecycle_1.0.0  
[53] munsell_0.5.0     reprex_2.0.0      callr_3.7.0       compiler_4.1.0   
[57] jquerylib_0.1.4   systemfonts_1.0.2 rlang_0.4.11      grid_4.1.0       
[61] rstudioapi_0.13   htmlwidgets_1.5.3 igraph_1.2.6      rmarkdown_2.11.2 
[65] gtable_0.3.0      codetools_0.2-18  DBI_1.1.1         R6_2.5.0         
[69] lubridate_1.7.10  fastmap_1.1.0     utf8_1.2.1        rprojroot_2.0.2  
[73] stringi_1.6.2     Rcpp_1.0.6        vctrs_0.3.8       dbplyr_2.1.1     
[77] tidyselect_1.1.1  xfun_0.24

Update Meeting: 11-11-2021

Jenny Sjaarda

2021-11-11

1 Progress update.

2 Results.