Last updated: 2021-11-02
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Knit directory: proxyMR/
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 658242d | Jenny Sjaarda | 2021-11-02 | wflow_publish("analysis/update_meeting_03_11_2021.Rmd") |
Rmd | b9d496f | Jenny Sjaarda | 2021-11-02 | wflow_rename("analysis/update_meetings_03_11_2021.Rmd", "analysis/update_meeting_03_11_2021.Rmd") |
Decided to run MVMR as follows: \(Y_p \sim X_i + Y_i + X_p\), using instruments for \({X_i, Y_i, X_p}\). In this MR, the coefficient for \(X_i\) would represent the direct \(X_i \rightarrow Y_p\) causal effect and hopefully this would be close to zero in most cases. Running simply the \(Y_p \sim X_i\) MR (with only \(X_i\) instruments), would give you the \(X_i \rightarrow Y_p\) total effect. Then we can compare the direct and total effect results.
A summary of the MVMR results are below, filtered to only traits with abs(correlation) < 0.8, and then BF significant with \(X_i\).
The column corr_traits
corresponds to the raw correlation between traits in the biobank. The columns IVW_meta_beta
and IVW_meta_pval
correspond to the meta-analyzed across sexes univariate results from the \(Y_p ~ X_i\) MR. The subsequent columns correspond to the MVMR MR results.
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] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
[5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
[7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
attached base packages:
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