Update Meeting: 23-09-2021
Jenny Sjaarda
2021-09-20
Last updated: 2021-09-23
Checks: 6 1
Knit directory: proxyMR/
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1 Last meeting summary.
- Because the results comparing \(\omega\) to \(\rho + \gamma\) were slightly confusing (see last meeting summary here), decided we should explore the impact of \(X_i\) on the \(Y_i \rightarrow Y_p\) causal effects.
| Version | Author | Date |
|---|---|---|
| fda4843 | jennysjaarda | 2021-09-14 |
- Two ideas:
- Identify overlap between \(X_i \rightarrow X_p\) IVs and \(Y_i \rightarrow Y_p\).
- Adjust the \(Y_i \rightarrow Y_p\) MR results for effects of \(X_i\) (as depicted below).
2 Results.
2.1 Overlap between IVs for \(X\) and \(Y\).
- Is simply calculating the SNP overlap good enough?
- Better to calculate signal overlap using LD at some \(r^2\) cutoff (what threshold would be useful?).
- For each IV used to estimate the causal effect \(Y_i \rightarrow Y_p\),
- Test if they are in LD with any IV used to estimate the causal effect \(X_i \rightarrow X_p\) at \(r^2\) >
0.9, - Repeat for all IVs for \(Y_i\) to calcualte the percentage overlap between the two sets of IVs.
- Define percentage overlap as: (# of \(Y_i\) IVs are in LD with at least one \(X_i\) IV at \(r^2\) >
0.9) / (total number of \(Y_i\) IVs used).
The results can be seen below. The YiXi_IV_exact_overlap column indicates the percentage of \(Y_i\) IVs that are an exact match with \(X_i\) IVs (same rs#). The YiXi_IV_sig_overlap column denotes the percentage overlap of signals, as defined above.
The results with the most overlap involve traits that are nearly identical.
2.2 Adjust \(Y_i \rightarrow Y_p\).
Performed MV MR \(Y_i \rightarrow Y_p\) relationships: \(Y_p \sim Y_i + X_i\). Where IVs were the same as the two trait MR (\(Y_p \sim Y_i\)) based on \(Y\), but the effect of the IVs on \(X_i\) were also included in the model.
2.3 Updated results with adjusted \(Y_i\)
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.5.2
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] knitr_1.33 DT_0.18 targets_0.7.0 forcats_0.5.1
[5] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.0.1
[9] tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 lubridate_1.7.10 ps_1.6.0 assertthat_0.2.1
[5] rprojroot_2.0.2 digest_0.6.27 utf8_1.2.1 R6_2.5.0
[9] cellranger_1.1.0 backports_1.2.1 reprex_2.0.0 evaluate_0.14
[13] highr_0.9 httr_1.4.2 pillar_1.6.1 rlang_0.4.11
[17] readxl_1.3.1 data.table_1.14.0 rstudioapi_0.13 whisker_0.4
[21] callr_3.7.0 rmarkdown_2.10 htmlwidgets_1.5.3 igraph_1.2.6
[25] munsell_0.5.0 broom_0.7.6 compiler_4.1.0 httpuv_1.6.1
[29] modelr_0.1.8 xfun_0.23 pkgconfig_2.0.3 htmltools_0.5.1.1
[33] tidyselect_1.1.1 workflowr_1.6.2 codetools_0.2-18 fansi_0.5.0
[37] crayon_1.4.1 tzdb_0.1.2 dbplyr_2.1.1 withr_2.4.2
[41] later_1.2.0 grid_4.1.0 jsonlite_1.7.2 gtable_0.3.0
[45] lifecycle_1.0.0 DBI_1.1.1 git2r_0.28.0 magrittr_2.0.1
[49] scales_1.1.1 cli_2.5.0 stringi_1.6.2 fs_1.5.0
[53] promises_1.2.0.1 xml2_1.3.2 ellipsis_0.3.2 generics_0.1.0
[57] vctrs_0.3.8 tools_4.1.0 glue_1.4.2 hms_1.1.0
[61] processx_3.5.2 yaml_2.2.1 colorspace_2.0-1 rvest_1.0.0
[65] haven_2.4.1