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Summarize all our results to get a feel for top traits (let’s call this: trait/trait MR). A logical addition that we didn’t discuss would be to meta-analyze across sexes and see what traits are significant in pooled analysis and then check which of these are significantly different between sexes.
Test linear association across bins (both age and time-together bins) using IVW estimated inversely weighted by SE.
Combine age and time-together bins into a grid of effect sizes to see if we see any pattern.
Move beyond our trait/trait MR into investigating how one trait impacts other traits to tease out direct vs indirect effects, two parts here:
Replicate permutation procedure from Tenesa paper. Do you still want to explore this?
Explore dietary traits - request estimates from Ninon?
Re (#4), for the trait/trait MR, I essentially followed a 2-sample MR procedure (just as a refresher for us both), so for example BMI partner vs BMI trait:
For the univariate analysis between two traits, I think what you were suggesting is to follow exactly the same procedure as above. So for example, disease partner vs BMI trait:
For the multivariate model, so if the model of interest is BMI ~ BMI + education + diet + activity level:
Does this look right (particularly for the multivariate model)? i.e. the final MR model only includes beta estimates, no raw genetic data.
I also understood that one of the traits to explore would be PCs. To me it seems logical that this would be a causal variable to explore (for eg, an MR being: BMI_beta ~ PCX_beta) rather than the other way around. If I have that right, then that means we would need IVs for PCs. At quick glance, Neale doesn’t have this available. Do you know if anyone in the group would have done this, otherwise I should run this GWAS.
sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.4
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 datasets utils methods base
other attached packages:
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[5] dplyr_1.0.6 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[9] tibble_3.1.2 ggplot2_3.3.3 tidyverse_1.3.1 targets_0.5.0.9000
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
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[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] httr_1.4.2 pillar_1.6.1 rlang_0.4.11 readxl_1.3.1
[17] rstudioapi_0.13 data.table_1.14.0 whisker_0.4 callr_3.7.0
[21] rmarkdown_2.8 htmlwidgets_1.5.3 igraph_1.2.6 munsell_0.5.0
[25] broom_0.7.6 compiler_4.1.0 httpuv_1.6.1 modelr_0.1.8
[29] xfun_0.23 pkgconfig_2.0.3 htmltools_0.5.1.1 tidyselect_1.1.1
[33] codetools_0.2-18 fansi_0.5.0 crayon_1.4.1 dbplyr_2.1.1
[37] withr_2.4.2 later_1.2.0 grid_4.1.0 jsonlite_1.7.2
[41] gtable_0.3.0 lifecycle_1.0.0 DBI_1.1.1 git2r_0.28.0
[45] magrittr_2.0.1 scales_1.1.1 cli_2.5.0 stringi_1.6.2
[49] renv_0.13.2-62 fs_1.5.0 promises_1.2.0.1 xml2_1.3.2
[53] ellipsis_0.3.2 generics_0.1.0 vctrs_0.3.8 tools_4.1.0
[57] glue_1.4.2 crosstalk_1.1.1 hms_1.1.0 processx_3.5.2
[61] yaml_2.2.1 colorspace_2.0-1 rvest_1.0.0 haven_2.4.1