Last updated: 2021-09-14
Checks: 7 0
Knit directory: proxyMR/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it's best to always run the code in an empty environment.
The command set.seed(20210602) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 110f384. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: FP_data.RData
Ignored: _targets/
Ignored: analysis/bgenie_GWAS/
Ignored: analysis/data_setup/
Ignored: analysis/process_Neale.out
Ignored: analysis/traitMR/
Ignored: data/Neale_SGG_directory_12_07_2021.csv
Ignored: data/Neale_SGG_directory_15_07_2021.csv
Ignored: data/processed/
Ignored: data/raw/
Ignored: household_MR_exhaustive_summary.rds
Ignored: output/figures/
Ignored: output/tables/traitMR/
Ignored: proxyMR_comparison.RData
Ignored: proxyMR_figure_data.RData
Ignored: proxymr_100_clustermq.out
Ignored: proxymr_101_clustermq.out
Ignored: proxymr_102_clustermq.out
Ignored: proxymr_103_clustermq.out
Ignored: proxymr_104_clustermq.out
Ignored: proxymr_105_clustermq.out
Ignored: proxymr_106_clustermq.out
Ignored: proxymr_107_clustermq.out
Ignored: proxymr_108_clustermq.out
Ignored: proxymr_109_clustermq.out
Ignored: proxymr_10_clustermq.out
Ignored: proxymr_110_clustermq.out
Ignored: proxymr_111_clustermq.out
Ignored: proxymr_112_clustermq.out
Ignored: proxymr_113_clustermq.out
Ignored: proxymr_114_clustermq.out
Ignored: proxymr_115_clustermq.out
Ignored: proxymr_116_clustermq.out
Ignored: proxymr_117_clustermq.out
Ignored: proxymr_118_clustermq.out
Ignored: proxymr_119_clustermq.out
Ignored: proxymr_11_clustermq.out
Ignored: proxymr_120_clustermq.out
Ignored: proxymr_121_clustermq.out
Ignored: proxymr_122_clustermq.out
Ignored: proxymr_123_clustermq.out
Ignored: proxymr_124_clustermq.out
Ignored: proxymr_125_clustermq.out
Ignored: proxymr_126_clustermq.out
Ignored: proxymr_127_clustermq.out
Ignored: proxymr_128_clustermq.out
Ignored: proxymr_129_clustermq.out
Ignored: proxymr_12_clustermq.out
Ignored: proxymr_130_clustermq.out
Ignored: proxymr_131_clustermq.out
Ignored: proxymr_132_clustermq.out
Ignored: proxymr_133_clustermq.out
Ignored: proxymr_134_clustermq.out
Ignored: proxymr_135_clustermq.out
Ignored: proxymr_136_clustermq.out
Ignored: proxymr_137_clustermq.out
Ignored: proxymr_138_clustermq.out
Ignored: proxymr_139_clustermq.out
Ignored: proxymr_13_clustermq.out
Ignored: proxymr_140_clustermq.out
Ignored: proxymr_14_clustermq.out
Ignored: proxymr_15_clustermq.out
Ignored: proxymr_16_clustermq.out
Ignored: proxymr_17_clustermq.out
Ignored: proxymr_18_clustermq.out
Ignored: proxymr_19_clustermq.out
Ignored: proxymr_1_clustermq.out
Ignored: proxymr_20_clustermq.out
Ignored: proxymr_21_clustermq.out
Ignored: proxymr_22_clustermq.out
Ignored: proxymr_23_clustermq.out
Ignored: proxymr_24_clustermq.out
Ignored: proxymr_25_clustermq.out
Ignored: proxymr_26_clustermq.out
Ignored: proxymr_27_clustermq.out
Ignored: proxymr_28_clustermq.out
Ignored: proxymr_29_clustermq.out
Ignored: proxymr_2_clustermq.out
Ignored: proxymr_30_clustermq.out
Ignored: proxymr_31_clustermq.out
Ignored: proxymr_32_clustermq.out
Ignored: proxymr_33_clustermq.out
Ignored: proxymr_34_clustermq.out
Ignored: proxymr_35_clustermq.out
Ignored: proxymr_36_clustermq.out
Ignored: proxymr_37_clustermq.out
Ignored: proxymr_38_clustermq.out
Ignored: proxymr_39_clustermq.out
Ignored: proxymr_3_clustermq.out
Ignored: proxymr_40_clustermq.out
Ignored: proxymr_41_clustermq.out
Ignored: proxymr_42_clustermq.out
Ignored: proxymr_43_clustermq.out
Ignored: proxymr_44_clustermq.out
Ignored: proxymr_45_clustermq.out
Ignored: proxymr_46_clustermq.out
Ignored: proxymr_47_clustermq.out
Ignored: proxymr_48_clustermq.out
Ignored: proxymr_49_clustermq.out
Ignored: proxymr_4_clustermq.out
Ignored: proxymr_50_clustermq.out
Ignored: proxymr_51_clustermq.out
Ignored: proxymr_52_clustermq.out
Ignored: proxymr_53_clustermq.out
Ignored: proxymr_54_clustermq.out
Ignored: proxymr_55_clustermq.out
Ignored: proxymr_56_clustermq.out
Ignored: proxymr_57_clustermq.out
Ignored: proxymr_58_clustermq.out
Ignored: proxymr_59_clustermq.out
Ignored: proxymr_5_clustermq.out
Ignored: proxymr_60_clustermq.out
Ignored: proxymr_61_clustermq.out
Ignored: proxymr_62_clustermq.out
Ignored: proxymr_63_clustermq.out
Ignored: proxymr_64_clustermq.out
Ignored: proxymr_65_clustermq.out
Ignored: proxymr_66_clustermq.out
Ignored: proxymr_67_clustermq.out
Ignored: proxymr_68_clustermq.out
Ignored: proxymr_69_clustermq.out
Ignored: proxymr_6_clustermq.out
Ignored: proxymr_70_clustermq.out
Ignored: proxymr_71_clustermq.out
Ignored: proxymr_72_clustermq.out
Ignored: proxymr_73_clustermq.out
Ignored: proxymr_74_clustermq.out
Ignored: proxymr_75_clustermq.out
Ignored: proxymr_76_clustermq.out
Ignored: proxymr_77_clustermq.out
Ignored: proxymr_78_clustermq.out
Ignored: proxymr_79_clustermq.out
Ignored: proxymr_7_clustermq.out
Ignored: proxymr_80_clustermq.out
Ignored: proxymr_81_clustermq.out
Ignored: proxymr_82_clustermq.out
Ignored: proxymr_83_clustermq.out
Ignored: proxymr_84_clustermq.out
Ignored: proxymr_85_clustermq.out
Ignored: proxymr_86_clustermq.out
Ignored: proxymr_87_clustermq.out
Ignored: proxymr_88_clustermq.out
Ignored: proxymr_89_clustermq.out
Ignored: proxymr_8_clustermq.out
Ignored: proxymr_90_clustermq.out
Ignored: proxymr_91_clustermq.out
Ignored: proxymr_92_clustermq.out
Ignored: proxymr_93_clustermq.out
Ignored: proxymr_94_clustermq.out
Ignored: proxymr_95_clustermq.out
Ignored: proxymr_96_clustermq.out
Ignored: proxymr_97_clustermq.out
Ignored: proxymr_98_clustermq.out
Ignored: proxymr_99_clustermq.out
Ignored: proxymr_9_clustermq.out
Ignored: renv/library/
Ignored: renv/staging/
Ignored: traits_corr2_update.rds
Untracked files:
Untracked: Rplots.pdf
Untracked: analysis/figures_scratch.Rmd
Untracked: analysis/group_meeting_pres_figures_080921.Rmd
Untracked: analysis/latex_test2.Rmd
Untracked: output/tables/household_correlations.final_filter.csv
Unstaged changes:
Modified: analysis/update_meeting_06_09_2021.Rmd
Modified: analysis/update_meeting_25_08_2021.Rmd
Modified: output/tables/define_Neale_categories.csv
Modified: output/tables/define_Neale_categories_filled.csv
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/phenotype_selection.Rmd) and HTML (docs/phenotype_selection.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 | 0365f37 | jennysjaarda | 2021-09-14 | add phenotype and couple selection schematic links |
| html | 70b6430 | Jenny Sjaarda | 2021-09-14 | Build site. |
| Rmd | 5afdbdc | Jenny Sjaarda | 2021-09-14 | update some docs |
Phenotypes were selected according to the following scheme
Note that as none of the filtering criteria apply any BF correction for the number of phenotypes, the order of the steps does not matter.
An overview of phenotype selection and exclusions can be seen here.
All SGG phenotypes went internal processing according the PHESANT pipeline. Some traits were excluded for various reasons (genetic trait, insufficient sample size, etc.).
Number of traits initially = 3118.
Traits included and their correlations amongst couples can be seen below.
Limited to only traits that were processed by Neale and had both sex-specific and joint summary statistics available (most traits that were excluded did not have all 3 subgroups were not available, some other traits were excluded by the Neale group).
Number remaining after filter = 1278.
The traits that are in the SGG database but were not included due to Neale filtering can be seen below. Can compare with the Neale Manifest (search by ID) to get a sense of why traits were excluded (likely there is not a GWAS file for both_sexes, male and female).
Traits were excluded if the correlation among couples was < 0.1 (i.e. \(\sqrt{(r^2)}\)).
Number remaining after filter = 510.
The traits remaining after this filter can be seen below.
Phenotypes were excluded if the number of valid IV was < 5. IVs were defined based on a p-value of p < 0 in both_sexes after clumping (performed in PLINK with the options --clump-kb 10000 and --clump-r2 0.001 using the 1000 Genomes European samples as a reference).
Number remaining after filter = 174.
The traits remaining after IV filtering can be seen below.
Using the sex-specific summary statistics, heterogeneity between sexes was calculated. IVs that showed significant evidence of heterogeneity between sexes were included (p < 0.05/[number of IVs]). After filtering IVs with significant heterogeneity, phenotypes were filtered to again have IVs < 5.
Number remaining after filter = 170.
The traits remaining after sex-heterogeneity filtering can be seen below.
Phenotypes were limited to continuous_irnt or ordinal (i.e. binary traits were removed).
Number remaining after filter = 131.
The traits remaining after sex-heterogeneity filtering can be seen below.
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:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] knitr_1.33 DT_0.18.1 forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.7 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[9] tibble_3.1.2 ggplot2_3.3.4 tidyverse_1.3.1 targets_0.5.0.9001
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 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] 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_2.9 htmlwidgets_1.5.3 igraph_1.2.6 munsell_0.5.0
[25] broom_0.7.7 compiler_4.1.0 httpuv_1.6.1 modelr_0.1.8
[29] xfun_0.24 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