Last updated: 2022-11-09

Checks: 7 0

Knit directory: meSuSie_Analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

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.

Environment: empty

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.

Seed: set.seed(20220530)

The command set.seed(20220530) 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.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 65fba54

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 65fba54. 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: 


working directory clean

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/HDL_APOE.Rmd) and HTML (docs/HDL_APOE.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 65fba54 borangao 2022-11-09 Build site.
html 65fba54 borangao 2022-11-09 Build site.

MESuSiE captures shared causal signal

This is to reproduce the real data analysis result of fine-mapping HDL across EUR and AFR ancestries in the manuscript. The processed data and manual annotations can be found at the data directory.

###Load the data
load("/net/fantasia/home/borang/Susie_Mult/website_material/real_data/HDL_APOE.RData")
library(MESuSiE)

GWAS across ancestries

We’ll start with exploring the region by locuszoom plot.

Version Author Date
65fba54 borangao 2022-11-09

We can see that rs429358 is the leading SNP in the UKBB, and also has a strong marginal association in the African ancestry with -log10(P-value)>40. rs429358 is a missense variant in the APOE region, which is known to be associated with lipid metabolism. With all these information together, it suggests rs429358 can be the shared causal variant for both ancestries.

Univariate fine-mapping

We’ll do the analysis by performing univariate fine-mapping method in each ancestry.

Version Author Date
65fba54 borangao 2022-11-09

Univariate SuSiE detects signals in both ancestries, but no shared causal signal is found.

Multi-ancestry fine-mapping

This motivates the multiple-ancestry fine-mapping techniques.

Version Author Date
65fba54 borangao 2022-11-09

Our proposed method MESuSiE detects the leading signal rs429358 as shared signals, and other shared causal signals in the regions, while Paintor can not distinguish detected signal as shared or ancestry-specific.


sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] DescTools_0.99.45   gridExtra_2.3       VennDiagram_1.7.3  
 [4] futile.logger_1.4.3 ggpmisc_0.4.7       ggpp_0.4.4         
 [7] patchwork_1.1.1     tidyr_1.2.0         dplyr_1.0.9        
[10] data.table_1.14.2   ggpubr_0.4.0        ggplot2_3.3.6      
[13] MESuSiE_1.0         workflowr_1.7.0    

loaded via a namespace (and not attached):
 [1] fs_1.5.2             httr_1.4.3           rprojroot_2.0.3     
 [4] tools_4.2.2          backports_1.4.1      bslib_0.3.1         
 [7] utf8_1.2.2           R6_2.5.1             DBI_1.1.2           
[10] colorspace_2.0-3     withr_2.5.0          tidyselect_1.1.2    
[13] Exact_3.1            processx_3.8.0       compiler_4.2.2      
[16] git2r_0.30.1         cli_3.4.1            quantreg_5.93       
[19] formatR_1.12         SparseM_1.81         expm_0.999-6        
[22] labeling_0.4.2       sass_0.4.1           scales_1.2.1        
[25] mvtnorm_1.1-3        callr_3.7.3          proxy_0.4-27        
[28] stringr_1.4.0        digest_0.6.30        rmarkdown_2.14      
[31] pkgconfig_2.0.3      htmltools_0.5.2      highr_0.9           
[34] fastmap_1.1.0        rlang_1.0.6          readxl_1.4.0        
[37] rstudioapi_0.13      jquerylib_0.1.4      generics_0.1.2      
[40] farver_2.1.1         jsonlite_1.8.3       car_3.0-13          
[43] magrittr_2.0.3       Matrix_1.4-1         Rcpp_1.0.8.3        
[46] munsell_0.5.0        fansi_1.0.3          abind_1.4-5         
[49] lifecycle_1.0.3      stringi_1.7.6        whisker_0.4         
[52] yaml_2.3.5           carData_3.0-5        MASS_7.3-57         
[55] rootSolve_1.8.2.3    promises_1.2.0.1     lmom_2.8            
[58] lattice_0.20-45      splines_4.2.2        knitr_1.39          
[61] ps_1.7.2             pillar_1.8.1         boot_1.3-28         
[64] gld_2.6.5            ggsignif_0.6.3       futile.options_1.0.1
[67] glue_1.6.2           evaluate_0.18        getPass_0.2-2       
[70] lambda.r_1.2.4       vctrs_0.5.0          httpuv_1.6.5        
[73] MatrixModels_0.5-0   cellranger_1.1.0     gtable_0.3.1        
[76] purrr_0.3.4          assertthat_0.2.1     xfun_0.31           
[79] broom_0.8.0          e1071_1.7-11         rstatix_0.7.0       
[82] later_1.3.0          class_7.3-20         survival_3.3-1      
[85] tibble_3.1.7         ellipsis_0.3.2