Last updated: 2022-11-09

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Knit directory: meSuSie_Analysis/

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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