Last updated: 2022-11-09

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Knit directory: meSuSie_Analysis/

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MESuSiE captures ancestry-specific causal signal

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

load("/net/fantasia/home/borang/Susie_Mult/website_material/real_data/TC_ARIC4.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 rs6601924 is the leading SNP in the UKBB, but no strong marginal associations in the African ancestry with maximum -log10(P-value) less than 4. This suggests there is European ancestry-specific causal variant within the region.

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 European ancestry, and no signal is found in African ancestry

Multi-ancestry fine-mapping

We further check the performance of MESuSiE in the scenario with only ancestry-specific causal variant.

Version Author Date
65fba54 borangao 2022-11-09

Our proposed method MESuSiE detects the leading signal rs6601924 as European ancestry-specific signal, Paintor also detects the leading variant rs6601924 as signal, while 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