Installation and Motivating Example
borangao
2022-11-09
Last updated: 2023-10-09
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Installation of MESuSiE
library(devtools)
# Install MESuSiE
install_github("borangao/MESuSiE",dependencies = FALSE)
# Load MESuSiE
library(MESuSiE)Motivating Example
The motivating example is based on a toy dataset outlined in the manuscript.
1. Data Characteristics
The dataset contains:
- Six SNPs categorized into three distinct LD clusters.
- Within each cluster, the SNPs show strong correlations.
- Between clusters, SNP correlations are weak.
SNP Grouping
- Cluster 1: Contains SNP 1 and 2.
- Cluster 2: Contains SNP 3 and 4.
- Cluster 3: Contains SNP 5 and 6.
Causal SNPs:
Based on our simulations, the SNPs are postulated to have the following causal relationships:
- SNP 4: Simulated as a shared causal SNP across ancestries.
- SNP 2: Simulated as the causal SNP specific to the BB ancestry.
- SNP 6: Simulated as the causal SNP specific to the WB ancestry.
2. Input of MESuSiE
The data is provided with the package. For the input of the MESuSiE, we require a list of summary statistics and a list of LD matrices from multiple ancestries.
data(summ_stat_list)
data(LD_list)
summ_stat_list$WB
SNP Beta Se Z N POS
rs1890449 SNP1 -0.001806456 0.001825742 -0.9894367 3e+05 1
rs3122053 SNP2 -0.001749892 0.001825742 -0.9584555 3e+05 2
rs6600259 SNP3 0.019230827 0.001825742 10.5331576 3e+05 3
rs6681089 SNP4 0.019113531 0.001825742 10.4689120 3e+05 4
rs3008244 SNP5 0.018599221 0.001825742 10.1872128 3e+05 5
rs3008245 SNP6 0.018806821 0.001825742 10.3009200 3e+05 6
$BB
SNP Beta Se Z N POS
rs1890449 SNP1 0.0146503149 0.001825742 8.0243079 3e+05 1
rs3122053 SNP2 0.0196888866 0.001825742 10.7840473 3e+05 2
rs6600259 SNP3 0.0176200508 0.001825742 9.6508993 3e+05 3
rs6681089 SNP4 0.0192921442 0.001825742 10.5667426 3e+05 4
rs3008244 SNP5 -0.0017014483 0.001825742 -0.9319216 3e+05 5
rs3008245 SNP6 0.0007568107 0.001825742 0.4145223 3e+05 6Each element of the summary statistics list is a data frame of summary statistics from each ancestry with column name SNP, Beta, Se, Z, and N representing SNP information, marginal effect size, standard error, Z-scores and number of sample.
LD_list$WB
SNP1 SNP2 SNP3 SNP4 SNP5
rs1890449 1.0000000000 0.9985345076 0.008849574 0.008352736 0.0004252546
rs3122053 0.9985345076 1.0000000000 0.009187116 0.008718572 0.0006605135
rs6600259 0.0088495745 0.0091871156 1.000000000 0.992043794 -0.0238684132
rs6681089 0.0083527360 0.0087185716 0.992043794 1.000000000 -0.0263527244
rs3008244 0.0004252546 0.0006605135 -0.023868413 -0.026352724 1.0000000000
rs3008245 0.0005909073 0.0008813672 -0.023679608 -0.025942562 0.9961393233
SNP6
rs1890449 0.0005909073
rs3122053 0.0008813672
rs6600259 -0.0236796083
rs6681089 -0.0259425621
rs3008244 0.9961393233
rs3008245 1.0000000000
$BB
SNP1 SNP2 SNP3 SNP4 SNP5
rs1890449 1.000000000 0.627749083 0.006781938 0.01153312 -0.02141473
rs3122053 0.627749083 1.000000000 0.008521627 0.01231062 -0.01548369
rs6600259 0.006781938 0.008521627 1.000000000 0.92899225 0.01440080
rs6681089 0.011533117 0.012310618 0.928992254 1.00000000 0.01766183
rs3008244 -0.021414730 -0.015483693 0.014400801 0.01766183 1.00000000
rs3008245 -0.035179346 -0.019228441 0.012404065 0.01611964 0.79215823
SNP6
rs1890449 -0.03517935
rs3122053 -0.01922844
rs6600259 0.01240406
rs6681089 0.01611964
rs3008244 0.79215823
rs3008245 1.00000000Each element of the LD matrices list is a matrix of LD from each ancestry, with column name being SNP name matched up with the summary statistics.
3. Analysis
MESuSiE
MESuSiE_res<-meSuSie_core(LD_list,summ_stat_list,L=10)*************************************************************
Multiple Ancestry Sum of Single Effect Model (MESuSiE)
Visit http://www.xzlab.org/software.html For Update
(C) 2022 Boran Gao, Xiang Zhou
GNU General Public License
*************************************************************
# Start data processing for sufficient statistics
# Create MESuSiE object
# Start data analysis
# Data analysis is done, and now generates result
Potential causal SNPs with PIP > 0.5: SNP2 SNP4 SNP6
Credible sets for effects:
$cs
$cs$L1
[1] 4
$cs$L2
[1] 2
$cs$L3
[1] 5 6
$cs_category
L1 L2 L3
"WB_BB" "BB" "WB"
$purity
min.abs.corr mean.abs.corr median.abs.corr
L1 1.0000000 1.0000000 1.0000000
L2 1.0000000 1.0000000 1.0000000
L3 0.9961393 0.9980697 0.9980697
$cs_index
[1] 1 2 3
$coverage
[1] 0.9997947 1.0000000 1.0000000
$requested_coverage
[1] 0.95
Use MESuSiE_Plot() for visualization
# Total time used for the analysis: 0 minsMESuSiE_res$pip_config WB BB WB_BB
[1,] 0.07142857 0.07142857 0.02380952
[2,] 0.07142857 0.63214915 0.36785085
[3,] 0.07142857 0.07142857 0.02380952
[4,] 0.07142857 0.07142857 0.99979466
[5,] 0.18126816 0.07142857 0.05082666
[6,] 0.56691986 0.07142857 0.20098531We observed that three SNPs have a posterior inclusion probability (PIP) exceeding the 0.5 threshold. Upon further examination of the PIP for ancestry-specificity and shared traits, we identified that SNP4 is shared, while SNP2 and SNP6 are ancestry-specific. The categories within the credible set indicate the ancestries affected by the SNPs present in the set.
SuSiE
library(susieR)
susie_WB<-susie_rss(summ_stat_list$WB$Z,LD_list$WB)
susie_BB<-susie_rss(summ_stat_list$BB$Z,LD_list$BB)
susie_WB$pip SNP1 SNP2 SNP3 SNP4 SNP5 SNP6
0.0000000 0.0000000 0.6052634 0.3947366 0.2398731 0.7601269 susie_BB$pip SNP1 SNP2 SNP3 SNP4 SNP5 SNP6
1.015177e-11 1.000000e+00 1.704749e-04 9.998295e-01 0.000000e+00 0.000000e+00 After executing the univariate SuSiE analysis, we determined that SNP 3 and 6 are signals specific to Europeans, while SNP 2 and 4 emerged as signals specific to Africans, using a PIP threshold of 0.5.
Paintor
##We load the Paintor result directly
paintor_res<-read.table("/net/fantasia/home/borang/Susie_Mult/Revision_Round_1/Simulation/091223/data/toy_example/result/fig1.mcmc.paintor",header=T)
paintor_res$Posterior_Prob[1] 0.040756 0.999980 0.987828 0.999956 0.993764 0.995080Paintor identifies SNP2-5 as signals without distinguishing ancestry-specific or shared causal variant.
4. Visualization
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3; LAPACK version 3.9.0
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
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpubr_0.6.0 cowplot_1.1.1 dplyr_1.1.2 ggplot2_3.4.2
[5] susieR_0.11.84 MESuSiE_1.0 devtools_2.4.3 usethis_2.2.1
[9] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 farver_2.1.1 fastmap_1.1.1
[4] reshape_0.8.9 promises_1.2.0.1 digest_0.6.30
[7] lifecycle_1.0.3 ellipsis_0.3.2 processx_3.8.0
[10] magrittr_2.0.3 compiler_4.3.1 rlang_1.1.1
[13] sass_0.4.6 progress_1.2.2 tools_4.3.1
[16] utf8_1.2.3 yaml_2.3.7 knitr_1.39
[19] ggsignif_0.6.4 labeling_0.4.2 prettyunits_1.2.0
[22] pkgbuild_1.4.2 curl_5.0.1 plyr_1.8.8
[25] abind_1.4-5 pkgload_1.3.1 withr_2.5.1
[28] purrr_1.0.1 grid_4.3.1 fansi_1.0.5
[31] git2r_0.32.0 colorspace_2.1-0 scales_1.2.1
[34] cli_3.6.1 rmarkdown_2.22 crayon_1.5.2
[37] generics_0.1.3 remotes_2.4.2 rstudioapi_0.14
[40] httr_1.4.6 RcppArmadillo_0.11.1.1.0 sessioninfo_1.2.2
[43] cachem_1.0.8 stringr_1.5.0 parallel_4.3.1
[46] matrixStats_1.0.0 vctrs_0.6.2 Matrix_1.5-4.1
[49] jsonlite_1.8.3 carData_3.0-5 car_3.1-2
[52] callr_3.7.3 hms_1.1.2 mixsqp_0.3-48
[55] ggrepel_0.9.1 rstatix_0.7.2 irlba_2.3.5.1
[58] jquerylib_0.1.4 tidyr_1.3.0 glue_1.6.2
[61] nloptr_2.0.3 ps_1.7.2 stringi_1.7.12
[64] gtable_0.3.1 later_1.3.1 RcppZiggurat_0.1.6
[67] munsell_0.5.0 tibble_3.2.1 pillar_1.9.0
[70] htmltools_0.5.5 R6_2.5.1 rprojroot_2.0.3
[73] evaluate_0.18 lattice_0.20-45 highr_0.10
[76] backports_1.4.1 Rfast_2.0.6 memoise_2.0.1
[79] broom_1.0.5 httpuv_1.6.11 bslib_0.5.0
[82] Rcpp_1.0.11 gridExtra_2.3 whisker_0.4.1
[85] xfun_0.39 fs_1.6.2 getPass_0.2-2
[88] pkgconfig_2.0.3