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library(ctwas)
library(data.table)
suppressMessages({library(plotly)})
library(tidyr)
library(plyr)
library(stringr)
source("~/causalTWAS/causal-TWAS/analysis/summarize_ctwas_plots.R")
source('~/causalTWAS/causal-TWAS/analysis/summarize_twas-coloc_plots.R')
source('~/causalTWAS/causal-TWAS/analysis/summarize_focus_plots.R')
source('~/causalTWAS/causal-TWAS/analysis/summarize_smr_plots.R')
source('~/causalTWAS/causal-TWAS/analysis/summarize_mrjti_plots.R')
source('~/causalTWAS/causal-TWAS/code/qqplot.R')
pgenfn = "/home/simingz/causalTWAS/ukbiobank/ukb_pgen_s200.113/ukb-s200.113_pgenfs.txt"
ld_pgenfn = "/home/simingz/causalTWAS/ukbiobank/ukb_pgen_s200.113/ukb-s200.113.2_pgenfs.txt"
outputdir = "/home/simingz/causalTWAS/simulations/simulation_ctwas_rss_20230321/" # /
comparedir = "/home/simingz/causalTWAS/simulations/simulation_ctwas_rss_20230321_compare/"
runtag = "ukb-s200.113-adi"
configtags = 1
simutags = paste(rep(1:9, each = length(1:5)), 1:5, sep = "-")

pgenfs <- read.table(pgenfn, header = F, stringsAsFactors = F)[,1]
pvarfs <- sapply(pgenfs, prep_pvar, outputdir = outputdir)

ld_pgenfs <- read.table(ld_pgenfn, header = F, stringsAsFactors = F)[,1]
ld_pvarfs <- sapply(ld_pgenfs, prep_pvar, outputdir = outputdir)

pgens <- lapply(1:length(pgenfs), function(x) prep_pgen(pgenf = pgenfs[x],pvarf = pvarfs[x]))

Analysis description

n.ori <- 200000 # number of samples
n <- pgenlibr::GetRawSampleCt(pgens[[1]])
p <- sum(unlist(lapply(pgens, pgenlibr::GetVariantCt))) # number of SNPs
J <- 8021 # number of genes

Data

The same as 20210416. We tried a few additional settings with small SNP heritablity (0.2) or large gene heritablity (0.5) following the suggestions by reviewers.

Analysis

ctwas

  1. Get z scores for gene expression. We used expression models and LD reference to get z scores for gene expression.

  2. Run ctwas_rss ctwas_rss algorithm first runs on all regions to get rough estimate for gene and SNP prior. Then run on small regions (having small probablities of having > 1 causal signals based on rough estimates) to get more accurate estimate. To lower computational burden, we downsampled SNPs (0.1) to estimate parameters. With the estimated parameters, we then run susie for all regions using both genes and downsampled SNPs with specified \(L\). After this, for regions with strong gene signals, we rerun susie with full SNPs using specified \(L\).

Configurations

ld_regions ='EUR', We used LDetect to define regions. To match UKbiobank data, we use the ‘EUR’ population

thin = 0.1, downsampled SNPs to 1/10 for parameter estimation step

niter1 =3, run niter1 =3 iterations first to get some rough parameter estimates.

prob_single = 0.8, the probability of a region having at most 1 singal has to be at least 0.8 to be selected for the parameter estimation step. This probability is obtained by using the PIPs from the first few iterations.

niter2 = 30, run niter2 = 30 for parameter estimation step

L = 5 , after parameter estimation, for running susie for all regions.

group_prior = NULL, the initiating prior parameters we used for running susie for each region is uniform prior for genes and SNPs.

group_prior_var = NULL, the initiating prior variance parameters we used for running susie for each region follows susie_rss’s default (50).

max_SNP_region = 5000, the maximum number of SNPs for re-running susie on strong gene signal regions is 5000.

ctwas results

Results: Each row shows parameter estimation results from 5 simulation runs with similar settings (i.e. pi1 and PVE for genes and SNPs). Results from each run were represented by one dot, dots with the same color come from the same run. truth: the true parameters, selected_truth: the truth in selected regions that were used to estimate parameters, ctwas: ctwas estimated parameters (using summary statistics as input). We run FUSION following default settings and adjust p values by BH method to get expected FDP.

We run FUSION following default settings and adjust p values by BH method to get expected FDP. We have also used Bonferroni correction for p values.

We ran coloc for all genes with TWAS p < 1e-4. We use PP4 (SNP associate with both traits). We ran SMR+HEIDI, using eQTL summary statistics GTEx v.7. We filter the results by requiring p_HEIDI > 0.05. The plots are based on SMR p value adjusted by BH method to get expected FDP.

FOCUS results contains nothing.

plot_par <- function(configtag, runtag, simutags){
  source(paste0(outputdir, "config", configtag, ".R"))
  phenofs <- paste0(outputdir, runtag, "_simu", simutags, "-pheno.Rd")
  susieIfs <- paste0(outputdir, runtag, "_simu", simutags, "_config", configtag, ".s2.susieIrssres.Rd")
  susieIfs2 <- paste0(outputdir, runtag, "_simu",simutags, "_config", configtag,".s2.susieIrss.txt")

  mtx <- show_param(phenofs, susieIfs, susieIfs2, thin = thin)
  par(mfrow=c(1,3))
  cat("simulations ", paste(simutags, sep=",") , ": ")
  cat("mean gene PVE:", mean(mtx[, "PVE.gene_truth"]), ",", "mean SNP PVE:", mean(mtx[, "PVE.SNP_truth"]), "\n")
  plot_param(mtx)
}

plot_PIP <- function(configtag, runtag,  simutags){
   phenofs <- paste0(outputdir, runtag, "_simu", simutags, "-pheno.Rd")
   susieIfs <- paste0(outputdir, runtag, "_simu",simutags, "_config", configtag,".susieIrss.txt")

   f1 <- caliPIP_plot(phenofs, susieIfs)
   f2 <- ncausal_plot(phenofs, susieIfs) 
   gridExtra::grid.arrange(f1, f2, ncol =2)
}

plot_fusion_coloc <- function(configtag, runtag,  simutags){
    phenofs <- paste0(outputdir, runtag, "_simu", simutags, "-pheno.Rd")
    fusioncolocfs <- paste0(comparedir, runtag, "_simu", simutags, ".Adipose_Subcutaneous.coloc.result")
    
    f1 <- caliFUSIONp_plot(phenofs, fusioncolocfs)
    f2 <- ncausalFUSIONp_plot(phenofs, fusioncolocfs)
    f3 <- caliFUSIONbon_plot(phenofs, fusioncolocfs)
    f4 <- ncausalFUSIONbon_plot(phenofs, fusioncolocfs)
    f5 <- caliPP4_plot(phenofs, fusioncolocfs, twas.p = 0.05/J)
    f6 <- ncausalPP4_plot(phenofs, fusioncolocfs, twas.p = 0.05/J)
    gridExtra::grid.arrange(f1, f2, ncol=2)
    gridExtra::grid.arrange(f3, f4, ncol=2)
    gridExtra::grid.arrange(f5, f6, ncol=2)
}
    
plot_focus <- function(configtag, runtag,  simutags){
    phenofs <- paste0(outputdir, runtag, "_simu", simutags, "-pheno.Rd")
    focusfs <- paste0(comparedir, runtag, "_simu", simutags, ".Adipose_Subcutaneous.focus.tsv")
    
    f1 <- califocusPIP_plot(phenofs, focusfs)
    f2 <- ncausalfocusPIP_plot(phenofs, focusfs)
    gridExtra::grid.arrange(f1, f2, ncol=2)
}

plot_smr <- function(configtag, runtag,  simutags){
    phenofs <- paste0(outputdir, runtag, "_simu", simutags, "-pheno.Rd")
    smrfs <- paste0(comparedir, runtag, "_simu", simutags, ".Adipose_Subcutaneous.smr")
    
    f1 <- caliSMRp_plot(phenofs, smrfs)
    f2 <- ncausalSMRp_plot(phenofs, smrfs)
    gridExtra::grid.arrange(f1, f2, ncol=2)
}

plot_mrjti <- function(configtag, runtag,  simutags){
    phenofs <- paste0(outputdir, runtag, "_simu", simutags, "-pheno.Rd")
    mrfs <- paste0(comparedir, runtag, "_simu", simutags, ".Adipose_Subcutaneous.mrjti.result")
    
    f1 <- caliMR_plot(phenofs, mrfs)
    f2 <- ncausalMR_plot(phenofs, mrfs)
    gridExtra::grid.arrange(f1, f2, ncol=2)
}
configtag <- 1
runtag = "ukb-s200.113-adi"

simutags <- paste(1, c(1:24,26:54, 56:70), sep = "-")
plot_par(configtag, runtag, simutags)
simulations  1-1 1-2 1-3 1-4 1-5 1-6 1-7 1-8 1-9 1-10 1-11 1-12 1-13 1-14 1-15 1-16 1-17 1-18 1-19 1-20 1-21 1-22 1-23 1-24 1-26 1-27 1-28 1-29 1-30 1-31 1-32 1-33 1-34 1-35 1-36 1-37 1-38 1-39 1-40 1-41 1-42 1-43 1-44 1-45 1-46 1-47 1-48 1-49 1-50 1-51 1-52 1-53 1-54 1-56 1-57 1-58 1-59 1-60 1-61 1-62 1-63 1-64 1-65 1-66 1-67 1-68 1-69 1-70 : mean gene PVE: 0.00510787 , mean SNP PVE: 0.1995698 

plot_PIP(configtag, runtag, simutags)

#plot_fusion_coloc(configtag, runtag, simutags)
# plot_focus(configtag, runtag, simutags)
#print("FOCUS result is null")
#plot_smr(configtag, runtag, simutags)

simutags <- paste(2, 1:30, sep = "-")
plot_par(configtag, runtag, simutags)
simulations  2-1 2-2 2-3 2-4 2-5 2-6 2-7 2-8 2-9 2-10 2-11 2-12 2-13 2-14 2-15 2-16 2-17 2-18 2-19 2-20 2-21 2-22 2-23 2-24 2-25 2-26 2-27 2-28 2-29 2-30 : mean gene PVE: 0.01072636 , mean SNP PVE: 0.2000549 

plot_PIP(configtag, runtag, simutags)

# plot_fusion_coloc(configtag, runtag, simutags)
# #plot_focus(configtag, runtag, simutags)
# plot_smr(configtag, runtag, simutags)

simutags <- paste(3, 1:10, sep = "-")
plot_par(configtag, runtag, simutags)
simulations  3-1 3-2 3-3 3-4 3-5 3-6 3-7 3-8 3-9 3-10 : mean gene PVE: 0.05220515 , mean SNP PVE: 0.2000557 

plot_PIP(configtag, runtag, simutags)

# plot_fusion_coloc(configtag, runtag, simutags)
# #plot_focus(configtag, runtag, simutags)
# plot_smr(configtag, runtag, simutags)

simutags <- paste(4, 1:10, sep = "-")
plot_par(configtag, runtag, simutags)
simulations  4-1 4-2 4-3 4-4 4-5 4-6 4-7 4-8 4-9 4-10 : mean gene PVE: 0.1040863 , mean SNP PVE: 0.1996897 

plot_PIP(configtag, runtag, simutags)

# plot_fusion_coloc(configtag, runtag, simutags)
# #plot_focus(configtag, runtag, simutags)
# plot_smr(configtag, runtag, simutags)

simutags <- paste(5, c(1:10), sep = "-")
plot_par(configtag, runtag, simutags)
simulations  5-1 5-2 5-3 5-4 5-5 5-6 5-7 5-8 5-9 5-10 : mean gene PVE: 0.155653 , mean SNP PVE: 0.1993414 

plot_PIP(configtag, runtag, simutags)

# plot_fusion_coloc(configtag, runtag, simutags)
# #plot_focus(configtag, runtag, simutags)
# plot_smr(configtag, runtag, simutags)

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:   /software/R-4.1.0-no-openblas-el7-x86_64/lib64/R/lib/libRblas.so
LAPACK: /software/R-4.1.0-no-openblas-el7-x86_64/lib64/R/lib/libRlapack.so

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

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

other attached packages:
 [1] ggpubr_0.4.0      plotrix_3.8-2     cowplot_1.1.1     stringr_1.4.0    
 [5] plyr_1.8.6        tidyr_1.1.3       plotly_4.9.4.1    ggplot2_3.3.5    
 [9] data.table_1.14.0 ctwas_0.1.29     

loaded via a namespace (and not attached):
 [1] httr_1.4.2        sass_0.4.0        jsonlite_1.7.2    viridisLite_0.4.0
 [5] foreach_1.5.1     carData_3.0-4     pgenlibr_0.3.2    logging_0.10-108 
 [9] bslib_0.4.2       assertthat_0.2.1  highr_0.9         cellranger_1.1.0 
[13] yaml_2.2.1        pillar_1.6.1      backports_1.2.1   lattice_0.20-44  
[17] glue_1.4.2        digest_0.6.27     promises_1.2.0.1  ggsignif_0.6.2   
[21] colorspace_2.0-2  htmltools_0.5.5   httpuv_1.6.1      Matrix_1.3-3     
[25] pkgconfig_2.0.3   broom_0.7.8       haven_2.4.1       purrr_0.3.4      
[29] scales_1.1.1      openxlsx_4.2.4    later_1.2.0       rio_0.5.27       
[33] git2r_0.28.0      tibble_3.1.2      farver_2.1.0      generics_0.1.0   
[37] car_3.0-11        ellipsis_0.3.2    cachem_1.0.5      withr_2.5.0      
[41] lazyeval_0.2.2    cli_3.6.1         readxl_1.3.1      magrittr_2.0.1   
[45] crayon_1.4.1      evaluate_0.20     fs_1.6.1          fansi_0.5.0      
[49] rstatix_0.7.0     forcats_0.5.1     foreign_0.8-81    tools_4.1.0      
[53] hms_1.1.0         lifecycle_1.0.3   munsell_0.5.0     ggsci_2.9        
[57] zip_2.2.0         compiler_4.1.0    jquerylib_0.1.4   rlang_1.1.0      
[61] grid_4.1.0        iterators_1.0.13  rstudioapi_0.13   htmlwidgets_1.5.3
[65] labeling_0.4.2    rmarkdown_2.21    gtable_0.3.0      codetools_0.2-18 
[69] abind_1.4-5       DBI_1.1.1         curl_4.3.2        R6_2.5.0         
[73] gridExtra_2.3     knitr_1.42        dplyr_1.0.7       fastmap_1.1.0    
[77] utf8_1.2.1        workflowr_1.6.2   rprojroot_2.0.2   stringi_1.6.2    
[81] Rcpp_1.0.9        vctrs_0.3.8       tidyselect_1.1.1  xfun_0.38