Last updated: 2025-05-25

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Rmd	c791833	XSun	2025-05-21	update
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Introduction

For non-psychiatric traits, we ran a cTWAS analysis using sQTL data from all 49 GTEx tissues.
For psychiatric traits, we used only the 9 GTEx brain tissues.
We ranked tissues by their G-test p-values & Fisher p-values and selected those passing the Bonferroni threshold (0.05 divided by the number of tissues) for the multi-group cTWAS analysis.
Default settings were used for computing z-scores, assembling input data, and estimating model parameters.

Mem: 50g/node got killed, 100g/node

Remove tissues with <200 samples. (2) Remove some tissues that are not related to our traits, including Testis, Nerve Tibial, Ovary, Prostate, Uterus, Vagina, Breast Mammary Tissue.

z_gene <- compute_gene_z(z_snp, weights, ncore=10)

res <- assemble_region_data(region_info, 
                                z_snp, 
                                z_gene, 
                                weights,
                                snp_map,
                                maxSNP = Inf,
                                min_group_size = 100,
                                thin = 1,
                                adjust_boundary_genes = TRUE,
                                ncore = 15)

param <- est_param(region_data, 
                       group_prior_var_structure = "shared_all",
                       null_method = "ctwas",
                       niter_prefit = 3,
                       min_gene = 0,
                       min_var = 2,                          
                       min_p_single_effect = 0.8,
                       niter = 200, 
                       ncore = 15,
                       verbose=TRUE)

library(ctwas)

Warning: replacing previous import 'utils::download.file' by
'restfulr::download.file' when loading 'rtracklayer'

source("/project/xinhe/xsun/multi_group_ctwas/data/samplesize.R")

trait_nopsy <- c("LDL-ukb-d-30780_irnt","aFib-ebi-a-GCST006414","ATH_gtexukb","BMI-panukb","HB-panukb",
             "Height-panukb","HTN-panukb","IBD-ebi-a-GCST004131","PLT-panukb","RA-panukb","RBC-panukb",
             "SBP-ukb-a-360","T1D-GCST90014023","WBC-ieu-b-30","T2D-panukb")

trait_psy <- c("SCZ-ieu-b-5102","ASD-ieu-a-1185","BIP-ieu-b-5110","MDD-ieu-b-102","PD-ieu-b-7","ADHD-ieu-a-1183","NS-ukb-a-230")

DT::datatable(matrix())

folder_results <- "/project/xinhe/xsun/multi_group_ctwas/21.tissue_selection_0511/results/S_thin1_shared_all_mingene0_exclude_brainprocess/"

Non-psychiatric traits

Tissue selection

converge_df <- c()
for (trait in trait_nopsy){

  param <- readRDS(paste0(folder_results,trait,"/",trait,".thin1.shared_all.param.RDS"))

  gwas_n <- samplesize[trait]

  param_summarized_fisher <- summarize_param(param = param,gwas_n = gwas_n,enrichment_test = "fisher",alternative = "greater")
  param_summarized_G <- summarize_param(param = param,gwas_n = gwas_n,enrichment_test = "G")

  param_df <- data.frame(
    group = names(param_summarized_fisher$group_size),
    group_size = as.numeric(param_summarized_fisher$group_size[names(param_summarized_fisher$group_size)]),
    group_pve = as.numeric(param_summarized_fisher$group_pve[names(param_summarized_fisher$group_size)]),
    prop_heritability = as.numeric(param_summarized_fisher$prop_heritability[names(param_summarized_fisher$group_size)]),
    log_enrichment = as.numeric(param_summarized_fisher$log_enrichment[names(param_summarized_fisher$group_size)]),
    log_enrichment_se = as.numeric(param_summarized_fisher$log_enrichment_se[names(param_summarized_fisher$group_size)]),
    enrichment_pval_fisher = as.numeric(param_summarized_fisher$enrichment_pval[names(param_summarized_fisher$group_size)]),
    enrichment_pval_G = as.numeric(param_summarized_G$enrichment_pval[names(param_summarized_G$group_size)])
  )

  param_df$total_pve <- param_summarized_fisher$total_pve

  param_df$prop_heritability <- paste0(round(param_df$prop_heritability * 100, 5), "%")

  param_df <- param_df[order(param_df$enrichment_pval_fisher,decreasing = F),]

  param_df_qtl <- param_df[-nrow(param_df),]
  threshold <- 0.05/(nrow(param_df_qtl)-1)



  cat("<br>")
  cat(knitr::knit_print(DT::datatable(param_df, caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;',trait,options = list(pageLength = 10)))))
  cat("<br>")


  cat("<br>")
  cat("<br>")
  print(paste0("p-value cutoff(0.05/num_tissue) = ",threshold))
  cat("<br>")
  cat("<br>")
  cat(paste0("Number of selected tissue -- fisher = ",min(10,sum(param_df_qtl$enrichment_pval_fisher < threshold)),"\n"))
  cat("<br>")
  cat(paste0(
    head(param_df_qtl$group[param_df_qtl$enrichment_pval_fisher < threshold], 10),
    collapse = " "
  ))
  cat("<br>")
  cat("<br>")
  cat("<br>")
    cat(paste0("Number of selected tissue -- G = ",min(10,sum(param_df_qtl$enrichment_pval_G < threshold)),"\n"))
  cat("<br>")
  cat(paste0(
    head(param_df_qtl$group[param_df_qtl$enrichment_pval_G < threshold], 10),
    collapse = " "
  ))
  cat("<br>")
  cat("<br>")
  cat("<br>")

  EM_iter <- length(param$loglik_iters)
  converge <- param$converged
  converge_df <- rbind(converge_df,c(trait,EM_iter,converge))
}

[1] “p-value cutoff(0.05/num_tissue) = 0.00208333333333333”

Number of selected tissue – fisher = 2
Pituitary|sQTL Skin_Sun_Exposed_Lower_leg|sQTL

Number of selected tissue – G = 2
Pituitary|sQTL Skin_Sun_Exposed_Lower_leg|sQTL

EM convergence

colnames(converge_df) <- c("trait","num_EM_iter","converge")
cat("<br>")

DT::datatable(converge_df,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','EM convergence '),options = list(pageLength = 30) )

cat("<br>")

Psychiatric traits

Tissue selection

converge_df <- c()
for (trait in trait_psy){
  
  param <- readRDS(paste0(folder_results,trait,"/",trait,".thin1.shared_all.param.RDS"))
  
  gwas_n <- samplesize[trait]

  param_summarized_fisher <- summarize_param(param = param,gwas_n = gwas_n,enrichment_test = "fisher",alternative = "greater")
  param_summarized_G <- summarize_param(param = param,gwas_n = gwas_n,enrichment_test = "G")
  
  param_df <- data.frame(
    group = names(param_summarized_fisher$group_size),
    group_size = as.numeric(param_summarized_fisher$group_size[names(param_summarized_fisher$group_size)]),
    group_pve = as.numeric(param_summarized_fisher$group_pve[names(param_summarized_fisher$group_size)]),
    prop_heritability = as.numeric(param_summarized_fisher$prop_heritability[names(param_summarized_fisher$group_size)]),
    log_enrichment = as.numeric(param_summarized_fisher$log_enrichment[names(param_summarized_fisher$group_size)]),
    log_enrichment_se = as.numeric(param_summarized_fisher$log_enrichment_se[names(param_summarized_fisher$group_size)]),
    enrichment_pval_fisher = as.numeric(param_summarized_fisher$enrichment_pval[names(param_summarized_fisher$group_size)]),
    enrichment_pval_G = as.numeric(param_summarized_G$enrichment_pval[names(param_summarized_G$group_size)])
  )

  param_df$total_pve <- param_summarized_fisher$total_pve
  
  param_df$prop_heritability <- paste0(round(param_df$prop_heritability * 100, 5), "%")
  
  param_df <- param_df[order(param_df$enrichment_pval_fisher,decreasing = F),]
  
  param_df_qtl <- param_df[-nrow(param_df),]
  threshold <- 0.05/(nrow(param_df_qtl)-1)
  
  
  
  cat("<br>")
  cat(knitr::knit_print(DT::datatable(param_df, caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;',trait,options = list(pageLength = 10)))))
  cat("<br>")
  
  
  cat("<br>")
  cat("<br>")
  print(paste0("p-value cutoff(0.05/num_tissue) = ",threshold))
  cat("<br>")
  cat("<br>")
  cat(paste0("Number of selected tissue -- fisher = ",min(10,sum(param_df_qtl$enrichment_pval_fisher < threshold)),"\n"))
  cat("<br>")
  cat(paste0(
    head(param_df_qtl$group[param_df_qtl$enrichment_pval_fisher < threshold], 10), 
    collapse = " "
  ))
  cat("<br>")
  cat("<br>")
  cat("<br>")
    cat(paste0("Number of selected tissue -- G = ",min(10,sum(param_df_qtl$enrichment_pval_G < threshold)),"\n"))
  cat("<br>")
  cat(paste0(
    head(param_df_qtl$group[param_df_qtl$enrichment_pval_G < threshold], 10), 
    collapse = " "
  ))
  cat("<br>")
  cat("<br>")
  cat("<br>")
 
  EM_iter <- length(param$loglik_iters)
  converge <- param$converged
  converge_df <- rbind(converge_df,c(trait,EM_iter,converge))
}

[1] “p-value cutoff(0.05/num_tissue) = 0.00714285714285714”

Number of selected tissue – fisher = 2
Brain_Cerebellar_Hemisphere|sQTL Brain_Cerebellum|sQTL

Number of selected tissue – G = 2
Brain_Cerebellar_Hemisphere|sQTL Brain_Cerebellum|sQTL

[1] “p-value cutoff(0.05/num_tissue) = 0.00714285714285714”

Number of selected tissue – fisher = 3
Brain_Frontal_Cortex_BA9|sQTL Brain_Hypothalamus|sQTL Brain_Caudate_basal_ganglia|sQTL

Number of selected tissue – G = 3
Brain_Frontal_Cortex_BA9|sQTL Brain_Hypothalamus|sQTL Brain_Caudate_basal_ganglia|sQTL

[1] “p-value cutoff(0.05/num_tissue) = 0.00714285714285714”

Number of selected tissue – fisher = 4
Brain_Cerebellar_Hemisphere|sQTL Brain_Nucleus_accumbens_basal_ganglia|sQTL Brain_Cortex|sQTL Brain_Frontal_Cortex_BA9|sQTL

Number of selected tissue – G = 4
Brain_Cerebellar_Hemisphere|sQTL Brain_Nucleus_accumbens_basal_ganglia|sQTL Brain_Cortex|sQTL Brain_Frontal_Cortex_BA9|sQTL

[1] “p-value cutoff(0.05/num_tissue) = 0.00714285714285714”

Number of selected tissue – fisher = 1
Brain_Cerebellar_Hemisphere|sQTL

Number of selected tissue – G = 1
Brain_Cerebellar_Hemisphere|sQTL

EM convergence

colnames(converge_df) <- c("trait","num_EM_iter","converge")
cat("<br>")

DT::datatable(converge_df,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','EM convergence '),options = list(pageLength = 30) )

cat("<br>")

sessionInfo()

R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so

locale:
[1] C

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

other attached packages:
[1] ctwas_0.5.19

loaded via a namespace (and not attached):
  [1] colorspace_2.0-3            rjson_0.2.21               
  [3] ellipsis_0.3.2              rprojroot_2.0.3            
  [5] XVector_0.36.0              locuszoomr_0.2.1           
  [7] GenomicRanges_1.48.0        base64enc_0.1-3            
  [9] fs_1.5.2                    rstudioapi_0.13            
 [11] DT_0.22                     ggrepel_0.9.1              
 [13] bit64_4.0.5                 AnnotationDbi_1.58.0       
 [15] fansi_1.0.3                 xml2_1.3.3                 
 [17] codetools_0.2-18            logging_0.10-108           
 [19] cachem_1.0.6                knitr_1.39                 
 [21] jsonlite_1.8.0              workflowr_1.7.0            
 [23] Rsamtools_2.12.0            dbplyr_2.1.1               
 [25] png_0.1-7                   readr_2.1.2                
 [27] compiler_4.2.0              httr_1.4.3                 
 [29] assertthat_0.2.1            Matrix_1.5-3               
 [31] fastmap_1.1.0               lazyeval_0.2.2             
 [33] cli_3.6.1                   later_1.3.0                
 [35] htmltools_0.5.2             prettyunits_1.1.1          
 [37] tools_4.2.0                 gtable_0.3.0               
 [39] glue_1.6.2                  GenomeInfoDbData_1.2.8     
 [41] dplyr_1.1.4                 rappdirs_0.3.3             
 [43] Rcpp_1.0.12                 Biobase_2.56.0             
 [45] jquerylib_0.1.4             vctrs_0.6.5                
 [47] Biostrings_2.64.0           rtracklayer_1.56.0         
 [49] crosstalk_1.2.0             xfun_0.41                  
 [51] stringr_1.5.1               irlba_2.3.5                
 [53] lifecycle_1.0.4             restfulr_0.0.14            
 [55] ensembldb_2.20.2            XML_3.99-0.14              
 [57] zlibbioc_1.42.0             zoo_1.8-10                 
 [59] scales_1.3.0                gggrid_0.2-0               
 [61] hms_1.1.1                   promises_1.2.0.1           
 [63] MatrixGenerics_1.8.0        ProtGenerics_1.28.0        
 [65] parallel_4.2.0              SummarizedExperiment_1.26.1
 [67] AnnotationFilter_1.20.0     LDlinkR_1.2.3              
 [69] yaml_2.3.5                  curl_4.3.2                 
 [71] memoise_2.0.1               ggplot2_3.5.1              
 [73] sass_0.4.1                  biomaRt_2.54.1             
 [75] stringi_1.7.6               RSQLite_2.3.1              
 [77] S4Vectors_0.34.0            BiocIO_1.6.0               
 [79] GenomicFeatures_1.48.3      BiocGenerics_0.42.0        
 [81] filelock_1.0.2              BiocParallel_1.30.3        
 [83] repr_1.1.4                  GenomeInfoDb_1.39.9        
 [85] rlang_1.1.2                 pkgconfig_2.0.3            
 [87] matrixStats_0.62.0          bitops_1.0-7               
 [89] evaluate_0.15               lattice_0.20-45            
 [91] purrr_1.0.2                 GenomicAlignments_1.32.0   
 [93] htmlwidgets_1.5.4           cowplot_1.1.1              
 [95] bit_4.0.4                   tidyselect_1.2.0           
 [97] magrittr_2.0.3              AMR_2.1.1                  
 [99] R6_2.5.1                    IRanges_2.30.0             
[101] generics_0.1.2              DelayedArray_0.22.0        
[103] DBI_1.2.2                   pgenlibr_0.3.3             
[105] pillar_1.9.0                whisker_0.4                
[107] mixsqp_0.3-43               KEGGREST_1.36.3            
[109] RCurl_1.98-1.7              tibble_3.2.1               
[111] crayon_1.5.1                utf8_1.2.2                 
[113] BiocFileCache_2.4.0         plotly_4.10.0              
[115] tzdb_0.4.0                  rmarkdown_2.25             
[117] progress_1.2.2              grid_4.2.0                 
[119] data.table_1.14.2           blob_1.2.3                 
[121] git2r_0.30.1                digest_0.6.29              
[123] tidyr_1.3.0                 httpuv_1.6.5               
[125] stats4_4.2.0                munsell_0.5.0              
[127] viridisLite_0.4.0           skimr_2.1.4                
[129] bslib_0.3.1

Tissue selection – splicing, tissue filtered by sample size, irrelevant tissues filtered out, brian tissue removed from non-psychiatric traits

XSun

2025-05-21

Introduction

Non-psychiatric traits

Tissue selection

EM convergence

Psychiatric traits

Tissue selection

EM convergence