Last updated: 2024-10-15

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Overview

Traits

aFib, IBD, LDL, SBP, SCZ, WBC

details

Tissues

The independent tissues are selected by single tissue analysis

Omics

eQTL, sQTL weights are from Predictdb

stQTL was a combination of Munro apa + rs QTL, if a gene has both rs-QTL and APA-QTL, we use rs-QTL.

Settings

stQTL from Munro

Weight processing:

PredictDB:

all the PredictDB are converted from FUSION weights

drop_strand_ambig = TRUE,
scale_by_ld_variance = F (FUSION converted weights)
load_predictdb_LD = F,

Parameter estimation and fine-mapping

niter_prefit = 5,
niter = 30(default),
filter_L = TRUE,
group_prior_var_structure = “shared_type”,
maxSNP = 20000,
min_nonSNP_PIP = 0.5,

e + s QTL from predictdb

Weight processing:

PredictDB (eqtl, sqtl)

drop_strand_ambig = TRUE,
scale_by_ld_variance = T
load_predictdb_LD = F,

Parameter estimation and fine-mapping

group_prior_var_structure = “shared_type”,
filter_L = TRUE,
filter_nonSNP_PIP = FALSE,
min_abs_corr = 0.1,

mem: 150g 10cores

library(ctwas)
library(ggplot2)
library(tidyverse)
library(pheatmap)

mapping_predictdb <- readRDS("/project2/xinhe/shared_data/multigroup_ctwas/weights/mapping_files/PredictDB_mapping.RDS")
mapping_munro <- readRDS("/project2/xinhe/shared_data/multigroup_ctwas/weights/mapping_files/Munro_mapping.RDS")
mapping_two <- rbind(mapping_predictdb,mapping_munro)

load("/project2/xinhe/shared_data/multigroup_ctwas/gwas/samplesize.rdata")


colors <- c(  "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728",  "#9467bd", "#8c564b", "#e377c2", "#7f7f7f",  "#bcbd22",  "#17becf",  "#f7b6d2",  "#c5b0d5",  "#9edae5", "#ffbb78",  "#98df8a",  "#ff9896" )

plot_piechart <- function(ctwas_parameters, colors, by) {
  # Create the initial data frame
  data <- data.frame(
    category = names(ctwas_parameters$prop_heritability),
    percentage = ctwas_parameters$prop_heritability
  )
  
  # Split the category into context and type
  data <- data %>%
    mutate(
      context = sub("\\|.*", "", category),
      type = sub(".*\\|", "", category)
    )
  
  # Aggregate the data based on the 'by' parameter
  if (by == "type") {
    data <- data %>%
      group_by(type) %>%
      summarize(percentage = sum(percentage)) %>%
      mutate(category = type)  # Use type as the new category
  } else if (by == "context") {
    data <- data %>%
      group_by(context) %>%
      summarize(percentage = sum(percentage)) %>%
      mutate(category = context)  # Use context as the new category
  } else {
    stop("Invalid 'by' parameter. Use 'type' or 'context'.")
  }
  
  # Calculate percentage labels for the chart
  data$percentage_label <- paste0(round(data$percentage * 100, 1), "%")
  
  # Create the pie chart
  pie <- ggplot(data, aes(x = "", y = percentage, fill = category)) +
    geom_bar(stat = "identity", width = 1) +
    coord_polar("y", start = 0) +
    theme_void() +  # Remove background and axes
    geom_text(aes(label = percentage_label), 
              position = position_stack(vjust = 0.5), size = 3) +  # Adjust size as needed
    scale_fill_manual(values = colors) +  # Custom colors
    labs(fill = "Category") +  # Legend title
    ggtitle("Percent of Heritability")  # Title
  
  return(pie)
}

draw_gene_piechart_type <- function(data, colors) {
  # Filter data based on combined_pip
  data <- data[data$combined_pip > 0.8, ]
  
  # Count occurrences by eQTL, sQTL, and stQTL
  byeQTL <- nrow(data[data$eQTL_pip / data$combined_pip > 0.8, ])
  bysQTL <- nrow(data[data$sQTL_pip / data$combined_pip > 0.8, ])
  bystQTL <- nrow(data[data$stQTL_pip / data$combined_pip > 0.8, ])
  
  # Count occurrences for combined sQTL and stQTL
  bysQTLstQTL <- nrow(data[((data$stQTL_pip + data$sQTL_pip) / data$combined_pip) > 0.8 &
                             data$stQTL_pip / data$combined_pip < 0.8 &
                             data$sQTL_pip / data$combined_pip < 0.8, ])
  
  # Count unspecified
  unspecified <- nrow(data) - byeQTL - bysQTL - bystQTL - bysQTLstQTL
  
  # Create vectors for plotting
  n <- c(byeQTL, bysQTL, bystQTL, bysQTLstQTL, unspecified)
  prop <- round(n / nrow(data), 3)
  labels = c("by eQTL", "by sQTL", "by stQTL", "by sQTL+stQTL", "unspecified")
  lab.ypos = cumsum(prop) - 0.5 * prop
  
  # Prepare the data frame for plotting
  df <- data.frame("n" = n,
                   "class" = labels,
                   "prop" = prop,
                   "lab.ypos" = lab.ypos)
  
  # Generate the pie chart
  ggplot(df, aes(x = "", y = prop, fill = class)) +
    geom_bar(width = 1, stat = "identity", color = "white") +
    coord_polar("y", start = 0) +
    geom_text(aes(label = n),
              position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = colors) +  # Ensure 'palette' is defined
    theme_void()
}

draw_gene_piechart_tissue <- function(data, colors){
  
  data <- data[data$combined_pip>0.8,]
  tissues <- colnames(data)[3:7]
  tissues <- sub("_pip$", "", tissues)
  colnames(data)[3:7] <- paste0("tissue",c(1:5))
  
  
  bytissue1 <- nrow(data[data$tissue1/data$combined_pip>0.8,])
  bytissue2 <- nrow(data[data$tissue2/data$combined_pip>0.8,])
  bytissue3 <- nrow(data[data$tissue3/data$combined_pip>0.8,])
  bytissue4 <- nrow(data[data$tissue4/data$combined_pip>0.8,])
  bytissue5 <- nrow(data[data$tissue5/data$combined_pip>0.8,])
  unspecified <- nrow(data)-bytissue1-bytissue2-bytissue3-bytissue4-bytissue5
  
  n <- c(bytissue1,bytissue2,bytissue3,bytissue4,bytissue5,unspecified)
  
  prop <- round(n/nrow(data),3)
  labels = c(tissues,"unspecified")
  lab.ypos = cumsum(prop) - 0.5*prop
  df <- data.frame("n" = n,
                   "class" = labels,
                   "prop" = prop,
                   "lab.ypos" = lab.ypos)
  p <- ggplot(df, aes(x = "", y = prop, fill = class)) +
    geom_bar(width = 1, stat = "identity", color = "white") +
    coord_polar("y", start = 0)+
    geom_text(aes(label = n),
              position = position_stack(vjust = 0.5))+
    scale_fill_manual(values = colors) +
    theme_void()
  
  return(p)
}

plot_heatmap <- function(heatmap_data, main) {
  
  rownames(heatmap_data) <- heatmap_data$gene_name
  heatmap_data <- heatmap_data %>% dplyr::select(-gene_name, -combined_pip)
  
  if(nrow(heatmap_data) ==1){
    
    heatmap_data <- rbind(heatmap_data,rep(0,ncol(heatmap_data)))
    rownames(heatmap_data)[2] <- "fake_gene_for_plotting"
    
  }
  
  heatmap_matrix <- as.matrix(heatmap_data)
  
  p <- pheatmap(heatmap_matrix,
                cluster_rows = F,   # Cluster the rows (genes)
                cluster_cols = F,   # Cluster the columns (QTL types)
                color = colorRampPalette(c("white", "red"))(50), # Color gradient
                display_numbers = TRUE, # Display numbers in cells
                main = main,labels_row = rownames(heatmap_data), silent = T)
  
  return(p)
}

aFib-ebi-a-GCST006414

trait <- "aFib-ebi-a-GCST006414"
gwas_n <- samplesize[trait]
tissue <- c("Heart_Atrial_Appendage","Artery_Tibial","Muscle_Skeletal","Stomach","Thyroid")

results_dir_multi <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_multi <- readRDS(paste0(results_dir_multi,trait,".ctwas.res.RDS"))

param_multi <- ctwas_res_multi$param
make_convergence_plots(param_multi, gwas_n, colors = colors)

ctwas_parameters_multi <- summarize_param(param_multi, gwas_n)
pve_pie_by_type_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "type")
pve_pie_by_context_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "context")

gridExtra::grid.arrange(pve_pie_by_type_multi,pve_pie_by_context_multi, ncol = 2)

Fine-mapping

susie_alpha_res_multi <- ctwas_res_multi$susie_alpha_res

susie_alpha_res_multi <- anno_susie_alpha_res(susie_alpha_res_multi,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:56:28 INFO::Annotating susie alpha result ...
2024-10-15 14:56:30 INFO::Map molecular traits to genes
2024-10-15 14:56:31 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)

combined_pip_by_context_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)


combined_pip_by_context_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

DT::datatable(combined_pip_by_type_cs_multi[combined_pip_by_type_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by omics'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_context_cs_multi[combined_pip_by_context_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by tissue'),options = list(pageLength = 5) )

pie1 <- draw_gene_piechart_type(data = combined_pip_by_type_multi,colors = colors)
pie2 <- draw_gene_piechart_tissue(data = combined_pip_by_context_multi,colors = colors)

gridExtra::grid.arrange(pie1,pie2, ncol = 2)

Comparing with single tissue + eQTL analysis

ctwas_res_single <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/10.single_tissue_1007/results/",trait,"/",tissue[1],"/",trait,"_",tissue[1], ".ctwas.res.RDS"))

susie_alpha_res_single <- ctwas_res_single$susie_alpha_res

susie_alpha_res_single <- anno_susie_alpha_res(susie_alpha_res_single,
                                        mapping_table = mapping_predictdb,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:56:44 INFO::Annotating susie alpha result ...
2024-10-15 14:56:44 INFO::Map molecular traits to genes

combined_pip_by_type_single <- combine_gene_pips(susie_alpha_res_single, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_sig_single <- combined_pip_by_type_single[combined_pip_by_type_single$combined_pip > 0.8,]
combined_pip_by_type_sig_multi <- combined_pip_by_type_multi[combined_pip_by_type_multi$combined_pip > 0.8,]

sprintf("Number of genes with PIP > 0.8  -- Multi-group = %s", nrow(combined_pip_by_type_sig_multi))

[1] "Number of genes with PIP > 0.8  -- Multi-group = 71"

sprintf("Number of genes with PIP > 0.8  -- single eQTL = %s", nrow(combined_pip_by_type_sig_single))

[1] "Number of genes with PIP > 0.8  -- single eQTL = 24"

sprintf("Number of overlapped genes = %s", sum(combined_pip_by_type_sig_single$gene_name %in% combined_pip_by_type_sig_multi$gene_name))

[1] "Number of overlapped genes = 23"

genes_not_reported <- combined_pip_by_type_sig_single$gene_name[!combined_pip_by_type_sig_single$gene_name %in%combined_pip_by_type_sig_multi$gene_name]

DT::datatable(combined_pip_by_type_sig_single[combined_pip_by_type_sig_single$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_type_multi[combined_pip_by_type_multi$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

gene_multi_unique_type <- combined_pip_by_type_sig_multi[!combined_pip_by_type_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]

p1 <- plot_heatmap(heatmap_data = gene_multi_unique_type, main = "Unique genes found by multi-group analysis")

combined_pip_by_context_sig_multi <- combined_pip_by_context_multi[combined_pip_by_context_multi$combined_pip > 0.8,]
gene_multi_unique_context <- combined_pip_by_context_sig_multi[!combined_pip_by_context_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]
p2 <- plot_heatmap(heatmap_data = gene_multi_unique_context, main = "Unique genes found by multi-group analysis")


g1 <- p1$gtable
g2 <- p2$gtable
gridExtra::grid.arrange(g1, g2, ncol=2)

LDL-ukb-d-30780_irnt

trait <- "LDL-ukb-d-30780_irnt"
gwas_n <- samplesize[trait]
tissue <- c("Liver","Spleen","Esophagus_Mucosa","Esophagus_Gastroesophageal_Junction","Skin_Not_Sun_Exposed_Suprapubic")

results_dir_multi <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_multi <- readRDS(paste0(results_dir_multi,trait,".ctwas.res.RDS"))

param_multi <- ctwas_res_multi$param
make_convergence_plots(param_multi, gwas_n, colors = colors)

ctwas_parameters_multi <- summarize_param(param_multi, gwas_n)
pve_pie_by_type_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "type")
pve_pie_by_context_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "context")

gridExtra::grid.arrange(pve_pie_by_type_multi,pve_pie_by_context_multi, ncol = 2)

Fine-mapping

susie_alpha_res_multi <- ctwas_res_multi$susie_alpha_res

susie_alpha_res_multi <- anno_susie_alpha_res(susie_alpha_res_multi,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:56:58 INFO::Annotating susie alpha result ...
2024-10-15 14:56:58 INFO::Map molecular traits to genes
2024-10-15 14:57:01 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)

combined_pip_by_context_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)


combined_pip_by_context_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

DT::datatable(combined_pip_by_type_cs_multi[combined_pip_by_type_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by omics'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_context_cs_multi[combined_pip_by_context_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by tissue'),options = list(pageLength = 5) )

pie1 <- draw_gene_piechart_type(data = combined_pip_by_type_multi,colors = colors)
pie2 <- draw_gene_piechart_tissue(data = combined_pip_by_context_multi,colors = colors)

gridExtra::grid.arrange(pie1,pie2, ncol = 2)

Comparing with single tissue + eQTL analysis

ctwas_res_single <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/10.single_tissue_1007/results/",trait,"/",tissue[1],"/",trait,"_",tissue[1], ".ctwas.res.RDS"))

susie_alpha_res_single <- ctwas_res_single$susie_alpha_res

susie_alpha_res_single <- anno_susie_alpha_res(susie_alpha_res_single,
                                        mapping_table = mapping_predictdb,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:57:13 INFO::Annotating susie alpha result ...
2024-10-15 14:57:13 INFO::Map molecular traits to genes

combined_pip_by_type_single <- combine_gene_pips(susie_alpha_res_single, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_sig_single <- combined_pip_by_type_single[combined_pip_by_type_single$combined_pip > 0.8,]
combined_pip_by_type_sig_multi <- combined_pip_by_type_multi[combined_pip_by_type_multi$combined_pip > 0.8,]

sprintf("Number of genes with PIP > 0.8  -- Multi-group = %s", nrow(combined_pip_by_type_sig_multi))

[1] "Number of genes with PIP > 0.8  -- Multi-group = 96"

sprintf("Number of genes with PIP > 0.8  -- single eQTL = %s", nrow(combined_pip_by_type_sig_single))

[1] "Number of genes with PIP > 0.8  -- single eQTL = 31"

sprintf("Number of overlapped genes = %s", sum(combined_pip_by_type_sig_single$gene_name %in% combined_pip_by_type_sig_multi$gene_name))

[1] "Number of overlapped genes = 28"

genes_not_reported <- combined_pip_by_type_sig_single$gene_name[!combined_pip_by_type_sig_single$gene_name %in%combined_pip_by_type_sig_multi$gene_name]

DT::datatable(combined_pip_by_type_sig_single[combined_pip_by_type_sig_single$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_type_multi[combined_pip_by_type_multi$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

gene_multi_unique_type <- combined_pip_by_type_sig_multi[!combined_pip_by_type_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]

p1 <- plot_heatmap(heatmap_data = gene_multi_unique_type, main = "Unique genes found by multi-group analysis")

combined_pip_by_context_sig_multi <- combined_pip_by_context_multi[combined_pip_by_context_multi$combined_pip > 0.8,]
gene_multi_unique_context <- combined_pip_by_context_sig_multi[!combined_pip_by_context_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]
p2 <- plot_heatmap(heatmap_data = gene_multi_unique_context, main = "Unique genes found by multi-group analysis")


g1 <- p1$gtable
g2 <- p2$gtable
gridExtra::grid.arrange(g1, g2, ncol=2)

IBD-ebi-a-GCST004131

trait <- "IBD-ebi-a-GCST004131"
gwas_n <- samplesize[trait]
tissue <- c("Whole_Blood","Adipose_Subcutaneous","Cells_Cultured_fibroblasts","Spleen","Testis")


results_dir_multi <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_multi <- readRDS(paste0(results_dir_multi,trait,".ctwas.res.RDS"))

param_multi <- ctwas_res_multi$param
make_convergence_plots(param_multi, gwas_n, colors = colors)

ctwas_parameters_multi <- summarize_param(param_multi, gwas_n)
pve_pie_by_type_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "type")
pve_pie_by_context_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "context")

gridExtra::grid.arrange(pve_pie_by_type_multi,pve_pie_by_context_multi, ncol = 2)

Fine-mapping

susie_alpha_res_multi <- ctwas_res_multi$susie_alpha_res

susie_alpha_res_multi <- anno_susie_alpha_res(susie_alpha_res_multi,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:57:23 INFO::Annotating susie alpha result ...
2024-10-15 14:57:23 INFO::Map molecular traits to genes
2024-10-15 14:57:25 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)

combined_pip_by_context_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)


combined_pip_by_context_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

DT::datatable(combined_pip_by_type_cs_multi[combined_pip_by_type_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by omics'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_context_cs_multi[combined_pip_by_context_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by tissue'),options = list(pageLength = 5) )

pie1 <- draw_gene_piechart_type(data = combined_pip_by_type_multi,colors = colors)
pie2 <- draw_gene_piechart_tissue(data = combined_pip_by_context_multi,colors = colors)

gridExtra::grid.arrange(pie1,pie2, ncol = 2)

Comparing with single tissue + eQTL analysis

ctwas_res_single <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/10.single_tissue_1007/results/",trait,"/",tissue[1],"/",trait,"_",tissue[1], ".ctwas.res.RDS"))

susie_alpha_res_single <- ctwas_res_single$susie_alpha_res

susie_alpha_res_single <- anno_susie_alpha_res(susie_alpha_res_single,
                                        mapping_table = mapping_predictdb,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:57:35 INFO::Annotating susie alpha result ...
2024-10-15 14:57:35 INFO::Map molecular traits to genes

combined_pip_by_type_single <- combine_gene_pips(susie_alpha_res_single, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_sig_single <- combined_pip_by_type_single[combined_pip_by_type_single$combined_pip > 0.8,]
combined_pip_by_type_sig_multi <- combined_pip_by_type_multi[combined_pip_by_type_multi$combined_pip > 0.8,]

sprintf("Number of genes with PIP > 0.8  -- Multi-group = %s", nrow(combined_pip_by_type_sig_multi))

[1] "Number of genes with PIP > 0.8  -- Multi-group = 47"

sprintf("Number of genes with PIP > 0.8  -- single eQTL = %s", nrow(combined_pip_by_type_sig_single))

[1] "Number of genes with PIP > 0.8  -- single eQTL = 11"

sprintf("Number of overlapped genes = %s", sum(combined_pip_by_type_sig_single$gene_name %in% combined_pip_by_type_sig_multi$gene_name))

[1] "Number of overlapped genes = 7"

genes_not_reported <- combined_pip_by_type_sig_single$gene_name[!combined_pip_by_type_sig_single$gene_name %in%combined_pip_by_type_sig_multi$gene_name]

DT::datatable(combined_pip_by_type_sig_single[combined_pip_by_type_sig_single$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_type_multi[combined_pip_by_type_multi$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

gene_multi_unique_type <- combined_pip_by_type_sig_multi[!combined_pip_by_type_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]

p1 <- plot_heatmap(heatmap_data = gene_multi_unique_type, main = "Unique genes found by multi-group analysis")

combined_pip_by_context_sig_multi <- combined_pip_by_context_multi[combined_pip_by_context_multi$combined_pip > 0.8,]
gene_multi_unique_context <- combined_pip_by_context_sig_multi[!combined_pip_by_context_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]
p2 <- plot_heatmap(heatmap_data = gene_multi_unique_context, main = "Unique genes found by multi-group analysis")


g1 <- p1$gtable
g2 <- p2$gtable
gridExtra::grid.arrange(g1, g2, ncol=2)

SBP-ukb-a-360

trait <- "SBP-ukb-a-360"
gwas_n <- samplesize[trait]
tissue <- c("Artery_Tibial","Heart_Atrial_Appendage","Adipose_Subcutaneous","Brain_Cortex","Skin_Sun_Exposed_Lower_leg")


results_dir_multi <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_multi <- readRDS(paste0(results_dir_multi,trait,".ctwas.res.RDS"))

param_multi <- ctwas_res_multi$param
make_convergence_plots(param_multi, gwas_n, colors = colors)

ctwas_parameters_multi <- summarize_param(param_multi, gwas_n)
pve_pie_by_type_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "type")
pve_pie_by_context_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "context")

gridExtra::grid.arrange(pve_pie_by_type_multi,pve_pie_by_context_multi, ncol = 2)

Fine-mapping

susie_alpha_res_multi <- ctwas_res_multi$susie_alpha_res

susie_alpha_res_multi <- anno_susie_alpha_res(susie_alpha_res_multi,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:57:47 INFO::Annotating susie alpha result ...
2024-10-15 14:57:47 INFO::Map molecular traits to genes
2024-10-15 14:57:51 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)

combined_pip_by_context_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)


combined_pip_by_context_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

DT::datatable(combined_pip_by_type_cs_multi[combined_pip_by_type_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by omics'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_context_cs_multi[combined_pip_by_context_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by tissue'),options = list(pageLength = 5) )

pie1 <- draw_gene_piechart_type(data = combined_pip_by_type_multi,colors = colors)
pie2 <- draw_gene_piechart_tissue(data = combined_pip_by_context_multi,colors = colors)

gridExtra::grid.arrange(pie1,pie2, ncol = 2)

Comparing with single tissue + eQTL analysis

ctwas_res_single <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/10.single_tissue_1007/results/",trait,"/",tissue[1],"/",trait,"_",tissue[1], ".ctwas.res.RDS"))

susie_alpha_res_single <- ctwas_res_single$susie_alpha_res

susie_alpha_res_single <- anno_susie_alpha_res(susie_alpha_res_single,
                                        mapping_table = mapping_predictdb,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:58:05 INFO::Annotating susie alpha result ...
2024-10-15 14:58:05 INFO::Map molecular traits to genes

combined_pip_by_type_single <- combine_gene_pips(susie_alpha_res_single, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_sig_single <- combined_pip_by_type_single[combined_pip_by_type_single$combined_pip > 0.8,]
combined_pip_by_type_sig_multi <- combined_pip_by_type_multi[combined_pip_by_type_multi$combined_pip > 0.8,]

sprintf("Number of genes with PIP > 0.8  -- Multi-group = %s", nrow(combined_pip_by_type_sig_multi))

[1] "Number of genes with PIP > 0.8  -- Multi-group = 70"

sprintf("Number of genes with PIP > 0.8  -- single eQTL = %s", nrow(combined_pip_by_type_sig_single))

[1] "Number of genes with PIP > 0.8  -- single eQTL = 29"

sprintf("Number of overlapped genes = %s", sum(combined_pip_by_type_sig_single$gene_name %in% combined_pip_by_type_sig_multi$gene_name))

[1] "Number of overlapped genes = 19"

genes_not_reported <- combined_pip_by_type_sig_single$gene_name[!combined_pip_by_type_sig_single$gene_name %in%combined_pip_by_type_sig_multi$gene_name]

DT::datatable(combined_pip_by_type_sig_single[combined_pip_by_type_sig_single$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_type_multi[combined_pip_by_type_multi$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

gene_multi_unique_type <- combined_pip_by_type_sig_multi[!combined_pip_by_type_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]

p1 <- plot_heatmap(heatmap_data = gene_multi_unique_type, main = "Unique genes found by multi-group analysis")

combined_pip_by_context_sig_multi <- combined_pip_by_context_multi[combined_pip_by_context_multi$combined_pip > 0.8,]
gene_multi_unique_context <- combined_pip_by_context_sig_multi[!combined_pip_by_context_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]
p2 <- plot_heatmap(heatmap_data = gene_multi_unique_context, main = "Unique genes found by multi-group analysis")


g1 <- p1$gtable
g2 <- p2$gtable
gridExtra::grid.arrange(g1, g2, ncol=2)

SCZ-ieu-b-5102

trait <- "SCZ-ieu-b-5102"
gwas_n <- samplesize[trait]
tissue <- c("Brain_Hippocampus","Adrenal_Gland","Brain_Spinal_cord_cervical_c-1","Spleen","Heart_Left_Ventricle")


results_dir_multi <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_multi <- readRDS(paste0(results_dir_multi,trait,".ctwas.res.RDS"))

param_multi <- ctwas_res_multi$param
make_convergence_plots(param_multi, gwas_n, colors = colors)

ctwas_parameters_multi <- summarize_param(param_multi, gwas_n)
pve_pie_by_type_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "type")
pve_pie_by_context_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "context")

gridExtra::grid.arrange(pve_pie_by_type_multi,pve_pie_by_context_multi, ncol = 2)

Fine-mapping

susie_alpha_res_multi <- ctwas_res_multi$susie_alpha_res

susie_alpha_res_multi <- anno_susie_alpha_res(susie_alpha_res_multi,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:58:19 INFO::Annotating susie alpha result ...
2024-10-15 14:58:19 INFO::Map molecular traits to genes
2024-10-15 14:58:19 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)

combined_pip_by_context_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)


combined_pip_by_context_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

DT::datatable(combined_pip_by_type_cs_multi[combined_pip_by_type_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by omics'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_context_cs_multi[combined_pip_by_context_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by tissue'),options = list(pageLength = 5) )

pie1 <- draw_gene_piechart_type(data = combined_pip_by_type_multi,colors = colors)
pie2 <- draw_gene_piechart_tissue(data = combined_pip_by_context_multi,colors = colors)

gridExtra::grid.arrange(pie1,pie2, ncol = 2)

Comparing with single tissue + eQTL analysis

ctwas_res_single <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/10.single_tissue_1007/results/",trait,"/",tissue[1],"/",trait,"_",tissue[1], ".ctwas.res.RDS"))

susie_alpha_res_single <- ctwas_res_single$susie_alpha_res

susie_alpha_res_single <- anno_susie_alpha_res(susie_alpha_res_single,
                                        mapping_table = mapping_predictdb,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:58:30 INFO::Annotating susie alpha result ...
2024-10-15 14:58:30 INFO::Map molecular traits to genes

combined_pip_by_type_single <- combine_gene_pips(susie_alpha_res_single, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_sig_single <- combined_pip_by_type_single[combined_pip_by_type_single$combined_pip > 0.8,]
combined_pip_by_type_sig_multi <- combined_pip_by_type_multi[combined_pip_by_type_multi$combined_pip > 0.8,]

sprintf("Number of genes with PIP > 0.8  -- Multi-group = %s", nrow(combined_pip_by_type_sig_multi))

[1] "Number of genes with PIP > 0.8  -- Multi-group = 42"

sprintf("Number of genes with PIP > 0.8  -- single eQTL = %s", nrow(combined_pip_by_type_sig_single))

[1] "Number of genes with PIP > 0.8  -- single eQTL = 14"

sprintf("Number of overlapped genes = %s", sum(combined_pip_by_type_sig_single$gene_name %in% combined_pip_by_type_sig_multi$gene_name))

[1] "Number of overlapped genes = 7"

genes_not_reported <- combined_pip_by_type_sig_single$gene_name[!combined_pip_by_type_sig_single$gene_name %in%combined_pip_by_type_sig_multi$gene_name]

DT::datatable(combined_pip_by_type_sig_single[combined_pip_by_type_sig_single$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_type_multi[combined_pip_by_type_multi$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

gene_multi_unique_type <- combined_pip_by_type_sig_multi[!combined_pip_by_type_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]

p1 <- plot_heatmap(heatmap_data = gene_multi_unique_type, main = "Unique genes found by multi-group analysis")

combined_pip_by_context_sig_multi <- combined_pip_by_context_multi[combined_pip_by_context_multi$combined_pip > 0.8,]
gene_multi_unique_context <- combined_pip_by_context_sig_multi[!combined_pip_by_context_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]
p2 <- plot_heatmap(heatmap_data = gene_multi_unique_context, main = "Unique genes found by multi-group analysis")


g1 <- p1$gtable
g2 <- p2$gtable
gridExtra::grid.arrange(g1, g2, ncol=2)

WBC-ieu-b-30

trait <- "WBC-ieu-b-30"
gwas_n <- samplesize[trait]
tissue <- c("Whole_Blood","Adipose_Subcutaneous","Esophagus_Muscularis","Cells_Cultured_fibroblasts","Thyroid")


results_dir_multi <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_multi <- readRDS(paste0(results_dir_multi,trait,".ctwas.res.RDS"))

param_multi <- ctwas_res_multi$param
make_convergence_plots(param_multi, gwas_n, colors = colors)

ctwas_parameters_multi <- summarize_param(param_multi, gwas_n)
pve_pie_by_type_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "type")
pve_pie_by_context_multi <- plot_piechart(ctwas_parameters = ctwas_parameters_multi, colors = colors, by = "context")

gridExtra::grid.arrange(pve_pie_by_type_multi,pve_pie_by_context_multi, ncol = 2)

Fine-mapping

susie_alpha_res_multi <- ctwas_res_multi$susie_alpha_res

susie_alpha_res_multi <- anno_susie_alpha_res(susie_alpha_res_multi,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:58:51 INFO::Annotating susie alpha result ...
2024-10-15 14:58:51 INFO::Map molecular traits to genes
2024-10-15 14:58:52 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)

combined_pip_by_context_cs_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)


combined_pip_by_context_multi <- combine_gene_pips(susie_alpha_res_multi, 
                                             group_by = "gene_name",
                                             by = "context",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

DT::datatable(combined_pip_by_type_cs_multi[combined_pip_by_type_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by omics'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_context_cs_multi[combined_pip_by_context_cs_multi$combined_pip>0.8,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Combined PIP by tissue'),options = list(pageLength = 5) )

pie1 <- draw_gene_piechart_type(data = combined_pip_by_type_multi,colors = colors)
pie2 <- draw_gene_piechart_tissue(data = combined_pip_by_context_multi,colors = colors)

gridExtra::grid.arrange(pie1,pie2, ncol = 2)

Comparing with single tissue + eQTL analysis

ctwas_res_single <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/10.single_tissue_1007/results/",trait,"/",tissue[1],"/",trait,"_",tissue[1], ".ctwas.res.RDS"))

susie_alpha_res_single <- ctwas_res_single$susie_alpha_res

susie_alpha_res_single <- anno_susie_alpha_res(susie_alpha_res_single,
                                        mapping_table = mapping_predictdb,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-15 14:59:24 INFO::Annotating susie alpha result ...
2024-10-15 14:59:24 INFO::Map molecular traits to genes

combined_pip_by_type_single <- combine_gene_pips(susie_alpha_res_single, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

combined_pip_by_type_sig_single <- combined_pip_by_type_single[combined_pip_by_type_single$combined_pip > 0.8,]
combined_pip_by_type_sig_multi <- combined_pip_by_type_multi[combined_pip_by_type_multi$combined_pip > 0.8,]

sprintf("Number of genes with PIP > 0.8  -- Multi-group = %s", nrow(combined_pip_by_type_sig_multi))

[1] "Number of genes with PIP > 0.8  -- Multi-group = 272"

sprintf("Number of genes with PIP > 0.8  -- single eQTL = %s", nrow(combined_pip_by_type_sig_single))

[1] "Number of genes with PIP > 0.8  -- single eQTL = 81"

sprintf("Number of overlapped genes = %s", sum(combined_pip_by_type_sig_single$gene_name %in% combined_pip_by_type_sig_multi$gene_name))

[1] "Number of overlapped genes = 61"

genes_not_reported <- combined_pip_by_type_sig_single$gene_name[!combined_pip_by_type_sig_single$gene_name %in%combined_pip_by_type_sig_multi$gene_name]

DT::datatable(combined_pip_by_type_sig_single[combined_pip_by_type_sig_single$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

DT::datatable(combined_pip_by_type_multi[combined_pip_by_type_multi$gene_name %in% genes_not_reported,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes not reported by multi-group analysis'),options = list(pageLength = 5) )

gene_multi_unique_type <- combined_pip_by_type_sig_multi[!combined_pip_by_type_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]

p1 <- plot_heatmap(heatmap_data = gene_multi_unique_type, main = "Unique genes found by multi-group analysis")

combined_pip_by_context_sig_multi <- combined_pip_by_context_multi[combined_pip_by_context_multi$combined_pip > 0.8,]
gene_multi_unique_context <- combined_pip_by_context_sig_multi[!combined_pip_by_context_sig_multi$gene_name %in%  combined_pip_by_type_sig_single$gene_name,]
p2 <- plot_heatmap(heatmap_data = gene_multi_unique_context, main = "Unique genes found by multi-group analysis")


g1 <- p1$gtable
g2 <- p2$gtable
gridExtra::grid.arrange(g1, g2, ncol=2)

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] pheatmap_1.0.12 forcats_0.5.1   stringr_1.5.1   dplyr_1.1.4    
 [5] purrr_1.0.2     readr_2.1.2     tidyr_1.3.0     tibble_3.2.1   
 [9] tidyverse_1.3.1 ggplot2_3.5.1   ctwas_0.4.15   

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        fs_1.5.2                   
  [9] rstudioapi_0.13             farver_2.1.0               
 [11] DT_0.22                     ggrepel_0.9.1              
 [13] bit64_4.0.5                 lubridate_1.8.0            
 [15] AnnotationDbi_1.58.0        fansi_1.0.3                
 [17] xml2_1.3.3                  codetools_0.2-18           
 [19] logging_0.10-108            cachem_1.0.6               
 [21] knitr_1.39                  jsonlite_1.8.0             
 [23] workflowr_1.7.0             Rsamtools_2.12.0           
 [25] broom_0.8.0                 dbplyr_2.1.1               
 [27] png_0.1-7                   compiler_4.2.0             
 [29] httr_1.4.3                  backports_1.4.1            
 [31] assertthat_0.2.1            Matrix_1.5-3               
 [33] fastmap_1.1.0               lazyeval_0.2.2             
 [35] cli_3.6.1                   later_1.3.0                
 [37] htmltools_0.5.2             prettyunits_1.1.1          
 [39] tools_4.2.0                 gtable_0.3.0               
 [41] glue_1.6.2                  GenomeInfoDbData_1.2.8     
 [43] rappdirs_0.3.3              Rcpp_1.0.12                
 [45] Biobase_2.56.0              cellranger_1.1.0           
 [47] jquerylib_0.1.4             vctrs_0.6.5                
 [49] Biostrings_2.64.0           rtracklayer_1.56.0         
 [51] crosstalk_1.2.0             xfun_0.41                  
 [53] rvest_1.0.2                 lifecycle_1.0.4            
 [55] irlba_2.3.5                 restfulr_0.0.14            
 [57] ensembldb_2.20.2            XML_3.99-0.14              
 [59] zlibbioc_1.42.0             zoo_1.8-10                 
 [61] scales_1.3.0                gggrid_0.2-0               
 [63] hms_1.1.1                   promises_1.2.0.1           
 [65] MatrixGenerics_1.8.0        ProtGenerics_1.28.0        
 [67] parallel_4.2.0              SummarizedExperiment_1.26.1
 [69] RColorBrewer_1.1-3          AnnotationFilter_1.20.0    
 [71] LDlinkR_1.2.3               yaml_2.3.5                 
 [73] curl_4.3.2                  gridExtra_2.3              
 [75] memoise_2.0.1               sass_0.4.1                 
 [77] biomaRt_2.54.1              stringi_1.7.6              
 [79] RSQLite_2.3.1               highr_0.9                  
 [81] S4Vectors_0.34.0            BiocIO_1.6.0               
 [83] GenomicFeatures_1.48.3      BiocGenerics_0.42.0        
 [85] filelock_1.0.2              BiocParallel_1.30.3        
 [87] GenomeInfoDb_1.39.9         rlang_1.1.2                
 [89] pkgconfig_2.0.3             matrixStats_0.62.0         
 [91] bitops_1.0-7                evaluate_0.15              
 [93] lattice_0.20-45             labeling_0.4.2             
 [95] GenomicAlignments_1.32.0    htmlwidgets_1.5.4          
 [97] cowplot_1.1.1               bit_4.0.4                  
 [99] tidyselect_1.2.0            magrittr_2.0.3             
[101] R6_2.5.1                    IRanges_2.30.0             
[103] generics_0.1.2              DelayedArray_0.22.0        
[105] DBI_1.2.2                   haven_2.5.0                
[107] pgenlibr_0.3.3              pillar_1.9.0               
[109] withr_2.5.0                 KEGGREST_1.36.3            
[111] RCurl_1.98-1.7              mixsqp_0.3-43              
[113] modelr_0.1.8                crayon_1.5.1               
[115] utf8_1.2.2                  BiocFileCache_2.4.0        
[117] plotly_4.10.0               tzdb_0.4.0                 
[119] rmarkdown_2.25              progress_1.2.2             
[121] readxl_1.4.0                grid_4.2.0                 
[123] data.table_1.14.2           blob_1.2.3                 
[125] git2r_0.30.1                reprex_2.0.1               
[127] digest_0.6.29               httpuv_1.6.5               
[129] stats4_4.2.0                munsell_0.5.0              
[131] viridisLite_0.4.0           bslib_0.3.1

6 Traits, 5 tissues, eQTL + sQTL + stQTL

XSun

2024-10-15

Overview

Traits

Tissues

Omics

Settings

stQTL from Munro

e + s QTL from predictdb

aFib-ebi-a-GCST006414

Fine-mapping

Comparing with single tissue + eQTL analysis

LDL-ukb-d-30780_irnt

Fine-mapping

Comparing with single tissue + eQTL analysis

IBD-ebi-a-GCST004131

Fine-mapping

Comparing with single tissue + eQTL analysis

SBP-ukb-a-360

Fine-mapping

Comparing with single tissue + eQTL analysis

SCZ-ieu-b-5102

Fine-mapping

Comparing with single tissue + eQTL analysis

WBC-ieu-b-30

Fine-mapping

Comparing with single tissue + eQTL analysis