Last updated: 2024-12-06

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Rmd	984b87f	XSun	2024-12-06	update
html	984b87f	XSun	2024-12-06	update
Rmd	4885dec	XSun	2024-12-05	update
html	4885dec	XSun	2024-12-05	update
Rmd	32ca054	XSun	2024-12-03	update

Introduction

We compare the post-processed results with the original results here: https://sq-96.github.io/multigroup_ctwas_analysis/multi_group_6traits_15weights_ess.html

library(ctwas)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(gridExtra)
library(dplyr)

ens_db <- EnsDb.Hsapiens.v86

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)


compute_pip_per_cs <- function(combined_data, susie_data) {
  # Initialize an empty list to store results
  details <- list()
  
  # Iterate over each unique gene name in the combined data
  unique_genes <- unique(combined_data$gene_name)
  
  for (genename in unique_genes) {
    # dplyr::filter susie data for the current gene
    susie_alpha_res_multi_per_gene <- susie_data %>%
      dplyr::filter(gene_name == genename)
    
    # Get all unique credible sets for the current gene
    cs_all <- unique(susie_alpha_res_multi_per_gene$susie_set[susie_alpha_res_multi_per_gene$in_cs])
    
    if (length(cs_all) > 1) {
      # dplyr::filter complete cases and those in credible sets
      susie_alpha_res_multi_per_gene <- susie_alpha_res_multi_per_gene %>%
        dplyr::filter(complete.cases(cs), in_cs)
      
      # Summarize the data
      summed_alpha_with_details <- susie_alpha_res_multi_per_gene %>%
        group_by(susie_set) %>%
        summarise(
          total_susie_alpha = round(sum(susie_alpha, na.rm = TRUE), digits = 3),
          num_molecular_traits = n(),
          ids_pip = paste0(id, "(", round(susie_alpha, digits = 3), ")", collapse = ", ")
        )
      
      # Add gene name to the summarized data
      summed_alpha_with_details$gene_name <- genename
      
      # Append the result to the details list
      details[[length(details) + 1]] <- summed_alpha_with_details
    }
  }
  
  # Combine all results into a single data frame
  final_details <- bind_rows(details)
  
  if(nrow(final_details) > 0){
    final_details <- final_details[,c("gene_name","susie_set","total_susie_alpha","num_molecular_traits","ids_pip")]
    colnames(final_details) <- c("gene_name","CS","total_PIP_CS","num_molecular_traits_CS","ids_pip_CS")
  }
  
  
  return(final_details)
}

aFib-ebi-a-GCST006414

trait <- "aFib-ebi-a-GCST006414"

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

finemap_res_origin <- ctwas_res_origin$finemap_res

Region merge

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))

finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]


finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]

finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")

DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )

LD-mismatch

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rmld_",trait,".rdata"))

sprintf("The number of problematic regions = %s", length(problematic_region_ids))

[1] "The number of problematic regions = 4"

sprintf("The number of problematic genes = %s", length(problematic_genes))

[1] "The number of problematic genes = 4"

sprintf("The number of problematic snps = %s", length(res_ldmismatch$problematic_snps))

[1] "The number of problematic snps = 1712"

sprintf("The number of flipped snps = %s", length(res_ldmismatch$flipped_snps))

[1] "The number of flipped snps = 22"

finemap_rm_res_problematic_region <- finemap_res_rm[finemap_res_rm$id %in% problematic_genes,]

finemap_noLD_res_problematic_region <- res_rmld$finemap_res
merge_regionmerge_nold <- merge(finemap_rm_res_problematic_region,finemap_noLD_res_problematic_region, by = "id")
merge_regionmerge_nold <- merge_regionmerge_nold[,c("id","susie_pip.x","susie_pip.y")]
colnames(merge_regionmerge_nold) <-  c("id","susie_pip_after_regionmerge","susie_pip_ld-mismatch-fixed") 

DT::datatable(merge_regionmerge_nold,caption = htmltools::tags$caption(style = 'caption-side: topleft; text-align = left; color:black;','PIP after region merge and fixed PIP for problematic genes'),options = list(pageLength = 5) )

finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]

p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("Original ctwas results") +
  theme_minimal()


finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]

p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("After region merge") +
  theme_minimal()


finemap_noLD_res_problematic_region_gene <- finemap_noLD_res_problematic_region[finemap_noLD_res_problematic_region$type !="SNP",]

p3 <- ggplot(data = finemap_noLD_res_problematic_region_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("After LD mismatch fixed") +
  theme_minimal()


grid.arrange(p1,p2,p3, ncol = 3)

Version	Author	Date
4885dec	XSun	2024-12-05

Comparing results without post processing

susie_alpha_res_origin <- ctwas_res_origin$susie_alpha_res
susie_alpha_res_origin <- anno_susie_alpha_res(susie_alpha_res_origin,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:21:56 INFO::Annotating susie alpha result ...
2024-12-06 13:21:56 INFO::Map molecular traits to genes
2024-12-06 13:21:57 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_origin <- combine_gene_pips(susie_alpha_res_origin, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_origin <- combined_pip_by_type_cs_origin[combined_pip_by_type_cs_origin$combined_pip > 0.8,]

susie_alpha_res_rm <- res_regionmerge$susie_alpha_res
susie_alpha_res_rm <- anno_susie_alpha_res(susie_alpha_res_rm,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:22:03 INFO::Annotating susie alpha result ...
2024-12-06 13:22:03 INFO::Map molecular traits to genes
2024-12-06 13:22:03 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rm <- combine_gene_pips(susie_alpha_res_rm, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rm <- combined_pip_by_type_cs_rm[combined_pip_by_type_cs_rm$combined_pip > 0.8,]



susie_alpha_res_rmld <- res_rmld$susie_alpha_res
susie_alpha_res_rmld <- anno_susie_alpha_res(susie_alpha_res_rmld,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:22:07 INFO::Annotating susie alpha result ...
2024-12-06 13:22:07 INFO::Map molecular traits to genes
2024-12-06 13:22:08 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rmld <- combine_gene_pips(susie_alpha_res_rmld, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rmld <- combined_pip_by_type_cs_rmld[combined_pip_by_type_cs_rmld$combined_pip > 0.8,]


origin_unique <- combined_pip_by_type_sig_origin[!combined_pip_by_type_sig_origin$gene_name %in% combined_pip_by_type_sig_rmld$gene_name,]

origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rm, by = "gene_name", all.x = TRUE)

# Then merge the result with combined_pip_by_type_cs_rmld
origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rmld, by = "gene_name", all.x = TRUE)
origin_unique_show <- origin_unique[,c("gene_name","combined_pip.x","combined_pip.y","combined_pip","eQTL_pip.x","eQTL_pip.y","eQTL_pip","sQTL_pip.x","sQTL_pip.y","sQTL_pip","stQTL_pip.x","stQTL_pip.x","stQTL_pip","combined_cs_id.x","combined_cs_id.y","combined_cs_id")]

colnames(origin_unique_show) <- c("gene_name","combined_pip_origin","combined_pip_rm","combined_pip_rmld","eQTL_pip_origin","eQTL_pip_rm","eQTL_pip_rmld","sQTL_pip_origin","sQTL_pip_rm","sQTL_pip_rmld","stQTL_pip_origin","stQTL_pip_origin","stQTL_pip_rmld","combined_cs_id_origin","combined_cs_id_rm","combined_cs_id_rmld")

DT::datatable(origin_unique_show,caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left; color: black;','Genes no longer significant after post processing'),options = list(pageLength = 5))

Exploring allelic heterogeneity

pip_per_cs <- compute_pip_per_cs(combined_pip_by_type_sig_rmld, susie_alpha_res_rmld)

sprintf("Number of genes having allelic heterogeneity = %s",length(unique(pip_per_cs$gene_name)))

[1] "Number of genes having allelic heterogeneity = 4"

if(nrow(pip_per_cs) > 0) {
  DT::datatable(pip_per_cs,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','PIP per CS'),options = list(pageLength = 5) )
}

LDL-ukb-d-30780_irnt

trait <- "LDL-ukb-d-30780_irnt"

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

finemap_res_origin <- ctwas_res_origin$finemap_res

Region merge

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))

finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]


finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]

finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")

DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )

LD-mismatch

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rmld_",trait,".rdata"))

sprintf("The number of problematic regions = %s", length(problematic_region_ids))

[1] "The number of problematic regions = 2"

sprintf("The number of problematic genes = %s", length(problematic_genes))

[1] "The number of problematic genes = 5"

sprintf("The number of problematic snps = %s", length(res_ldmismatch$problematic_snps))

[1] "The number of problematic snps = 325"

sprintf("The number of flipped snps = %s", length(res_ldmismatch$flipped_snps))

[1] "The number of flipped snps = 8"

finemap_rm_res_problematic_region <- finemap_res_rm[finemap_res_rm$id %in% problematic_genes,]

finemap_noLD_res_problematic_region <- res_rmld$finemap_res
merge_regionmerge_nold <- merge(finemap_rm_res_problematic_region,finemap_noLD_res_problematic_region, by = "id")
merge_regionmerge_nold <- merge_regionmerge_nold[,c("id","susie_pip.x","susie_pip.y")]
colnames(merge_regionmerge_nold) <-  c("id","susie_pip_after_regionmerge","susie_pip_ld-mismatch-fixed") 

DT::datatable(merge_regionmerge_nold,caption = htmltools::tags$caption(style = 'caption-side: topleft; text-align = left; color:black;','PIP after region merge and fixed PIP for problematic genes'),options = list(pageLength = 5) )

finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]

p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("Original ctwas results") +
  theme_minimal()


finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]

p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("After region merge") +
  theme_minimal()


finemap_noLD_res_problematic_region_gene <- finemap_noLD_res_problematic_region[finemap_noLD_res_problematic_region$type !="SNP",]

p3 <- ggplot(data = finemap_noLD_res_problematic_region_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("After LD mismatch fixed") +
  theme_minimal()


grid.arrange(p1,p2,p3, ncol = 3)

Version	Author	Date
4885dec	XSun	2024-12-05

Comparing results without post processing

susie_alpha_res_origin <- ctwas_res_origin$susie_alpha_res
susie_alpha_res_origin <- anno_susie_alpha_res(susie_alpha_res_origin,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:22:55 INFO::Annotating susie alpha result ...
2024-12-06 13:22:55 INFO::Map molecular traits to genes
2024-12-06 13:22:56 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_origin <- combine_gene_pips(susie_alpha_res_origin, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_origin <- combined_pip_by_type_cs_origin[combined_pip_by_type_cs_origin$combined_pip > 0.8,]

susie_alpha_res_rm <- res_regionmerge$susie_alpha_res
susie_alpha_res_rm <- anno_susie_alpha_res(susie_alpha_res_rm,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:23:00 INFO::Annotating susie alpha result ...
2024-12-06 13:23:00 INFO::Map molecular traits to genes
2024-12-06 13:23:01 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rm <- combine_gene_pips(susie_alpha_res_rm, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rm <- combined_pip_by_type_cs_rm[combined_pip_by_type_cs_rm$combined_pip > 0.8,]



susie_alpha_res_rmld <- res_rmld$susie_alpha_res
susie_alpha_res_rmld <- anno_susie_alpha_res(susie_alpha_res_rmld,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:23:07 INFO::Annotating susie alpha result ...
2024-12-06 13:23:07 INFO::Map molecular traits to genes
2024-12-06 13:23:07 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rmld <- combine_gene_pips(susie_alpha_res_rmld, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rmld <- combined_pip_by_type_cs_rmld[combined_pip_by_type_cs_rmld$combined_pip > 0.8,]


origin_unique <- combined_pip_by_type_sig_origin[!combined_pip_by_type_sig_origin$gene_name %in% combined_pip_by_type_sig_rmld$gene_name,]

origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rm, by = "gene_name", all.x = TRUE)

# Then merge the result with combined_pip_by_type_cs_rmld
origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rmld, by = "gene_name", all.x = TRUE)
origin_unique_show <- origin_unique[,c("gene_name","combined_pip.x","combined_pip.y","combined_pip","eQTL_pip.x","eQTL_pip.y","eQTL_pip","sQTL_pip.x","sQTL_pip.y","sQTL_pip","stQTL_pip.x","stQTL_pip.x","stQTL_pip","combined_cs_id.x","combined_cs_id.y","combined_cs_id")]

colnames(origin_unique_show) <- c("gene_name","combined_pip_origin","combined_pip_rm","combined_pip_rmld","eQTL_pip_origin","eQTL_pip_rm","eQTL_pip_rmld","sQTL_pip_origin","sQTL_pip_rm","sQTL_pip_rmld","stQTL_pip_origin","stQTL_pip_origin","stQTL_pip_rmld","combined_cs_id_origin","combined_cs_id_rm","combined_cs_id_rmld")

DT::datatable(origin_unique_show,caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left; color: black;','Genes no longer significant after post processing'),options = list(pageLength = 5))

Exploring allelic heterogeneity

pip_per_cs <- compute_pip_per_cs(combined_pip_by_type_sig_rmld, susie_alpha_res_rmld)

sprintf("Number of genes having allelic heterogeneity = %s",length(unique(pip_per_cs$gene_name)))

[1] "Number of genes having allelic heterogeneity = 5"

if(nrow(pip_per_cs) > 0) {
  DT::datatable(pip_per_cs,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','PIP per CS'),options = list(pageLength = 5) )
}

IBD-ebi-a-GCST004131

trait <- "IBD-ebi-a-GCST004131"

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

finemap_res_origin <- ctwas_res_origin$finemap_res

Region merge

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))

finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]


finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]

finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")

DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )

LD-mismatch

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rmld_",trait,".rdata"))

sprintf("The number of problematic regions = %s", length(problematic_region_ids))

[1] "The number of problematic regions = 2"

sprintf("The number of problematic genes = %s", length(problematic_genes))

[1] "The number of problematic genes = 2"

sprintf("The number of problematic snps = %s", length(res_ldmismatch$problematic_snps))

[1] "The number of problematic snps = 326"

sprintf("The number of flipped snps = %s", length(res_ldmismatch$flipped_snps))

[1] "The number of flipped snps = 3"

finemap_rm_res_problematic_region <- finemap_res_rm[finemap_res_rm$id %in% problematic_genes,]

finemap_noLD_res_problematic_region <- res_rmld$finemap_res
merge_regionmerge_nold <- merge(finemap_rm_res_problematic_region,finemap_noLD_res_problematic_region, by = "id")
merge_regionmerge_nold <- merge_regionmerge_nold[,c("id","susie_pip.x","susie_pip.y")]
colnames(merge_regionmerge_nold) <-  c("id","susie_pip_after_regionmerge","susie_pip_ld-mismatch-fixed") 

DT::datatable(merge_regionmerge_nold,caption = htmltools::tags$caption(style = 'caption-side: topleft; text-align = left; color:black;','PIP after region merge and fixed PIP for problematic genes'),options = list(pageLength = 5) )

finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]

p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("Original ctwas results") +
  theme_minimal()


finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]

p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("After region merge") +
  theme_minimal()


finemap_noLD_res_problematic_region_gene <- finemap_noLD_res_problematic_region[finemap_noLD_res_problematic_region$type !="SNP",]

p3 <- ggplot(data = finemap_noLD_res_problematic_region_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("After LD mismatch fixed") +
  theme_minimal()


grid.arrange(p1,p2,p3, ncol = 3)

Version	Author	Date
4885dec	XSun	2024-12-05

Comparing results without post processing

susie_alpha_res_origin <- ctwas_res_origin$susie_alpha_res
susie_alpha_res_origin <- anno_susie_alpha_res(susie_alpha_res_origin,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:23:46 INFO::Annotating susie alpha result ...
2024-12-06 13:23:46 INFO::Map molecular traits to genes
2024-12-06 13:23:46 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_origin <- combine_gene_pips(susie_alpha_res_origin, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_origin <- combined_pip_by_type_cs_origin[combined_pip_by_type_cs_origin$combined_pip > 0.8,]

susie_alpha_res_rm <- res_regionmerge$susie_alpha_res
susie_alpha_res_rm <- anno_susie_alpha_res(susie_alpha_res_rm,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:23:49 INFO::Annotating susie alpha result ...
2024-12-06 13:23:49 INFO::Map molecular traits to genes
2024-12-06 13:23:49 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rm <- combine_gene_pips(susie_alpha_res_rm, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rm <- combined_pip_by_type_cs_rm[combined_pip_by_type_cs_rm$combined_pip > 0.8,]



susie_alpha_res_rmld <- res_rmld$susie_alpha_res
susie_alpha_res_rmld <- anno_susie_alpha_res(susie_alpha_res_rmld,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:23:52 INFO::Annotating susie alpha result ...
2024-12-06 13:23:52 INFO::Map molecular traits to genes
2024-12-06 13:23:52 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rmld <- combine_gene_pips(susie_alpha_res_rmld, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rmld <- combined_pip_by_type_cs_rmld[combined_pip_by_type_cs_rmld$combined_pip > 0.8,]


origin_unique <- combined_pip_by_type_sig_origin[!combined_pip_by_type_sig_origin$gene_name %in% combined_pip_by_type_sig_rmld$gene_name,]

origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rm, by = "gene_name", all.x = TRUE)

# Then merge the result with combined_pip_by_type_cs_rmld
origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rmld, by = "gene_name", all.x = TRUE)
origin_unique_show <- origin_unique[,c("gene_name","combined_pip.x","combined_pip.y","combined_pip","eQTL_pip.x","eQTL_pip.y","eQTL_pip","sQTL_pip.x","sQTL_pip.y","sQTL_pip","stQTL_pip.x","stQTL_pip.x","stQTL_pip","combined_cs_id.x","combined_cs_id.y","combined_cs_id")]

colnames(origin_unique_show) <- c("gene_name","combined_pip_origin","combined_pip_rm","combined_pip_rmld","eQTL_pip_origin","eQTL_pip_rm","eQTL_pip_rmld","sQTL_pip_origin","sQTL_pip_rm","sQTL_pip_rmld","stQTL_pip_origin","stQTL_pip_origin","stQTL_pip_rmld","combined_cs_id_origin","combined_cs_id_rm","combined_cs_id_rmld")

DT::datatable(origin_unique_show,caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left; color: black;','Genes no longer significant after post processing'),options = list(pageLength = 5))

Exploring allelic heterogeneity

pip_per_cs <- compute_pip_per_cs(combined_pip_by_type_sig_rmld, susie_alpha_res_rmld)

sprintf("Number of genes having allelic heterogeneity = %s",length(unique(pip_per_cs$gene_name)))

[1] "Number of genes having allelic heterogeneity = 1"

if(nrow(pip_per_cs) > 0) {
  DT::datatable(pip_per_cs,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','PIP per CS'),options = list(pageLength = 5) )
}

SBP-ukb-a-360

trait <- "SBP-ukb-a-360"

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

finemap_res_origin <- ctwas_res_origin$finemap_res

Region merge

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))

finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]


finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]

finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")

DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )

LD-mismatch

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rmld_",trait,".rdata"))

sprintf("The number of problematic regions = %s", length(problematic_region_ids))

[1] "The number of problematic regions = 4"

sprintf("The number of problematic genes = %s", length(problematic_genes))

[1] "The number of problematic genes = 10"

sprintf("The number of problematic snps = %s", length(res_ldmismatch$problematic_snps))

[1] "The number of problematic snps = 742"

sprintf("The number of flipped snps = %s", length(res_ldmismatch$flipped_snps))

[1] "The number of flipped snps = 148"

finemap_rm_res_problematic_region <- finemap_res_rm[finemap_res_rm$id %in% problematic_genes,]

finemap_noLD_res_problematic_region <- res_rmld$finemap_res
merge_regionmerge_nold <- merge(finemap_rm_res_problematic_region,finemap_noLD_res_problematic_region, by = "id")
merge_regionmerge_nold <- merge_regionmerge_nold[,c("id","susie_pip.x","susie_pip.y")]
colnames(merge_regionmerge_nold) <-  c("id","susie_pip_after_regionmerge","susie_pip_ld-mismatch-fixed") 

DT::datatable(merge_regionmerge_nold,caption = htmltools::tags$caption(style = 'caption-side: topleft; text-align = left; color:black;','PIP after region merge and fixed PIP for problematic genes'),options = list(pageLength = 5) )

finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]

p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("Original ctwas results") +
  theme_minimal()


finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]

p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("After region merge") +
  theme_minimal()


finemap_noLD_res_problematic_region_gene <- finemap_noLD_res_problematic_region[finemap_noLD_res_problematic_region$type !="SNP",]

p3 <- ggplot(data = finemap_noLD_res_problematic_region_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("After LD mismatch fixed") +
  theme_minimal()


grid.arrange(p1,p2,p3, ncol = 3)

Version	Author	Date
4885dec	XSun	2024-12-05

Comparing results without post processing

susie_alpha_res_origin <- ctwas_res_origin$susie_alpha_res
susie_alpha_res_origin <- anno_susie_alpha_res(susie_alpha_res_origin,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:24:42 INFO::Annotating susie alpha result ...
2024-12-06 13:24:42 INFO::Map molecular traits to genes
2024-12-06 13:24:43 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_origin <- combine_gene_pips(susie_alpha_res_origin, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_origin <- combined_pip_by_type_cs_origin[combined_pip_by_type_cs_origin$combined_pip > 0.8,]

susie_alpha_res_rm <- res_regionmerge$susie_alpha_res
susie_alpha_res_rm <- anno_susie_alpha_res(susie_alpha_res_rm,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:24:49 INFO::Annotating susie alpha result ...
2024-12-06 13:24:49 INFO::Map molecular traits to genes
2024-12-06 13:24:50 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rm <- combine_gene_pips(susie_alpha_res_rm, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rm <- combined_pip_by_type_cs_rm[combined_pip_by_type_cs_rm$combined_pip > 0.8,]



susie_alpha_res_rmld <- res_rmld$susie_alpha_res
susie_alpha_res_rmld <- anno_susie_alpha_res(susie_alpha_res_rmld,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:24:55 INFO::Annotating susie alpha result ...
2024-12-06 13:24:55 INFO::Map molecular traits to genes
2024-12-06 13:24:55 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rmld <- combine_gene_pips(susie_alpha_res_rmld, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rmld <- combined_pip_by_type_cs_rmld[combined_pip_by_type_cs_rmld$combined_pip > 0.8,]


origin_unique <- combined_pip_by_type_sig_origin[!combined_pip_by_type_sig_origin$gene_name %in% combined_pip_by_type_sig_rmld$gene_name,]

origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rm, by = "gene_name", all.x = TRUE)

# Then merge the result with combined_pip_by_type_cs_rmld
origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rmld, by = "gene_name", all.x = TRUE)
origin_unique_show <- origin_unique[,c("gene_name","combined_pip.x","combined_pip.y","combined_pip","eQTL_pip.x","eQTL_pip.y","eQTL_pip","sQTL_pip.x","sQTL_pip.y","sQTL_pip","stQTL_pip.x","stQTL_pip.x","stQTL_pip","combined_cs_id.x","combined_cs_id.y","combined_cs_id")]

colnames(origin_unique_show) <- c("gene_name","combined_pip_origin","combined_pip_rm","combined_pip_rmld","eQTL_pip_origin","eQTL_pip_rm","eQTL_pip_rmld","sQTL_pip_origin","sQTL_pip_rm","sQTL_pip_rmld","stQTL_pip_origin","stQTL_pip_origin","stQTL_pip_rmld","combined_cs_id_origin","combined_cs_id_rm","combined_cs_id_rmld")

DT::datatable(origin_unique_show,caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left; color: black;','Genes no longer significant after post processing'),options = list(pageLength = 5))

Exploring allelic heterogeneity

pip_per_cs <- compute_pip_per_cs(combined_pip_by_type_sig_rmld, susie_alpha_res_rmld)

sprintf("Number of genes having allelic heterogeneity = %s",length(unique(pip_per_cs$gene_name)))

[1] "Number of genes having allelic heterogeneity = 2"

if(nrow(pip_per_cs) > 0) {
  DT::datatable(pip_per_cs,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','PIP per CS'),options = list(pageLength = 5) )
}

SCZ-ieu-b-5102

trait <- "SCZ-ieu-b-5102"

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

finemap_res_origin <- ctwas_res_origin$finemap_res

Region merge

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))

finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]


finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]

finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")

DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )

LD-mismatch

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rmld_",trait,".rdata"))

sprintf("The number of problematic regions = %s", length(problematic_region_ids))

[1] "The number of problematic regions = 3"

sprintf("The number of problematic genes = %s", length(problematic_genes))

[1] "The number of problematic genes = 7"

sprintf("The number of problematic snps = %s", length(res_ldmismatch$problematic_snps))

[1] "The number of problematic snps = 169"

sprintf("The number of flipped snps = %s", length(res_ldmismatch$flipped_snps))

[1] "The number of flipped snps = 2"

finemap_rm_res_problematic_region <- finemap_res_rm[finemap_res_rm$id %in% problematic_genes,]

finemap_noLD_res_problematic_region <- res_rmld$finemap_res
merge_regionmerge_nold <- merge(finemap_rm_res_problematic_region,finemap_noLD_res_problematic_region, by = "id")
merge_regionmerge_nold <- merge_regionmerge_nold[,c("id","susie_pip.x","susie_pip.y")]
colnames(merge_regionmerge_nold) <-  c("id","susie_pip_after_regionmerge","susie_pip_ld-mismatch-fixed") 

DT::datatable(merge_regionmerge_nold,caption = htmltools::tags$caption(style = 'caption-side: topleft; text-align = left; color:black;','PIP after region merge and fixed PIP for problematic genes'),options = list(pageLength = 5) )

finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]

p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("Original ctwas results") +
  theme_minimal()


finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]

p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("After region merge") +
  theme_minimal()


finemap_noLD_res_problematic_region_gene <- finemap_noLD_res_problematic_region[finemap_noLD_res_problematic_region$type !="SNP",]

p3 <- ggplot(data = finemap_noLD_res_problematic_region_gene, aes(x= abs(z), y= susie_pip)) + 
  geom_point() +
  ggtitle("After LD mismatch fixed") +
  theme_minimal()


grid.arrange(p1,p2,p3, ncol = 3)

Version	Author	Date
4885dec	XSun	2024-12-05

Comparing results without post processing

susie_alpha_res_origin <- ctwas_res_origin$susie_alpha_res
susie_alpha_res_origin <- anno_susie_alpha_res(susie_alpha_res_origin,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:25:42 INFO::Annotating susie alpha result ...
2024-12-06 13:25:42 INFO::Map molecular traits to genes
2024-12-06 13:25:42 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_origin <- combine_gene_pips(susie_alpha_res_origin, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_origin <- combined_pip_by_type_cs_origin[combined_pip_by_type_cs_origin$combined_pip > 0.8,]

susie_alpha_res_rm <- res_regionmerge$susie_alpha_res
susie_alpha_res_rm <- anno_susie_alpha_res(susie_alpha_res_rm,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:25:45 INFO::Annotating susie alpha result ...
2024-12-06 13:25:45 INFO::Map molecular traits to genes
2024-12-06 13:25:46 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rm <- combine_gene_pips(susie_alpha_res_rm, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rm <- combined_pip_by_type_cs_rm[combined_pip_by_type_cs_rm$combined_pip > 0.8,]



susie_alpha_res_rmld <- res_rmld$susie_alpha_res
susie_alpha_res_rmld <- anno_susie_alpha_res(susie_alpha_res_rmld,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:25:48 INFO::Annotating susie alpha result ...
2024-12-06 13:25:48 INFO::Map molecular traits to genes
2024-12-06 13:25:49 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rmld <- combine_gene_pips(susie_alpha_res_rmld, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rmld <- combined_pip_by_type_cs_rmld[combined_pip_by_type_cs_rmld$combined_pip > 0.8,]


origin_unique <- combined_pip_by_type_sig_origin[!combined_pip_by_type_sig_origin$gene_name %in% combined_pip_by_type_sig_rmld$gene_name,]

origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rm, by = "gene_name", all.x = TRUE)

# Then merge the result with combined_pip_by_type_cs_rmld
origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rmld, by = "gene_name", all.x = TRUE)
origin_unique_show <- origin_unique[,c("gene_name","combined_pip.x","combined_pip.y","combined_pip","eQTL_pip.x","eQTL_pip.y","eQTL_pip","sQTL_pip.x","sQTL_pip.y","sQTL_pip","stQTL_pip.x","stQTL_pip.x","stQTL_pip","combined_cs_id.x","combined_cs_id.y","combined_cs_id")]

colnames(origin_unique_show) <- c("gene_name","combined_pip_origin","combined_pip_rm","combined_pip_rmld","eQTL_pip_origin","eQTL_pip_rm","eQTL_pip_rmld","sQTL_pip_origin","sQTL_pip_rm","sQTL_pip_rmld","stQTL_pip_origin","stQTL_pip_origin","stQTL_pip_rmld","combined_cs_id_origin","combined_cs_id_rm","combined_cs_id_rmld")

DT::datatable(origin_unique_show,caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left; color: black;','Genes no longer significant after post processing'),options = list(pageLength = 5))

Exploring allelic heterogeneity

pip_per_cs <- compute_pip_per_cs(combined_pip_by_type_sig_rmld, susie_alpha_res_rmld)

sprintf("Number of genes having allelic heterogeneity = %s",length(unique(pip_per_cs$gene_name)))

[1] "Number of genes having allelic heterogeneity = 0"

if(nrow(pip_per_cs) > 0) {
  DT::datatable(pip_per_cs,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','PIP per CS'),options = list(pageLength = 5) )
}

WBC-ieu-b-30

trait <- "WBC-ieu-b-30"

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

finemap_res_origin <- ctwas_res_origin$finemap_res

Region merge

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))

finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]


finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]

finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")

DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )

LD-mismatch

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rmld_",trait,".rdata"))

sprintf("The number of problematic regions = %s", length(problematic_region_ids))

[1] "The number of problematic regions = 16"

sprintf("The number of problematic genes = %s", length(problematic_genes))

[1] "The number of problematic genes = 37"

sprintf("The number of problematic snps = %s", length(res_ldmismatch$problematic_snps))

[1] "The number of problematic snps = 1092"

sprintf("The number of flipped snps = %s", length(res_ldmismatch$flipped_snps))

[1] "The number of flipped snps = 17"

finemap_rm_res_problematic_region <- finemap_res_rm[finemap_res_rm$id %in% problematic_genes,]

finemap_noLD_res_problematic_region <- res_rmld$finemap_res
merge_regionmerge_nold <- merge(finemap_rm_res_problematic_region,finemap_noLD_res_problematic_region, by = "id")
merge_regionmerge_nold <- merge_regionmerge_nold[,c("id","susie_pip.x","susie_pip.y")]
colnames(merge_regionmerge_nold) <-  c("id","susie_pip_after_regionmerge","susie_pip_ld-mismatch-fixed")

DT::datatable(merge_regionmerge_nold,caption = htmltools::tags$caption(style = 'caption-side: topleft; text-align = left; color:black;','PIP after region merge and fixed PIP for problematic genes'),options = list(pageLength = 5) )

finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]

p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) +
  geom_point() +
  ggtitle("Original ctwas results") +
  theme_minimal()


finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]

p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) +
  geom_point() +
  ggtitle("After region merge") +
  theme_minimal()


finemap_noLD_res_problematic_region_gene <- finemap_noLD_res_problematic_region[finemap_noLD_res_problematic_region$type !="SNP",]

p3 <- ggplot(data = finemap_noLD_res_problematic_region_gene, aes(x= abs(z), y= susie_pip)) +
  geom_point() +
  ggtitle("After LD mismatch fixed") +
  theme_minimal()


grid.arrange(p1,p2,p3, ncol = 3)

Version	Author	Date
984b87f	XSun	2024-12-06

Comparing results without post processing

susie_alpha_res_origin <- ctwas_res_origin$susie_alpha_res
susie_alpha_res_origin <- anno_susie_alpha_res(susie_alpha_res_origin,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:27:08 INFO::Annotating susie alpha result ...
2024-12-06 13:27:08 INFO::Map molecular traits to genes
2024-12-06 13:27:09 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_origin <- combine_gene_pips(susie_alpha_res_origin,
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_origin <- combined_pip_by_type_cs_origin[combined_pip_by_type_cs_origin$combined_pip > 0.8,]

susie_alpha_res_rm <- res_regionmerge$susie_alpha_res
susie_alpha_res_rm <- anno_susie_alpha_res(susie_alpha_res_rm,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:27:21 INFO::Annotating susie alpha result ...
2024-12-06 13:27:21 INFO::Map molecular traits to genes
2024-12-06 13:27:22 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rm <- combine_gene_pips(susie_alpha_res_rm,
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rm <- combined_pip_by_type_cs_rm[combined_pip_by_type_cs_rm$combined_pip > 0.8,]



susie_alpha_res_rmld <- res_rmld$susie_alpha_res
susie_alpha_res_rmld <- anno_susie_alpha_res(susie_alpha_res_rmld,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-12-06 13:27:34 INFO::Annotating susie alpha result ...
2024-12-06 13:27:34 INFO::Map molecular traits to genes
2024-12-06 13:27:35 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_cs_rmld <- combine_gene_pips(susie_alpha_res_rmld,
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = T)
combined_pip_by_type_sig_rmld <- combined_pip_by_type_cs_rmld[combined_pip_by_type_cs_rmld$combined_pip > 0.8,]


origin_unique <- combined_pip_by_type_sig_origin[!combined_pip_by_type_sig_origin$gene_name %in% combined_pip_by_type_sig_rmld$gene_name,]

origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rm, by = "gene_name", all.x = TRUE)

# Then merge the result with combined_pip_by_type_cs_rmld
origin_unique <- merge(origin_unique, combined_pip_by_type_cs_rmld, by = "gene_name", all.x = TRUE)
origin_unique_show <- origin_unique[,c("gene_name","combined_pip.x","combined_pip.y","combined_pip","eQTL_pip.x","eQTL_pip.y","eQTL_pip","sQTL_pip.x","sQTL_pip.y","sQTL_pip","stQTL_pip.x","stQTL_pip.x","stQTL_pip","combined_cs_id.x","combined_cs_id.y","combined_cs_id")]

colnames(origin_unique_show) <- c("gene_name","combined_pip_origin","combined_pip_rm","combined_pip_rmld","eQTL_pip_origin","eQTL_pip_rm","eQTL_pip_rmld","sQTL_pip_origin","sQTL_pip_rm","sQTL_pip_rmld","stQTL_pip_origin","stQTL_pip_origin","stQTL_pip_rmld","combined_cs_id_origin","combined_cs_id_rm","combined_cs_id_rmld")

DT::datatable(origin_unique_show,caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left; color: black;','Genes no longer significant after post processing'),options = list(pageLength = 5))

Exploring allelic heterogeneity

pip_per_cs <- compute_pip_per_cs(combined_pip_by_type_sig_rmld, susie_alpha_res_rmld)

sprintf("Number of genes having allelic heterogeneity = %s",length(unique(pip_per_cs$gene_name)))

[1] "Number of genes having allelic heterogeneity = 12"

if(nrow(pip_per_cs) > 0) {
  DT::datatable(pip_per_cs,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','PIP per CS'),options = list(pageLength = 5) )
}

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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] dplyr_1.1.4               gridExtra_2.3            
 [3] ggplot2_3.5.1             EnsDb.Hsapiens.v86_2.99.0
 [5] ensembldb_2.20.2          AnnotationFilter_1.20.0  
 [7] GenomicFeatures_1.48.3    AnnotationDbi_1.58.0     
 [9] Biobase_2.56.0            GenomicRanges_1.48.0     
[11] GenomeInfoDb_1.39.9       IRanges_2.30.0           
[13] S4Vectors_0.34.0          BiocGenerics_0.42.0      
[15] ctwas_0.4.20             

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] fs_1.5.2                    rstudioapi_0.13            
  [9] farver_2.1.0                DT_0.22                    
 [11] ggrepel_0.9.1               bit64_4.0.5                
 [13] fansi_1.0.3                 xml2_1.3.3                 
 [15] codetools_0.2-18            logging_0.10-108           
 [17] cachem_1.0.6                knitr_1.39                 
 [19] jsonlite_1.8.0              workflowr_1.7.0            
 [21] Rsamtools_2.12.0            dbplyr_2.1.1               
 [23] png_0.1-7                   readr_2.1.2                
 [25] compiler_4.2.0              httr_1.4.3                 
 [27] assertthat_0.2.1            Matrix_1.5-3               
 [29] fastmap_1.1.0               lazyeval_0.2.2             
 [31] cli_3.6.1                   later_1.3.0                
 [33] htmltools_0.5.2             prettyunits_1.1.1          
 [35] tools_4.2.0                 gtable_0.3.0               
 [37] glue_1.6.2                  GenomeInfoDbData_1.2.8     
 [39] rappdirs_0.3.3              Rcpp_1.0.12                
 [41] jquerylib_0.1.4             vctrs_0.6.5                
 [43] Biostrings_2.64.0           rtracklayer_1.56.0         
 [45] crosstalk_1.2.0             xfun_0.41                  
 [47] stringr_1.5.1               lifecycle_1.0.4            
 [49] irlba_2.3.5                 restfulr_0.0.14            
 [51] XML_3.99-0.14               zlibbioc_1.42.0            
 [53] zoo_1.8-10                  scales_1.3.0               
 [55] gggrid_0.2-0                hms_1.1.1                  
 [57] promises_1.2.0.1            MatrixGenerics_1.8.0       
 [59] ProtGenerics_1.28.0         parallel_4.2.0             
 [61] SummarizedExperiment_1.26.1 LDlinkR_1.2.3              
 [63] yaml_2.3.5                  curl_4.3.2                 
 [65] memoise_2.0.1               sass_0.4.1                 
 [67] biomaRt_2.54.1              stringi_1.7.6              
 [69] RSQLite_2.3.1               highr_0.9                  
 [71] BiocIO_1.6.0                filelock_1.0.2             
 [73] BiocParallel_1.30.3         rlang_1.1.2                
 [75] pkgconfig_2.0.3             matrixStats_0.62.0         
 [77] bitops_1.0-7                evaluate_0.15              
 [79] lattice_0.20-45             purrr_1.0.2                
 [81] labeling_0.4.2              GenomicAlignments_1.32.0   
 [83] htmlwidgets_1.5.4           cowplot_1.1.1              
 [85] bit_4.0.4                   tidyselect_1.2.0           
 [87] magrittr_2.0.3              R6_2.5.1                   
 [89] generics_0.1.2              DelayedArray_0.22.0        
 [91] DBI_1.2.2                   withr_2.5.0                
 [93] pgenlibr_0.3.3              pillar_1.9.0               
 [95] whisker_0.4                 KEGGREST_1.36.3            
 [97] RCurl_1.98-1.7              mixsqp_0.3-43              
 [99] tibble_3.2.1                crayon_1.5.1               
[101] utf8_1.2.2                  BiocFileCache_2.4.0        
[103] plotly_4.10.0               tzdb_0.4.0                 
[105] rmarkdown_2.25              progress_1.2.2             
[107] grid_4.2.0                  data.table_1.14.2          
[109] blob_1.2.3                  git2r_0.30.1               
[111] digest_0.6.29               tidyr_1.3.0                
[113] httpuv_1.6.5                munsell_0.5.0              
[115] viridisLite_0.4.0           bslib_0.3.1

6 Traits, 5 tissues, eQTL + sQTL + stQTL – compute ukbb LD, eqtl, sqtl from predictdb; stQTL from Munro et al; post-processing

XSun

2024-12-03

Introduction

aFib-ebi-a-GCST006414

Region merge

LD-mismatch

Comparing results without post processing

Exploring allelic heterogeneity

LDL-ukb-d-30780_irnt

Region merge

LD-mismatch

Comparing results without post processing

Exploring allelic heterogeneity

IBD-ebi-a-GCST004131

Region merge

LD-mismatch

Comparing results without post processing

Exploring allelic heterogeneity

SBP-ukb-a-360

Region merge

LD-mismatch

Comparing results without post processing

Exploring allelic heterogeneity

SCZ-ieu-b-5102

Region merge

LD-mismatch

Comparing results without post processing

Exploring allelic heterogeneity

WBC-ieu-b-30

Region merge

LD-mismatch

Comparing results without post processing

Exploring allelic heterogeneity