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Run S-LDSC on data from Hormozdiari, F. et al. Nature Genetics 2018 paper

  • Use the GTEx_FE_META_TISSUE_GE_MaxCPP annotation (MaxCPP annotation computed from fixed-effect meta-analysis of eQTLs from 44 GTEx tissues)
  • Compute the GTEx_FE_META_TISSUE_GE_MaxCPP annotation conditional on baselineLD_v1.1

TRAITS=("PASS_BMI1" "PASS_Rheumatoid_Arthritis" "PASS_Schizophrenia" "UKB_460K.blood_WHITE_COUNT" "UKB_460K.blood_PLATELET_COUNT")

for trait in "${TRAITS[@]}"
do
  sbatch ~/projects/analysis_pipelines/code/sldsc_annot_GTEx_QTL_separate_example.sbatch ${trait} GTEx_FE_META_TISSUE_GE_MaxCPP
done

Extract S-LDSC enrichment for the GTEx_FE_META_TISSUE_GE_MaxCPP annotation

  • Note: the enrichment values of those traits should match those in the supplementary table 10 of Hormozdiari, F. et al. Nature Genetics 2018 paper.
## barplot for S-LDSC enrichment 
barplot_enrichment <- function(result_sLDSC, ylim = NULL, title = "", horizontal = FALSE){
  
  result_sLDSC$Enrichment_L <- result_sLDSC$Enrichment - 1.96*result_sLDSC$Enrichment_std_error
  result_sLDSC$Enrichment_H <- result_sLDSC$Enrichment + 1.96*result_sLDSC$Enrichment_std_error
  
  p <- ggplot(result_sLDSC, aes(x = Category, y = Enrichment))+
    geom_bar(position=position_dodge(), stat="identity", width = 0.5) +
    geom_errorbar(aes(ymin=Enrichment_L,
                      ymax=Enrichment_H),
                  width=.1,                    # Width of the error bars
                  position=position_dodge(.9)) + 
    ylab("Enrichment")+ xlab("") +
    ggtitle(title) +
    geom_hline(yintercept = 1,linetype="dotted", colour = "red")+
    theme_classic() +
    theme(axis.text.x = element_text(angle=30, hjust=1, size = 14))
  if(!is.null(ylim)){
    p <- p + coord_cartesian(ylim = ylim)
  }
  
  if(horizontal){
    p <- p + coord_flip()
  }
  print(p)
}

change_trait_names <- function(trait_namelist){
  trait_namelist <- gsub("PASS_","", trait_namelist)
  trait_namelist <- gsub("BMI1","BMI", trait_namelist)
  trait_namelist <- gsub("Rheumatoid_Arthritis","Rheumatoid Arthritis", trait_namelist)
  trait_namelist <- gsub("UKB_460K.blood_WHITE_COUNT","White Blood Cell Count", trait_namelist)
  trait_namelist <- gsub("UKB_460K.blood_PLATELET_COUNT","Platelet Count", trait_namelist)
}
library(foreach)
library(doParallel)
Loading required package: iterators
Loading required package: parallel
registerDoParallel(cores = 6)

dir_results <- paste0("/project2/xinhe/kevinluo/ldsc/results/sLDSC_Hormozdiari_NG2018/LDSC_QTL/results_sLDSC/")

trait_name_list <- c("PASS_BMI1", "PASS_Rheumatoid_Arthritis", "PASS_Schizophrenia", "UKB_460K.blood_WHITE_COUNT", "UKB_460K.blood_PLATELET_COUNT")

prefix_annot <- "GTEx_FE_META_TISSUE_GE_MaxCPP"

result_sLDSC <- foreach(trait = trait_name_list, .combine = rbind)%dopar%{
  sldsc_results <- read.table(paste0(dir_results,"/", trait, "/baselineLDv1.1/", trait, "_", prefix_annot, "_baselineLDv1.1.results"), header = T, stringsAsFactors = F)
  est <- sldsc_results[sldsc_results$Category == "L2_1",]
  est$Category <- trait
  est
}

result_sLDSC$Category <- change_trait_names(result_sLDSC$Category)
DT::datatable(format(result_sLDSC, digits = 3), options = list(scrollX = TRUE, keys = TRUE, pageLength = 20),rownames = F)

Plot S-LDSC enrichment for the GTEx_FE_META_TISSUE_GE_MaxCPP annotation

  • Error bars represent 95% CI
library(ggplot2)

barplot_enrichment(result_sLDSC)

Version Author Date
511d991 kevinlkx 2020-07-07
b70df47 kevinlkx 2020-07-07 

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

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

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

other attached packages:
[1] ggplot2_3.3.0     doParallel_1.0.14 iterators_1.0.12  foreach_1.5.0    
[5] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6      pillar_1.4.4      compiler_3.5.1    later_1.0.0      
 [5] git2r_0.27.1      tools_3.5.1       digest_0.6.25     tibble_3.0.1     
 [9] lifecycle_0.2.0   jsonlite_1.6      evaluate_0.14     gtable_0.3.0     
[13] pkgconfig_2.0.3   rlang_0.4.6       shiny_1.4.0.2     crosstalk_1.0.0  
[17] yaml_2.2.0        xfun_0.14         fastmap_1.0.1     withr_2.1.2      
[21] dplyr_0.8.5       stringr_1.4.0     knitr_1.28        vctrs_0.3.0      
[25] fs_1.3.1          htmlwidgets_1.5.1 tidyselect_0.2.5  rprojroot_1.3-2  
[29] DT_0.13           grid_3.5.1        glue_1.4.1        R6_2.4.1         
[33] rmarkdown_2.1     farver_2.0.3      purrr_0.3.4       magrittr_1.5     
[37] whisker_0.4       ellipsis_0.3.1    backports_1.1.7   scales_1.1.1     
[41] codetools_0.2-15  promises_1.1.0    htmltools_0.4.0   assertthat_0.2.1 
[45] colorspace_1.4-1  mime_0.9          xtable_1.8-4      httpuv_1.5.3.1   
[49] labeling_0.3      stringi_1.4.6     munsell_0.5.0     crayon_1.3.4