Last updated: 2020-03-17
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Knit directory: m6AQTL_reproducibleDocument/
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library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionm6AQTL <- readRDS("~/m6AQTL/m6A_QTL_results/linear_model/m6AQTL.m6APeak_logOR_GC.IP.adjusted_qqnorm.15PCs.fastQTL.nominals.rds")
eQTL <- readRDS("~/m6AQTL/GWAS_summary/eQTL_summary_stats.RDS")
sQTL <- readRDS("~/m6AQTL/GWAS_summary/sQTL_summary_stats.RDS")
sQTL$snps <- gsub("[.]",":",sQTL$snps)
eQTL$snps <- gsub("[.]",":",eQTL$snps)
eQTL$snps <- gsub("chr","",eQTL$snps)
sQTL$snps <- gsub("chr","",sQTL$snps)To plot the QQ plot, we select SNPs with P<1e-4
m6AQTL_lead <- m6AQTL[m6AQTL$pvalue<1e-4,]
eQTL_lead <- eQTL[eQTL$pvalue < 1e-4,]
sQTL_lead <- sQTL[ sQTL$pvalue < 1e-4,]
m6AQTL_lead$snps <- gsub("chr","",m6AQTL_lead$SNP)RA_gwas <-readRDS("~/m6AQTL/GWAS_summary/GWAS_summary_stats.RDS/RA_meta_stats.RDS")
RA_gwas$m6AQTL <- RA_gwas$eQTL <- RA_gwas$sQTL <- RA_gwas$CtlQTL <- 0
RA_gwas[which(paste(RA_gwas$CHR,RA_gwas$POS,sep = ":") %in% m6AQTL_lead$snps),"m6AQTL" ] <- 1
RA_gwas[which(paste(RA_gwas$CHR,RA_gwas$POS,sep = ":") %in% eQTL_lead$snps), "eQTL"] <- 1
RA_gwas[which(paste(RA_gwas$CHR,RA_gwas$POS,sep = ":") %in% sQTL_lead$snps), "sQTL"] <- 1
barplot(1:8,col = cbbPalette)
cbbPalette <- c( "#E69F00","#D55E00", "#56B4E9", "#000000", "#F0E442", "#0072B2", "#CC79A7","#009E73")
RA_gwas <- RA_gwas[RA_gwas$P > 1e-30,]
RA_p_values <- list('m6AQTL'=dplyr::filter(RA_gwas,m6AQTL==1)$P,
'eQTL'=dplyr::filter(RA_gwas,eQTL==1)$P,
'sQTL'=dplyr::filter(RA_gwas,sQTL==1)$P,
'Genome wide'=RA_gwas$P)
m6Amonster::qqplot.pvalue(RA_p_values,legendSize = 2)+theme(axis.title = element_text(size = 16,face = "bold"),axis.text = element_text(size = 14,face = "bold"), legend.title = element_blank(),legend.text = element_text(size = 14,face = "bold"),axis.line = element_line(size = 1) )+scale_color_manual(values = cbbPalette)cad_gwas <- readRDS("~/m6AQTL/GWAS_summary/GWAS_summary_stats.RDS/CAD_stats.RDS")
cad_gwas$m6AQTL <- cad_gwas$eQTL <- cad_gwas$sQTL <- cad_gwas$CtlQTL <- 0
cad_gwas$m6AQTL[which(paste(cad_gwas$CHR ,cad_gwas$POS,sep = ":") %in% m6AQTL_lead$snps )] <- 1
cad_gwas$eQTL[which(paste(cad_gwas$CHR ,cad_gwas$POS,sep = ":") %in% eQTL_lead$snps )] <- 1
cad_gwas$sQTL[which(paste(cad_gwas$CHR ,cad_gwas$POS,sep = ":") %in% sQTL_lead$snps )] <- 1
cad_gwas <- dplyr::filter(cad_gwas,P>1e-30 )
cad_p_values <- list('m6AQTL'=dplyr::filter(cad_gwas,m6AQTL==1)$P,
'eQTL'=dplyr::filter(cad_gwas,eQTL==1)$P,
'sQTL'=dplyr::filter(cad_gwas,sQTL==1)$P,
'Genome wide'=cad_gwas$P)
m6Amonster::qqplot.pvalue(cad_p_values,legendSize = 2)+theme(axis.title = element_text(size = 16,face = "bold"),axis.text = element_text(size = 14,face = "bold"), legend.title = element_blank(),legend.text = element_text(size = 14,face = "bold"),axis.line = element_line(size = 1) )+scale_color_manual(values = cbbPalette)PlatletCount_gwas <- readRDS("~/m6AQTL/GWAS_summary/GWAS_collection.RDS/plt_GWAS_processed.RDS")
PlatletCount_gwas$m6AQTL <- PlatletCount_gwas$eQTL <- PlatletCount_gwas$sQTL <- PlatletCount_gwas$CtlQTL <- 0
PlatletCount_gwas$m6AQTL[which(paste(PlatletCount_gwas$CHR ,PlatletCount_gwas$POS,sep = ":") %in% paste0("chr",m6AQTL_lead$snps) )] <- 1
PlatletCount_gwas$eQTL[which(paste(PlatletCount_gwas$CHR ,PlatletCount_gwas$POS,sep = ":") %in% paste0("chr",eQTL_lead$snps) )] <- 1
PlatletCount_gwas$sQTL[which(paste(PlatletCount_gwas$CHR ,PlatletCount_gwas$POS,sep = ":") %in% paste0("chr",sQTL_lead$snps) )] <- 1
PlatletCount_gwas$P <- as.numeric( PlatletCount_gwas$P )
PlatletCount_gwas <- dplyr::filter(PlatletCount_gwas,P>1e-30 )
PlatletCount_p_values <- list('m6AQTL'=dplyr::filter(PlatletCount_gwas,m6AQTL==1)$P,
'eQTL'=dplyr::filter(PlatletCount_gwas,eQTL==1)$P,
'sQTL'=dplyr::filter(PlatletCount_gwas,sQTL==1)$P,
'Genome wide'=PlatletCount_gwas$P)
m6Amonster::qqplot.pvalue(PlatletCount_p_values,legendSize = 2)+theme(axis.title = element_text(size = 16,face = "bold"),axis.text = element_text(size = 14,face = "bold"), legend.title = element_blank(),legend.text = element_text(size = 14,face = "bold"),axis.line = element_line(size = 1) )+scale_color_manual(values = cbbPalette)Lymphocyte_gwas <- readRDS("~/m6AQTL/GWAS_summary/GWAS_collection.RDS/lymph_GWAS_processed.RDS")
Lymphocyte_gwas$m6AQTL <- Lymphocyte_gwas$eQTL <- Lymphocyte_gwas$sQTL <- Lymphocyte_gwas$CtlQTL <- 0
Lymphocyte_gwas$m6AQTL[which(paste(Lymphocyte_gwas$CHR ,Lymphocyte_gwas$POS,sep = ":") %in% paste0("chr",m6AQTL_lead$snps) )] <- 1
Lymphocyte_gwas$eQTL[which(paste(Lymphocyte_gwas$CHR ,Lymphocyte_gwas$POS,sep = ":") %in% paste0("chr",eQTL_lead$snps) )] <- 1
Lymphocyte_gwas$sQTL[which(paste(Lymphocyte_gwas$CHR ,Lymphocyte_gwas$POS,sep = ":") %in% paste0("chr",sQTL_lead$snps) )] <- 1
Lymphocyte_gwas$P <- as.numeric( Lymphocyte_gwas$P )
Lymphocyte_gwas <- dplyr::filter(Lymphocyte_gwas,P>1e-30 )
Lymphocyte_p_values <- list('m6AQTL'=dplyr::filter(Lymphocyte_gwas,m6AQTL==1)$P,
'eQTL'=dplyr::filter(Lymphocyte_gwas,eQTL==1)$P,
'sQTL'=dplyr::filter(Lymphocyte_gwas,sQTL==1)$P,
'Genome wide'=Lymphocyte_gwas$P)
m6Amonster::qqplot.pvalue(Lymphocyte_p_values,legendSize = 2)+theme(axis.title = element_text(size = 16,face = "bold"),axis.text = element_text(size = 14,face = "bold"), legend.title = element_blank(),legend.text = element_text(size = 14,face = "bold"),axis.line = element_line(size = 1) )+scale_color_manual(values = cbbPalette)LeukocyteCount_gwas <- readRDS("~/m6AQTL/GWAS_summary/GWAS_collection.RDS/wbc_GWAS_processed.RDS")
LeukocyteCount_gwas$m6AQTL <- LeukocyteCount_gwas$eQTL <- LeukocyteCount_gwas$sQTL <- LeukocyteCount_gwas$CtlQTL <- 0
LeukocyteCount_gwas$m6AQTL[which(paste(LeukocyteCount_gwas$CHR ,LeukocyteCount_gwas$POS,sep = ":") %in% paste0("chr",m6AQTL_lead$snps) )] <- 1
LeukocyteCount_gwas$eQTL[which(paste(LeukocyteCount_gwas$CHR ,LeukocyteCount_gwas$POS,sep = ":") %in% paste0("chr",eQTL_lead$snps) )] <- 1
LeukocyteCount_gwas$sQTL[which(paste(LeukocyteCount_gwas$CHR ,LeukocyteCount_gwas$POS,sep = ":") %in% paste0("chr",sQTL_lead$snps) )] <- 1
LeukocyteCount_gwas$P <- as.numeric( LeukocyteCount_gwas$P )
LeukocyteCount_gwas <- dplyr::filter(LeukocyteCount_gwas,P>1e-30 )
LeukocyteCount_p_values <- list('m6AQTL'=dplyr::filter(LeukocyteCount_gwas,m6AQTL==1)$P,
'eQTL'=dplyr::filter(LeukocyteCount_gwas,eQTL==1)$P,
'sQTL'=dplyr::filter(LeukocyteCount_gwas,sQTL==1)$P,
'Genome wide'=LeukocyteCount_gwas$P)
m6Amonster::qqplot.pvalue(LeukocyteCount_p_values,legendSize = 2)+theme(axis.title = element_text(size = 16,face = "bold"),axis.text = element_text(size = 14,face = "bold"), legend.title = element_blank(),legend.text = element_text(size = 14,face = "bold"),axis.line = element_line(size = 1) )+scale_color_manual(values = cbbPalette)UlcerateiveColitis_gwas <- readRDS("~/m6AQTL/GWAS_summary/GWAS_collection.RDS/ulcerative_colitis_GWAS_processed.RDS")
UlcerateiveColitis_gwas$m6AQTL <- UlcerateiveColitis_gwas$eQTL <- UlcerateiveColitis_gwas$sQTL <- UlcerateiveColitis_gwas$CtlQTL <- 0
UlcerateiveColitis_gwas$m6AQTL[which(paste(UlcerateiveColitis_gwas$CHR ,UlcerateiveColitis_gwas$POS,sep = ":") %in% paste0("chr",m6AQTL_lead$snps) )] <- 1
UlcerateiveColitis_gwas$eQTL[which(paste(UlcerateiveColitis_gwas$CHR ,UlcerateiveColitis_gwas$POS,sep = ":") %in% paste0("chr",eQTL_lead$snps) )] <- 1
UlcerateiveColitis_gwas$sQTL[which(paste(UlcerateiveColitis_gwas$CHR ,UlcerateiveColitis_gwas$POS,sep = ":") %in% paste0("chr",sQTL_lead$snps) )] <- 1
UlcerateiveColitis_gwas$P <- as.numeric( UlcerateiveColitis_gwas$P )
UlcerateiveColitis_gwas <- dplyr::filter(UlcerateiveColitis_gwas,P>1e-30 )
UlcerateiveColitis_p_values <- list('m6AQTL'=dplyr::filter(UlcerateiveColitis_gwas,m6AQTL==1)$P,
'eQTL'=dplyr::filter(UlcerateiveColitis_gwas,eQTL==1)$P,
'sQTL'=dplyr::filter(UlcerateiveColitis_gwas,sQTL==1)$P,
'Genome wide'=UlcerateiveColitis_gwas$P)
m6Amonster::qqplot.pvalue(UlcerateiveColitis_p_values,legendSize = 2)+theme(axis.title = element_text(size = 16,face = "bold"),axis.text = element_text(size = 14,face = "bold"), legend.title = element_blank(),legend.text = element_text(size = 14,face = "bold"),axis.line = element_line(size = 1) )+scale_color_manual(values = cbbPalette)Eosinophil_gwas <- readRDS("~/m6AQTL/GWAS_summary/GWAS_collection.RDS/Eosinophil_count_GWAS_processed.RDS")
Eosinophil_gwas$m6AQTL <- Eosinophil_gwas$eQTL <- Eosinophil_gwas$sQTL <- Eosinophil_gwas$CtlQTL <- 0
Eosinophil_gwas$m6AQTL[which(paste(Eosinophil_gwas$CHR ,Eosinophil_gwas$POS,sep = ":") %in% paste0("chr",m6AQTL_lead$snps) )] <- 1
Eosinophil_gwas$eQTL[which(paste(Eosinophil_gwas$CHR ,Eosinophil_gwas$POS,sep = ":") %in% paste0("chr",eQTL_lead$snps) )] <- 1
Eosinophil_gwas$sQTL[which(paste(Eosinophil_gwas$CHR ,Eosinophil_gwas$POS,sep = ":") %in% paste0("chr",sQTL_lead$snps) )] <- 1
Eosinophil_gwas$P <- as.numeric( Eosinophil_gwas$P )
Eosinophil_gwas <- dplyr::filter(Eosinophil_gwas,P>1e-30 )
Eosinophil_p_values <- list('m6AQTL'=dplyr::filter(Eosinophil_gwas,m6AQTL==1)$P,
'eQTL'=dplyr::filter(Eosinophil_gwas,eQTL==1)$P,
'sQTL'=dplyr::filter(Eosinophil_gwas,sQTL==1)$P,
#'Control-SNPs'=dplyr::filter(Eosinophil_gwas,CtlQTL==1)$P,
'Genome wide'=Eosinophil_gwas$P)
m6Amonster::qqplot.pvalue(Eosinophil_p_values,legendSize = 2)+theme(axis.title = element_text(size = 16,face = "bold"),axis.text = element_text(size = 14,face = "bold"), legend.title = element_blank(),legend.text = element_text(size = 14,face = "bold"),axis.line = element_line(size = 1) )+scale_color_manual(values = cbbPalette)
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 17.10
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_0.8.0.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.1 knitr_1.22 magrittr_1.5 workflowr_1.3.0
[5] tidyselect_0.2.5 R6_2.4.0 rlang_0.4.0 stringr_1.4.0
[9] tools_3.5.3 xfun_0.6 git2r_0.25.2 htmltools_0.3.6
[13] yaml_2.2.0 rprojroot_1.3-2 digest_0.6.18 assertthat_0.2.1
[17] tibble_2.1.1 crayon_1.3.4 purrr_0.3.2 fs_1.3.0
[21] glue_1.3.1 evaluate_0.13 rmarkdown_1.12 stringi_1.4.3
[25] compiler_3.5.3 pillar_1.3.1 backports_1.1.4 pkgconfig_2.0.2