Last updated: 2023-02-21

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Knit directory: DO_Opioid/

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Unstaged changes:
    Modified:   _workflowr.yml
    Modified:   analysis/Plot_DO_Fentanyl_Cohort2_mapping.Rmd
    Modified:   analysis/Plot_DO_Fentanyl_combining2Cohort_mapping.Rmd
    Modified:   analysis/Plot_DO_fentanyl.Rmd
    Modified:   analysis/marker_violin.Rmd

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Rmd 8ecb9e6 xhyuo 2020-06-01 Plot_DO_morphine_secondbatch

Last update: 2023-02-21

loading libraries

library(ggplot2)
library(gridExtra)
library(GGally)
library(parallel)
library(qtl2)
library(parallel)
library(survival)
library(regress)
library(abind)
library(openxlsx)

rz.transform <- function(y) {
  rankY=rank(y, ties.method="average", na.last="keep")
  rzT=qnorm(rankY/(length(na.exclude(rankY))+1))
  return(rzT)
}
load("code/do.colors.RData")

Read phenotype data

# Read phenotype data -----------------------------------------------------
second_set <- read.xlsx(xlsxFile = "data/MasterMorphine Second Set DO.xlsx",
                        sheet = 1)
colnames(second_set)[2] <- "Sex"
colnames(second_set)[6] <- "Status24"
#character
second_set[,c("ID", "Sex", "GROUP", "Status24")] <- lapply(second_set[, c("ID", "Sex", "GROUP", "Status24")], as.character)
second_set$Status_bin <- ifelse(second_set$Status24 == "ALIVE", 1, 0)
rownames(second_set) <- second_set$ID

#remove outlier
second_set <- second_set[second_set$ID != "empty",]
#remove negative survival time subjects
second_set <- second_set[!(!is.na(second_set$Survival.Time) & second_set$Survival.Time < 0),]
second_set$Survival.Time[!is.na(second_set$Survival.Time) & second_set$Survival.Time > 15] <- NA
second_set$Recovery.Time[!is.na(second_set$Recovery.Time) & second_set$Recovery.Time > 24] <- NA
second_set$Min.depression[!is.na(second_set$Min.depression) & second_set$Min.depression > 1] <- NA

#do
do.morphine <- get(load("output/second_set_gm.RData"))
do.pheno <- merge(second_set, do.morphine$covar, by.x = "row.names", by.y = "row.names", all.x = TRUE)

#first batch
do.pheno1 <- read.csv("data/pheno_qtl2.csv")
colnames(do.pheno1)[1] <- "ID"
do.pheno1$Survival.Time <- as.numeric(do.pheno1$Survival.Time)
do.pheno1$Recovery.Time <- as.numeric(do.pheno1$Recovery.Time)
do.pheno1$Min.depression <- as.numeric(do.pheno1$Min.depression)
do.pheno1$ngen <- "29"

#boxplot
#survival time
surv <- do.pheno[,c(2,8,15)]
#surv <- rbind(surv, do.pheno1[,c(1,2,5)])
surv <- surv[complete.cases(surv), ]
surv$ngen <- factor(surv$ngen, levels = as.character(c(30:34)))
p1 <- ggplot(surv, aes(x=ngen, y=Survival.Time, group = ngen, fill = ngen, alpha = 0.9)) + 
  geom_boxplot(show.legend = F , outlier.size = 1.5, notchwidth = 0.85) +
  geom_jitter(color="black", size=0.8, alpha=0.9) +
  scale_fill_brewer(palette="Blues") +
  ylab("Survival Time") +
  xlab("Generation") +
  labs(fill = "") +
  theme(legend.position = "none",
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"),
        text = element_text(size=16), 
        axis.title=element_text(size=16)) +
  guides(shape = guide_legend(override.aes = list(size = 12)))
p1

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reco <- do.pheno[,c(2,9,15)]
#reco <- rbind(reco, do.pheno1[,c(1,3,5)])
reco <- reco[complete.cases(reco), ]
reco$ngen <- factor(reco$ngen, levels = as.character(c(30:35)))
p2 <- ggplot(reco, aes(x=ngen, y=Recovery.Time, group = ngen, fill = ngen, alpha = 0.9)) + 
  geom_boxplot(show.legend = F , outlier.size = 1.5, notchwidth = 0.85) +
  geom_jitter(color="black", size=0.8, alpha=0.9) +
  scale_fill_brewer(palette="Blues") +
  ylab("Recovery Time") +
  xlab("Generation") +
  labs(fill = "") +
  theme(legend.position = "none",
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"),
        text = element_text(size=16), 
        axis.title=element_text(size=16)) +
  guides(shape = guide_legend(override.aes = list(size = 12)))
p2

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dep <- do.pheno[,c(2,10,15)]
#dep <- rbind(dep, do.pheno1[,c(1,4,5)])
dep <- dep[complete.cases(dep), ]
dep$ngen <- factor(dep$ngen, levels = as.character(c(30:35)))
p3 <- ggplot(dep, aes(x=ngen, y=Min.depression, group = ngen, fill = ngen, alpha = 0.9)) + 
  geom_boxplot(show.legend = F , outlier.size = 1.5, notchwidth = 0.85) +
  geom_jitter(color="black", size=0.8, alpha=0.9) +
  scale_fill_brewer(palette="Blues") +
  ylab("Min depression") +
  xlab("Generation") +
  labs(fill = "") +
  theme(legend.position = "none",
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"),
        text = element_text(size=16), 
        axis.title=element_text(size=16)) +
  guides(shape = guide_legend(override.aes = list(size = 12)))
p3

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#histogram
a <- ggplot(data=do.pheno, aes(do.pheno$Survival.Time)) + 
  geom_histogram() +
  ylab("Number of DO mice") + xlab("Survival Time (h)")
  
b <- ggplot(data=do.pheno, aes(do.pheno$Recovery.Time)) + 
  geom_histogram() +
  ylab("Number of DO mice") + xlab("Recovery Time (h)")

c <- ggplot(data=do.pheno, aes(do.pheno$Min.depression)) + 
  geom_histogram() +
  ylab("Number of DO mice") + xlab("Respiratory Depression (% of baseline)")

grid.arrange(a,b,c)
# Warning: Use of `do.pheno$Survival.Time` is discouraged. Use `Survival.Time`
# instead.
# Warning: Removed 211 rows containing non-finite values (stat_bin).
# Warning: Use of `do.pheno$Recovery.Time` is discouraged. Use `Recovery.Time`
# instead.
# Warning: Removed 166 rows containing non-finite values (stat_bin).
# Warning: Use of `do.pheno$Min.depression` is discouraged. Use `Min.depression`
# instead.
# Warning: Removed 17 rows containing non-finite values (stat_bin).

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Plot qtl mapping on array

Plot by using its output qtl.morphine.out.second_set.RData

load("output/qtl.morphine.out.second_set.RData")
#pmap and gmap
chr.names <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19", "X")
gmap <- list()
pmap <- list()
for (chr in chr.names){
  gmap[[chr]] <-  do.morphine$gmap[[chr]]
  pmap[[chr]] <-  do.morphine$pmap[[chr]]
}
attr(gmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
attr(pmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)

#genome-wide plot
for(i in names(qtl.morphine.out)){
  par(mar=c(5.1, 4.1, 1.1, 1.1))
  ymx <- maxlod(qtl.morphine.out[[i]]) # overall maximum LOD score
  plot(qtl.morphine.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, 10))
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
  title(main = paste0("DO_morphine_",i))
}

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#save genome-wide plot
for(i in names(qtl.morphine.out)){
  png(file = paste0("output/DO_morphine_secondbatch_", i, ".png"), width = 16, height =8, res=300, units = "in")
  par(mar=c(5.1, 4.1, 1.1, 1.1))
  ymx <- maxlod(qtl.morphine.out[[i]]) # overall maximum LOD score
  plot(qtl.morphine.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, 10))
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
  title(main = paste0("DO_morphine_",i))
  dev.off()
}

#peaks coeff plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
  print(peaks)
  for(p in 1:dim(peaks)[1]) {
    print(p)
    chr <-peaks[p,3]
    #coeff plot
    par(mar=c(4.1, 4.1, 0.6, 0.6))
    plot_coefCC(coef_c1[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
  }
}
# [1] "Survival.Time"
#   lodindex lodcolumn chr      pos      lod    ci_lo     ci_hi
# 1        1    pheno1   2 72.08345 6.672136 71.80655  72.11478
# 2        1    pheno1   3 98.06064 7.005262 97.69551 159.92550
# 3        1    pheno1   6 94.86810 8.427626 93.64635  95.01090
# [1] 1

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# [1] 2

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# [1] 3

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# [1] "Recovery.Time"
#   lodindex lodcolumn chr       pos      lod     ci_lo    ci_hi
# 1        1    pheno1   1  59.35852 6.107005  59.07336 194.5045
# 2        1    pheno1   4 156.04818 6.519153 114.73873 156.1199
# 3        1    pheno1   8 117.07865 6.973714 115.52733 117.7515
# [1] 1

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# [1] 2

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# [1] 3

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# [1] "Min.depression"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   6 93.69909 6.677445 93.60388 94.87509
# [1] 1

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# [1] "Status_bin"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   9 68.12998 6.087016 43.84234 123.7027
# [1] 1

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#save peaks coeff plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
  fname <- paste("output/DO_morphine_secondbatch_",i,"_coefplot.pdf",sep="")
  pdf(file = fname, width = 16, height =8)
  for(p in 1:dim(peaks)[1]) {
    print(p)
    chr <-peaks[p,3]
    #coeff plot
    par(mar=c(4.1, 4.1, 0.6, 0.6))
    plot_coefCC(coef_c1[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
  }
  dev.off()
}
# [1] "Survival.Time"
# [1] 1
# [1] 2
# [1] 3
# [1] "Recovery.Time"
# [1] 1
# [1] 2
# [1] 3
# [1] "Min.depression"
# [1] 1
# [1] "Status_bin"
# [1] 1

#save peaks coeff blup plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
  fname <- paste("output/DO_morphine_secondbatch_",i,"_coefplot_blup.pdf",sep="")
  pdf(file = fname, width = 16, height =8)
  for(p in 1:dim(peaks)[1]) {
    print(p)
    chr <-peaks[p,3]
    #coeff plot
    par(mar=c(4.1, 4.1, 0.6, 0.6))
    plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
  }
  dev.off()
}
# [1] "Survival.Time"
# [1] 1
# [1] 2
# [1] 3
# [1] "Recovery.Time"
# [1] 1
# [1] 2
# [1] 3
# [1] "Min.depression"
# [1] 1
# [1] "Status_bin"
# [1] 1

Plot qtl mapping on 69K

Plot by using its output qtl.morphine.69k.out.second_set.RData

load("output/qtl.morphine.69k.out.second_set.RData")
#pmap and gmap
load("data/69k_grid_pgmap.RData")

#genome-wide plot
for(i in names(qtl.morphine.out)){
  par(mar=c(5.1, 4.1, 1.1, 1.1))
  ymx <- maxlod(qtl.morphine.out[[i]]) # overall maximum LOD score
  plot(qtl.morphine.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, 10))
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
  title(main = paste0("DO_morphine_",i))
}

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c191146 xhyuo 2020-06-16

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#save genome-wide plot
for(i in names(qtl.morphine.out)){
  png(file = paste0("output/DO_morphine_secondbatch_69k_", i, ".png"), width = 16, height =8, res=300, units = "in")
  par(mar=c(5.1, 4.1, 1.1, 1.1))
  ymx <- maxlod(qtl.morphine.out[[i]]) # overall maximum LOD score
  plot(qtl.morphine.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, 10))
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
  title(main = paste0("DO_morphine_",i))
  dev.off()
}

#peaks coeff plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
  print(peaks)
  for(p in 1:dim(peaks)[1]) {
    print(p)
    chr <-peaks[p,3]
    #coeff plot
    par(mar=c(4.1, 4.1, 0.6, 0.6))
    plot_coefCC(coef_c1[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
  }
}
# [1] "Survival.Time"
#   lodindex lodcolumn chr      pos      lod    ci_lo     ci_hi
# 1        1    pheno1   3 98.04809 7.056873 97.51576 160.01710
# 2        1    pheno1   6 94.88068 8.514711 93.61447  95.01174
# [1] 1

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# [1] 2

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# [1] "Recovery.Time"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   1  59.37548 6.120300  59.01588  62.32984
# 2        1    pheno1   4 155.99563 6.203463 114.75702 156.13631
# 3        1    pheno1   8 117.09888 6.925422 115.52682 117.86788
# [1] 1

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# [1] 2

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# [1] 3

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# [1] "Min.depression"
#   lodindex lodcolumn chr       pos      lod      ci_lo     ci_hi
# 1        1    pheno1   6  94.17733 6.736136  93.600320  94.87681
# 2        1    pheno1  12  12.26505 6.147825   9.396249  16.88098
# 3        1    pheno1  19  44.14928 6.171079  20.756178  55.72002
# 4        1    pheno1   X 163.26639 7.254464 162.637334 164.13916
# [1] 1

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# [1] 2

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# [1] 3

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# [1] 4

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# [1] "Status_bin"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   9 68.16325 6.081293 43.77453 123.6993
# [1] 1

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#save peaks coeff plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
  fname <- paste("output/DO_morphine_secondbatch_69k_",i,"_coefplot.pdf",sep="")
  pdf(file = fname, width = 16, height =8)
  for(p in 1:dim(peaks)[1]) {
    print(p)
    chr <-peaks[p,3]
    #coeff plot
    par(mar=c(4.1, 4.1, 0.6, 0.6))
    plot_coefCC(coef_c1[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
  }
  dev.off()
}
# [1] "Survival.Time"
# [1] 1
# [1] 2
# [1] "Recovery.Time"
# [1] 1
# [1] 2
# [1] 3
# [1] "Min.depression"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] "Status_bin"
# [1] 1

#save peaks coeff blup plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
  fname <- paste("output/DO_morphine_secondbatch_69k_",i,"_coefplot_blup.pdf",sep="")
  pdf(file = fname, width = 16, height =8)
  for(p in 1:dim(peaks)[1]) {
    print(p)
    chr <-peaks[p,3]
    #coeff plot
    par(mar=c(4.1, 4.1, 0.6, 0.6))
    plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
  }
  dev.off()
}
# [1] "Survival.Time"
# [1] 1
# [1] 2
# [1] "Recovery.Time"
# [1] 1
# [1] 2
# [1] 3
# [1] "Min.depression"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] "Status_bin"
# [1] 1

Plot qtl mapping on array male

load("output/qtl.morphine.out.second_set.male.RData")
#pmap and gmap
chr.names <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19", "X")
gmap <- list()
pmap <- list()
for (chr in chr.names){
  gmap[[chr]] <-  do.morphine$gmap[[chr]]
  pmap[[chr]] <-  do.morphine$pmap[[chr]]
}
attr(gmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
attr(pmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)

#genome-wide plot
for(i in names(qtl.morphine.out)){
  par(mar=c(5.1, 4.1, 1.1, 1.1))
  ymx <- maxlod(qtl.morphine.out[[i]]) # overall maximum LOD score
  plot(qtl.morphine.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, max(sapply(qtl.morphine.out, maxlod)) +2))
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
  title(main = paste0("DO_morphine_",i))
}

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#peaks coeff plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
  print(peaks)
  for(p in 1:dim(peaks)[1]) {
    print(p)
    chr <-peaks[p,3]
    #coeff plot
    par(mar=c(4.1, 4.1, 0.6, 0.6))
    plot_coefCC(coef_c1[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
  }
}
# [1] "Survival.Time"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   1 120.12762 6.572499  40.49547 191.33153
# 2        1    pheno1   3 125.94945 6.714517 107.22292 127.25264
# 3        1    pheno1   6  93.28072 6.085423  92.70653 139.26401
# 4        1    pheno1  10  77.27719 6.883233  55.59002  78.16454
# 5        1    pheno1  11  94.11858 7.028132  93.89976  94.27434
# 6        1    pheno1  18  84.44421 7.071786  61.47853  85.27749
# 7        1    pheno1  19  11.86392 6.344164  11.74479  17.03980
# [1] 1

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# [1] 2

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# [1] 3

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# [1] 4

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# [1] 5

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# [1] 6

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# [1] 7

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# [1] "Recovery.Time"
#   lodindex lodcolumn chr     pos      lod    ci_lo    ci_hi
# 1        1    pheno1   2 45.6708 6.041384 43.98518 167.0778
# [1] 1

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# [1] "Min.depression"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   9 122.9964 7.582778 122.2267 124.0099
# [1] 1

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# [1] "Status_bin"
#   lodindex lodcolumn chr       pos      lod      ci_lo     ci_hi
# 1        1    pheno1   5 148.11200 6.160116 147.986160 148.43807
# 2        1    pheno1   8  37.41968 6.546587  37.029877  37.85219
# 3        1    pheno1  10 108.33120 6.527988 108.088576 108.51289
# 4        1    pheno1  12  80.14229 6.091948   9.247594 103.28398
# 5        1    pheno1  15  88.17969 8.118696  87.323190  88.41707
# [1] 1

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# [1] 2

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# [1] 3

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# [1] 4

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# [1] 5

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Plot qtl mapping on array female

load("output/qtl.morphine.out.second_set.female.RData")
#pmap and gmap
chr.names <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19", "X")
gmap <- list()
pmap <- list()
for (chr in chr.names){
  gmap[[chr]] <-  do.morphine$gmap[[chr]]
  pmap[[chr]] <-  do.morphine$pmap[[chr]]
}
attr(gmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
attr(pmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)

#genome-wide plot
for(i in names(qtl.morphine.out)){
  par(mar=c(5.1, 4.1, 1.1, 1.1))
  ymx <- maxlod(qtl.morphine.out[[i]]) # overall maximum LOD score
  plot(qtl.morphine.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, max(sapply(qtl.morphine.out, maxlod)) +2))
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
  abline(h=summary(qtl.morphine.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
  title(main = paste0("DO_morphine_",i))
}

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#peaks coeff plot
for(i in names(qtl.morphine.out)){
  print(i)
  peaks <- find_peaks(qtl.morphine.out[[i]], map=pmap, threshold=6, drop=1.5)
  print(peaks)
  for(p in 1:dim(peaks)[1]) {
    print(p)
    chr <-peaks[p,3]
    #coeff plot
    par(mar=c(4.1, 4.1, 0.6, 0.6))
    plot_coefCC(coef_c1[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.morphine.out[[i]], bgcolor="gray95", legend=NULL)
    plot(out_snps[[i]][[p]]$lod, out_snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=out_genes[[i]][[p]])
  }
}
# [1] "Survival.Time"
#   lodindex lodcolumn chr       pos      lod      ci_lo     ci_hi
# 1        1    pheno1   2  70.95263 6.278855  57.899382 173.54300
# 2        1    pheno1   3  97.98775 6.445479  35.675687 130.29440
# 3        1    pheno1   4  30.34673 6.677948  28.777007  30.81005
# 4        1    pheno1   8 115.44116 7.553628 115.137119 115.98481
# 5        1    pheno1  15  29.67993 6.173329   7.017907  47.72534
# 6        1    pheno1  19  37.85897 6.861602  37.488231  38.17204
# [1] 1

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# [1] 3

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# [1] 4

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# [1] 5

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# [1] 6

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# [1] "Recovery.Time"
#   lodindex lodcolumn chr        pos      lod      ci_lo      ci_hi
# 1        1    pheno1   3 123.593760 6.464420 117.457785 123.638818
# 2        1    pheno1   4 156.048178 6.417408 114.723317 156.119928
# 3        1    pheno1   5  92.743909 6.654942  37.152061  95.838678
# 4        1    pheno1  10   5.988497 7.279613   5.445056   8.819529
# 5        1    pheno1  13  60.185807 6.793807  59.610678  63.542749
# [1] 1

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# [1] 2

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# [1] 3

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# [1] 4

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# [1] 5

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# [1] "Min.depression"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   6  93.69909 6.430461  93.60934  94.86810
# 2        1    pheno1  19  43.49508 8.046388  43.20074  44.03063
# 3        1    pheno1   X 163.31238 7.736391 160.41596 164.23444
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# [1] 2

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# [1] 3

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# [1] "Status_bin"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   3 53.017859 6.760367 52.833289 53.290619
# 2        1    pheno1   6  4.124793 6.678702  3.200188  5.832646
# 3        1    pheno1   9 68.129978 6.719334 67.169326 68.687010
# 4        1    pheno1  12  4.923560 7.088994  3.804905  5.324666
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# [1] 2

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# [1] 3

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# [1] 4

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sessionInfo()
# R version 4.0.3 (2020-10-10)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Ubuntu 20.04.2 LTS
# 
# Matrix products: default
# BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.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=C             
#  [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] openxlsx_4.2.3  abind_1.4-5     regress_1.3-21  survival_3.2-7 
# [5] qtl2_0.24       GGally_2.1.0    gridExtra_2.3   ggplot2_3.3.3  
# [9] workflowr_1.6.2
# 
# loaded via a namespace (and not attached):
#  [1] tidyselect_1.1.0   xfun_0.21          purrr_0.3.4        lattice_0.20-41   
#  [5] splines_4.0.3      colorspace_2.0-0   vctrs_0.3.6        generics_0.1.0    
#  [9] htmltools_0.5.1.1  yaml_2.2.1         blob_1.2.1         rlang_1.0.2       
# [13] later_1.1.0.1      pillar_1.4.7       glue_1.4.2         withr_2.4.1       
# [17] DBI_1.1.1          bit64_4.0.5        RColorBrewer_1.1-2 lifecycle_1.0.0   
# [21] plyr_1.8.6         stringr_1.4.0      munsell_0.5.0      gtable_0.3.0      
# [25] zip_2.1.1          evaluate_0.14      memoise_2.0.0      labeling_0.4.2    
# [29] knitr_1.31         fastmap_1.1.0      httpuv_1.5.5       highr_0.8         
# [33] Rcpp_1.0.6         promises_1.2.0.1   scales_1.1.1       cachem_1.0.4      
# [37] farver_2.0.3       fs_1.5.0           bit_4.0.4          digest_0.6.27     
# [41] stringi_1.5.3      dplyr_1.0.4        grid_4.0.3         rprojroot_2.0.2   
# [45] cli_2.3.0          tools_4.0.3        magrittr_2.0.1     tibble_3.0.6      
# [49] RSQLite_2.2.3      crayon_1.4.1       whisker_0.4        pkgconfig_2.0.3   
# [53] Matrix_1.3-2       ellipsis_0.3.1     data.table_1.13.6  assertthat_0.2.1  
# [57] rmarkdown_2.6      reshape_0.8.8      R6_2.5.0           git2r_0.28.0      
# [61] compiler_4.0.3

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