Last updated: 2023-02-21

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Knit directory: DO_Opioid/

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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)
library(tidyverse)

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 Master Fentanyl DO Study Sheet.xlsx-----------------------------------------------------
do.pheno1 <- read.xlsx("data/Master Fentanyl DO Study Sheet.xlsx", sheet = 1,
                       rows = 1:399, na.strings = "NaN", detectDates = TRUE)
#remove zombie mice
do.pheno1 <- do.pheno1 %>%
  dplyr::filter(is.na(Censor)) %>%
  dplyr::mutate(ID = replace(ID, (ID == 31009 & File == 20210519), 31109)) %>% #second 31009 is really 31109
  dplyr::mutate(Survival = replace(Survival, (ID %in% c(30931, 31084)), "ALIVE")) %>% #30931, 31084 should be ALIVE
  dplyr::mutate(Survival = replace(Survival, (ID %in% c(31049,30901)), "DEAD")) %>% #31049,30901 should be DEAD
  dplyr::mutate(Status_bin = ifelse(Survival == "ALIVE", 1, 0)) %>%
  dplyr::mutate(ID = as.character(ID)) %>%
  dplyr::arrange(ID, desc(File)) %>%
  dplyr::distinct(ID, .keep_all = TRUE) %>%
  dplyr::rename(., MouseID = ID)

#Fentanyl_alternate_metrics.xlsx
do.pheno.alt <- read.xlsx("data/Fentanyl_alternate_metrics.xlsx", sheet = 1,
                          na.strings = "NaN") %>%
  dplyr::rename(., MouseID = Mouse.ID) %>%
  dplyr::mutate(MouseID = as.character(MouseID)) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\(", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\)", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\%/", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\.%", "")) %>%
  dplyr::filter(!(if_all(2:11, is.na))) %>%
  dplyr::distinct(MouseID, .keep_all = TRUE)
#get sex and dob
do.pheno1 <- left_join(do.pheno.alt, do.pheno1) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\(", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\)", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\.%", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\-", "")) %>%
  dplyr::select(MouseID, Sex, dob, Generation,
                Time.to.Dead.Hr:RR.Recovery.Rate.Hr,
                Min.Depression.BR, Time.to.Mostly.Dead.Hr, Time.to.Recovery.Hr,
                Status_bin, Survival) %>%
  dplyr::mutate(Survival = replace(Survival, (!is.na(Time.to.Dead.Hr) & is.na(Survival)), "DEAD")) %>%
  dplyr::mutate(event = ifelse(Survival == "DEAD", 1, 0)) %>% #event 0=alive, 1=dead.
  dplyr::mutate(Survival.Time48 = replace(Time.to.Dead.Hr, (Survival == "ALIVE" & is.na(Time.to.Dead.Hr)), 24.00)) %>%
  dplyr::mutate(cohort = "cohort1")

# Read phenotype data Composite Post Kevins Program Group 2 Fentanyl Prepped for Hao.xlsx-----------------------------------------------------
#batch 20220502
do.pheno2 <- readxl::read_excel(path = "data/Composite Post Kevins Program Group 2 Fentanyl Prepped for Hao.xlsx",
                                col_types = c("text", "text", "text", "date", "numeric", "numeric", "date", rep("numeric", 9), "date"))
# Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
# Expecting date in D263 / R263C4: got 'BLANK'
# Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
# Expecting numeric in F263 / R263C6: got 'BLANK'
do.pheno2 <- do.pheno2 %>%
  dplyr::rename_with(., ~str_remove_all(.x, " ")) %>%
  dplyr::filter(!is.na(MouseID)) %>%
  dplyr::filter(MouseID != "BLANK") %>%
  dplyr::mutate(EarTag = replace(EarTag, (MouseID == 35151), "ALIVE")) %>% #35151 should be ALIVE
  dplyr::mutate(EarTag = replace(EarTag, (MouseID %in% c(35263, 35108)), "DEAD")) %>% #  35263 35108 should be DEAD
  dplyr::mutate(Status_bin = case_when(
    EarTag == "DEAD" ~ 0,
    (EarTag == "ALIVE" | is.na(EarTag)) ~ 1
  )) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\(", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\)", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\%/", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\.%", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\%", "")) %>%
  dplyr::rename_with(., ~ str_replace(.x, "\\-", "")) %>%
  dplyr::select(MouseID,
                Survival = EarTag,
                Sex,
                dob = DOB,
                Time.to.Dead.Hr = TimetoDeadHr,
                RR.Depression.Rate.Hr = RRDepressionRateHrSLOPE,
                Time.to.Steady.RR.Depression.Hr = TimetoSteadyRRDepressionHr,
                Min.Depression.RR = MinDepressionRR,
                SteadyState.Depression.Duration.Hr = SteadyStateDepressionDurationHrINTERVAL,
                Time.to.Threshold.Recovery.Hr = TimetoThresholdRecoveryHr,
                Time.to.Projected.Recovery.Hr = TimetoProjectedRecoveryHr,
                Start.of.Recovery.Hr = StartofRecoveryHr,
                RR.Recovery.Rate.Hr = RRRecoveryRateHrSLOPE,
                Status_bin) %>%
  dplyr::mutate(Survival = replace(Survival, is.na(Survival), "ALIVE")) %>%
  dplyr::mutate(event = ifelse(Survival == "DEAD", 1, 0)) %>% #event 0=alive, 1=dead.
  dplyr::mutate(Survival.Time48 = replace(Time.to.Dead.Hr, (Survival == "ALIVE" & is.na(Time.to.Dead.Hr)), 48.00)) %>%
  dplyr::mutate(cohort = "cohort2")

#truncated greater than 24 hrs as missing
do.pheno2 <- do.pheno2 %>%
  dplyr::mutate(Time.to.Dead.Hr                    = ifelse(Time.to.Dead.Hr > 24, NA_real_, Time.to.Dead.Hr)) %>%
  dplyr::mutate(SteadyState.Depression.Duration.Hr = ifelse(SteadyState.Depression.Duration.Hr > 24, NA_real_, SteadyState.Depression.Duration.Hr)) %>%
  dplyr::mutate(Time.to.Threshold.Recovery.Hr      = ifelse(Time.to.Threshold.Recovery.Hr > 24, NA_real_, Time.to.Threshold.Recovery.Hr)) %>%
  dplyr::mutate(Time.to.Projected.Recovery.Hr      = ifelse(Time.to.Projected.Recovery.Hr > 24, NA_real_, Time.to.Projected.Recovery.Hr)) %>%
  dplyr::mutate(Start.of.Recovery.Hr               = ifelse(Start.of.Recovery.Hr > 24, NA_real_, Start.of.Recovery.Hr))

#merge with do.pheno
do.pheno <- bind_rows(do.pheno1, do.pheno2)

#data point
table(do.pheno[is.na(do.pheno$Time.to.Dead.Hr), ]$cohort)
# 
# cohort1 cohort2 
#     212     138

# Read genotype data -----------------------------------------------------------
load("data/Jackson_Lab_Bubier_MURGIGV01/gm_DO765_qc_newid.RData")#gm_after_qc
overlap.id  = intersect(ind_ids(gm_after_qc), as.character(do.pheno$MouseID))

#subset
gm = gm_after_qc[overlap.id, ]
do.pheno = do.pheno[do.pheno$MouseID %in% overlap.id, ]
do.pheno = do.pheno[match(ind_ids(gm), do.pheno$MouseID) , ]
rownames(do.pheno) = do.pheno$MouseID
all.equal(ind_ids(gm), do.pheno$MouseID)
# [1] TRUE

#covar
do.pheno$Sex <- as.factor(do.pheno$Sex)
#addcovar
options(na.action='na.pass')
addcovar = model.matrix(~Sex, data = do.pheno[, c("Sex"), drop=F])[,-1, drop=F]
colnames(addcovar) <- c("sex")

#boxplot on the raw data------------
for(i in 5:17){
  df <- do.pheno[,c(1,2,i)]
  df <- df[complete.cases(df), ]
  p <- ggplot(df, aes(x=Sex, y=df[,3], group = Sex, fill = Sex, 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(paste0(colnames(df)[3])) +
    xlab("Sex") +
    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=21), 
          axis.title=element_text(size=21)) +
    guides(shape = guide_legend(override.aes = list(size = 12)))
  print(p)
}

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#histogram
for(i in 5:17){
  df <- do.pheno[,c(1,2,i)]
  p <- ggplot(data=df, aes(x=df[,3])) + 
    geom_histogram() +
    ylab("Number of DO mice") + 
    xlab(paste0(colnames(df)[3])) +
    theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
          panel.background = element_blank(), axis.line = element_line(colour = "black"),
          text = element_text(size=21))
  print(p)
}
# Warning: Removed 337 rows containing non-finite values (stat_bin).

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#boxplot on the rankz data------------
for(i in 5:17){
  df <- do.pheno[,c(1,2,i)]
  df <- df[complete.cases(df), ]
  p <- ggplot(df, aes(x=Sex, y= rz.transform(df[,3]), group = Sex, fill = Sex, 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(paste0("rz.transformed ", colnames(df)[3])) +
    xlab("Sex") +
    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=21), 
          axis.title=element_text(size=21)) +
    guides(shape = guide_legend(override.aes = list(size = 12)))
  print(p)
}

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#histogram
for(i in 5:17){
  df <- do.pheno[,c(1,2,i)]
  p <- ggplot(data=df, aes(x=rz.transform(df[,3]))) + 
    geom_histogram() +
    ylab("Number of DO mice") + 
    xlab(paste0("rz.transformed ", colnames(df)[3])) +
    theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
          panel.background = element_blank(), axis.line = element_line(colour = "black"),
          text = element_text(size=21))
  print(p)
}
# Warning: Removed 337 rows containing non-finite values (stat_bin).

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Plot qtl mapping on array

Plot by using its output qtl.fentanyl.combine2cohort.out.RData

load("output/Fentanyl/qtl.fentanyl.combine2cohort.out.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]] <-  gm$gmap[[chr]]
  pmap[[chr]] <-  gm$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.out)){
  par(mar=c(5.1, 4.1, 1.1, 1.1))
  ymx <- maxlod(qtl.out[[i]]) # overall maximum LOD score
  plot(qtl.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, 12))
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
  title(main = paste0("do_Fentanyl_combine2cohort_",i))
}

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pdf(file = paste0("output/Fentanyl/do_Fentanyl_combine2cohort.pdf"), width = 16, height =8)
#save genome-wide plo
for(i in names(qtl.out)){
  par(mar=c(5.1, 4.1, 1.1, 1.1))
  ymx <- maxlod(qtl.out[[i]]) # overall maximum LOD score
  plot(qtl.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, 10))
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
  title(main = paste0("do_Fentanyl_combine2cohort_",i))
}
dev.off()
# png 
#   2
  
#peaks coeff plot
for(i in names(qtl.out)){
  print(i)
  peaks <- find_peaks(qtl.out[[i]], map=pmap, threshold=6, drop=1.5)
  if(nrow(peaks) > 0) {
    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.out[[i]], bgcolor="gray95", legend=NULL)
    plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.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] "Time.to.Dead.Hr"
#   lodindex lodcolumn chr       pos       lod     ci_lo     ci_hi
# 1        1    pheno1   4 155.62894 10.526649 155.33847 155.75940
# 2        1    pheno1  15 100.98983  6.626767  87.42813 101.41826
# 3        1    pheno1  17  23.96788  6.327276  21.57341  26.00445
# [1] 1

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# [1] 2

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# [1] 3

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# [1] "RR.Depression.Rate.Hr"
# [1] "Time.to.Steady.RR.Depression.Hr"
# [1] "Min.Depression.RR"
#   lodindex lodcolumn chr      pos      lod    ci_lo     ci_hi
# 1        1    pheno1  15 70.84631 6.097157 69.53075  72.33644
# 2        1    pheno1   X 99.32996 6.439452 98.70699 101.15048
# [1] 1

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# [1] 2

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# [1] "Mean.Depression.BR"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   2 179.93697 6.642010 99.48509 180.36898
# 2        1    pheno1  15  28.43474 6.957455 27.40518  72.41165
# 3        1    pheno1  17  44.82475 6.816106 44.68776  45.38912
# [1] 1

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# [1] 2

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# [1] 3

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# [1] "SteadyState.Depression.Duration.Hr"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   1 152.78955 6.378383 151.00100 153.36692
# 2        1    pheno1  10  42.02827 7.628133  41.66399  43.86722
# [1] 1

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# [1] 2

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# [1] "Time.to.Threshold.Recovery.Hr"
# [1] "Time.to.Projected.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   1 151.2661 6.756996 150.4074 151.4769
# [1] 1

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# [1] "Start.of.Recovery.Hr"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   1 152.78955 6.310882 149.02638 153.36692
# 2        1    pheno1  10  42.16135 7.694886  41.66399  43.86722
# 3        1    pheno1  15  93.60493 6.059735  88.73612  93.88737
# [1] 1

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# [1] 2

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# [1] 3

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# [1] "RR.Recovery.Rate.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1  14 20.35257 6.513019 20.15234 23.74698
# 2        1    pheno1  18 65.37854 6.187039 61.59291 81.48908
# [1] 1

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# [1] 2

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# [1] "Min.Depression.BR"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   9 103.0304 6.065324 38.64793 103.0599
# [1] 1

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# [1] "Time.to.Mostly.Dead.Hr"
#   lodindex lodcolumn chr      pos      lod   ci_lo    ci_hi
# 1        1    pheno1  19 35.84901 6.359785 35.4551 37.47958
# [1] 1

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# [1] "Time.to.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo     ci_hi
# 1        1    pheno1   5 117.7803 6.310941 66.67504 117.79601
# 2        1    pheno1   6  53.5762 6.096846 53.51503  53.91867
# [1] 1

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# [1] 2

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# [1] "Status_bin"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   1 150.38336 6.177322  40.80906 185.20043
# 2        1    pheno1   2 117.58205 6.005846  37.99636 118.56768
# 3        1    pheno1   5  64.02294 7.173261  59.01797 130.37092
# 4        1    pheno1  12 119.19756 8.201177 118.59974 120.01743
# 5        1    pheno1  19  53.40556 6.139452  53.08070  57.06435
# [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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#save peaks coeff plot
for(i in names(qtl.out)){
  print(i)
  peaks <- find_peaks(qtl.out[[i]], map=pmap, threshold=6, drop=1.5)
  if(nrow(peaks) > 0){
      fname <- paste("output/do_Fentanyl_combine2cohort_", str_replace_all(i, "[[:punct:]]", "") ,"_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.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] "Time.to.Dead.Hr"
# [1] 1
# [1] 2
# [1] 3
# [1] "RR.Depression.Rate.Hr"
# [1] "Time.to.Steady.RR.Depression.Hr"
# [1] "Min.Depression.RR"
# [1] 1
# [1] 2
# [1] "Mean.Depression.BR"
# [1] 1
# [1] 2
# [1] 3
# [1] "SteadyState.Depression.Duration.Hr"
# [1] 1
# [1] 2
# [1] "Time.to.Threshold.Recovery.Hr"
# [1] "Time.to.Projected.Recovery.Hr"
# [1] 1
# [1] "Start.of.Recovery.Hr"
# [1] 1
# [1] 2
# [1] 3
# [1] "RR.Recovery.Rate.Hr"
# [1] 1
# [1] 2
# [1] "Min.Depression.BR"
# [1] 1
# [1] "Time.to.Mostly.Dead.Hr"
# [1] 1
# [1] "Time.to.Recovery.Hr"
# [1] 1
# [1] 2
# [1] "Status_bin"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] 5

#save peaks coeff blup plot
for(i in names(qtl.out)){
  print(i)
  peaks <- find_peaks(qtl.out[[i]], map=pmap, threshold=6, drop=1.5)
  if(nrow(peaks) > 0) {
      fname <- paste("output/do_Fentanyl_combine2cohort_", str_replace_all(i, "[[:punct:]]", "") ,"_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.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] "Time.to.Dead.Hr"
# [1] 1
# [1] 2
# [1] 3
# [1] "RR.Depression.Rate.Hr"
# [1] "Time.to.Steady.RR.Depression.Hr"
# [1] "Min.Depression.RR"
# [1] 1
# [1] 2
# [1] "Mean.Depression.BR"
# [1] 1
# [1] 2
# [1] 3
# [1] "SteadyState.Depression.Duration.Hr"
# [1] 1
# [1] 2
# [1] "Time.to.Threshold.Recovery.Hr"
# [1] "Time.to.Projected.Recovery.Hr"
# [1] 1
# [1] "Start.of.Recovery.Hr"
# [1] 1
# [1] 2
# [1] 3
# [1] "RR.Recovery.Rate.Hr"
# [1] 1
# [1] 2
# [1] "Min.Depression.BR"
# [1] 1
# [1] "Time.to.Mostly.Dead.Hr"
# [1] 1
# [1] "Time.to.Recovery.Hr"
# [1] 1
# [1] 2
# [1] "Status_bin"
# [1] 1
# [1] 2
# [1] 3
# [1] 4
# [1] 5

heritability

#plot heritability by qtl2 array --------------
herit <- data.frame(Phenotype = names(unlist(qtl.hsq)),
                    Heritability = round(unlist(qtl.hsq),3))
herit <- herit %>%
  arrange(desc(Heritability))
herit$Phenotype <- factor(herit$Phenotype, levels = herit$Phenotype)
#histgram
p2 <- ggplot(data=herit, aes(x=Phenotype, y=Heritability)) + #, fill=Domain, color = Domain)) +
  geom_bar(stat="identity", fill = "blue", color = "blue", show.legend = FALSE) +
  scale_y_continuous(breaks=seq(0.0, 1.0, 0.1)) +
  geom_text(aes(label = Heritability, y = Heritability + 0.005), position = position_dodge(0.9),vjust = 0) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  ggtitle("DO do_Fentanyl_combine2cohort Heritability by qtl2 array")
p2

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#plot heritability by GCTA
h <- read.csv("data/FinalReport/GCTA/DO_Fentanyl_combining2Cohort/DO_Fentanyl_combining2Cohort_heritability_by_GCTA.csv", header = TRUE)
h <- h %>%
  arrange(desc(Heritability))
h$Phenotype <- factor(h$Phenotype, levels = h$Phenotype)
h$Heritability <- round(h$Heritability,2)
#histgram
pdf(file = paste0("data/FinalReport/GCTA/DO_Fentanyl_combining2Cohort/DO_Fentanyl_combining2Cohort","_heritability_by_GCTA.pdf"), height = 10, width = 10)
p3<-ggplot(data=h, aes(x=Phenotype, y=Heritability)) + #, fill=Domain, color = Domain)) +
  geom_bar(stat="identity", fill = "blue", color = "blue", show.legend = FALSE) +
  scale_y_continuous(breaks=seq(0.0, 1.0, 0.1)) +
  geom_text(aes(label = Heritability, y = Heritability + 0.005), position = position_dodge(0.9),vjust = 0) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  ggtitle("Heritability by GCTA")
p3
dev.off()
# png 
#   2
# png 
#   2
p3

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Plot qtl mapping on array male

load("output/Fentanyl/qtl.fentanyl.combine2cohort.out.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]] <-  gm$gmap[[chr]]
  pmap[[chr]] <-  gm$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.out)){
  par(mar=c(5.1, 4.1, 1.1, 1.1))
  ymx <- maxlod(qtl.out[[i]]) # overall maximum LOD score
  plot(qtl.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, max(sapply(qtl.out, maxlod)) +2))
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
  title(main = paste0("do_Fentanyl_combine2cohort_",i))
}

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#peaks coeff plot
for(i in names(qtl.out)){
  print(i)
  peaks <- find_peaks(qtl.out[[i]], map=pmap, threshold=6, drop=1.5)
  if(nrow(peaks) > 0) {
    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.out[[i]], bgcolor="gray95", legend=NULL)
    plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.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] "Time.to.Dead.Hr"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   2 132.62489 6.124096 50.81340 173.86901
# 2        1    pheno1  10  99.92521 6.492863 99.28182 105.40417
# 3        1    pheno1  17  23.96788 6.725145 21.59514  25.08344
# [1] 1

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# [1] "RR.Depression.Rate.Hr"
# [1] "Time.to.Steady.RR.Depression.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1  17 33.66364 6.021735 7.725897 35.02144
# [1] 1

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# [1] "Min.Depression.RR"
#   lodindex lodcolumn chr     pos      lod    ci_lo    ci_hi
# 1        1    pheno1  14 101.798 6.961191 100.3058 103.3399
# [1] 1

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# [1] "Mean.Depression.BR"
# [1] "SteadyState.Depression.Duration.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   4 32.63908 7.066593 31.85079 97.16594
# 2        1    pheno1   7 78.55130 6.765506 78.43609 79.07406
# 3        1    pheno1  15 82.95976 6.039949 82.61311 84.16285
# [1] 1

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# [1] "Time.to.Threshold.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1  10 60.61789 6.163288 59.50257 61.79358
# [1] 1

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# [1] "Time.to.Projected.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   1 180.4362 8.101813 180.3185 180.5471
# [1] 1

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# [1] "Start.of.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   4 32.63908 7.105498 31.85079 97.15470
# 2        1    pheno1   7 78.55130 6.935599 78.43609 79.07697
# 3        1    pheno1  15 82.95976 6.085487 76.08766 84.17742
# [1] 1

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# [1] "RR.Recovery.Rate.Hr"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   3 152.71526 6.057558 151.92564 153.04206
# 2        1    pheno1  12  82.52881 6.219487  46.37269  82.84506
# [1] 1

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# [1] "Min.Depression.BR"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   6 124.4987 6.882637 122.7691 125.5986
# [1] 1

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# [1] "Time.to.Mostly.Dead.Hr"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   2 115.56002 6.027351 37.81483 132.19814
# 2        1    pheno1  10  16.47363 7.647309 16.28585  18.20019
# 3        1    pheno1  11  30.45329 6.071616 28.81595  31.56268
# 4        1    pheno1  17  80.09690 6.490398 23.55316  80.36008
# [1] 1

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# [1] 2

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# [1] "Time.to.Recovery.Hr"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   3 148.59948 6.440788 53.60570 148.61250
# 2        1    pheno1   4  22.26230 6.109573 17.41576 137.08866
# 3        1    pheno1  10  70.01089 7.242967 68.18103  70.90480
# 4        1    pheno1  14  80.19357 6.242259 79.67940  80.21217
# 5        1    pheno1  18  23.93757 6.888692 22.73513  24.98189
# [1] 1

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# [1] "Status_bin"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   1 185.11874 6.058648 41.25093 185.84088
# 2        1    pheno1  15  94.25093 7.334412 92.19023  94.31234
# 3        1    pheno1  18  16.19240 6.685013 15.31898  20.05685
# 4        1    pheno1  19  49.78637 6.595691 45.70921  53.50149
# [1] 1

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Plot qtl mapping on array female

load("output/Fentanyl/qtl.fentanyl.combine2cohort.out.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]] <-  gm$gmap[[chr]]
  pmap[[chr]] <-  gm$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.out)){
  par(mar=c(5.1, 4.1, 1.1, 1.1))
  ymx <- maxlod(qtl.out[[i]]) # overall maximum LOD score
  plot(qtl.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, max(sapply(qtl.out, maxlod)) +2))
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
  abline(h=summary(qtl.operm[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
  title(main = paste0("do_Fentanyl_combine2cohort_",i))
}

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#peaks coeff plot
for(i in names(qtl.out)){
  print(i)
  peaks <- find_peaks(qtl.out[[i]], map=pmap, threshold=6, drop=1.5)
  if(nrow(peaks) > 0) {
    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.out[[i]], bgcolor="gray95", legend=NULL)
    plot_coefCC(coef_c2[[i]][[p]], pmap[chr], scan1_output=qtl.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] "Time.to.Dead.Hr"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   2 132.62489 6.124096 50.81340 173.86901
# 2        1    pheno1  10  99.92521 6.492863 99.28182 105.40417
# 3        1    pheno1  17  23.96788 6.725145 21.59514  25.08344
# [1] 1

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# [1] "RR.Depression.Rate.Hr"
# [1] "Time.to.Steady.RR.Depression.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1  17 33.66364 6.021735 7.725897 35.02144
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# [1] "Min.Depression.RR"
#   lodindex lodcolumn chr     pos      lod    ci_lo    ci_hi
# 1        1    pheno1  14 101.798 6.961191 100.3058 103.3399
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# [1] "Mean.Depression.BR"
# [1] "SteadyState.Depression.Duration.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   4 32.63908 7.066593 31.85079 97.16594
# 2        1    pheno1   7 78.55130 6.765506 78.43609 79.07406
# 3        1    pheno1  15 82.95976 6.039949 82.61311 84.16285
# [1] 1

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# [1] "Time.to.Threshold.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1  10 60.61789 6.163288 59.50257 61.79358
# [1] 1

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# [1] "Time.to.Projected.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   1 180.4362 8.101813 180.3185 180.5471
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# [1] "Start.of.Recovery.Hr"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   4 32.63908 7.105498 31.85079 97.15470
# 2        1    pheno1   7 78.55130 6.935599 78.43609 79.07697
# 3        1    pheno1  15 82.95976 6.085487 76.08766 84.17742
# [1] 1

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# [1] "RR.Recovery.Rate.Hr"
#   lodindex lodcolumn chr       pos      lod     ci_lo     ci_hi
# 1        1    pheno1   3 152.71526 6.057558 151.92564 153.04206
# 2        1    pheno1  12  82.52881 6.219487  46.37269  82.84506
# [1] 1

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# [1] "Min.Depression.BR"
#   lodindex lodcolumn chr      pos      lod    ci_lo    ci_hi
# 1        1    pheno1   6 124.4987 6.882637 122.7691 125.5986
# [1] 1

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# [1] "Time.to.Mostly.Dead.Hr"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   2 115.56002 6.027351 37.81483 132.19814
# 2        1    pheno1  10  16.47363 7.647309 16.28585  18.20019
# 3        1    pheno1  11  30.45329 6.071616 28.81595  31.56268
# 4        1    pheno1  17  80.09690 6.490398 23.55316  80.36008
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# [1] "Time.to.Recovery.Hr"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   3 148.59948 6.440788 53.60570 148.61250
# 2        1    pheno1   4  22.26230 6.109573 17.41576 137.08866
# 3        1    pheno1  10  70.01089 7.242967 68.18103  70.90480
# 4        1    pheno1  14  80.19357 6.242259 79.67940  80.21217
# 5        1    pheno1  18  23.93757 6.888692 22.73513  24.98189
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# [1] "Status_bin"
#   lodindex lodcolumn chr       pos      lod    ci_lo     ci_hi
# 1        1    pheno1   1 185.11874 6.058648 41.25093 185.84088
# 2        1    pheno1  15  94.25093 7.334412 92.19023  94.31234
# 3        1    pheno1  18  16.19240 6.685013 15.31898  20.05685
# 4        1    pheno1  19  49.78637 6.595691 45.70921  53.50149
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8611541 xhyuo 2023-02-21
# [1] 3

Version Author Date
8611541 xhyuo 2023-02-21

Version Author Date
8611541 xhyuo 2023-02-21

Version Author Date
8611541 xhyuo 2023-02-21
# [1] 4

Version Author Date
8611541 xhyuo 2023-02-21

Version Author Date
8611541 xhyuo 2023-02-21

Version Author Date
8611541 xhyuo 2023-02-21

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] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.4     purrr_0.3.4    
#  [5] readr_1.4.0     tidyr_1.1.2     tibble_3.0.6    tidyverse_1.3.0
#  [9] openxlsx_4.2.3  abind_1.4-5     regress_1.3-21  survival_3.2-7 
# [13] qtl2_0.24       GGally_2.1.0    gridExtra_2.3   ggplot2_3.3.3  
# [17] workflowr_1.6.2
# 
# loaded via a namespace (and not attached):
#  [1] httr_1.4.2         bit64_4.0.5        jsonlite_1.7.2     splines_4.0.3     
#  [5] modelr_0.1.8       assertthat_0.2.1   highr_0.8          blob_1.2.1        
#  [9] cellranger_1.1.0   yaml_2.2.1         pillar_1.4.7       RSQLite_2.2.3     
# [13] backports_1.2.1    lattice_0.20-41    glue_1.4.2         digest_0.6.27     
# [17] RColorBrewer_1.1-2 promises_1.2.0.1   rvest_0.3.6        colorspace_2.0-0  
# [21] htmltools_0.5.1.1  httpuv_1.5.5       Matrix_1.3-2       plyr_1.8.6        
# [25] pkgconfig_2.0.3    broom_0.7.4        haven_2.3.1        scales_1.1.1      
# [29] whisker_0.4        later_1.1.0.1      git2r_0.28.0       farver_2.0.3      
# [33] generics_0.1.0     ellipsis_0.3.1     cachem_1.0.4       withr_2.4.1       
# [37] cli_2.3.0          magrittr_2.0.1     crayon_1.4.1       readxl_1.3.1      
# [41] memoise_2.0.0      evaluate_0.14      fs_1.5.0           xml2_1.3.2        
# [45] tools_4.0.3        data.table_1.13.6  hms_1.0.0          lifecycle_1.0.0   
# [49] reprex_1.0.0       munsell_0.5.0      zip_2.1.1          compiler_4.0.3    
# [53] rlang_1.0.2        grid_4.0.3         rstudioapi_0.13    labeling_0.4.2    
# [57] rmarkdown_2.6      gtable_0.3.0       DBI_1.1.1          reshape_0.8.8     
# [61] R6_2.5.0           lubridate_1.7.9.2  knitr_1.31         fastmap_1.1.0     
# [65] bit_4.0.4          rprojroot_2.0.2    stringi_1.5.3      Rcpp_1.0.6        
# [69] vctrs_0.3.6        dbplyr_2.1.0       tidyselect_1.1.0   xfun_0.21

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