CC_SARS

workflowr

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Last updated: 2023-09-29

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Knit directory: DO_Opioid/

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Unstaged changes:
    Modified:   .gitignore
    Modified:   _workflowr.yml
    Deleted:    analysis/CC_SARS-2.Rmd
    Modified:   analysis/marker_violin.Rmd
    Modified:   output/CC_SARS_Chr16_QTL_interval.pdf
    Modified:   output/CC_SARS_Chr16_plotGeno.pdf
    Modified:   output/CC_SARS_Chr16_plotGeno.png

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/CC_SARS.Rmd) and HTML (docs/CC_SARS.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	acc8369	xhyuo	2023-09-29	qtl mapping conditional on Chr16 young
html	1bf1450	xhyuo	2023-09-01	Build site.
Rmd	9f2fe4c	xhyuo	2023-09-01	qtl mapping dpi
html	87576d1	xhyuo	2023-06-07	Build site.
Rmd	9fd2684	xhyuo	2023-06-07	snp annotation csv
html	ac4e5fd	xhyuo	2023-06-07	Build site.
Rmd	501e447	xhyuo	2023-06-07	snp annotation
html	446ef80	xhyuo	2023-05-12	Build site.
Rmd	4cdafe6	xhyuo	2023-05-12	CC_SARS plotGeno pheno label
html	5d16c51	xhyuo	2023-05-04	Build site.
Rmd	650b581	xhyuo	2023-05-04	CC_SARS plotGeno png
html	20c7a92	xhyuo	2023-05-04	Build site.
Rmd	8cd8b14	xhyuo	2023-05-04	CC_SARS plotGeno save
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Rmd	f14e74b	xhyuo	2023-04-20	CC_SARS plotGeno2
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Rmd	5f9d416	xhyuo	2023-04-20	CC_SARS plotGeno
html	7670d84	xhyuo	2023-04-12	Build site.
Rmd	c81156f	xhyuo	2023-04-12	CC_SARS qtl plot remove noninformative marker
html	c3bafbc	xhyuo	2023-04-12	Build site.
Rmd	7062be2	xhyuo	2023-04-12	CC_SARS qtl remove noninformative marker
html	d3baa96	xhyuo	2023-04-11	Build site.
Rmd	d2e27f6	xhyuo	2023-04-11	CC_SARS qtl peak
html	c2d7684	xhyuo	2023-04-07	Build site.
Rmd	f1fa513	xhyuo	2023-04-07	CC_SARS qtl new lod drop
html	ea484cc	xhyuo	2023-04-06	Build site.
Rmd	b315db7	xhyuo	2023-04-06	CC_SARS qtl lod drop
html	80390e8	xhyuo	2023-04-05	Build site.
Rmd	3efc2fb	xhyuo	2023-04-05	CC_SARS sex qtl
html	b864fd1	xhyuo	2023-03-30	Build site.
Rmd	558a2a4	xhyuo	2023-03-30	CC_SARS qtl
html	6a0b66f	xhyuo	2023-03-22	Build site.
Rmd	ffa3ffd	xhyuo	2023-03-22	CC_SARS

---

Last update: 2023-09-29

Loading libraries

library(ggplot2)
library(gridExtra)
library(GGally)
library(parallel)
library(qtl)
library(parallel)
library(survival)
library(regress)
library(abind)
library(tidyverse)
library(broman)
library(qtl2)
library(qtlcharts)
library(DT)
library(biomaRt)
library(vroom)
source("code/PLINKtoCSVR.R")

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

Read phenotype data

# Read phenotype data -----------------------------------------------------
pheno <- readxl::read_xlsx(path = "data/CC_SARS-1/Menachery 2022 SARS-CoV-1 Study.xlsx", skip = 1,
                        sheet = 1)
pheno <- pheno %>%
  slice(-1) %>%
  dplyr::mutate(across(7:18, as.numeric))
# Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
colnames(pheno)[1] <- "AnimalID"
colnames(pheno)[7:11] <- paste0("DPI-g.", 0:4)
colnames(pheno)[12:16] <- paste0("DPI-perct.", 0:4)
colnames(pheno)[17] <- "Lung_titer_4DPI_PFU_per_ml"
colnames(pheno)[18] <- "Log10_Lung_titer_4DPI_PFU_per_ml"
#add another column for log10 (+1) so that it will not be NA
pheno <- pheno %>%
  dplyr::mutate(Log10_1_Lung_titer_4DPI_PFU_per_ml = log10(Lung_titer_4DPI_PFU_per_ml + 1))

#
pheno <- pheno %>%
  dplyr::mutate(strain = str_sub(Strain, 1, 4), .after = Strain) %>%
  tidyr::unite(IID, c("strain", "Gender", "AnimalID"), sep = "_", remove = FALSE) %>%
  tidyr::separate(Parents, c("MID", "FID"))

#boxplot
p1 <- ggplot(pheno, aes(x=Gender, y=Lung_titer_4DPI_PFU_per_ml, group = Gender, fill = Gender, 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("Lung titer 4DPI [PFU/ml]") +
  xlab("Gender") +
  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

Past versions of “Read phenotype data-1.png”

Version	Author	Date
6a0b66f	xhyuo	2023-03-22

p2 <- ggplot(pheno, aes(x=Gender, y=Log10_1_Lung_titer_4DPI_PFU_per_ml, group = Gender, fill = Gender, 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("Log10 (Lung titer)") +
  xlab("Gender") +
  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

Past versions of “Read phenotype data-2.png”

Version	Author	Date
b864fd1	xhyuo	2023-03-30
6a0b66f	xhyuo	2023-03-22

QC on the genotype data

batch.name <- list.files("data/CC_SARS-1/", pattern = "*-raw-genotypes.txt", full.names = TRUE)

#read data
geno <- map_dfr(batch.name, ~read.table(.x, header = TRUE, sep = "\t"))
geno <- geno %>% 
  dplyr::mutate(Sample.Name = gsub(" ", "", Sample.Name))

#missing
percent_missing <- geno %>%
  dplyr::filter(Allele1...Forward == "-" & Allele2...Forward == "-") %>%
  group_by(Sample.Name) %>%
  tally() %>%
  dplyr::mutate(Percent_missing_genotype_data = 100*n/n_distinct(geno$SNP.Name)) %>%
  dplyr::mutate(Mouse=seq_along(Sample.Name))

#iplot
iplot(percent_missing$Mouse, 
      percent_missing$Percent_missing_genotype_data, 
      indID=paste0(percent_missing$Sample.Name, " (", round(percent_missing$Percent_missing_genotype_data,2), "%)"),
      chartOpts=list(xlab="Mouse",  ylab="Percent missing genotype data", ylim=c(0, 100)))

Sex

#sex
#xint and yint
#Xint
Xint <- geno %>%
  dplyr::filter(Chromosome == "X") %>%
  dplyr::mutate(xint = (X+Y)/2) %>%
  reshape2::dcast(. , Sample.Name ~ SNP.Name, value.var = "xint") %>%
  column_to_rownames(var = "Sample.Name")
#Yint
Yint <- geno %>%
  dplyr::filter(Chromosome == "Y") %>%
  dplyr::mutate(yint = (X+Y)/2) %>%
  reshape2::dcast(. , Sample.Name ~ SNP.Name, value.var = "yint") %>%
  column_to_rownames(var = "Sample.Name")

#sex order
all.equal(rownames(Xint), rownames(Yint))
# [1] TRUE
sex <- str_extract(rownames(Xint), "(?<=_)[^_]*(?=_)")

x_pval <- apply(Xint, 2, function(a) t.test(a ~ sex)$p.value)
y_pval <- apply(Yint, 2, function(a) t.test(a ~ sex)$p.value)

xint_ave <- rowMeans(Xint[, x_pval < 0.05/length(x_pval)], na.rm=TRUE)
yint_ave <- rowMeans(Yint[, y_pval < 0.05/length(y_pval)], na.rm=TRUE)

point_colors <- as.character( brocolors("web")[c("green", "purple")] )
labels <- paste0(names(xint_ave))
iplot(xint_ave, yint_ave, group=sex, indID=labels,
      chartOpts=list(pointcolor=point_colors, pointsize=4,
                     xlab="Average X chr intensity", ylab="Average Y chr intensity"))

Genotype format on PLINK and R/qtl

minimuga <- read.csv("https://raw.githubusercontent.com/kbroman/MUGAarrays/c4e92e32300d97055408fbc26c84e0005a873d4a/UWisc/mini_uwisc_v2.csv")

#remove bad samples MVVM_F_879, VMVM_F_583
pheno <- pheno %>%
  dplyr::filter(!(IID %in% c("MVVM_F_879", "VMVM_F_583")))
geno <- geno %>%
  dplyr::filter(!(Sample.Name %in% c("MVVM_F_879", "VMVM_F_583"))) %>%
  dplyr::filter(!(Chromosome %in% c("Y", "MT", "PAR", "0"))) %>%
  dplyr::left_join(., minimuga[, c(1,4)], by = c("SNP.Name"  = "marker"))

#chromosome (1-22, X, Y or 0 if unplaced)
#rs# or snp identifier
#Genetic distance (morgans)
#Base-pair position (bp units)
     
map <- geno %>% 
  dplyr::select(Chromosome, SNP.Name, cM_cox, bp = Position) %>%
  #dplyr::mutate(morgan = 0, .before = bp) %>%
  #dplyr::filter(Chromosome %in% c("Y", "MT", "PAR", "0"))
  distinct()

ped.pheno <- geno %>% 
  tidyr::unite("geno", Allele1...Forward, Allele2...Forward, sep = " ", remove = FALSE) %>%
  dplyr::select(Sample.Name, SNP.Name, geno) %>%
  dplyr::mutate(geno = case_when(
    geno == "- -" ~ "0 0",
    TRUE ~ as.character(geno)
  )) %>%
  reshape2::dcast(. , Sample.Name ~ SNP.Name, value.var = "geno") %>%
  left_join(x = pheno[, c(1,3,7,8,10:14, 16:22)], y = ., by = c("IID" = "Sample.Name")) %>%
  dplyr::mutate(FFID = paste0(FID, "_", MID), .before = 1)

#qc on the marker
qc_marker <- data.frame(name = colnames(ped.pheno)[-1:-17],
                        letter = map_chr(18:ncol(ped.pheno), function(x){
                          paste0(unique(unlist(strsplit(pull(ped.pheno[,x]), " "))), collapse = "")
                          }),
                        length = map_dbl(18:ncol(ped.pheno), function(x){
                          y  = unique(unlist(strsplit(pull(ped.pheno[,x]), " ")))
                          length(y[y != "-"])
                          })
                        )
#select marker with length > 1
ped.pheno <- cbind(ped.pheno[, 1:17], ped.pheno[, qc_marker[qc_marker$length > 1, "name"]])
#update map
map <- map %>% 
  dplyr::filter(SNP.Name %in% qc_marker[qc_marker$length > 1, "name"])
all.equal(map$SNP.Name, colnames(ped.pheno)[-1:-17])
# [1] TRUE
write.table(map, file = "data/CC_SARS-1/cc_sars.map", sep = " ", quote = F, col.names = FALSE, row.names = FALSE)

#ped
ped <- ped.pheno %>% 
  dplyr::mutate(Gender = if_else(Gender == "M", 1, 2)) %>%
  dplyr::select(FFID, IID, FID, MID, Gender, 17:last_col())
write.table(ped, file = "data/CC_SARS-1/cc_sars.ped", sep = " ", quote = F, col.names = FALSE, row.names = FALSE)
#pheno
write.table(ped.pheno[, c(1, 2, 6:17)], file = "data/CC_SARS-1/cc_sars.pheno", sep = " ", quote = F, col.names = TRUE, row.names = FALSE)
  
#read plink format
system("cd /projects/compsci/vmp/USERS/heh/DO_Opioid/data/CC_SARS-1; /projects/csna/csna_workflow/code/plink-1.07-x86_64/plink --noweb --file cc_sars  --missing-phenotype NA --mind 0.1 --geno 0.1 --recode --out cc_sars_qc")

QTL mapping with removing non-informative markers

cross <- PLINKtoCSVR(ped = "data/CC_SARS-1/cc_sars_qc.ped",
                     map = "data/CC_SARS-1/cc_sars_qc.map",
                     out = "data/CC_SARS-1/cross.csvr")
#  --Read the following data:
#    282  individuals
#    2785  markers
#    2  phenotypes
# Warning in summary.cross(cross): Some markers at the same position on chr
# 1,2,3,5,6,7,8,9,10,11,12,13,14,15,16,18,19,23; use jittermap().
#  --Cross type: f2
cross$pheno <- cbind(cross$pheno, ped.pheno[, c(6:17)])
#rankz on DPI
cross$pheno <- cross$pheno %>%
  dplyr::mutate(across(3:11, ~rz.transform(.x)))
#convert to cross2
cross2 <- convert2cross2(cross)

plotMap(cross) 

Past versions of “QTL mapping QTL mapping without removing non-informative markers-1.png”

Version	Author	Date
20c7a92	xhyuo	2023-05-04
c3bafbc	xhyuo	2023-04-12

plotPheno(cross, pheno.col = 14, xlab = "", main = "Log10_1_Lung_titer_4DPI_PFU_per_ml")

Past versions of “QTL mapping QTL mapping without removing non-informative markers-2.png”

Version	Author	Date
20c7a92	xhyuo	2023-05-04
c3bafbc	xhyuo	2023-04-12

plotPheno(cross, pheno.col = 12, xlab = "", main = "Lung_titer_4DPI_PFU_per_ml")

Past versions of “QTL mapping QTL mapping without removing non-informative markers-3.png”

Version	Author	Date
20c7a92	xhyuo	2023-05-04
c3bafbc	xhyuo	2023-04-12

plotPheno(cross, pheno.col = 13, xlab = "", main = "Log10_Lung_titer_4DPI_PFU_per_ml")

Past versions of “QTL mapping QTL mapping without removing non-informative markers-4.png”

Version	Author	Date
20c7a92	xhyuo	2023-05-04
c3bafbc	xhyuo	2023-04-12

#0/1 for females/males
covars <- model.matrix(~ sex, cross$pheno)[,-1]
f2_qtl <- calc.genoprob(cross)

#1
out_1 <- scanone(f2_qtl, pheno.col = 14, model = "2part", n.cluster = 10)
#out_1_operm <- scanone(f2_qtl, pheno.col = 1, model = "2part", n.perm = 100, n.cluster = 10)
#save(out_1_operm, file = "output/out_1_operm.RData")
load("output/out_1_operm.RData")
summary(out_1[,1:3], perms = out_1_operm[,1], alpha = 0.05, pvalues = TRUE)
#              chr  pos lod.p.mu pval
# gUNCHS042457  16 25.5     52.6    0
summary(out_1_operm[,1], alpha=c(0.05))
# LOD thresholds (100 permutations)
#    lod.p.mu
# 5%     9.06

#3
out_3 <- scanone(f2_qtl, pheno.col = 13, model = "normal", n.cluster = 10)
# Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 165 individuals with missing phenotypes.
#
plot(out_1, col = c("red"), main = "Log10_1_Lung_titer_4DPI_PFU_per_ml", ylab = "LOD")
add.threshold(out_1, perms = out_1_operm, alpha = 0.05, col="magenta")

Past versions of “QTL mapping QTL mapping without removing non-informative markers-5.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01
5d16c51	xhyuo	2023-05-04
20c7a92	xhyuo	2023-05-04
c3bafbc	xhyuo	2023-04-12

plot(out_3, col = c("gray"), main = "Log10_Lung_titer_4DPI_PFU_per_ml", ylab = "LOD")

Past versions of “QTL mapping QTL mapping without removing non-informative markers-6.png”

Version	Author	Date
20c7a92	xhyuo	2023-05-04
c3bafbc	xhyuo	2023-04-12

#1.5 drop interval on cM
drop_interval = find_peaks(out_1[, 3, drop = F], map = cross2$gmap, drop = 1.5, threshold = 9.2)
drop_interval
#   lodindex lodcolumn chr    pos      lod  ci_lo  ci_hi
# 1        1  lod.p.mu  16 25.536 52.56001 24.714 25.676

#chr16
plot(out_1, col = c("red"), chr = drop_interval$chr, ylab = "LOD")
plot(out_3, col = c("gray"), chr = drop_interval$chr, add = TRUE, ylab = "LOD")

Past versions of “QTL mapping QTL mapping without removing non-informative markers-7.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01
20c7a92	xhyuo	2023-05-04
7670d84	xhyuo	2023-04-12
c3bafbc	xhyuo	2023-04-12

#peak
peak = lodint(out_1, chr = drop_interval$chr)
peak
#              chr    pos lod.p.mu    lod.p   lod.mu
# gUNC26636079  16 24.714 50.68370 19.18999 31.49371
# gUNCHS042457  16 25.536 52.56001 21.05561 31.49602
# UNC26651633   16 25.676 49.95863 19.90604 30.52492
#peak marker
marker = rownames(peak)[which.max(peak$lod.p.mu)]
par(mar=c(3, 5, 4, 3))
plotPXG(f2_qtl, marker = marker, jitter = 0.25, pheno.col = 1, infer = F, 
          main = marker, ylab = "Log10_1_Lung_titer_4DPI_PFU_per_ml",
          mgp = c(3.4,1,0))

Past versions of “QTL mapping QTL mapping without removing non-informative markers-8.png”

Version	Author	Date
5d16c51	xhyuo	2023-05-04
20c7a92	xhyuo	2023-05-04
c3bafbc	xhyuo	2023-04-12

# Calculate error LOD scores
#cross <- calc.errorlod(cross, error.prob = 0.01)
#save(cross, file = "data/CC_SARS-1/cross.RData")
load("data/CC_SARS-1/cross.RData")

#chr16 1.5 drop interval on bp-----------------------------------------------------------------------
peak_bp = map[map$Chromosome == drop_interval$chr & map$cM_cox == drop_interval$pos & !is.na(map$cM_cox), "bp"]/10^6
peak_bp
# [1] 36.35104

start = map[map$Chromosome == drop_interval$chr & map$cM_cox == drop_interval$ci_lo & !is.na(map$cM_cox), "bp"]/10^6
start
# [1] 35.15812
end   = map[map$Chromosome == drop_interval$chr & map$cM_cox == drop_interval$ci_hi & !is.na(map$cM_cox), "bp"]/10^6
end
# [1] 36.57991
#genes in the qtl region
query_variants <- create_variant_query_func("data/cc_variants.sqlite")
query_genes <- create_gene_query_func("data/mouse_genes_mgi.sqlite")
chr16_gene <- query_genes(chr = 16, start, end)

#add bp
out_1_bp <- out_1 %>%
  rownames_to_column(.) %>%
  left_join(minimuga[, c(1,3,4)], by = c("rowname" = "marker")) %>%
  dplyr::mutate(pos = bp_mm10/10^6) %>%
  column_to_rownames()
class(out_1_bp) <- c("scanone","data.frame")

#plot
layout(mat = matrix(c(1:2), 
                    nrow = 2, 
                    ncol = 1),
       heights = c(1, 2))    # Heights of the two rows
# Plot 1
par(mar = c(0.01, 5, 2, 0.5))
#Create the base plot
plot(out_1_bp, col = c("red"), chr = 16, main = "CC_SARS Chr16 QTL interval", xlab = "", ylab = "LOD", xlim = c(start, end), ylim = c(0, 60))
text(peak_bp, 55, paste0(drop_interval$chr, "@", drop_interval$pos, "cM"), cex = 0.75)
segments(x0 = peak_bp,
         x1 = peak_bp,
         y0 = 0,
         y1 = drop_interval$lod,
         col="black", lty=2, lwd=1)
# Plot 2
par(mar = c(5.10, 5, 0, 0.5))
plot_genes(chr16_gene, bgcolor="white", xlim = c(start, end))

Past versions of “QTL mapping QTL mapping without removing non-informative markers-9.png”

Version	Author	Date
20c7a92	xhyuo	2023-05-04
c3bafbc	xhyuo	2023-04-12

#save plot
pdf(file = "data/CC_SARS-1/CC_SARS_Chr16_QTL_interval.pdf", width = 6, height = 6)
#plot
layout(mat = matrix(c(1:2), 
                    nrow = 2, 
                    ncol = 1),
       heights = c(1, 2))    # Heights of the two rows
# Plot 1
par(mar = c(0.01, 5, 2, 0.5))
#Create the base plot
plot(out_1_bp, col = c("red"), chr = 16, main = "CC_SARS Chr16 QTL interval", xlab = "", ylab = "LOD", xlim = c(start, end), ylim = c(0, 60))
text(peak_bp, 55, paste0(drop_interval$chr, "@", drop_interval$pos, "cM"), cex = 0.75)
segments(x0 = peak_bp,
         x1 = peak_bp,
         y0 = 0,
         y1 = drop_interval$lod,
         col="black", lty=2, lwd=1)
# Plot 2
par(mar = c(5.10, 5, 0, 0.5))
plot_genes(chr16_gene, bgcolor="white", xlim = c(start, end))
dev.off()
# png 
#   2

#B10160031421 16@21.63
#gUNCHS042457 16@25.536
#gUNC26706586 16@28.373
#gUNC26899499 16@34.29
#mUNC26936620 16@35.998
#S6J162881072 16@40.738
# plot genotype data, flagging genotypes with error LOD > cutoff
#sort the pheno to have top and bottom individuals 
top_ind <- cross$pheno %>%
  rownames_to_column(var = "id") %>%
  slice_max(., Pheno, n = 10) %>%
  pull(id)

bottom_ind <- cross$pheno %>%
  rownames_to_column(var = "id") %>%
  slice_min(., Pheno, n = 10, with_ties = FALSE) %>%
  pull(id)

pheno_ind <- cross$pheno[c(top_ind, bottom_ind), ]
#save plot
png(file = "data/CC_SARS-1/CC_SARS_Chr16_plotGeno.png", width = 800, height = 600)
par(mar = c(5, 3, 6, 6))
plotGeno(cross, chr = 16, ind = as.numeric(c(top_ind, bottom_ind)), xlim = c(0, 50), main = "")
par(las = 2)
axis(side = 4, at= 1:20, labels =  paste0(rownames(pheno_ind), " | ", round(pheno_ind$Pheno,3)))
#add horizontal line at y=20
abline(v = c(21.63, 25.536, 28.373, 34.29, 35.998, 40.738), col = c("red"), lty = c(2, 1, rep(2,4)), lwd = c(1))
xtick <- c(21.63, 25.536, 28.373, 34.29, 35.998, 40.738)
labels <- paste0(c("B10160031421", "gUNCHS042457", "gUNC26706586", "gUNC26899499", "mUNC26936620", "S6J162881072"))
axis(side = 3, at = xtick, labels = FALSE)
text(x = xtick-0.05,  par("usr")[1], 
     labels = labels, srt = 90, pos = 1, xpd = TRUE, offset = 0.2, cex = 0.7)
dev.off()
# png 
#   2

#knitr::include_graphics("output/CC_SARS_Chr16_plotGeno.png")

#save plot
pdf(file = "data/CC_SARS-1/CC_SARS_Chr16_plotGeno.pdf", width = 12, height = 10)
par(mar = c(5, 3, 6, 6))
plotGeno(cross, chr = 16, ind = as.numeric(c(top_ind, bottom_ind)), xlim = c(0, 50), main = "")
par(las = 2)
axis(side = 4, at= 1:20, labels =  paste0(rownames(pheno_ind), " | ", round(pheno_ind$Pheno,3)))
#add horizontal line at y=20
abline(v = c(21.63, 25.536, 28.373, 34.29, 35.998, 40.738), col = c("red"), lty = c(2, 1, rep(2,4)), lwd = c(1))
xtick <- c(21.63, 25.536, 28.373, 34.29, 35.998, 40.738)
labels <- paste0(c("B10160031421", "gUNCHS042457", "gUNC26706586", "gUNC26899499", "mUNC26936620", "S6J162881072"))
axis(side = 3, at = xtick, labels = FALSE)
text(x = xtick-0.05,  par("usr")[1], 
     labels = labels, srt = 90, pos = 1, xpd = TRUE, offset = 0.2, cex = 0.7)
dev.off()
# png 
#   2

#par(mar = c(5, 3, 6, 6))
plotGeno(cross, chr = 16, ind = as.numeric(c(top_ind, bottom_ind)), xlim = c(0, 50), main = "")
par(las = 2)
axis(side = 4, at= 1:20, labels =  paste0(rownames(pheno_ind), " | ", round(pheno_ind$Pheno,3)))
#add horizontal line at y=20
abline(v = c(21.63, 25.536, 28.373, 34.29, 35.998, 40.738), col = c("red"), lty = c(2, 1, rep(2,4)), lwd = c(1))
xtick <- c(21.63, 25.536, 28.373, 34.29, 35.998, 40.738)
labels <- paste0(c("B10160031421", "gUNCHS042457", "gUNC26706586", "gUNC26899499", "mUNC26936620", "S6J162881072"))
axis(side = 3, at = xtick, labels = FALSE)
text(x = xtick-0.05,  par("usr")[1], 
     labels = labels, srt = 90, pos = 1, xpd = TRUE, offset = 0.2, cex = 0.7)

Past versions of “QTL mapping QTL mapping without removing non-informative markers-10.png”

Version	Author	Date
ac4e5fd	xhyuo	2023-06-07
5d16c51	xhyuo	2023-05-04
20c7a92	xhyuo	2023-05-04

Interactive qtl plot

#interactive qtl plot
iplotScanone(out_1, f2_qtl,chr = drop_interval$chr)

snp annotation

snp.list <- read.csv("data/CC_SARS-1/20000 to MVAR.csv", header = TRUE)

# view the available databases GRCm38
listEnsembl(version = 102)
#         biomart                version
# 1         genes      Ensembl Genes 102
# 2 mouse_strains      Mouse strains 102
# 3          snps  Ensembl Variation 102
# 4    regulation Ensembl Regulation 102
## set up connection to ensembl database
ensembl <- useEnsembl(biomart = "snps", version=102)

# serach the available datasets (species)
searchDatasets(mart = ensembl, pattern = "Mouse")
#                dataset
# 20       mmusculus_snp
# 21 mmusculus_structvar
#                                                                      description
# 20 Mouse Short Variants (SNPs and indels excluding flagged variants) (GRCm38.p6)
# 21                                         Mouse Structural Variants (GRCm38.p6)
#      version
# 20 GRCm38.p6
# 21 GRCm38.p6

ensembl <- useEnsembl(biomart = 'snps', 
                      dataset = c('mmusculus_snp'),
                      version = 102)

# check the available "filters" - things you can filter for
ensembl_filters  <- listFilters(ensembl)
ensembl_attributes <- listAttributes(ensembl)

# To find the correct name for the Ensembl ID we can filter the name column
# Set the filter type and values
ourFilterType <- "snp_filter"

# get the Ensembl IDs from our results table
filterValues <- na.omit(snp.list$rs)

# Set the list of attributes
attributeNames <- c("refsnp_source",'refsnp_id',
                    "chr_name",
                    "chrom_start",
                    "consequence_type_tv",
                    "ensembl_gene_stable_id"
                    )

# run the query
annot_snp <- getBM(attributes=attributeNames, filters = ourFilterType, values = filterValues, 
               mart = ensembl)

#annotation gene
ensembl <- useEnsembl(biomart = 'genes', 
                      dataset = 'mmusculus_gene_ensembl',
                      version = 102)
annot_gene <- getBM(attributes=c("ensembl_gene_id",
                                 "external_gene_name"), 
                    filters = "ensembl_gene_id", 
                    values = unique(na.omit(annot_snp$ensembl_gene_stable_id)), 
                    mart = ensembl)

annot_snp = annot_snp %>%
  left_join(annot_gene, by = c("ensembl_gene_stable_id" = "ensembl_gene_id")) %>%
  tidyr::unite("consequence_type_tv|gene_name",  
               c("consequence_type_tv", "external_gene_name"), sep = "|")
               
               
snp.list.annot <- left_join(snp.list, annot_snp, by = c("rs" = "refsnp_id"))   
 
 
#display snp.list.annot
DT::datatable(snp.list.annot[, c(-10:-12)], filter = list(position = 'top', clear = FALSE),
              extensions = 'Buttons', options = list(dom = 'Bfrtip',
                                                     buttons = c('csv', 'excel'),
                                                     pageLength = 40, 
                                                     scrollY = "300px", 
                                                     scrollX = "40px"))
# Warning in instance$preRenderHook(instance): It seems your data is too big
# for client-side DataTables. You may consider server-side processing: https://
# rstudio.github.io/DT/server.html

QTL mapping on DPI

dpi_out <- scanone(f2_qtl, pheno.col = 3:11, addcovar = covars, n.cluster = 20)
save(dpi_out, file = "data/CC_SARS-1/dpi_out.RData")
dpi_operm <- scanone(f2_qtl, pheno.col = 3:11, addcovar = covars, n.cluster = 20, n.perm = 1000)
#  -Running permutations via a cluster of 20 nodes.
save(dpi_operm, file = "data/CC_SARS-1/dpi_operm.RData")
load("data/CC_SARS-1/dpi_out.RData")
load("data/CC_SARS-1/dpi_operm.RData")
load("data/CC_SARS-1/cross.RData")
query_variants <- create_variant_query_func("data/cc_variants.sqlite")
query_genes <- create_gene_query_func("data/mouse_genes_mgi.sqlite")
variants <- list()
for(i in 1:9){
  print(paste0("pheno = ", i))
  plot(dpi_out, lodcolumn = i, col = c("blue"), ylim = c(0, max(max(dpi_out[, i + 2]), 
                                                                summary(dpi_operm[,i], alpha=c(0.05))[[1]])))
  add.threshold(dpi_out, lodcolumn = i, perms = dpi_operm, alpha = 0.05, col="red")
  #1.5 drop interval on cM
  drop_interval = find_peaks(dpi_out[,i+2, drop=F], map = cross2$gmap, drop = 1.5, threshold =
                               summary(dpi_operm[,i], alpha=c(0.05))[[1]])
  print(drop_interval)
  if(nrow(drop_interval) != 0){
    for(j in 1:nrow(drop_interval)){
      print(paste0("row = ", j))
      chr = drop_interval[j, "chr"]
      pos = drop_interval[j, "pos"]
      ci_lo = drop_interval[j, "ci_lo"]
      ci_hi = drop_interval[j, "ci_hi"]
      lod = drop_interval[j, "lod"]
      # 1.5 drop interval on bp
      peak_bp = map[map$Chromosome == chr & map$cM_cox == pos & !is.na(map$cM_cox), "bp"]/10^6
      start = unique(map[map$Chromosome == chr & map$cM_cox == ci_lo & !is.na(map$cM_cox), "bp"]/10^6)
      end   = unique(map[map$Chromosome == chr & map$cM_cox == ci_hi & !is.na(map$cM_cox), "bp"]/10^6)
      #genes in the qtl region
      chr_gene <- query_genes(chr, start, end)
      snp <- query_variants(chr, start, end)
      variants[[i]] <- snp %>%
        dplyr::mutate(Pheno = colnames(dpi_out)[[i+2]], .before = 1)

      # #add bp
      # out_bp <- dpi_out[, c(1,2,i+2)] %>%
      #   rownames_to_column(.) %>%
      #   left_join(minimuga[, c(1,3,4)], by = c("rowname" = "marker")) %>%
      #   dplyr::mutate(pos = bp_mm10/10^6) %>%
      #   column_to_rownames()
      # class(out_bp) <- c("scanone","data.frame")
      
      #plot
      # if((ci_hi - ci_lo) < 30){
      #   layout(mat = matrix(c(1:2), 
      #                       nrow = 2, 
      #                       ncol = 1),
      #          heights = c(1, 2))    # Heights of the two rows
      #   # Plot 1
      #   par(mar = c(0.01, 5, 2, 0.5))
      #   #Create the base plot
      #   plot(out_bp, col = c("red"), chr = chr, main = paste0("CC_SARS Chr", chr, " QTL interval"), xlab = "", ylab = "LOD", xlim = c(start, end), ylim = c(0, max(max(dpi_out[, i + 2]),
      #                                                                                                                                                              summary(dpi_operm[,i], alpha=c(0.05))[[1]])))
      #   text(peak_bp, peak_bp-5, paste0(chr, "@", pos, "cM"), cex = 0.75)
      #   segments(x0 = peak_bp,
      #            x1 = peak_bp,
      #            y0 = 0,
      #            y1 = lod,
      #            col="black", lty=2, lwd=1)
      #   # Plot 2
      #   par(mar = c(5.10, 5, 0, 0.5))
      #   plot_genes(chr_gene, bgcolor="white", xlim = c(start, end))
      # }
    }
  }
}
# [1] "pheno = 1"

Past versions of “QTL mapping on DPI-1.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01

# [1] lodindex  lodcolumn chr       pos       lod       ci_lo     ci_hi    
# <0 rows> (or 0-length row.names)
# [1] "pheno = 2"

Past versions of “QTL mapping on DPI-2.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01

# [1] lodindex  lodcolumn chr       pos       lod       ci_lo     ci_hi    
# <0 rows> (or 0-length row.names)
# [1] "pheno = 3"

Past versions of “QTL mapping on DPI-3.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01

#   lodindex lodcolumn chr    pos      lod ci_lo  ci_hi
# 1        1   DPI-g.2  13  3.459 4.056407 2.245 65.465
# 2        1   DPI-g.2  15 33.852 4.170061 1.972 48.270
# [1] "row = 1"
# [1] "row = 2"
# [1] "pheno = 4"

Past versions of “QTL mapping on DPI-4.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01

#   lodindex lodcolumn chr   pos      lod ci_lo ci_hi
# 1        1   DPI-g.3  15 34.04 4.406689 2.422 48.27
# [1] "row = 1"
# [1] "pheno = 5"

Past versions of “QTL mapping on DPI-5.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01

# [1] lodindex  lodcolumn chr       pos       lod       ci_lo     ci_hi    
# <0 rows> (or 0-length row.names)
# [1] "pheno = 6"

Past versions of “QTL mapping on DPI-6.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01

# [1] lodindex  lodcolumn chr       pos       lod       ci_lo     ci_hi    
# <0 rows> (or 0-length row.names)
# [1] "pheno = 7"

Past versions of “QTL mapping on DPI-7.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01

#   lodindex   lodcolumn chr    pos      lod  ci_lo ci_hi
# 1        1 DPI-perct.2  15 37.829 6.495021 32.951 56.09
# [1] "row = 1"
# [1] "pheno = 8"

Past versions of “QTL mapping on DPI-8.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01

#   lodindex   lodcolumn chr    pos      lod  ci_lo ci_hi
# 1        1 DPI-perct.3  15 38.933 5.554559 32.951 56.09
# [1] "row = 1"
# [1] "pheno = 9"

Past versions of “QTL mapping on DPI-9.png”

Version	Author	Date
1bf1450	xhyuo	2023-09-01

#   lodindex   lodcolumn chr    pos     lod  ci_lo  ci_hi
# 1        1 DPI-perct.4  15 51.087 5.54724 33.852 57.158
# [1] "row = 1"
variants_all <- bind_rows(variants)
#display variants_all
vroom::vroom_write(variants_all, file = "data/CC_SARS-1/variants_all.csv", delim = ",", col_names = TRUE, quote = "none", num_threads = 10)

QTL mapping conditioning on 16 locus

#pull out the top marker in out_3
out_3_peak <- find_peaks(out_3[, 3, drop = F], map = cross2$gmap, drop = 1.5)
out_3_marker <- find.marker(cross, 16, 24.456)
out_3_marker_covar = as.data.frame(pull.markers(cross, out_3_marker)$geno$`16`$data[,1, drop=F])
out_3_marker_covar_matrix = model.matrix(~gUNC26630094, out_3_marker_covar)[,-1]
out_3_condi_chr16 <- scanone(f2_qtl, pheno.col = 13, addcovar = out_3_marker_covar_matrix,
                             model = "normal", n.cluster = 10,
                             method = "mr-imp")
# Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 165 individuals with missing phenotypes.
out_3_condi_chr16_operm <- scanone(f2_qtl, pheno.col = 13, n.perm = 100, 
                                   addcovar = out_3_marker_covar_matrix,
                                   model = "normal", n.cluster = 10,
                                   method = "mr-imp")
#  -Running permutations via a cluster of 10 nodes.
save(out_3_condi_chr16_operm, file = "data/CC_SARS-1/out_3_condi_chr16_operm.RData")
load("data/CC_SARS-2/out_3_condi_chr16_operm.RData")
summary(out_3_condi_chr16[,1:3], perms = out_3_condi_chr16_operm[,1], alpha = 0.05, pvalues = TRUE)
#             chr  pos  lod pval
# UNC26651633  16 25.7 10.4    0
summary(out_3_condi_chr16_operm[,1], alpha=c(0.05))
# LOD thresholds (100 permutations)
#    lod
# 5% 4.1
summary(out_3_condi_chr16_operm[,1], alpha=c(0.1))
# LOD thresholds (100 permutations)
#      lod
# 10% 3.78

plot(out_3_condi_chr16, col = c("red"), main = "Log10_Lung_titer_4DPI_PFU_per_ml (QTL mapping conditional on Chr16 locus)", ylab = "LOD")
add.threshold(out_3_condi_chr16, perms = out_3_condi_chr16_operm, alpha = 0.05, col="magenta")
add.threshold(out_3_condi_chr16, perms = out_3_condi_chr16_operm, alpha = 0.1, col="blue")

plot(out_3_condi_chr16, col = c("red"), chr = 16, ylab = "LOD")

Session information

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
# 
# Random number generation:
#  RNG:     L'Ecuyer-CMRG 
#  Normal:  Inversion 
#  Sample:  Rejection 
#  
# 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] vroom_1.5.4       biomaRt_2.46.3    DT_0.17           qtlcharts_0.12-10
#  [5] qtl2_0.24         broman_0.72-4     forcats_0.5.1     stringr_1.4.0    
#  [9] dplyr_1.0.4       purrr_0.3.4       readr_1.4.0       tidyr_1.1.2      
# [13] tibble_3.0.6      tidyverse_1.3.0   abind_1.4-5       regress_1.3-21   
# [17] survival_3.2-7    qtl_1.47-9        GGally_2.1.0      gridExtra_2.3    
# [21] ggplot2_3.3.3     workflowr_1.6.2  
# 
# loaded via a namespace (and not attached):
#  [1] fs_1.5.0             lubridate_1.7.9.2    bit64_4.0.5         
#  [4] progress_1.2.2       RColorBrewer_1.1-2   httr_1.4.2          
#  [7] rprojroot_2.0.2      tools_4.0.3          backports_1.2.1     
# [10] R6_2.5.0             BiocGenerics_0.36.1  DBI_1.1.1           
# [13] colorspace_2.0-0     withr_2.4.1          prettyunits_1.1.1   
# [16] tidyselect_1.1.0     curl_4.3             bit_4.0.4           
# [19] compiler_4.0.3       git2r_0.28.0         Biobase_2.50.0      
# [22] cli_2.3.0            rvest_0.3.6          xml2_1.3.2          
# [25] labeling_0.4.2       scales_1.1.1         askpass_1.1         
# [28] rappdirs_0.3.3       digest_0.6.27        rmarkdown_2.6       
# [31] pkgconfig_2.0.3      htmltools_0.5.1.1    highr_0.8           
# [34] dbplyr_2.1.0         fastmap_1.1.0        htmlwidgets_1.5.3   
# [37] rlang_1.0.2          readxl_1.3.1         rstudioapi_0.13     
# [40] RSQLite_2.2.3        farver_2.0.3         generics_0.1.0      
# [43] jsonlite_1.7.2       crosstalk_1.1.1      magrittr_2.0.1      
# [46] Matrix_1.3-2         S4Vectors_0.28.1     Rcpp_1.0.6          
# [49] munsell_0.5.0        lifecycle_1.0.0      stringi_1.5.3       
# [52] whisker_0.4          yaml_2.2.1           BiocFileCache_1.14.0
# [55] plyr_1.8.6           grid_4.0.3           blob_1.2.1          
# [58] promises_1.2.0.1     crayon_1.4.1         lattice_0.20-41     
# [61] haven_2.3.1          splines_4.0.3        hms_1.0.0           
# [64] knitr_1.31           pillar_1.4.7         reshape2_1.4.4      
# [67] stats4_4.0.3         reprex_1.0.0         XML_3.99-0.5        
# [70] glue_1.4.2           evaluate_0.14        data.table_1.13.6   
# [73] modelr_0.1.8         tzdb_0.1.2           vctrs_0.3.6         
# [76] httpuv_1.5.5         cellranger_1.1.0     openssl_1.4.3       
# [79] gtable_0.3.0         reshape_0.8.8        assertthat_0.2.1    
# [82] cachem_1.0.4         xfun_0.21            broom_0.7.4         
# [85] later_1.1.0.1        IRanges_2.24.1       AnnotationDbi_1.52.0
# [88] memoise_2.0.0        ellipsis_0.3.1

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