Last updated: 2022-07-12
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Knit directory: Serreze-T1D_Workflow/
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Unstaged changes:
Modified: analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_5.batches.Rmd
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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.
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We will load the data and subset indivials out that are in the groups of interest. We will create a binary phenotype from this (PBS ==0, ICI == 1).
load("data/gm_allqc_5.batches_mis.RData")
#gm_allqc
gm=gm_allqc
gm
Object of class cross2 (crosstype "bc")
Total individuals 308
No. genotyped individuals 308
No. phenotyped individuals 308
No. with both geno & pheno 308
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131356
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770
#pr <- readRDS("data/serreze_probs_allqc.rds")
#pr <- readRDS("data/serreze_probs.rds")
##extracting animals with ici and pbs group status
miceinfo <- gm$covar[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
table(miceinfo$group)
ICI PBS
104 34
mice.ids <- rownames(miceinfo)
gm <- gm[mice.ids]
gm
Object of class cross2 (crosstype "bc")
Total individuals 138
No. genotyped individuals 138
No. phenotyped individuals 138
No. with both geno & pheno 138
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131356
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770
#pr.qc <- pr
#for (i in 1:20){pr.qc[[i]] = pr.qc[[i]][mice.ids,,]}
#bin_pheno <- NULL
#bin_pheno$PBS <- ifelse(gm$covar$group == "PBS", 1, 0)
#bin_pheno$ICI <- ifelse(gm$covar$group == "ICI", 1, 0)
#bin_pheno <- as.data.frame(bin_pheno)
#rownames(bin_pheno) <- rownames(gm$covar)
gm$covar$ICI.vs.PBS <- ifelse(gm$covar$group == "PBS", 0, 1)
gm.full <- gm
##adding peaks as covariates: UNCHS008487, UNC8250659, UNC18240977
g.covar <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/sample_geno_AHB.csv")
colnames(g.covar) <- gsub("\\.","-", colnames(g.covar))
rownames(g.covar) <- g.covar$marker
#g.covar <- g.covar[-1]
mar.covar <- g.covar[g.covar$marker == "UNCHS008487"| g.covar$marker == "UNC8250659"| g.covar$marker == "UNC18240977",]
mar.covar <- mar.covar[-1]
mar.covar[mar.covar=='A'] <- 0
mar.covar[mar.covar=='H'] <- 1
mar.covar[mar.covar=='B'] <- 2
mar.covar[mar.covar=='-'] <- ''
#mar.covar <- pull_markers(gm, c("UNCHS008487", "UNC8250659", "UNC18240977"))
mar.covar.g <- t(mar.covar)
mar.covar.g <- as.data.frame(mar.covar.g)
covars <- merge(gm$covar, mar.covar.g, by='row.names', sort=F)
table(covars$group)
ICI PBS
104 34
rownames(covars) <- covars$Row.names
covars <- covars[-1]
##removing problmetic marker
gm <- drop_markers(gm, "UNCHS013106")
##dropping monomorphic markers within the dataset
g <- do.call("cbind", gm$geno)
gf_mar <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
#gn_mar <- t(apply(g, 2, function(a) table(factor(a, 1:2))))
gf_mar <- gf_mar[gf_mar[,2] != "NaN",]
count <- rowSums(gf_mar <=0.05)
low_freq_df <- merge(as.data.frame(gf_mar),as.data.frame(count), by="row.names",all=T)
low_freq_df[is.na(low_freq_df)] <- ''
low_freq_df <- low_freq_df[low_freq_df$count == 1,]
rownames(low_freq_df) <- low_freq_df$Row.names
low_freq <- find_markerpos(gm, rownames(low_freq_df))
low_freq$id <- rownames(low_freq)
nrow(low_freq)
[1] 98665
low_freq_bad <- merge(low_freq,low_freq_df, by="row.names",all=T)
names(low_freq_bad)[1] <- c("marker")
gf_mar <- gf_mar[gf_mar[,2] != "NaN",]
MAF <- apply(gf_mar, 1, function(x) min(x))
MAF <- as.data.frame(MAF)
MAF$index <- 1:nrow(gf_mar)
gf_mar_maf <- merge(gf_mar,as.data.frame(MAF), by="row.names")
gf_mar_maf <- gf_mar_maf[order(gf_mar_maf$index),]
gfmar <- NULL
gfmar$gfmar_mar_0 <- sum(gf_mar_maf$MAF==0)
gfmar$gfmar_mar_1 <- sum(gf_mar_maf$MAF< 0.01)
gfmar$gfmar_mar_5 <- sum(gf_mar_maf$MAF< 0.05)
gfmar$gfmar_mar_10 <- sum(gf_mar_maf$MAF< 0.10)
gfmar$gfmar_mar_15 <- sum(gf_mar_maf$MAF< 0.15)
gfmar$gfmar_mar_25 <- sum(gf_mar_maf$MAF< 0.25)
gfmar$gfmar_mar_50 <- sum(gf_mar_maf$MAF< 0.50)
gfmar$total_snps <- nrow(as.data.frame(gf_mar_maf))
gfmar <- t(as.data.frame(gfmar))
gfmar <- as.data.frame(gfmar)
gfmar$count <- gfmar$V1
gfmar[c(2)] %>%
kable(escape = F,align = c("ccccccccc"),linesep ="\\hline") %>%
kable_styling(full_width = F) %>%
kable_styling("striped", full_width = F) %>%
row_spec(8 ,bold=T,color= "white",background = "black")
count | |
---|---|
gfmar_mar_0 | 88924 |
gfmar_mar_1 | 92460 |
gfmar_mar_5 | 98665 |
gfmar_mar_10 | 99264 |
gfmar_mar_15 | 99368 |
gfmar_mar_25 | 100303 |
gfmar_mar_50 | 130381 |
total_snps | 131355 |
gm_qc <- drop_markers(gm, low_freq_bad$marker)
gm_qc <- drop_nullmarkers(gm_qc)
gm = gm_qc
gm
Object of class cross2 (crosstype "bc")
Total individuals 138
No. genotyped individuals 138
No. phenotyped individuals 138
No. with both geno & pheno 138
No. phenotypes 1
No. covariates 7
No. phenotype covariates 0
No. chromosomes 20
Total markers 32690
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 1396 1628 1692 1064 940
17 18 19 X
401 1095 1067 575
markers <- marker_names(gm)
gmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/genetic_map.csv")
pmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/physical_map.csv")
#mapdf <- merge(gmapdf,pmapdf, by=c("marker","chr"), all=T)
#rownames(mapdf) <- mapdf$marker
#mapdf <- mapdf[markers,]
#names(mapdf) <- c('marker','chr','gmapdf','pmapdf')
#mapdfnd <- mapdf[!duplicated(mapdf[c(2:3)]),]
pr.qc <- calc_genoprob(gm)
#gm
Xcovar <- get_x_covar(gm)
#addcovar = model.matrix(~Sex, data = covars)[,-1]
addcovar = model.matrix(~UNCHS008487+UNC8250659, data = covars)[,-1]
#K <- calc_kinship(pr.qc, type = "loco")
#heatmap(K[[1]])
#K.overall <- calc_kinship(pr.qc, type = "overall")
#heatmap(K.overall)
kinship <- calc_kinship(pr.qc)
heatmap(kinship)
#operm <- scan1perm(pr.qc, gm$covar$phenos, Xcovar=Xcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, addcovar = addcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, n_perm=2000)
operm <- scan1perm(pr.qc, gm$covar["ICI.vs.PBS"], model="binary", n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap), addcovar = addcovar)
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
names(summary_table) <- c("autosomes","X")
summary_table$significance.level <- rownames(summary_table)
rownames(summary_table) <- NULL
summary_table[c(3,1:2)] %>%
kable(escape = F,align = c("ccc")) %>%
kable_styling("striped", full_width = T) %>%
column_spec(1, bold=TRUE)
significance.level | autosomes | X |
---|---|---|
0.01 | 3.090048 | 3.657299 |
0.05 | 3.029934 | 3.124699 |
0.1 | 2.954606 | 2.428212 |
The figures below show QTL maps for each phenotype
out <- scan1(pr.qc, gm$covar["ICI.vs.PBS"], Xcovar=Xcovar, model="binary", addcovar = addcovar)
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
plot_lod<-function(out,map){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- 11 # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " [positions in cM] \n(using same scale as eoi vs ici for easier comparison)"))
add_threshold(map, summary(operm, alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
}
}
plot_lod(out,gm$gmap)
The table below shows QTL peaks associated with the phenotype. We use the 95% threshold from the permutations to find peaks.
peaks <- find_peaks(out, gm$gmap, threshold=summary(operm,alpha=0.05)$A, thresholdX = summary(operm,alpha=0.05)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks$marker <- find_marker(gm$gmap, chr=peaks$chr,pos=peaks$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]
rownames(peaks) <- NULL
#print(kable(peaks, "html")
# %>% kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
# )
print(kable(peaks, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
#peaks[] %>%
# kable(escape = F,align = c("cccccccc")) %>%
# kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
#plot only peak chromosomes
plot_lod_chr<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
ymx <- 11
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]\n(using same scale as eoi vs. ici for easier comparison)"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
}
}
for(i in unique(peaks$chr)){
#for (i in 1:nrow(peaks)){
#plot_lod_chr(out,gm$gmap, peaks$chr[i])
plot_lod_chr(out,gm$gmap, i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}
phenotype | chr | pos | lod | ci_lo | ci_hi | marker |
---|---|---|---|---|---|---|
ICI.vs.PBS | 18 | 2.934 | 3.788369 | 1.972 | 4.621 | UNCHS045343 |
print("peaks in MB positions")
[1] “peaks in MB positions”
peaks_mba <- find_peaks(out, gm$pmap, threshold=summary(operm,alpha=0.05)$A, thresholdX = summary(operm,alpha=0.05)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks_mba$marker <- find_marker(gm$pmap, chr=peaks_mba$chr,pos=peaks_mba$pos)
names(peaks_mba)[2] <- c("phenotype")
peaks_mba <- peaks_mba[-1]
#peaks_mbl <- list()
##corresponding info in Mb
#for(i in 1:nrow(peaks)){
# #lodindex <- peaks$lodindex[i]
# phenotype <- peaks$phenotype[i]
# chr <- as.character(peaks$chr[i])
# lod <- peaks$lod[i]
# mark <- peaks$marker[i]
# pos <- mapdf[mapdf$marker==mark,]$pmapdf
# ci_lo <- mapdfnd$pmapdf[which(mapdfnd$gmapdf == peaks$ci_lo[i] & mapdfnd$chr == peaks$chr[i])]
# ci_hi <- mapdfnd$pmapdf[which(mapdfnd$gmapdf == peaks$ci_hi[i] & mapdfnd$chr == peaks$chr[i])]
# peaks_mb=as.data.frame(cbind(phenotype, chr, pos, lod, ci_lo, ci_hi, mark))
# names(peaks_mb)[7] <- c("marker")
# peaks_mbl[[i]] <- peaks_mb
#}
#peaks_mba2 <- do.call(rbind, peaks_mbl)
#peaks_mba2 <- as.data.frame(peaks_mba)
#peaks_mba[,c("chr", "pos", "lod", "ci_lo", "ci_hi")] <- sapply(peaks_mba[,c("chr", "pos", "lod", "ci_lo", "ci_hi")], as.numeric)
rownames(peaks_mba) <- NULL
#print(kable(peaks_mba, "html")
# %>% kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
# )
print(kable(peaks_mba, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
#peaks_mba[] %>%
# kable(escape = F,align = c("cccccccc")) %>%
# kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
plot_lod_chr_mb<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
ymx <- 11
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]\n(using same scale as eoi vs. ici for easier comparison)"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
}
}
for(i in unique(peaks_mba$chr)){
#for (i in 1:nrow(peaks_mba)){
#plot_lod_chr_mb(out,gm$pmap, peaks_mba$chr[i])
plot_lod_chr_mb(out,gm$pmap,i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}
phenotype | chr | pos | lod | ci_lo | ci_hi | marker |
---|---|---|---|---|---|---|
ICI.vs.PBS | 18 | 4.861199 | 3.788369 | 3.01212 | 7.97679 | UNCHS045343 |
For each peak LOD location we give a list of gene
query_variants <- create_variant_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/cc_variants.sqlite")
query_genes <- create_gene_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/mouse_genes_mgi.sqlite")
if(nrow(peaks) >0){
for (i in 1:nrow(peaks)){
#for (i in 1:1){
#Plot 1
#marker = find_marker(gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i])
#gp <- g[,marker]
#gp[gp==1] <- "AA"
#gp[gp==2] <- "AB"
#gp[gp==0] <- NA
#plot_pxg(gp, gm$covar[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
#title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
###dev.off()
g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
#par(mar=c(4.1, 4.1, 1.5, 0.6))
plot_pxg(g, gm$covar[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
##dev.off()
chr = peaks$chr[i]
# Plot 2
pr_sub <- pull_genoprobint(pr.qc, gm$gmap, chr, c(peaks$ci_lo[i], peaks$ci_hi[i]))
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar)
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], Xcovar=Xcovar)
#coeff <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
#coeff_sub <- scan1coef(pr_sub[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
blup <- scan1blup(pr.qc[,chr], gm$covar[peaks$phenotype[i]], addcovar = addcovar)
blup_sub <- scan1blup(pr_sub[,chr], gm$covar[peaks$phenotype[i]], addcovar = addcovar)
write.csv(as.data.frame(blup_sub), paste0("data/ici.vs.pbs_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_5.batches_mis_conditional_2-peaks-chr3-4.csv"), quote=F)
#plot_coef(coeff,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i]," [scan1coeff; positions in cM])")
# )
#plot_coef(coeff_sub,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i],"; 1.5 LOD drop interval [scan1coeff; positions in cM] ) ")
# )
plot_coef(blup,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," [scan1blup; positions in cM])")
)
plot_coef(blup_sub,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i],"; 1.5 LOD drop interval [scan1blup; positions in cM])")
)
# Plot 3
#c2effB <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary", contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
#c2effBb <- scan1blup(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]],Xcovar=Xcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
#plot(c2effB, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
#plot(c2effBb, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
##last_coef <- unclass(c2effB)[nrow(c2effB),2:3] # last two coefficients
##for(t in seq(along=last_coef))
## axis(side=4, at=last_coef[t], names(last_coef)[t], tick=FALSE)
#Table 1
chr = peaks_mba$chr[i]
start=as.numeric(peaks_mba$ci_lo[i])
end=as.numeric(peaks_mba$ci_hi[i])
genesgss = query_genes(chr, start, end)
write.csv(genesgss, file=paste0("data/ici.vs.pbs_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_5.batches_mis_conditional_2-peaks-chr3-4.csv"), quote=F)
rownames(genesgss) <- NULL
genesgss$strand_old = genesgss$strand
genesgss$strand[genesgss$strand=="+"] <- "positive"
genesgss$strand[genesgss$strand=="-"] <- "negative"
#genesgss <-
#table <-
#genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")] %>%
#kable(escape = F,align = c("ccccccccccc")) %>%
#kable_styling("striped", full_width = T) #%>%
#cat #%>%
#column_spec(1, bold=TRUE)
#
#print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], escape = F,align = c("ccccccccccc")))
print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}
chr | type | start | stop | strand | ID | Name | Dbxref | gene_id | mgi_type | description |
---|---|---|---|---|---|---|---|---|---|---|
18 | pseudogene | 3.015908 | 3.016159 | positive | MGI_C57BL6J_6275399 | Gm50072 | ENSEMBL:ENSMUSG00000117547 | MGI:6275399 | pseudogene | predicted gene, 50072 |
18 | pseudogene | 3.026901 | 3.027882 | negative | MGI_C57BL6J_3852490 | Vmn1r-ps151 | NCBI_Gene:100312519,ENSEMBL:ENSMUSG00000093774 | MGI:3852490 | pseudogene | vomeronasal 1 receptor, pseudogene 151 |
18 | pseudogene | 3.080778 | 3.081476 | negative | MGI_C57BL6J_3852491 | Vmn1r-ps152 | NCBI_Gene:100312520,ENSEMBL:ENSMUSG00000093444 | MGI:3852491 | pseudogene | vomeronasal 1 receptor, pseudogene 152 |
18 | pseudogene | 3.105167 | 3.105396 | negative | MGI_C57BL6J_6275400 | Gm50073 | ENSEMBL:ENSMUSG00000117531 | MGI:6275400 | pseudogene | predicted gene, 50073 |
18 | gene | 3.122492 | 3.130012 | negative | MGI_C57BL6J_3852494 | Vmn1r238 | NCBI_Gene:100312476,ENSEMBL:ENSMUSG00000091539 | MGI:3852494 | protein coding gene | vomeronasal 1 receptor, 238 |
18 | pseudogene | 3.139032 | 3.139305 | negative | MGI_C57BL6J_6275401 | Gm50074 | ENSEMBL:ENSMUSG00000117353 | MGI:6275401 | pseudogene | predicted gene, 50074 |
18 | pseudogene | 3.171447 | 3.171932 | negative | MGI_C57BL6J_6275402 | Gm50075 | ENSEMBL:ENSMUSG00000117464 | MGI:6275402 | pseudogene | predicted gene, 50075 |
18 | gene | 3.266048 | 3.338176 | negative | MGI_C57BL6J_88495 | Crem | NCBI_Gene:12916,ENSEMBL:ENSMUSG00000063889 | MGI:88495 | protein coding gene | cAMP responsive element modulator |
18 | gene | 3.336416 | 3.366863 | positive | MGI_C57BL6J_3643252 | Gm6225 | NCBI_Gene:633947,ENSEMBL:ENSMUSG00000097746 | MGI:3643252 | lncRNA gene | predicted gene 6225 |
18 | gene | 3.382970 | 3.436700 | positive | MGI_C57BL6J_1918995 | Cul2 | NCBI_Gene:71745,ENSEMBL:ENSMUSG00000024231 | MGI:1918995 | protein coding gene | cullin 2 |
18 | pseudogene | 3.446654 | 3.447503 | positive | MGI_C57BL6J_3648491 | Gm6235 | NCBI_Gene:621501,ENSEMBL:ENSMUSG00000117370 | MGI:3648491 | pseudogene | predicted gene 6235 |
18 | pseudogene | 3.466371 | 3.466502 | negative | MGI_C57BL6J_6275423 | Gm50088 | ENSEMBL:ENSMUSG00000117389 | MGI:6275423 | pseudogene | predicted gene, 50088 |
18 | gene | 3.471630 | 3.474315 | positive | MGI_C57BL6J_3645649 | G430049J08Rik | ENSEMBL:ENSMUSG00000096528 | MGI:3645649 | unclassified gene | RIKEN cDNA G430049J08 gene |
18 | pseudogene | 3.484218 | 3.485193 | positive | MGI_C57BL6J_6275424 | Gm50089 | ENSEMBL:ENSMUSG00000117469 | MGI:6275424 | pseudogene | predicted gene, 50089 |
18 | gene | 3.507923 | 3.516404 | positive | MGI_C57BL6J_1915260 | Bambi | NCBI_Gene:68010,ENSEMBL:ENSMUSG00000024232 | MGI:1915260 | protein coding gene | BMP and activin membrane-bound inhibitor |
18 | pseudogene | 3.558675 | 3.560037 | positive | MGI_C57BL6J_6275425 | Gm50090 | ENSEMBL:ENSMUSG00000117454 | MGI:6275425 | pseudogene | predicted gene, 50090 |
18 | gene | 3.616281 | 3.618242 | positive | MGI_C57BL6J_6275426 | Gm50091 | ENSEMBL:ENSMUSG00000117506 | MGI:6275426 | unclassified gene | predicted gene, 50091 |
18 | gene | 3.770741 | 3.770804 | negative | MGI_C57BL6J_5453420 | Gm23643 | ENSEMBL:ENSMUSG00000088480 | MGI:5453420 | snRNA gene | predicted gene, 23643 |
18 | pseudogene | 3.857730 | 3.858585 | negative | MGI_C57BL6J_3647465 | Rpl7a-ps6 | NCBI_Gene:435549,ENSEMBL:ENSMUSG00000117463 | MGI:3647465 | pseudogene | ribosomal protein L7A, pseudogene 6 |
18 | gene | 3.860108 | 3.860430 | positive | MGI_C57BL6J_5454409 | Gm24632 | ENSEMBL:ENSMUSG00000084719 | MGI:5454409 | unclassified non-coding RNA gene | predicted gene, 24632 |
18 | pseudogene | 4.010143 | 4.011335 | positive | MGI_C57BL6J_3646622 | Gm7378 | NCBI_Gene:664867,ENSEMBL:ENSMUSG00000117395 | MGI:3646622 | pseudogene | predicted gene 7378 |
18 | pseudogene | 4.052351 | 4.052771 | negative | MGI_C57BL6J_3644899 | Gm6248 | NCBI_Gene:621666,ENSEMBL:ENSMUSG00000117565 | MGI:3644899 | pseudogene | predicted gene 6248 |
18 | gene | 4.165832 | 4.182236 | positive | MGI_C57BL6J_1914578 | Lyzl1 | NCBI_Gene:67328,ENSEMBL:ENSMUSG00000024233 | MGI:1914578 | protein coding gene | lysozyme-like 1 |
18 | pseudogene | 4.198320 | 4.199135 | negative | MGI_C57BL6J_3708638 | Gm10557 | NCBI_Gene:383374,ENSEMBL:ENSMUSG00000073647 | MGI:3708638 | pseudogene | predicted gene 10557 |
18 | gene | 4.244121 | 4.250779 | negative | MGI_C57BL6J_5624542 | Gm41657 | NCBI_Gene:105246358 | MGI:5624542 | lncRNA gene | predicted gene, 41657 |
18 | pseudogene | 4.293097 | 4.294538 | negative | MGI_C57BL6J_3647488 | Gm7400 | NCBI_Gene:664909,ENSEMBL:ENSMUSG00000117482 | MGI:3647488 | pseudogene | predicted gene 7400 |
18 | gene | 4.331325 | 4.353412 | negative | MGI_C57BL6J_1346878 | Map3k8 | NCBI_Gene:26410,ENSEMBL:ENSMUSG00000024235 | MGI:1346878 | protein coding gene | mitogen-activated protein kinase kinase kinase 8 |
18 | gene | 4.353547 | 4.368945 | positive | MGI_C57BL6J_1921165 | 4833419F23Rik | NCBI_Gene:73915,ENSEMBL:ENSMUSG00000117401 | MGI:1921165 | lncRNA gene | RIKEN cDNA 4833419F23 gene |
18 | gene | 4.353644 | 4.380723 | positive | MGI_C57BL6J_6275434 | Gm50096 | ENSEMBL:ENSMUSG00000117594 | MGI:6275434 | lncRNA gene | predicted gene, 50096 |
18 | gene | 4.373669 | 4.379985 | negative | MGI_C57BL6J_5477359 | Gm26865 | ENSEMBL:ENSMUSG00000097641 | MGI:5477359 | lncRNA gene | predicted gene, 26865 |
18 | gene | 4.375592 | 4.398798 | positive | MGI_C57BL6J_1914690 | Mtpap | NCBI_Gene:67440,ENSEMBL:ENSMUSG00000024234 | MGI:1914690 | protein coding gene | mitochondrial poly(A) polymerase |
18 | gene | 4.399328 | 4.400910 | positive | MGI_C57BL6J_6275321 | Gm50023 | ENSEMBL:ENSMUSG00000117579 | MGI:6275321 | lncRNA gene | predicted gene, 50023 |
18 | pseudogene | 4.484469 | 4.485501 | positive | MGI_C57BL6J_3644066 | Gm7411 | NCBI_Gene:664931,ENSEMBL:ENSMUSG00000117640 | MGI:3644066 | pseudogene | predicted gene 7411 |
18 | gene | 4.541612 | 4.562599 | negative | MGI_C57BL6J_5624543 | Gm41658 | NCBI_Gene:105246359 | MGI:5624543 | lncRNA gene | predicted gene, 41658 |
18 | gene | 4.590763 | 4.616064 | positive | MGI_C57BL6J_6275323 | Gm50024 | ENSEMBL:ENSMUSG00000117521 | MGI:6275323 | lncRNA gene | predicted gene, 50024 |
18 | gene | 4.590792 | 4.682869 | positive | MGI_C57BL6J_2685174 | Jcad | NCBI_Gene:240185,ENSEMBL:ENSMUSG00000033960 | MGI:2685174 | protein coding gene | junctional cadherin 5 associated |
18 | pseudogene | 4.743061 | 4.743876 | positive | MGI_C57BL6J_3645969 | Gm5047 | NCBI_Gene:268988,ENSEMBL:ENSMUSG00000117585 | MGI:3645969 | pseudogene | predicted gene 5047 |
18 | gene | 4.812486 | 4.850959 | positive | MGI_C57BL6J_3642809 | Gm10556 | NCBI_Gene:100038439,ENSEMBL:ENSMUSG00000097043 | MGI:3642809 | lncRNA gene | predicted gene 10556 |
18 | gene | 4.820303 | 4.826659 | negative | MGI_C57BL6J_5593428 | Gm34269 | NCBI_Gene:102637466 | MGI:5593428 | lncRNA gene | predicted gene, 34269 |
18 | gene | 4.869777 | 4.912263 | positive | MGI_C57BL6J_5624544 | Gm41659 | NCBI_Gene:105246360 | MGI:5624544 | lncRNA gene | predicted gene, 41659 |
18 | gene | 4.920245 | 5.119299 | positive | MGI_C57BL6J_2147319 | Svil | NCBI_Gene:225115,ENSEMBL:ENSMUSG00000024236 | MGI:2147319 | protein coding gene | supervillin |
18 | gene | 4.959636 | 4.960300 | positive | MGI_C57BL6J_3642287 | BC025933 | NA | NA | protein coding gene | cDNA sequence BC025933 |
18 | gene | 5.042818 | 5.048867 | negative | MGI_C57BL6J_5593485 | Gm34326 | NCBI_Gene:102637544 | MGI:5593485 | lncRNA gene | predicted gene, 34326 |
18 | gene | 5.139349 | 5.142068 | positive | MGI_C57BL6J_5593700 | Gm34541 | NCBI_Gene:102637831 | MGI:5593700 | lncRNA gene | predicted gene, 34541 |
18 | gene | 5.141762 | 5.165731 | negative | MGI_C57BL6J_5477176 | Gm26682 | NCBI_Gene:102637682,ENSEMBL:ENSMUSG00000097888 | MGI:5477176 | lncRNA gene | predicted gene, 26682 |
18 | gene | 5.210027 | 5.334963 | negative | MGI_C57BL6J_2444919 | Zfp438 | NCBI_Gene:240186,ENSEMBL:ENSMUSG00000050945 | MGI:2444919 | protein coding gene | zinc finger protein 438 |
18 | gene | 5.348258 | 5.361861 | negative | MGI_C57BL6J_6275385 | Gm50064 | ENSEMBL:ENSMUSG00000117520 | MGI:6275385 | lncRNA gene | predicted gene, 50064 |
18 | gene | 5.368541 | 5.370482 | negative | MGI_C57BL6J_6275387 | Gm50065 | ENSEMBL:ENSMUSG00000117617 | MGI:6275387 | lncRNA gene | predicted gene, 50065 |
18 | gene | 5.389802 | 5.402870 | negative | MGI_C57BL6J_5593789 | Gm34630 | NCBI_Gene:102637944 | MGI:5593789 | lncRNA gene | predicted gene, 34630 |
18 | gene | 5.491501 | 5.593505 | negative | MGI_C57BL6J_3642044 | Gm10125 | NCBI_Gene:791318,ENSEMBL:ENSMUSG00000063087 | MGI:3642044 | lncRNA gene | predicted gene 10125 |
18 | gene | 5.572824 | 5.575055 | negative | MGI_C57BL6J_5477069 | Gm26575 | ENSEMBL:ENSMUSG00000097926 | MGI:5477069 | lncRNA gene | predicted gene, 26575 |
18 | gene | 5.588043 | 5.591711 | negative | MGI_C57BL6J_3780408 | Gm2238 | ENSEMBL:ENSMUSG00000103138 | MGI:3780408 | unclassified gene | predicted gene 2238 |
18 | gene | 5.591860 | 5.775468 | positive | MGI_C57BL6J_1344313 | Zeb1 | NCBI_Gene:21417,ENSEMBL:ENSMUSG00000024238 | MGI:1344313 | protein coding gene | zinc finger E-box binding homeobox 1 |
18 | pseudogene | 5.611558 | 5.611930 | negative | MGI_C57BL6J_3801802 | Gm16147 | ENSEMBL:ENSMUSG00000090078 | MGI:3801802 | pseudogene | predicted gene 16147 |
18 | gene | 5.614473 | 5.614583 | positive | MGI_C57BL6J_5452486 | Gm22709 | ENSEMBL:ENSMUSG00000080543 | MGI:5452486 | miRNA gene | predicted gene, 22709 |
18 | gene | 5.885791 | 5.889876 | negative | MGI_C57BL6J_5593899 | Gm34740 | NCBI_Gene:102638095 | MGI:5593899 | lncRNA gene | predicted gene, 34740 |
18 | gene | 5.945183 | 5.966908 | positive | MGI_C57BL6J_5593963 | Gm34804 | NCBI_Gene:102638178,ENSEMBL:ENSMUSG00000117508 | MGI:5593963 | lncRNA gene | predicted gene, 34804 |
18 | gene | 6.004578 | 6.015278 | positive | MGI_C57BL6J_5624548 | Gm41663 | NCBI_Gene:105246364 | MGI:5624548 | lncRNA gene | predicted gene, 41663 |
18 | gene | 6.014253 | 6.024013 | negative | MGI_C57BL6J_5624547 | Gm41662 | NCBI_Gene:105246363,ENSEMBL:ENSMUSG00000117548 | MGI:5624547 | lncRNA gene | predicted gene, 41662 |
18 | gene | 6.024427 | 6.136098 | negative | MGI_C57BL6J_1922665 | Arhgap12 | NCBI_Gene:75415,ENSEMBL:ENSMUSG00000041225 | MGI:1922665 | protein coding gene | Rho GTPase activating protein 12 |
18 | pseudogene | 6.029179 | 6.030456 | negative | MGI_C57BL6J_5011425 | Gm19240 | NCBI_Gene:100418482 | MGI:5011425 | pseudogene | predicted gene, 19240 |
18 | gene | 6.201002 | 6.242174 | negative | MGI_C57BL6J_1098268 | Kif5b | NCBI_Gene:16573,ENSEMBL:ENSMUSG00000006740 | MGI:1098268 | protein coding gene | kinesin family member 5B |
18 | gene | 6.213913 | 6.227324 | positive | MGI_C57BL6J_4937863 | Gm17036 | ENSEMBL:ENSMUSG00000091165 | MGI:4937863 | lncRNA gene | predicted gene 17036 |
18 | gene | 6.234853 | 6.326693 | positive | MGI_C57BL6J_5624549 | Gm41664 | NCBI_Gene:105246365,ENSEMBL:ENSMUSG00000117519 | MGI:5624549 | lncRNA gene | predicted gene, 41664 |
18 | pseudogene | 6.332591 | 6.333082 | positive | MGI_C57BL6J_3646174 | Rpl27-ps3 | NCBI_Gene:621100,ENSEMBL:ENSMUSG00000073640 | MGI:3646174 | pseudogene | ribosomal protein L27, pseudogene 3 |
18 | pseudogene | 6.368360 | 6.368797 | positive | MGI_C57BL6J_6275267 | Gm49986 | ENSEMBL:ENSMUSG00000117387 | MGI:6275267 | pseudogene | predicted gene, 49986 |
18 | pseudogene | 6.371352 | 6.371660 | negative | MGI_C57BL6J_3648803 | Gm6291 | NCBI_Gene:102637200,ENSEMBL:ENSMUSG00000095614 | MGI:3648803 | pseudogene | predicted gene 6291 |
18 | gene | 6.435951 | 6.516108 | negative | MGI_C57BL6J_1278322 | Epc1 | NCBI_Gene:13831,ENSEMBL:ENSMUSG00000024240 | MGI:1278322 | protein coding gene | enhancer of polycomb homolog 1 |
18 | gene | 6.490143 | 6.496962 | positive | MGI_C57BL6J_5579235 | Gm28529 | ENSEMBL:ENSMUSG00000101320 | MGI:5579235 | lncRNA gene | predicted gene 28529 |
18 | gene | 6.490564 | 6.490646 | negative | MGI_C57BL6J_3811424 | Mir1893 | miRBase:MI0008327,NCBI_Gene:100316773,ENSEMBL:ENSMUSG00000084514 | MGI:3811424 | miRNA gene | microRNA 1893 |
18 | pseudogene | 6.557130 | 6.557796 | positive | MGI_C57BL6J_3645335 | Gm7464 | NCBI_Gene:665047,ENSEMBL:ENSMUSG00000117425 | MGI:3645335 | pseudogene | predicted gene 7464 |
18 | gene | 6.603629 | 6.640435 | positive | MGI_C57BL6J_1918151 | 4921524L21Rik | NCBI_Gene:70901,ENSEMBL:ENSMUSG00000039540 | MGI:1918151 | protein coding gene | RIKEN cDNA 4921524L21 gene |
18 | pseudogene | 6.687843 | 6.691317 | negative | MGI_C57BL6J_6275405 | Gm50077 | ENSEMBL:ENSMUSG00000117620 | MGI:6275405 | pseudogene | predicted gene, 50077 |
18 | gene | 6.733905 | 6.794429 | positive | MGI_C57BL6J_102790 | Rab18 | NCBI_Gene:19330,ENSEMBL:ENSMUSG00000073639 | MGI:102790 | protein coding gene | RAB18, member RAS oncogene family |
18 | pseudogene | 6.760073 | 6.760959 | negative | MGI_C57BL6J_3648356 | Gm7466 | NCBI_Gene:665053,ENSEMBL:ENSMUSG00000117359 | MGI:3648356 | pseudogene | predicted gene 7466 |
18 | gene | 6.788768 | 6.792137 | positive | MGI_C57BL6J_6275412 | Gm50081 | ENSEMBL:ENSMUSG00000117461 | MGI:6275412 | lncRNA gene | predicted gene, 50081 |
18 | gene | 6.910459 | 6.936621 | negative | MGI_C57BL6J_3826781 | Gm2350 | ENSEMBL:ENSMUSG00000117652 | MGI:3826781 | lncRNA gene | predicted gene 2350 |
18 | gene | 6.934518 | 7.004780 | negative | MGI_C57BL6J_2687286 | Mkx | NCBI_Gene:210719,ENSEMBL:ENSMUSG00000061013 | MGI:2687286 | protein coding gene | mohawk homeobox |
18 | gene | 7.004963 | 7.066322 | positive | MGI_C57BL6J_5826270 | Gm46633 | NCBI_Gene:108168391,ENSEMBL:ENSMUSG00000117356 | MGI:5826270 | lncRNA gene | predicted gene, 46633 |
18 | pseudogene | 7.041939 | 7.044195 | negative | MGI_C57BL6J_3642728 | Gm10350 | NCBI_Gene:664778,ENSEMBL:ENSMUSG00000117381 | MGI:3642728 | pseudogene | predicted gene 10350 |
18 | gene | 7.088209 | 7.298304 | negative | MGI_C57BL6J_1922184 | Armc4 | NCBI_Gene:74934,ENSEMBL:ENSMUSG00000061802 | MGI:1922184 | protein coding gene | armadillo repeat containing 4 |
18 | pseudogene | 7.107181 | 7.108183 | negative | MGI_C57BL6J_5010766 | Gm18581 | NCBI_Gene:100417382,ENSEMBL:ENSMUSG00000117360 | MGI:5010766 | pseudogene | predicted gene, 18581 |
18 | gene | 7.150344 | 7.151777 | negative | MGI_C57BL6J_1915234 | 4930415O11Rik | ENSEMBL:ENSMUSG00000117383 | MGI:1915234 | unclassified gene | RIKEN cDNA 4930415O11 gene |
18 | gene | 7.332445 | 7.346299 | negative | MGI_C57BL6J_5594138 | Gm34979 | NCBI_Gene:102638402 | MGI:5594138 | lncRNA gene | predicted gene, 34979 |
18 | gene | 7.347959 | 7.626893 | negative | MGI_C57BL6J_1922989 | Mpp7 | NCBI_Gene:75739,ENSEMBL:ENSMUSG00000057440 | MGI:1922989 | protein coding gene | membrane protein, palmitoylated 7 (MAGUK p55 subfamily member 7) |
18 | pseudogene | 7.548932 | 7.549781 | positive | MGI_C57BL6J_5011133 | Gm18948 | NCBI_Gene:100418013,ENSEMBL:ENSMUSG00000117556 | MGI:5011133 | pseudogene | predicted gene, 18948 |
18 | pseudogene | 7.581482 | 7.582130 | positive | MGI_C57BL6J_3644282 | Fabp5l2 | NCBI_Gene:622384,ENSEMBL:ENSMUSG00000094334 | MGI:3644282 | pseudogene | fatty acid binding protein 5-like 2 |
18 | pseudogene | 7.684076 | 7.693411 | negative | MGI_C57BL6J_5010948 | Gm18763 | NCBI_Gene:100417683,ENSEMBL:ENSMUSG00000117542 | MGI:5010948 | pseudogene | predicted gene, 18763 |
18 | pseudogene | 7.798784 | 7.799900 | negative | MGI_C57BL6J_3780506 | Gm2336 | NCBI_Gene:100039606 | MGI:3780506 | pseudogene | predicted gene 2336 |
18 | gene | 7.835177 | 7.835283 | positive | MGI_C57BL6J_5454069 | Gm24292 | ENSEMBL:ENSMUSG00000064822 | MGI:5454069 | snRNA gene | predicted gene, 24292 |
18 | pseudogene | 7.844684 | 7.844935 | negative | MGI_C57BL6J_6275329 | Gm50027 | ENSEMBL:ENSMUSG00000117462 | MGI:6275329 | pseudogene | predicted gene, 50027 |
18 | gene | 7.868018 | 7.869113 | negative | MGI_C57BL6J_3641793 | Gm9993 | ENSEMBL:ENSMUSG00000117505 | MGI:3641793 | lncRNA gene | predicted gene 9993 |
18 | gene | 7.868832 | 7.929028 | positive | MGI_C57BL6J_2387357 | Wac | NCBI_Gene:225131,ENSEMBL:ENSMUSG00000024283 | MGI:2387357 | protein coding gene | WW domain containing adaptor with coiled-coil |
18 | gene | 7.894300 | 7.895792 | positive | MGI_C57BL6J_1924412 | A430102J17Rik | NA | NA | unclassified gene | RIKEN cDNA A430102J17 gene |
18 | pseudogene | 7.932728 | 7.933556 | positive | MGI_C57BL6J_3643245 | Gm7502 | NCBI_Gene:665121,ENSEMBL:ENSMUSG00000091019 | MGI:3643245 | pseudogene | predicted gene 7502 |
gm
Object of class cross2 (crosstype "bc")
Total individuals 138
No. genotyped individuals 138
No. phenotyped individuals 138
No. with both geno & pheno 138
No. phenotypes 1
No. covariates 7
No. phenotype covariates 0
No. chromosomes 20
Total markers 32690
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 1396 1628 1692 1064 940
17 18 19 X
401 1095 1067 575
#detach("package:qtl2", unload=TRUE)
#library(qtl)
cross <- qtl::read.cross("csv", file = "data/ici.vs.pbs_gm_qtl_snpsqc_5.batches_mis_conditional_2-peaks-chr3-4.csv",alleles=c("A","B"))
--Read the following data:
138 individuals
32690 markers
5 phenotypes
--Cross type: bc
cross <- qtl::jittermap(cross)
summary(cross)
Backcross
No. individuals: 138
No. phenotypes: 5
Percent phenotyped: 100 100 100 99.3 98.6
No. chromosomes: 20
Autosomes: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
X chr: X
Total markers: 32690
No. markers: 2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069
1396 1628 1692 1064 940 401 1095 1067 575
Percent genotyped: 99.5
Genotypes (%):
Autosomes: AA:54.3 AB:45.7
X chromosome: AA:52.7 AB:47.3
cross.probs <- qtl::calc.genoprob(cross)
print("method == hk")
[1] "method == hk"
add.covars = qtl::pull.pheno(cross.probs, c("UNCHS008487","UNC8250659"))
scanone.hk <-qtl::scanone(cross.probs, pheno.col="ICI.vs.PBS" , model="binary", method="hk", addcovar = add.covars)
operm.hk <- qtl::scanone(cross.probs, method = "hk", pheno.col="ICI.vs.PBS", n.perm = 10, perm.Xsp = TRUE, model="binary", verbose=FALSE, addcovar = add.covars)
plot(operm.hk)
print(summary(operm.hk, alpha=c(0.01, 0.05, 0.1)))
Autosome LOD thresholds (10 permutations)
lod
1% 4.86
5% 4.49
10% 4.03
X chromosome LOD thresholds (183 permutations)
lod
1% 4.15
5% 3.62
10% 2.94
#plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "steelblue")
#qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.01, col = 'blue')
#qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.05, col = 'red')
#qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.1, col = 'purple')
ymx <- maxlod(out) # overall maximum LOD score
plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "slateblue", ylim=c(0, ymx+0.5))
title(main = paste0(colnames(out), " [positions in cM]"))
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.01, col = 'blue')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.05, col = 'red')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.1, col = 'purple')
ymx <- 11
plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "slateblue", ylim=c(0, ymx+0.5))
title(main = paste0(colnames(out), " [positions in cM]\n(using same scale as eoi vs ici for easier comparison)"))
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.01, col = 'blue')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.05, col = 'red')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.1, col = 'purple')
print(as.data.frame(summary(scanone.hk, perms=operm.hk, pvalues=TRUE, format="allpeaks")))
chr pos lod pval
UNCHS000141 1 4.439109 1.4810382 0.7191035
UNC2981117 2 27.613431 0.8724495 1.0000000
JAX00521745 3 20.613360 0.7586221 1.0000000
JAX00556948 4 41.038754 0.7726880 1.0000000
UNC8675939 5 6.193174 2.0684019 0.5185858
UNC10923091 6 18.321306 0.6856206 1.0000000
UNCHS020066 7 34.113657 1.2936681 0.8168395
UNC15524531 8 59.462590 1.9315437 0.5185858
UNC17203597 9 68.527929 0.6928610 1.0000000
UNCHS028001 10 21.783362 1.0219015 0.9118239
UNC20465557 11 80.904972 0.7841081 1.0000000
UNC21787084 12 53.288332 0.7505664 1.0000000
JAX00351755 13 7.044200 0.9809396 1.0000000
UNC24056202 14 30.156730 1.8824837 0.5185858
UNC25275535 15 11.967146 0.8880487 1.0000000
UNCHS042071 16 13.176096 0.7730967 1.0000000
UNCrs47191360 17 18.433013 1.2636005 0.8168395
UNCHS045343 18 2.934001 3.3781249 0.2096965
UNC29920552 19 8.810092 1.9159935 0.5185858
UNCHS048314 X 8.974041 1.9849011 0.6619190
print("all peaks with a p-value less or equal to 0.05 (suggestive)")
[1] "all peaks with a p-value less or equal to 0.05 (suggestive)"
print(as.data.frame(summary(scanone.hk, perms=operm.hk, alpha=0.05, pvalues=TRUE, format="allpeaks")))
[1] chr pos lod
<0 rows> (or 0-length row.names)
#print("method == ehk")
#scanone.ehk <-qtl::scanone(cross.probs, pheno.col="ICI.vs.PBS" , model="binary", method="ehk")
#operm.ehk <- qtl::scanone(cross.probs, method = "ehk", pheno.col="ICI.vs.PBS", n.perm = 10, perm.Xsp = TRUE, model="binary", verbose=FALSE)
#plot(operm.ehk)
#print(summary(operm.ehk, alpha=c(0.01, 0.05, 0.1)))
#plot(scanone.ehk, bandcol = "grey90",lty=1, cex=1, col = "steelblue")
#qtl::add.threshold(scanone.ehk, perms= operm.ehk, alpha=0.01, col = 'blue')
#qtl::add.threshold(scanone.ehk, perms= operm.ehk, alpha=0.05, col = 'red')
#qtl::add.threshold(scanone.ehk, perms= operm.ehk, alpha=0.1, col = 'purple')
#print(as.data.frame(summary(scanone.ehk)))
#print(as.data.frame(summary(scanone.ehk, perms=operm.ehk, alpha=0.05, pvalues=TRUE, format="allpeaks")))
#gm
Xcovar <- get_x_covar(gm)
#addcovar = model.matrix(~Sex, data = covars)[,-1]
addcovar = model.matrix(~UNCHS008487+UNC18240977, data = covars)[,-1]
#K <- calc_kinship(pr.qc, type = "loco")
#heatmap(K[[1]])
#K.overall <- calc_kinship(pr.qc, type = "overall")
#heatmap(K.overall)
kinship <- calc_kinship(pr.qc)
heatmap(kinship)
#operm <- scan1perm(pr.qc, gm$covar$phenos, Xcovar=Xcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, addcovar = addcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, n_perm=2000)
operm <- scan1perm(pr.qc, gm$covar["ICI.vs.PBS"], model="binary", n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap), addcovar = addcovar)
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
names(summary_table) <- c("autosomes","X")
summary_table$significance.level <- rownames(summary_table)
rownames(summary_table) <- NULL
summary_table[c(3,1:2)] %>%
kable(escape = F,align = c("ccc")) %>%
kable_styling("striped", full_width = T) %>%
column_spec(1, bold=TRUE)
significance.level | autosomes | X |
---|---|---|
0.01 | 2.738061 | 2.910347 |
0.05 | 2.671339 | 2.756903 |
0.1 | 2.587731 | 2.556243 |
The figures below show QTL maps for each phenotype
out <- scan1(pr.qc, gm$covar["ICI.vs.PBS"], Xcovar=Xcovar, model="binary", addcovar = addcovar)
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
plot_lod<-function(out,map){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- 11 # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " [positions in cM] \n(using same scale as eoi vs ici for easier comparison)"))
add_threshold(map, summary(operm, alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
}
}
plot_lod(out,gm$gmap)
The table below shows QTL peaks associated with the phenotype. We use the 95% threshold from the permutations to find peaks.
peaks <- find_peaks(out, gm$gmap, threshold=summary(operm,alpha=0.05)$A, thresholdX = summary(operm,alpha=0.05)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks$marker <- find_marker(gm$gmap, chr=peaks$chr,pos=peaks$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]
rownames(peaks) <- NULL
#print(kable(peaks, "html")
# %>% kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
# )
print(kable(peaks, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
#peaks[] %>%
# kable(escape = F,align = c("cccccccc")) %>%
# kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
#plot only peak chromosomes
plot_lod_chr<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
ymx <- 11
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]\n(using same scale as eoi vs. ici for easier comparison)"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
}
}
for(i in unique(peaks$chr)){
#for (i in 1:nrow(peaks)){
#plot_lod_chr(out,gm$gmap, peaks$chr[i])
plot_lod_chr(out,gm$gmap, i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}
phenotype | chr | pos | lod | ci_lo | ci_hi | marker |
---|---|---|---|---|---|---|
ICI.vs.PBS | 18 | 2.934 | 3.309497 | 1.972 | 3.857 | UNCHS045343 |
print("peaks in MB positions")
[1] “peaks in MB positions”
peaks_mba <- find_peaks(out, gm$pmap, threshold=summary(operm,alpha=0.05)$A, thresholdX = summary(operm,alpha=0.05)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks_mba$marker <- find_marker(gm$pmap, chr=peaks_mba$chr,pos=peaks_mba$pos)
names(peaks_mba)[2] <- c("phenotype")
peaks_mba <- peaks_mba[-1]
#peaks_mbl <- list()
##corresponding info in Mb
#for(i in 1:nrow(peaks)){
# #lodindex <- peaks$lodindex[i]
# phenotype <- peaks$phenotype[i]
# chr <- as.character(peaks$chr[i])
# lod <- peaks$lod[i]
# mark <- peaks$marker[i]
# pos <- mapdf[mapdf$marker==mark,]$pmapdf
# ci_lo <- mapdfnd$pmapdf[which(mapdfnd$gmapdf == peaks$ci_lo[i] & mapdfnd$chr == peaks$chr[i])]
# ci_hi <- mapdfnd$pmapdf[which(mapdfnd$gmapdf == peaks$ci_hi[i] & mapdfnd$chr == peaks$chr[i])]
# peaks_mb=as.data.frame(cbind(phenotype, chr, pos, lod, ci_lo, ci_hi, mark))
# names(peaks_mb)[7] <- c("marker")
# peaks_mbl[[i]] <- peaks_mb
#}
#peaks_mba2 <- do.call(rbind, peaks_mbl)
#peaks_mba2 <- as.data.frame(peaks_mba)
#peaks_mba[,c("chr", "pos", "lod", "ci_lo", "ci_hi")] <- sapply(peaks_mba[,c("chr", "pos", "lod", "ci_lo", "ci_hi")], as.numeric)
rownames(peaks_mba) <- NULL
#print(kable(peaks_mba, "html")
# %>% kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
# )
print(kable(peaks_mba, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
#peaks_mba[] %>%
# kable(escape = F,align = c("cccccccc")) %>%
# kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
plot_lod_chr_mb<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
ymx <- 11
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]\n(using same scale as eoi vs. ici for easier comparison)"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
}
}
for(i in unique(peaks_mba$chr)){
#for (i in 1:nrow(peaks_mba)){
#plot_lod_chr_mb(out,gm$pmap, peaks_mba$chr[i])
plot_lod_chr_mb(out,gm$pmap,i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}
phenotype | chr | pos | lod | ci_lo | ci_hi | marker |
---|---|---|---|---|---|---|
ICI.vs.PBS | 18 | 4.861199 | 3.309497 | 3.01212 | 5.584328 | UNCHS045343 |
For each peak LOD location we give a list of gene
query_variants <- create_variant_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/cc_variants.sqlite")
query_genes <- create_gene_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/mouse_genes_mgi.sqlite")
if(nrow(peaks) >0){
for (i in 1:nrow(peaks)){
#for (i in 1:1){
#Plot 1
#marker = find_marker(gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i])
#gp <- g[,marker]
#gp[gp==1] <- "AA"
#gp[gp==2] <- "AB"
#gp[gp==0] <- NA
#plot_pxg(gp, gm$covar[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
#title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
###dev.off()
g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
#par(mar=c(4.1, 4.1, 1.5, 0.6))
plot_pxg(g, gm$covar[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
##dev.off()
chr = peaks$chr[i]
# Plot 2
pr_sub <- pull_genoprobint(pr.qc, gm$gmap, chr, c(peaks$ci_lo[i], peaks$ci_hi[i]))
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar)
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], Xcovar=Xcovar)
#coeff <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
#coeff_sub <- scan1coef(pr_sub[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
blup <- scan1blup(pr.qc[,chr], gm$covar[peaks$phenotype[i]], addcovar = addcovar)
blup_sub <- scan1blup(pr_sub[,chr], gm$covar[peaks$phenotype[i]], addcovar = addcovar)
write.csv(as.data.frame(blup_sub), paste0("data/ici.vs.pbs_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_5.batches_mis_conditional_2-peaks-chr3-10.csv"), quote=F)
#plot_coef(coeff,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i]," [scan1coeff; positions in cM])")
# )
#plot_coef(coeff_sub,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i],"; 1.5 LOD drop interval [scan1coeff; positions in cM] ) ")
# )
plot_coef(blup,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," [scan1blup; positions in cM])")
)
plot_coef(blup_sub,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i],"; 1.5 LOD drop interval [scan1blup; positions in cM])")
)
# Plot 3
#c2effB <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary", contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
#c2effBb <- scan1blup(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]],Xcovar=Xcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
#plot(c2effB, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
#plot(c2effBb, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
##last_coef <- unclass(c2effB)[nrow(c2effB),2:3] # last two coefficients
##for(t in seq(along=last_coef))
## axis(side=4, at=last_coef[t], names(last_coef)[t], tick=FALSE)
#Table 1
chr = peaks_mba$chr[i]
start=as.numeric(peaks_mba$ci_lo[i])
end=as.numeric(peaks_mba$ci_hi[i])
genesgss = query_genes(chr, start, end)
write.csv(genesgss, file=paste0("data/ici.vs.pbs_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_5.batches_mis_conditional_2-peaks-chr3-10.csv"), quote=F)
rownames(genesgss) <- NULL
genesgss$strand_old = genesgss$strand
genesgss$strand[genesgss$strand=="+"] <- "positive"
genesgss$strand[genesgss$strand=="-"] <- "negative"
#genesgss <-
#table <-
#genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")] %>%
#kable(escape = F,align = c("ccccccccccc")) %>%
#kable_styling("striped", full_width = T) #%>%
#cat #%>%
#column_spec(1, bold=TRUE)
#
#print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], escape = F,align = c("ccccccccccc")))
print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}
chr | type | start | stop | strand | ID | Name | Dbxref | gene_id | mgi_type | description |
---|---|---|---|---|---|---|---|---|---|---|
18 | pseudogene | 3.015908 | 3.016159 | positive | MGI_C57BL6J_6275399 | Gm50072 | ENSEMBL:ENSMUSG00000117547 | MGI:6275399 | pseudogene | predicted gene, 50072 |
18 | pseudogene | 3.026901 | 3.027882 | negative | MGI_C57BL6J_3852490 | Vmn1r-ps151 | NCBI_Gene:100312519,ENSEMBL:ENSMUSG00000093774 | MGI:3852490 | pseudogene | vomeronasal 1 receptor, pseudogene 151 |
18 | pseudogene | 3.080778 | 3.081476 | negative | MGI_C57BL6J_3852491 | Vmn1r-ps152 | NCBI_Gene:100312520,ENSEMBL:ENSMUSG00000093444 | MGI:3852491 | pseudogene | vomeronasal 1 receptor, pseudogene 152 |
18 | pseudogene | 3.105167 | 3.105396 | negative | MGI_C57BL6J_6275400 | Gm50073 | ENSEMBL:ENSMUSG00000117531 | MGI:6275400 | pseudogene | predicted gene, 50073 |
18 | gene | 3.122492 | 3.130012 | negative | MGI_C57BL6J_3852494 | Vmn1r238 | NCBI_Gene:100312476,ENSEMBL:ENSMUSG00000091539 | MGI:3852494 | protein coding gene | vomeronasal 1 receptor, 238 |
18 | pseudogene | 3.139032 | 3.139305 | negative | MGI_C57BL6J_6275401 | Gm50074 | ENSEMBL:ENSMUSG00000117353 | MGI:6275401 | pseudogene | predicted gene, 50074 |
18 | pseudogene | 3.171447 | 3.171932 | negative | MGI_C57BL6J_6275402 | Gm50075 | ENSEMBL:ENSMUSG00000117464 | MGI:6275402 | pseudogene | predicted gene, 50075 |
18 | gene | 3.266048 | 3.338176 | negative | MGI_C57BL6J_88495 | Crem | NCBI_Gene:12916,ENSEMBL:ENSMUSG00000063889 | MGI:88495 | protein coding gene | cAMP responsive element modulator |
18 | gene | 3.336416 | 3.366863 | positive | MGI_C57BL6J_3643252 | Gm6225 | NCBI_Gene:633947,ENSEMBL:ENSMUSG00000097746 | MGI:3643252 | lncRNA gene | predicted gene 6225 |
18 | gene | 3.382970 | 3.436700 | positive | MGI_C57BL6J_1918995 | Cul2 | NCBI_Gene:71745,ENSEMBL:ENSMUSG00000024231 | MGI:1918995 | protein coding gene | cullin 2 |
18 | pseudogene | 3.446654 | 3.447503 | positive | MGI_C57BL6J_3648491 | Gm6235 | NCBI_Gene:621501,ENSEMBL:ENSMUSG00000117370 | MGI:3648491 | pseudogene | predicted gene 6235 |
18 | pseudogene | 3.466371 | 3.466502 | negative | MGI_C57BL6J_6275423 | Gm50088 | ENSEMBL:ENSMUSG00000117389 | MGI:6275423 | pseudogene | predicted gene, 50088 |
18 | gene | 3.471630 | 3.474315 | positive | MGI_C57BL6J_3645649 | G430049J08Rik | ENSEMBL:ENSMUSG00000096528 | MGI:3645649 | unclassified gene | RIKEN cDNA G430049J08 gene |
18 | pseudogene | 3.484218 | 3.485193 | positive | MGI_C57BL6J_6275424 | Gm50089 | ENSEMBL:ENSMUSG00000117469 | MGI:6275424 | pseudogene | predicted gene, 50089 |
18 | gene | 3.507923 | 3.516404 | positive | MGI_C57BL6J_1915260 | Bambi | NCBI_Gene:68010,ENSEMBL:ENSMUSG00000024232 | MGI:1915260 | protein coding gene | BMP and activin membrane-bound inhibitor |
18 | pseudogene | 3.558675 | 3.560037 | positive | MGI_C57BL6J_6275425 | Gm50090 | ENSEMBL:ENSMUSG00000117454 | MGI:6275425 | pseudogene | predicted gene, 50090 |
18 | gene | 3.616281 | 3.618242 | positive | MGI_C57BL6J_6275426 | Gm50091 | ENSEMBL:ENSMUSG00000117506 | MGI:6275426 | unclassified gene | predicted gene, 50091 |
18 | gene | 3.770741 | 3.770804 | negative | MGI_C57BL6J_5453420 | Gm23643 | ENSEMBL:ENSMUSG00000088480 | MGI:5453420 | snRNA gene | predicted gene, 23643 |
18 | pseudogene | 3.857730 | 3.858585 | negative | MGI_C57BL6J_3647465 | Rpl7a-ps6 | NCBI_Gene:435549,ENSEMBL:ENSMUSG00000117463 | MGI:3647465 | pseudogene | ribosomal protein L7A, pseudogene 6 |
18 | gene | 3.860108 | 3.860430 | positive | MGI_C57BL6J_5454409 | Gm24632 | ENSEMBL:ENSMUSG00000084719 | MGI:5454409 | unclassified non-coding RNA gene | predicted gene, 24632 |
18 | pseudogene | 4.010143 | 4.011335 | positive | MGI_C57BL6J_3646622 | Gm7378 | NCBI_Gene:664867,ENSEMBL:ENSMUSG00000117395 | MGI:3646622 | pseudogene | predicted gene 7378 |
18 | pseudogene | 4.052351 | 4.052771 | negative | MGI_C57BL6J_3644899 | Gm6248 | NCBI_Gene:621666,ENSEMBL:ENSMUSG00000117565 | MGI:3644899 | pseudogene | predicted gene 6248 |
18 | gene | 4.165832 | 4.182236 | positive | MGI_C57BL6J_1914578 | Lyzl1 | NCBI_Gene:67328,ENSEMBL:ENSMUSG00000024233 | MGI:1914578 | protein coding gene | lysozyme-like 1 |
18 | pseudogene | 4.198320 | 4.199135 | negative | MGI_C57BL6J_3708638 | Gm10557 | NCBI_Gene:383374,ENSEMBL:ENSMUSG00000073647 | MGI:3708638 | pseudogene | predicted gene 10557 |
18 | gene | 4.244121 | 4.250779 | negative | MGI_C57BL6J_5624542 | Gm41657 | NCBI_Gene:105246358 | MGI:5624542 | lncRNA gene | predicted gene, 41657 |
18 | pseudogene | 4.293097 | 4.294538 | negative | MGI_C57BL6J_3647488 | Gm7400 | NCBI_Gene:664909,ENSEMBL:ENSMUSG00000117482 | MGI:3647488 | pseudogene | predicted gene 7400 |
18 | gene | 4.331325 | 4.353412 | negative | MGI_C57BL6J_1346878 | Map3k8 | NCBI_Gene:26410,ENSEMBL:ENSMUSG00000024235 | MGI:1346878 | protein coding gene | mitogen-activated protein kinase kinase kinase 8 |
18 | gene | 4.353547 | 4.368945 | positive | MGI_C57BL6J_1921165 | 4833419F23Rik | NCBI_Gene:73915,ENSEMBL:ENSMUSG00000117401 | MGI:1921165 | lncRNA gene | RIKEN cDNA 4833419F23 gene |
18 | gene | 4.353644 | 4.380723 | positive | MGI_C57BL6J_6275434 | Gm50096 | ENSEMBL:ENSMUSG00000117594 | MGI:6275434 | lncRNA gene | predicted gene, 50096 |
18 | gene | 4.373669 | 4.379985 | negative | MGI_C57BL6J_5477359 | Gm26865 | ENSEMBL:ENSMUSG00000097641 | MGI:5477359 | lncRNA gene | predicted gene, 26865 |
18 | gene | 4.375592 | 4.398798 | positive | MGI_C57BL6J_1914690 | Mtpap | NCBI_Gene:67440,ENSEMBL:ENSMUSG00000024234 | MGI:1914690 | protein coding gene | mitochondrial poly(A) polymerase |
18 | gene | 4.399328 | 4.400910 | positive | MGI_C57BL6J_6275321 | Gm50023 | ENSEMBL:ENSMUSG00000117579 | MGI:6275321 | lncRNA gene | predicted gene, 50023 |
18 | pseudogene | 4.484469 | 4.485501 | positive | MGI_C57BL6J_3644066 | Gm7411 | NCBI_Gene:664931,ENSEMBL:ENSMUSG00000117640 | MGI:3644066 | pseudogene | predicted gene 7411 |
18 | gene | 4.541612 | 4.562599 | negative | MGI_C57BL6J_5624543 | Gm41658 | NCBI_Gene:105246359 | MGI:5624543 | lncRNA gene | predicted gene, 41658 |
18 | gene | 4.590763 | 4.616064 | positive | MGI_C57BL6J_6275323 | Gm50024 | ENSEMBL:ENSMUSG00000117521 | MGI:6275323 | lncRNA gene | predicted gene, 50024 |
18 | gene | 4.590792 | 4.682869 | positive | MGI_C57BL6J_2685174 | Jcad | NCBI_Gene:240185,ENSEMBL:ENSMUSG00000033960 | MGI:2685174 | protein coding gene | junctional cadherin 5 associated |
18 | pseudogene | 4.743061 | 4.743876 | positive | MGI_C57BL6J_3645969 | Gm5047 | NCBI_Gene:268988,ENSEMBL:ENSMUSG00000117585 | MGI:3645969 | pseudogene | predicted gene 5047 |
18 | gene | 4.812486 | 4.850959 | positive | MGI_C57BL6J_3642809 | Gm10556 | NCBI_Gene:100038439,ENSEMBL:ENSMUSG00000097043 | MGI:3642809 | lncRNA gene | predicted gene 10556 |
18 | gene | 4.820303 | 4.826659 | negative | MGI_C57BL6J_5593428 | Gm34269 | NCBI_Gene:102637466 | MGI:5593428 | lncRNA gene | predicted gene, 34269 |
18 | gene | 4.869777 | 4.912263 | positive | MGI_C57BL6J_5624544 | Gm41659 | NCBI_Gene:105246360 | MGI:5624544 | lncRNA gene | predicted gene, 41659 |
18 | gene | 4.920245 | 5.119299 | positive | MGI_C57BL6J_2147319 | Svil | NCBI_Gene:225115,ENSEMBL:ENSMUSG00000024236 | MGI:2147319 | protein coding gene | supervillin |
18 | gene | 4.959636 | 4.960300 | positive | MGI_C57BL6J_3642287 | BC025933 | NA | NA | protein coding gene | cDNA sequence BC025933 |
18 | gene | 5.042818 | 5.048867 | negative | MGI_C57BL6J_5593485 | Gm34326 | NCBI_Gene:102637544 | MGI:5593485 | lncRNA gene | predicted gene, 34326 |
18 | gene | 5.139349 | 5.142068 | positive | MGI_C57BL6J_5593700 | Gm34541 | NCBI_Gene:102637831 | MGI:5593700 | lncRNA gene | predicted gene, 34541 |
18 | gene | 5.141762 | 5.165731 | negative | MGI_C57BL6J_5477176 | Gm26682 | NCBI_Gene:102637682,ENSEMBL:ENSMUSG00000097888 | MGI:5477176 | lncRNA gene | predicted gene, 26682 |
18 | gene | 5.210027 | 5.334963 | negative | MGI_C57BL6J_2444919 | Zfp438 | NCBI_Gene:240186,ENSEMBL:ENSMUSG00000050945 | MGI:2444919 | protein coding gene | zinc finger protein 438 |
18 | gene | 5.348258 | 5.361861 | negative | MGI_C57BL6J_6275385 | Gm50064 | ENSEMBL:ENSMUSG00000117520 | MGI:6275385 | lncRNA gene | predicted gene, 50064 |
18 | gene | 5.368541 | 5.370482 | negative | MGI_C57BL6J_6275387 | Gm50065 | ENSEMBL:ENSMUSG00000117617 | MGI:6275387 | lncRNA gene | predicted gene, 50065 |
18 | gene | 5.389802 | 5.402870 | negative | MGI_C57BL6J_5593789 | Gm34630 | NCBI_Gene:102637944 | MGI:5593789 | lncRNA gene | predicted gene, 34630 |
18 | gene | 5.491501 | 5.593505 | negative | MGI_C57BL6J_3642044 | Gm10125 | NCBI_Gene:791318,ENSEMBL:ENSMUSG00000063087 | MGI:3642044 | lncRNA gene | predicted gene 10125 |
18 | gene | 5.572824 | 5.575055 | negative | MGI_C57BL6J_5477069 | Gm26575 | ENSEMBL:ENSMUSG00000097926 | MGI:5477069 | lncRNA gene | predicted gene, 26575 |
gm
Object of class cross2 (crosstype "bc")
Total individuals 138
No. genotyped individuals 138
No. phenotyped individuals 138
No. with both geno & pheno 138
No. phenotypes 1
No. covariates 7
No. phenotype covariates 0
No. chromosomes 20
Total markers 32690
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 1396 1628 1692 1064 940
17 18 19 X
401 1095 1067 575
#detach("package:qtl2", unload=TRUE)
#library(qtl)
cross <- qtl::read.cross("csv", file = "data/ici.vs.pbs_gm_qtl_snpsqc_5.batches_mis_conditional_2-peaks-chr3-10.csv",alleles=c("A","B"))
--Read the following data:
138 individuals
32690 markers
5 phenotypes
--Cross type: bc
cross <- qtl::jittermap(cross)
summary(cross)
Backcross
No. individuals: 138
No. phenotypes: 5
Percent phenotyped: 100 100 100 99.3 98.6
No. chromosomes: 20
Autosomes: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
X chr: X
Total markers: 32690
No. markers: 2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069
1396 1628 1692 1064 940 401 1095 1067 575
Percent genotyped: 99.5
Genotypes (%):
Autosomes: AA:54.3 AB:45.7
X chromosome: AA:52.7 AB:47.3
cross.probs <- qtl::calc.genoprob(cross)
print("method == hk")
[1] "method == hk"
add.covars = qtl::pull.pheno(cross.probs, c("UNCHS008487","UNC18240977"))
scanone.hk <-qtl::scanone(cross.probs, pheno.col="ICI.vs.PBS" , model="binary", method="hk", addcovar = add.covars)
operm.hk <- qtl::scanone(cross.probs, method = "hk", pheno.col="ICI.vs.PBS", n.perm = 10, perm.Xsp = TRUE, model="binary", verbose=FALSE, addcovar = add.covars)
plot(operm.hk)
print(summary(operm.hk, alpha=c(0.01, 0.05, 0.1)))
Autosome LOD thresholds (10 permutations)
lod
1% 3.62
5% 3.43
10% 3.19
X chromosome LOD thresholds (183 permutations)
lod
1% 2.57
5% 2.53
10% 2.49
#plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "steelblue")
#qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.01, col = 'blue')
#qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.05, col = 'red')
#qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.1, col = 'purple')
ymx <- maxlod(out) # overall maximum LOD score
plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "slateblue", ylim=c(0, ymx+0.5))
title(main = paste0(colnames(out), " [positions in cM]"))
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.01, col = 'blue')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.05, col = 'red')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.1, col = 'purple')
ymx <- 11
plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "slateblue", ylim=c(0, ymx+0.5))
title(main = paste0(colnames(out), " [positions in cM]\n(using same scale as eoi vs ici for easier comparison)"))
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.01, col = 'blue')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.05, col = 'red')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.1, col = 'purple')
print(as.data.frame(summary(scanone.hk, perms=operm.hk, pvalues=TRUE, format="allpeaks")))
chr pos lod pval
UNCHS000141 1 4.439109 1.4810382 0.9118239
UNC2981117 2 27.613431 0.8724495 1.0000000
JAX00521745 3 20.613360 0.7586221 1.0000000
JAX00556948 4 41.038754 0.7726880 1.0000000
UNC8675939 5 6.193174 2.0684019 0.7191035
UNC10923091 6 18.321306 0.6856206 1.0000000
UNCHS020066 7 34.113657 1.2936681 1.0000000
UNC15524531 8 59.462590 1.9315437 0.8168395
UNC17203597 9 68.527929 0.6928610 1.0000000
UNCHS028001 10 21.783362 1.0219015 1.0000000
UNC20465557 11 80.904972 0.7841081 1.0000000
UNC21787084 12 53.288332 0.7505664 1.0000000
JAX00351755 13 7.044200 0.9809396 1.0000000
UNC24056202 14 30.156730 1.8824837 0.8168395
UNC25275535 15 11.967146 0.8880487 1.0000000
UNCHS042071 16 13.176096 0.7730967 1.0000000
UNCrs47191360 17 18.433013 1.2636005 1.0000000
UNCHS045343 18 2.934001 3.3781249 0.1051672
UNC29920552 19 8.810092 1.9159935 0.8168395
UNCHS048314 X 8.974041 1.9849011 0.5288989
print("all peaks with a p-value less or equal to 0.05 (suggestive)")
[1] "all peaks with a p-value less or equal to 0.05 (suggestive)"
print(as.data.frame(summary(scanone.hk, perms=operm.hk, alpha=0.05, pvalues=TRUE, format="allpeaks")))
[1] chr pos lod
<0 rows> (or 0-length row.names)
#print("method == ehk")
#scanone.ehk <-qtl::scanone(cross.probs, pheno.col="ICI.vs.PBS" , model="binary", method="ehk")
#operm.ehk <- qtl::scanone(cross.probs, method = "ehk", pheno.col="ICI.vs.PBS", n.perm = 10, perm.Xsp = TRUE, model="binary", verbose=FALSE)
#plot(operm.ehk)
#print(summary(operm.ehk, alpha=c(0.01, 0.05, 0.1)))
#plot(scanone.ehk, bandcol = "grey90",lty=1, cex=1, col = "steelblue")
#qtl::add.threshold(scanone.ehk, perms= operm.ehk, alpha=0.01, col = 'blue')
#qtl::add.threshold(scanone.ehk, perms= operm.ehk, alpha=0.05, col = 'red')
#qtl::add.threshold(scanone.ehk, perms= operm.ehk, alpha=0.1, col = 'purple')
#print(as.data.frame(summary(scanone.ehk)))
#print(as.data.frame(summary(scanone.ehk, perms=operm.ehk, alpha=0.05, pvalues=TRUE, format="allpeaks")))
#gm
Xcovar <- get_x_covar(gm)
#addcovar = model.matrix(~Sex, data = covars)[,-1]
addcovar = model.matrix(~UNC8250659+UNC18240977, data = covars)[,-1]
#K <- calc_kinship(pr.qc, type = "loco")
#heatmap(K[[1]])
#K.overall <- calc_kinship(pr.qc, type = "overall")
#heatmap(K.overall)
kinship <- calc_kinship(pr.qc)
heatmap(kinship)
#operm <- scan1perm(pr.qc, gm$covar$phenos, Xcovar=Xcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, addcovar = addcovar, n_perm=2000)
#operm <- scan1perm(pr.qc, gm$covar$phenos, n_perm=2000)
operm <- scan1perm(pr.qc, gm$covar["ICI.vs.PBS"], model="binary", n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap), addcovar = addcovar)
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
names(summary_table) <- c("autosomes","X")
summary_table$significance.level <- rownames(summary_table)
rownames(summary_table) <- NULL
summary_table[c(3,1:2)] %>%
kable(escape = F,align = c("ccc")) %>%
kable_styling("striped", full_width = T) %>%
column_spec(1, bold=TRUE)
significance.level | autosomes | X |
---|---|---|
0.01 | 3.572011 | 3.009770 |
0.05 | 3.301449 | 2.940770 |
0.1 | 2.962412 | 2.850537 |
The figures below show QTL maps for each phenotype
out <- scan1(pr.qc, gm$covar["ICI.vs.PBS"], Xcovar=Xcovar, model="binary", addcovar = addcovar)
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
plot_lod<-function(out,map){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- 11 # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " [positions in cM] \n(using same scale as eoi vs ici for easier comparison)"))
add_threshold(map, summary(operm, alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
}
}
plot_lod(out,gm$gmap)
The table below shows QTL peaks associated with the phenotype. We use the 95% threshold from the permutations to find peaks.
peaks <- find_peaks(out, gm$gmap, threshold=summary(operm,alpha=0.05)$A, thresholdX = summary(operm,alpha=0.05)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks$marker <- find_marker(gm$gmap, chr=peaks$chr,pos=peaks$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]
rownames(peaks) <- NULL
#print(kable(peaks, "html")
# %>% kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
# )
print(kable(peaks, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
#peaks[] %>%
# kable(escape = F,align = c("cccccccc")) %>%
# kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
#plot only peak chromosomes
plot_lod_chr<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
ymx <- 11
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]\n(using same scale as eoi vs. ici for easier comparison)"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
}
}
for(i in unique(peaks$chr)){
#for (i in 1:nrow(peaks)){
#plot_lod_chr(out,gm$gmap, peaks$chr[i])
plot_lod_chr(out,gm$gmap, i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}
phenotype | chr | pos | lod | ci_lo | ci_hi | marker |
---|---|---|---|---|---|---|
ICI.vs.PBS | 18 | 2.934 | 3.616886 | 1.972 | 4.621 | UNCHS045343 |
print("peaks in MB positions")
[1] “peaks in MB positions”
peaks_mba <- find_peaks(out, gm$pmap, threshold=summary(operm,alpha=0.05)$A, thresholdX = summary(operm,alpha=0.05)$X, peakdrop=3, drop=1.5)
if(nrow(peaks) >0){
peaks_mba$marker <- find_marker(gm$pmap, chr=peaks_mba$chr,pos=peaks_mba$pos)
names(peaks_mba)[2] <- c("phenotype")
peaks_mba <- peaks_mba[-1]
#peaks_mbl <- list()
##corresponding info in Mb
#for(i in 1:nrow(peaks)){
# #lodindex <- peaks$lodindex[i]
# phenotype <- peaks$phenotype[i]
# chr <- as.character(peaks$chr[i])
# lod <- peaks$lod[i]
# mark <- peaks$marker[i]
# pos <- mapdf[mapdf$marker==mark,]$pmapdf
# ci_lo <- mapdfnd$pmapdf[which(mapdfnd$gmapdf == peaks$ci_lo[i] & mapdfnd$chr == peaks$chr[i])]
# ci_hi <- mapdfnd$pmapdf[which(mapdfnd$gmapdf == peaks$ci_hi[i] & mapdfnd$chr == peaks$chr[i])]
# peaks_mb=as.data.frame(cbind(phenotype, chr, pos, lod, ci_lo, ci_hi, mark))
# names(peaks_mb)[7] <- c("marker")
# peaks_mbl[[i]] <- peaks_mb
#}
#peaks_mba2 <- do.call(rbind, peaks_mbl)
#peaks_mba2 <- as.data.frame(peaks_mba)
#peaks_mba[,c("chr", "pos", "lod", "ci_lo", "ci_hi")] <- sapply(peaks_mba[,c("chr", "pos", "lod", "ci_lo", "ci_hi")], as.numeric)
rownames(peaks_mba) <- NULL
#print(kable(peaks_mba, "html")
# %>% kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
# )
print(kable(peaks_mba, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
#peaks_mba[] %>%
# kable(escape = F,align = c("cccccccc")) %>%
# kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
plot_lod_chr_mb<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
ymx <- 11
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]\n(using same scale as eoi vs. ici for easier comparison)"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
}
}
for(i in unique(peaks_mba$chr)){
#for (i in 1:nrow(peaks_mba)){
#plot_lod_chr_mb(out,gm$pmap, peaks_mba$chr[i])
plot_lod_chr_mb(out,gm$pmap,i)
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}
phenotype | chr | pos | lod | ci_lo | ci_hi | marker |
---|---|---|---|---|---|---|
ICI.vs.PBS | 18 | 4.861199 | 3.616886 | 3.01212 | 7.97679 | UNCHS045343 |
For each peak LOD location we give a list of gene
query_variants <- create_variant_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/cc_variants.sqlite")
query_genes <- create_gene_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/mouse_genes_mgi.sqlite")
if(nrow(peaks) >0){
for (i in 1:nrow(peaks)){
#for (i in 1:1){
#Plot 1
#marker = find_marker(gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i])
#gp <- g[,marker]
#gp[gp==1] <- "AA"
#gp[gp==2] <- "AB"
#gp[gp==0] <- NA
#plot_pxg(gp, gm$covar[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
#title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
###dev.off()
g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
#par(mar=c(4.1, 4.1, 1.5, 0.6))
plot_pxg(g, gm$covar[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
##dev.off()
chr = peaks$chr[i]
# Plot 2
pr_sub <- pull_genoprobint(pr.qc, gm$gmap, chr, c(peaks$ci_lo[i], peaks$ci_hi[i]))
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar)
#coeff <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], Xcovar=Xcovar)
#coeff <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
#coeff_sub <- scan1coef(pr_sub[,chr], gm$covar[peaks$lodcolumn[i]], model="binary")
blup <- scan1blup(pr.qc[,chr], gm$covar[peaks$phenotype[i]], addcovar = addcovar)
blup_sub <- scan1blup(pr_sub[,chr], gm$covar[peaks$phenotype[i]], addcovar = addcovar)
write.csv(as.data.frame(blup_sub), paste0("data/ici.vs.pbs_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_5.batches_mis_conditional_2-peaks-chr4-10.csv"), quote=F)
#plot_coef(coeff,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i]," [scan1coeff; positions in cM])")
# )
#plot_coef(coeff_sub,
# gm$gmap, columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$lodcolumn[i],"; 1.5 LOD drop interval [scan1coeff; positions in cM] ) ")
# )
plot_coef(blup,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," [scan1blup; positions in cM])")
)
plot_coef(blup_sub,
gm$gmap, columns=1:2,
bgcolor="gray95", legend="bottomleft",
main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i],"; 1.5 LOD drop interval [scan1blup; positions in cM])")
)
# Plot 3
#c2effB <- scan1coef(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], model="binary", contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
#c2effBb <- scan1blup(pr.qc[,chr], gm$covar[peaks$lodcolumn[i]], contrasts=cbind(a=c(-1, 0), d=c(0, -1)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]], addcovar = addcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
##c2effB <- scan1coef(pr[,chr], cross$pheno[,peaks$lodcolumn[i]],Xcovar=Xcovar, contrasts=cbind(mu=c(1,1,1), a=c(-1, 0, 1), d=c(0, 1, 0)))
#plot(c2effB, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
#plot(c2effBb, gm$gmap[chr], columns=1:2,
# bgcolor="gray95", legend="bottomleft",
# main = paste("chr", chr=peaks$chr[i], "pos", peaks$pos[i], "(",peaks$lodcolumn[i],")")
# )
##last_coef <- unclass(c2effB)[nrow(c2effB),2:3] # last two coefficients
##for(t in seq(along=last_coef))
## axis(side=4, at=last_coef[t], names(last_coef)[t], tick=FALSE)
#Table 1
chr = peaks_mba$chr[i]
start=as.numeric(peaks_mba$ci_lo[i])
end=as.numeric(peaks_mba$ci_hi[i])
genesgss = query_genes(chr, start, end)
write.csv(genesgss, file=paste0("data/ici.vs.pbs_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_5.batches_mis_conditional_2-peaks-chr4-10.csv"), quote=F)
rownames(genesgss) <- NULL
genesgss$strand_old = genesgss$strand
genesgss$strand[genesgss$strand=="+"] <- "positive"
genesgss$strand[genesgss$strand=="-"] <- "negative"
#genesgss <-
#table <-
#genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")] %>%
#kable(escape = F,align = c("ccccccccccc")) %>%
#kable_styling("striped", full_width = T) #%>%
#cat #%>%
#column_spec(1, bold=TRUE)
#
#print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], escape = F,align = c("ccccccccccc")))
print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}
chr | type | start | stop | strand | ID | Name | Dbxref | gene_id | mgi_type | description |
---|---|---|---|---|---|---|---|---|---|---|
18 | pseudogene | 3.015908 | 3.016159 | positive | MGI_C57BL6J_6275399 | Gm50072 | ENSEMBL:ENSMUSG00000117547 | MGI:6275399 | pseudogene | predicted gene, 50072 |
18 | pseudogene | 3.026901 | 3.027882 | negative | MGI_C57BL6J_3852490 | Vmn1r-ps151 | NCBI_Gene:100312519,ENSEMBL:ENSMUSG00000093774 | MGI:3852490 | pseudogene | vomeronasal 1 receptor, pseudogene 151 |
18 | pseudogene | 3.080778 | 3.081476 | negative | MGI_C57BL6J_3852491 | Vmn1r-ps152 | NCBI_Gene:100312520,ENSEMBL:ENSMUSG00000093444 | MGI:3852491 | pseudogene | vomeronasal 1 receptor, pseudogene 152 |
18 | pseudogene | 3.105167 | 3.105396 | negative | MGI_C57BL6J_6275400 | Gm50073 | ENSEMBL:ENSMUSG00000117531 | MGI:6275400 | pseudogene | predicted gene, 50073 |
18 | gene | 3.122492 | 3.130012 | negative | MGI_C57BL6J_3852494 | Vmn1r238 | NCBI_Gene:100312476,ENSEMBL:ENSMUSG00000091539 | MGI:3852494 | protein coding gene | vomeronasal 1 receptor, 238 |
18 | pseudogene | 3.139032 | 3.139305 | negative | MGI_C57BL6J_6275401 | Gm50074 | ENSEMBL:ENSMUSG00000117353 | MGI:6275401 | pseudogene | predicted gene, 50074 |
18 | pseudogene | 3.171447 | 3.171932 | negative | MGI_C57BL6J_6275402 | Gm50075 | ENSEMBL:ENSMUSG00000117464 | MGI:6275402 | pseudogene | predicted gene, 50075 |
18 | gene | 3.266048 | 3.338176 | negative | MGI_C57BL6J_88495 | Crem | NCBI_Gene:12916,ENSEMBL:ENSMUSG00000063889 | MGI:88495 | protein coding gene | cAMP responsive element modulator |
18 | gene | 3.336416 | 3.366863 | positive | MGI_C57BL6J_3643252 | Gm6225 | NCBI_Gene:633947,ENSEMBL:ENSMUSG00000097746 | MGI:3643252 | lncRNA gene | predicted gene 6225 |
18 | gene | 3.382970 | 3.436700 | positive | MGI_C57BL6J_1918995 | Cul2 | NCBI_Gene:71745,ENSEMBL:ENSMUSG00000024231 | MGI:1918995 | protein coding gene | cullin 2 |
18 | pseudogene | 3.446654 | 3.447503 | positive | MGI_C57BL6J_3648491 | Gm6235 | NCBI_Gene:621501,ENSEMBL:ENSMUSG00000117370 | MGI:3648491 | pseudogene | predicted gene 6235 |
18 | pseudogene | 3.466371 | 3.466502 | negative | MGI_C57BL6J_6275423 | Gm50088 | ENSEMBL:ENSMUSG00000117389 | MGI:6275423 | pseudogene | predicted gene, 50088 |
18 | gene | 3.471630 | 3.474315 | positive | MGI_C57BL6J_3645649 | G430049J08Rik | ENSEMBL:ENSMUSG00000096528 | MGI:3645649 | unclassified gene | RIKEN cDNA G430049J08 gene |
18 | pseudogene | 3.484218 | 3.485193 | positive | MGI_C57BL6J_6275424 | Gm50089 | ENSEMBL:ENSMUSG00000117469 | MGI:6275424 | pseudogene | predicted gene, 50089 |
18 | gene | 3.507923 | 3.516404 | positive | MGI_C57BL6J_1915260 | Bambi | NCBI_Gene:68010,ENSEMBL:ENSMUSG00000024232 | MGI:1915260 | protein coding gene | BMP and activin membrane-bound inhibitor |
18 | pseudogene | 3.558675 | 3.560037 | positive | MGI_C57BL6J_6275425 | Gm50090 | ENSEMBL:ENSMUSG00000117454 | MGI:6275425 | pseudogene | predicted gene, 50090 |
18 | gene | 3.616281 | 3.618242 | positive | MGI_C57BL6J_6275426 | Gm50091 | ENSEMBL:ENSMUSG00000117506 | MGI:6275426 | unclassified gene | predicted gene, 50091 |
18 | gene | 3.770741 | 3.770804 | negative | MGI_C57BL6J_5453420 | Gm23643 | ENSEMBL:ENSMUSG00000088480 | MGI:5453420 | snRNA gene | predicted gene, 23643 |
18 | pseudogene | 3.857730 | 3.858585 | negative | MGI_C57BL6J_3647465 | Rpl7a-ps6 | NCBI_Gene:435549,ENSEMBL:ENSMUSG00000117463 | MGI:3647465 | pseudogene | ribosomal protein L7A, pseudogene 6 |
18 | gene | 3.860108 | 3.860430 | positive | MGI_C57BL6J_5454409 | Gm24632 | ENSEMBL:ENSMUSG00000084719 | MGI:5454409 | unclassified non-coding RNA gene | predicted gene, 24632 |
18 | pseudogene | 4.010143 | 4.011335 | positive | MGI_C57BL6J_3646622 | Gm7378 | NCBI_Gene:664867,ENSEMBL:ENSMUSG00000117395 | MGI:3646622 | pseudogene | predicted gene 7378 |
18 | pseudogene | 4.052351 | 4.052771 | negative | MGI_C57BL6J_3644899 | Gm6248 | NCBI_Gene:621666,ENSEMBL:ENSMUSG00000117565 | MGI:3644899 | pseudogene | predicted gene 6248 |
18 | gene | 4.165832 | 4.182236 | positive | MGI_C57BL6J_1914578 | Lyzl1 | NCBI_Gene:67328,ENSEMBL:ENSMUSG00000024233 | MGI:1914578 | protein coding gene | lysozyme-like 1 |
18 | pseudogene | 4.198320 | 4.199135 | negative | MGI_C57BL6J_3708638 | Gm10557 | NCBI_Gene:383374,ENSEMBL:ENSMUSG00000073647 | MGI:3708638 | pseudogene | predicted gene 10557 |
18 | gene | 4.244121 | 4.250779 | negative | MGI_C57BL6J_5624542 | Gm41657 | NCBI_Gene:105246358 | MGI:5624542 | lncRNA gene | predicted gene, 41657 |
18 | pseudogene | 4.293097 | 4.294538 | negative | MGI_C57BL6J_3647488 | Gm7400 | NCBI_Gene:664909,ENSEMBL:ENSMUSG00000117482 | MGI:3647488 | pseudogene | predicted gene 7400 |
18 | gene | 4.331325 | 4.353412 | negative | MGI_C57BL6J_1346878 | Map3k8 | NCBI_Gene:26410,ENSEMBL:ENSMUSG00000024235 | MGI:1346878 | protein coding gene | mitogen-activated protein kinase kinase kinase 8 |
18 | gene | 4.353547 | 4.368945 | positive | MGI_C57BL6J_1921165 | 4833419F23Rik | NCBI_Gene:73915,ENSEMBL:ENSMUSG00000117401 | MGI:1921165 | lncRNA gene | RIKEN cDNA 4833419F23 gene |
18 | gene | 4.353644 | 4.380723 | positive | MGI_C57BL6J_6275434 | Gm50096 | ENSEMBL:ENSMUSG00000117594 | MGI:6275434 | lncRNA gene | predicted gene, 50096 |
18 | gene | 4.373669 | 4.379985 | negative | MGI_C57BL6J_5477359 | Gm26865 | ENSEMBL:ENSMUSG00000097641 | MGI:5477359 | lncRNA gene | predicted gene, 26865 |
18 | gene | 4.375592 | 4.398798 | positive | MGI_C57BL6J_1914690 | Mtpap | NCBI_Gene:67440,ENSEMBL:ENSMUSG00000024234 | MGI:1914690 | protein coding gene | mitochondrial poly(A) polymerase |
18 | gene | 4.399328 | 4.400910 | positive | MGI_C57BL6J_6275321 | Gm50023 | ENSEMBL:ENSMUSG00000117579 | MGI:6275321 | lncRNA gene | predicted gene, 50023 |
18 | pseudogene | 4.484469 | 4.485501 | positive | MGI_C57BL6J_3644066 | Gm7411 | NCBI_Gene:664931,ENSEMBL:ENSMUSG00000117640 | MGI:3644066 | pseudogene | predicted gene 7411 |
18 | gene | 4.541612 | 4.562599 | negative | MGI_C57BL6J_5624543 | Gm41658 | NCBI_Gene:105246359 | MGI:5624543 | lncRNA gene | predicted gene, 41658 |
18 | gene | 4.590763 | 4.616064 | positive | MGI_C57BL6J_6275323 | Gm50024 | ENSEMBL:ENSMUSG00000117521 | MGI:6275323 | lncRNA gene | predicted gene, 50024 |
18 | gene | 4.590792 | 4.682869 | positive | MGI_C57BL6J_2685174 | Jcad | NCBI_Gene:240185,ENSEMBL:ENSMUSG00000033960 | MGI:2685174 | protein coding gene | junctional cadherin 5 associated |
18 | pseudogene | 4.743061 | 4.743876 | positive | MGI_C57BL6J_3645969 | Gm5047 | NCBI_Gene:268988,ENSEMBL:ENSMUSG00000117585 | MGI:3645969 | pseudogene | predicted gene 5047 |
18 | gene | 4.812486 | 4.850959 | positive | MGI_C57BL6J_3642809 | Gm10556 | NCBI_Gene:100038439,ENSEMBL:ENSMUSG00000097043 | MGI:3642809 | lncRNA gene | predicted gene 10556 |
18 | gene | 4.820303 | 4.826659 | negative | MGI_C57BL6J_5593428 | Gm34269 | NCBI_Gene:102637466 | MGI:5593428 | lncRNA gene | predicted gene, 34269 |
18 | gene | 4.869777 | 4.912263 | positive | MGI_C57BL6J_5624544 | Gm41659 | NCBI_Gene:105246360 | MGI:5624544 | lncRNA gene | predicted gene, 41659 |
18 | gene | 4.920245 | 5.119299 | positive | MGI_C57BL6J_2147319 | Svil | NCBI_Gene:225115,ENSEMBL:ENSMUSG00000024236 | MGI:2147319 | protein coding gene | supervillin |
18 | gene | 4.959636 | 4.960300 | positive | MGI_C57BL6J_3642287 | BC025933 | NA | NA | protein coding gene | cDNA sequence BC025933 |
18 | gene | 5.042818 | 5.048867 | negative | MGI_C57BL6J_5593485 | Gm34326 | NCBI_Gene:102637544 | MGI:5593485 | lncRNA gene | predicted gene, 34326 |
18 | gene | 5.139349 | 5.142068 | positive | MGI_C57BL6J_5593700 | Gm34541 | NCBI_Gene:102637831 | MGI:5593700 | lncRNA gene | predicted gene, 34541 |
18 | gene | 5.141762 | 5.165731 | negative | MGI_C57BL6J_5477176 | Gm26682 | NCBI_Gene:102637682,ENSEMBL:ENSMUSG00000097888 | MGI:5477176 | lncRNA gene | predicted gene, 26682 |
18 | gene | 5.210027 | 5.334963 | negative | MGI_C57BL6J_2444919 | Zfp438 | NCBI_Gene:240186,ENSEMBL:ENSMUSG00000050945 | MGI:2444919 | protein coding gene | zinc finger protein 438 |
18 | gene | 5.348258 | 5.361861 | negative | MGI_C57BL6J_6275385 | Gm50064 | ENSEMBL:ENSMUSG00000117520 | MGI:6275385 | lncRNA gene | predicted gene, 50064 |
18 | gene | 5.368541 | 5.370482 | negative | MGI_C57BL6J_6275387 | Gm50065 | ENSEMBL:ENSMUSG00000117617 | MGI:6275387 | lncRNA gene | predicted gene, 50065 |
18 | gene | 5.389802 | 5.402870 | negative | MGI_C57BL6J_5593789 | Gm34630 | NCBI_Gene:102637944 | MGI:5593789 | lncRNA gene | predicted gene, 34630 |
18 | gene | 5.491501 | 5.593505 | negative | MGI_C57BL6J_3642044 | Gm10125 | NCBI_Gene:791318,ENSEMBL:ENSMUSG00000063087 | MGI:3642044 | lncRNA gene | predicted gene 10125 |
18 | gene | 5.572824 | 5.575055 | negative | MGI_C57BL6J_5477069 | Gm26575 | ENSEMBL:ENSMUSG00000097926 | MGI:5477069 | lncRNA gene | predicted gene, 26575 |
18 | gene | 5.588043 | 5.591711 | negative | MGI_C57BL6J_3780408 | Gm2238 | ENSEMBL:ENSMUSG00000103138 | MGI:3780408 | unclassified gene | predicted gene 2238 |
18 | gene | 5.591860 | 5.775468 | positive | MGI_C57BL6J_1344313 | Zeb1 | NCBI_Gene:21417,ENSEMBL:ENSMUSG00000024238 | MGI:1344313 | protein coding gene | zinc finger E-box binding homeobox 1 |
18 | pseudogene | 5.611558 | 5.611930 | negative | MGI_C57BL6J_3801802 | Gm16147 | ENSEMBL:ENSMUSG00000090078 | MGI:3801802 | pseudogene | predicted gene 16147 |
18 | gene | 5.614473 | 5.614583 | positive | MGI_C57BL6J_5452486 | Gm22709 | ENSEMBL:ENSMUSG00000080543 | MGI:5452486 | miRNA gene | predicted gene, 22709 |
18 | gene | 5.885791 | 5.889876 | negative | MGI_C57BL6J_5593899 | Gm34740 | NCBI_Gene:102638095 | MGI:5593899 | lncRNA gene | predicted gene, 34740 |
18 | gene | 5.945183 | 5.966908 | positive | MGI_C57BL6J_5593963 | Gm34804 | NCBI_Gene:102638178,ENSEMBL:ENSMUSG00000117508 | MGI:5593963 | lncRNA gene | predicted gene, 34804 |
18 | gene | 6.004578 | 6.015278 | positive | MGI_C57BL6J_5624548 | Gm41663 | NCBI_Gene:105246364 | MGI:5624548 | lncRNA gene | predicted gene, 41663 |
18 | gene | 6.014253 | 6.024013 | negative | MGI_C57BL6J_5624547 | Gm41662 | NCBI_Gene:105246363,ENSEMBL:ENSMUSG00000117548 | MGI:5624547 | lncRNA gene | predicted gene, 41662 |
18 | gene | 6.024427 | 6.136098 | negative | MGI_C57BL6J_1922665 | Arhgap12 | NCBI_Gene:75415,ENSEMBL:ENSMUSG00000041225 | MGI:1922665 | protein coding gene | Rho GTPase activating protein 12 |
18 | pseudogene | 6.029179 | 6.030456 | negative | MGI_C57BL6J_5011425 | Gm19240 | NCBI_Gene:100418482 | MGI:5011425 | pseudogene | predicted gene, 19240 |
18 | gene | 6.201002 | 6.242174 | negative | MGI_C57BL6J_1098268 | Kif5b | NCBI_Gene:16573,ENSEMBL:ENSMUSG00000006740 | MGI:1098268 | protein coding gene | kinesin family member 5B |
18 | gene | 6.213913 | 6.227324 | positive | MGI_C57BL6J_4937863 | Gm17036 | ENSEMBL:ENSMUSG00000091165 | MGI:4937863 | lncRNA gene | predicted gene 17036 |
18 | gene | 6.234853 | 6.326693 | positive | MGI_C57BL6J_5624549 | Gm41664 | NCBI_Gene:105246365,ENSEMBL:ENSMUSG00000117519 | MGI:5624549 | lncRNA gene | predicted gene, 41664 |
18 | pseudogene | 6.332591 | 6.333082 | positive | MGI_C57BL6J_3646174 | Rpl27-ps3 | NCBI_Gene:621100,ENSEMBL:ENSMUSG00000073640 | MGI:3646174 | pseudogene | ribosomal protein L27, pseudogene 3 |
18 | pseudogene | 6.368360 | 6.368797 | positive | MGI_C57BL6J_6275267 | Gm49986 | ENSEMBL:ENSMUSG00000117387 | MGI:6275267 | pseudogene | predicted gene, 49986 |
18 | pseudogene | 6.371352 | 6.371660 | negative | MGI_C57BL6J_3648803 | Gm6291 | NCBI_Gene:102637200,ENSEMBL:ENSMUSG00000095614 | MGI:3648803 | pseudogene | predicted gene 6291 |
18 | gene | 6.435951 | 6.516108 | negative | MGI_C57BL6J_1278322 | Epc1 | NCBI_Gene:13831,ENSEMBL:ENSMUSG00000024240 | MGI:1278322 | protein coding gene | enhancer of polycomb homolog 1 |
18 | gene | 6.490143 | 6.496962 | positive | MGI_C57BL6J_5579235 | Gm28529 | ENSEMBL:ENSMUSG00000101320 | MGI:5579235 | lncRNA gene | predicted gene 28529 |
18 | gene | 6.490564 | 6.490646 | negative | MGI_C57BL6J_3811424 | Mir1893 | miRBase:MI0008327,NCBI_Gene:100316773,ENSEMBL:ENSMUSG00000084514 | MGI:3811424 | miRNA gene | microRNA 1893 |
18 | pseudogene | 6.557130 | 6.557796 | positive | MGI_C57BL6J_3645335 | Gm7464 | NCBI_Gene:665047,ENSEMBL:ENSMUSG00000117425 | MGI:3645335 | pseudogene | predicted gene 7464 |
18 | gene | 6.603629 | 6.640435 | positive | MGI_C57BL6J_1918151 | 4921524L21Rik | NCBI_Gene:70901,ENSEMBL:ENSMUSG00000039540 | MGI:1918151 | protein coding gene | RIKEN cDNA 4921524L21 gene |
18 | pseudogene | 6.687843 | 6.691317 | negative | MGI_C57BL6J_6275405 | Gm50077 | ENSEMBL:ENSMUSG00000117620 | MGI:6275405 | pseudogene | predicted gene, 50077 |
18 | gene | 6.733905 | 6.794429 | positive | MGI_C57BL6J_102790 | Rab18 | NCBI_Gene:19330,ENSEMBL:ENSMUSG00000073639 | MGI:102790 | protein coding gene | RAB18, member RAS oncogene family |
18 | pseudogene | 6.760073 | 6.760959 | negative | MGI_C57BL6J_3648356 | Gm7466 | NCBI_Gene:665053,ENSEMBL:ENSMUSG00000117359 | MGI:3648356 | pseudogene | predicted gene 7466 |
18 | gene | 6.788768 | 6.792137 | positive | MGI_C57BL6J_6275412 | Gm50081 | ENSEMBL:ENSMUSG00000117461 | MGI:6275412 | lncRNA gene | predicted gene, 50081 |
18 | gene | 6.910459 | 6.936621 | negative | MGI_C57BL6J_3826781 | Gm2350 | ENSEMBL:ENSMUSG00000117652 | MGI:3826781 | lncRNA gene | predicted gene 2350 |
18 | gene | 6.934518 | 7.004780 | negative | MGI_C57BL6J_2687286 | Mkx | NCBI_Gene:210719,ENSEMBL:ENSMUSG00000061013 | MGI:2687286 | protein coding gene | mohawk homeobox |
18 | gene | 7.004963 | 7.066322 | positive | MGI_C57BL6J_5826270 | Gm46633 | NCBI_Gene:108168391,ENSEMBL:ENSMUSG00000117356 | MGI:5826270 | lncRNA gene | predicted gene, 46633 |
18 | pseudogene | 7.041939 | 7.044195 | negative | MGI_C57BL6J_3642728 | Gm10350 | NCBI_Gene:664778,ENSEMBL:ENSMUSG00000117381 | MGI:3642728 | pseudogene | predicted gene 10350 |
18 | gene | 7.088209 | 7.298304 | negative | MGI_C57BL6J_1922184 | Armc4 | NCBI_Gene:74934,ENSEMBL:ENSMUSG00000061802 | MGI:1922184 | protein coding gene | armadillo repeat containing 4 |
18 | pseudogene | 7.107181 | 7.108183 | negative | MGI_C57BL6J_5010766 | Gm18581 | NCBI_Gene:100417382,ENSEMBL:ENSMUSG00000117360 | MGI:5010766 | pseudogene | predicted gene, 18581 |
18 | gene | 7.150344 | 7.151777 | negative | MGI_C57BL6J_1915234 | 4930415O11Rik | ENSEMBL:ENSMUSG00000117383 | MGI:1915234 | unclassified gene | RIKEN cDNA 4930415O11 gene |
18 | gene | 7.332445 | 7.346299 | negative | MGI_C57BL6J_5594138 | Gm34979 | NCBI_Gene:102638402 | MGI:5594138 | lncRNA gene | predicted gene, 34979 |
18 | gene | 7.347959 | 7.626893 | negative | MGI_C57BL6J_1922989 | Mpp7 | NCBI_Gene:75739,ENSEMBL:ENSMUSG00000057440 | MGI:1922989 | protein coding gene | membrane protein, palmitoylated 7 (MAGUK p55 subfamily member 7) |
18 | pseudogene | 7.548932 | 7.549781 | positive | MGI_C57BL6J_5011133 | Gm18948 | NCBI_Gene:100418013,ENSEMBL:ENSMUSG00000117556 | MGI:5011133 | pseudogene | predicted gene, 18948 |
18 | pseudogene | 7.581482 | 7.582130 | positive | MGI_C57BL6J_3644282 | Fabp5l2 | NCBI_Gene:622384,ENSEMBL:ENSMUSG00000094334 | MGI:3644282 | pseudogene | fatty acid binding protein 5-like 2 |
18 | pseudogene | 7.684076 | 7.693411 | negative | MGI_C57BL6J_5010948 | Gm18763 | NCBI_Gene:100417683,ENSEMBL:ENSMUSG00000117542 | MGI:5010948 | pseudogene | predicted gene, 18763 |
18 | pseudogene | 7.798784 | 7.799900 | negative | MGI_C57BL6J_3780506 | Gm2336 | NCBI_Gene:100039606 | MGI:3780506 | pseudogene | predicted gene 2336 |
18 | gene | 7.835177 | 7.835283 | positive | MGI_C57BL6J_5454069 | Gm24292 | ENSEMBL:ENSMUSG00000064822 | MGI:5454069 | snRNA gene | predicted gene, 24292 |
18 | pseudogene | 7.844684 | 7.844935 | negative | MGI_C57BL6J_6275329 | Gm50027 | ENSEMBL:ENSMUSG00000117462 | MGI:6275329 | pseudogene | predicted gene, 50027 |
18 | gene | 7.868018 | 7.869113 | negative | MGI_C57BL6J_3641793 | Gm9993 | ENSEMBL:ENSMUSG00000117505 | MGI:3641793 | lncRNA gene | predicted gene 9993 |
18 | gene | 7.868832 | 7.929028 | positive | MGI_C57BL6J_2387357 | Wac | NCBI_Gene:225131,ENSEMBL:ENSMUSG00000024283 | MGI:2387357 | protein coding gene | WW domain containing adaptor with coiled-coil |
18 | gene | 7.894300 | 7.895792 | positive | MGI_C57BL6J_1924412 | A430102J17Rik | NA | NA | unclassified gene | RIKEN cDNA A430102J17 gene |
18 | pseudogene | 7.932728 | 7.933556 | positive | MGI_C57BL6J_3643245 | Gm7502 | NCBI_Gene:665121,ENSEMBL:ENSMUSG00000091019 | MGI:3643245 | pseudogene | predicted gene 7502 |
gm
Object of class cross2 (crosstype "bc")
Total individuals 138
No. genotyped individuals 138
No. phenotyped individuals 138
No. with both geno & pheno 138
No. phenotypes 1
No. covariates 7
No. phenotype covariates 0
No. chromosomes 20
Total markers 32690
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069 1396 1628 1692 1064 940
17 18 19 X
401 1095 1067 575
#detach("package:qtl2", unload=TRUE)
#library(qtl)
cross <- qtl::read.cross("csv", file = "data/ici.vs.pbs_gm_qtl_snpsqc_5.batches_mis_conditional_2-peaks-chr4-10.csv",alleles=c("A","B"))
--Read the following data:
138 individuals
32690 markers
5 phenotypes
--Cross type: bc
cross <- qtl::jittermap(cross)
summary(cross)
Backcross
No. individuals: 138
No. phenotypes: 5
Percent phenotyped: 100 100 100 98.6 98.6
No. chromosomes: 20
Autosomes: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
X chr: X
Total markers: 32690
No. markers: 2952 2874 2058 2071 1955 2053 1862 1700 2009 1229 2069
1396 1628 1692 1064 940 401 1095 1067 575
Percent genotyped: 99.5
Genotypes (%):
Autosomes: AA:54.3 AB:45.7
X chromosome: AA:52.7 AB:47.3
cross.probs <- qtl::calc.genoprob(cross)
print("method == hk")
[1] "method == hk"
add.covars = qtl::pull.pheno(cross.probs, c("UNC8250659","UNC18240977"))
scanone.hk <-qtl::scanone(cross.probs, pheno.col="ICI.vs.PBS" , model="binary", method="hk", addcovar = add.covars)
operm.hk <- qtl::scanone(cross.probs, method = "hk", pheno.col="ICI.vs.PBS", n.perm = 10, perm.Xsp = TRUE, model="binary", verbose=FALSE, addcovar = add.covars)
plot(operm.hk)
print(summary(operm.hk, alpha=c(0.01, 0.05, 0.1)))
Autosome LOD thresholds (10 permutations)
lod
1% 2.51
5% 2.51
10% 2.50
X chromosome LOD thresholds (183 permutations)
lod
1% 3.17
5% 2.82
10% 2.37
#plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "steelblue")
#qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.01, col = 'blue')
#qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.05, col = 'red')
#qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.1, col = 'purple')
ymx <- maxlod(out) # overall maximum LOD score
plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "slateblue", ylim=c(0, ymx+0.5))
title(main = paste0(colnames(out), " [positions in cM]"))
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.01, col = 'blue')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.05, col = 'red')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.1, col = 'purple')
ymx <- 11
plot(scanone.hk, bandcol = "grey90",lty=1, cex=1, col = "slateblue", ylim=c(0, ymx+0.5))
title(main = paste0(colnames(out), " [positions in cM]\n(using same scale as eoi vs ici for easier comparison)"))
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.01, col = 'blue')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.05, col = 'red')
qtl::add.threshold(scanone.hk, perms= operm.hk, alpha=0.1, col = 'purple')
print(as.data.frame(summary(scanone.hk, perms=operm.hk, pvalues=TRUE, format="allpeaks")))
chr pos lod pval
UNCHS000141 1 4.439109 1.4810382 1.0000000
UNC2981117 2 27.613431 0.8724495 1.0000000
JAX00521745 3 20.613360 0.7586221 1.0000000
JAX00556948 4 41.038754 0.7726880 1.0000000
UNC8675939 5 6.193174 2.0684019 0.4165181
UNC10923091 6 18.321306 0.6856206 1.0000000
UNCHS020066 7 34.113657 1.2936681 1.0000000
UNC15524531 8 59.462590 1.9315437 0.5185858
UNC17203597 9 68.527929 0.6928610 1.0000000
UNCHS028001 10 21.783362 1.0219015 1.0000000
UNC20465557 11 80.904972 0.7841081 1.0000000
UNC21787084 12 53.288332 0.7505664 1.0000000
JAX00351755 13 7.044200 0.9809396 1.0000000
UNC24056202 14 30.156730 1.8824837 0.6195366
UNC25275535 15 11.967146 0.8880487 1.0000000
UNCHS042071 16 13.176096 0.7730967 1.0000000
UNCrs47191360 17 18.433013 1.2636005 1.0000000
UNCHS045343 18 2.934001 3.3781249 0.0000000
UNC29920552 19 8.810092 1.9159935 0.5185858
UNCHS048314 X 8.974041 1.9849011 0.3472122
print("all peaks with a p-value less or equal to 0.05 (suggestive)")
[1] "all peaks with a p-value less or equal to 0.05 (suggestive)"
print(as.data.frame(summary(scanone.hk, perms=operm.hk, alpha=0.05, pvalues=TRUE, format="allpeaks")))
chr pos lod pval
UNCHS045343 18 2.934001 3.378125 0
#print("method == ehk")
#scanone.ehk <-qtl::scanone(cross.probs, pheno.col="ICI.vs.PBS" , model="binary", method="ehk")
#operm.ehk <- qtl::scanone(cross.probs, method = "ehk", pheno.col="ICI.vs.PBS", n.perm = 10, perm.Xsp = TRUE, model="binary", verbose=FALSE)
#plot(operm.ehk)
#print(summary(operm.ehk, alpha=c(0.01, 0.05, 0.1)))
#plot(scanone.ehk, bandcol = "grey90",lty=1, cex=1, col = "steelblue")
#qtl::add.threshold(scanone.ehk, perms= operm.ehk, alpha=0.01, col = 'blue')
#qtl::add.threshold(scanone.ehk, perms= operm.ehk, alpha=0.05, col = 'red')
#qtl::add.threshold(scanone.ehk, perms= operm.ehk, alpha=0.1, col = 'purple')
#print(as.data.frame(summary(scanone.ehk)))
#print(as.data.frame(summary(scanone.ehk, perms=operm.ehk, alpha=0.05, pvalues=TRUE, format="allpeaks")))
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] abind_1.4-5 qtl2_0.22 reshape2_1.4.3 ggplot2_3.3.6
[5] tibble_3.1.7 psych_2.2.5 readxl_1.4.0 cluster_2.0.7-1
[9] dplyr_1.0.9 optparse_1.6.6 rhdf5_2.26.2 mclust_5.4.5
[13] tidyr_1.2.0 data.table_1.14.2 knitr_1.29 kableExtra_1.3.4
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.3 bit64_0.9-7 viridisLite_0.3.0 assertthat_0.2.1
[5] highr_0.8 blob_1.2.1 cellranger_1.1.0 yaml_2.2.1
[9] gdtools_0.2.1 pillar_1.7.0 RSQLite_2.2.0 backports_1.1.5
[13] lattice_0.20-35 glue_1.6.2 digest_0.6.25 promises_1.1.0
[17] rvest_1.0.2 colorspace_1.4-1 htmltools_0.5.2 httpuv_1.5.2
[21] plyr_1.8.6 pkgconfig_2.0.3 purrr_0.3.4 scales_1.1.1
[25] webshot_0.5.3 svglite_1.2.3 qtl_1.46-2 getopt_1.20.3
[29] later_1.0.0 git2r_0.26.1 generics_0.0.2 ellipsis_0.3.2
[33] withr_2.5.0 cli_3.3.0 mnormt_1.5-6 magrittr_2.0.3
[37] crayon_1.5.1 memoise_1.1.0 evaluate_0.14 fs_1.3.2
[41] fansi_0.4.1 nlme_3.1-137 xml2_1.3.2 tools_3.5.1
[45] lifecycle_1.0.1 stringr_1.4.0 Rhdf5lib_1.4.3 munsell_0.5.0
[49] compiler_3.5.1 systemfonts_0.1.1 rlang_1.0.3 grid_3.5.1
[53] rstudioapi_0.13 rmarkdown_2.3 gtable_0.3.0 DBI_1.1.0
[57] R6_2.4.1 fastmap_1.1.0 bit_1.1-15.2 utf8_1.1.4
[61] rprojroot_1.3-2 stringi_1.4.6 parallel_3.5.1 Rcpp_1.0.4.6
[65] vctrs_0.4.1 tidyselect_1.1.2 xfun_0.15