QTL Analysis - binary [ICI-Early vs PBS]
Belinda Cornes
2022-04-15
Last updated: 2022-04-15
Checks: 5 2
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
Modified: analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_5.batches_mis.Rmd
Modified: analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_snpsqc_5.batches.Rmd
Modified: analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_snpsqc_5.batches_mis.Rmd
Modified: analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_snpsqc_dis_no-x_updated_5.batches.Rmd
Modified: analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_snpsqc_dis_no-x_updated_5.batches_mis.Rmd
Modified: analysis/4.1.1_qtl.analysis_binary_ici.vs.eoi_snpsqc_dis_no-x_updated.Rmd
Modified: analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
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Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
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Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
Modified: analysis/index_5.batches.Rmd
Modified: analysis/index_5.batches_additional.Rmd
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_5.batches_mis.Rmd) and HTML (docs/4.1.1_qtl.analysis_binary_ici-early.vs.pbs_5.batches_mis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a8c2d1a | Belinda Cornes | 2022-04-13 | additonal analysis - peaks and continuous variables |
| html | a8c2d1a | Belinda Cornes | 2022-04-13 | additonal analysis - peaks and continuous variables |
Loading Data
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-Early == 1).
load("data/gm_allqc_5.batches_mis.RData")
#gm_allqc
gm=gm_allqc
gmObject 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 #removing ici-late
table(gm$covar$group)
(A2 Parental) (DQ8 Parental) B6.g7 Parental EOI F1
2 2 1 164 1
ICI PBS
104 34 ici.late_ids <- gm$covar[gm$covar$"diabetic status" == "T1D>17",]$id
length(ici.late_ids)[1] 17ici.late_ids [1] "NG00198-ICI-LATE" "NG00329-ICI-LATE" "NG00343-ICI-LATE" "NG00386-ICI-LATE"
[5] "NG00442-ICI-LATE" "NG00443-ICI-LATE" "NG00526-ICI-LATE" "NG00522-ICI-LATE"
[9] "NG00790-ICI-LATE" "NG00874-ICI-LATE" "NG00929-ICI-LATE" "NG01225-ICI-LATE"
[13] "NG01303-ICI-LATE" "NG01020-ICI-LATE" "NG01397-ICI-LATE" "NG01389-ICI-LATE"
[17] "NG01548-ICI-LATE"gm <- gm[paste0("-",as.character(ici.late_ids)),]
##extracting animals with ici-early and pbs group status
miceinfo <- gm$covar[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
table(miceinfo$group)
ICI PBS
87 34 mice.ids <- rownames(miceinfo)
gm <- gm[mice.ids]
gmObject of class cross2 (crosstype "bc")
Total individuals 121
No. genotyped individuals 121
No. phenotyped individuals 121
No. with both geno & pheno 121
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-Early <- ifelse(gm$covar$group == "ICI-Early", 1, 0)
#bin_pheno <- as.data.frame(bin_pheno)
#rownames(bin_pheno) <- rownames(gm$covar)
gm$covar$ICI_Early.vs.PBS <- ifelse(gm$covar$group == "PBS", 0, 1)
gm.full <- gm
##removing problmetic marker
gm <- drop_markers(gm, "UNCHS013106")
markers <- marker_names(gm)
gmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/genetic_map.csv")
pmapdf <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/physical_map.csv")
#mapdf <- merge(gmapdf,pmapdf, by=c("marker","chr"), all=T)
#rownames(mapdf) <- mapdf$marker
#mapdf <- mapdf[markers,]
#names(mapdf) <- c('marker','chr','gmapdf','pmapdf')
#mapdfnd <- mapdf[!duplicated(mapdf[c(2:3)]),]
pr.qc <- calc_genoprob(gm)
gmObject of class cross2 (crosstype "bc")
Total individuals 121
No. genotyped individuals 121
No. phenotyped individuals 121
No. with both geno & pheno 121
No. phenotypes 1
No. covariates 7
No. phenotype covariates 0
No. chromosomes 20
Total markers 131355
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7585 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770 Genome-wide scan
For each of the phenotype analyzed, permutations were used for each model to obtain genome-wide LOD significance threshold for p < 0.01, p < 0.05, p < 0.10, respectively, separately for X and automsomes (A).
The table shows the estimated significance thresholds from permutation test.
We also looked at the kinship to see how correlated each sample is. Kinship values between pairs of samples range between 0 (no relationship) and 1.0 (completely identical). The darker the colour the more indentical the pairs are.
Xcovar <- get_x_covar(gm)
#addcovar = model.matrix(~Sex, 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$covar["ICI_Early.vs.PBS"], model="binary", n_perm=10, perm_Xsp=TRUE, chr_lengths=chr_lengths(gm$gmap))
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
names(summary_table) <- c("autosomes","X")
summary_table$significance.level <- rownames(summary_table)
rownames(summary_table) <- NULL
summary_table[c(3,1:2)] %>%
kable(escape = F,align = c("ccc")) %>%
kable_styling("striped", full_width = T) %>%
column_spec(1, bold=TRUE)| significance.level | autosomes | X |
|---|---|---|
| 0.01 | 3.496879 | 3.769675 |
| 0.05 | 3.388343 | 3.649370 |
| 0.1 | 3.252337 | 3.492049 |
The figures below show QTL maps for each phenotype
out <- scan1(pr.qc, gm$covar["ICI_Early.vs.PBS"], Xcovar=Xcovar, model="binary")
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 <- 14 # 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$gmap)
LOD peaks
The table below shows QTL peaks associated with the phenotype. We use the 95% threshold from the permutations to find peaks.
Centimorgan (cM)
peaks <- find_peaks(out, gm$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, escape = F, align = c("cccccccc"), "html")
%>% 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 <- 14
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]"))
}[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 3.38834334440836 [autosomes]/3.64937035323678 [x-chromosome]”
Megabase (MB)
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, escape = F, align = c("cccccccc"), "html")
%>% 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 <- 14
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]"))
}[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 3.38834334440836 [autosomes]/3.64937035323678 [x-chromosome]”
QTL effects
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
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]])
blup_sub <- scan1blup(pr_sub[,chr], gm$covar[peaks$phenotype[i]])
write.csv(as.data.frame(blup_sub), paste0("data/ici-early.vs.pbs_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_5.batches_mis.csv"), quote=F)
#plot_coef(coeff,
# gm$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-early.vs.pbs_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_5.batches_mis.csv"), quote=F)
rownames(genesgss) <- NULL
genesgss$strand_old = genesgss$strand
genesgss$strand[genesgss$strand=="+"] <- "positive"
genesgss$strand[genesgss$strand=="-"] <- "negative"
#genesgss <-
#table <-
#genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")] %>%
#kable(escape = F,align = c("ccccccccccc")) %>%
#kable_styling("striped", full_width = T) #%>%
#cat #%>%
#column_spec(1, bold=TRUE)
#
#print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], escape = F,align = c("ccccccccccc")))
print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))
#table
}
} else {
print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05)$A, " [autosomes]/",summary(operm,alpha=0.05)$X, " [x-chromosome]"))
}[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 3.38834334440836 [autosomes]/3.64937035323678 [x-chromosome]”
R/qtl
scanone
gmObject of class cross2 (crosstype "bc")
Total individuals 121
No. genotyped individuals 121
No. phenotyped individuals 121
No. with both geno & pheno 121
No. phenotypes 1
No. covariates 7
No. phenotype covariates 0
No. chromosomes 20
Total markers 131355
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7585 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770 #detach("package:qtl2", unload=TRUE)
#library(qtl)
cross <- qtl::read.cross("csv", file = "data/ici-early.vs.pbs_gm_qtl_5.batches_mis.csv",alleles=c("A","B")) --Read the following data:
121 individuals
131355 markers
3 phenotypes
--Cross type: bc cross <- qtl::jittermap(cross)
summary(cross) Backcross
No. individuals: 121
No. phenotypes: 3
Percent phenotyped: 100 100 100
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: 131355
No. markers: 9956 9987 7848 7585 7609 7736 7399 6458 6713 6385 7143
6110 6082 5966 5346 5015 5080 4605 3562 4770
Percent genotyped: 99.5
Genotypes (%):
Autosomes: AA:88.1 AB:11.9
X chromosome: AA:93.9 AB:6.1 cross.probs <- qtl::calc.genoprob(cross)
print("method == hk")[1] "method == hk"scanone.hk <-qtl::scanone(cross.probs, pheno.col="ICI_Early.vs.PBS" , model="binary", method="hk")
operm.hk <- qtl::scanone(cross.probs, method = "hk", pheno.col="ICI_Early.vs.PBS", n.perm = 10, perm.Xsp = TRUE, model="binary", verbose=FALSE)
plot(operm.hk)
print(summary(operm.hk, alpha=c(0.01, 0.05, 0.1)))Autosome LOD thresholds (10 permutations)
lod
1% 3.55
5% 3.30
10% 2.99
X chromosome LOD thresholds (182 permutations)
lod
1% 3.98
5% 3.77
10% 3.49#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 <- 14
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 ici vs. eoi 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
UNCHS001755 1 47.671576 1.699092 1.0000000
UNCHS005266 2 40.908610 1.819169 0.9118988
UNC6045859 3 50.620447 1.696850 1.0000000
UNC8441460 4 80.550063 1.697048 1.0000000
JAX00574407 5 6.213413 3.247353 0.1052020
UNCHS017906 6 37.001766 1.692501 1.0000000
JAX00233659 7 87.998157 1.696685 1.0000000
JAX00667121 8 24.443849 2.827988 0.4166281
UNC17203329 9 68.519175 1.403886 1.0000000
JAX00302150 10 74.547218 2.296889 0.6196652
UNC20281687 11 67.380801 1.671169 1.0000000
UNCHS032933 12 8.461913 1.824421 0.9118988
UNC23451375 13 64.806834 1.124814 1.0000000
JAX00380911 14 30.154517 2.827013 0.4166281
UNC25275535 15 11.968141 1.494207 1.0000000
UNC26790882 16 30.832641 1.806118 0.9118988
UNCHS045288 17 60.699043 1.876482 0.8169482
JAX00450853 18 2.913076 3.162299 0.1052020
UNCHS047674 19 32.059844 1.908255 0.8169482
XiB2 X 45.699951 1.696699 0.9999549print("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_Early.vs.PBS" , model="binary", method="ehk")
#operm.ehk <- qtl::scanone(cross.probs, method = "ehk", pheno.col="ICI_Early.vs.PBS", n.perm = 1000, 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 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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_1.4.4 ggplot2_3.3.5
[5] tibble_3.1.2 psych_2.0.7 readxl_1.3.1 cluster_2.1.0
[9] dplyr_1.0.8 optparse_1.6.6 rhdf5_2.28.1 mclust_5.4.6
[13] tidyr_1.0.2 data.table_1.14.0 knitr_1.33 kableExtra_1.1.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.1 bit64_4.0.5 viridisLite_0.4.0 assertthat_0.2.1
[5] highr_0.9 blob_1.2.1 cellranger_1.1.0 yaml_2.2.1
[9] pillar_1.6.1 RSQLite_2.2.7 backports_1.2.1 lattice_0.20-38
[13] glue_1.4.2 digest_0.6.27 promises_1.1.0 rvest_0.3.5
[17] colorspace_2.0-2 htmltools_0.5.1.1 httpuv_1.5.2 plyr_1.8.6
[21] pkgconfig_2.0.3 purrr_0.3.4 scales_1.1.1 webshot_0.5.2
[25] qtl_1.46-2 whisker_0.4 getopt_1.20.3 later_1.0.0
[29] git2r_0.26.1 generics_0.0.2 ellipsis_0.3.2 cachem_1.0.5
[33] withr_2.4.2 cli_3.0.0 mnormt_1.5-7 magrittr_2.0.1
[37] crayon_1.4.1 memoise_2.0.0 evaluate_0.14 fs_1.4.1
[41] fansi_0.5.0 nlme_3.1-142 xml2_1.3.1 tools_3.6.2
[45] hms_0.5.3 lifecycle_1.0.1 stringr_1.4.0 Rhdf5lib_1.6.3
[49] munsell_0.5.0 compiler_3.6.2 rlang_1.0.2 grid_3.6.2
[53] rstudioapi_0.13 rmarkdown_2.1 gtable_0.3.0 DBI_1.1.1
[57] R6_2.5.0 fastmap_1.1.0 bit_4.0.4 utf8_1.2.1
[61] rprojroot_1.3-2 readr_1.3.1 stringi_1.7.2 parallel_3.6.2
[65] Rcpp_1.0.7 vctrs_0.3.8 tidyselect_1.1.2 xfun_0.24