QTL Analysis - binary [ICI-Early vs PBS & ICI-Late ]
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
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
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
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
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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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 & ICI-Late ==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 table(gm$covar$"diabetic status")
Both Projects Diabetes Proj ICI-No Diabetes Myocarditis Spont. T1D
1 3 164 2 34
T1D <17 T1D<17 T1D>17
36 51 17 table(gm$covar$group)
(A2 Parental) (DQ8 Parental) B6.g7 Parental EOI F1
2 2 1 164 1
ICI PBS
104 34 #ICI-Early vs (PBS-T1D + ICI > 17 weeks of age)
##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
104 34 mice.ids <- rownames(miceinfo)
gm <- gm[mice.ids]
gmObject 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 table(gm$covar$"diabetic status")
Spont. T1D T1D <17 T1D<17 T1D>17
34 36 51 17 table(gm$covar$group)
ICI PBS
104 34 #Creating a new lable for pbs+ici late and ici-early
table(gm$covar$group)
ICI PBS
104 34 gm$covar$group.new <- ifelse(gm$covar$"diabetic status" == "T1D>17" | gm$covar$group == "PBS", "PBS_ICI.Late", "ICI.Early")
#binary Yes/No T1D
gm$covar$ICI.Early.vs.PBS_ICI.Late <- ifelse(gm$covar$group.new == "PBS_ICI.Late", 0, 1)
table(gm$covar$"diabetic status")
Spont. T1D T1D <17 T1D<17 T1D>17
34 36 51 17 table(gm$covar$group)
ICI PBS
104 34 table(gm$covar$group.new)
ICI.Early PBS_ICI.Late
87 51 table(gm$covar$ICI.Early.vs.PBS_ICI.Late)
0 1
51 87 gm.full <- gm
##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] 98665low_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
gmObject 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 8
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$marker
#mapdf <- mapdf[markers,]
#names(mapdf) <- c('marker','chr','gmapdf','pmapdf')
#mapdfnd <- mapdf[!duplicated(mapdf[c(2:3)]),]
pr.qc <- calc_genoprob(gm)Genome-wide scan
For each of the phenotype analyzed, permutations were used for each model to obtain genome-wide LOD significance threshold for p < 0.01, p < 0.05, p < 0.10, respectively, separately for X and automsomes (A).
The table shows the estimated significance thresholds from permutation test.
We also looked at the kinship to see how correlated each sample is. Kinship values between pairs of samples range between 0 (no relationship) and 1.0 (completely identical). The darker the colour the more indentical the pairs are.
#gm
Xcovar <- get_x_covar(gm)
#addcovar = model.matrix(~Sex, data = covars)[,-1]
#K <- calc_kinship(pr.qc, type = "loco")
#heatmap(K[[1]])
#K.overall <- calc_kinship(pr.qc, type = "overall")
#heatmap(K.overall)
kinship <- calc_kinship(pr.qc)
heatmap(kinship)
#operm <- scan1perm(pr.qc, gm$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_ICI.Late"], 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 | 2.986273 | 3.360565 |
| 0.05 | 2.945796 | 3.030636 |
| 0.1 | 2.895076 | 2.599185 |
The figures below show QTL maps for each phenotype
out <- scan1(pr.qc, gm$covar["ICI.Early.vs.PBS_ICI.Late"], Xcovar=Xcovar, model="binary")
summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01, 0.05, 0.1))))
plot_lod<-function(out,map){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- 11 # overall maximum LOD score
plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " [positions in cM] \n(using same scale as eoi vs ici for easier comparison)"))
add_threshold(map, summary(operm, alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
}
}
plot_lod(out,gm$gmap)
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$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]
rownames(peaks) <- NULL
#print(kable(peaks, "html")
# %>% kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
# )
print(kable(peaks, escape = F, align = c("cccccccc"), "html")
%>% kable_styling("striped", full_width = T)%>%
column_spec(1, bold=TRUE)
)
#peaks[] %>%
# kable(escape = F,align = c("cccccccc")) %>%
# kable_styling("striped", full_width = T) %>%
# column_spec(1, bold=TRUE)
#plot only peak chromosomes
plot_lod_chr<-function(out,map,chrom){
for (i in 1:dim(out)[2]){
#png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i], "_lod.png"))
#par(mar=c(5.1, 6.1, 1.1, 1.1))
ymx <- maxlod(out) # overall maximum LOD score
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
#for (j in 1: dim(summary_table)[1]){
# abline(h=summary_table[j, i],col="red")
# text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
#}
#dev.off()
ymx <- 11
plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
#legend("topright", lwd=2, colnames(out)[i], bg="gray90")
title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]\n(using same scale as eoi vs. ici for easier comparison)"))
add_threshold(map, summary(operm,alpha=0.1), col = 'purple')
add_threshold(map, summary(operm, alpha=0.05), col = 'red')
add_threshold(map, summary(operm, alpha=0.01), col = 'blue')
}
}
for(i in unique(peaks$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)$X, " [x-chromosome]"))
}| phenotype | chr | pos | lod | ci_lo | ci_hi | marker |
|---|---|---|---|---|---|---|
| ICI.Early.vs.PBS_ICI.Late | 18 | 2.934 | 3.032745 | 1.972 | 3.857 | UNCHS045343 |
Megabase (MB)
print("peaks in MB positions")[1] “peaks in MB positions”
peaks_mba <- find_peaks(out, gm$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, "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)$X, " [x-chromosome]"))
}| phenotype | chr | pos | lod | ci_lo | ci_hi | marker |
|---|---|---|---|---|---|---|
| ICI.Early.vs.PBS_ICI.Late | 18 | 4.861199 | 3.032745 | 3.01212 | 5.584328 | UNCHS045343 |
QTL effects
For each peak LOD location we give a list of gene
query_variants <- create_variant_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/cc_variants.sqlite")
query_genes <- create_gene_query_func("/Users/corneb/Documents/MyJax/CS/Projects/support.files/qtl2/mouse_genes_mgi.sqlite")
if(nrow(peaks) >0){
for (i in 1:nrow(peaks)){
#for (i in 1:1){
#Plot 1
#marker = find_marker(gm$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$phenotype[i]])
blup_sub <- scan1blup(pr_sub[,chr], gm$covar[peaks$phenotype[i]])
write.csv(as.data.frame(blup_sub), paste0("data/ici-early.vs.pbs.ici-late_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_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$ci_hi[i])
genesgss = query_genes(chr, start, end)
write.csv(genesgss, file=paste0("data/ici-early.vs.pbs.ici-late_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_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))
}
} 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 |
R/qtl
scanone
gmObject 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 8
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-early.vs.pbs.ici-late_gm_qtl_snpsqc_5.batches_mis.csv",alleles=c("A","B")) --Read the following data:
138 individuals
32690 markers
3 phenotypes
--Cross type: bc cross <- qtl::jittermap(cross)
summary(cross) Backcross
No. individuals: 138
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: 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"scanone.hk <-qtl::scanone(cross.probs, pheno.col="ICI.Early.vs.PBS_ICI.Late" , model="binary", method="hk")
operm.hk <- qtl::scanone(cross.probs, method = "hk", pheno.col="ICI.Early.vs.PBS_ICI.Late", 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% 2.73
5% 2.66
10% 2.58
X chromosome LOD thresholds (183 permutations)
lod
1% 3.28
5% 3.11
10% 2.89#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
JAX00266954 1 56.986863 0.5405355 1.0000000
UNC3037495 2 29.574490 1.7768438 0.6195366
UNC6243788 3 60.421545 0.7632080 1.0000000
UNCHS011877 4 40.235752 1.1136643 1.0000000
UNC8675939 5 6.193174 1.3273453 1.0000000
UNC10755464 6 11.704209 1.5934115 0.8168395
UNCHS020066 7 34.113657 1.2971960 1.0000000
UNC15524531 8 59.462590 0.9546319 1.0000000
UNC17203597 9 68.527929 1.2619483 1.0000000
UNCHS029502 10 69.918145 1.1417450 1.0000000
UNCHS030712 11 32.135951 1.2272963 1.0000000
UNCHS034209 12 39.433105 0.4136511 1.0000000
JAX00358619 13 28.616612 0.2183678 1.0000000
UNC24056202 14 30.156730 1.6290750 0.8168395
UNC25635404 15 22.240175 0.7597238 1.0000000
UNCHS041628 16 3.550089 0.2694622 1.0000000
UNCrs47191360 17 18.433013 0.8259964 1.0000000
UNCHS045343 18 2.934001 3.0327459 0.0000000
UNC29834997 19 4.086020 1.3894202 0.9118239
UNCHS048314 X 8.974041 1.5255441 0.9412683print("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 3.032746 0#print("method == ehk")
#scanone.ehk <-qtl::scanone(cross.probs, pheno.col="ICI.Early.vs.PBS_ICI.Late" , model="binary", method="ehk")
#operm.ehk <- qtl::scanone(cross.probs, method = "ehk", pheno.col="ICI.Early.vs.PBS_ICI.Late", 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 getopt_1.20.3 later_1.0.0 git2r_0.26.1
[29] generics_0.0.2 ellipsis_0.3.2 cachem_1.0.5 withr_2.4.2
[33] cli_3.0.0 mnormt_1.5-7 magrittr_2.0.1 crayon_1.4.1
[37] memoise_2.0.0 evaluate_0.14 fs_1.4.1 fansi_0.5.0
[41] nlme_3.1-142 xml2_1.3.1 tools_3.6.2 hms_0.5.3
[45] lifecycle_1.0.1 stringr_1.4.0 Rhdf5lib_1.6.3 munsell_0.5.0
[49] compiler_3.6.2 rlang_1.0.2 grid_3.6.2 rstudioapi_0.13
[53] rmarkdown_2.1 gtable_0.3.0 DBI_1.1.1 R6_2.5.0
[57] fastmap_1.1.0 bit_4.0.4 utf8_1.2.1 rprojroot_1.3-2
[61] readr_1.3.1 stringi_1.7.2 parallel_3.6.2 Rcpp_1.0.7
[65] vctrs_0.3.8 tidyselect_1.1.2 xfun_0.24