Load packages
library(qtl)
library(ASMap)
library(dplyr)
library(corrplot)## Warning: package 'corrplot' was built under R version 4.2.3
library(LinkageMapView)Import genotypic data. for now, all markers are on 1 chr
data<-read.cross("csvr",".","raw_data.csv",genotypes=c("a","h","b"),map.function="kosambi")## --Read the following data:
## 152 individuals
## 791 markers
## 14 phenotypes
## Warning in summary.cross(cross): Some chromosomes > 1000 cM in length; there may be a problem with the genetic map.
## (Perhaps it is in basepairs?)
## --Cross type: f2
Convert cross to bcsft type f2 to use ASMap package
data<-convert2bcsft(data,F.gen=2,estimate.map=F)
cat("Phenos:")## Phenos:
cbind(head(phenames(data),-1))## [,1]
## [1,] "AFF"
## [2,] "AFS"
## [3,] "AFL"
## [4,] "AG"
## [5,] "AYS"
## [6,] "AYL"
## [7,] "FOB1"
## [8,] "FOB2"
## [9,] "FOB3"
## [10,] "F_AUDPC"
## [11,] "Cold"
## [12,] "FOB3_bin"
## [13,] "BDM"
n<-11 #normal phenotypes countLook at the pattern of missing data. Black pixels indicate missing genotypes.
plotMissing(data)
Omit the individuals with more than 85% of total markers -> less than 672 markers. Briefly estimate map first.
data<-quickEst(data,map.function="kosambi")
sg<-statGen(data,bychr=F,stat.type="miss",id='index')
data1<-subset(data,ind=om<-sg$miss<(totmar(data))*0.85)
cat(if(F%in%om) which(!om)else "no","ind omitted for missing > 85% mar")## no ind omitted for missing > 85% mar
Plot the number of genotyped markers per individual as well as the number of genotyped individuals per marker
par(mfrow=c(1,2), las=1)
plot(ntyped(data1), ylim=c(0,totmar(data1)+50),ylab="No. typed markers",main="Markers by Individual")
mtext("A",adj=0)
plot(ntyped(data1, "mar"), ylim=c(0,(nind(data1)+15)),ylab="No. typed individuals",main="Individuals by Marker")
mtext("B",adj=0)
Plot the genotype frequencies per individual
g <- pull.geno(data1)
gfreq <- apply(g, 1, function(a) table(factor(a, levels=1:3)))
gfreq <- t(t(gfreq) / colSums(gfreq))
par(mfrow=c(1,3), las=1)
for(i in 1:3){
plot(gfreq[i,], ylab="Genotype frequency",ylim=c(0,1))
abline(h=mean(gfreq[i,]),lty=3,col="red",lwd=3)
mtext(c("AA", "AB", "BB")[i])}
par(mfrow=c(1,1));title("Genotypes' Frequency and Segregation Ratio",line = 2.5)
Compare the genotypes for all pairs of individuals
cg<-comparegeno(data1);cgr<-cg[lower.tri((cg))]
hist(cgr, breaks=seq(0, 1, len=101),xlab="No. matching genotypes", main="Matching Pairs of Individuals")
rug(cgr)
#mark the outlier with a red arrow
x<-max(cgr)
arrow.plot(x,50,0,-1,true.angle = T,arrow.ex=30, length=.1,col='red', lwd=2)
text(x,70,paste0(round(100*x,1),"%"),adj=c(0.5,0.2))
Omit individuals with more than 90% identical markers
wh<-which(cg>0.9,arr=T)
data2<-subset(data1,ind=-wh[,2])
cat(nind(data1)-nind(data2),"ind omitted for",paste0(round(100*x,1),"%"),"identical geno\n",paste0("#", data1$pheno$index[wh[,2]]))## 2 ind omitted for 96.9% identical geno
## #132 #134
Pull out markers from cross temporarily
cat(totmar(data2),'total mar\n')## 791 total mar
data3<-pullCross(data2,type="missing",pars=list(miss.thresh=0.1))
cat(totmar(data3),'total mar\n')## 760 total mar
cat(totmar(data2)-totmar(data3),"mar pulled for missing\n")## 31 mar pulled for missing
data4<-pullCross(data3,type="seg.distortion",pars=list(seg.thresh=0.001))
cat(totmar(data4),'total mar\n')## 726 total mar
cat(totmar(data3)-totmar(data4),"mar pulled for seg. distortion\n")## 34 mar pulled for seg. distortion
data5<-pullCross(data4,type="co.located")
cat(totmar(data5),'total mar\n')## 567 total mar
cat(totmar(data4)-totmar(data5),"mar pulled for co. located")## 159 mar pulled for co. located
Plot optional p. values to determine distance threshold for marker clustering. I chose pValue= 1e-7 (on y axis), meaning->> split linkage groups with more than 30cM gap between markers, according to 150 ind population (on x axis)
cat(nind(data5),"individuals are used to cluster markers")## 150 individuals are used to cluster markers
pValue(dist=seq(25,40,by=5),pop.size=110:190)
LOD(Logarithm of the Odds)= statistical measure of the likelihood that two loci (positions on a chromosome) are linked and therefore inherited together, rather than assorting independently.
Form linkage groups with LOD=7, raw map
data5<-mstmap(data5,bychr=F,p.value=1e-7,id='index')## Number of linkage groups: 28
## The size of the linkage groups are: 39 32 47 33 27 12 19 10 38 18 28 19 27 8 11 40 11 13 24 17 16 20 17 1 21 1 16 2
## The number of bins in each linkage group: 34 23 35 26 20 9 13 8 30 14 25 15 20 8 10 35 11 11 18 14 14 13 17 1 20 1 12 2
plotMap(data5,alternate.chrid = T)
Profile individuals’ genotype statistics
Omit missing/ double crossover (dxo)/ xo statistics outlier individuals
data6<-subsetCross(data5,ind=!pg$xo.lambda)
cat(nind(data5)-nind(data6),"ind omitted by profileGen")## 1 ind omitted by profileGen
Double check dxo. An unusually high rate of double crossovers might indicate genotyping errors
pg1<-profileGen(data6,bychr=F,stat.type=c("xo","dxo","miss"),id="index",xo.lambda=median(pg1$stat$xo),layout=c(1,3),lty=2,cex=0.7)
Re-construct map. The genotyping errors can distort the distances between markers, the order of the inputted markers is respected
data7<-mstmap(data6,bychr=F,anchor=T,p.value=1e-7,id='index')## Number of linkage groups: 28
## The size of the linkage groups are: 39 18 28 19 27 8 11 40 11 13 24 32 17 16 20 17 1 21 1 16 2 47 33 27 12 19 10 38
## The number of bins in each linkage group: 33 14 25 15 20 8 10 35 11 11 17 22 14 13 13 16 1 20 1 12 2 35 26 19 9 13 8 30
Push back markers to the map
cat(totmar(data7),'total mar ')## 567 total mar
data8<-pushCross(data7,type="co.located")
cat(totmar(data8),'total mar ')## 726 total mar
cat(totmar(data8)-totmar(data7),"mar pushed for co. located")## 159 mar pushed for co. located
Re-construct final map by adding markers to existing LGs (linkage groups)
Drop LGs with less than 2 markers
mndrop<-markernames(data9,nmar(data9)<2)
data10<-drop.markers(data9,as.character(mndrop))
cat(totmar(data10),'total mar')## 724 total mar
cat(totmar(data9)-totmar(data10),"mar omitted for LG < 2 mar")## 2 mar omitted for LG < 2 mar
Rename chr by numerical order
x<-1:nchr(data10)
for (i in x) {names(data10$geno)[i]<-paste0("LG",i)}Plot genetic map illustration, final map
plot.map(data10,alternate.chrid=T)
LGs summary table
summaryMap(data10)## n.mar length ave.spacing max.spacing
## LG1 66 58.5 0.9 12.8
## LG2 13 34.4 2.9 14.6
## LG3 24 22.3 1.0 4.7
## LG4 33 64.0 2.0 11.5
## LG5 22 29.9 1.4 6.3
## LG6 23 36.1 1.6 15.2
## LG7 20 33.8 1.8 21.9
## LG8 26 48.9 2.0 11.2
## LG9 29 52.6 1.9 17.0
## LG10 18 43.9 2.6 8.9
## LG11 16 56.6 3.8 25.6
## LG12 2 1.9 1.9 1.9
## LG13 67 56.7 0.9 9.9
## LG14 34 58.7 1.8 10.8
## LG15 47 20.8 0.5 4.5
## LG16 12 41.1 3.7 18.5
## LG17 19 46.6 2.6 10.4
## LG18 10 15.7 1.7 8.5
## LG19 53 114.3 2.2 23.2
## LG20 44 60.7 1.4 14.4
## LG21 19 40.9 2.3 19.6
## LG22 27 77.1 3.0 14.7
## LG23 8 14.9 2.1 4.1
## LG24 11 49.3 4.9 26.9
## LG25 70 47.8 0.7 6.5
## LG26 11 86.3 8.6 28.8
## overall 724 1213.7 1.7 28.8
Estimate recombination fraction
data10<-est.rf(data10)Heatmap of LOD and Rf
heatMap(data10,lmax = 70,main='')
mtext("Pairwise Recombination Fractions and LOD Scores",cex=1.1,line=3.2,adj=0.4,font=2)
Plot Pairwise LOD vs. Rf
rf<-pull.rf(data10);lod<-pull.rf(data10,what="lod")
plot(as.numeric(rf),as.numeric(lod),xlab="Recombination fraction",ylab="LOD score",main=paste("Pairwise LOD vs. Rf for",totmar(data10),"Markers"))
The evaluation looks good:
The heatmap is continuous with low gradient along the chr meaning that the markers within a chromosome are gradually distant from each other.
On the LOD/rf scatter plot there is a trend line with no outliers.
Jitter map- to avoid marker overlaping by slightly adding gaps between them
data<-jittermap(data10)Create a df of parents phenotype
cold_values <- c(5, 5, 4, 6, 7, 9, 5, 6, 6, 7, NA, NA, 7, 8, 7, 8, 8, 8, 9, 9, 9, 8, NA, NA)
faudpc_values <- c(rep(0, 12), rep(c(90, 142), each = 6))
parents <- data.frame(rbind(matrix(0,12,9), matrix(5,12,9)),faudpc_values, Cold = cold_values,row.names = c(paste0("P1_", 1:12), paste0("DP_", 1:12)))
ant_values <-c(5, 4.5, 4.5, 4.5, 4.5, 3.5, 4, 4.5, 4, 4.5, 4, 4)
parents[13:24,1:3] <-matrix(ant_values,12,3)
colnames(parents) <-phenames(data)[1:n]Check the phenotypic distribution
Plot histograms or barplots
cbind(phenames(data)) ## [,1]
## [1,] "AFF"
## [2,] "AFS"
## [3,] "AFL"
## [4,] "AG"
## [5,] "AYS"
## [6,] "AYL"
## [7,] "FOB1"
## [8,] "FOB2"
## [9,] "FOB3"
## [10,] "F_AUDPC"
## [11,] "Cold"
## [12,] "FOB3_bin"
## [13,] "BDM"
## [14,] "index"
Plot the correlation matrices using corrplot
mat<-cor(pull.pheno(data,c(1:10,12,13,11)),use = "complete.obs")
corrplot(mat,type = 'upper',method = "color", addCoef.col = "orange", tl.col = "black", tl.srt = 35,tl.cex=0.7,number.cex=0.5) 
Now, for each group of phenotypes
Fusarium
mat<-cor(pull.pheno(data,c(7:10,12)),use = "complete.obs")
corrplot(mat, type='upper',method = "color", addCoef.col = "white", tl.col = "black", tl.srt = 35)
Anthocyanin
mat<-cor(pull.pheno(data,1:6),use = "complete.obs")
corrplot(mat, type='upper',method = "color", addCoef.col = "white", tl.col = "black", tl.srt = 35)
There is a strong correlation between the phenotypes in the same group. the different conditions (in anthocyanin) or repetitions (in fusarium) had a limited effect on the resulting phenotype.
Perform genome scans to identify QTL
## Loading precomputed genome scans from genome_scans.Rdata, scan2_part1.Rdata and scan2_lod_part2.Rdata to save time.
Setting a QTL detection threshold according to permutation tests. The lower the precentage (5%), the better significance of the QTL.
(thresh1.hk<-summary(scan1perm,alpha=c(0.63,0.1,0.05))) ## LOD thresholds (1000 permutations)
## AFF AFS AFL AG AYS AYL FOB1 FOB2 FOB3 F_AUDPC Cold
## 63% 2.24 2.24 2.22 2.21 2.24 2.22 2.24 2.22 2.25 2.24 2.26
## 10% 3.30 3.29 3.28 3.26 3.31 3.35 3.40 3.38 3.28 3.36 3.33
## 5% 3.66 3.65 3.66 3.63 3.66 3.65 3.71 3.74 3.64 3.75 3.67
(thresh2.em<-summary(scan1perm.bin,alpha=c(0.63,0.1,0.05)))## LOD thresholds (1000 permutations)
## FOB3_bin BDM
## 63% 2.21 2.26
## 10% 3.29 3.40
## 5% 3.54 3.88
Scan for additional QTL after reducing the masking effect of QTL with major peaks.
Threshold colors
thcol<-c('blue','green','red')Plot LOD curves per phenotype, QTL peaks
for(i in 1:(length(phenames(data))-3)){
p<-phenames(data)[i]
plot(scan1,lodcolumn=i,main=p,ylab="LOD",bandcol="gray80",ylim=c(0,max(scan1[,2+i])+0.5),alternate.chrid = T)
abline(h=thresh1.hk[,i],lty='dotted',lwd=2,col=thcol)
for(j in 1:3){
if(thresh1.hk[j,i]/par('usr')[4]<1){
mtext(rownames(thresh1.hk)[j],side=4,font=2,adj=thresh1.hk[j,i]/(par('usr')[4]-0.2),col=thcol[j])
}
}
}
for(i in 1:2){
p<-phenames(data)[i+n]
plot(scan1.bin,lodcolumn=i,main=p,ylab="LOD",bandcol="gray80",ylim=c(0,max(scan1.bin[,2+i])+0.5),alternate.chrid = T)
abline(h=thresh2.em[,i],lty='dotted',lwd=2,col=thcol)
for(j in 1:3){
if(thresh2.em[j,i]/par('usr')[4]<1){
mtext(rownames(thresh2.em)[j],side=4,font=2,adj=thresh2.em[j,i]/(par('usr')[4]-0.2),col=thcol[j])
}
}
}
The add QTL scan found nothing
#normal
qtlist<-summary(scan1,perms=scan1perm,format="tabByCol",alpha=0.95,ci.function="bayesint",pvalues=T)
data<-calc.genoprob(data,2,map.function="kosambi")
qtlist.aq<-list()
s.aq<-list()
for (i in 1:n){
p<-phenames(data)[i]
if(!is.null(out.aq[[p]])){
s<-summary(out.aq[[p]],format="tabByCol",perms=scan1perm[,p],alpha=0.95,ci.function="bayesint",pvalues=T)
if(nrow(s[[1]])>0){
qtlist.aq[p]<-s
qtlist.aq[[p]]<-cbind.data.frame(Trait=p,qtlist.aq[[p]])
s.aq[[p]]<-summary(out.aq[[p]],perms=scan1perm[,p],alpha=0.63)
if(nrow(s.aq[[p]])>0){
rqtl[[p]]<-addtoqtl(data,rqtl[[p]],s.aq[[p]][,1],s.aq[[p]][,2])
}
}
}
}
for (i in 1:length(qtlist)){
if(colnames(qtlist[[i]])[1]!="Trait"){
qtlist[[i]]<-cbind.data.frame(Trait=names(qtlist[i]),qtlist[[i]])
}
}
qtldf<-do.call(rbind.data.frame,c(qtlist,make.row.names=F))
for (i in 1:n){
if(names(qtlist[i])%in%names(qtlist.aq)){
qtlist[[i]]<-rbind(qtlist[[i]],qtlist.aq[[phenames(data)[i]]])
}
}
qtldf.aq<-do.call(rbind.data.frame,c(qtlist,make.row.names=F))
#binary
qtlist<-summary(scan1.bin,perms=scan1perm.bin,format="tabByCol",alpha=0.95,ci.function="bayesint",pvalues=T)
data<-calc.genoprob(data,3,map.function="kosambi")
for (i in 1:2){
p<-phenames(data)[i+n]
if(!is.null(out.aq.bin[[p]])){
s<-summary(out.aq.bin[[p]],perms=scan1perm.bin[,p],alpha=0.95,format="tabByCol",ci.function="bayesint",pvalues=T)
if(nrow(s[[1]])>0){
qtlist.aq[p]<-s
qtlist.aq[[p]]<-cbind.data.frame(Trait=p,qtlist.aq[[p]])
s.aq[[p]]<-summary(out.aq.bin[[p]],perms=scan1perm.bin[,p],alpha=0.63)
if(nrow(s.aq[[p]])>0){
rqtl.bin[[p]]<-addtoqtl(data,rqtl.bin[[p]],s.aq[[p]][,1],s.aq[[p]][,2])
}
}
}
}
for (i in 1:length(qtlist)){
qtlist[[i]]<-cbind.data.frame(Trait=names(qtlist[i]),qtlist[[i]])
}
for (i in 1:length(qtlist)){
qtldf<-rbind.data.frame(qtldf,qtlist[[i]],make.row.names=F)
}
for (i in 1:2){
if(names(qtlist[i])%in%names(qtlist.aq)){
qtlist[[i]]<-rbind(qtlist[[i]],qtlist.aq[[phenames(data)[i+n]]])
}
}
qtldf.aq.bin<-do.call(rbind.data.frame,c(qtlist,make.row.names=F))
qtldf.aq<-rbind(qtldf.aq,qtldf.aq.bin)QTL summary as dataframe for final report. QTL at alp= 0.99 and sig *** levels
su<-1-summary(data)$missing.phe
qtldf.aq<-qtldf.aq%>%
mutate("Len of LG"=round(chrlen(data)[chr],1),.after=chr)%>%
mutate("Len of QTL"=round(ci.high-ci.low,1),.after="Len of LG")%>%
mutate("Flanking markers"=paste0(chr,"_m",find.marker(data,chr,ci.low),"-",chr,"_m",find.marker(data,chr,ci.high)))%>%
mutate("Central marker"=paste0(chr,"_m",find.marker(data,chr,pos)))%>%
mutate("Pval"=paste0(pval,if_else(pval<0.63,"*",""),if_else(pval<0.1,"*",""), if_else(pval<0.05,"*","")))%>%
select(!c(pval,ci.low,ci.high))%>%
rename("QTL's LG"=chr)%>%
mutate("No. Inds/% phenotyped"=paste0(nind(data)*su[find.pheno(data,Trait)]," ind / ",round(100*su[find.pheno(data,Trait)],1),"%"),.after=Trait)%>%
mutate("pos"=round(pos,1))%>%
mutate("lod"=round(lod,1))
qtldf.aq## Trait No. Inds/% phenotyped QTL's LG Len of LG Len of QTL pos lod
## 1 AFF 149 ind / 100% LG13 56.7 28.6 56.3 2.8
## 2 AFF 149 ind / 100% LG14 58.7 57.2 12.0 2.6
## 3 AFF 149 ind / 100% LG16 41.1 41.1 32.9 1.9
## 4 AFF 149 ind / 100% LG17 46.6 32.0 1.1 1.9
## 5 AFF 149 ind / 100% LG19 114.3 80.0 34.2 2.5
## 6 AFF 149 ind / 100% LG20 60.7 26.0 18.0 2.9
## 7 AFF 149 ind / 100% LG22 77.1 77.1 77.1 1.8
## 8 AFF 149 ind / 100% LG24 49.3 22.0 31.7 3.1
## 9 AFS 149 ind / 100% LG13 56.7 45.1 56.7 2.3
## 10 AFS 149 ind / 100% LG14 58.7 52.4 12.0 2.7
## 11 AFS 149 ind / 100% LG17 46.6 28.0 1.1 2.1
## 12 AFS 149 ind / 100% LG19 114.3 82.0 34.2 2.5
## 13 AFS 149 ind / 100% LG20 60.7 26.0 24.0 3.1
## 14 AFS 149 ind / 100% LG24 49.3 22.0 31.7 3.3
## 15 AFS 149 ind / 100% LG26 86.3 86.3 0.0 2.0
## 16 AFL 149 ind / 100% LG13 56.7 17.1 56.3 2.8
## 17 AFL 149 ind / 100% LG14 58.7 18.0 4.0 2.7
## 18 AFL 149 ind / 100% LG16 41.1 15.1 32.9 2.3
## 19 AFL 149 ind / 100% LG17 46.6 46.0 1.1 1.9
## 20 AFL 149 ind / 100% LG19 114.3 80.8 36.0 2.5
## 21 AFL 149 ind / 100% LG20 60.7 26.0 18.0 2.7
## 22 AFL 149 ind / 100% LG24 49.3 22.0 31.7 2.8
## 23 AG 149 ind / 100% LG2 34.4 22.4 31.4 2.0
## 24 AG 149 ind / 100% LG13 56.7 41.7 56.3 2.3
## 25 AG 149 ind / 100% LG14 58.7 58.0 4.0 1.9
## 26 AG 149 ind / 100% LG16 41.1 41.1 32.9 2.2
## 27 AG 149 ind / 100% LG19 114.3 48.0 15.9 2.5
## 28 AG 149 ind / 100% LG20 60.7 26.0 24.0 2.4
## 29 AYS 149 ind / 100% LG13 56.7 45.1 56.3 2.1
## 30 AYS 149 ind / 100% LG16 41.1 41.1 34.0 1.9
## 31 AYS 149 ind / 100% LG19 114.3 79.6 40.0 2.4
## 32 AYL 149 ind / 100% LG13 56.7 56.7 56.3 1.8
## 33 AYL 149 ind / 100% LG14 58.7 57.2 12.8 2.2
## 34 AYL 149 ind / 100% LG16 41.1 17.1 34.6 1.9
## 35 AYL 149 ind / 100% LG19 114.3 88.0 38.0 2.4
## 36 FOB1 117 ind / 78.5% LG4 64.0 28.0 2.0 2.1
## 37 FOB1 117 ind / 78.5% LG25 47.8 34.0 7.9 1.9
## 38 FOB2 118 ind / 79.2% LG19 114.3 94.0 15.9 1.9
## 39 FOB2 118 ind / 79.2% LG25 47.8 40.0 6.6 2.6
## 40 FOB3 118 ind / 79.2% LG6 36.1 24.0 24.0 2.3
## 41 FOB3 118 ind / 79.2% LG13 56.7 40.0 17.8 2.0
## 42 FOB3 118 ind / 79.2% LG17 46.6 22.9 16.2 2.6
## 43 FOB3 118 ind / 79.2% LG22 77.1 65.1 56.0 1.7
## 44 FOB3 118 ind / 79.2% LG25 47.8 32.0 6.6 3.6
## 45 F_AUDPC 118 ind / 79.2% LG25 47.8 32.0 6.6 3.1
## 46 Cold 149 ind / 100% LG21 40.9 26.1 24.0 2.0
## 47 FOB3_bin 118 ind / 79.2% LG6 36.1 22.0 27.0 2.5
## 48 FOB3_bin 118 ind / 79.2% LG9 52.6 39.0 51.0 1.8
## 49 FOB3_bin 118 ind / 79.2% LG13 56.7 56.7 17.8 2.1
## 50 FOB3_bin 118 ind / 79.2% LG17 46.6 27.0 16.2 1.9
## 51 FOB3_bin 118 ind / 79.2% LG20 60.7 60.7 3.0 1.9
## 52 FOB3_bin 118 ind / 79.2% LG22 77.1 54.0 57.0 1.8
## 53 FOB3_bin 118 ind / 79.2% LG25 47.8 17.3 6.6 4.5
## 54 FOB3_bin 118 ind / 79.2% LG25 47.8 22.8 21.0 3.0
## 55 BDM 149 ind / 100% LG6 36.1 26.0 27.0 3.1
## 56 BDM 149 ind / 100% LG13 56.7 42.0 12.4 1.8
## 57 BDM 149 ind / 100% LG19 114.3 108.0 8.4 2.7
## 58 BDM 149 ind / 100% LG25 47.8 23.5 14.9 2.1
## 59 BDM 149 ind / 100% LG6 36.1 13.9 10.1 2.4
## Flanking markers Central marker Pval
## 1 LG13_m213796-LG13_m152483 LG13_m95830 0.232*
## 2 LG14_m187879-LG14_m3973 LG14_m232116 0.34*
## 3 LG16_m98366-LG16_m1644 LG16_m256179 0.858
## 4 LG17_m66485-LG17_m2335 LG17_m26429 0.888
## 5 LG19_m62803-LG19_m3026 LG19_m164757 0.426*
## 6 LG20_m66136-LG20_m233715 LG20_m99144 0.207*
## 7 LG22_m172088-LG22_m55365 LG22_m55365 0.931
## 8 LG24_m115022-LG24_m158249 LG24_m115022 0.144*
## 9 LG13_m239247-LG13_m152483 LG13_m152483 0.608*
## 10 LG14_m187879-LG14_m140735 LG14_m232116 0.312*
## 11 LG17_m66485-LG17_m186932 LG17_m26429 0.762
## 12 LG19_m248944-LG19_m3026 LG19_m164757 0.432*
## 13 LG20_m66136-LG20_m233715 LG20_m249499 0.156*
## 14 LG24_m115022-LG24_m158249 LG24_m115022 0.097**
## 15 LG26_m228710-LG26_m99216 LG26_m228710 0.828
## 16 LG13_m116284-LG13_m152483 LG13_m95830 0.269*
## 17 LG14_m187879-LG14_m99155 LG14_m33308 0.303*
## 18 LG16_m256633-LG16_m1644 LG16_m256179 0.577*
## 19 LG17_m66485-LG17_m242212 LG17_m26429 0.895
## 20 LG19_m255575-LG19_m38508 LG19_m164757 0.423*
## 21 LG20_m66136-LG20_m233715 LG20_m99144 0.308*
## 22 LG24_m115022-LG24_m158249 LG24_m115022 0.269*
## 23 LG2_m27444-LG2_m221845 LG2_m23665 0.781
## 24 LG13_m110230-LG13_m152483 LG13_m95830 0.589*
## 25 LG14_m187879-LG14_m2633 LG14_m33308 0.837
## 26 LG16_m98366-LG16_m1644 LG16_m256179 0.639
## 27 LG19_m195831-LG19_m176482 LG19_m62803 0.461*
## 28 LG20_m66136-LG20_m233715 LG20_m249499 0.48*
## 29 LG13_m239247-LG13_m152483 LG13_m95830 0.734
## 30 LG16_m98366-LG16_m1644 LG16_m85999 0.833
## 31 LG19_m222416-LG19_m38508 LG19_m164757 0.532*
## 32 LG13_m131941-LG13_m152483 LG13_m95830 0.882
## 33 LG14_m187879-LG14_m3973 LG14_m232116 0.679
## 34 LG16_m256633-LG16_m1644 LG16_m85999 0.838
## 35 LG19_m195831-LG19_m38508 LG19_m164757 0.532*
## 36 LG4_m158167-LG4_m179103 LG4_m158167 0.703
## 37 LG25_m148704-LG25_m118219 LG25_m42909 0.871
## 38 LG19_m234493-LG19_m3026 LG19_m62803 0.859
## 39 LG25_m148704-LG25_m196841 LG25_m26868 0.333*
## 40 LG6_m249118-LG6_m34544 LG6_m51714 0.562*
## 41 LG13_m131941-LG13_m116284 LG13_m129765 0.814
## 42 LG17_m26429-LG17_m186932 LG17_m181813 0.38*
## 43 LG22_m204703-LG22_m55365 LG22_m3697 0.93
## 44 LG25_m148704-LG25_m198798 LG25_m26868 0.051**
## 45 LG25_m148704-LG25_m198798 LG25_m26868 0.158*
## 46 LG21_m78042-LG21_m103080 LG21_m168769 0.809
## 47 LG6_m102306-LG6_m34544 LG6_m51714 0.457*
## 48 LG9_m74935-LG9_m61458 LG9_m235200 0.9
## 49 LG13_m131941-LG13_m152483 LG13_m129765 0.71
## 50 LG17_m66485-LG17_m186932 LG17_m181813 0.856
## 51 LG20_m213579-LG20_m112128 LG20_m66136 0.832
## 52 LG22_m102393-LG22_m213924 LG22_m159568 0.897
## 53 LG25_m86542-LG25_m152494 LG25_m26868 0.007***
## 54 LG25_m148704-LG25_m108773 LG25_m121157 0.189*
## 55 LG6_m201323-LG6_m34544 LG6_m51714 0.158*
## 56 LG13_m131941-LG13_m116284 LG13_m32070 0.929
## 57 LG19_m195831-LG19_m251298 LG19_m234493 0.345*
## 58 LG25_m148704-LG25_m83002 LG25_m153207 0.749
## 59 LG6_m162041-LG6_m51714 LG6_m201323 0.514*
QTL pairs summary
#normal
c.thr1<-list()
for(i in 1:n){
(thr1<-summary(scan2, perms=scan2perm, alpha=0.2,lodcolumn=i,pvalues=T))
if(i==1){c.thr1[[phenames(data)[i]]]<-thr1
}else c.thr1[[phenames(data)[i]]]<-thr1
}
for (i in 1:length(c.thr1)){
if(nrow(c.thr1[[i]])>0&&colnames(c.thr1[[i]])[1]!="Trait"){
c.thr1[[i]]<-cbind.data.frame(Trait=names(c.thr1[i]),c.thr1[[i]])
}
}
thr1df<-do.call(rbind.data.frame,c(c.thr1,make.row.names=F))
#binary
c.thr2<-list()
for(i in 1:2){
(thr2<-summary(scan2.bin, perms=scan2perm.bin, alpha=0.2,lodcolumn=i,pvalues=T))
if(i==1){c.thr2[[phenames(data)[i+n]]]<-thr2
}else c.thr2[[phenames(data)[i+n]]]<-thr2
}
for (i in 1:length(c.thr2)){
if(nrow(c.thr2[[i]])>0&&colnames(c.thr2[[i]])[1]!="Trait"){
c.thr2[[i]]<-cbind.data.frame(Trait=names(c.thr2[i]),c.thr2[[i]])
}
}
thr2df<-do.call(rbind.data.frame,c(c.thr2,make.row.names=F))
thr<-rbind(thr1df,thr2df)QTL pairs
#normal
data<-calc.genoprob(data,2,map.function="kosambi")
qtlist<-summary(scan1,perms=scan1perm,format="tabByCol",alpha=0.63,ci.function="bayesint")
sc2thr1<-summary(scan2perm,alpha=0.2)
#rearrange the threshold list
th<-vector('list',5)
for(j in 1:5){th[[j]]<-t(sc2thr1[[j]])}
m<-do.call('cbind',th)
dimnames(m)<-list(phenames(data)[1:n],names(sc2thr1)[1:5])
out.ap<-list();qtlpairs<-list();s.fq<-list();out.fq<-list()
for(i in 1:n){
p<-phenames(data)[i]
if(length(qtlist[[p]][,1])>0){
out.ap[[p]]<-addpair(data,qtlist[[p]][,1],p,rqtl[[p]],method="hk",verbose=T)
qtlpairs[[p]]<-summary(out.ap[[p]],thresholds=m[p,])
s.fq[[p]]<-summary(out.fq[[p]]<- fitqtl(data,p,rqtl[[p]],method="hk",get.ests=T))
}
}## Warning in addpair(data, qtlist[[p]][, 1], p, rqtl[[p]], method = "hk", : Dropping 31 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 31 individuals with missing phenotypes.
## Warning in addpair(data, qtlist[[p]][, 1], p, rqtl[[p]], method = "hk", : Dropping 31 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 31 individuals with missing phenotypes.
## Warning in addpair(data, qtlist[[p]][, 1], p, rqtl[[p]], method = "hk", : Dropping 31 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 31 individuals with missing phenotypes.
#binary
data<-calc.genoprob(data,10,map.function="kosambi")
qtlist.bin<-summary(scan1.bin,perms=scan1perm.bin,format="tabByCol",alpha=0.63,ci.function="bayesint")
sc2thr2<-summary(scan2perm.bin,alpha=0.2)
#rearrange the threshold list
th<-vector('list',5)
for(j in 1:5){th[[j]]<-t(sc2thr2[[j]])}
m.bin<-do.call('cbind',th)
dimnames(m.bin)<-list(phenames(data)[n+1:2],names(sc2thr2)[1:5])
out.ap.bin<-list();rqtl2.bin<-list()
for(i in 1:2){
p<-phenames(data)[i+n]
if(length(qtlist.bin[[p]][,1])>0){
q<-rbind(qtlist.bin[[p]][,-c(3,4)],s.aq[[p]])
rqtl2.bin[[p]]<-refineqtl(data,p,makeqtl(data,q[,1],q[,2],what="prob"),maxit.fitqtl=1e+6,tol=0.05,method="hk",model="binary")
out.ap.bin[[p]]<-addpair(data,q[,1],p,rqtl2.bin[[p]],maxit=1e+6,tol=0.2,method="hk",model="binary",verbose=T)
qtlpairs[[p]]<-summary(out.ap.bin[[p]],thresholds=m.bin[p,])
s.fq[[p]]<-summary(out.fq[[p]]<- fitqtl(data,p,rqtl2.bin[[p]],maxit=1e+6,tol=0.01,method="hk",model="binary",get.ests=T))
}
}## Warning in matchchr(chr, names(cross$geno)): Dropping duplicate chromosomes
## Warning in addpair(data, q[, 1], p, rqtl2.bin[[p]], maxit = 1e+06, tol = 0.2, : Dropping 31 individuals with missing phenotypes.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl.obj, covar = covar, formula =
## formula, : Didn't converge.
## Warning in fitqtlengine(pheno = pheno, qtl = qtl, covar = covar, formula = formula, : Dropping 31 individuals with missing phenotypes.
## Warning in matchchr(chr, names(cross$geno)): Dropping duplicate chromosomes
for (i in 1:length(qtlpairs)){
if(nrow(qtlpairs[[i]])>0 && names(qtlpairs[[i]])[1]!="Trait"){
qtlpairs[[i]]<-cbind.data.frame(Trait=names(qtlpairs[i]),qtlpairs[[i]])
}
}
qtlpairsdf<-do.call(rbind.data.frame,c(qtlpairs,make.row.names=F))
##interacting QTL detected for AFL
qtlpairsdf<-c(qtlpairsdf,thr=m[qtlpairsdf[,1],])One interactive QTL pair was found in the add pair scan for AFL phenotype.
alp<-0.63
colorlist<-RColorBrewer::brewer.pal(8,"Set1")Genetic map to pdf
qtldf_initial<-\(){
# make a df to pass qtl info
qtldf <- data.frame(
chr = character(),
qtl = character(),
so = numeric(),
si = numeric(),
ei = numeric(),
eo = numeric(),
col = character(),
stringsAsFactors = F
)
return(qtldf)
}
outfile<-file.path("results/basil_linkage_map.pdf")
main<-"Basil Genetic Map"
qtldf<-qtldf_initial()
setting<-list(mapthis=data,outfile=outfile,main=main,ruler=T,maxnbrcolsfordups=2,dupnbr=T,lg.col='lightblue1',lgw=0.15,labdist=0.15,lgperrow=3)
do.call(lmv.linkage.plot,setting)## Required pdf.width = 7.11466666666667
## Required pdf.height = 51.2411333333333
## Using pdf.width = 8
## Using pdf.height = 52
Anthocyanin QTL map
qtldf<-qtldf_initial()
for (i in 1:6) {
qtls<-summary(scan1,perms=scan1perm,alpha=alp,lodcolumn=i)[,c(1:2,2+i)]
if(nrow(qtls)>0){
for (j in 1:nrow(qtls)) {
bay <-bayesint(scan1[,c(1:2,2+i)],chr=qtls$chr[j])
qtldf <- rbind(qtldf,
data.frame(
chr = qtls$chr[j],
qtl = colnames(bay)[3],
so = bay$pos[1],
si = bay$pos[2],
ei = bay$pos[2],
eo = bay$pos[3],
col=colorlist[(i+1)]))
}
}
}
outfile<-file.path("results/basil_QTLs.anthocyanin.pdf")
mapthese<-paste0("LG",sort(unique(as.numeric(qtldf$chr))))
main<-paste0("Basil Genetic Map + QTLs for Anthocyanin (",paste0(mapthese,collapse = ","),")")
setting<-modifyList(setting,list(outfile=outfile,mapthese=mapthese,main=main))
setting$qtldf<-qtldf
do.call(lmv.linkage.plot,setting)## Required pdf.width = 12.865
## Required pdf.height = 14.6325333333333
## Using pdf.width = 13
## Using pdf.height = 15
Fusarium QTL map
qtldf<-qtldf_initial()
for (i in 7:10) {
qtls<-summary(scan1,perms=scan1perm,alpha=alp,lodcolumn=i)[,c(1:2,2+i)]
if(nrow(qtls)>0){
for (j in 1:nrow(qtls)) {
bay <-bayesint(scan1[,c(1:2,2+i)],chr=qtls$chr[j])
qtldf <- rbind(qtldf,
data.frame(
chr = qtls$chr[j],
qtl = colnames(bay)[3],
so = bay$pos[1],
si = bay$pos[2],
ei = bay$pos[2],
eo = bay$pos[3],
col=colorlist[(i-5)]))
}
}
}
i<-1
p<-phenames(data)[i+n]
qtls<-summary(scan1.bin,perms=scan1perm.bin,format="tabByCol",alpha=alp,ci.function="bayesint")
if(nrow(qtls[[p]])>0){
for (j in 1:(nrow(qtls[[p]])+1)) {
if(j==3){
qtls[[p]]<-rbind(qtls[[p]],cbind(s.aq[[p]][,-3],`ci.low`=bayesint(out.aq.bin[[p]],s.aq[[p]][,1])[1,2],`ci.high`=bayesint(out.aq.bin[[p]],s.aq[[p]][,1])[3,2],`lod`=s.aq[[p]][,3]))
}
qtldf<-rbind(qtldf,
data.frame(
chr = qtls[[p]]$chr[j],
qtl = p,
so = qtls[[p]]$`ci.low`[j],
si = qtls[[p]]$pos[j],
ei = qtls[[p]]$pos[j],
eo = qtls[[p]]$`ci.high`[j],
col=colorlist[7]))
}
}
outfile<-file.path("results/basil_QTLs.fusarium.pdf")
mapthese<-paste0("LG",sort(unique(as.numeric(qtldf$chr))))
main<-paste0("Basil Genetic Map + QTLs for Fusarium (",paste0(mapthese,collapse = ","),")")
setting<-modifyList(setting,list(outfile=outfile,mapthese=mapthese,main=main))
setting$qtldf<-qtldf
do.call(lmv.linkage.plot,setting)## Required pdf.width = 10.6533333333333
## Required pdf.height = 8.0864
## Using pdf.width = 11
## Using pdf.height = 9
Downy Mildew QTL map
qtldf<-qtldf_initial()
i<-2
p<-phenames(data)[i+n]
if(nrow(qtls[[p]])>0){
for (j in 1:(nrow(qtls[[p]])+1)) {
if(j==3){
qtls[[p]]<-rbind(qtls[[p]],cbind(s.aq[[p]][,-3],`ci.low`=bayesint(out.aq.bin[[p]],s.aq[[p]][,1])[1,2],`ci.high`=bayesint(out.aq.bin[[p]],s.aq[[p]][,1])[3,2],`lod`=s.aq[[p]][,3]))
}
qtldf<-rbind(qtldf,
data.frame(
chr = qtls[[p]]$chr[j],
qtl = p,
so = qtls[[p]]$`ci.low`[j],
si = qtls[[p]]$pos[j],
ei = qtls[[p]]$pos[j],
eo = qtls[[p]]$`ci.high`[j],
col=colorlist[7]))
}
}
outfile<-file.path("results/basil_QTLs.BDM.pdf")
mapthese<-paste0("LG",sort(unique(as.numeric(qtldf$chr))))
main<-paste0("Basil Genetic Map + QTLs for Downy Mildew (",paste0(mapthese,collapse = ","),")")
setting<-modifyList(setting,list(outfile=outfile,mapthese=mapthese,main=main))
setting$qtldf<-qtldf
do.call(lmv.linkage.plot,setting)## Required pdf.width = 6.46766666666667
## Required pdf.height = 6.74613333333333
## Using pdf.width = 7
## Using pdf.height = 7
The pdf files are in the results folder.
There are some matching chromosomes for each phenotypes’ group. For example, the fusarium group is mainly on LG25.
The Anthocyanin group is mainly on LG19 where there’s also BDM resistance locus. This is an interesting finding. It’s worth checking out the markers on this position, align them to the genome and find out what this locus is responible for, if there is a connection between anthocyanin and downy mildew resistance. The phenotypes’ correlation showed no connection though.
Thanks for reading my project, please share your thoughts with me!