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gapit_functions.txt
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if(!require(gplots)) install.packages("gplots")
if(!require(genetics)) install.packages("genetics")
if(!require(ape)) install.packages("ape")
if(!require(compiler)) install.packages("compiler")
if(!require(grid)) install.packages("grid")
if(!require(bigmemory)) install.packages("bigmemory")
if(!require(EMMREML)) install.packages("EMMREML")
if(!require(scatterplot3d)) install.packages("scatterplot3d")
if(!require(lme4)) install.packages("lme4")
# if(!require(rgl)) install.packages("rgl")
if(!'multtest'%in% installed.packages()[,"Package"]){
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("multtest")
BiocManager::install("snpStats")
}
`GAPIT.0000` <-
function(){
##############################################################################################
#GAPIT: Genome Association and Prediction Integrated Tool
#Objective 1: State of art methods for high power, accuracy and speed;
#Objective 2: User friendly by design, help documents, and web forum;
#Objective 3: Comprehensive output to interpret data and results;
#Objective 4: Informative tables and high quality figures for reports and publication;
#Methods implimented:
# 1. GLM (Structure or Q method for GWAS, Pritchard et. al. Genetics, 2000)
# 2. MLM (Q+K, Yu et. al. Nature Genetics, 2006)
# 3. gBLUP (Marker based kinship, Zhang et. al. Journal of Animal Science, 2007)
# 4. PCA (Zhao et. al. Plos Genetics, 2007)
# 5. EMMA (Kang et. al. Genetics, 2008)
# 6. CMLM (Zhang et. al. Nature Genetics, 2010)
# 7. EMMAx (Kang et. al. Nature Genetics, 2010)
# 8. P3D (Zhang et. al. Nature Genetics, 2010)
# 9. FaST-LMM (Lippert et. al. Nature Methods, 2011)
# 10. ECMLM (Li et. al. BMC Bioogy, 2014)
# 11. SUPER (Wang et. al. PLoS One, 2014)
#Designed by Zhiwu Zhang
#Authors of paper on Bioinformatics (2012, 28:2397-2399): Alex Lipka, Feng Tian, Qishan Wang, Xiaolei Liu, Meng Li,You Tang and Zhiwu Zhang
#Authors of paper on Plant Genome (2016, Vol 9, No. 2): You Tang, Xiaolei Liu, Jiabo Wang, Meng Li, Qishan Wang, Feng Tian, Zhongbin Su, Yuchun Pan, Di Liu, Alexander E. Lipka, Edward S. Buckler, and Zhiwu Zhang
#if(!require(multtest))
#{
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install("multtest")
# #source("http://www.bioconductor.org/biocLite.R")
# #biocLite("multtest")
#}
GAPIT.Version="2024.07.26, GAPIT 3.5"
return(GAPIT.Version)
}
#=============================================================================================
#Object: To calculate Area Under (ROC) Curve (AUC)
#Straitegy: NA
#Output: P value
#intput: beta-power and alpha-fdr or type I error
#Authors: Zhiwu Zhang
#Last update: December 18, 2015
##############################################################################################
GAPIT.AUC=function(beta=NULL,alpha=NULL){
n=length(beta)
#plot(alpha,beta,type="b")
db=beta[-1]-beta[-n]
da=1-.5*(alpha[-1]+alpha[-n])
ab=da*db
AUC=sum(ab)
return(AUC)
}
#=============================================================================================
#Object: To generate binary phenotype
#Straitegy: NA
#Output: binary phenotype (0 and 1's)
#intput: genetic effect (x), hertiability (h2) and ratio of 1's (r)
#Authors: Zhiwu Zhang
#Last update: March 18, 2016
##############################################################################################
`GAPIT.BIPH` <-
function(x=0,h2=.5,r=.25){
#To assign probability for given standard normal variable x and h2
#Author: Zhiwu Zhang
#Last update: Febuary 27, 2016
p = stats::pnorm(x)
srp=1-p-r
sh=1/(1-sqrt(h2))
adj=(r-.5)*(1-sqrt(h2))
f=1/(1+exp(sh*srp))+adj
return(f)
}
#=============================================================================================
`Blink` <- function(Y = NULL,
QTN.position = NULL,
GD = NULL,
GM = NULL,
CV = NULL,
DPP = 100000000,
kinship.algorithm = "FARM-CPU",
file.output = TRUE,
cutOff = 0.01,
method.GLM = "FarmCPU.LM",
Prior = NULL,
ncpus = 1,
maxLoop = 10,
LD = 0.7,
threshold.output = .0001,
alpha = c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1),
WS = c(1e0,1e3,1e4,1e5,1e6,1e7),
GP = NULL,
FDRcut = FALSE,
maxOut = 10,
converge = 1,
iteration.output = FALSE,
acceleration = 0,
threshold = NA,
model = "A",
MAF.calculate = FALSE,
plot.style = "FarmCPU",
p.threshold = NA,
maf.threshold = 0,
bound = FALSE,
method.sub = "reward",
method.sub.final = "reward",
stepwise = FALSE,
BIC.method = "naive",
LD.wise = FALSE,
time.cal = FALSE,
Prediction = FALSE){
# Jiabo modified the Blink GS codes in 2020.9
print("----------------------Welcome to Blink----------------------")
time.start=proc.time()
nm=nrow(GM)
ny=nrow(Y)
if(is.na(threshold)){
threshold = floor(ny / log(ny))
}
ngd = nrow(GD)
orientation = "col"
theSNP = 2
ns = nrow(GD)
seqQTN = NULL
if(nm == ngd){
orientation = "row"
theSNP = 1
ns = ncol(GD)
}
if(maf.threshold > 0) {
MAF.calculate = TRUE
}
if(MAF.calculate==FALSE){
MAF=NA
}else{
MAF=apply(GD,theSNP,mean)
MAF=matrix(MAF,nrow=1)
MAF=apply(X = MAF,
MARGIN = 2,
function(x){ min(1 - x/2, x/2) }
)
}
MAF.index = 1:nm
if(maf.threshold > 0) {
MAF.index = MAF > maf.threshold
MAF = MAF[MAF.index]
}
ac=NULL
if(acceleration != 0){
ac = rep(1.0, nm)
}
index = which(GM[,3] == 0 )
if(length(index) > 0){
GM[index,3]=1
}
P = GP
gc()
if(ncol(GD) > nm & orientation == "col"){
if( bigmemory::is.big.matrix(GD) ){
GD = bigmemory::deepcopy(GD,
rows=1:nrow(GD),
cols=2:ncol(GD))
}else{
GD=as.matrix(GD[,-1])
}
}
# GD=as.matrix(GD)
gc()
shift = 0
for(trait in 2:2){
name.of.trait = colnames(Y)[trait]
index = MAF.index
seqTaxa = which(!is.na(Y[,trait]))
Y1 = Y[seqTaxa,]
if(!is.null(CV)){
CV1 = CV[seqTaxa,] #Thanks for jloat's suggestion in Jul 23 2021
no.cv=ncol(CV)
}else{
CV1=NULL
no.cv=0
}
# print(no.cv)
if(orientation == "col"){
if(bigmemory::is.big.matrix(GD)){
GD1=bigmemory::deepcopy(GD,rows=seqTaxa,cols=index)
}else{
GD1=GD[seqTaxa,index]
}
} else {
if(bigmemory::is.big.matrix(GD)){
GD1=bigmemory::deepcopy(GD,rows=index,cols=seqTaxa)
}else{
GD1=GD[index,seqTaxa]
GD1=as.matrix(GD1)
}
}
LD.time = rep(0,maxLoop)
BIC.time = rep(0,maxLoop)
GLM.time = rep(0,maxLoop)
theLoop = 0
theConverge = 0
seqQTN.save = c(0)
isDone = FALSE
name.of.trait2 = name.of.trait
while(!isDone) {
theLoop = theLoop + 1
print(paste("----------------------Iteration:",theLoop,"----------------------",sep=" "))
if(iteration.output){
name.of.trait2 = paste("Iteration_",
theLoop,".",
name.of.trait,
sep="")
}
myPrior = FarmCPU.Prior(GM = GM,
P = P,
Prior = Prior,
kinship.algorithm = kinship.algorithm)
if(!is.null(myPrior)){
if(theLoop!=1){
seqQTN.p = myPrior
if(theLoop == 2){
bonferroniCutOff = cutOff/nm
sp = sort(seqQTN.p)
spd = abs(cutOff - sp * nm/cutOff)
index_fdr = grep(min(spd), spd)[1]
FDRcutoff = cutOff * index_fdr/nm
if(FDRcut){
index.p = seqQTN.p < (FDRcutoff)
}else{
index.p = seqQTN.p < (bonferroniCutOff)
}
if(!is.na(p.threshold)){
index.p = seqQTN.p < p.threshold
}
index.p[ is.na(index.p) ] = FALSE
seqQTN.selected = as.numeric( which( index.p ) )
}else{
index.p = seqQTN.p < (1/nm)
if(!is.na(p.threshold)){
index.p=seqQTN.p<p.threshold
}
index.p[is.na(index.p)] = FALSE
seqQTN.selected = as.numeric(which(index.p))
}
Porder = order(myPrior[seqQTN.selected],
na.last = TRUE,
decreasing = FALSE)
t1 = proc.time()
if(length(Porder) > 1){
if(LD.wise & (length(Porder) > threshold)){
max_Porder = max(Porder)
if(max_Porder > 10000) max_Porder = 10000
step_bin = 10
Porder_new = rep(Porder[1],threshold)
Po = 1
for( po in 2:max_Porder){
if(min(abs(seqQTN.selected[Porder[po]]-seqQTN.selected[Porder_new]))>step_bin){
Po = Po + 1
Porder_new[Po]=Porder[po]
if (Po >=threshold) break
}
}
Porder=Porder_new
}
if(bigmemory::is.big.matrix(GD1)){
if(orientation=="col"){
GDnew=bigmemory::deepcopy(GD1,cols=seqQTN.selected)
GDneo=bigmemory::deepcopy(GDnew,cols=Porder)
}else{
GDnew=bigmemory::deepcopy(GD1,rows=seqQTN.selected)
GDneo=bigmemory::deepcopy(GDnew,rows=Porder)
}
} else {
if(orientation=="col"){
GDnew=GD1[,seqQTN.selected]
GDneo=GDnew[,Porder]
}else{
GDnew=GD1[seqQTN.selected,]
GDneo=GDnew[Porder,]
}
}
print("LD remove is working....")
print("Number SNPs for LD remove:")
print(length(Porder))
Psort=Blink.LDRemove(Porder=Porder,GDneo=GDneo,bound=bound,LD=LD,model=model,orientation=orientation)
seqQTN.can=seqQTN.selected[Psort]
t2=proc.time()
print("Model selection based on BIC is working....")
print("Number of SNPs for BIC selection:")
print(length(seqQTN.can))
myBIC = Blink.BICselection(Psort = seqQTN.can,
GD = GD1,
Y = Y1,
orientation = orientation,
BIC.method = BIC.method)
seqQTN = myBIC$seqQTN
#if(theLoop==6) print(seqQTN)
t3 = proc.time()
LD.time[theLoop] = as.numeric(t2)[3] - as.numeric(t1)[3]
BIC.time[theLoop] = as.numeric(t3)[3] - as.numeric(t2)[3]
}else if(length(Porder) == 1){
print("LD remove is working....")
print("Model selection based on BIC is working....")
seqQTN=seqQTN.selected
}else{
seqQTN=NULL
}
}
}else{
seqQTN=NULL
}
if(theLoop==2){
if(!is.na(p.threshold)){
if(min(myPrior,na.rm=TRUE)>p.threshold){
seqQTN=NULL
print("Top snps have little effect, set seqQTN to NULL!")
}
}else{
sp=sort(seqQTN.p)
spd=abs(cutOff-sp*nm/cutOff)
index_fdr=grep(min(spd),spd)[1]
FDRcutoff=cutOff*index_fdr/nm
if(FDRcut){
index.cutoff=FDRcutoff
}else{
index.cutoff=bonferroniCutOff
}
# index.p=seqQTN.p<(FDRcutoff)
if(min(myPrior,na.rm=TRUE)>index.cutoff){
seqQTN=NULL
print("Top snps have little effect, set seqQTN to NULL!")
}
}
}
if(theLoop==2&&is.null(seqQTN)|length(seqQTN)==0&&theLoop==2){
print(paste("seqQTN is:",seqQTN,",stop here",sep=""))
if(!isDone | iteration.output){
gc()
p.GLM=GWAS[,4]
p.GLM.log=-log10(stats::quantile(p.GLM,na.rm=TRUE,0.05))
bonf.log=1.3
bonf.compare=p.GLM.log/bonf.log
p.FARMCPU.log=-log10(p.GLM)/bonf.compare
GWAS[,4]=10^(-p.FARMCPU.log)
GWAS[,4][which(GWAS[,4]>1)]=1
colnames(GWAS)=c(colnames(GM),"P.value","maf","nobs","Rsquare.of.Model.without.SNP","Rsquare.of.Model.with.SNP","FDR_Adjusted_P-values")
Vp=stats::var(Y1[,2],na.rm=TRUE)
# if(file.output) GAPIT.Report(name.of.trait=name.of.trait2,GWAS=GWAS,pred=NULL,tvalue=NULL,stderr=stderr,Vp=Vp,DPP=DPP,cutOff=cutOff,threshold.output=threshold.output,MAF=MAF,seqQTN=QTN.position,MAF.calculate=MAF.calculate,plot.style=plot.style)
# myPower=GAPIT.Power(WS=WS, alpha=alpha, maxOut=maxOut,seqQTN=QTN.position,GM=GM,GWAS=GWAS,MaxBP=1e10)
}
break
}
if(theLoop>1){
if(all(seqQTN.save!=0 & seqQTN.save!=-1 & !is.null(seqQTN))){
seqQTN=union(seqQTN,seqQTN.save)
}
}
if(theLoop>2 ){
if( length(Porder)>1){
BIC=Blink.BICselection(Psort=seqQTN,GD=GD1,Y=Y1,orientation=orientation,BIC.method=BIC.method)
seqQTN = BIC$seqQTN
}
}
print("seqQTN:")
print(seqQTN)
theConverge=length(intersect(seqQTN,seqQTN.save))/length(union(seqQTN,seqQTN.save))
isDone=((theLoop>=maxLoop)|(theConverge>=converge))
if(!is.null(seqQTN)) seqQTN.save=seqQTN
gc()
if(!is.null(seqQTN)){
if(orientation=="col"){
theCV=cbind(CV1,GD1[,seqQTN])
}else{
if(length(seqQTN)>1){
theCV1=t(GD1[seqQTN,])
theCV=cbind(CV1,theCV1)
}else{
theCV=cbind(CV1,GD1[seqQTN,])
}
}
}else{
theCV=CV1
}
t4=proc.time()
if(!is.null(theCV)) theCV=as.matrix(theCV)
#if(theLoop==4) write.table(theCV,"CV.txt",col.names=F,row.names=F,quote=F,sep="\t")
myGLM=FarmCPU.LM(y=Y1[,trait],GDP=GD1,w=theCV,orientation=orientation)
#print(dim(myGLM$P))
#myGLM=Blink.LM(y=Y1[,trait],GDP=GD1,w=theCV,orientation=orientation)
#print(dim(myGLM$P))
if(!isDone){
myGLM=Blink.SUB(GM=GM,GLM=myGLM,QTN=GM[seqQTN,],method=method.sub,model=model,no.cv=no.cv)
}else{
#save(myGLM,file="myGLM_last.Rdata")
myGLM=Blink.SUB(GM=GM,GLM=myGLM,QTN=GM[seqQTN,],method=method.sub,model=model,no.cv=no.cv)
}
t5=proc.time()
GLM.time[theLoop]=as.numeric(t5)[3]-as.numeric(t4)[3]
P=myGLM$P[,ncol(myGLM$P)-shift]
index=which(ac>1)
P[P==0] <- min(P[P!=0],na.rm=TRUE)*0.01
P[is.na(P)] =1
# print(str(myGLM))
gc()
nf=ncol(myGLM$P)/4
tvalue=myGLM$P[,nf*2-shift]
stderr=myGLM$P[,3*nf-shift]
B=myGLM$B
GWAS=cbind(GM[MAF.index,],P,MAF,NA,NA,NA,NA)
colnames(GWAS)=c(colnames(GM),"P.value","maf","nobs","Rsquare.of.Model.without.SNP","Rsquare.of.Model.with.SNP","FDR_Adjusted_P-values")
Vp=stats::var(Y1[,2],na.rm=TRUE)
# if(file.output){
# if(theLoop==1&&is.null(CV)){
# GAPIT.Report(name.of.trait=name.of.trait2,GWAS=GWAS,pred=NULL,ypred=NULL,tvalue=NULL,stderr=stderr,Vp=Vp,DPP=DPP,cutOff=cutOff,threshold.output=threshold.output,MAF=MAF,seqQTN=QTN.position,MAF.calculate=MAF.calculate,plot.style=plot.style)
# }else{
# GAPIT.Report(name.of.trait=name.of.trait2,GWAS=GWAS,pred=NULL,ypred=NULL,tvalue=NULL,stderr=stderr,Vp=Vp,DPP=DPP,cutOff=cutOff,threshold.output=threshold.output,MAF=MAF,seqQTN=QTN.position,MAF.calculate=MAF.calculate,plot.style=plot.style)
# }
# }# end of file.out
} #end of theLoop
PEV=NULL
if(Prediction){
YP = cbind(Y[,1],Y[,trait])
p.rank = order(GWAS[,4],na.last = T,decreasing=F)
if(sum(GWAS[p.rank,4]<p.threshold)>0){
seqQTN = p.rank[GWAS[p.rank,4] < p.threshold]
}else{
seqQTN = p.rank[1]
}
if(!bigmemory::is.big.matrix(GD)){
if(orientation=="col"){
GDpred = GD[,seqQTN]
}else{
if(length(seqQTN)>1){
GDpred = t(GD[seqQTN,])
}else{
GDpred = as.matrix(GD[seqQTN,])
}
}
}else{
if(orientation=="col"){
GDpred = bigmemory::deepcopy(GD,cols=seqQTN)
}else{
GDpred = bigmemory::deepcopy(GD,rows=seqQTN)
}
}
}
if(time.cal){
print("LD.time(sec):")
print(LD.time[1:theLoop])
print("BIC.time(sec):")
print(BIC.time[1:theLoop])
print("GLM.time(sec):")
print(GLM.time[1:theLoop])
}
time.end=proc.time()
time.all=as.numeric(time.end)[3]-as.numeric(time.start)[3]
print(paste("-------------Blink finished successfully in",round(time.all,2),"seconds!-----------------"))
# print(proc.time())
# write.table(GWAS,paste(name.of.trait2,"_GWAS.txt",sep=""),sep="\t",col.names=T,row.names=F)
}#end of phenotype
return(list(GWAS=GWAS,myGLM=myGLM,PEV = PEV,seqQTN=seqQTN,Beta=B))
}# end of function Blink
`Blink.BICselection` <- function(Y,
Psort = NULL,
CV = NULL,
GD = NULL,
orientation = NULL,
BIC.method = "even"){
#Objects: fixed model selection using BIC
#Input:Y,GD,Psort
# BIC.method: Naive: detect all SNPs of Psort
# even: detect some SNPs, step=floor(sqrt(m))+1
# fixed: detect the SNPs by fixed steps. Default is 20
# log: detect the SNPs by log(10,N) transform
# ln: detect the SNPs by ln transform
#Output: seqQTN: SNP position
#Author: Yao Zhou
#Last update: 01/05/2016, modified 03/31/2016
GD = as.matrix(GD)
n=nrow(Y)
threshold=floor(n/log(n))
if(threshold < length(Psort)){
seqQTN=Psort[1:threshold]
}else{
seqQTN=Psort
}
y=Y[,2]
s=0
a=0
m=length(seqQTN)
pmatrix=matrix(1,m,m)
if(BIC.method=="naive"){
position=seq(1:m)
} else if(BIC.method=="even"){
step.length=floor(sqrt(m))+1
step=floor(m/step.length)
if ((m-step*step.length)>=(0.5*step.length)) {
step=step+1
}
if (step.length>m) {
step.length=m
step=1
}
position=seq(step,m,step)
if(position[length(position)]<m) position=append(position,m)
} else if(BIC.method=="lg"){
if(m==1){
position =c(1)
}else{
le=seq(1:m)
step=le/log10(le)
for(i in 2:m){
le[i]=le[i-1]+step[i]
le=round(le)
if(le[i]>m){
position=le[1:i]
break
}
}
}
} else if(BIC.method=="ln"){
if(m==1){
position =c(1)
}else{
le=seq(1:m)
step=le/log(le)
for(i in 2:m){
le[i]=le[i-1]+step[i]
le=round(le)
if(le[i]>m){
position=le[1:i]
break
}
}
}
} else if(BIC.method=="fixed"){
if(m>20){
position=floor(seq(1,m,m/20))
}else{
position=seq(1:m)
}
}else{
print("please choose one method for BIC")
# break
}
BICv=rep(NA,length(position))
if(is.null(CV)){
w=as.matrix(rep(1,n))
ww=n
ncov=2
}else{
CV=as.matrix(CV)
w=cbind(1,CV)
ww=crossprod(w)
ncov=ncol(ww)+1
}
wwi=MASS::ginv(ww)
pos.pre=0
k=0
for(pos in position){
if(pos>m) pos=m
if(orientation=="col"){
x=GD[,seqQTN[(pos.pre+1):pos]]
}else{
x=GD[seqQTN[(pos.pre+1):pos],]
if(is.matrix(x)){
x=t(x)
}else{
x=as.matrix(x)
}
}
k=k+1
pos.pre=pos
x=as.matrix(x)
if(k==1){
ww=crossprod(w,w)
}else{
WW=matrix(0,(nwc+nxc),(nwc+nxc))
WW[1:nwc,1:nwc]=ww
WW[1:nwc,(nwc+1):(nwc+nxc)]=xw
WW[(nwc+1):(nxc+nwc),1:nwc]=wx
WW[(nwc+1):(nwc+nxc),(nwc+1):(nwc+nxc)]=xx
ww=WW
}
nwc = ncol(w)
nxc = ncol(x)
iXX = matrix(0,(nwc+nxc),(nwc+nxc))
xx = crossprod(x,x)
xw = crossprod(x,w)
wx = crossprod(w,x)
t1 = wwi %*% wx
t2 = xx - xw %*% t1
if (!is.null(t2)){
M22 = MASS::ginv(t2)
t3=xw %*% wwi
M21=-M22 %*% t3
M12=-t1 %*% M22
M11=wwi + t1 %*% M22 %*% t3
iXX[1:nwc,1:nwc]=M11
iXX[(nwc+1):(nwc+nxc),(nwc+1):(nwc+nxc)]=M22
iXX[(nwc+1):(nwc+nxc),1:nwc]=M21
iXX[1:nwc,(nwc+1):(nwc+nxc)]=M12
w=cbind(w,x)
wy=crossprod(w,y)
wwi=iXX
beta=wwi %*% wy
yp= w %*% beta
ve=as.numeric(stats::var(yp-y))
RSS= (yp-y)^2
n2LL=n*log(2*pi)+n*log(ve)+2*sum(RSS/(2*ve))
# BICv[k]=n2LL+2*(nwc+nxc-1)*log(n)
BICv[k]=n2LL+(nwc+nxc-1)*log(n)
df=(n-pos-1)
MSE=sum(RSS)/df
se=sqrt(diag(iXX)*MSE)
tvalue=beta/se
pvalue <- 2 * stats::pt(abs(tvalue), df,lower.tail = FALSE)
pmatrix[1:pos,pos]=pvalue[ncov:length(pvalue)]
}
}
seqQTN=Psort[1:position[which(BICv==min(BICv,na.rm=T))]]
pvalue=as.numeric(pmatrix[1:length(seqQTN),length(seqQTN)])
return(list(seqQTN=seqQTN,pvalue=pvalue,BIC=BICv))
}
`Blink.LDRemoveBlock`<-function(GDneo=NULL,LD=NULL,Porder=NULL,bound=FALSE,model="A",orientation=NULL){
#`Blink.LDRemove`<-function(GDneo=NULL,LD=NULL,Porder=NULL,bound=FALSE,model="A",orientation=NULL){
#Objects: Calculate LD and remove the correlated SNPs
#Authors: Yao Zhou
#Last Update: 03/03/16
if (model=="D"){
GDneo=1-abs(GDneo-1)
}
GDneo=as.matrix(GDneo)
if(min(ncol(GDneo),nrow(GDneo))<201) bound=FALSE
if(orientation=="col"){
n=nrow(GDneo)
if(bound){
GDneo=GDneo[sample(n,200,replace=F),]
}
}else{
n=ncol(GDneo)
if(bound){
GDneo=GDneo[,sample(n,200,replace=F)]
}
GDneo=t(GDneo)
}
# cat("ncol(GDneo) is",ncol(GDneo),"\n")
corr = stats::cor(GDneo)
corr[is.na(corr)]=1
corr[abs(corr)<=LD]=0
corr[abs(corr)>LD]=1
Psort=as.numeric(matrix(1,1,ncol(corr)))
# print(ncol(corr))
for(i in 2:ncol(corr)){
p.a=Psort[1:(i-1)]
p.b=as.numeric(corr[1:(i-1),i])
index=(p.a==p.b)
index[(p.a==0)&(p.b==0)]=FALSE
if(sum(index)!=0) Psort[i]=0
}
seqQTN=Porder[Psort==1]
return(seqQTN)
}
`Blink.LDRemove`<-function(GDneo=NULL,LD=0.7,Porder=NULL,bound=FALSE,model="A",orientation="row",block=1000,LD.num =50){
#Objects: LD remove, especially length(Porder)>10000
#Authors: Yao Zhou
#Last update: 08/15/2016
GDneo = as.matrix(GDneo)
if (orientation == "row") {
SNP.index = apply(GDneo,1, stats::sd)!=0
GDneo = GDneo[SNP.index,]
} else {
SNP.index = apply(GDneo, 2, stats::sd) != 0
GDneo = GDneo[, SNP.index]
}
Porder = Porder[SNP.index]
l = block
seqQTN=NULL
lp=length(Porder)
k=ceiling(lp/l)
GDneo=as.matrix(GDneo)
if(min(ncol(GDneo),nrow(GDneo))<201) bound=FALSE
if(orientation=="col"){
n=nrow(GDneo)
if(bound){
GDneo=GDneo[sample(n,200,replace=F),]
}
}else{
n=ncol(GDneo)
if(bound){
GDneo=GDneo[,sample(n,200,replace=F)]
}
GDneo=t(GDneo)
}
for(i in 1:k){
bottom=(i-1)*l+1
up=l*i
if(up>lp) up = lp
Porderb=Porder[bottom:up]
index = seq(bottom:up)
GDneob = GDneo[,index]
# cat("i is ",i,"\n")
# print(length(index))
seqQTNs = Blink.LDRemoveBlock(GDneo=GDneob,LD=LD,Porder=Porderb,orientation="col",model=model)
# print(seqQTN)
seqQTN = append(seqQTN,seqQTNs)
if(k >1){
index1 = which(Porder %in% seqQTN)
Porderb = Porder[index1]
GDneob = GDneo[,index1]
if(length(index1)>1){
seqQTN = Blink.LDRemoveBlock(GDneo=GDneob,LD=LD,Porder=Porderb,orientation="col",model=model)
}else{
seqQTN = Porderb
}
}
if(LD.num < length(seqQTN)) break
}
rm(GDneob,Porderb)
return(seqQTN)
}
`Blink.LM` <-function(y,w=NULL,GDP,orientation="col"){
N=length(y) #Total number of taxa, including missing ones
direction=2
if(orientation=="row"){
GDP=t(GDP)
}
ntest=ncol(GDP)
if(orientation=="row"){
B <- matrix(NA,nrow=nrow(GDP),ncol=ncol(w)+1)
}else{
B <- matrix(NA,nrow=ncol(GDP),ncol=ncol(w)+1)
}
print(dim(B))
if(!is.null(w)){
nf=length(w)/N
w=matrix(as.numeric(as.matrix(w)),N,nf )
w=cbind(rep(1,N),w)#add overall mean indicator
q0=ncol(w) #Number of fixed effect excluding gnetic effects
}else{
w=rep(1,N)
nf=0
q0=1
}
y=matrix(as.numeric(as.matrix(y)),N,1 )
n=N
k=1 #number of genetic effect: 1 and 2 for A and AD respectively
q1=(q0+1) # vecter index for the posistion of genetic effect (a)
q2=(q0+1):(q0+2) # vecter index for the posistion of genetic effect (a and d)
df=n-q0-k #residual df (this should be varied based on validating d)
iXX=matrix(0,q0+k,q0+k) #Reserve the maximum size of inverse of LHS
ww=crossprod(w,w)
wy=crossprod(w,y)
yy=crossprod(y,y)
# wwi=solve(ww) Revised by Jiabo on 2021.3.4
wwi <- try(solve(ww),silent=TRUE)
if(inherits(wwi, "try-error")){
print("!!!!!")
wwi <- MASS::ginv(ww)
}
#Statistics on the reduced model without marker
rhs=wy
gc()
y=as.matrix(y)
gy=crossprod(GDP,y)
gw=crossprod(w,GDP)
bw=crossprod(gw,wwi)
lbw=ncol(bw)
P <- matrix(NA,nrow=ncol(GDP),ncol=4*(nf+1))
for(i in 1:ntest){
x=GDP[,i]
xy=gy[i,1]
xw=gw[,i]
xx=crossprod(x,x)
# B21 <- crossprod(xw, wwi)
B21=matrix(bw[i,],1,lbw)
t2=B21%*%xw #I have problem of using crossprod and tcrossprod here
B22 <- xx - t2
invB22=1/B22
NeginvB22B21 <- crossprod(-invB22,B21)
iXX11 <- wwi + as.numeric(invB22)*crossprod(B21,B21)
iXX[1:q0,1:q0]=iXX11
iXX[q1,q1]=invB22
iXX[q1,1:q0]=NeginvB22B21
iXX[1:q0,q1]=NeginvB22B21
rhs=c(wy,xy)
beta <- crossprod(iXX,rhs) #both a and d go in
df=n-q0-1
ve=(yy-crossprod(beta,rhs))/df
se=sqrt(diag(iXX)*ve)
tvalue=beta/se
pvalue <- 2 * stats::pt(abs(tvalue), df,lower.tail = FALSE)
if(!is.na(abs(B22[1,1]))){
if(abs(B22[1,1])<10e-8)pvalue[]=NA}
P[i,]=c(beta[-1],tvalue[-1],se[-1],pvalue[-1])
print(length(beta))
# B[i,]=beta[length(beta)]
}
return(list(P=P,PF=NULL,beta=beta))
} #end of function
`Blink.Pred` <- function(GD = NULL,
Y = NULL,
CV = NULL,
orientation = "col"){
## Objects: Prediction using significant pseudo QTNs
## Input: Y, CV and GD
## Output: Predicted Phenotype
## Authors: Yao Zhou
## Last update: 2/6/2017
if(bigmemory::is.big.matrix(GD)) GD = as.matrix(GD)
if(orientation =="row"){
GD = t(GD)
if(nrow(GD)==1) GD = t(GD)
}
seqTaxa=which(!is.na(Y[,2]))
Y1 = Y[seqTaxa,2]
GD1 = GD[seqTaxa,]
if(is.null(CV)){
mylm = stats::lm(Y1 ~ GD1)
PEV = stats::predict(mylm,as.data.frame(GD))
}else{
CV1 = CV[seqTaxa,]
mylm = stats::lm(Y1 ~ CV1 + GD1)
PEV = stats::predict(mylm,as.data.frame(cbind(CV,GD)))
}
return(PEV)
}
`Blink.SUB` <-
function(GM=NULL,GLM=NULL,QTN=NULL,method="mean",useapply=TRUE,model="A",no.cv=0){
#Input: FarmCPU.GLM object
#Input: QTN - s by 3 matrix for SNP name, chromosome and BP
#Input: method - options are "penalty", "reward","mean","median",and "onsite"
#Requirement: P has row name of SNP. s<=t. covariates of QTNs are next to SNP
#Output: GLM with the last column of P updated by the substituded p values
#Authors: Xiaolei Liu and Zhiwu Zhang
# Last update: Febuary 26, 2013
##############################################################################
if(is.null(GLM$P)) return(NULL) #P is required
if(is.null(QTN)) return(NULL) #QTN is required
position=match(QTN[,1], GM[,1], nomatch = 0)
nqtn=length(position)
if(model=="A"){
index=(ncol(GLM$P)-nqtn):(ncol(GLM$P)-1)
spot=ncol(GLM$P)
}else{
index=(ncol(GLM$P)-nqtn-1):(ncol(GLM$P)-2)
spot=ncol(GLM$P)-1
}
if(ncol(GLM$P)!=1){
if(length(index)>1){
if(method=="penalty") P.QTN=apply(GLM$P[,index],2,max,na.rm=TRUE)
if(method=="reward") P.QTN=apply(GLM$P[,index],2,min,na.rm=TRUE)
if(method=="mean") P.QTN=apply(GLM$P[,index],2,mean,na.rm=TRUE)
if(method=="median") P.QTN = apply(GLM$P[,index], 2, stats::median, na.rm = TRUE)
if(method=="onsite") P.QTN=GLM$P0[(length(GLM$P0)-nqtn+1):length(GLM$P0)]
}else{
if(method=="penalty") P.QTN=max(GLM$P[,index],na.rm=TRUE)
if(method=="reward") P.QTN=min(GLM$P[,index],na.rm=TRUE)
if(method=="mean") P.QTN=mean(GLM$P[,index],na.rm=TRUE)
if(method=="median") P.QTN=stats::median(GLM$P[,index], stats::median,na.rm=TRUE)
if(method=="onsite") P.QTN=GLM$P0[(length(GLM$P0)-nqtn+1):length(GLM$P0)]
}
GLM$P[position,spot]=P.QTN
# print(position)
# print(GLM$betapred)
GLM$B[position,]=GLM$betapred[(no.cv+1):length(GLM$betapred)]
}
return(GLM)
}#The function FarmCPU.SUB ends here
`Blink.cor`<-function(Y,GD,w=NULL,orientation="row",ms=ms,n=ny,m=nm){
#Objects: calculate R value with covariates
#Input: pheontype(nx1), ms is marker size for slicing the genotype, genotype(orientation="row", mxn or orientation="col", nxm,) and covariates(nxp)
# n is individual number, m is marker number, p is covariate number
#Output: abs(r)
#Author: Yao Zhou
#Last updated: Jun 28, 2016
if(!is.matrix(Y)) Y=as.matrix(Y)
# Orthogonolize phenotype w.r.t. covariates
{
if(!is.null(w)){
w = cbind(1,w)
}else{
w = matrix(1,n,1)
}
if(!is.matrix(w)) w = as.matrix(w)
qw = qr(w)
if( min(abs(diag(qr.R(qw)))) < .Machine$double.eps * m ) {
stop("Colinear or zero covariates detected");
}
w = qr.Q(qw)
tw=t(w)
rm(qw)
}
# Orthogonolize phenotype w.r.t. covariates
{
Y = Y - w%*%crossprod(w,Y)
colsq = colSums(Y^2)
div = sqrt(colsq)
Y = Y/div
rm(colsq,div)
}
time.start = proc.time()
#Orthogonolize genotype w.r.t. covariates
{
if(orientation == "row"){
rabs = matrix(NA,nrow = nrow(GD),ncol = 1)
ntest = nrow(GD)
ns = ceiling(ntest/ms)
for(i in 1:ns){
bottom=(ms*(i-1)+1)
if(i<ns){
up=ms*i
}else{
up=ntest
}
GDs = GD[bottom:up,]
GDs = GDs - tcrossprod(GDs,tw)%*%tw
rowsq= rowSums(GDs^2)
div = sqrt(rowsq)
GDs=GDs/div
rabs[bottom:up,1]=abs(GDs%*%Y)
}
}else{
rabs = matrix(NA,nrow = ncol(GD),ncol = 1)
ntest = ncol(GD)
ns = ceiling(ntest/ms)
for(i in 1:ns){
bottom=(ms*(i-1)+1)
if(i<ns){
up=ms*i
}else{