Nothing
FASTmrMLM<-function(gen,phe,outATCG,genRaw,kk,psmatrix,svpal,svrad,svmlod,Genformat,CLO){
inputform<-Genformat
svlod<-svmlod
if(is.null(kk)){
if(is.null(gen)==TRUE)
{
warning("Please input correct genotype dataset !")
}else{
XX1<-t(gen)
x<-XX1[3:nrow(XX1),]
rownames(x)<-NULL
colnames(x)<-NULL
X1<-as.matrix(x)
rm(x,XX1)
gc()
n<-nrow(X1)
m<-ncol(X1)
########kinship##########
#kk1<-(X1%*%t(X1))/m
kk1<-mrMLM::multiplication_speed(X1,t(X1))/m
kk<-as.matrix(kk1)
}
rm(kk1,X1)
gc()
}
if(is.null(psmatrix)){
flagps<-1
}else{
flagps<-0
}
if(is.null(svpal)==TRUE||is.null(svrad)==TRUE||is.null(svlod)==TRUE){
warning("Please set parameter!")
}
if((svpal<0)||(svpal>1))
{
warning("Please input critical P-value between 0 and 1!")
}
if(svrad<0)
{
warning("Please input search radius (kb) of candidate gene: > 0 !")
}
if(svlod<0)
{
warning("Please input critical LOD score: > 0 !")
}
if(exists("gen")==FALSE)
{
warning("Please input correct genotype dataset !")
}
if(exists("phe")==FALSE)
{
warning("Please input correct phenotype dataset !")
}
if(exists("kk")==FALSE)
{
warning("Please input correct kinship (K) dataset !")
}
if((exists("gen")==TRUE)&&(exists("phe")==TRUE)&&(ncol(gen)!=(nrow(phe)+2)))
{
warning("Sample size in genotypic dataset doesn't equal to the sample size in phenotypic dataset!")
}
if((exists("gen")==TRUE)&&(exists("phe")==TRUE)&&(exists("kk")==TRUE)&&((ncol(gen)==(nrow(phe)+2)))&&(svpal>=0)&&(svpal<=1)&&(svrad>0)&&(svmlod>=0))
{
parmsShow<-NULL
wan<-NULL
parms<-NULL
parms.pchange<-NULL
multinormal<-function(y,mean,sigma)
{
pdf_value<-(1/sqrt(2*3.14159265358979323846*sigma))*exp(-(y-mean)*(y-mean)/(2*sigma));
return (pdf_value)
}
ebayes_EM<-function(x,z,y)
{
n<-nrow(z);k<-ncol(z)
if(abs(min(eigen(crossprod(x,x))$values))<1e-6){
b<-solve(crossprod(x,x)+diag(ncol(x))*1e-8)%*%crossprod(x,y)
}else{
b<-solve(crossprod(x,x))%*%(crossprod(x,y))
}
v0<-as.numeric(crossprod((y-x%*%b),(y-x%*%b))/n)
u<-matrix(rep(0,k),k,1)
v<-matrix(rep(0,k),k,1)
s<-matrix(rep(0,k),k,1)
for(i in 1:k)
{
zz<-z[,i]
s[i]<-((crossprod(zz,zz)+1e-100)^(-1))*v0
u[i]<-s[i]*crossprod(zz,(y-x%*%b))/v0
v[i]<-u[i]^2+s[i]
}
vv<-matrix(rep(0,n*n),n,n);
for(i in 1:k)
{
zz<-z[,i]
vv=vv+tcrossprod(zz,zz)*v[i]
}
vv<-vv+diag(n)*v0
iter<-0;err<-1000;iter_max<-500;err_max<-1e-8
tau<-0;omega<-0
while((iter<iter_max)&&(err>err_max))
{
iter<-iter+1
v01<-v0
v1<-v
b1<-b
vi<-solve(vv)
xtv<-crossprod(x,vi)
if(ncol(x)==1)
{
b<-((xtv%*%x)^(-1))*(xtv%*%y)
}else{
if(abs(min(eigen(xtv%*%x)$values))<1e-6){
b<-solve((xtv%*%x)+diag(ncol(x))*1e-8)%*%(xtv%*%y)
}else{
b<-solve(xtv%*%x)%*%(xtv%*%y)
}
}
r<-y-x%*%b
ss<-matrix(rep(0,n),n,1)
for(i in 1:k)
{
zz<-z[,i]
zztvi<-crossprod(zz,vi)
u[i]<-v[i]*zztvi%*%r
s[i]<-v[i]*(1-zztvi%*%zz*v[i])
v[i]<-(u[i]^2+s[i]+omega)/(tau+3)
ss<-ss+zz*u[i]
}
v0<-as.numeric(crossprod(r,(r-ss))/n)
vv<-matrix(rep(0,n*n),n,n)
for(i in 1:k)
{
zz<-z[,i]
vv<-vv+tcrossprod(zz,zz)*v[i]
}
vv<-vv+diag(n)*v0
err<-(crossprod((b1-b),(b1-b))+(v01-v0)^2+crossprod((v1-v),(v1-v)))/(2+k)
beta<-t(b)
sigma2<-v0
}
wang<-matrix(rep(0,k),k,1)
for (i in 1:k){
stderr<-sqrt(s[i]+1e-20)
t<-abs(u[i])/stderr
f<-t*t
p<-pchisq(f,1,lower.tail = F)
wang[i]<-p
}
return(list(u=u,sigma2=sigma2,wang=wang))
}
likelihood<-function(xxn,xxx,yn,bbo)
{
nq<-ncol(xxx)
ns<-nrow(yn)
at1<-0
if(is.null(bbo)==TRUE){
ww1<-1:ncol(xxx)
ww1<-as.matrix(ww1)
}else{
ww1<-as.matrix(which(abs(bbo)>1e-5))
}
at1<-dim(ww1)[1]
lod<-matrix(rep(0,nq),nq,1)
if(at1>0.5)
ad<-cbind(xxn,xxx[,ww1])
else
ad<-xxn
if(abs(min(eigen(crossprod(ad,ad))$values))<1e-6)
bb<-solve(crossprod(ad,ad)+diag(ncol(ad))*0.01)%*%crossprod(ad,yn)
else
bb<-solve(crossprod(ad,ad))%*%crossprod(ad,yn)
vv1<-as.numeric(crossprod((yn-ad%*%bb),(yn-ad%*%bb))/ns);
ll1<-sum(log(abs(multinormal(yn,ad%*%bb,vv1))))
sub<-1:ncol(ad);
if(at1>0.5)
{
for(i in 1:at1)
{
ij<-which(sub!=sub[i+ncol(xxn)])
ad1<-ad[,ij]
if(abs(min(eigen(crossprod(ad1,ad1))$values))<1e-6)
bb1<-solve(crossprod(ad1,ad1)+diag(ncol(ad1))*0.01)%*%crossprod(ad1,yn)
else
bb1<-solve(crossprod(ad1,ad1))%*%crossprod(ad1,yn)
vv0<-as.numeric(crossprod((yn-ad1%*%bb1),(yn-ad1%*%bb1))/ns);
ll0<-sum(log(abs(multinormal(yn,ad1%*%bb1,vv0))))
lod[ww1[i]]<--2.0*(ll0-ll1)/(2.0*log(10))
}
}
return (lod)
}
mixed1<-function(xu,yu,theta1){
loglike<-function(theta1){
lambda<-exp(theta1)
logdt<-sum(log(lambda*delta+1))
h<-1/(lambda*delta+1)
yy<-sum(yu*h*yu)
yx<-matrix(0,q,1)
xx<-matrix(0,q,q)
for(i in 1:q){
yx[i]<-sum(yu*h*xu[,i])
for(j in 1:q){
xx[i,j]<-sum(xu[,i]*h*xu[,j])
}
}
loglike<- -0.5*logdt-0.5*(n-q)*log(yy-t(yx)%*%solve(xx)%*%yx)-0.5*log(det(xx))-0.5*(n-q)
return(-loglike)
}
grad<-function(theta1){
lambda<-exp(theta1)
h<-1/(lambda*delta+1)
d<-diag(delta,nrow(X1),nrow(X1))
hinv<-diag(1/(lambda*delta+1),nrow(X1),nrow(X1))
yy<-sum(yu*h*yu)
yx<-matrix(0,q,1)
xx<-matrix(0,q,q)
for(i in 1:q){
yx[i]<-sum(yu*h*xu[,i])
for(j in 1:q){
xx[i,j]<-sum(xu[,i]*h*xu[,j])
}
}
pp=hinv- hinv%*%xu%*%solve(xx)%*%t(xu)%*%hinv
sigma<-(yy-t(yx)%*%solve(xx)%*%yx)/(n-q)
f= -0.5*{sum(diag(pp%*%d))-1/sigma*(t(yu)%*%pp%*%d%*%pp%*%yu)}
return(c(-f))
}
parm<-optim(par=theta,fn=loglike,gr=grad,hessian = TRUE,method="L-BFGS-B",lower=-50,upper=10)
lambda<-(parm$par)
return(c(lambda))
}
lll<- function(theta){
lambdak<-exp(theta)
deth<-1+lambdak*g1
tmp<-lambdak*1/deth
yHy<-yy-zy%*%tmp%*%zy
yHx<-yx-zx%*%tmp%*%zy
xHx<-xx-zx%*%tmp%*%t(zx)
logdt2<-log(deth)
ll<- -0.5*logdt2-0.5*(n-q)*log(yHy-t(yHx)%*%solve(xHx)%*%yHx)-0.5*log(det(xHx))
return(-ll)
}
grad2<- function(theta){
lambdak<-exp(theta)
deth<-1+lambdak*g1
tmp<-lambdak*1/deth
yHy<-yy-zy%*%tmp%*%zy
yHx<-yx-zx%*%tmp%*%zy
xHx<-xx-zx%*%tmp%*%t(zx)
zHy<-zy-zz%*%tmp%*%zy
zHx<-zx-zx%*%tmp%*%zz
zHz<-zz-zz%*%tmp%*%zz
sigma2<-(yHy-t(yHx)%*%solve(xHx)%*%yHx)/(n-q)
f<- -0.5*{(zHz-t(zHx)%*%solve(xHx)%*%zHx)-(zHy-t(zHx)%*%solve(xHx)%*%yHx)^2/sigma2}
return(c(-f))
}
fixed2<-function(lambdak){
deth<-1+lambdak*g1
tmp<-lambdak*1/deth
yHy<-yy-zy%*%tmp%*%zy
yHx<-yx-zx%*%tmp%*%zy
xHx<-xx-zx%*%tmp%*%t(zx)
zHy<-zy-zz%*%tmp%*%zy
zHx<-zx-zx%*%tmp%*%zz
zHz<-zz-zz%*%tmp%*%zz
beta<-solve(xHx,yHx)
tmp2<-solve(xHx)
sigma2<-(yHy-t(yHx)%*%tmp2%*%yHx)/(n-q)
gamma<-lambdak*zHy-lambdak*t(zHx)%*%tmp2%*%yHx
var<-abs((lambdak*diag(1)-lambdak*zHz*lambdak)*as.numeric(sigma2))
wald<-gamma^2/var
stderr<-sqrt(diag(var))
p_value<-pchisq(wald,1,lower.tail = F)
result<-list(gamma,stderr,beta,sigma2,p_value,wald)
return(result)
}
y<-as.matrix(phe)
XX1<-t(gen)
x<-XX1[3:nrow(XX1),]
rownames(x)<-NULL
colnames(x)<-NULL
X1<-as.matrix(x)
rm(x)
gc()
n<-nrow(X1)
m<-ncol(X1)
########kinship##########
xxx<-matrix(1,n,1)
xxx<-matrix()
if (is.null(psmatrix)==TRUE)
{
xxx<-matrix(1,n,1)
}else{
ps<-as.matrix(psmatrix)
xxx<-cbind(matrix(1,n,1),ps)
}
qq<-eigen(kk)
delta<-qq[[1]]
d<-diag(delta,n,n)
uu<-qq[[2]]
q<-ncol(xxx)
waving<-svrad
xu<-t(uu)%*%xxx
zkk<-t(uu)%*%X1
theta1<-0
theta<-0
rm(kk,d,qq)
gc()
ll<-numeric()
y<-as.matrix(y)
yu<-t(uu)%*%y
ll<-numeric()
omeg<-mixed1(xu,yu,theta1)
delta1<-1/sqrt(delta*exp(omeg)+1)
d1<-diag(delta1,nrow(X1),nrow(X1))
yc<-d1%*%yu
yy<-sum(yc*1*yc)
xc<-d1%*%xu
yx<-matrix(0,q,1)
for(i in 1:q){
yx[i]<-sum(yc*1*xc[,i])
}
binv<-diag(1,nrow(X1),nrow(X1))
xx<-matrix(0,q,q)
for(i in 1:q){
for(j in 1:q){
xx[i,j]<-sum(xc[,i]*1*xc[,j])
}
}
zkk1<-d1%*%zkk
rm(d1,uu)
gc()
cl.cores <- detectCores()
if((cl.cores<=2)||(is.null(CLO)==FALSE)){
cl.cores<-1
}else if(cl.cores>2){
if(cl.cores>10){
cl.cores<-10
}else {
cl.cores <- detectCores()-1
}
}
cl <- makeCluster(cl.cores)
registerDoParallel(cl)
mat=foreach(j=1:m, .multicombine=TRUE, .combine = 'rbind')%dopar%
{
zc<-as.matrix(zkk1[,j])
uu1<-as.matrix(zc)%*%t(as.matrix(zc))
g1<-sum(diag(uu1))
zy<-as.matrix(sum(yc*1*zc))
zz<-as.matrix(sum(zc*1*zc))
zx<-matrix(0,q,1)
for(i in 1:q){
zx[i]<-sum(xc[,i]*1*zc)
}
par<-tryCatch(optim(par=theta,fn=lll,hessian = TRUE,gr=grad2,method="L-BFGS-B",lower=-10,upper=10), error=function(e) optim(par=theta,fn=lll,hessian = TRUE,method="L-BFGS-B",lower=-10,upper=10))
lambda<-exp(par$par)
conv<-par$convergence
fn1<-par$value
hess<-par$hessian
parmfix<-fixed2(lambda)
gamma<-parmfix[[1]]
stderr<-parmfix[[2]]
beta<-parmfix[[3]][1,]
sigma2<-parmfix[[4]]
p_wald<-parmfix[[5]]
sigma2g<-lambda*sigma2
wald<-parmfix[[6]]
fn0<-lll(c(-Inf))
lrt<-2*abs(fn0-fn1)
p_lrt<-pchisq(lrt,1,lower.tail = F)
parm0<-c(j,beta,sigma2,sigma2g,gamma,stderr,wald,p_wald)
}
stopCluster(cl)
rm(zkk,zkk1)
gc()
ll<-rbind(ll,mat)
parms1<-as.matrix(ll)
rownames(parms1)<-NULL
newparm<-cbind(gen[,1:2],parms1[,2:8])
parms<-newparm
parms.pchange<-parms
parmsp<-as.matrix(parms.pchange[,9])
locsub<-which(parmsp==0)
if(length(locsub)!=0){
pmin<-min(parmsp[parmsp!=0])
subvalue<-10^(1.1*log10(pmin))
parms.pchange[locsub,9]<-subvalue
}else{
parms.pchange<-parms
}
if(inputform==1){
#output result1 using mrMLM numeric format
parmsShow<-parms
tempparms<-parms[,3:9]
tempparms[,7]<--log10(tempparms[,7])
tempparms[which(abs(tempparms)>=1e-4)]<-round(tempparms[which(abs(tempparms)>=1e-4)],4)
tempparms[which(abs(tempparms)<1e-4)]<-as.numeric(sprintf("%.4e",tempparms[which(abs(tempparms)<1e-4)]))
parmsShow<-cbind(genRaw[-1,1],parms[,1:2],tempparms,genRaw[-1,4])
colnames(parmsShow)<-c("RS#","Chromosome","Marker position (bp)","Mean","Sigma2","Sigma2_k","SNP effect (FASTmrMLM)","Sigma2_k_posteriori","Wald","'-log10(P) (FASTmrMLM)'","Genotype for code 1")
}
if(inputform==2){
#output result1 using mrMLM character format
parmsShow<-parms
outATCG<-matrix(outATCG,,1)
tempparms<-parms[,3:9]
tempparms[,7]<--log10(tempparms[,7])
tempparms[which(abs(tempparms)>=1e-4)]<-round(tempparms[which(abs(tempparms)>=1e-4)],4)
tempparms[which(abs(tempparms)<1e-4)]<-as.numeric(sprintf("%.4e",tempparms[which(abs(tempparms)<1e-4)]))
parmsShow<-cbind(genRaw[-1,1],parms[,1:2],tempparms,outATCG)
colnames(parmsShow)<-c("RS#","Chromosome","Marker position (bp)","Mean","Sigma2","Sigma2_k","SNP effect (FASTmrMLM)","Sigma2_k_posteriori","Wald","'-log10(P) (FASTmrMLM)'","Genotype for code 1")
}
if(inputform==3){
#output result1 using TASSEL format
parmsShow<-parms
outATCG<-matrix(outATCG,,1)
outATCG<-unlist(strsplit(outATCG,""))
outATCG<-matrix(outATCG[c(TRUE,FALSE)],,1)
tempparms<-parms[,3:9]
tempparms[,7]<--log10(tempparms[,7])
tempparms[which(abs(tempparms)>=1e-4)]<-round(tempparms[which(abs(tempparms)>=1e-4)],4)
tempparms[which(abs(tempparms)<1e-4)]<-as.numeric(sprintf("%.4e",tempparms[which(abs(tempparms)<1e-4)]))
parmsShow<-cbind(genRaw[-1,1],parms[,1:2],tempparms,outATCG)
colnames(parmsShow)<-c("RS#","Chromosome","Marker position (bp)","Mean","Sigma2","Sigma2_k","SNP effect (FASTmrMLM)","Sigma2_k_posteriori","Wald","'-log10(P) (FASTmrMLM)'","Genotype for code 1")
}
p<-as.vector(parms1[,8])
ans<-p.adjust(p, method = "bonferroni", n = length(p))
rm(gen)
gc()
##########p is parameter########
sigg<-as.vector(which(p<=svpal))
le1<-length(sigg)
if(le1!=0){
if (length(which(ans<0.05))!=0)
{
siggbh<-which(ans<0.05)
nnn1<-cbind(XX1[1,],XX1[2,])
setloci<-siggbh
setposi<-c(XX1[2,siggbh])
num<-dim(nnn1)[1]
endresult<-numeric()
for (t in 1:length(siggbh))
{
for (i in 1:num){
temp<-numeric()
if ((XX1[1,i]==XX1[1,(setloci[t])])&&(abs(nnn1[i,2]-setposi[t])<=waving))
{
temp<-cbind(matrix(nnn1[i,],1,),i)
endresult<-rbind(endresult,temp)
}
}
}
end<-as.vector(endresult[,3])
sigg2<-sigg[!sigg%in% end]
sigg1<-sort(c(siggbh,sigg2))
}else{
sigg1<-sigg
}
if (length(sigg1)>nrow(X1))
{
larsres<-lars(X1[,sigg1], y, type = "lar",trace = FALSE, normalize = TRUE, intercept = TRUE, eps = .Machine$double.eps, use.Gram=FALSE)
larsc2<-sigg1[which(larsres$entry!=0)]
if(length(which(larsres$entry>nrow(X1)))!=0)
{
ad1<-sigg1[which(larsres$entry>nrow(X1))]
larsc<-larsc2[!larsc2%in%ad1]
}else{
larsc<-larsc2
}
}else{
larsc<-sigg1
}
z<-matrix(1,nrow(X1),1)
z<-matrix()
if (is.null(psmatrix)==TRUE)
{
z<-matrix(1,nrow(X1),1)
}else{
z<-cbind(matrix(1,nrow(X1),1),psmatrix)
}
le1<-length(larsc)
xxxnew11<-as.matrix(X1[,larsc])
u1<-ebayes_EM(z,xxxnew11,y)
obj<-u1$u
result1<-matrix(0,m,1)
for (i in 1:le1)
{
result1[(larsc)[i],1]=obj[i]
}
Res<- t(as.matrix((rowSums(result1)/ncol(result1))))
Res1<-as.vector(Res)
sig1<-which(abs(Res1)>=1e-5)
le2<-length(which(abs(Res1)>=1e-5))
if(le2!=0){
bbo<-matrix(0,le2,1)
for (i in 1:le2){
bbo[i,]=Res1[sig1[i]]
}
xxxx<-as.matrix(X1[,sig1])
yn<-as.matrix(y)
xxn<-z
lod<-likelihood(xxn,xxxx,yn,bbo)
her1<-vector(length=le2)
for (i in 1:le2){
p1<-length(as.vector(which(X1[,sig1[i]]==1)))/length(X1[,sig1[i]])
p2<-1-p1
her1[i]=((p1+p2)-(p1-p2)^2)*(Res1[sig1[i]])^2
}
if(var(y)>=sum(her1)+u1$sigma2){
her<-(her1/as.vector(var(y)))*100
}else{
her<-(her1/(sum(her1)+u1$sigma2))*100
}
slod<-cbind(sig1,lod,her)
if(length(which(slod[,2]>=svlod))>=1){
if(length(which(slod[,2]>=svlod))==1){
sslod<-t(as.matrix(slod[which(slod[,2]>=svlod),]))
sig1<-slod[which(slod[,2]>=svlod),1]
}else if(length(which(slod[,2]>=svlod))>1){
sslod<-slod[which(slod[,2]>=svlod),]
sig1<-sslod[,1]
}
xxxx<-as.matrix(X1[,sig1])
lod<-sslod[,2]
her<-sslod[,3]
ii<-as.vector(sig1)
qqq<-matrix(0,nrow=length(ii),ncol=6)
qqq[,1]=as.matrix(ii)
for (j in 1:length(ii)){
qqq[j,2]=XX1[1,ii[j]]
qqq[j,3]=XX1[2,ii[j]]
qqq[j,4]=result1[ii[j],]
qqq[j,5]=lod[j]
qqq[j,6]=her[j]
}
rm(XX1,X1)
gc()
id<-which(qqq[,5]==0)
if(length(id)!=dim(qqq)[1]){
if(length(id)!=0){
qqq1<-qqq[-id,]
}else{
qqq1<-qqq
}
xxmaf<-t(xxxx)
leng.maf<-dim(xxmaf)[2]
maf.fun<-function(snp){
leng<-length(snp)
snp1<-length(which(snp==1))
snp11<-length(which(snp==-1))
snp0<-length(which(snp==0))
ma1<-(2*snp1+snp0)/(2*leng)
ma2<-(2*snp11+snp0)/(2*leng)
maf<-min(ma1,ma2)
return(maf)
}
maf<-apply(xxmaf,1,maf.fun)
maf<-as.matrix(round(maf,4))
vee<-round(u1$sigma2,4)
pee<-round(var(y),4)
if(nrow(qqq1)>1){
result<-as.matrix(qqq1[,-1])
vees<-matrix("",nrow = nrow(result),1)
pees<-matrix("",nrow = nrow(result),1)
pees[1,1]<-pee
vees[1,1]<-vee
}else{
result<-t(as.matrix(qqq1[,-1]))
pees<-as.matrix(pee)
vees<-as.matrix(vee)
}
if(nrow(qqq1)>1){
result<-as.matrix(qqq1[,-1])
result<-result
temp<-as.matrix(result[,3:5])
temp[which(abs(temp)>=1e-4)]<-round(temp[abs(temp)>=1e-4],4)
temp[which(abs(temp)<1e-4)]<-as.numeric(sprintf("%.4e",temp[abs(temp)<1e-4]))
wan<-cbind(result[,1:2],temp)
snp<-parmsShow[,11]
}else{
result<-t(as.matrix(qqq1[,-1]))
result<-result
temp<-t(as.matrix(result[,3:5]))
temp[which(abs(temp)>=1e-4)]<-round(temp[abs(temp)>=1e-4],4)
temp[which(abs(temp)<1e-4)]<-as.numeric(sprintf("%.4e",temp[abs(temp)<1e-4]))
wan<-cbind(t(as.matrix(result[,1:2])),temp)
snp<-parmsShow[,11]
}
if(inputform==1){
genRaw<-as.data.frame(genRaw)
genraw<-genRaw[-1,1:4]
wan_len<-dim(wan)[1]
marker<-character()
snp<-character()
for(i in 1:wan_len){
chr_pos<-which(genraw[,2]==wan[i,1])
new_matrix<-genraw[chr_pos,]
posi_pos<-which(new_matrix[,3]==wan[i,2])
mark<-matrix(new_matrix[posi_pos,1],1,)
marker<-rbind(marker,mark)
sn<-matrix(new_matrix[posi_pos,4],1,)
snp<-rbind(snp,sn)
}
}
if(inputform==2){
genRaw<-as.data.frame(genRaw)
genraw<-genRaw[-1,1:4]
wan_len<-dim(wan)[1]
marker<-character()
snp<-character()
for(i in 1:wan_len){
chr_pos<-which(genraw[,2]==wan[i,1])
new_matrix<-genraw[chr_pos,]
posi_pos<-which(new_matrix[,3]==wan[i,2])
mark<-matrix(new_matrix[posi_pos,1],1,)
marker<-rbind(marker,mark)
sn<-matrix(new_matrix[posi_pos,4],1,)
snp<-rbind(snp,sn)
}
}
if(inputform==3){
genRaw<-as.data.frame(genRaw)
genraw<-genRaw[-1,c(1,3,4,12)]
wan_len<-dim(wan)[1]
marker<-character()
snp<-character()
for(i in 1:wan_len){
chr_pos<-which(genraw[,2]==wan[i,1])
new_matrix<-genraw[chr_pos,]
posi_pos<-which(new_matrix[,3]==wan[i,2])
mark<-matrix(new_matrix[posi_pos,1],1,)
marker<-rbind(marker,mark)
sn<-matrix(new_matrix[posi_pos,4],1,)
snp<-rbind(snp,sn)
}
}
wan<-cbind(marker,wan,maf,snp,vees,pees)
tempwan <- wan
lodscore1 <- as.numeric(tempwan[,5])
log10P <- as.matrix(round(-log10(pchisq(lodscore1*4.605,1,lower.tail = F)),4))
if(nrow(tempwan)>1){
tempwan1 <- cbind(tempwan[,1:5],log10P,tempwan[,6:10])
}else{
tempwan1 <- cbind(t(as.matrix(tempwan[,1:5])),log10P,t(as.matrix(tempwan[,6:10])))
}
wan <- tempwan1
colnames(wan)<-c("RS#","Chromosome","Marker position (bp)","QTN effect","LOD score","'-log10(P)'","r2 (%)","MAF","Genotype for code 1","Var_error","Var_phen (total)")
wan<-as.data.frame(wan)
}
}
}
}
parmsShow<-parmsShow[,-c(4,5,6,8,9)]
output<-list(result1=parmsShow,result2=wan)
return(output)
}
}
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