//NOTE checking for r[j] <0 (marker ordering) can ahppen at contract
vector recombination_frequencies(const unsigned int nmark, const cvector position, const vector mapdistance)
{
// contract: if DEBUG is_valid(positionarray)
// info("Estimating recombinant frequencies");
vector r = newvector(nmark);
for (unsigned int j=0; j<nmark; j++) {
r[j]= RFUNKNOWN;
if ((position[j]==MLEFT)||(position[j]==MMIDDLE)) {
r[j]= recombination_frequentie((mapdistance[j+1]-mapdistance[j]));
if (r[j]<0) {
Rprintf("ERROR: Position=%d r[j]=%f\n", position[j], r[j]);
fatal("Recombination frequency is negative, (Marker ordering problem ?)");
return NULL;
}
}
}
return r;
}
/*
* mapQTL moves a QTL along the chromosome and calculated at each map position
* the QTL likelihood. Uses either all cofactors, or selected cofactors only
*/
double mapQTL(int Nind, int Nmark, cvector cofactor, cvector selcofactor,
MQMMarkerMatrix marker, cvector position, vector mapdistance, vector y,
vector r, ivector ind, int Naug, double variance, char
printoutput, vector *informationcontent, matrix *Frun, int run,
char REMLorML, bool fitQTL, bool dominance, int em, double
windowsize, double stepsize, double stepmin, double stepmax,
MQMCrossType crosstype, int verbose) {
//Rprintf("INFO: mapQTL function called.\n");
int j, jj, jjj=0;
int Nloci = Nmark+1;
vector Fy = newvector(Naug);
cvector QTLcofactor = newcvector(Nloci);
cvector saveQTLcofactor = newcvector(Nloci);
double infocontent;
vector info0 = newvector(Nind);
vector info1 = newvector(Nind);
vector info2 = newvector(Nind);
vector weight = newvector(Naug);
weight[0]= -1.0;
/* fit QTL on top of markers (but: should also be done with routine QTLmixture() for exact ML) */
cvector newcofactor= newcvector(Nmark);
cvector direction = newcvector(Nmark);
vector cumdistance = newvector(Nmark+1);
double QTLlikelihood=0.0;
for (j=0; j<Nmark; j++) {
if (position[j]==MLEFT)
cumdistance[j]= -50*log(1-2.0*r[j]);
else if (position[j]==MMIDDLE)
cumdistance[j]= cumdistance[j-1]-50*log(1-2.0*r[j]);
}
double savelogL=0.0; // log-likelihood of model with all selected cofactors
/* fit QTL on top of markers (full ML) fit QTL between markers (full ML) */
// cout << "please wait (mixture calculus may take quite a lot of time)" << endl;
/* estimate variance in mixture model with all marker cofactors */
// cout << "estimate variance in mixture model with all cofactors" << endl;
variance= -1.0;
savelogL= 2.0*QTLmixture(marker, cofactor, r, position, y, ind, Nind, Naug, Nmark, &variance, em, &weight, REMLorML, fitQTL, dominance, crosstype, verbose);
if (verbose==1){ info("INFO: log-likelihood of full model= %f\n", savelogL/2); }
// augment data for missing QTL observations (x 3)
fitQTL=true;
int newNaug = 3 * Naug;
Free(weight);
weight = newvector(newNaug);
weight[0] = 1.0;
vector weight0 = newvector(newNaug);
weight0[0] = -1.0;
vector QTLr = newvector(Nloci);
vector QTLmapdistance = newvector(Nloci);
cvector QTLposition = newcvector(Nloci);
MQMMarkerMatrix QTLloci = (MQMMarkerMatrix)Calloc(Nloci, MQMMarkerVector);
double moveQTL = stepmin;
char nextinterval= 'n', firsttime='y';
double maxF=0.0, savebaseNoQTLModel=0.0;
int baseNoQTLModel=0, step=0;
for (j=0; j<Nmark; j++) {
/* fit a QTL in two steps:
1. move QTL along marker interval j -> j+1 with steps of stepsize=20 cM, starting from -20 cM up to 220 cM
2. all marker-cofactors in the neighborhood of the QTL are dropped by using cM='windows' as criterium
*/
nextinterval= 'n';
#ifndef STANDALONE
R_CheckUserInterrupt(); /* check for ^C */
R_FlushConsole();
#endif
while (nextinterval=='n') { // step 1:
// Rprintf("DEBUG testing STEP 1");
if (position[j]==MLEFT) {
if (moveQTL<=mapdistance[j]) {
QTLposition[j]= position[j];
QTLposition[j+1]= MMIDDLE;
QTLr[j]= recombination_frequentie((mapdistance[j]-moveQTL));
QTLr[j+1]= r[j];
QTLloci[j+1]= marker[j];
QTLloci[j]= marker[Nloci-1];
QTLmapdistance[j]= moveQTL;
QTLmapdistance[j+1]= mapdistance[j];
if (firsttime=='y') weight[0]= -1.0;
moveQTL+= stepsize;
} else if (moveQTL<=mapdistance[j+1]) {
QTLposition[j]= position[j];
QTLposition[j+1]= MMIDDLE;
QTLr[j]= recombination_frequentie((moveQTL-mapdistance[j]));
QTLr[j+1]= recombination_frequentie((mapdistance[j+1]-moveQTL)); //r[j];
QTLloci[j]= marker[j];
QTLloci[j+1]= marker[Nloci-1];
QTLmapdistance[j]= mapdistance[j];
QTLmapdistance[j+1]= moveQTL;
moveQTL+= stepsize;
} else nextinterval= 'y';
} else if (position[j]==MMIDDLE) {
if (moveQTL<=mapdistance[j+1]) {
QTLposition[j]= position[j];
QTLposition[j+1]= MMIDDLE;
QTLr[j]= recombination_frequentie((moveQTL-mapdistance[j])); //0.0;
QTLr[j+1]= recombination_frequentie((mapdistance[j+1]-moveQTL)); //r[j];
QTLloci[j]= marker[j];
QTLloci[j+1]= marker[Nloci-1];
QTLmapdistance[j]= mapdistance[j];
QTLmapdistance[j+1]= moveQTL;
moveQTL+= stepsize;
} else nextinterval= 'y';
} else if (position[j]==MRIGHT) {
if (moveQTL<=stepmax) {
QTLposition[j]= MMIDDLE;
QTLposition[j+1]= MRIGHT;
QTLr[j]= recombination_frequentie((moveQTL-mapdistance[j])); //0.0;
QTLr[j+1]= r[j]; // note r[j]=999.0
QTLloci[j]= marker[j];
QTLloci[j+1]= marker[Nloci-1];
QTLmapdistance[j]= mapdistance[j];
QTLmapdistance[j+1]= moveQTL;
moveQTL+= stepsize;
} else {
nextinterval= 'y';
moveQTL= stepmin;
}
} else if (position[j]==MUNLINKED) {
QTLposition[j]= MLEFT;
QTLposition[j+1]= MRIGHT; //position[j] ?? MRIGHT ?
QTLr[j]= 0.0;
QTLr[j+1]= r[j];
QTLloci[j+1]= marker[j];
QTLloci[j]= marker[Nloci-1];
QTLmapdistance[j]= mapdistance[j];
QTLmapdistance[j+1]= mapdistance[j];
if (firsttime=='y') weight[0]= -1.0;
nextinterval= 'y';
moveQTL= stepmin;
}
if (nextinterval=='n') { // QTLcofactor[j]= MAA;
// QTLcofactor[j+1]= MAA;
for (jj=0; jj<j; jj++) {
QTLposition[jj]= position[jj];
QTLr[jj]= r[jj];
QTLloci[jj]= marker[jj];
QTLmapdistance[jj]= mapdistance[jj];
QTLcofactor[jj]= selcofactor[jj];
}
for (jj=j+1; jj<Nmark; jj++) {
QTLposition[jj+1]= position[jj];
QTLr[jj+1]= r[jj];
QTLloci[jj+1]= marker[jj];
QTLcofactor[jj+1]= selcofactor[jj];
QTLmapdistance[jj+1]= mapdistance[jj];
QTLcofactor[jj+1]= selcofactor[jj];
}
// step 2:
// Rprintf("DEBUG testing STEP 2");
if ((position[j]==MLEFT)&&((moveQTL-stepsize)<=mapdistance[j])) {
QTLcofactor[j]= MNOCOF;
QTLcofactor[j+1]=
(((QTLmapdistance[j+1]-QTLmapdistance[j])<windowsize) ? MNOCOF : selcofactor[j]);
} else {
QTLcofactor[j+1]= MNOCOF;
QTLcofactor[j]=
(((QTLmapdistance[j+1]-QTLmapdistance[j])<windowsize) ? MNOCOF : selcofactor[j]);
}
if ((position[j]==MLEFT)||(position[j]==MMIDDLE)) {
jjj=j+2;
while (QTLposition[jjj]==MMIDDLE) {
if ((position[j]==MLEFT)&&((moveQTL-stepsize)<=mapdistance[j]))
QTLcofactor[jjj]=
(((QTLmapdistance[jjj]-QTLmapdistance[j])<windowsize) ? MNOCOF : QTLcofactor[jjj]);
else
QTLcofactor[jjj]=
(((QTLmapdistance[jjj]-QTLmapdistance[j+1])<windowsize) ? MNOCOF : QTLcofactor[jjj]);
jjj++;
}
QTLcofactor[jjj]=
(((QTLmapdistance[jjj]-QTLmapdistance[j+1])<windowsize) ? MNOCOF : QTLcofactor[jjj]);
}
if ((position[j]==MMIDDLE)||(position[j]==MRIGHT)) {
jjj=j-1;
while (QTLposition[jjj]==MMIDDLE) {
QTLcofactor[jjj]= (((QTLmapdistance[j+1]-QTLmapdistance[jjj])<windowsize) ? MNOCOF : QTLcofactor[jjj]);
jjj--;
}
QTLcofactor[jjj]= (((QTLmapdistance[j+1]-QTLmapdistance[jjj])<windowsize) ? MNOCOF : QTLcofactor[jjj]);
}
// fit no-QTL model at current map position (cofactors only)
if (firsttime=='y') {
for (jj=0; jj<Nloci; jj++) saveQTLcofactor[jj]= QTLcofactor[jj];
baseNoQTLModel=1;
firsttime='n';
} else {
baseNoQTLModel=0;
for (jj=0; jj<Nloci; jj++) baseNoQTLModel+= (saveQTLcofactor[jj]==QTLcofactor[jj] ? 0 : 1);
}
// Rprintf("fitting NO-QTL model\n");
if (baseNoQTLModel!=0) { // new base no-QTL model
if ((position[j]==MLEFT)&&((moveQTL-stepsize)<=mapdistance[j])) QTLcofactor[j]= MSEX;
else QTLcofactor[j+1]= MSEX;
// Rprintf("INFO: Before base model\n", QTLlikelihood/-2);
QTLlikelihood= -2.0*QTLmixture(QTLloci, QTLcofactor, QTLr, QTLposition, y, ind, Nind, Naug, Nloci, &variance, em, &weight0, REMLorML, fitQTL, dominance, crosstype, verbose);
// Rprintf("INFO: log-likelihood of NO QTL model= %f\n", QTLlikelihood/-2);
weight0[0]= -1.0;
savebaseNoQTLModel= QTLlikelihood;
if ((position[j]==MLEFT)&&((moveQTL-stepsize)<=mapdistance[j])) QTLcofactor[j]= MNOCOF;
else QTLcofactor[j+1]= MNOCOF;
for (jj=0; jj<Nloci; jj++) saveQTLcofactor[jj]= QTLcofactor[jj];
} else
QTLlikelihood= savebaseNoQTLModel;
// fit QTL-model (plus cofactors) at current map position
// MNOTAA= QTL
if ((position[j]==MLEFT)&&((moveQTL-stepsize)<=mapdistance[j])) QTLcofactor[j]= MQTL;
else QTLcofactor[j+1]= MQTL;
if (REMLorML==MH) weight[0]= -1.0;
QTLlikelihood+=2.0*QTLmixture(QTLloci, QTLcofactor, QTLr, QTLposition, y, ind, Nind, Naug, Nloci, &variance, em, &weight, REMLorML, fitQTL, dominance, crosstype, verbose);
//this is the place we error at, because the likelihood is not correct.
if (QTLlikelihood<-0.05) {
Rprintf("WARNING: Negative QTLlikelihood=%f versus BASE MODEL: %f\nThis applies to the QTL at %d\n", QTLlikelihood, (savebaseNoQTLModel/-2), j); //return 0;}
}
maxF= (maxF<QTLlikelihood ? QTLlikelihood : maxF);
if (run>0) (*Frun)[step][run]+= QTLlikelihood;
else (*Frun)[step][0]+= QTLlikelihood;
/* Each individual has condition multilocus probabilities for being 0, 1 or 2 at the QTL.
Calculate the maximum per individu. Calculate the mean of this maximum, averaging over all individuals
This is the information content plotted.
*/
infocontent= 0.0;
for (int i=0; i<Nind; i++) {
info0[i]= 0.0; // qq
info1[i]= 0.0; // Qq
info2[i]= 0.0; // QQ
}
for (int i=0; i<Naug; i++) {
info0[ind[i]]+= weight[i];
info1[ind[i]]+= weight[i+Naug];
info2[ind[i]]+= weight[i+2*Naug];
}
for (int i=0; i<Nind; i++)
if (info0[i]<info1[i]) infocontent+= (info1[i]<info2[i] ? info2[i] : info1[i]);
else infocontent+= (info0[i]<info2[i] ? info2[i] : info0[i]);
(*informationcontent)[step]+=infocontent/Nind;
step++;
}
}
}
fitQTL=false;
freevector(direction);
Free(info0);
Free(info1);
Free(info2);
Free(weight);
Free(weight0);
Free(QTLr);
Free(QTLposition);
Free(Fy);
Free(newcofactor);
Free(QTLcofactor);
Free(cumdistance);
Free(QTLmapdistance);
Free(QTLloci);
Free(saveQTLcofactor);
return maxF; //QTLlikelihood;
}
