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+/*
+ Genome-wide Efficient Mixed Model Association (GEMMA)
+ Copyright (C) 2011 Xiang Zhou
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <http://www.gnu.org/licenses/>.
+*/
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <string>
+#include <iomanip>
+#include <bitset>
+#include <vector>
+#include <map>
+#include <set>
+#include <cstring>
+#include <cmath>
+#include <stdio.h>
+#include <stdlib.h>
+
+#include "gsl/gsl_vector.h"
+#include "gsl/gsl_matrix.h"
+#include "gsl/gsl_linalg.h"
+#include "gsl/gsl_blas.h"
+#include "gsl/gsl_cdf.h"
+
+#include "lapack.h"
+#include "gzstream.h"
+
+#ifdef FORCE_FLOAT
+#include "param_float.h"
+#include "varcov_float.h"
+#include "io_float.h"
+#include "mathfunc_float.h"
+#else
+#include "param.h"
+#include "varcov.h"
+#include "io.h"
+#include "mathfunc.h"
+#endif
+
+
+using namespace std;
+
+
+
+
+void VARCOV::CopyFromParam (PARAM &cPar)
+{
+ d_pace=cPar.d_pace;
+
+ file_bfile=cPar.file_bfile;
+ file_geno=cPar.file_geno;
+ file_out=cPar.file_out;
+ path_out=cPar.path_out;
+
+ time_opt=0.0;
+
+ window_cm=cPar.window_cm;
+ window_bp=cPar.window_bp;
+ window_ns=cPar.window_ns;
+
+ indicator_idv=cPar.indicator_idv;
+ indicator_snp=cPar.indicator_snp;
+ snpInfo=cPar.snpInfo;
+
+ return;
+}
+
+
+void VARCOV::CopyToParam (PARAM &cPar)
+{
+ cPar.time_opt=time_opt;
+ return;
+}
+
+
+
+//chr rs ps n_idv allele1 allele0 af var window_size r2 (r2_11,n_11,r2_12,n_12...r2_1m,n_1m)
+void VARCOV::WriteCov (const int flag, const vector<SNPINFO> &snpInfo_sub, const vector<vector<double> > &Cov_mat)
+{
+ string file_cov;
+ file_cov=path_out+"/"+file_out;
+ file_cov+=".cor.txt";
+
+ ofstream outfile;
+
+ if (flag==0) {
+ outfile.open (file_cov.c_str(), ofstream::out);
+ if (!outfile) {cout<<"error writing file: "<<file_cov<<endl; return;}
+
+ outfile<<"chr"<<"\t"<<"rs"<<"\t"<<"ps"<<"\t"<<"n_mis"
+ <<"\t"<<"n_obs"<<"\t"<<"allele1"<<"\t"<<"allele0"
+ <<"\t"<<"af"<<"\t"<<"window_size"
+ <<"\t"<<"var"<<"\t"<<"cor"<<endl;
+ } else {
+ outfile.open (file_cov.c_str(), ofstream::app);
+ if (!outfile) {cout<<"error writing file: "<<file_cov<<endl; return;}
+
+ for (size_t i=0; i<Cov_mat.size(); i++) {
+ outfile<<snpInfo_sub[i].chr<<"\t"<<snpInfo_sub[i].rs_number<<"\t"<<snpInfo_sub[i].base_position<<"\t"<<snpInfo_sub[i].n_miss<<"\t"<<snpInfo_sub[i].n_idv<<"\t"<<snpInfo_sub[i].a_minor<<"\t"<<snpInfo_sub[i].a_major<<"\t"<<fixed<<setprecision(3)<<snpInfo_sub[i].maf<<"\t"<<Cov_mat[i].size()-1<<"\t";
+ outfile<<scientific<<setprecision(6)<<Cov_mat[i][0]<<"\t";
+
+ if (Cov_mat[i].size()==1) {
+ outfile<<"NA";
+ } else {
+ for (size_t j=1; j<Cov_mat[i].size(); j++) {
+ if (j==(Cov_mat[i].size()-1)) {
+ outfile<<Cov_mat[i][j];
+ } else {
+ outfile<<Cov_mat[i][j]<<",";
+ }
+ }
+ }
+
+ outfile<<endl;
+ }
+ }
+
+ outfile.close();
+ outfile.clear();
+ return;
+}
+
+
+
+
+// sort SNPs first based on chromosomes then based on bp (or cm, if bp is not available)
+//if chr1=chr2 and bp1=bp2, then return with the same order
+bool CompareSNPinfo (const SNPINFO &snpInfo1, const SNPINFO &snpInfo2)
+{
+ int c_chr=snpInfo1.chr.compare(snpInfo2.chr);
+ long int c_bp=snpInfo1.base_position-snpInfo2.base_position;
+
+ if(c_chr<0) {
+ return true;
+ } else if (c_chr>0) {
+ return false;
+ } else {
+ if (c_bp<0) {
+ return true;
+ } else if (c_bp>0) {
+ return false;
+ } else {
+ return true;
+ }
+ }
+}
+
+
+// do not sort SNPs (because gzip files do not support random access)
+// then calculate n_nb, the number of neighbours, for each snp
+void VARCOV::CalcNB (vector<SNPINFO> &snpInfo_sort)
+{
+ // stable_sort(snpInfo_sort.begin(), snpInfo_sort.end(), CompareSNPinfo);
+
+ size_t t2=0, n_nb=0;
+ for (size_t t=0; t<indicator_snp.size(); ++t) {
+ if (indicator_snp[t]==0) {continue;}
+
+ if (snpInfo_sort[t].chr=="-9" || (snpInfo_sort[t].cM==-9 && window_cm!=0) || (snpInfo_sort[t].base_position==-9 && window_bp!=0) ) {
+ snpInfo_sort[t].n_nb=0; continue;
+ }
+
+ if (t==indicator_snp.size()-1) {snpInfo_sort[t].n_nb=0; continue;}
+
+ t2=t+1; n_nb=0;
+
+ while (t2<indicator_snp.size() && snpInfo_sort[t2].chr==snpInfo_sort[t].chr && indicator_snp[t2]==0) {t2++;}
+
+ while (t2<indicator_snp.size() && snpInfo_sort[t2].chr==snpInfo_sort[t].chr && (snpInfo_sort[t2].cM-snpInfo_sort[t].cM<window_cm || window_cm==0) && (snpInfo_sort[t2].base_position-snpInfo_sort[t].base_position<window_bp || window_bp==0) && (n_nb<window_ns|| window_ns==0) ) {
+ t2++; n_nb++;
+ while (t2<indicator_snp.size() && snpInfo_sort[t2].chr==snpInfo_sort[t].chr && indicator_snp[t2]==0) {t2++;}
+ }
+
+ snpInfo_sort[t].n_nb=n_nb;
+ }
+
+ return;
+}
+
+
+
+//vector double is centered to have mean 0
+void Calc_Cor(vector<vector<double> > &X_mat, vector<double> &cov_vec)
+{
+ cov_vec.clear();
+
+ double v1, v2, r;
+ vector<double> x_vec=X_mat[0];
+
+ lapack_ddot(x_vec, x_vec, v1);
+ cov_vec.push_back(v1/(double)x_vec.size() );
+
+ for (size_t i=1; i<X_mat.size(); i++) {
+ lapack_ddot(X_mat[i], x_vec, r);
+ lapack_ddot(X_mat[i], X_mat[i], v2);
+ r/=sqrt(v1*v2);
+
+ cov_vec.push_back(r);
+ }
+
+ return;
+}
+
+/*
+//somehow this can produce nan for some snps; perhaps due to missing values?
+//vector int is not centered, and have 0/1/2 values only
+//missing value is -9; to calculate covariance, these values are replaced by
+void Calc_Cor(const vector<vector<int> > &X_mat, vector<double> &cov_vec)
+{
+ cov_vec.clear();
+
+ int d1, d2, m1, m2, n1, n2, n12;
+ double m1d, m2d, m12d, v1d, v2d, v;
+
+ vector<int> x_vec=X_mat[0];
+
+ //calculate m2
+ m2=0; n2=0;
+ for (size_t j=0; j<x_vec.size(); j++) {
+ d2=x_vec[j];
+ if (d2==-9) {continue;}
+ m2+=d2; n2++;
+ }
+ m2d=(double)m2/(double)n2;
+
+ for (size_t i=0; i<X_mat.size(); i++) {
+ //calculate m1
+ m1=0; n1=0;
+ for (size_t j=0; j<x_vec.size(); j++) {
+ d1=X_mat[i][j];
+ if (d1==-9) {continue;}
+ m1+=d1; n1++;
+ }
+ m1d=(double)m1/(double)n1;
+
+ //calculate v1
+ m1=0; m2=0; m12d=0; n12=0;
+ for (size_t j=0; j<x_vec.size(); j++) {
+ d1=X_mat[i][j];
+ if (d1==-9) {
+ n12++;
+ } else if (d1!=0) {
+ if (d1==1) {m12d+=1;} else if (d1==2) {m12d+=4;} else {m12d+=(double)(d1*d1);}
+ }
+ }
+
+ v1d=((double)m12d+m1d*(double)m2+m2d*(double)m1+(double)n12*m1d*m2d)/(double)x_vec.size()-m1d*m2d;
+
+ //calculate covariance
+ if (i!=0) {
+ m1=0; m2=0; m12d=0; n12=0;
+ for (size_t j=0; j<x_vec.size(); j++) {
+ d1=X_mat[i][j]; d2=x_vec[j];
+ if (d1==-9 && d2==-9) {
+ n12++;
+ } else if (d1==-9) {
+ m2+=d2;
+ } else if (d2==-9) {
+ m1+=d1;
+ } else if (d1!=0 && d2!=0) {
+ if (d1==1) {m12d+=d2;} else if (d1==2) {m12d+=d2+d2;} else {m12d+=(double)(d1*d2);}
+ }
+ }
+
+ v=((double)m12d+m1d*(double)m2+m2d*(double)m1+(double)n12*m1d*m2d)/(double)x_vec.size()-m1d*m2d;
+ v/=sqrt(v1d*v2d);
+ } else {
+ v2d=v1d;
+ v=v1d/(double)x_vec.size();
+ }
+
+ cov_vec.push_back(v);
+ }
+ return;
+}
+*/
+
+
+//read the genotype file again, calculate r2 between pairs of SNPs within a window, output the file every 10K SNPs
+//the output results are sorted based on chr and bp
+//output format similar to assoc.txt files (remember n_miss is replaced by n_idv)
+
+//r2 between the current SNP and every following SNPs within the window_size (which can vary if cM was used)
+//read bimbam mean genotype file and calculate the covariance matrix for neighboring SNPs
+//output values at 10000-SNP-interval
+void VARCOV::AnalyzeBimbam ()
+{
+ igzstream infile (file_geno.c_str(), igzstream::in);
+ if (!infile) {cout<<"error reading genotype file:"<<file_geno<<endl; return;}
+
+ //calculate the number of right-hand-side neighbours for each SNP
+ vector<SNPINFO> snpInfo_sub;
+ CalcNB(snpInfo);
+
+ size_t ni_test=0;
+ for (size_t i=0; i<indicator_idv.size(); i++) {
+ ni_test+=indicator_idv[i];
+ }
+
+ gsl_vector *geno=gsl_vector_alloc (ni_test);
+ double geno_mean;
+
+ vector<double> x_vec, cov_vec;
+ vector<vector<double> > X_mat, Cov_mat;
+
+ for (size_t i=0; i<ni_test; i++) {
+ x_vec.push_back(0);
+ }
+
+ WriteCov (0, snpInfo_sub, Cov_mat);
+
+ size_t t2=0, inc;
+ int n_nb=0;
+
+ for (size_t t=0; t<indicator_snp.size(); ++t) {
+ if (t%d_pace==0 || t==(indicator_snp.size()-1)) {ProgressBar ("Reading SNPs ", t, indicator_snp.size()-1);}
+ if (indicator_snp[t]==0) {continue;}
+ // if (t>=2) {break;}
+
+ if (X_mat.size()==0) {
+ n_nb=snpInfo[t].n_nb+1;
+ } else {
+ n_nb=snpInfo[t].n_nb-n_nb+1;
+ }
+
+ for (int i=0; i<n_nb; i++) {
+ if (X_mat.size()==0) {t2=t;}
+
+ //read a line of the snp is filtered out
+ inc=0;
+ while (t2<indicator_snp.size() && indicator_snp[t2]==0) {
+ t2++; inc++;
+ }
+
+ Bimbam_ReadOneSNP (inc, indicator_idv, infile, geno, geno_mean);
+ gsl_vector_add_constant (geno, -1.0*geno_mean);
+
+ for (size_t j=0; j<geno->size; j++) {
+ x_vec[j]=gsl_vector_get(geno, j);
+ }
+ X_mat.push_back(x_vec);
+
+ t2++;
+ }
+
+ n_nb=snpInfo[t].n_nb;
+
+ Calc_Cor(X_mat, cov_vec);
+ Cov_mat.push_back(cov_vec);
+ snpInfo_sub.push_back(snpInfo[t]);
+
+ X_mat.erase(X_mat.begin());
+
+ //write out var/cov values
+ if (Cov_mat.size()==10000) {
+ WriteCov (1, snpInfo_sub, Cov_mat);
+ Cov_mat.clear();
+ snpInfo_sub.clear();
+ }
+ }
+
+ if (Cov_mat.size()!=0) {
+ WriteCov (1, snpInfo_sub, Cov_mat);
+ Cov_mat.clear();
+ snpInfo_sub.clear();
+ }
+
+ gsl_vector_free(geno);
+
+ infile.close();
+ infile.clear();
+
+ return;
+}
+
+
+
+
+void VARCOV::AnalyzePlink ()
+{
+ string file_bed=file_bfile+".bed";
+ ifstream infile (file_bed.c_str(), ios::binary);
+ if (!infile) {cout<<"error reading bed file:"<<file_bed<<endl; return;}
+
+ //calculate the number of right-hand-side neighbours for each SNP
+ vector<SNPINFO> snpInfo_sub;
+ CalcNB(snpInfo);
+
+ size_t ni_test=0;
+ for (size_t i=0; i<indicator_idv.size(); i++) {
+ ni_test+=indicator_idv[i];
+ }
+
+ gsl_vector *geno=gsl_vector_alloc (ni_test);
+ double geno_mean;
+
+ vector<double> x_vec, cov_vec;
+ vector<vector<double> > X_mat, Cov_mat;
+
+ for (size_t i=0; i<ni_test; i++) {
+ x_vec.push_back(0);
+ }
+
+ WriteCov (0, snpInfo_sub, Cov_mat);
+
+ size_t t2=0, inc;
+ int n_nb=0;
+
+ for (size_t t=0; t<indicator_snp.size(); ++t) {
+ if (t%d_pace==0 || t==(indicator_snp.size()-1)) {ProgressBar ("Reading SNPs ", t, indicator_snp.size()-1);}
+ if (indicator_snp[t]==0) {continue;}
+ // if (t>=2) {break;}
+
+ if (X_mat.size()==0) {
+ n_nb=snpInfo[t].n_nb+1;
+ } else {
+ n_nb=snpInfo[t].n_nb-n_nb+1;
+ }
+
+ for (int i=0; i<n_nb; i++) {
+ if (X_mat.size()==0) {t2=t;}
+
+ //read a line of the snp is filtered out
+ inc=0;
+ while (t2<indicator_snp.size() && indicator_snp[t2]==0) {
+ t2++; inc++;
+ }
+
+ Plink_ReadOneSNP (t2, indicator_idv, infile, geno, geno_mean);
+ gsl_vector_add_constant (geno, -1.0*geno_mean);
+
+ for (size_t j=0; j<geno->size; j++) {
+ x_vec[j]=gsl_vector_get(geno, j);
+ }
+ X_mat.push_back(x_vec);
+
+ t2++;
+ }
+
+ n_nb=snpInfo[t].n_nb;
+
+ Calc_Cor(X_mat, cov_vec);
+ Cov_mat.push_back(cov_vec);
+ snpInfo_sub.push_back(snpInfo[t]);
+
+ X_mat.erase(X_mat.begin());
+
+ //write out var/cov values
+ if (Cov_mat.size()==10000) {
+ WriteCov (1, snpInfo_sub, Cov_mat);
+ Cov_mat.clear();
+ snpInfo_sub.clear();
+ }
+ }
+
+ if (Cov_mat.size()!=0) {
+ WriteCov (1, snpInfo_sub, Cov_mat);
+ Cov_mat.clear();
+ snpInfo_sub.clear();
+ }
+
+ gsl_vector_free(geno);
+
+ infile.close();
+ infile.clear();
+
+ return;
+}
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