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assoc.cpp
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assoc.cpp
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//////////////////////////////////////////////////////////////////
// //
// PLINK (c) 2005-2009 Shaun Purcell //
// //
// This file is distributed under the GNU General Public //
// License, Version 2. Please see the file COPYING for more //
// details //
// //
//////////////////////////////////////////////////////////////////
#include <iostream>
#include <fstream>
#include <iomanip>
#include <cmath>
#include <algorithm>
#include <map>
#include "plink.h"
#include "fisher.h"
#include "stats.h"
#include "helper.h"
#include "options.h"
#include "crandom.h"
#include "sets.h"
#include "perm.h"
#include "phase.h"
using namespace std;
extern ofstream LOG;
////////////////////////////////////////
// Standard 2x2 allelic association tests
// Genotypic, and quantitative trait association
// Permutation (within-cluster)
void Plink::calcAssociationWithPermutation(Perm & perm)
{
// SNP-major mode analyses?
if (par::assoc_glm)
{
if (par::SNP_major)
SNP2Ind();
}
else if (!par::SNP_major)
Ind2SNP();
//////////////////////////////
// Profile-based set test?
// if ( par::set_score )
// pS->profileTestInitialise();
//////////////////////////////
// LD-clump within each set?
if ( par::set_test && par::set_r2 )
{
printLOG("Performing LD-based set test, with parameters:\n");
printLOG(" r-squared (--set-r2) = " dbl2str( par::set_r2_val ) "\n" );
printLOG(" p-value (--set-p) = " dbl2str( chiprobP(par::set_chisq_threshold,1) ) "\n" );
printLOG(" max # SNPs (--set-max) = " int2str( par::set_max ) "\n" );
pS->makeLDSets();
}
//////////////////////////////
// Step-wise set tests
if ( par::set_step )
{
vector_t r = pS->fitStepwiseModel();
shutdown();
}
// Basic association testing results
vector<double> original;
//////////////////////
// Empirical p-values
////////////////////
// Number of tests
int ntests = nl_all;
if ( par::set_test && ( par::set_r2 || par::set_score ) )
ntests = pS->snpset.size();
perm.setTests(ntests);
// Case/control missingness test statistics
vector<double> missing;
// Observed marginals
int aff;
int unf;
// Odds ratio
vector<double> odds(nl_all);
//////////////////////////////////////////
// Working vectors for assoc_test_alt_perm
vector<int> a1;
vector<int> a2;
vector<int> a0;
// Expected values for the 2x2 test
vector<double> exp_afffreq1;
vector<double> exp_afffreq2;
vector<double> exp_unffreq1;
vector<double> exp_unffreq2;
if (par::assoc_test_alt_perm)
{
a1.resize(nl_all);
a2.resize(nl_all);
a0.resize(nl_all);
exp_afffreq1.resize(nl_all);
exp_afffreq2.resize(nl_all);
exp_unffreq1.resize(nl_all);
exp_unffreq2.resize(nl_all);
}
////////////////////////////////
// Set up permutation structure
// (we need to perform this step
// whether or not we also
// subsequently permute)
perm.setPermClusters(*this);
perm.originalOrder();
////////////////////////////////////////////////////////
// Perform a test of missingness by case/control status
if (par::test_missing && par::bt)
original = testMiss(perm,true);
//////////////////////////////////////////
// Calculate original association results
else if (par::bt)
{
if (par::full_model_assoc)
{
////////////////////////////////////
// Full model case/control test
original = fullModelAssoc(true,perm);
}
else if (par::assoc_glm)
{
///////////////////////////
// Logistic regression test
original = glmAssoc(true,perm);
}
else if (par::CMH_test_1)
{
//////////////////////////////////////////
// 2 x 2 x K Cochran-Mantel-Haenszel test
original = calcMantelHaenszel_2x2xK(perm, true);
}
else if ( par::test_hap_GLM )
{
///////////////////////////////////
// Haplotypic GLM tests (logistic)
string f = par::output_file_name ".assoc.hap.logistic";
printLOG("Writing haplotype results to [ " f " ]\n");
haplo->HTEST.open(f.c_str(), ios::out);
haplo->HTEST.precision(3);
haplo->HTEST << setw(4) << "NSNP" << " "
<< setw(4) << "NHAP" << " "
<< setw(4) << "CHR" << " "
<< setw(12) << "BP1" << " "
<< setw(12) << "BP2" << " "
<< setw(par::pp_maxsnp) << "SNP1" << " "
<< setw(par::pp_maxsnp) << "SNP2" << " ";
if ( ! par::test_hap_GLM_omnibus )
{
haplo->HTEST << setw(12) << "HAPLOTYPE" << " "
<< setw(8) << "F" << " "
<< setw(8) << "OR" << " ";
}
haplo->HTEST << setw(8) << "STAT" << " "
<< setw(8) << "P" << "\n";
original = haplo->phaseAllHaplotypes(true,perm);
haplo->HTEST.close();
}
else
{
//////////////////////////////////////
// Standard allelic case/control test
original = testAssoc(aff,unf,
a1,a2,a0,
odds,
exp_afffreq1, exp_afffreq2,
exp_unffreq1, exp_unffreq2,
perm,
true);
}
}
else if (par::qt)
{
if (par::assoc_glm)
{
///////////////////////////
// Linear regression test
original = glmAssoc(true,perm);
}
else if ( par::test_hap_GLM )
{
///////////////////////////
// Haplotypic GLM tests (linear)
string f = par::output_file_name ".assoc.hap.linear";
printLOG("Writing haplotype results to [ " f " ]\n");
haplo->HTEST.open(f.c_str(), ios::out);
haplo->HTEST.precision(3);
haplo->HTEST << setw(4) << "NSNP" << " "
<< setw(4) << "NHAP" << " "
<< setw(4) << "CHR" << " "
<< setw(12) << "BP1" << " "
<< setw(12) << "BP2" << " "
<< setw(par::pp_maxsnp) << "SNP1" << " "
<< setw(par::pp_maxsnp) << "SNP2" << " ";
if ( ! par::test_hap_GLM_omnibus )
{
haplo->HTEST << setw(12) << "HAPLOTYPE" << " "
<< setw(8) << "F" << " "
<< setw(8) << "BETA" << " ";
}
haplo->HTEST << setw(8) << "STAT" << " "
<< setw(8) << "P" << "\n";
original = haplo->phaseAllHaplotypes(true,perm);
haplo->HTEST.close();
}
else
{
////////////////////////////////////
// Quantitative trait regression
original = testQAssoc(true,perm);
}
}
// If we didn't know how many values to expect back,
// resize now (i.e. from haplotype tests)
if ( par::test_hap_GLM )
{
ntests = original.size();
perm.setTests(ntests);
}
////////////////////////////
// Display corrected p-values?
string f0 = ".assoc";
if (par::test_missing && par::bt)
f0 = ".missing";
else if (par::qt && !par::assoc_glm)
f0 = ".qassoc";
else if (par::bt && par::assoc_glm)
f0 = ".logistic";
else if (par::qt && par::assoc_glm)
f0 = ".linear";
else if (par::CMH_test_1)
f0 = ".cmh";
else if (par::test_hap_GLM)
f0 = par::bt ? ".hap.logistic" : ".hap.linear";
else if (par::full_model_assoc)
{
if (par::model_perm_best && par::permute)
{
f0 = ".model.best";
printLOG("Using BEST of ALLELIC, DOM and REC for --model permutation\n");
}
else if (par::model_perm_gen && par::permute)
{f0 = ".model.gen"; printLOG("Using GENO for --model permutation\n"); }
else if (par::model_perm_dom)
{f0 = ".model.dom"; printLOG("Using DOM for --model permutation/adjusted p-values\n"); }
else if (par::model_perm_rec)
{f0 = ".model.rec"; printLOG("Using REC for --model permutation/adjusted p-values\n"); }
else if (par::model_perm_trend)
{f0 = ".model.trend"; printLOG("Using CA-trend test for --model permutation/adjusted p-values\n"); }
}
if (par::fisher_test )
f0 = ".fisher";
// Profile-based set-test?
if ( par::set_test && par::set_r2 )
f0 = ".set";
else if ( par::set_test && par::set_score )
f0 = ".set.score";
// Assumes we have 1 df chi-square statistics
if (par::multtest)
{
vector<double> obp(0);
if ( par::fisher_test )
{
for (int l=0; l<ntests;l )
{
double chi = original[l] > 0 && realnum(original[l]) ? inverse_chiprob( 1-original[l] , 1) : -9;
obp.push_back( chi ) ;
}
}
else
{
for (int l=0; l<ntests;l )
obp.push_back(original[l]);
}
multcomp(obp,f0);
}
////////////////////////////
// If no permutation, then
// leave now
if (!par::permute)
return;
////////////////////////
// Score original sets
vector<int> setsigsize;
if (par::set_test)
{
if ( par::set_r2 || par::set_score )
{
// Score...
// original = pS->profileTestScore();
original = pS->fitLDSetTest(original,true);
// ...and save # of significant SNPs
setsigsize.clear();
for (int i=0; i<pS->profileSNPs.size(); i )
setsigsize.push_back( pS->s_min[i] );
}
else
pS->cumulativeSetSum_WITHLABELS(*this,original);
}
/////////////////////////////
// Ordered/rank permutation?
if (par::mperm_rank)
perm.setOriginalRanking(original);
//////////////////////
// Verbose dumping?
if (par::mperm_save_all)
printLOG("Dumping all permutation statistics to [ "
par::output_file_name ".mperm.dump.all ]\n");
else if (par::mperm_save_best)
printLOG("Dumping best permutation statistics to [ "
par::output_file_name ".mperm.dump.best ]\n");
//////////////////////
// Begin permutations
bool finished = par::replicates == 0 ? true : false;
while(!finished)
{
// Store permuted results
vector<double> pr(ntests);
if (par::perm_genedrop)
{
if (par::perm_genedrop_and_swap)
perm.permuteInCluster();
perm.geneDrop();
}
else
perm.permuteInCluster();
if (par::test_missing)
pr = testMiss(perm,false);
else if ((!par::assoc_test_alt_perm)
|| par::qt
|| par::full_model_assoc
|| par::CMH_test_1
|| par::assoc_glm)
{
if (par::qt)
{
if (par::assoc_glm)
pr = glmAssoc(false,perm);
else if ( par::test_hap_GLM )
pr = haplo->phaseAllHaplotypes(false,perm);
else
pr = testQAssoc(false,perm);
}
else if (par::full_model_assoc)
pr = fullModelAssoc(false,perm);
else if (par::assoc_glm)
pr = glmAssoc(false,perm);
else if (par::CMH_test_1)
pr = calcMantelHaenszel_2x2xK(perm, false);
else if ( par::test_hap_GLM )
pr = haplo->phaseAllHaplotypes(false,perm);
else
pr = testAssoc(aff,unf,
a1,a2,a0,
odds,
exp_afffreq1, exp_afffreq2,
exp_unffreq1, exp_unffreq2,
perm,
false);
}
else
{
/////////////////////////
// For binary traits only
// -------------
// | A | B | E | aff
// -------------
// | C | D | F | unf
// -------------
// a1 a2 a0
// a1 most likely to be common, followed by a2, then a0
// save aff unf (do not alter by locus)
// and a1,a2,a0 marginals (which do alter by locus)
// then we only need count A and B in each subsequent replicate:
//
int A, B, C, D, M;
///////////////////////////////
// Iterate over SNPs
vector<CSNP*>::iterator s = SNP.begin();
int l=0;
while ( s != SNP.end() )
{
// In adaptive mode, possibly skip this test
if (par::adaptive_perm && (!perm.snp_test[l]))
{
s ;
l ;
continue;
}
/////////////////
// clear counts
D=M=0;
/////////////////
// Autosomal or haploid?
bool X=false, haploid=false;
if (par::chr_sex[locus[l]->chr]) X=true;
else if (par::chr_haploid[locus[l]->chr]) haploid=true;
/////////////////////////////
// Iterate over individuals
vector<bool>::iterator i1 = (*s)->one.begin();
vector<bool>::iterator i2 = (*s)->two.begin();
vector<Individual*>::iterator gperson = sample.begin();
while ( gperson != sample.end() )
{
// Phenotype for this person (i.e. might be permuted)
Individual * pperson = (*gperson)->pperson;
// SNP alleles
bool s1 = *i1;
bool s2 = *i2;
if ( ! pperson->missing )
{
if (! pperson->aff ) // unaffected
{
if ( haploid || ( X && (*gperson)->sex ) )
{
if ( s2 )
D ; // (hemi, one count)
else if ( s1 ) M ; // (missing, one count)
}
else
{
if ( s2 )
{
if (!s1) D ; // (het, one A count)
else D =2; // (hom, two B count)
}
else if ( s1 ) M =2; // (missing, two B count)
}
}
}
// Next individual
gperson ;
i1 ;
i2 ;
}
// reconstruct rest of 2x2 table
C = unf - D - M;
A = a1[l] - C;
B = a2[l] - D;
pr[l] = ( (A - exp_afffreq1[l]) * ( A - exp_afffreq1[l] ) ) / exp_afffreq1[l]
( (C - exp_unffreq1[l]) * ( C - exp_unffreq1[l] ) ) / exp_unffreq1[l]
( (B - exp_afffreq2[l]) * ( B - exp_afffreq2[l] ) ) / exp_afffreq2[l]
( (D - exp_unffreq2[l]) * ( D - exp_unffreq2[l] ) ) / exp_unffreq2[l];
// Next SNP
s ;
l ;
}
}
//////////////////////
// Make sets?
if (par::set_test)
{
if ( par::set_r2 )
pr = pS->fitLDSetTest(pr,false);
else if ( par::set_score )
pr = pS->profileTestScore();
else
pS->cumulativeSetSum_WITHOUTLABELS(pr,perm.current_reps() 1);
}
////////////////////////////////
// Standard permutation counting
finished = perm.update(pr,original);
} // next permutation
if (!par::silent)
cout << "\n\n";
/////////////////////////////////////////////////////
// //
// Calculate SET-based empirical p-values //
// //
/////////////////////////////////////////////////////
if (par::set_test && ! (par::set_r2 || par::set_score) )
{
printLOG("Calculating empirical SET-based p-values\n");
pS->empiricalSetPValues();
}
/////////////////////////////////////////////////////
// //
// Display basic permutation results //
// //
/////////////////////////////////////////////////////
ofstream ASC;
string f;
if (par::adaptive_perm) f = par::output_file_name f0 ".perm";
else f = par::output_file_name f0 ".mperm";
ASC.open(f.c_str(),ios::out);
ASC.precision(4);
printLOG("Writing permutation association results to [ " f " ] \n");
if ( par::test_hap_GLM )
ASC << setw(10) << "TEST" << " ";
else if ( ! ( par::set_score || par::set_r2 ) )
{
ASC << setw(4) << "CHR" << " "
<< setw(par::pp_maxsnp)<< "SNP" << " ";
}
else
{
ASC << setw(12) << "SET" << " "
<< setw(6) << "NSNP" << " "
<< setw(6) << "NSIG" << " "
<< setw(6) << "ISIG" << " ";
}
ASC << setw(12) << "EMP1" << " ";
if ( !par::set_r2 )
{
if (par::adaptive_perm)
ASC << setw(12)<< "NP" << " ";
else if ( par::mperm_rank )
ASC << setw(12)<< "EMP3" << " "
<< setw(12)<< "RANK" << " ";
else
ASC << setw(12)<< "EMP2" << " ";
}
else
ASC << "SNPS";
ASC << "\n";
vector<double> pv(0);
for (int l=0; l<ntests; l )
{
// Skip?, if filtering p-values
if ( par::pfilter && perm.pvalue(l) > par::pfvalue )
continue;
if ( par::test_hap_GLM )
{
ASC << setw(10) << ("T" int2str(l)) << " ";
}
else if ( ! (par::set_score || par::set_r2 ) )
{
ASC << setw(4) << locus[l]->chr << " "
<< setw(par::pp_maxsnp) << locus[l]->name << " ";
}
else
{
ASC << setw(12) << setname[l] << " "
<< setw(6) << pS->snpset[l].size() << " "
<< setw(6) << pS->numSig[l] << " "
<< setw(6) << pS->selectedSNPs[l].size() << " ";
}
// All tests are 1 df
double p = chiprobP(original[l],1);
// ... except 2df genotypic test
if ( par::model_perm_gen )
p = chiprobP(original[l],2);
if (par::multtest)
pv.push_back(p);
ASC << setw(12) << perm.pvalue(l) << " ";
if ( ! par::set_r2 )
{
if (par::adaptive_perm)
ASC << setw(12) << perm.reps_done(l) << " ";
else if ( par::mperm_rank )
ASC << setw(12) << perm.max_pvalue(l) << " "
<< setw(12) << perm.rank(l) << " ";
else
ASC << setw(12) << perm.max_pvalue(l) << " ";
}
else
{
if ( pS->selectedSNPs[l].size() == 0 )
ASC << "NA";
else
for (int j=0; j<pS->selectedSNPs[l].size(); j )
{
ASC << locus[ snpset[l][pS->selectedSNPs[l][j]] ]->name;
if ( j < pS->selectedSNPs[l].size() - 1 )
ASC << "|";
}
}
ASC << "\n";
}
ASC.close();
////////////////////////////////////////////////////////
// //
// Display SET-based results (sum statistics) //
// //
////////////////////////////////////////////////////////
if (par::set_test && ! (par::set_r2 || par::set_score) )
{
f = par::output_file_name f0 ".set";
ASC.open(f.c_str(),ios::out);
printLOG("Writing set-based association results to [ " f " ] \n");
ASC.clear();
ASC << setw(12) << "SET" << " "
<< setw(6) << "NSNP" << " "
<< setw(6) << "S" << " "
<< setw(par::pp_maxsnp) << "SNP" << " "
<< setw(12) << "T" << " "
<< setw(12) << "P_0" << " "
<< setw(12) << "P_1" << " "
<< setw(12) << "P_2" << " "
<< "\n";
for (int i=0;i<pS->pv_set.size();i )
{
if ( pS->pv_set[i].size()>0 ) ASC << "\n";
for (int j=0;j<pS->pv_set[i].size();j )
{
ASC << setw(12) << setname[i] << " "
<< setw(6) << pS->snpset[i].size() << " "
<< setw(6)
<< string("S" int2str(j 1 pS->s_min[i])) << " "
<< setw(par::pp_maxsnp)
<< pS->setsort[i][j] << " "
<< setw(12)
<< pS->stat_set[i][j][0] << " "
<< setw(12)
<< pS->pv_set[i][j][0] << " "
<< setw(12)
<< pS->pv_maxG_set[i][j]/(par::replicates 1) << " "
<< setw(12)
<< pS->pv_maxE_set[i][j]/(par::replicates 1) << "\n";
}
}
ASC.close();
}
// Call destructor to close PDUMP file
perm.closeDUMP();
}
/////////////////////////////////////////////////
// Basic C/C association test
vector<double> Plink::testAssoc(int & aff,
int & unf,
vector<int> & a1,
vector<int> & a2,
vector<int> & a0,
vector<double> & odds,
vector<double> & exp_afffreq1,
vector<double> & exp_afffreq2,
vector<double> & exp_unffreq1,
vector<double> & exp_unffreq2,
Perm & perm,
bool display)
{
ofstream ASC;
if (display)
{
////////////////////
// Display results
string f = par::output_file_name ".assoc";
if ( par::fisher_test )
f = ".fisher";
ASC.open(f.c_str(),ios::out);
ASC.precision(4);
printLOG("Writing main association results to [ " f " ] \n");
ASC << setw(4) << "CHR" << " "
<< setw(par::pp_maxsnp)<< "SNP" << " "
<< setw(10) << "BP" << " "
<< setw(4) << "A1" << " ";
if ( par::assoc_counts )
ASC << setw(8) << "C_A" << " "
<< setw(8) << "C_U" << " ";
else
ASC << setw(8) << "F_A" << " "
<< setw(8) << "F_U" << " ";
ASC << setw(4) << "A2" << " ";
if ( ! par::fisher_test )
ASC << setw(12)<< "CHISQ" << " ";
ASC << setw(12)<< "P" << " "
<< setw(12)<< "OR" << " " ;
if (par::display_ci)
ASC << setw(12)<< "SE" << " "
<< setw(12) << string("L" dbl2str(par::ci_level*100)) << " "
<< setw(12) << string("U" dbl2str(par::ci_level*100)) << " ";
ASC << "\n";
}
vector<double> original(nl_all);
///////////////////////////////
// Iterate over SNPs
vector<CSNP*>::iterator s = SNP.begin();
int l=0;
while ( s != SNP.end() )
{
// In adaptive mode, possibly skip this test
if (par::adaptive_perm && (!perm.snp_test[l]))
{
s ;
l ;
continue;
}
int A1 = 0, A2 = 0, A0 = 0;
int U1 = 0, U2 = 0, U0 = 0;
bool X=false, haploid=false;
if (par::chr_sex[locus[l]->chr]) X=true;
else if (par::chr_haploid[locus[l]->chr]) haploid=true;
/////////////////////////////
// Iterate over individuals
vector<bool>::iterator i1 = (*s)->one.begin();
vector<bool>::iterator i2 = (*s)->two.begin();
vector<Individual*>::iterator gperson = sample.begin();
while ( gperson != sample.end() )
{
// Phenotype for this person (i.e. might be permuted)
Individual * pperson = (*gperson)->pperson;
// Is this individual missing?
if ( pperson->missing )
{
// Next person
gperson ;
i1 ;
i2 ;
continue;
}
// SNP alleles
bool s1 = *i1;
bool s2 = *i2;
// Type of marker
if ( haploid || ( X && (*gperson)->sex ) )
{
/////////////////////////////////////
// Haploid marker (or male X)
if (pperson->aff) // if affected
{
if (!s1)
{
if (!s2)
A1 ;
}
else
{
if ( s2 )
A2 ;
else A0 ;
}
}
else // unaffected if not missing
{
if (!s1)
{
if (!s2)
U1 ;
}
else
{
if ( s2)
U2 ;
else U0 ;
}
}
}
else
{
/////////////////////////////////////
// Autosomal marker
if (pperson->aff) // if affected
{
if (!s1)
{
if (!s2)
A1 =2;
else
{ A1 ; A2 ; }
}
else
{
if ( s2 )
A2 =2;
else A0 =2;
}
}
else // unaffected if not missing
{
if (!s1)
{
if (!s2)
U1 =2;
else
{ U1 ; U2 ; }
}
else
{
if ( s2 )
U2 =2;