diff options
| author | xiangzhou | 2016-05-23 17:05:35 -0400 |
|---|---|---|
| committer | xiangzhou | 2016-05-23 17:05:35 -0400 |
| commit | 943e970c9cbc184dcca679fbe455f48c32242cdc (patch) | |
| tree | 70369d969dee3432e09e345c8106a541ac0d5a76 /src/gemma.cpp | |
| parent | 3ab77854a52ac016b9e2b824858f5f0c695d4170 (diff) | |
| download | pangemma-943e970c9cbc184dcca679fbe455f48c32242cdc.tar.gz | |
version 0.95alpha
Diffstat (limited to 'src/gemma.cpp')
| -rw-r--r-- | src/gemma.cpp | 1840 |
1 files changed, 1416 insertions, 424 deletions
diff --git a/src/gemma.cpp b/src/gemma.cpp index b8693a8..3b9fe29 100644 --- a/src/gemma.cpp +++ b/src/gemma.cpp @@ -39,9 +39,11 @@ #include "vc_float.h" #include "lm_float.h" //for LM class #include "bslmm_float.h" //for BSLMM class +#include "ldr_float.h" //for LDR class #include "lmm_float.h" //for LMM class, and functions CalcLambda, CalcPve, CalcVgVe #include "mvlmm_float.h" //for MVLMM class #include "prdt_float.h" //for PRDT class +#include "varcov_float.h" //for MVLMM class #include "mathfunc_float.h" //for a few functions #else #include "io.h" @@ -49,9 +51,11 @@ #include "vc.h" #include "lm.h" #include "bslmm.h" +#include "ldr.h" #include "lmm.h" #include "mvlmm.h" #include "prdt.h" +#include "varcov.h" #include "mathfunc.h" #endif @@ -60,26 +64,23 @@ using namespace std; -GEMMA::GEMMA(void): -version("0.95alpha"), date("08/08/2014"), year("2011") +GEMMA::GEMMA(void): +version("0.95alpha"), date("07/11/2015"), year("2011") {} void GEMMA::PrintHeader (void) { cout<<endl; cout<<"*********************************************************"<<endl; - cout<<" Genome-wide Efficient Mixed Model Association (GEMMA) "<<endl; + cout<<" Genome-wide Efficient Mixed Model Association (GEMMA) "<<endl; cout<<" Version "<<version<<", "<<date<<" "<<endl; - cout<<" Visit "<<endl; - cout<<" http://stephenslab.uchicago.edu/software.html "<<endl; - cout<<" http://home.uchicago.edu/~xz7/software.html "<<endl; - cout<<" For Possible Updates "<<endl; + cout<<" Visit http://www.xzlab.org/software.html For Updates "<<endl; cout<<" (C) "<<year<<" Xiang Zhou "<<endl; - cout<<" GNU General Public License "<<endl; - cout<<" For Help, Type ./gemma -h "<<endl; + cout<<" GNU General Public License "<<endl; + cout<<" For Help, Type ./gemma -h "<<endl; cout<<"*********************************************************"<<endl; cout<<endl; - + return; } @@ -89,13 +90,13 @@ void GEMMA::PrintLicense (void) cout<<endl; cout<<"The Software Is Distributed Under GNU General Public License, But May Also Require The Following Notifications."<<endl; cout<<endl; - + cout<<"Including Lapack Routines In The Software May Require The Following Notification:"<<endl; cout<<"Copyright (c) 1992-2010 The University of Tennessee and The University of Tennessee Research Foundation. All rights reserved."<<endl; cout<<"Copyright (c) 2000-2010 The University of California Berkeley. All rights reserved."<<endl; - cout<<"Copyright (c) 2006-2010 The University of Colorado Denver. All rights reserved."<<endl; + cout<<"Copyright (c) 2006-2010 The University of Colorado Denver. All rights reserved."<<endl; cout<<endl; - + cout<<"$COPYRIGHT$"<<endl; cout<<"Additional copyrights may follow"<<endl; cout<<"$HEADER$"<<endl; @@ -113,9 +114,9 @@ void GEMMA::PrintLicense (void) <<"THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE " <<"OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."<<endl; cout<<endl; - - - + + + return; } @@ -124,9 +125,9 @@ void GEMMA::PrintLicense (void) void GEMMA::PrintHelp(size_t option) { if (option==0) { - cout<<endl; + cout<<endl; cout<<" GEMMA version "<<version<<", released on "<<date<<endl; - cout<<" implemented by Xiang Zhou"<<endl; + cout<<" implemented by Xiang Zhou"<<endl; cout<<endl; cout<<" type ./gemma -h [num] for detailed helps"<<endl; cout<<" options: " << endl; @@ -135,72 +136,116 @@ void GEMMA::PrintHelp(size_t option) cout<<" 3: SNP QC"<<endl; cout<<" 4: calculate relatedness matrix"<<endl; cout<<" 5: perform eigen decomposition"<<endl; - cout<<" 6: perform variance component estiamtion"<<endl; + cout<<" 6: perform variance component estimation"<<endl; cout<<" 7: fit a linear model"<<endl; cout<<" 8: fit a linear mixed model"<<endl; cout<<" 9: fit a multivariate linear mixed model"<<endl; cout<<" 10: fit a Bayesian sparse linear mixed model"<<endl; cout<<" 11: obtain predicted values"<<endl; - cout<<" 12: note"<<endl; + cout<<" 12: calculate snp variance covariance"<<endl; + cout<<" 13: note"<<endl; cout<<endl; - } - + } + if (option==1) { cout<<" QUICK GUIDE" << endl; cout<<" to generate a relatedness matrix: "<<endl; cout<<" ./gemma -bfile [prefix] -gk [num] -o [prefix]"<<endl; cout<<" ./gemma -g [filename] -p [filename] -gk [num] -o [prefix]"<<endl; + cout<<" to generate the S matrix: "<<endl; + cout<<" ./gemma -bfile [prefix] -gs -o [prefix]"<<endl; + cout<<" ./gemma -p [filename] -g [filename] -gs -o [prefix]"<<endl; + cout<<" ./gemma -bfile [prefix] -cat [filename] -gs -o [prefix]"<<endl; + cout<<" ./gemma -p [filename] -g [filename] -cat [filename] -gs -o [prefix]"<<endl; + cout<<" ./gemma -bfile [prefix] -sample [num] -gs -o [prefix]"<<endl; + cout<<" ./gemma -p [filename] -g [filename] -sample [num] -gs -o [prefix]"<<endl; + cout<<" to generate the q vector: "<<endl; + cout<<" ./gemma -beta [filename] -gq -o [prefix]"<<endl; + cout<<" ./gemma -beta [filename] -cat [filename] -gq -o [prefix]"<<endl; + cout<<" to generate the ldsc weigthts: "<<endl; + cout<<" ./gemma -beta [filename] -gw -o [prefix]"<<endl; + cout<<" ./gemma -beta [filename] -cat [filename] -gw -o [prefix]"<<endl; cout<<" to perform eigen decomposition of the relatedness matrix: "<<endl; cout<<" ./gemma -bfile [prefix] -k [filename] -eigen -o [prefix]"<<endl; cout<<" ./gemma -g [filename] -p [filename] -k [filename] -eigen -o [prefix]"<<endl; cout<<" to estimate variance components: "<<endl; - cout<<" ./gemma -bfile [prefix] -k [filename] -vc -o [prefix]"<<endl; - cout<<" ./gemma -p [filename] -k [filename] -vc -o [prefix]"<<endl; - cout<<" ./gemma -bfile [prefix] -mk [filename] -vc -o [prefix]"<<endl; - cout<<" ./gemma -p [filename] -mk [filename] -vc -o [prefix]"<<endl; + cout<<" ./gemma -bfile [prefix] -k [filename] -vc [num] -o [prefix]"<<endl; + cout<<" ./gemma -p [filename] -k [filename] -vc [num] -o [prefix]"<<endl; + cout<<" ./gemma -bfile [prefix] -mk [filename] -vc [num] -o [prefix]"<<endl; + cout<<" ./gemma -p [filename] -mk [filename] -vc [num] -o [prefix]"<<endl; + cout<<" ./gemma -beta [filename] -cor [filename] -vc [num] -o [prefix]"<<endl; + cout<<" ./gemma -beta [filename] -cor [filename] -cat [filename] -vc [num] -o [prefix]"<<endl; + cout<<" options for the above two commands: -crt -windowbp [num]"<<endl; + cout<<" ./gemma -mq [filename] -ms [filename] -mv [filename] -vc [num] -o [prefix]"<<endl; + cout<<" or with summary statistics, replace bfile with mbfile, or g or mg; vc=1 for HE weights and vc=2 for LDSC weights"<<endl; + cout<<" ./gemma -beta [filename] -bfile [filename] -cat [filename] -wsnp [filename] -wcat [filename] -vc [num] -o [prefix]"<<endl; + cout<<" ./gemma -beta [filename] -bfile [filename] -cat [filename] -wsnp [filename] -wcat [filename] -ci [num] -o [prefix]"<<endl; cout<<" to fit a linear mixed model: "<<endl; cout<<" ./gemma -bfile [prefix] -k [filename] -lmm [num] -o [prefix]"<<endl; - cout<<" ./gemma -g [filename] -p [filename] -a [filename] -k [filename] -lmm [num] -o [prefix]"<<endl; + cout<<" ./gemma -g [filename] -p [filename] -a [filename] -k [filename] -lmm [num] -o [prefix]"<<endl; + cout<<" to fit a linear mixed model to test g by e effects: "<<endl; + cout<<" ./gemma -bfile [prefix] -gxe [filename] -k [filename] -lmm [num] -o [prefix]"<<endl; + cout<<" ./gemma -g [filename] -p [filename] -a [filename] -gxe [filename] -k [filename] -lmm [num] -o [prefix]"<<endl; + cout<<" to fit a univariate linear mixed model with different residual weights for different individuals: "<<endl; + cout<<" ./gemma -bfile [prefix] -weight [filename] -k [filename] -lmm [num] -o [prefix]"<<endl; + cout<<" ./gemma -g [filename] -p [filename] -a [filename] -weight [filename] -k [filename] -lmm [num] -o [prefix]"<<endl; cout<<" to fit a multivariate linear mixed model: "<<endl; cout<<" ./gemma -bfile [prefix] -k [filename] -lmm [num] -n [num1] [num2] -o [prefix]"<<endl; - cout<<" ./gemma -g [filename] -p [filename] -a [filename] -k [filename] -lmm [num] -n [num1] [num2] -o [prefix]"<<endl; + cout<<" ./gemma -g [filename] -p [filename] -a [filename] -k [filename] -lmm [num] -n [num1] [num2] -o [prefix]"<<endl; cout<<" to fit a Bayesian sparse linear mixed model: "<<endl; cout<<" ./gemma -bfile [prefix] -bslmm [num] -o [prefix]"<<endl; cout<<" ./gemma -g [filename] -p [filename] -a [filename] -bslmm [num] -o [prefix]"<<endl; cout<<" to obtain predicted values: "<<endl; cout<<" ./gemma -bfile [prefix] -epm [filename] -emu [filename] -ebv [filename] -k [filename] -predict [num] -o [prefix]"<<endl; cout<<" ./gemma -g [filename] -p [filename] -epm [filename] -emu [filename] -ebv [filename] -k [filename] -predict [num] -o [prefix]"<<endl; + cout<<" to calculate correlations between SNPs: "<<endl; + cout<<" ./gemma -bfile [prefix] -calccor -o [prefix]"<<endl; + cout<<" ./gemma -g [filename] -p [filename] -calccor -o [prefix]"<<endl; cout<<endl; } - + if (option==2) { cout<<" FILE I/O RELATED OPTIONS" << endl; - cout<<" -bfile [prefix] "<<" specify input PLINK binary ped file prefix."<<endl; - cout<<" requires: *.fam, *.bim and *.bed files"<<endl; + cout<<" -bfile [prefix] "<<" specify input PLINK binary ped file prefix."<<endl; + cout<<" requires: *.fam, *.bim and *.bed files"<<endl; cout<<" missing value: -9"<<endl; cout<<" -g [filename] "<<" specify input BIMBAM mean genotype file name"<<endl; - cout<<" format: rs#1, allele0, allele1, genotype for individual 1, genotype for individual 2, ..."<<endl; - cout<<" rs#2, allele0, allele1, genotype for individual 1, genotype for individual 2, ..."<<endl; - cout<<" ..."<<endl; - cout<<" missing value: NA"<<endl; + cout<<" format: rs#1, allele0, allele1, genotype for individual 1, genotype for individual 2, ..."<<endl; + cout<<" rs#2, allele0, allele1, genotype for individual 1, genotype for individual 2, ..."<<endl; + cout<<" ..."<<endl; + cout<<" missing value: NA"<<endl; cout<<" -p [filename] "<<" specify input BIMBAM phenotype file name"<<endl; - cout<<" format: phenotype for individual 1"<<endl; - cout<<" phenotype for individual 2"<<endl; + cout<<" format: phenotype for individual 1"<<endl; + cout<<" phenotype for individual 2"<<endl; + cout<<" ..."<<endl; + cout<<" missing value: NA"<<endl; + cout<<" -a [filename] "<<" specify input BIMBAM SNP annotation file name (optional)"<<endl; + cout<<" format: rs#1, base_position, chr_number"<<endl; + cout<<" rs#2, base_position, chr_number"<<endl; cout<<" ..."<<endl; - cout<<" missing value: NA"<<endl; - cout<<" -a [filename] "<<" specify input BIMBAM SNP annotation file name (optional)"<<endl; - cout<<" format: rs#1, base_position, chr_number"<<endl; - cout<<" rs#2, base_position, chr_number"<<endl; + // WJA added + cout<<" -oxford [prefix] "<<" specify input Oxford genotype bgen file prefix."<<endl; + cout<<" requires: *.bgen, *.sample files"<<endl; + + cout<<" -gxe [filename] "<<" specify input file that contains a column of environmental factor for g by e tests"<<endl; + cout<<" format: variable for individual 1"<<endl; + cout<<" variable for individual 2"<<endl; cout<<" ..."<<endl; - cout<<" -k [filename] "<<" specify input kinship/relatedness matrix file name"<<endl; - cout<<" -mk [filename] "<<" specify input file which contains a list of kinship/relatedness matrices"<<endl; - cout<<" -u [filename] "<<" specify input file containing the eigen vectors of the kinship/relatedness matrix"<<endl; - cout<<" -d [filename] "<<" specify input file containing the eigen values of the kinship/relatedness matrix"<<endl; - cout<<" -c [filename] "<<" specify input covariates file name (optional)"<<endl; - cout<<" format: covariate 1 for individual 1, ... , covariate c for individual 1"<<endl; - cout<<" covariate 1 for individual 2, ... , covariate c for individual 2"<<endl; + cout<<" missing value: NA"<<endl; + cout<<" -widv [filename] "<<" specify input file that contains a column of residual weights"<<endl; + cout<<" format: variable for individual 1"<<endl; + cout<<" variable for individual 2"<<endl; cout<<" ..."<<endl; - cout<<" missing value: NA"<<endl; + cout<<" missing value: NA"<<endl; + cout<<" -k [filename] "<<" specify input kinship/relatedness matrix file name"<<endl; + cout<<" -mk [filename] "<<" specify input file which contains a list of kinship/relatedness matrices"<<endl; + cout<<" -u [filename] "<<" specify input file containing the eigen vectors of the kinship/relatedness matrix"<<endl; + cout<<" -d [filename] "<<" specify input file containing the eigen values of the kinship/relatedness matrix"<<endl; + cout<<" -c [filename] "<<" specify input covariates file name (optional)"<<endl; + cout<<" -cat [filename] "<<" specify input category file name (optional), which contains rs cat1 cat2 ..."<<endl; + cout<<" -beta [filename] "<<" specify input beta file name (optional), which contains rs beta se_beta n_total (or n_mis and n_obs) estimates from a lm model"<<endl; + cout<<" -cor [filename] "<<" specify input correlation file name (optional), which contains rs window_size correlations from snps"<<endl; + cout<<" missing value: NA"<<endl; cout<<" note: the intercept (a column of 1s) may need to be included"<<endl; cout<<" -epm [filename] "<<" specify input estimated parameter file name"<<endl; cout<<" -en [n1] [n2] [n3] [n4] "<<" specify values for the input estimated parameter file (with a header)"<<endl; @@ -210,74 +255,81 @@ void GEMMA::PrintHelp(size_t option) cout<<" n4: estimated gamma column number (0 to ignore)"<<endl; cout<<" default: 2 4 5 6 if -ebv is not specified; 2 0 5 6 if -ebv is specified"<<endl; cout<<" -ebv [filename] "<<" specify input estimated random effect (breeding value) file name"<<endl; - cout<<" format: value for individual 1"<<endl; - cout<<" value for individual 2"<<endl; + cout<<" format: value for individual 1"<<endl; + cout<<" value for individual 2"<<endl; cout<<" ..."<<endl; - cout<<" missing value: NA"<<endl; + cout<<" missing value: NA"<<endl; cout<<" -emu [filename] "<<" specify input log file name containing estimated mean"<<endl; cout<<" -mu [num] "<<" specify input estimated mean value"<<endl; cout<<" -gene [filename] "<<" specify input gene expression file name"<<endl; - cout<<" format: header"<<endl; - cout<<" gene1, count for individual 1, count for individual 2, ..."<<endl; - cout<<" gene2, count for individual 1, count for individual 2, ..."<<endl; + cout<<" format: header"<<endl; + cout<<" gene1, count for individual 1, count for individual 2, ..."<<endl; + cout<<" gene2, count for individual 1, count for individual 2, ..."<<endl; cout<<" ..."<<endl; - cout<<" missing value: not allowed"<<endl; + cout<<" missing value: not allowed"<<endl; cout<<" -r [filename] "<<" specify input total read count file name"<<endl; - cout<<" format: total read count for individual 1"<<endl; - cout<<" total read count for individual 2"<<endl; + cout<<" format: total read count for individual 1"<<endl; + cout<<" total read count for individual 2"<<endl; cout<<" ..."<<endl; - cout<<" missing value: NA"<<endl; + cout<<" missing value: NA"<<endl; cout<<" -snps [filename] "<<" specify input snps file name to only analyze a certain set of snps"<<endl; - cout<<" format: rs#1"<<endl; - cout<<" rs#2"<<endl; + cout<<" format: rs#1"<<endl; + cout<<" rs#2"<<endl; cout<<" ..."<<endl; - cout<<" missing value: NA"<<endl; + cout<<" missing value: NA"<<endl; cout<<" -silence "<<" silent terminal display"<<endl; cout<<" -km [num] "<<" specify input kinship/relatedness file type (default 1)."<<endl; cout<<" options: 1: \"n by n matrix\" format"<<endl; cout<<" 2: \"id id value\" format"<<endl; - cout<<" -n [num] "<<" specify phenotype column in the phenotype/*.fam file (optional; default 1)"<<endl; + cout<<" -n [num] "<<" specify phenotype column in the phenotype/*.fam file (optional; default 1)"<<endl; cout<<" -pace [num] "<<" specify terminal display update pace (default 100000 SNPs or 100000 iterations)."<<endl; - cout<<" -outdir [path] "<<" specify output directory path (default \"./output/\")"<<endl; - cout<<" -o [prefix] "<<" specify output file prefix (default \"result\")"<<endl; - cout<<" output: prefix.cXX.txt or prefix.sXX.txt from kinship/relatedness matrix estimation"<<endl; - cout<<" output: prefix.assoc.txt and prefix.log.txt form association tests"<<endl; + cout<<" -outdir [path] "<<" specify output directory path (default \"./output/\")"<<endl; + cout<<" -o [prefix] "<<" specify output file prefix (default \"result\")"<<endl; + cout<<" output: prefix.cXX.txt or prefix.sXX.txt from kinship/relatedness matrix estimation"<<endl; + cout<<" output: prefix.assoc.txt and prefix.log.txt form association tests"<<endl; cout<<endl; } - + if (option==3) { cout<<" SNP QC OPTIONS" << endl; - cout<<" -miss [num] "<<" specify missingness threshold (default 0.05)" << endl; - cout<<" -maf [num] "<<" specify minor allele frequency threshold (default 0.01)" << endl; - cout<<" -hwe [num] "<<" specify HWE test p value threshold (default 0; no test)" << endl; - cout<<" -r2 [num] "<<" specify r-squared threshold (default 0.9999)" << endl; - cout<<" -notsnp "<<" minor allele frequency cutoff is not used" << endl; + cout<<" -miss [num] "<<" specify missingness threshold (default 0.05)" << endl; + cout<<" -maf [num] "<<" specify minor allele frequency threshold (default 0.01)" << endl; + cout<<" -hwe [num] "<<" specify HWE test p value threshold (default 0; no test)" << endl; + cout<<" -r2 [num] "<<" specify r-squared threshold (default 0.9999)" << endl; + cout<<" -notsnp "<<" minor allele frequency cutoff is not used" << endl; cout<<endl; } - + if (option==4) { cout<<" RELATEDNESS MATRIX CALCULATION OPTIONS" << endl; - cout<<" -gk [num] "<<" specify which type of kinship/relatedness matrix to generate (default 1)" << endl; + cout<<" -gk [num] "<<" specify which type of kinship/relatedness matrix to generate (default 1)" << endl; cout<<" options: 1: centered XX^T/p"<<endl; cout<<" 2: standardized XX^T/p"<<endl; cout<<" note: non-polymorphic SNPs are excluded "<<endl; cout<<endl; } - + if (option==5) { cout<<" EIGEN-DECOMPOSITION OPTIONS" << endl; - cout<<" -eigen "<<" specify to perform eigen decomposition of the loaded relatedness matrix" << endl; + cout<<" -eigen "<<" specify to perform eigen decomposition of the loaded relatedness matrix" << endl; cout<<endl; } if (option==6) { cout<<" VARIANCE COMPONENT ESTIMATION OPTIONS" << endl; - cout<<" -vc "<<" specify to perform variance component estimation for the loaded relatedness matrix/matrices" << endl; + cout<<" -vc "<<" specify to perform variance component estimation for the loaded relatedness matrix/matrices" << endl; + cout<<" options (with kinship file): 1: HE regression (default)"<<endl; + cout<<" 2: REML"<<endl; + cout<<" options (with beta/cor files): 1: Centered genotypes (default)"<<endl; + cout<<" 2: Standardized genotypes"<<endl; + cout<<" -crt -windowbp [num]"<<" specify the window size based on bp (default 1000000; 1Mb)"<<endl; + cout<<" -crt -windowcm [num]"<<" specify the window size based on cm (default 0)"<<endl; + cout<<" -crt -windowns [num]"<<" specify the window size based on number of snps (default 0)"<<endl; cout<<endl; } - + if (option==7) { - cout<<" LINEAR MODEL OPTIONS" << endl; + cout<<" LINEAR MODEL OPTIONS" << endl; cout<<" -lm [num] "<<" specify analysis options (default 1)."<<endl; cout<<" options: 1: Wald test"<<endl; cout<<" 2: Likelihood ratio test"<<endl; @@ -285,21 +337,21 @@ void GEMMA::PrintHelp(size_t option) cout<<" 4: 1-3"<<endl; cout<<endl; } - + if (option==8) { - cout<<" LINEAR MIXED MODEL OPTIONS" << endl; + cout<<" LINEAR MIXED MODEL OPTIONS" << endl; cout<<" -lmm [num] "<<" specify analysis options (default 1)."<<endl; - cout<<" options: 1: Wald test"<<endl; + cout<<" options: 1: Wald test"<<endl; cout<<" 2: Likelihood ratio test"<<endl; cout<<" 3: Score test"<<endl; cout<<" 4: 1-3"<<endl; cout<<" 5: Parameter estimation in the null model only"<<endl; - cout<<" -lmin [num] "<<" specify minimal value for lambda (default 1e-5)" << endl; - cout<<" -lmax [num] "<<" specify maximum value for lambda (default 1e+5)" << endl; - cout<<" -region [num] "<<" specify the number of regions used to evaluate lambda (default 10)" << endl; + cout<<" -lmin [num] "<<" specify minimal value for lambda (default 1e-5)" << endl; + cout<<" -lmax [num] "<<" specify maximum value for lambda (default 1e+5)" << endl; + cout<<" -region [num] "<<" specify the number of regions used to evaluate lambda (default 10)" << endl; cout<<endl; } - + if (option==9) { cout<<" MULTIVARIATE LINEAR MIXED MODEL OPTIONS" << endl; cout<<" -pnr "<<" specify the pvalue threshold to use the Newton-Raphson's method (default 0.001)"<<endl; @@ -310,51 +362,63 @@ void GEMMA::PrintHelp(size_t option) cout<<" -crt "<<" specify to output corrected pvalues for these pvalues that are below the -pnr threshold"<<endl; cout<<endl; } - + if (option==10) { cout<<" MULTI-LOCUS ANALYSIS OPTIONS" << endl; cout<<" -bslmm [num] "<<" specify analysis options (default 1)."<<endl; - cout<<" options: 1: BSLMM"<<endl; - cout<<" 2: standard ridge regression/GBLUP (no mcmc)"<<endl; - cout<<" 3: probit BSLMM (requires 0/1 phenotypes)"<<endl; - + cout<<" options: 1: BSLMM"<<endl; + cout<<" 2: standard ridge regression/GBLUP (no mcmc)"<<endl; + cout<<" 3: probit BSLMM (requires 0/1 phenotypes)"<<endl; + + cout<<" -ldr [num] "<<" specify analysis options (default 1)."<<endl; + cout<<" options: 1: LDR"<<endl; + cout<<" MCMC OPTIONS" << endl; - cout<<" Prior" << endl; - cout<<" -hmin [num] "<<" specify minimum value for h (default 0)" << endl; - cout<<" -hmax [num] "<<" specify maximum value for h (default 1)" << endl; - cout<<" -rmin [num] "<<" specify minimum value for rho (default 0)" << endl; - cout<<" -rmax [num] "<<" specify maximum value for rho (default 1)" << endl; - cout<<" -pmin [num] "<<" specify minimum value for log10(pi) (default log10(1/p), where p is the number of analyzed SNPs )" << endl; - cout<<" -pmax [num] "<<" specify maximum value for log10(pi) (default log10(1) )" << endl; - cout<<" -smin [num] "<<" specify minimum value for |gamma| (default 0)" << endl; - cout<<" -smax [num] "<<" specify maximum value for |gamma| (default 300)" << endl; - + cout<<" Prior" << endl; + cout<<" -hmin [num] "<<" specify minimum value for h (default 0)" << endl; + cout<<" -hmax [num] "<<" specify maximum value for h (default 1)" << endl; + cout<<" -rmin [num] "<<" specify minimum value for rho (default 0)" << endl; + cout<<" -rmax [num] "<<" specify maximum value for rho (default 1)" << endl; + cout<<" -pmin [num] "<<" specify minimum value for log10(pi) (default log10(1/p), where p is the number of analyzed SNPs )" << endl; + cout<<" -pmax [num] "<<" specify maximum value for log10(pi) (default log10(1) )" << endl; + cout<<" -smin [num] "<<" specify minimum value for |gamma| (default 0)" << endl; + cout<<" -smax [num] "<<" specify maximum value for |gamma| (default 300)" << endl; + cout<<" Proposal" << endl; - cout<<" -gmean [num] "<<" specify the mean for the geometric distribution (default: 2000)" << endl; - cout<<" -hscale [num] "<<" specify the step size scale for the proposal distribution of h (value between 0 and 1, default min(10/sqrt(n),1) )" << endl; - cout<<" -rscale [num] "<<" specify the step size scale for the proposal distribution of rho (value between 0 and 1, default min(10/sqrt(n),1) )" << endl; - cout<<" -pscale [num] "<<" specify the step size scale for the proposal distribution of log10(pi) (value between 0 and 1, default min(5/sqrt(n),1) )" << endl; - + cout<<" -gmean [num] "<<" specify the mean for the geometric distribution (default: 2000)" << endl; + cout<<" -hscale [num] "<<" specify the step size scale for the proposal distribution of h (value between 0 and 1, default min(10/sqrt(n),1) )" << endl; + cout<<" -rscale [num] "<<" specify the step size scale for the proposal distribution of rho (value between 0 and 1, default min(10/sqrt(n),1) )" << endl; + cout<<" -pscale [num] "<<" specify the step size scale for the proposal distribution of log10(pi) (value between 0 and 1, default min(5/sqrt(n),1) )" << endl; + cout<<" Others" << endl; - cout<<" -w [num] "<<" specify burn-in steps (default 100,000)" << endl; - cout<<" -s [num] "<<" specify sampling steps (default 1,000,000)" << endl; - cout<<" -rpace [num] "<<" specify recording pace, record one state in every [num] steps (default 10)" << endl; - cout<<" -wpace [num] "<<" specify writing pace, write values down in every [num] recorded steps (default 1000)" << endl; - cout<<" -seed [num] "<<" specify random seed (a random seed is generated by default)" << endl; - cout<<" -mh [num] "<<" specify number of MH steps in each iteration (default 10)" << endl; - cout<<" requires: 0/1 phenotypes and -bslmm 3 option"<<endl; + cout<<" -w [num] "<<" specify burn-in steps (default 100,000)" << endl; + cout<<" -s [num] "<<" specify sampling steps (default 1,000,000)" << endl; + cout<<" -rpace [num] "<<" specify recording pace, record one state in every [num] steps (default 10)" << endl; + cout<<" -wpace [num] "<<" specify writing pace, write values down in every [num] recorded steps (default 1000)" << endl; + cout<<" -seed [num] "<<" specify random seed (a random seed is generated by default)" << endl; + cout<<" -mh [num] "<<" specify number of MH steps in each iteration (default 10)" << endl; + cout<<" requires: 0/1 phenotypes and -bslmm 3 option"<<endl; cout<<endl; } - + if (option==11) { cout<<" PREDICTION OPTIONS" << endl; cout<<" -predict [num] "<<" specify prediction options (default 1)."<<endl; - cout<<" options: 1: predict for individuals with missing phenotypes"<<endl; - cout<<" 2: predict for individuals with missing phenotypes, and convert the predicted values to probability scale. Use only for files fitted with -bslmm 3 option"<<endl; + cout<<" options: 1: predict for individuals with missing phenotypes"<<endl; + cout<<" 2: predict for individuals with missing phenotypes, and convert the predicted values to probability scale. Use only for files fitted with -bslmm 3 option"<<endl; cout<<endl; } - + if (option==12) { + cout<<" CALC CORRELATION OPTIONS" << endl; + cout<<" -calccor "<<endl; + cout<<" -windowbp [num] "<<" specify the window size based on bp (default 1000000; 1Mb)" << endl; + cout<<" -windowcm [num] "<<" specify the window size based on cm (default 0; not used)" << endl; + cout<<" -windowns [num] "<<" specify the window size based on number of snps (default 0; not used)" << endl; + cout<<endl; + } + + if (option==13) { cout<<" NOTE"<<endl; cout<<" 1. Only individuals with non-missing phenotoypes and covariates will be analyzed."<<endl; cout<<" 2. Missing genotoypes will be repalced with the mean genotype of that SNP."<<endl; @@ -363,17 +427,29 @@ void GEMMA::PrintHelp(size_t option) cout<<" 5. For bslmm analysis, in addition to 3, memory should be large enough to hold the whole genotype matrix."<<endl; cout<<endl; } - + return; } - +//options +//gk: 21-22 +//gs: 25-26 +//gq: 27-28 +//eigen: 31-32 +//lmm: 1-5 +//bslmm: 11-13 +//predict: 41-43 +//lm: 51 +//vc: 61 +//ci: 66-67 +//calccor: 71 +//gw: 72 void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) { string str; - - for(int i = 1; i < argc; i++) { + + for(int i = 1; i < argc; i++) { if (strcmp(argv[i], "-bfile")==0 || strcmp(argv[i], "--bfile")==0 || strcmp(argv[i], "-b")==0) { if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} ++i; @@ -381,6 +457,13 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) str.assign(argv[i]); cPar.file_bfile=str; } + else if (strcmp(argv[i], "-mbfile")==0 || strcmp(argv[i], "--mbfile")==0 || strcmp(argv[i], "-mb")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_mbfile=str; + } else if (strcmp(argv[i], "-silence")==0) { cPar.mode_silence=true; } @@ -391,6 +474,13 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) str.assign(argv[i]); cPar.file_geno=str; } + else if (strcmp(argv[i], "-mg")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_mgeno=str; + } else if (strcmp(argv[i], "-p")==0) { if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} ++i; @@ -405,6 +495,42 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) str.assign(argv[i]); cPar.file_anno=str; } + // WJA added + else if (strcmp(argv[i], "-oxford")==0 || strcmp(argv[i], "--oxford")==0 || strcmp(argv[i], "-x")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_oxford=str; + } + else if (strcmp(argv[i], "-gxe")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_gxe=str; + } + else if (strcmp(argv[i], "-widv")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_weight=str; + } + else if (strcmp(argv[i], "-wsnp")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_wsnp=str; + } + else if (strcmp(argv[i], "-wcat")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_wcat=str; + } else if (strcmp(argv[i], "-k")==0) { if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} ++i; @@ -440,6 +566,62 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) str.assign(argv[i]); cPar.file_cvt=str; } + else if (strcmp(argv[i], "-cat")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_cat=str; + } + else if (strcmp(argv[i], "-mcat")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_mcat=str; + } + else if (strcmp(argv[i], "-beta")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_beta=str; + } + else if (strcmp(argv[i], "-cor")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_cor=str; + } + else if (strcmp(argv[i], "-study")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_study=str; + } + else if (strcmp(argv[i], "-ref")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_ref=str; + } + else if (strcmp(argv[i], "-mstudy")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_mstudy=str; + } + else if (strcmp(argv[i], "-mref")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.file_mref=str; + } else if (strcmp(argv[i], "-epm")==0) { if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} ++i; @@ -447,7 +629,7 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) str.assign(argv[i]); cPar.file_epm=str; } - else if (strcmp(argv[i], "-en")==0) { + else if (strcmp(argv[i], "-en")==0) { while (argv[i+1] != NULL && argv[i+1][0] != '-') { ++i; str.clear(); @@ -503,7 +685,7 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) str.clear(); str.assign(argv[i]); cPar.k_mode=atoi(str.c_str()); - } + } else if (strcmp(argv[i], "-n")==0) { (cPar.p_column).clear(); while (argv[i+1] != NULL && argv[i+1][0] != '-') { @@ -533,7 +715,7 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) str.clear(); str.assign(argv[i]); cPar.file_out=str; - } + } else if (strcmp(argv[i], "-miss")==0) { if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} ++i; @@ -566,31 +748,101 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) cPar.maf_level=-1; } else if (strcmp(argv[i], "-gk")==0) { - if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -eigen -vc -lm -lmm -bslmm -predict options is allowed."<<endl; break;} + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=21; continue;} ++i; str.clear(); str.assign(argv[i]); cPar.a_mode=20+atoi(str.c_str()); - } + } + else if (strcmp(argv[i], "-gs")==0) { + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} + if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=25; continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.a_mode=24+atoi(str.c_str()); + } + else if (strcmp(argv[i], "-gq")==0) { + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} + if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=27; continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.a_mode=26+atoi(str.c_str()); + } + else if (strcmp(argv[i], "-gw")==0) { + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} + if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=72; continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.a_mode=71+atoi(str.c_str()); + } + else if (strcmp(argv[i], "-sample")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.ni_subsample=atoi(str.c_str()); + } else if (strcmp(argv[i], "-eigen")==0) { - if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -eigen -vc -lm -lmm -bslmm -predict options is allowed."<<endl; break;} + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=31; continue;} ++i; str.clear(); str.assign(argv[i]); cPar.a_mode=30+atoi(str.c_str()); - } + } + else if (strcmp(argv[i], "-calccor")==0) { + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} + if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=71; continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.a_mode=70+atoi(str.c_str()); + } else if (strcmp(argv[i], "-vc")==0) { - if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -eigen -vc -lm -lmm -bslmm -predict options is allowed."<<endl; break;} + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=61; continue;} ++i; str.clear(); str.assign(argv[i]); cPar.a_mode=60+atoi(str.c_str()); - } + } + else if (strcmp(argv[i], "-ci")==0) { + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} + if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=66; continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.a_mode=65+atoi(str.c_str()); + } + else if (strcmp(argv[i], "-pve")==0) { + double s=0; + while (argv[i+1] != NULL && (argv[i+1][0] != '-' || !isalpha(argv[i+1][1]) ) ) { + ++i; + str.clear(); + str.assign(argv[i]); + cPar.v_pve.push_back(atof(str.c_str())); + s+=atof(str.c_str()); + } + if (s==1) { + cout<<"summation of pve equals one."<<endl; + } + } + else if (strcmp(argv[i], "-blocks")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.n_block=atoi(str.c_str()); + } + else if (strcmp(argv[i], "-noconstrain")==0) { + cPar.noconstrain=true; + } else if (strcmp(argv[i], "-lm")==0) { - if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -eigen -vc -lm -lmm -bslmm -predict options is allowed."<<endl; break;} + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=51; continue;} ++i; str.clear(); @@ -598,7 +850,7 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) cPar.a_mode=50+atoi(str.c_str()); } else if (strcmp(argv[i], "-fa")==0 || strcmp(argv[i], "-lmm")==0) { - if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -eigen -vc -lm -lmm -bslmm -predict options is allowed."<<endl; break;} + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=1; continue;} ++i; str.clear(); @@ -665,13 +917,21 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) cPar.crt=1; } else if (strcmp(argv[i], "-bslmm")==0) { - if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -eigen -vc -lm -lmm -bslmm -predict options is allowed."<<endl; break;} + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=11; continue;} ++i; str.clear(); str.assign(argv[i]); cPar.a_mode=10+atoi(str.c_str()); } + else if (strcmp(argv[i], "-ldr")==0) { + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} + if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=14; continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.a_mode=13+atoi(str.c_str()); + } else if (strcmp(argv[i], "-hmin")==0) { if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} ++i; @@ -799,25 +1059,46 @@ void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) cPar.n_mh=atoi(str.c_str()); } else if (strcmp(argv[i], "-predict")==0) { - if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -eigen -vc -lm -lmm -bslmm -predict options is allowed."<<endl; break;} + if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;} if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=41; continue;} ++i; str.clear(); str.assign(argv[i]); cPar.a_mode=40+atoi(str.c_str()); } + else if (strcmp(argv[i], "-windowcm")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.window_cm=atof(str.c_str()); + } + else if (strcmp(argv[i], "-windowbp")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.window_bp=atoi(str.c_str()); + } + else if (strcmp(argv[i], "-windowns")==0) { + if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;} + ++i; + str.clear(); + str.assign(argv[i]); + cPar.window_ns=atoi(str.c_str()); + } else {cout<<"error! unrecognized option: "<<argv[i]<<endl; cPar.error=true; continue;} } - + //change prediction mode to 43, if the epm file is not provided if (cPar.a_mode==41 && cPar.file_epm.empty()) {cPar.a_mode=43;} - + return; } -void GEMMA::BatchRun (PARAM &cPar) +void GEMMA::BatchRun (PARAM &cPar) { clock_t time_begin, time_start; time_begin=clock(); @@ -828,25 +1109,26 @@ void GEMMA::BatchRun (PARAM &cPar) if (cPar.error==true) {cout<<"error! fail to read files. "<<endl; return;} cPar.CheckData(); if (cPar.error==true) {cout<<"error! fail to check data. "<<endl; return;} - //Prediction for bslmm + + //Prediction for bslmm if (cPar.a_mode==41 || cPar.a_mode==42) { gsl_vector *y_prdt; - + y_prdt=gsl_vector_alloc (cPar.ni_total-cPar.ni_test); //set to zero gsl_vector_set_zero (y_prdt); - + PRDT cPRDT; cPRDT.CopyFromParam(cPar); - + //add breeding value if needed if (!cPar.file_kin.empty() && !cPar.file_ebv.empty()) { cout<<"Adding Breeding Values ... "<<endl; - + gsl_matrix *G=gsl_matrix_alloc (cPar.ni_total, cPar.ni_total); gsl_vector *u_hat=gsl_vector_alloc (cPar.ni_test); - + //read kinship matrix and set u_hat vector<int> indicator_all; size_t c_bv=0; @@ -854,13 +1136,13 @@ void GEMMA::BatchRun (PARAM &cPar) indicator_all.push_back(1); if (cPar.indicator_bv[i]==1) {gsl_vector_set(u_hat, c_bv, cPar.vec_bv[i]); c_bv++;} } - + ReadFile_kin (cPar.file_kin, indicator_all, cPar.mapID2num, cPar.k_mode, cPar.error, G); if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;} - - //read u - cPRDT.AddBV(G, u_hat, y_prdt); - + + //read u + cPRDT.AddBV(G, u_hat, y_prdt); + gsl_matrix_free(G); gsl_vector_free(u_hat); } @@ -872,10 +1154,10 @@ void GEMMA::BatchRun (PARAM &cPar) else { cPRDT.AnalyzeBimbam (y_prdt); } - + //add mu gsl_vector_add_constant(y_prdt, cPar.pheno_mean); - + //convert y to probability if needed if (cPar.a_mode==42) { double d; @@ -885,51 +1167,51 @@ void GEMMA::BatchRun (PARAM &cPar) gsl_vector_set(y_prdt, i, d); } } - - + + cPRDT.CopyToParam(cPar); - + cPRDT.WriteFiles(y_prdt); - + gsl_vector_free(y_prdt); } - - + + //Prediction with kinship matrix only; for one or more phenotypes if (cPar.a_mode==43) { - //first, use individuals with full phenotypes to obtain estimates of Vg and Ve + //first, use individuals with full phenotypes to obtain estimates of Vg and Ve gsl_matrix *Y=gsl_matrix_alloc (cPar.ni_test, cPar.n_ph); - gsl_matrix *W=gsl_matrix_alloc (Y->size1, cPar.n_cvt); + gsl_matrix *W=gsl_matrix_alloc (Y->size1, cPar.n_cvt); gsl_matrix *G=gsl_matrix_alloc (Y->size1, Y->size1); - gsl_matrix *U=gsl_matrix_alloc (Y->size1, Y->size1); + gsl_matrix *U=gsl_matrix_alloc (Y->size1, Y->size1); gsl_matrix *UtW=gsl_matrix_alloc (Y->size1, W->size2); gsl_matrix *UtY=gsl_matrix_alloc (Y->size1, Y->size2); gsl_vector *eval=gsl_vector_alloc (Y->size1); - + gsl_matrix *Y_full=gsl_matrix_alloc (cPar.ni_cvt, cPar.n_ph); gsl_matrix *W_full=gsl_matrix_alloc (Y_full->size1, cPar.n_cvt); //set covariates matrix W and phenotype matrix Y - //an intercept should be included in W, + //an intercept should be included in W, cPar.CopyCvtPhen (W, Y, 0); cPar.CopyCvtPhen (W_full, Y_full, 1); - - gsl_matrix *Y_hat=gsl_matrix_alloc (Y_full->size1, cPar.n_ph); - gsl_matrix *G_full=gsl_matrix_alloc (Y_full->size1, Y_full->size1); + + gsl_matrix *Y_hat=gsl_matrix_alloc (Y_full->size1, cPar.n_ph); + gsl_matrix *G_full=gsl_matrix_alloc (Y_full->size1, Y_full->size1); gsl_matrix *H_full=gsl_matrix_alloc (Y_full->size1*Y_hat->size2, Y_full->size1*Y_hat->size2); - + //read relatedness matrix G, and matrix G_full ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G); if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;} ReadFile_kin (cPar.file_kin, cPar.indicator_cvt, cPar.mapID2num, cPar.k_mode, cPar.error, G_full); if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;} - + //center matrix G CenterMatrix (G); CenterMatrix (G_full); - + //eigen-decomposition and calculate trace_G cout<<"Start Eigen-Decomposition..."<<endl; - time_start=clock(); + time_start=clock(); cPar.trace_G=EigenDecomp (G, U, eval, 0); cPar.trace_G=0.0; for (size_t i=0; i<eval->size; i++) { @@ -937,8 +1219,8 @@ void GEMMA::BatchRun (PARAM &cPar) cPar.trace_G+=gsl_vector_get (eval, i); } cPar.trace_G/=(double)eval->size; - cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); - + cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); + //calculate UtW and Uty CalcUtX (U, W, UtW); CalcUtX (U, Y, UtY); @@ -948,7 +1230,7 @@ void GEMMA::BatchRun (PARAM &cPar) if (cPar.n_ph==1) { gsl_vector *beta=gsl_vector_alloc (W->size2); gsl_vector *se_beta=gsl_vector_alloc (W->size2); - + double lambda, logl, vg, ve; gsl_vector_view UtY_col=gsl_matrix_column (UtY, 0); @@ -959,29 +1241,29 @@ void GEMMA::BatchRun (PARAM &cPar) cout<<"REMLE estimate for vg in the null model = "<<vg<<endl; cout<<"REMLE estimate for ve in the null model = "<<ve<<endl; cPar.vg_remle_null=vg; cPar.ve_remle_null=ve; - + //obtain Y_hat from fixed effects - gsl_vector_view Yhat_col=gsl_matrix_column (Y_hat, 0); + gsl_vector_view Yhat_col=gsl_matrix_column (Y_hat, 0); gsl_blas_dgemv (CblasNoTrans, 1.0, W_full, beta, 0.0, &Yhat_col.vector); - + //obtain H gsl_matrix_set_identity (H_full); gsl_matrix_scale (H_full, ve); gsl_matrix_scale (G_full, vg); gsl_matrix_add (H_full, G_full); - - //free matrices + + //free matrices gsl_vector_free(beta); gsl_vector_free(se_beta); - } else { + } else { gsl_matrix *Vg=gsl_matrix_alloc (cPar.n_ph, cPar.n_ph); gsl_matrix *Ve=gsl_matrix_alloc (cPar.n_ph, cPar.n_ph); gsl_matrix *B=gsl_matrix_alloc (cPar.n_ph, W->size2); gsl_matrix *se_B=gsl_matrix_alloc (cPar.n_ph, W->size2); - + //obtain estimates CalcMvLmmVgVeBeta (eval, UtW, UtY, cPar.em_iter, cPar.nr_iter, cPar.em_prec, cPar.nr_prec, cPar.l_min, cPar.l_max, cPar.n_region, Vg, Ve, B, se_B); - + cout<<"REMLE estimate for Vg in the null model: "<<endl; for (size_t i=0; i<Vg->size1; i++) { for (size_t j=0; j<=i; j++) { @@ -1004,110 +1286,250 @@ void GEMMA::BatchRun (PARAM &cPar) cPar.Ve_remle_null.push_back(gsl_matrix_get (Ve, i, j) ); } } - + //obtain Y_hat from fixed effects gsl_blas_dgemm (CblasNoTrans, CblasTrans, 1.0, W_full, B, 0.0, Y_hat); - + //obtain H KroneckerSym(G_full, Vg, H_full); for (size_t i=0; i<G_full->size1; i++) { gsl_matrix_view H_sub=gsl_matrix_submatrix (H_full, i*Ve->size1, i*Ve->size2, Ve->size1, Ve->size2); gsl_matrix_add (&H_sub.matrix, Ve); } - - //free matrices + + //free matrices gsl_matrix_free (Vg); gsl_matrix_free (Ve); gsl_matrix_free (B); gsl_matrix_free (se_B); } - + PRDT cPRDT; - + cPRDT.CopyFromParam(cPar); - + cout<<"Predicting Missing Phentypes ... "<<endl; - time_start=clock(); + time_start=clock(); cPRDT.MvnormPrdt(Y_hat, H_full, Y_full); - cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); + cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); cPRDT.WriteFiles(Y_full); - + gsl_matrix_free(Y); - gsl_matrix_free(W); + gsl_matrix_free(W); gsl_matrix_free(G); - gsl_matrix_free(U); + gsl_matrix_free(U); gsl_matrix_free(UtW); gsl_matrix_free(UtY); gsl_vector_free(eval); - + gsl_matrix_free(Y_full); gsl_matrix_free(Y_hat); gsl_matrix_free(W_full); - gsl_matrix_free(G_full); + gsl_matrix_free(G_full); gsl_matrix_free(H_full); } - - + + //Generate Kinship matrix - if (cPar.a_mode==21 || cPar.a_mode==22) { + if (cPar.a_mode==21 || cPar.a_mode==22) { cout<<"Calculating Relatedness Matrix ... "<<endl; - + gsl_matrix *G=gsl_matrix_alloc (cPar.ni_total, cPar.ni_total); - + time_start=clock(); cPar.CalcKin (G); cPar.time_G=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); if (cPar.error==true) {cout<<"error! fail to calculate relatedness matrix. "<<endl; return;} - + if (cPar.a_mode==21) { cPar.WriteMatrix (G, "cXX"); } else { cPar.WriteMatrix (G, "sXX"); } - + gsl_matrix_free (G); } - - + + //Compute the LDSC weights (not implemented yet) + if (cPar.a_mode==72) { + cout<<"Calculating Weights ... "<<endl; + + VARCOV cVarcov; + cVarcov.CopyFromParam(cPar); + + if (!cPar.file_bfile.empty()) { + cVarcov.AnalyzePlink (); + } else { + cVarcov.AnalyzeBimbam (); + } + + cVarcov.CopyToParam(cPar); + } + + + //Compute the S matrix (and its variance), that is used for variance component estimation using summary statistics + if (cPar.a_mode==25 || cPar.a_mode==26) { + cout<<"Calculating the S Matrix ... "<<endl; + + gsl_matrix *S=gsl_matrix_alloc (cPar.n_vc*2, cPar.n_vc); + gsl_vector *ns=gsl_vector_alloc (cPar.n_vc+1); + gsl_matrix_set_zero(S); + gsl_vector_set_zero(ns); + + gsl_matrix_view S_mat=gsl_matrix_submatrix(S, 0, 0, cPar.n_vc, cPar.n_vc); + gsl_matrix_view Svar_mat=gsl_matrix_submatrix (S, cPar.n_vc, 0, cPar.n_vc, cPar.n_vc); + gsl_vector_view ns_vec=gsl_vector_subvector(ns, 0, cPar.n_vc); + + gsl_matrix *K=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc*cPar.ni_test); + gsl_matrix *A=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc*cPar.ni_test); + gsl_matrix_set_zero (K); + gsl_matrix_set_zero (A); + + gsl_vector *y=gsl_vector_alloc (cPar.ni_test); + gsl_matrix *W=gsl_matrix_alloc (cPar.ni_test, cPar.n_cvt); + + cPar.CopyCvtPhen (W, y, 0); + + set<string> setSnps_beta; + map <string, double> mapRS2wA, mapRS2wK; + + cPar.ObtainWeight(setSnps_beta, mapRS2wK); + + time_start=clock(); + cPar.CalcS (mapRS2wA, mapRS2wK, W, A, K, &S_mat.matrix, &Svar_mat.matrix, &ns_vec.vector); + cPar.time_G=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); + if (cPar.error==true) {cout<<"error! fail to calculate the S matrix. "<<endl; return;} + + gsl_vector_set (ns, cPar.n_vc, cPar.ni_test); + + cPar.WriteMatrix (S, "S"); + cPar.WriteVector (ns, "size"); + cPar.WriteVar ("snps"); + /* + cout<<scientific; + for (size_t i=0; i<cPar.n_vc; i++) { + for (size_t j=0; j<cPar.n_vc; j++) { + cout<<gsl_matrix_get(S, i, j)<<" "; + } + cout<<endl; + } + + for (size_t i=cPar.n_vc; i<cPar.n_vc*2; i++) { + for (size_t j=0; j<cPar.n_vc; j++) { + cout<<gsl_matrix_get(S, i, j)<<" "; + } + cout<<endl; + } + */ + gsl_matrix_free (S); + gsl_vector_free (ns); + + gsl_matrix_free (A); + gsl_matrix_free (K); + + gsl_vector_free (y); + gsl_matrix_free (K); + } + + //Compute the q vector, that is used for variance component estimation using summary statistics + if (cPar.a_mode==27 || cPar.a_mode==28) { + gsl_matrix *Vq=gsl_matrix_alloc (cPar.n_vc, cPar.n_vc); + gsl_vector *q=gsl_vector_alloc (cPar.n_vc); + gsl_vector *s=gsl_vector_alloc (cPar.n_vc+1); + gsl_vector_set_zero (q); + gsl_vector_set_zero (s); + + gsl_vector_view s_vec=gsl_vector_subvector(s, 0, cPar.n_vc); + + vector<size_t> vec_cat, vec_ni; + vector<double> vec_weight, vec_z2; + map<string, double> mapRS2weight; + mapRS2weight.clear(); + + time_start=clock(); + ReadFile_beta (cPar.file_beta, cPar.mapRS2cat, mapRS2weight, vec_cat, vec_ni, vec_weight, vec_z2, cPar.ni_total, cPar.ns_total, cPar.ns_test); + cout<<"## number of total individuals = "<<cPar.ni_total<<endl; + cout<<"## number of total SNPs = "<<cPar.ns_total<<endl; + cout<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl; + cout<<"## number of variance components = "<<cPar.n_vc<<endl; + cout<<"Calculating the q vector ... "<<endl; + Calcq (cPar.n_block, vec_cat, vec_ni, vec_weight, vec_z2, Vq, q, &s_vec.vector); + cPar.time_G=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); + + if (cPar.error==true) {cout<<"error! fail to calculate the q vector. "<<endl; return;} + + gsl_vector_set (s, cPar.n_vc, cPar.ni_total); + + cPar.WriteMatrix (Vq, "Vq"); + cPar.WriteVector (q, "q"); + cPar.WriteVector (s, "size"); + /* + for (size_t i=0; i<cPar.n_vc; i++) { + cout<<gsl_vector_get(q, i)<<endl; + } + */ + gsl_matrix_free (Vq); + gsl_vector_free (q); + gsl_vector_free (s); + } + + + //Calculate SNP covariance + if (cPar.a_mode==71) { + VARCOV cVarcov; + cVarcov.CopyFromParam(cPar); + + if (!cPar.file_bfile.empty()) { + cVarcov.AnalyzePlink (); + } else { + cVarcov.AnalyzeBimbam (); + } + + cVarcov.CopyToParam(cPar); + } + + //LM if (cPar.a_mode==51 || cPar.a_mode==52 || cPar.a_mode==53 || cPar.a_mode==54) { //Fit LM gsl_matrix *Y=gsl_matrix_alloc (cPar.ni_test, cPar.n_ph); - gsl_matrix *W=gsl_matrix_alloc (Y->size1, cPar.n_cvt); - - //set covariates matrix W and phenotype matrix Y - //an intercept should be included in W, + gsl_matrix *W=gsl_matrix_alloc (Y->size1, cPar.n_cvt); + + //set covariates matrix W and phenotype matrix Y + //an intercept should be included in W, cPar.CopyCvtPhen (W, Y, 0); - + //Fit LM or mvLM - if (cPar.n_ph==1) { + if (cPar.n_ph==1) { LM cLm; cLm.CopyFromParam(cPar); - + gsl_vector_view Y_col=gsl_matrix_column (Y, 0); - - if (!cPar.file_gene.empty()) { + + if (!cPar.file_gene.empty()) { cLm.AnalyzeGene (W, &Y_col.vector); //y is the predictor, not the phenotype } else if (!cPar.file_bfile.empty()) { cLm.AnalyzePlink (W, &Y_col.vector); + } else if (!cPar.file_oxford.empty()) { + cLm.Analyzebgen (W, &Y_col.vector); } else { cLm.AnalyzeBimbam (W, &Y_col.vector); } - + cLm.WriteFiles(); cLm.CopyToParam(cPar); } /* - else { + else { MVLM cMvlm; - cMvlm.CopyFromParam(cPar); - + cMvlm.CopyFromParam(cPar); + if (!cPar.file_bfile.empty()) { cMvlm.AnalyzePlink (W, Y); } else { cMvlm.AnalyzeBimbam (W, Y); } - + cMvlm.WriteFiles(); cMvlm.CopyToParam(cPar); } @@ -1115,27 +1537,202 @@ void GEMMA::BatchRun (PARAM &cPar) //release all matrices and vectors gsl_matrix_free (Y); gsl_matrix_free (W); - } + } //VC estimation with one or multiple kinship matrices //REML approach only //if file_kin or file_ku/kd is provided, then a_mode is changed to 5 already, in param.cpp - //for one phenotype only; - if (cPar.a_mode==61) { + //for one phenotype only; + if (cPar.a_mode==61 || cPar.a_mode==62) { + if (!cPar.file_beta.empty() ) { + //need to obtain a common set of SNPs between beta file and the genotype file; these are saved in mapRS2wA and mapRS2wK + //normalize the weight in mapRS2wK to have an average of one; each element of mapRS2wA is 1 + //update indicator_snps, so that the numbers are in accordance with mapRS2wK + set<string> setSnps_beta; + ReadFile_snps_header (cPar.file_beta, setSnps_beta); + + map <string, double> mapRS2wA, mapRS2wK; + cPar.ObtainWeight(setSnps_beta, mapRS2wK); + + cPar.UpdateSNP (mapRS2wK); + + //setup matrices and vectors + gsl_matrix *S=gsl_matrix_alloc (cPar.n_vc*2, cPar.n_vc); + gsl_matrix *Vq=gsl_matrix_alloc (cPar.n_vc, cPar.n_vc); + gsl_vector *q=gsl_vector_alloc (cPar.n_vc); + gsl_vector *s=gsl_vector_alloc (cPar.n_vc+1); + + gsl_matrix *K=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc*cPar.ni_test); + gsl_matrix *A=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc*cPar.ni_test); + + gsl_vector *y=gsl_vector_alloc (cPar.ni_test); + gsl_matrix *W=gsl_matrix_alloc (cPar.ni_test, cPar.n_cvt); + + gsl_matrix_set_zero (K); + gsl_matrix_set_zero (A); + + gsl_matrix_set_zero(S); + gsl_matrix_set_zero(Vq); + gsl_vector_set_zero (q); + gsl_vector_set_zero (s); + + cPar.CopyCvtPhen (W, y, 0); + + gsl_matrix_view S_mat=gsl_matrix_submatrix(S, 0, 0, cPar.n_vc, cPar.n_vc); + gsl_matrix_view Svar_mat=gsl_matrix_submatrix (S, cPar.n_vc, 0, cPar.n_vc, cPar.n_vc); + gsl_vector_view s_vec=gsl_vector_subvector(s, 0, cPar.n_vc); + + vector<size_t> vec_cat, vec_ni; + vector<double> vec_weight, vec_z2; + + //read beta, based on the mapRS2wK + ReadFile_beta (cPar.file_beta, cPar.mapRS2cat, mapRS2wK, vec_cat, vec_ni, vec_weight, vec_z2, cPar.ni_study, cPar.ns_study, cPar.ns_test); + + cout<<"Study Panel: "<<endl; + cout<<"## number of total individuals = "<<cPar.ni_study<<endl; + cout<<"## number of total SNPs = "<<cPar.ns_study<<endl; + cout<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl; + cout<<"## number of variance components = "<<cPar.n_vc<<endl; + + //compute q + Calcq (cPar.n_block, vec_cat, vec_ni, vec_weight, vec_z2, Vq, q, &s_vec.vector); + + //compute S + time_start=clock(); + cPar.CalcS (mapRS2wA, mapRS2wK, W, A, K, &S_mat.matrix, &Svar_mat.matrix, &s_vec.vector); + cPar.time_G+=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); + if (cPar.error==true) {cout<<"error! fail to calculate the S matrix. "<<endl; return;} + + //compute vc estimates + CalcVCss(Vq, &S_mat.matrix, &Svar_mat.matrix, q, &s_vec.vector, cPar.ni_study, cPar.v_pve, cPar.v_se_pve, cPar.pve_total, cPar.se_pve_total, cPar.v_sigma2, cPar.v_se_sigma2, cPar.v_enrich, cPar.v_se_enrich); + + //if LDSC weights, then compute the weights and run the above steps again + if (cPar.a_mode==62) { + //compute the weights and normalize the weights for A + cPar.UpdateWeight (1, mapRS2wK, cPar.ni_study, &s_vec.vector, mapRS2wA); + + //read beta file again, and update weigths vector + ReadFile_beta (cPar.file_beta, cPar.mapRS2cat, mapRS2wA, vec_cat, vec_ni, vec_weight, vec_z2, cPar.ni_study, cPar.ns_total, cPar.ns_test); + + //compute q + Calcq (cPar.n_block, vec_cat, vec_ni, vec_weight, vec_z2, Vq, q, &s_vec.vector); + + //compute S + time_start=clock(); + cPar.CalcS (mapRS2wA, mapRS2wK, W, A, K, &S_mat.matrix, &Svar_mat.matrix, &s_vec.vector); + cPar.time_G+=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); + if (cPar.error==true) {cout<<"error! fail to calculate the S matrix. "<<endl; return;} + + //compute vc estimates + CalcVCss(Vq, &S_mat.matrix, &Svar_mat.matrix, q, &s_vec.vector, cPar.ni_study, cPar.v_pve, cPar.v_se_pve, cPar.pve_total, cPar.se_pve_total, cPar.v_sigma2, cPar.v_se_sigma2, cPar.v_enrich, cPar.v_se_enrich); + } + + gsl_vector_set (s, cPar.n_vc, cPar.ni_test); + + cPar.WriteMatrix (S, "S"); + cPar.WriteMatrix (Vq, "Vq"); + cPar.WriteVector (q, "q"); + cPar.WriteVector (s, "size"); + + gsl_matrix_free (S); + gsl_matrix_free (Vq); + gsl_vector_free (q); + gsl_vector_free (s); + + gsl_matrix_free (A); + gsl_matrix_free (K); + gsl_vector_free (y); + gsl_matrix_free (W); + } else if (!cPar.file_study.empty() || !cPar.file_mstudy.empty()) { + if (!cPar.file_study.empty()) { + string sfile=cPar.file_study+".size.txt"; + CountFileLines (sfile, cPar.n_vc); + } else { + string file_name; + igzstream infile (cPar.file_mstudy.c_str(), igzstream::in); + if (!infile) {cout<<"error! fail to open mstudy file: "<<cPar.file_study<<endl; return;} + + safeGetline(infile, file_name); + + infile.clear(); + infile.close(); + + string sfile=file_name+".size.txt"; + CountFileLines (sfile, cPar.n_vc); + } + + cPar.n_vc=cPar.n_vc-1; + + gsl_matrix *S=gsl_matrix_alloc (2*cPar.n_vc, cPar.n_vc); + gsl_matrix *Vq=gsl_matrix_alloc (cPar.n_vc, cPar.n_vc); + //gsl_matrix *V=gsl_matrix_alloc (cPar.n_vc+1, (cPar.n_vc*(cPar.n_vc+1))/2*(cPar.n_vc+1) ); + //gsl_matrix *Vslope=gsl_matrix_alloc (n_lines+1, (n_lines*(n_lines+1))/2*(n_lines+1) ); + gsl_vector *q=gsl_vector_alloc (cPar.n_vc); + gsl_vector *s_study=gsl_vector_alloc (cPar.n_vc); + gsl_vector *s_ref=gsl_vector_alloc (cPar.n_vc); + gsl_vector *s=gsl_vector_alloc (cPar.n_vc+1); + + gsl_matrix_set_zero(S); + gsl_matrix_view S_mat=gsl_matrix_submatrix(S, 0, 0, cPar.n_vc, cPar.n_vc); + gsl_matrix_view Svar_mat=gsl_matrix_submatrix (S, cPar.n_vc, 0, cPar.n_vc, cPar.n_vc); + + gsl_matrix_set_zero(Vq); + //gsl_matrix_set_zero(V); + //gsl_matrix_set_zero(Vslope); + gsl_vector_set_zero(q); + gsl_vector_set_zero(s_study); + gsl_vector_set_zero(s_ref); + + if (!cPar.file_study.empty()) { + ReadFile_study(cPar.file_study, Vq, q, s_study, cPar.ni_study); + } else { + ReadFile_mstudy(cPar.file_mstudy, Vq, q, s_study, cPar.ni_study); + } + + if (!cPar.file_ref.empty()) { + ReadFile_ref(cPar.file_ref, &S_mat.matrix, &Svar_mat.matrix, s_ref, cPar.ni_ref); + } else { + ReadFile_mref(cPar.file_mref, &S_mat.matrix, &Svar_mat.matrix, s_ref, cPar.ni_ref); + } + + cout<<"## number of variance components = "<<cPar.n_vc<<endl; + cout<<"## number of individuals in the sample = "<<cPar.ni_study<<endl; + cout<<"## number of individuals in the reference = "<<cPar.ni_ref<<endl; + + CalcVCss(Vq, &S_mat.matrix, &Svar_mat.matrix, q, s_study, cPar.ni_study, cPar.v_pve, cPar.v_se_pve, cPar.pve_total, cPar.se_pve_total, cPar.v_sigma2, cPar.v_se_sigma2, cPar.v_enrich, cPar.v_se_enrich); + + gsl_vector_view s_sub=gsl_vector_subvector (s, 0, cPar.n_vc); + gsl_vector_memcpy (&s_sub.vector, s_ref); + gsl_vector_set (s, cPar.n_vc, cPar.ni_ref); + + cPar.WriteMatrix (S, "S"); + cPar.WriteMatrix (Vq, "Vq"); + cPar.WriteVector (q, "q"); + cPar.WriteVector (s, "size"); + + gsl_matrix_free (S); + gsl_matrix_free (Vq); + //gsl_matrix_free (V); + //gsl_matrix_free (Vslope); + gsl_vector_free (q); + gsl_vector_free (s_study); + gsl_vector_free (s_ref); + gsl_vector_free (s); + } else { gsl_matrix *Y=gsl_matrix_alloc (cPar.ni_test, cPar.n_ph); gsl_matrix *W=gsl_matrix_alloc (Y->size1, cPar.n_cvt); gsl_matrix *G=gsl_matrix_alloc (Y->size1, Y->size1*cPar.n_vc ); - //set covariates matrix W and phenotype matrix Y - //an intercept should be included in W, + //set covariates matrix W and phenotype matrix Y + //an intercept should be included in W, cPar.CopyCvtPhen (W, Y, 0); //read kinship matrices if (!(cPar.file_mk).empty()) { ReadFile_mk (cPar.file_mk, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G); if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;} - + //center matrix G, and obtain v_traceG double d=0; (cPar.v_traceG).clear(); @@ -1152,7 +1749,7 @@ void GEMMA::BatchRun (PARAM &cPar) } else if (!(cPar.file_kin).empty()) { ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G); if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;} - + //center matrix G CenterMatrix (G); @@ -1167,8 +1764,8 @@ void GEMMA::BatchRun (PARAM &cPar) /* //eigen-decomposition and calculate trace_G cout<<"Start Eigen-Decomposition..."<<endl; - time_start=clock(); - + time_start=clock(); + if (cPar.a_mode==31) { cPar.trace_G=EigenDecomp (G, U, eval, 1); } else { @@ -1182,14 +1779,14 @@ void GEMMA::BatchRun (PARAM &cPar) } cPar.trace_G/=(double)eval->size; - cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); + cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); } else { ReadFile_eigenU (cPar.file_ku, cPar.error, U); if (cPar.error==true) {cout<<"error! fail to read the U file. "<<endl; return;} - - ReadFile_eigenD (cPar.file_kd, cPar.error, eval); + + ReadFile_eigenD (cPar.file_kd, cPar.error, eval); if (cPar.error==true) {cout<<"error! fail to read the D file. "<<endl; return;} - + cPar.trace_G=0.0; for (size_t i=0; i<eval->size; i++) { if (gsl_vector_get(eval, i)<1e-10) {gsl_vector_set(eval, i, 0);} @@ -1202,7 +1799,7 @@ void GEMMA::BatchRun (PARAM &cPar) if (cPar.n_ph==1) { // if (cPar.n_vc==1) { /* - //calculate UtW and Uty + //calculate UtW and Uty CalcUtX (U, W, UtW); CalcUtX (U, Y, UtY); @@ -1228,10 +1825,10 @@ void GEMMA::BatchRun (PARAM &cPar) cPar.beta_remle_null.push_back(gsl_matrix_get(B, 0, i) ); cPar.se_beta_remle_null.push_back(gsl_matrix_get(se_B, 0, i) ); } - + CalcPve (eval, UtW, &UtY_col.vector, cPar.l_remle_null, cPar.trace_G, cPar.pve_null, cPar.pve_se_null); cPar.PrintSummary(); - + //calculate and output residuals if (cPar.a_mode==5) { gsl_vector *Utu_hat=gsl_vector_alloc (Y->size1); @@ -1239,11 +1836,11 @@ void GEMMA::BatchRun (PARAM &cPar) gsl_vector *u_hat=gsl_vector_alloc (Y->size1); gsl_vector *e_hat=gsl_vector_alloc (Y->size1); gsl_vector *y_hat=gsl_vector_alloc (Y->size1); - + //obtain Utu and Ute gsl_vector_memcpy (y_hat, &UtY_col.vector); gsl_blas_dgemv (CblasNoTrans, -1.0, UtW, &beta.vector, 1.0, y_hat); - + double d, u, e; for (size_t i=0; i<eval->size; i++) { d=gsl_vector_get (eval, i); @@ -1252,37 +1849,210 @@ void GEMMA::BatchRun (PARAM &cPar) gsl_vector_set (Utu_hat, i, u); gsl_vector_set (Ute_hat, i, e); } - + //obtain u and e gsl_blas_dgemv (CblasNoTrans, 1.0, U, Utu_hat, 0.0, u_hat); gsl_blas_dgemv (CblasNoTrans, 1.0, U, Ute_hat, 0.0, e_hat); - - //output residuals + + //output residuals cPar.WriteVector(u_hat, "residU"); cPar.WriteVector(e_hat, "residE"); - + gsl_vector_free(u_hat); gsl_vector_free(e_hat); gsl_vector_free(y_hat); - } -*/ + } +*/ // } else { gsl_vector_view Y_col=gsl_matrix_column (Y, 0); VC cVc; - cVc.CopyFromParam(cPar); - cVc.CalcVCreml (G, W, &Y_col.vector); + cVc.CopyFromParam(cPar); + if (cPar.a_mode==61) { + cVc.CalcVChe (G, W, &Y_col.vector); + } else { + cVc.CalcVCreml (cPar.noconstrain, G, W, &Y_col.vector); + } cVc.CopyToParam(cPar); - //obtain pve from sigma2 //obtain se_pve from se_sigma2 - + //} - } + } + } + + } + - + //compute confidence intervals with additional summary statistics + //we do not check the sign of z-scores here, but they have to be matched with the genotypes + if (cPar.a_mode==66 || cPar.a_mode==67) { + //read reference file first + gsl_matrix *S=gsl_matrix_alloc (cPar.n_vc, cPar.n_vc); + gsl_matrix *Svar=gsl_matrix_alloc (cPar.n_vc, cPar.n_vc); + gsl_vector *s_ref=gsl_vector_alloc (cPar.n_vc); + + gsl_matrix_set_zero(S); + gsl_matrix_set_zero(Svar); + gsl_vector_set_zero(s_ref); + + if (!cPar.file_ref.empty()) { + ReadFile_ref(cPar.file_ref, S, Svar, s_ref, cPar.ni_ref); + } else { + ReadFile_mref(cPar.file_mref, S, Svar, s_ref, cPar.ni_ref); + } + + //need to obtain a common set of SNPs between beta file and the genotype file; these are saved in mapRS2wA and mapRS2wK + //normalize the weight in mapRS2wK to have an average of one; each element of mapRS2wA is 1 + set<string> setSnps_beta; + ReadFile_snps_header (cPar.file_beta, setSnps_beta); + + //obtain the weights for wA, which contains the SNP weights for SNPs used in the model + map <string, double> mapRS2wK; + cPar.ObtainWeight(setSnps_beta, mapRS2wK); + + //set up matrices and vector + gsl_matrix *Xz=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc); + gsl_matrix *XWz=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc); + gsl_matrix *XtXWz=gsl_matrix_alloc (mapRS2wK.size(), cPar.n_vc*cPar.n_vc); + gsl_vector *w=gsl_vector_alloc (mapRS2wK.size()); + gsl_vector *w1=gsl_vector_alloc (mapRS2wK.size()); + gsl_vector *z=gsl_vector_alloc (mapRS2wK.size()); + gsl_vector *s_vec=gsl_vector_alloc (cPar.n_vc); + + vector<size_t> vec_cat, vec_size; + vector<double> vec_z; + + map <string, double> mapRS2z, mapRS2wA; + map <string, string> mapRS2A1; + string file_str; + + //update s_vec, the number of snps in each category + for (size_t i=0; i<cPar.n_vc; i++) { + vec_size.push_back(0); + } + + for (map<string, double>::const_iterator it=mapRS2wK.begin(); it!=mapRS2wK.end(); ++it) { + vec_size[cPar.mapRS2cat[it->first]]++; + } + + for (size_t i=0; i<cPar.n_vc; i++) { + gsl_vector_set(s_vec, i, vec_size[i]); + } + + //update mapRS2wA using v_pve and s_vec + if (cPar.a_mode==66) { + for (map<string, double>::const_iterator it=mapRS2wK.begin(); it!=mapRS2wK.end(); ++it) { + mapRS2wA[it->first]=1; + } + } else { + cPar.UpdateWeight (0, mapRS2wK, cPar.ni_test, s_vec, mapRS2wA); + } + + //read in z-scores based on allele 0, and save that into a vector + ReadFile_beta (cPar.file_beta, mapRS2wA, mapRS2A1, mapRS2z); + + //update snp indicator, save weights to w, save z-scores to vec_z, save category label to vec_cat + //sign of z is determined by matching alleles + cPar.UpdateSNPnZ (mapRS2wA, mapRS2A1, mapRS2z, w, z, vec_cat); + + //compute an n by k matrix of X_iWz + cout<<"Calculating Xz ... "<<endl; + + gsl_matrix_set_zero(Xz); + gsl_vector_set_all (w1, 1); + + if (!cPar.file_bfile.empty() ) { + file_str=cPar.file_bfile+".bed"; + PlinkXwz (file_str, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, vec_cat, w1, z, 0, Xz); + } else if (!cPar.file_geno.empty()) { + BimbamXwz (cPar.file_geno, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, vec_cat, w1, z, 0, Xz); + } else if (!cPar.file_mbfile.empty() ){ + MFILEXwz (1, cPar.file_mbfile, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, vec_cat, w1, z, Xz); + } else if (!cPar.file_mgeno.empty()) { + MFILEXwz (0, cPar.file_mgeno, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, vec_cat, w1, z, Xz); + } + /* + cout<<"Xz: "<<endl; + for (size_t i=0; i<5; i++) { + for (size_t j=0; j<cPar.n_vc; j++) { + cout<<gsl_matrix_get (Xz, i, j)<<" "; + } + cout<<endl; + } + */ + if (cPar.a_mode==66) { + gsl_matrix_memcpy (XWz, Xz); + } else if (cPar.a_mode==67) { + cout<<"Calculating XWz ... "<<endl; + + gsl_matrix_set_zero(XWz); + + if (!cPar.file_bfile.empty() ) { + file_str=cPar.file_bfile+".bed"; + PlinkXwz (file_str, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, vec_cat, w, z, 0, XWz); + } else if (!cPar.file_geno.empty()) { + BimbamXwz (cPar.file_geno, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, vec_cat, w, z, 0, XWz); + } else if (!cPar.file_mbfile.empty() ){ + MFILEXwz (1, cPar.file_mbfile, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, vec_cat, w, z, XWz); + } else if (!cPar.file_mgeno.empty()) { + MFILEXwz (0, cPar.file_mgeno, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, vec_cat, w, z, XWz); + } + } + /* + cout<<"XWz: "<<endl; + for (size_t i=0; i<5; i++) { + cout<<gsl_vector_get (w, i)<<endl; + for (size_t j=0; j<cPar.n_vc; j++) { + cout<<gsl_matrix_get (XWz, i, j)<<" "; + } + cout<<endl; + } + */ + //compute an p by k matrix of X_j^TWX_iWz + cout<<"Calculating XtXWz ... "<<endl; + gsl_matrix_set_zero(XtXWz); + + if (!cPar.file_bfile.empty() ) { + file_str=cPar.file_bfile+".bed"; + PlinkXtXwz (file_str, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, XWz, 0, XtXWz); + } else if (!cPar.file_geno.empty()) { + BimbamXtXwz (cPar.file_geno, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, XWz, 0, XtXWz); + } else if (!cPar.file_mbfile.empty() ){ + MFILEXtXwz (1, cPar.file_mbfile, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, XWz, XtXWz); + } else if (!cPar.file_mgeno.empty()) { + MFILEXtXwz (0, cPar.file_mgeno, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, XWz, XtXWz); + } + /* + cout<<"XtXWz: "<<endl; + for (size_t i=0; i<5; i++) { + for (size_t j=0; j<cPar.n_vc; j++) { + cout<<gsl_matrix_get (XtXWz, i, j)<<" "; + } + cout<<endl; + } + */ + //compute confidence intervals + CalcCIss(Xz, XWz, XtXWz, S, Svar, w, z, s_vec, vec_cat, cPar.v_pve, cPar.v_se_pve, cPar.pve_total, cPar.se_pve_total, cPar.v_sigma2, cPar.v_se_sigma2, cPar.v_enrich, cPar.v_se_enrich); + + //write files + //cPar.WriteMatrix (XWz, "XWz"); + //cPar.WriteMatrix (XtXWz, "XtXWz"); + //cPar.WriteVector (w, "w"); + + gsl_matrix_free(S); + gsl_matrix_free(Svar); + gsl_vector_free(s_ref); + + gsl_matrix_free(Xz); + gsl_matrix_free(XWz); + gsl_matrix_free(XtXWz); + gsl_vector_free(w); + gsl_vector_free(w1); + gsl_vector_free(z); + gsl_vector_free(s_vec); } - - + + //LMM or mvLMM or Eigen-Decomposition if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 || cPar.a_mode==5 || cPar.a_mode==31) { //Fit LMM or mvLMM or eigen gsl_matrix *Y=gsl_matrix_alloc (cPar.ni_test, cPar.n_ph); @@ -1290,33 +2060,62 @@ void GEMMA::BatchRun (PARAM &cPar) gsl_matrix *B=gsl_matrix_alloc (Y->size2, W->size2); //B is a d by c matrix gsl_matrix *se_B=gsl_matrix_alloc (Y->size2, W->size2); gsl_matrix *G=gsl_matrix_alloc (Y->size1, Y->size1); - gsl_matrix *U=gsl_matrix_alloc (Y->size1, Y->size1); + gsl_matrix *U=gsl_matrix_alloc (Y->size1, Y->size1); gsl_matrix *UtW=gsl_matrix_alloc (Y->size1, W->size2); gsl_matrix *UtY=gsl_matrix_alloc (Y->size1, Y->size2); gsl_vector *eval=gsl_vector_alloc (Y->size1); - - //set covariates matrix W and phenotype matrix Y - //an intercept should be included in W, + gsl_vector *env=gsl_vector_alloc (Y->size1); + gsl_vector *weight=gsl_vector_alloc (Y->size1); + + //set covariates matrix W and phenotype matrix Y + //an intercept should be included in W, cPar.CopyCvtPhen (W, Y, 0); - - //read relatedness matrix G + if (!cPar.file_gxe.empty()) {cPar.CopyGxe (env);} + + //read relatedness matrix G if (!(cPar.file_kin).empty()) { ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G); if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;} - + //center matrix G CenterMatrix (G); - + + //is residual weights are provided, then + if (!cPar.file_weight.empty()) { + cPar.CopyWeight (weight); + double d, wi, wj; + for (size_t i=0; i<G->size1; i++) { + wi=gsl_vector_get(weight, i); + for (size_t j=i; j<G->size2; j++) { + wj=gsl_vector_get(weight, j); + d=gsl_matrix_get(G, i, j); + if (wi<=0 || wj<=0) {d=0;} else {d/=sqrt(wi*wj);} + gsl_matrix_set(G, i, j, d); + if (j!=i) {gsl_matrix_set(G, j, i, d);} + } + } + } + //eigen-decomposition and calculate trace_G cout<<"Start Eigen-Decomposition..."<<endl; - time_start=clock(); - + time_start=clock(); + if (cPar.a_mode==31) { cPar.trace_G=EigenDecomp (G, U, eval, 1); } else { cPar.trace_G=EigenDecomp (G, U, eval, 0); } + if (!cPar.file_weight.empty()) { + double wi; + for (size_t i=0; i<U->size1; i++) { + wi=gsl_vector_get(weight, i); + if (wi<=0) {wi=0;} else {wi=sqrt(wi);} + gsl_vector_view Urow=gsl_matrix_row (U, i); + gsl_vector_scale (&Urow.vector, wi); + } + } + cPar.trace_G=0.0; for (size_t i=0; i<eval->size; i++) { if (gsl_vector_get (eval, i)<1e-10) {gsl_vector_set (eval, i, 0);} @@ -1324,14 +2123,14 @@ void GEMMA::BatchRun (PARAM &cPar) } cPar.trace_G/=(double)eval->size; - cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); + cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); } else { ReadFile_eigenU (cPar.file_ku, cPar.error, U); if (cPar.error==true) {cout<<"error! fail to read the U file. "<<endl; return;} - - ReadFile_eigenD (cPar.file_kd, cPar.error, eval); + + ReadFile_eigenD (cPar.file_kd, cPar.error, eval); if (cPar.error==true) {cout<<"error! fail to read the D file. "<<endl; return;} - + cPar.trace_G=0.0; for (size_t i=0; i<eval->size; i++) { if (gsl_vector_get(eval, i)<1e-10) {gsl_vector_set(eval, i, 0);} @@ -1339,14 +2138,29 @@ void GEMMA::BatchRun (PARAM &cPar) } cPar.trace_G/=(double)eval->size; } - + if (cPar.a_mode==31) { cPar.WriteMatrix(U, "eigenU"); cPar.WriteVector(eval, "eigenD"); - } else { - //calculate UtW and Uty + } else if (!cPar.file_gene.empty() ) { + //calculate UtW and Uty CalcUtX (U, W, UtW); - CalcUtX (U, Y, UtY); + CalcUtX (U, Y, UtY); + + LMM cLmm; + cLmm.CopyFromParam(cPar); + + gsl_vector_view Y_col=gsl_matrix_column (Y, 0); + gsl_vector_view UtY_col=gsl_matrix_column (UtY, 0); + + cLmm.AnalyzeGene (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector); //y is the predictor, not the phenotype + + cLmm.WriteFiles(); + cLmm.CopyToParam(cPar); + } else { + //calculate UtW and Uty + CalcUtX (U, W, UtW); + CalcUtX (U, Y, UtY); //calculate REMLE/MLE estimate and pve for univariate model if (cPar.n_ph==1) { @@ -1372,10 +2186,10 @@ void GEMMA::BatchRun (PARAM &cPar) cPar.beta_remle_null.push_back(gsl_matrix_get(B, 0, i) ); cPar.se_beta_remle_null.push_back(gsl_matrix_get(se_B, 0, i) ); } - + CalcPve (eval, UtW, &UtY_col.vector, cPar.l_remle_null, cPar.trace_G, cPar.pve_null, cPar.pve_se_null); cPar.PrintSummary(); - + //calculate and output residuals if (cPar.a_mode==5) { gsl_vector *Utu_hat=gsl_vector_alloc (Y->size1); @@ -1383,11 +2197,11 @@ void GEMMA::BatchRun (PARAM &cPar) gsl_vector *u_hat=gsl_vector_alloc (Y->size1); gsl_vector *e_hat=gsl_vector_alloc (Y->size1); gsl_vector *y_hat=gsl_vector_alloc (Y->size1); - + //obtain Utu and Ute gsl_vector_memcpy (y_hat, &UtY_col.vector); gsl_blas_dgemv (CblasNoTrans, -1.0, UtW, &beta.vector, 1.0, y_hat); - + double d, u, e; for (size_t i=0; i<eval->size; i++) { d=gsl_vector_get (eval, i); @@ -1396,81 +2210,106 @@ void GEMMA::BatchRun (PARAM &cPar) gsl_vector_set (Utu_hat, i, u); gsl_vector_set (Ute_hat, i, e); } - + //obtain u and e gsl_blas_dgemv (CblasNoTrans, 1.0, U, Utu_hat, 0.0, u_hat); gsl_blas_dgemv (CblasNoTrans, 1.0, U, Ute_hat, 0.0, e_hat); - - //output residuals + + //output residuals cPar.WriteVector(u_hat, "residU"); cPar.WriteVector(e_hat, "residE"); - + gsl_vector_free(u_hat); gsl_vector_free(e_hat); gsl_vector_free(y_hat); - } - } - + } + } + //Fit LMM or mvLMM if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4) { - if (cPar.n_ph==1) { + if (cPar.n_ph==1) { LMM cLmm; cLmm.CopyFromParam(cPar); - + gsl_vector_view Y_col=gsl_matrix_column (Y, 0); gsl_vector_view UtY_col=gsl_matrix_column (UtY, 0); - - if (!cPar.file_gene.empty()) { - cLmm.AnalyzeGene (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector); //y is the predictor, not the phenotype - } else if (!cPar.file_bfile.empty()) { - cLmm.AnalyzePlink (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector); - } else { - cLmm.AnalyzeBimbam (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector); - } - + + if (!cPar.file_bfile.empty()) { + if (cPar.file_gxe.empty()) { + cLmm.AnalyzePlink (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector); + } else { + cLmm.AnalyzePlinkGXE (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector, env); + } + } + // WJA added + else if(!cPar.file_oxford.empty()) { + cLmm.Analyzebgen (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector); + } + else { + if (cPar.file_gxe.empty()) { + cLmm.AnalyzeBimbam (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector); + } else { + cLmm.AnalyzeBimbamGXE (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector, env); + } + } + cLmm.WriteFiles(); cLmm.CopyToParam(cPar); - } else { + } else { MVLMM cMvlmm; - cMvlmm.CopyFromParam(cPar); - + cMvlmm.CopyFromParam(cPar); + if (!cPar.file_bfile.empty()) { - cMvlmm.AnalyzePlink (U, eval, UtW, UtY); - } else { - cMvlmm.AnalyzeBimbam (U, eval, UtW, UtY); + if (cPar.file_gxe.empty()) { + cMvlmm.AnalyzePlink (U, eval, UtW, UtY); + } else { + cMvlmm.AnalyzePlinkGXE (U, eval, UtW, UtY, env); + } + } + else if(!cPar.file_oxford.empty()) + { + cMvlmm.Analyzebgen (U, eval, UtW, UtY); + } + else { + if (cPar.file_gxe.empty()) { + cMvlmm.AnalyzeBimbam (U, eval, UtW, UtY); + } else { + cMvlmm.AnalyzeBimbamGXE (U, eval, UtW, UtY, env); + } } - + cMvlmm.WriteFiles(); cMvlmm.CopyToParam(cPar); } } } - - + + //release all matrices and vectors gsl_matrix_free (Y); gsl_matrix_free (W); gsl_matrix_free(B); gsl_matrix_free(se_B); - gsl_matrix_free (G); + gsl_matrix_free (G); gsl_matrix_free (U); gsl_matrix_free (UtW); gsl_matrix_free (UtY); gsl_vector_free (eval); - } - - + gsl_vector_free (env); + } + + //BSLMM if (cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) { gsl_vector *y=gsl_vector_alloc (cPar.ni_test); - gsl_matrix *W=gsl_matrix_alloc (y->size, cPar.n_cvt); + gsl_matrix *W=gsl_matrix_alloc (y->size, cPar.n_cvt); gsl_matrix *G=gsl_matrix_alloc (y->size, y->size); - gsl_matrix *UtX=gsl_matrix_alloc (y->size, cPar.ns_test); - - //set covariates matrix W and phenotype vector y - //an intercept should be included in W, + gsl_matrix *UtX=gsl_matrix_alloc (y->size, cPar.ns_test); + + //set covariates matrix W and phenotype vector y + //an intercept should be included in W, cPar.CopyCvtPhen (W, y, 0); - + //center y, even for case/control data cPar.pheno_mean=CenterVector(y); @@ -1482,32 +2321,32 @@ void GEMMA::BatchRun (PARAM &cPar) //perform BSLMM analysis BSLMM cBslmm; cBslmm.CopyFromParam(cPar); - time_start=clock(); + time_start=clock(); cBslmm.MCMC(UtX, y); cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); cBslmm.CopyToParam(cPar); //else, if rho!=1 } else { - gsl_matrix *U=gsl_matrix_alloc (y->size, y->size); + gsl_matrix *U=gsl_matrix_alloc (y->size, y->size); gsl_vector *eval=gsl_vector_alloc (y->size); gsl_matrix *UtW=gsl_matrix_alloc (y->size, W->size2); gsl_vector *Uty=gsl_vector_alloc (y->size); - - //read relatedness matrix G - if (!(cPar.file_kin).empty()) { + + //read relatedness matrix G + if (!(cPar.file_kin).empty()) { cPar.ReadGenotypes (UtX, G, false); - + //read relatedness matrix G ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G); if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;} - + //center matrix G CenterMatrix (G); } else { cPar.ReadGenotypes (UtX, G, true); } - + //eigen-decomposition and calculate trace_G cout<<"Start Eigen-Decomposition..."<<endl; time_start=clock(); @@ -1518,39 +2357,39 @@ void GEMMA::BatchRun (PARAM &cPar) cPar.trace_G+=gsl_vector_get (eval, i); } cPar.trace_G/=(double)eval->size; - cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); - - //calculate UtW and Uty + cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); + + //calculate UtW and Uty CalcUtX (U, W, UtW); CalcUtX (U, y, Uty); - + //calculate REMLE/MLE estimate and pve CalcLambda ('L', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_mle_null, cPar.logl_mle_H0); CalcLambda ('R', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_remle_null, cPar.logl_remle_H0); CalcPve (eval, UtW, Uty, cPar.l_remle_null, cPar.trace_G, cPar.pve_null, cPar.pve_se_null); - + cPar.PrintSummary(); - + //Creat and calcualte UtX, use a large memory cout<<"Calculating UtX..."<<endl; - time_start=clock(); + time_start=clock(); CalcUtX (U, UtX); cPar.time_UtX=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); - + //perform BSLMM analysis BSLMM cBslmm; cBslmm.CopyFromParam(cPar); - time_start=clock(); - if (cPar.a_mode==12) { //ridge regression + time_start=clock(); + if (cPar.a_mode==12) { //ridge regression cBslmm.RidgeR(U, UtX, Uty, eval, cPar.l_remle_null); } else { //Run MCMC cBslmm.MCMC(U, UtX, Uty, eval, y); } cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); cBslmm.CopyToParam(cPar); - + //release all matrices and vectors - gsl_matrix_free (G); + gsl_matrix_free (G); gsl_matrix_free (U); gsl_matrix_free (UtW); gsl_vector_free (eval); @@ -1560,106 +2399,259 @@ void GEMMA::BatchRun (PARAM &cPar) gsl_matrix_free (W); gsl_vector_free (y); gsl_matrix_free (UtX); - } - - - + } + + + + //LDR + if (cPar.a_mode==14) { + gsl_vector *y=gsl_vector_alloc (cPar.ni_test); + gsl_matrix *W=gsl_matrix_alloc (y->size, cPar.n_cvt); + gsl_matrix *G=gsl_matrix_alloc (1, 1); + vector<vector<unsigned char> > Xt; + + //set covariates matrix W and phenotype vector y + //an intercept is included in W + cPar.CopyCvtPhen (W, y, 0); + + //read in genotype matrix X + cPar.ReadGenotypes (Xt, G, false); + + LDR cLdr; + cLdr.CopyFromParam(cPar); + time_start=clock(); + + cLdr.VB(Xt, W, y); + + cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0); + cLdr.CopyToParam(cPar); + + gsl_vector_free (y); + gsl_matrix_free (W); + gsl_matrix_free (G); + } + cPar.time_total=(clock()-time_begin)/(double(CLOCKS_PER_SEC)*60.0); - + return; } -void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) +void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) { string file_str; file_str=cPar.path_out+"/"+cPar.file_out; file_str+=".log.txt"; - + ofstream outfile (file_str.c_str(), ofstream::out); if (!outfile) {cout<<"error writing log file: "<<file_str.c_str()<<endl; return;} - + outfile<<"##"<<endl; outfile<<"## GEMMA Version = "<<version<<endl; - + outfile<<"##"<<endl; outfile<<"## Command Line Input = "; - for(int i = 1; i < argc; i++) { + for(int i = 0; i < argc; i++) { outfile<<argv[i]<<" "; } outfile<<endl; outfile<<"##"<<endl; - time_t rawtime; + time_t rawtime; time(&rawtime); tm *ptm = localtime (&rawtime); - outfile<<"## Date = "<<asctime(ptm)<<endl; + outfile<<"## Date = "<<asctime(ptm); //ptm->tm_year<<":"<<ptm->tm_month<<":"<<ptm->tm_day":"<<ptm->tm_hour<<":"<<ptm->tm_min<<endl; - + outfile<<"##"<<endl; outfile<<"## Summary Statistics:"<<endl; - outfile<<"## number of total individuals = "<<cPar.ni_total<<endl; - if (cPar.a_mode==43) { - outfile<<"## number of analyzed individuals = "<<cPar.ni_cvt<<endl; - outfile<<"## number of individuals with full phenotypes = "<<cPar.ni_test<<endl; - } else { - outfile<<"## number of analyzed individuals = "<<cPar.ni_test<<endl; - } - outfile<<"## number of covariates = "<<cPar.n_cvt<<endl; - outfile<<"## number of phenotypes = "<<cPar.n_ph<<endl; - if (cPar.a_mode==43) { - outfile<<"## number of observed data = "<<cPar.np_obs<<endl; - outfile<<"## number of missing data = "<<cPar.np_miss<<endl; - } - if (cPar.a_mode==61) { - outfile<<"## number of variance components = "<<cPar.n_vc<<endl; - } - - if (!(cPar.file_gene).empty()) { - outfile<<"## number of total genes = "<<cPar.ng_total<<endl; - outfile<<"## number of analyzed genes = "<<cPar.ng_test<<endl; - } else if (cPar.file_epm.empty()) { - outfile<<"## number of total SNPs = "<<cPar.ns_total<<endl; - outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl; + if (!cPar.file_cor.empty() || !cPar.file_study.empty() || !cPar.file_mstudy.empty() ) { + outfile<<"## number of total individuals in the sample = "<<cPar.ni_study<<endl; + outfile<<"## number of total individuals in the reference = "<<cPar.ni_ref<<endl; + //outfile<<"## number of total SNPs in the sample = "<<cPar.ns_study<<endl; + //outfile<<"## number of total SNPs in the reference panel = "<<cPar.ns_ref<<endl; + //outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl; + //outfile<<"## number of analyzed SNP pairs = "<<cPar.ns_pair<<endl; + outfile<<"## number of variance components = "<<cPar.n_vc<<endl; + + outfile<<"## pve estimates = "; + for (size_t i=0; i<cPar.v_pve.size(); i++) { + outfile<<" "<<cPar.v_pve[i]; + } + outfile<<endl; + + outfile<<"## se(pve) = "; + for (size_t i=0; i<cPar.v_se_pve.size(); i++) { + outfile<<" "<<cPar.v_se_pve[i]; + } + outfile<<endl; + + if (cPar.n_vc>1) { + outfile<<"## total pve = "<<cPar.pve_total<<endl; + outfile<<"## se(total pve) = "<<cPar.se_pve_total<<endl; + } + + outfile<<"## sigma2 per snp = "; + for (size_t i=0; i<cPar.v_sigma2.size(); i++) { + outfile<<" "<<cPar.v_sigma2[i]; + } + outfile<<endl; + + outfile<<"## se(sigma2 per snp) = "; + for (size_t i=0; i<cPar.v_se_sigma2.size(); i++) { + outfile<<" "<<cPar.v_se_sigma2[i]; + } + outfile<<endl; + + outfile<<"## enrichment = "; + for (size_t i=0; i<cPar.v_enrich.size(); i++) { + outfile<<" "<<cPar.v_enrich[i]; + } + outfile<<endl; + + outfile<<"## se(enrichment) = "; + for (size_t i=0; i<cPar.v_se_enrich.size(); i++) { + outfile<<" "<<cPar.v_se_enrich[i]; + } + outfile<<endl; + } else if (!cPar.file_beta.empty() && (cPar.a_mode==61 || cPar.a_mode==62) ) { + outfile<<"## number of total individuals in the sample = "<<cPar.ni_study<<endl; + outfile<<"## number of total individuals in the reference = "<<cPar.ni_total<<endl; + outfile<<"## number of total SNPs in the sample = "<<cPar.ns_study<<endl; + outfile<<"## number of total SNPs in the reference panel = "<<cPar.ns_total<<endl; + outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl; + outfile<<"## number of variance components = "<<cPar.n_vc<<endl; + } else if (!cPar.file_beta.empty() && (cPar.a_mode==66 || cPar.a_mode==67) ) { + outfile<<"## number of total individuals in the sample = "<<cPar.ni_total<<endl; + outfile<<"## number of total individuals in the reference = "<<cPar.ni_ref<<endl; + outfile<<"## number of total SNPs in the sample = "<<cPar.ns_total<<endl; + outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl; + outfile<<"## number of variance components = "<<cPar.n_vc<<endl; + + outfile<<"## pve estimates = "; + for (size_t i=0; i<cPar.v_pve.size(); i++) { + outfile<<" "<<cPar.v_pve[i]; + } + outfile<<endl; + + outfile<<"## se(pve) = "; + for (size_t i=0; i<cPar.v_se_pve.size(); i++) { + outfile<<" "<<cPar.v_se_pve[i]; + } + outfile<<endl; + + if (cPar.n_vc>1) { + outfile<<"## total pve = "<<cPar.pve_total<<endl; + outfile<<"## se(total pve) = "<<cPar.se_pve_total<<endl; + } + + outfile<<"## sigma2 per snp = "; + for (size_t i=0; i<cPar.v_sigma2.size(); i++) { + outfile<<" "<<cPar.v_sigma2[i]; + } + outfile<<endl; + + outfile<<"## se(sigma2 per snp) = "; + for (size_t i=0; i<cPar.v_se_sigma2.size(); i++) { + outfile<<" "<<cPar.v_se_sigma2[i]; + } + outfile<<endl; + + outfile<<"## enrichment = "; + for (size_t i=0; i<cPar.v_enrich.size(); i++) { + outfile<<" "<<cPar.v_enrich[i]; + } + outfile<<endl; + + outfile<<"## se(enrichment) = "; + for (size_t i=0; i<cPar.v_se_enrich.size(); i++) { + outfile<<" "<<cPar.v_se_enrich[i]; + } + outfile<<endl; } else { - outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl; + outfile<<"## number of total individuals = "<<cPar.ni_total<<endl; + + if (cPar.a_mode==43) { + outfile<<"## number of analyzed individuals = "<<cPar.ni_cvt<<endl; + outfile<<"## number of individuals with full phenotypes = "<<cPar.ni_test<<endl; + } else if (cPar.a_mode!=27 && cPar.a_mode!=28) { + outfile<<"## number of analyzed individuals = "<<cPar.ni_test<<endl; + } + + outfile<<"## number of covariates = "<<cPar.n_cvt<<endl; + outfile<<"## number of phenotypes = "<<cPar.n_ph<<endl; + if (cPar.a_mode==43) { + outfile<<"## number of observed data = "<<cPar.np_obs<<endl; + outfile<<"## number of missing data = "<<cPar.np_miss<<endl; + } + if (cPar.a_mode==25 || cPar.a_mode==26 || cPar.a_mode==27 || cPar.a_mode==28 || cPar.a_mode==61 || cPar.a_mode==62 || cPar.a_mode==66 || cPar.a_mode==67) { + outfile<<"## number of variance components = "<<cPar.n_vc<<endl; + } + + if (!(cPar.file_gene).empty()) { + outfile<<"## number of total genes = "<<cPar.ng_total<<endl; + outfile<<"## number of analyzed genes = "<<cPar.ng_test<<endl; + } else if (cPar.file_epm.empty()) { + outfile<<"## number of total SNPs = "<<cPar.ns_total<<endl; + outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl; + } else { + outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl; + } + + if (cPar.a_mode==13) { + outfile<<"## number of cases = "<<cPar.ni_case<<endl; + outfile<<"## number of controls = "<<cPar.ni_control<<endl; + } } - - if (cPar.a_mode==13) { - outfile<<"## number of cases = "<<cPar.ni_case<<endl; - outfile<<"## number of controls = "<<cPar.ni_control<<endl; - } - - - if (cPar.a_mode==61) { - // outfile<<"## REMLE log-likelihood in the null model = "<<cPar.logl_remle_H0<<endl; - if (cPar.n_ph==1) { - outfile<<"## pve estimate in the null model = "; - for (size_t i=0; i<cPar.v_pve.size(); i++) { - outfile<<" "<<cPar.v_pve[i]; - } - outfile<<endl; - - outfile<<"## se(pve) in the null model = "; - for (size_t i=0; i<cPar.v_se_pve.size(); i++) { - outfile<<" "<<cPar.v_se_pve[i]; - } - outfile<<endl; - - outfile<<"## sigma2 estimate in the null model = "; - for (size_t i=0; i<cPar.v_sigma2.size(); i++) { - outfile<<" "<<cPar.v_sigma2[i]; - } - outfile<<endl; - outfile<<"## se(sigma2) in the null model = "; - for (size_t i=0; i<cPar.v_se_sigma2.size(); i++) { - outfile<<" "<<cPar.v_se_sigma2[i]; - } - outfile<<endl; + if ( (cPar.a_mode==61 || cPar.a_mode==62) && cPar.file_cor.empty() && cPar.file_study.empty() && cPar.file_mstudy.empty() ) { + // outfile<<"## REMLE log-likelihood in the null model = "<<cPar.logl_remle_H0<<endl; + if (cPar.n_ph==1) { + outfile<<"## pve estimates = "; + for (size_t i=0; i<cPar.v_pve.size(); i++) { + outfile<<" "<<cPar.v_pve[i]; + } + outfile<<endl; + + outfile<<"## se(pve) = "; + for (size_t i=0; i<cPar.v_se_pve.size(); i++) { + outfile<<" "<<cPar.v_se_pve[i]; + } + outfile<<endl; + + if (cPar.n_vc>1) { + outfile<<"## total pve = "<<cPar.pve_total<<endl; + outfile<<"## se(total pve) = "<<cPar.se_pve_total<<endl; + } + + outfile<<"## sigma2 estimates = "; + for (size_t i=0; i<cPar.v_sigma2.size(); i++) { + outfile<<" "<<cPar.v_sigma2[i]; + } + outfile<<endl; + + outfile<<"## se(sigma2) = "; + for (size_t i=0; i<cPar.v_se_sigma2.size(); i++) { + outfile<<" "<<cPar.v_se_sigma2[i]; + } + outfile<<endl; + + if (!cPar.file_beta.empty() ) { + outfile<<"## enrichment = "; + for (size_t i=0; i<cPar.v_enrich.size(); i++) { + outfile<<" "<<cPar.v_enrich[i]; + } + outfile<<endl; + + outfile<<"## se(enrichment) = "; + for (size_t i=0; i<cPar.v_se_enrich.size(); i++) { + outfile<<" "<<cPar.v_se_enrich[i]; + } + outfile<<endl; + } /* outfile<<"## beta estimate in the null model = "; for (size_t i=0; i<cPar.beta_remle_null.size(); i++) { @@ -1672,19 +2664,19 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } outfile<<endl; */ - } + } } - + if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 || cPar.a_mode==5 || cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) { outfile<<"## REMLE log-likelihood in the null model = "<<cPar.logl_remle_H0<<endl; outfile<<"## MLE log-likelihood in the null model = "<<cPar.logl_mle_H0<<endl; if (cPar.n_ph==1) { //outfile<<"## lambda REMLE estimate in the null (linear mixed) model = "<<cPar.l_remle_null<<endl; - //outfile<<"## lambda MLE estimate in the null (linear mixed) model = "<<cPar.l_mle_null<<endl; + //outfile<<"## lambda MLE estimate in the null (linear mixed) model = "<<cPar.l_mle_null<<endl; outfile<<"## pve estimate in the null model = "<<cPar.pve_null<<endl; - outfile<<"## se(pve) in the null model = "<<cPar.pve_se_null<<endl; + outfile<<"## se(pve) in the null model = "<<cPar.pve_se_null<<endl; outfile<<"## vg estimate in the null model = "<<cPar.vg_remle_null<<endl; - outfile<<"## ve estimate in the null model = "<<cPar.ve_remle_null<<endl; + outfile<<"## ve estimate in the null model = "<<cPar.ve_remle_null<<endl; outfile<<"## beta estimate in the null model = "; for (size_t i=0; i<cPar.beta_remle_null.size(); i++) { outfile<<" "<<cPar.beta_remle_null[i]; @@ -1695,10 +2687,10 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) outfile<<" "<<cPar.se_beta_remle_null[i]; } outfile<<endl; - + } else { size_t c; - outfile<<"## REMLE estimate for Vg in the null model: "<<endl; + outfile<<"## REMLE estimate for Vg in the null model: "<<endl; for (size_t i=0; i<cPar.n_ph; i++) { for (size_t j=0; j<=i; j++) { c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j); @@ -1706,7 +2698,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } outfile<<endl; } - outfile<<"## se(Vg): "<<endl; + outfile<<"## se(Vg): "<<endl; for (size_t i=0; i<cPar.n_ph; i++) { for (size_t j=0; j<=i; j++) { c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j); @@ -1714,7 +2706,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } outfile<<endl; } - outfile<<"## REMLE estimate for Ve in the null model: "<<endl; + outfile<<"## REMLE estimate for Ve in the null model: "<<endl; for (size_t i=0; i<cPar.n_ph; i++) { for (size_t j=0; j<=i; j++) { c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j); @@ -1722,7 +2714,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } outfile<<endl; } - outfile<<"## se(Ve): "<<endl; + outfile<<"## se(Ve): "<<endl; for (size_t i=0; i<cPar.n_ph; i++) { for (size_t j=0; j<=i; j++) { c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j); @@ -1730,7 +2722,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } outfile<<endl; } - + outfile<<"## MLE estimate for Vg in the null model: "<<endl; for (size_t i=0; i<cPar.n_ph; i++) { for (size_t j=0; j<cPar.n_ph; j++) { @@ -1739,7 +2731,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } outfile<<endl; } - outfile<<"## se(Vg): "<<endl; + outfile<<"## se(Vg): "<<endl; for (size_t i=0; i<cPar.n_ph; i++) { for (size_t j=0; j<=i; j++) { c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j); @@ -1747,7 +2739,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } outfile<<endl; } - outfile<<"## MLE estimate for Ve in the null model: "<<endl; + outfile<<"## MLE estimate for Ve in the null model: "<<endl; for (size_t i=0; i<cPar.n_ph; i++) { for (size_t j=0; j<cPar.n_ph; j++) { c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j); @@ -1755,7 +2747,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } outfile<<endl; } - outfile<<"## se(Ve): "<<endl; + outfile<<"## se(Ve): "<<endl; for (size_t i=0; i<cPar.n_ph; i++) { for (size_t j=0; j<=i; j++) { c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j); @@ -1781,15 +2773,15 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } } } - + /* if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 || cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) { if (cPar.n_ph==1) { outfile<<"## REMLE vg estimate in the null model = "<<cPar.vg_remle_null<<endl; - outfile<<"## REMLE ve estimate in the null model = "<<cPar.ve_remle_null<<endl; + outfile<<"## REMLE ve estimate in the null model = "<<cPar.ve_remle_null<<endl; } else { size_t c; - outfile<<"## REMLE estimate for Vg in the null model: "<<endl; + outfile<<"## REMLE estimate for Vg in the null model: "<<endl; for (size_t i=0; i<cPar.n_ph; i++) { for (size_t j=0; j<=i; j++) { c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j); @@ -1797,7 +2789,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } outfile<<endl; } - outfile<<"## REMLE estimate for Ve in the null model: "<<endl; + outfile<<"## REMLE estimate for Ve in the null model: "<<endl; for (size_t i=0; i<cPar.n_ph; i++) { for (size_t j=0; j<=i; j++) { c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j); @@ -1808,15 +2800,15 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } } */ - - + + if (cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) { - outfile<<"## estimated mean = "<<cPar.pheno_mean<<endl; + outfile<<"## estimated mean = "<<cPar.pheno_mean<<endl; } - - if (cPar.a_mode==11 || cPar.a_mode==13) { + + if (cPar.a_mode==11 || cPar.a_mode==13) { outfile<<"##"<<endl; - outfile<<"## MCMC related:"<<endl; + outfile<<"## MCMC related:"<<endl; outfile<<"## initial value of h = "<<cPar.cHyp_initial.h<<endl; outfile<<"## initial value of rho = "<<cPar.cHyp_initial.rho<<endl; outfile<<"## initial value of pi = "<<exp(cPar.cHyp_initial.logp)<<endl; @@ -1824,7 +2816,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) outfile<<"## random seed = "<<cPar.randseed<<endl; outfile<<"## acceptance ratio = "<<(double)cPar.n_accept/(double)((cPar.w_step+cPar.s_step)*cPar.n_mh)<<endl; } - + outfile<<"##"<<endl; outfile<<"## Computation Time:"<<endl; outfile<<"## total computation time = "<<cPar.time_total<<" min "<<endl; @@ -1837,7 +2829,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) } if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 || cPar.a_mode==5 || cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) { outfile<<"## time on eigen-decomposition = "<<cPar.time_eigen<<" min "<<endl; - outfile<<"## time on calculating UtX = "<<cPar.time_UtX<<" min "<<endl; + outfile<<"## time on calculating UtX = "<<cPar.time_UtX<<" min "<<endl; } if ((cPar.a_mode>=1 && cPar.a_mode<=4) || (cPar.a_mode>=51 && cPar.a_mode<=54) ) { outfile<<"## time on optimization = "<<cPar.time_opt<<" min "<<endl; @@ -1855,7 +2847,7 @@ void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar) outfile<<"## time on predicting phenotypes = "<<cPar.time_opt<<" min "<<endl; } outfile<<"##"<<endl; - + outfile.close(); outfile.clear(); return; |