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Error in SAIGE GRM Step 1 : *** caught segfault *** address (nil), cause 'unknown' #151

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emilianatonini opened this issue Oct 13, 2024 · 0 comments
Open

Error in SAIGE GRM Step 1 : *** caught segfault *** address (nil), cause 'unknown' #151

emilianatonini opened this issue Oct 13, 2024 · 0 comments

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@emilianatonini
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Hi all,

I get the following error when running SAIGE GRM step 1. This only occurs for Chromosome 18. I am using
SAIGE GWAS GRM (v3.0.2) on the UKB RAP.

Any tips highly appreciated, thank you!

t2-t1
user system elapsed
1.231 1.355 0.086
NA th marker
*** caught segfault ***
address (nil), cause 'unknown'
Traceback:
1: Get_OneSNP_Geno(i - 1)
2: scoreTest_SPAGMMAT_forVarianceRatio_binaryTrait(obj.glmm.null = modglmm, obj.glm.null = fit0, Cutoff = SPAcutoff, maxiterPCG = maxiterPCG, tolPCG = tolPCG, numMarkers = numMarkers, varRatioOutFile = varRatioFile, ratioCVcutoff = ratioCVcutoff, testOut = SPAGMMATOut, plinkFile = plinkFile, chromosomeStartIndexVec = chromosomeStartIndexVec, chromosomeEndIndexVec = chromosomeEndIndexVec, isCateVarianceRatio = isCateVarianceRatio, cateVarRatioIndexVec = cateVarRatioIndexVec, IsSparseKin = IsSparseKin, sparseGRMFile = sparseGRMFile, sparseGRMSampleIDFile = sparseGRMSampleIDFile, numRandomMarkerforSparseKin = numRandomMarkerforSparseKin, relatednessCutoff = relatednessCutoff, useSparseGRMtoFitNULL = useSparseGRMtoFitNULL, nThreads = nThreads, cateVarRatioMinMACVecExclude = cateVarRatioMinMACVecExclude, cateVarRatioMaxMACVecInclude = cateVarRatioMaxMACVecInclude, minMAFforGRM = minMAFforGRM, isDiagofKinSetAsOne = isDiagofKinSetAsOne, includeNonautoMarkersforVarRatio = includeNonautoMarkersforVarRatio)
3: fitNULLGLMM(plinkFile = opt$plinkFile, phenoFile = opt$phenoFile, phenoCol = opt$phenoCol, traitType = opt$traitType, invNormalize = opt$invNormalize, covarColList = covars, qCovarCol = NULL, sampleIDColinphenoFile = opt$sampleIDColinphenoFile, tol = opt$tol, maxiter = opt$maxiter, tolPCG = opt$tolPCG, maxiterPCG = opt$maxiterPCG, nThreads = opt$nThreads, SPAcutoff = opt$SPAcutoff, numMarkers = opt$numRandomMarkerforVarianceRatio, skipModelFitting = opt$skipModelFitting, memoryChunk = opt$memoryChunk, tauInit = tauInit, LOCO = opt$LOCO, traceCVcutoff = opt$traceCVcutoff, ratioCVcutoff = opt$ratioCVcutoff, outputPrefix = opt$outputPrefix, outputPrefix_varRatio = opt$outputPrefix_varRatio, IsOverwriteVarianceRatioFile = opt$IsOverwriteVarianceRatioFile, IsSparseKin = opt$IsSparseKin, sparseGRMFile = opt$sparseGRMFile, sparseGRMSampleIDFile = opt$sparseGRMSampleIDFile, numRandomMarkerforSparseKin = opt$numRandomMarkerforSparseKin, relatednessCutoff = opt$relatednessCutoff, isCateVarianceRatio = opt$isCateVarianceRatio, cateVarRatioMinMACVecExclude = cateVarRatioMinMACVecExclude, cateVarRatioMaxMACVecInclude = cateVarRatioMaxMACVecInclude, isCovariateTransform = opt$isCovariateTransform, isDiagofKinSetAsOne = opt$isDiagofKinSetAsOne, useSparseSigmaConditionerforPCG = opt$useSparseSigmaConditionerforPCG, useSparseSigmaforInitTau = opt$useSparseSigmaforInitTau, minMAFforGRM = opt$minMAFforGRM, minCovariateCount = opt$minCovariateCount, includeNonautoMarkersforVarRatio = opt$includeNonautoMarkersforVarRatio, sexCol = opt$sexCol, FemaleCode = opt$FemaleCode, FemaleOnly = opt$FemaleOnly, MaleCode = opt$MaleCode, MaleOnly = opt$MaleOnly, noEstFixedEff = opt$noEstFixedEff, useSparseGRMtoFitNULL = opt$useSparseGRMtoFitNULL)
An irrecoverable exception occurred. R is aborting now ...

The entire run is as followed:
Logging initialized (priority)
Logging initialized (bulk)
Downloading bundled file resources.tar.gz

Unpacking resources.tar.gz to /
tar: Removing leading `/' from member names
dxpy/0.383.1 (Linux-5.15.0-1070-aws-x86_64-with-glibc2.29) Python/3.8.10
bash running (job ID job-1)
++ type -t main

  • [[ function == \f\u\n\c\t\i\o\n ]]
  • main
  • '[' ukb22418_c18_b0_v2 '!=' ukb22418_c18_b0_v2 ']'
  • '[' ukb22418_c18_b0_v2 '!=' ukb22418_c18_b0_v2 ']'
  • PIDDOCKER=7215
  • dx-download-all-inputs --parallel
  • PIDDL=7216
  • wait 7215
  • docker load -i /saige.tar.gz
    downloading file: file-FxXZxgjJkF6B6gJg1B6qXGjf to filesystem: /home/dnanexus/in/plink_bim/ukb22418_c18_b0_v2.bim
    downloading file: file-FxXb54QJkF6JV6g0Pp0gvb9p to filesystem: /home/dnanexus/in/plink_bed/ukb22418_c18_b0_v2.bed
    downloading file: file-Gkj6248JzjV8Y2KVJVv30KBP to filesystem: /home/dnanexus/in/plink_fam/ukb22418_c18_b0_v2.fam
    downloading file: file-Gv2zKFjJQgFx29Q3YJvJfggk to filesystem: /home/dnanexus/in/phenotype_file/phenotype.txt
    downloading file: file-GqbbqZ8JYVvKjq6FGZPypPYY to filesystem: /home/dnanexus/in/sparse_grm_mtx/saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx
    downloading file: file-GqbbqZ8JYVv05FYV9GZQFB05 to filesystem: /home/dnanexus/in/sparse_grm_sample_id_txt/saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx.sampleIDs.txt
    Downloading files using 8 threads+ wait 7216
    Loaded image: saige:latest
    ++ nproc --all
  • thr=32
    ++ docker run saige step1_fitNULLGLMM.R --help
    ++ grep SAIGE
    ++ cut -f1
    Loading required package: optparse
  • version='[1] optparse_1.7.5 SAIGE_0.44.5 '
  • echo 'SAIGE version in use: '''[1] optparse_1.7.5 SAIGE_0.44.5 ''''
    SAIGE version in use: '[1] optparse_1.7.5 SAIGE_0.44.5 '
    ++ basename /home/dnanexus/in/plink_bim/ukb22418_c18_b0_v2.bim
  • plink_file=ukb22418_c18_b0_v2.bim
  • plink_file=ukb22418_c18_b0_v2
    ++ basename /home/dnanexus/in/phenotype_file/phenotype.txt
  • pheno_file=phenotype.txt
  • mv in/phenotype_file/phenotype.txt in/plink_bed/ukb22418_c18_b0_v2.bed in/plink_bim/ukb22418_c18_b0_v2.bim in/plink_fam/ukb22418_c18_b0_v2.fam in/sparse_grm_mtx/saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx in/sparse_grm_sample_id_txt/saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx.sampleIDs.txt /home/dnanexus
  • cmd=()
  • cmd+=("step1_fitNULLGLMM.R")
  • cmd+=("--plinkFile=${plink_file}")
  • cmd+=("--phenoFile=${pheno_file}")
  • cmd+=("--phenoCol=${pheno_col}")
  • cmd+=("--traitType=${trait_type}")
  • cmd+=("--invNormalize=${inverse_normalize}")
  • cmd+=("--covarColList=${covariates}")
  • cmd+=("--sampleIDColinphenoFile=${sample_id_col}")
  • cmd+=("--nThreads=${thr}")
  • cmd+=("--outputPrefix=${output_file_prefix}_${plink_bim_prefix}")
  • cmd+=("--minMAFforGRM=${min_maf}")
  • cmd+=("--minCovariateCount=${min_covariate_count}")
  • cmd+=("--IsSparseKin=${is_sparse_k_in}")
  • cmd+=("--isCateVarianceRatio=${is_cate_variance_ratio}")
  • cmd+=("--cateVarRatioMinMACVecExclude=${cate_var_ratio_min_MAC_vec_exclude}")
  • cmd+=("--cateVarRatioMaxMACVecInclude=${cate_var_ratio_max_MAC_vec_include}")
  • saige_gene=false
  • '[' saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx '!=' '' ']'
  • '[' saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx.sampleIDs.txt '!=' '' ']'
  • cmd+=("--sparseGRMFile=${sparse_grm_mtx_name}")
  • cmd+=("--sparseGRMSampleIDFile=${sparse_grm_sample_id_txt_name}")
  • saige_gene=true
  • cmd+=(${extra_options})
  • docker run -v /home/dnanexus:/home/dnanexus -w /home/dnanexus saige step1_fitNULLGLMM.R --plinkFile=ukb22418_c18_b0_v2 --phenoFile=phenotype.txt --phenoCol=case --traitType=binary --invNormalize=false --covarColList=pc1,pc2,pc3,pc4 --sampleIDColinphenoFile=IID --nThreads=32 --outputPrefix=saige_step1_ukb22418_c18_b0_v2 --minMAFforGRM=0.01 --minCovariateCount=-1 --IsSparseKin=true --isCateVarianceRatio=true --cateVarRatioMinMACVecExclude=0.5,1.5,2.5,3.5,4.5,5.5,10.5,20.5 --cateVarRatioMaxMACVecInclude=1.5,2.5,3.5,4.5,5.5,10.5,20.5 --sparseGRMFile=saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx --sparseGRMSampleIDFile=saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx.sampleIDs.txt
    Loading required package: optparse
    R version 4.4.0 (2024-04-24)
    Platform: x86_64-pc-linux-gnu
    Running under: Ubuntu 20.04.6 LTS
    Matrix products: default
    BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so; LAPACK version 3.9.0
    locale:
    [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
    [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
    [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
    [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
    time zone: America/New_York
    tzcode source: system (glibc)
    attached base packages:
    [1] stats graphics grDevices utils datasets methods base
    other attached packages:
    [1] optparse_1.7.5 SAIGE_0.44.5
    loaded via a namespace (and not attached):
    [1] compiler_4.4.0 Matrix_1.7-0 Rcpp_1.0.12 getopt_1.20.4
    [5] grid_4.4.0 RcppParallel_5.1.7 lattice_0.22-6
    $plinkFile
    [1] "ukb22418_c18_b0_v2"
    $phenoFile
    [1] "phenotype.txt"
    $phenoCol
    [1] "case"
    $traitType
    [1] "binary"
    $invNormalize
    [1] FALSE
    $covarColList
    [1] "pc1,pc2,pc3,pc4"
    $sampleIDColinphenoFile
    [1] "IID"
    $tol
    [1] 0.02
    $maxiter
    [1] 20
    $tolPCG
    [1] 1e-05
    $maxiterPCG
    [1] 500
    $nThreads
    [1] 32
    $SPAcutoff
    [1] 2
    $numRandomMarkerforVarianceRatio
    [1] 30
    $skipModelFitting
    [1] FALSE
    $memoryChunk
    [1] 2
    $tauInit
    [1] "0,0"
    $LOCO
    [1] TRUE
    $traceCVcutoff
    [1] 0.0025
    $ratioCVcutoff
    [1] 0.001
    $outputPrefix
    [1] "saige_step1_ukb22418_c18_b0_v2"
    $IsOverwriteVarianceRatioFile
    [1] FALSE
    $IsSparseKin
    [1] TRUE
    $sparseGRMFile
    [1] "saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx"
    $sparseGRMSampleIDFile
    [1] "saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx.sampleIDs.txt"
    $numRandomMarkerforSparseKin
    [1] 2000
    $isCateVarianceRatio
    [1] TRUE
    $relatednessCutoff
    [1] 0.125
    $cateVarRatioMinMACVecExclude
    [1] "0.5,1.5,2.5,3.5,4.5,5.5,10.5,20.5"
    $cateVarRatioMaxMACVecInclude
    [1] "1.5,2.5,3.5,4.5,5.5,10.5,20.5"
    $isCovariateTransform
    [1] TRUE
    $isDiagofKinSetAsOne
    [1] FALSE
    $useSparseSigmaConditionerforPCG
    [1] FALSE
    $useSparseSigmaforInitTau
    [1] FALSE
    $useSparseGRMtoFitNULL
    [1] FALSE
    $minMAFforGRM
    [1] 0.01
    $minCovariateCount
    [1] -1
    $includeNonautoMarkersforVarRatio
    [1] FALSE
    $FemaleOnly
    [1] FALSE
    $MaleOnly
    [1] FALSE
    $sexCol
    [1] ""
    $FemaleCode
    [1] "1"
    $MaleCode
    [1] "0"
    $noEstFixedEff
    [1] FALSE
    $help
    [1] FALSE
    tauInit is 0 0
    cateVarRatioMinMACVecExclude is 0.5 1.5 2.5 3.5 4.5 5.5 10.5 20.5
    cateVarRatioMaxMACVecInclude is 1.5 2.5 3.5 4.5 5.5 10.5 20.5
    Markers in the Plink file with MAF >= 0.01 will be used to construct GRM
    32 threads are set to be used
    WARNING: leave-one-chromosome-out is activated! Note this option will only be applied to autosomal variants
    WARNING: Genetic variants needs to be ordered by chromosome and position in the Plink file
    chromosomeStartIndexVec: NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 NA NA NA NA
    chromosomeEndIndexVec: NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 21961 NA NA NA NA
    WARNING: The number of autosomal chromosomes is less than 2 and leave-one-chromosome-out can't be conducted!
    488377 samples have genotypes
    formula is case~pc1+pc2+pc3+pc4
    278031 samples have non-missing phenotypes
    210348 samples in geno file do not have phenotypes
    278029 samples will be used for analysis
    qr transformation has been performed on covariates
    colnames(data.new) is Y minus1 pc1 pc2 pc3 pc4
    out.transform$Param.transform$qrr: 5 5
    extract sparse GRM
    278029 samples have been used to fit the glmm null model
    [1] "print m4"
    [1] 278029 278029
    2 locationMat.n_rows
    34631367 locationMat.n_cols
    34631367 valueVec.n_elem
    1.01229
    0
    0
    0.984465
    1
    1
    0.998731
    2
    2
    1.09305
    3
    3
    0.531392
    4
    3
    0.531392
    3
    4
    1.03709
    4
    4
    1.0106
    5
    5
    0.558549
    122481
    5
    0.992529
    6
    6
    case is a binary trait
    glm:
    Call: glm(formula = formula.new, family = binomial, data = data.new)
    Coefficients:
    minus1 pc1 pc2 pc3 pc4
    3.65632 0.06761 -0.07310 -0.07380 -0.01411
    Degrees of Freedom: 278029 Total (i.e. Null); 278024 Residual
    Null Deviance: 385400
    Residual Deviance: 65580 AIC: 65590
    Start fitting the NULL GLMM
    user system elapsed
    94.573 30.487 85.795
    user system elapsed
    94.764 30.538 86.038
    [1] "Start reading genotype plink file here"
    nbyte: 122095
    nbyte: 69508
    reserve: 1526578560
    M: 21962, N: 488377
    size of genoVecofPointers: 2
    setgeno mark1
    setgeno mark2
    Oct 11 2024, 11:53 AM
    19331 markers with MAF >= 0.01 are used for GRM.
    setgeno mark5
    setgeno mark6
    time: 104771
    [1] "Genotype reading is done"
    inital tau is 1 0.1
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 8
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    Tau:
    [1] 1.0 0.1
    Fixed-effect coefficients:
    [,1]
    [1,] 3.65633082
    [2,] 0.06132995
    [3,] -0.07537051
    [4,] -0.07795184
    [5,] -0.01548827
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 8
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 8
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 9
    Variance component estimates:
    [1] 1.0000000 0.1000002
    Iteration 1 1 0.1000002 :
    tau0_v1: 1 0.1000002
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 8
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    CPU: 57% (32 cores) * Memory: 6001/255008MB * Storage: 11/1121GB * Net: 10↓/0↑MBps
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    Tau:
    [1] 1.0000000 0.1000002
    Fixed-effect coefficients:
    [,1]
    [1,] 3.65633082
    [2,] 0.06132997
    [3,] -0.07537050
    [4,] -0.07795196
    [5,] -0.01548825
    t_end_Get_Coef - t_begin_Get_Coef
    user system elapsed
    1607.440 3.770 50.946
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 8
    iter from getPCG1ofSigmaAndVector 8
    iter from getPCG1ofSigmaAndVector 7
    iter from getPCG1ofSigmaAndVector 9
    t_end_fitglmmaiRPCG - t_begin_fitglmmaiRPCG
    user system elapsed
    7629.888 54.931 243.056
    [1] 0.000000000 0.003800582
    tau: 1 0.1008395
    tau0: 1 0.1000002
    Final 1 0.1008395 :
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 8
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    Tau:
    [1] 1.0000000 0.1008395
    Fixed-effect coefficients:
    [,1]
    [1,] 3.66198969
    [2,] 0.06158396
    [3,] -0.07500686
    [4,] -0.07808167
    [5,] -0.01548948
    t_end_null - t_begin, fitting the NULL model without LOCO took
    user system elapsed
    20196.063 120.550 755.072
    iter from getPCG1ofSigmaAndVector 16
    t1_Rinv_1 is 286551
    Nglmm 28276.51
    user system elapsed
    20779.854 152.574 861.364
    t_end - t_begin, fitting the NULL model took
    user system elapsed
    20685.281 122.087 775.569
    Start estimating variance ratios
    Family: binomial
    Link function: logit
    iter from getPCG1ofSigmaAndVector 8
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    sparse GRM will be used
    sparse GRM has been specified
    read in sparse GRM from saige_gene_step0_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseGRM.mtx
    length(sparseGRMSampleID$IndexGRM): 488377
    nrow(sparseGRMSampleID): 488377
    278029 samples have been used to fit the glmm null model
    [1] 278029 3
    IID IndexInModel IndexGRM
    216453 4909201 1 380145
    228385 5123288 2 400950
    146444 3644608 3 257293
    60813 2098941 4 107185
    121202 3189582 5 213117
    227067 5099694 6 398662
    write sparse Sigma to saige_step1_ukb22418_c18_b0_v2.varianceRatio.txt_relatednessCutoff_0.125_2000_randomMarkersUsed.sparseSigma.mtx
    Oct 11 2024, 12:08 PM
    Categorical variance ratios will be estimated
    0.5 < MAC <= 1.5
    1.5 < MAC <= 2.5
    2.5 < MAC <= 3.5
    3.5 < MAC <= 4.5
    4.5 < MAC <= 5.5
    5.5 < MAC <= 10.5
    10.5 < MAC <= 20.5
    20.5 < MAC
    iter from getPCG1ofSigmaAndVector 8
    CPU: 62% (32 cores) * Memory: 10911/255008MB * Storage: 12/1121GB * Net: 0↓/0↑MBps
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    iter from getPCG1ofSigmaAndVector 9
    15422 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    Oct 11 2024, 12:10 PM
    t2-t1
    user system elapsed
    60.434 5.162 62.403
    13653 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.070 1.331 0.084
    1289 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.129 1.340 0.085
    12833 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.314 1.391 0.086
    5986 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.249 1.651 0.091
    14325 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.236 1.468 0.085
    15368 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 2
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.260 1.456 0.085
    12838 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.107 1.603 0.085
    15411 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.235 1.480 0.085
    8048 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.231 1.484 0.085
    [1] "OK"
    [1] "OK1"
    15361 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.339 1.376 0.085
    6013 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.236 1.468 0.084
    7852 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.241 1.464 0.085
    12842 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.396 1.523 0.091
    12787 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.369 1.559 0.092
    8131 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.250 1.683 0.091
    13590 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 2
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.396 1.523 0.094
    3573 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.271 1.702 0.093
    37 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.072 1.480 0.092
    15388 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.347 1.552 0.093
    [1] "OK"
    [1] "OK1"
    8120 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.344 1.588 0.091
    8040 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.272 1.656 0.092
    12788 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.158 1.571 0.093
    16272 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.220 1.540 0.092
    8091 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.307 1.582 0.090
    14208 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.100 1.428 0.081
    889 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.244 1.456 0.091
    12834 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.279 1.643 0.091
    19634 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.325 1.631 0.092
    19764 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 2
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.281 1.655 0.091
    [1] "OK"
    [1] "OK1"
    [1] "OK2"
    CV for variance ratio estimate using 30 markers is 3.332616e-06 < 0.001
    varRatio 1.000246
    [1] 1.000246
    [,1]
    [1,] 1.000246
    7835 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.290 1.652 0.092
    15436 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.247 1.679 0.091
    15417 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.311 1.635 0.092
    13591 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.337 1.595 0.092
    66 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.382 1.572 0.092
    8133 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.213 1.715 0.092
    903 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.364 1.584 0.092
    8105 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.286 1.647 0.092
    899 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.195 1.726 0.093
    4829 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.068 1.410 0.081
    [1] "OK"
    [1] "OK1"
    4915 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.375 1.580 0.092
    11423 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.327 1.548 0.092
    15429 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    0.956 1.199 0.072
    7850 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.348 1.503 0.092
    12799 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.308 1.592 0.092
    8043 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.167 1.688 0.092
    15371 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    0.698 0.907 0.052
    7780 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.342 1.532 0.093
    12839 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.381 1.596 0.094
    6007 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.290 1.666 0.093
    [1] "OK"
    [1] "OK1"
    6030 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.393 1.596 0.094
    15409 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.316 1.654 0.093
    10190 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.268 1.723 0.094
    5521 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.324 1.620 0.092
    255 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.269 1.712 0.094
    4501 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.387 1.572 0.093
    5529 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.274 1.711 0.093
    5322 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.308 1.651 0.093
    5609 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.253 1.728 0.094
    16248 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.288 1.668 0.093
    [1] "OK"
    [1] "OK1"
    [1] "OK2"
    CV for variance ratio estimate using 30 markers is 2.33958e-06 < 0.001
    varRatio 1.00028
    [1] 1.00028
    [,1]
    [1,] 1.000246
    [2,] 1.000280
    5993 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.297 1.552 0.092
    15364 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.179 1.588 0.093
    12774 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.441 1.501 0.094
    5977 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.280 1.707 0.093
    13652 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.278 1.710 0.093
    11439 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.273 1.684 0.093
    12796 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.338 1.592 0.093
    18022 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.323 1.572 0.093
    5608 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.185 1.587 0.090
    15375 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.288 1.672 0.092
    [1] "OK"
    [1] "OK1"
    6135 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.362 1.622 0.093
    6041 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.182 1.444 0.084
    11113 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.168 1.558 0.085
    19763 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.305 1.420 0.086
    5606 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.387 1.336 0.085
    7825 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.284 1.446 0.086
    12784 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.255 1.464 0.085
    5618 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.042 1.228 0.073
    19314 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.197 1.441 0.086
    17059 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    0.901 1.391 0.074
    [1] "OK"
    [1] "OK1"
    16347 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 3
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.198 1.536 0.086
    12776 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.274 1.470 0.086
    4741 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.232 1.511 0.086
    5979 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.208 1.486 0.086
    12804 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.233 1.508 0.086
    8196 th marker
    G0 2 2 2 2 2 2 2 2 2 2
    iter from getPCG1ofSigmaAndVector 4
    t1
    t1again
    [1] "pcginvSigma"
    t2-t1
    user system elapsed
    1.231 1.355 0.086
    NA th marker
    *** caught segfault ***
    address (nil), cause 'unknown'
    Traceback:
    1: Get_OneSNP_Geno(i - 1)
    2: scoreTest_SPAGMMAT_forVarianceRatio_binaryTrait(obj.glmm.null = modglmm, obj.glm.null = fit0, Cutoff = SPAcutoff, maxiterPCG = maxiterPCG, tolPCG = tolPCG, numMarkers = numMarkers, varRatioOutFile = varRatioFile, ratioCVcutoff = ratioCVcutoff, testOut = SPAGMMATOut, plinkFile = plinkFile, chromosomeStartIndexVec = chromosomeStartIndexVec, chromosomeEndIndexVec = chromosomeEndIndexVec, isCateVarianceRatio = isCateVarianceRatio, cateVarRatioIndexVec = cateVarRatioIndexVec, IsSparseKin = IsSparseKin, sparseGRMFile = sparseGRMFile, sparseGRMSampleIDFile = sparseGRMSampleIDFile, numRandomMarkerforSparseKin = numRandomMarkerforSparseKin, relatednessCutoff = relatednessCutoff, useSparseGRMtoFitNULL = useSparseGRMtoFitNULL, nThreads = nThreads, cateVarRatioMinMACVecExclude = cateVarRatioMinMACVecExclude, cateVarRatioMaxMACVecInclude = cateVarRatioMaxMACVecInclude, minMAFforGRM = minMAFforGRM, isDiagofKinSetAsOne = isDiagofKinSetAsOne, includeNonautoMarkersforVarRatio = includeNonautoMarkersforVarRatio)
    3: fitNULLGLMM(plinkFile = opt$plinkFile, phenoFile = opt$phenoFile, phenoCol = opt$phenoCol, traitType = opt$traitType, invNormalize = opt$invNormalize, covarColList = covars, qCovarCol = NULL, sampleIDColinphenoFile = opt$sampleIDColinphenoFile, tol = opt$tol, maxiter = opt$maxiter, tolPCG = opt$tolPCG, maxiterPCG = opt$maxiterPCG, nThreads = opt$nThreads, SPAcutoff = opt$SPAcutoff, numMarkers = opt$numRandomMarkerforVarianceRatio, skipModelFitting = opt$skipModelFitting, memoryChunk = opt$memoryChunk, tauInit = tauInit, LOCO = opt$LOCO, traceCVcutoff = opt$traceCVcutoff, ratioCVcutoff = opt$ratioCVcutoff, outputPrefix = opt$outputPrefix, outputPrefix_varRatio = opt$outputPrefix_varRatio, IsOverwriteVarianceRatioFile = opt$IsOverwriteVarianceRatioFile, IsSparseKin = opt$IsSparseKin, sparseGRMFile = opt$sparseGRMFile, sparseGRMSampleIDFile = opt$sparseGRMSampleIDFile, numRandomMarkerforSparseKin = opt$numRandomMarkerforSparseKin, relatednessCutoff = opt$relatednessCutoff, isCateVarianceRatio = opt$isCateVarianceRatio, cateVarRatioMinMACVecExclude = cateVarRatioMinMACVecExclude, cateVarRatioMaxMACVecInclude = cateVarRatioMaxMACVecInclude, isCovariateTransform = opt$isCovariateTransform, isDiagofKinSetAsOne = opt$isDiagofKinSetAsOne, useSparseSigmaConditionerforPCG = opt$useSparseSigmaConditionerforPCG, useSparseSigmaforInitTau = opt$useSparseSigmaforInitTau, minMAFforGRM = opt$minMAFforGRM, minCovariateCount = opt$minCovariateCount, includeNonautoMarkersforVarRatio = opt$includeNonautoMarkersforVarRatio, sexCol = opt$sexCol, FemaleCode = opt$FemaleCode, FemaleOnly = opt$FemaleOnly, MaleCode = opt$MaleCode, MaleOnly = opt$MaleOnly, noEstFixedEff = opt$noEstFixedEff, useSparseGRMtoFitNULL = opt$useSparseGRMtoFitNULL)
    An irrecoverable exception occurred. R is aborting now ...
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