+-----------------------------+
                      |                       ___   |
                      |   BOLT-LMM, v2.3.2   /_ /   |
                      |   March 10, 2018      /_/   |
                      |   Po-Ru Loh            //   |
                      |                        /    |
                      +-----------------------------+

Copyright (C) 2014-2018 Harvard University.
Distributed under the GNU GPLv3 open source license.

Compiled with USE_SSE: fast aligned memory access
Compiled with USE_MKL: Intel Math Kernel Library linear algebra
Boost version: 1_58

Command line options:

../bolt \
    --bfile=EUR_subset \
    --remove=EUR_subset.remove \
    --exclude=EUR_subset.exclude \
    --exclude=EUR_subset.exclude2 \
    --phenoFile=EUR_subset.pheno2.covars \
    --phenoCol=PHENO \
    --covarFile=EUR_subset.pheno2.covars \
    --covarCol=CAT_COV \
    --qCovarCol=QCOV{1:2} \
    --modelSnps=EUR_subset.modelSnps \
    --lmm \
    --LDscoresFile=../tables/LDSCORE.1000G_EUR.tab.gz \
    --numThreads=2 \
    --statsFile=example.stats \
    --dosageFile=EUR_subset.dosage.chr17first100 \
    --dosageFile=EUR_subset.dosage.chr22last100.gz \
    --dosageFidIidFile=EUR_subset.dosage.indivs \
    --statsFileDosageSnps=example.dosageSnps.stats \
    --impute2FileList=EUR_subset.impute2FileList.txt \
    --impute2FidIidFile=EUR_subset.impute2.indivs \
    --statsFileImpute2Snps=example.impute2Snps.stats \
    --dosage2FileList=EUR_subset.dosage2FileList.txt \
    --statsFileDosage2Snps=example.dosage2Snps.stats 

Verifying contents of --dosage2FileList: EUR_subset.dosage2FileList.txt
Checking map file EUR_subset.dosage2.chr17first100.map and 2-dosage genotype file EUR_subset.dosage2.chr17first100.gz
Checking map file EUR_subset.dosage2.chr17second100.map and 2-dosage genotype file EUR_subset.dosage2.chr17second100
Checking map file EUR_subset.dosage2.chr22last100.map and 2-dosage genotype file EUR_subset.dosage2.chr22last100.gz

Setting number of threads to 2
fam: EUR_subset.fam
bim(s): EUR_subset.bim
bed(s): EUR_subset.bed

=== Reading genotype data ===

Total indivs in PLINK data: Nbed = 379
Reading remove file (indivs to remove): EUR_subset.remove
Removed 6 individual(s)
Total indivs stored in memory: N = 373
Reading bim file #1: EUR_subset.bim
    Read 54051 snps
Total snps in PLINK data: Mbed = 54051
Reading exclude file (SNPs to exclude): EUR_subset.exclude
Excluded 5405 SNP(s)
Reading exclude file (SNPs to exclude): EUR_subset.exclude2
Excluded 43171 SNP(s)
Reading list of SNPs to include in model (i.e., GRM): EUR_subset.modelSnps
WARNING: SNP has been excluded: rs1882989
WARNING: SNP has been excluded: rs112221137
WARNING: SNP has been excluded: rs35840960
WARNING: SNP has been excluded: rs62057022
WARNING: SNP has been excluded: rs1882990
Included 2431 SNP(s) in model in 1 variance component(s)
WARNING: 24594 SNP(s) had been excluded

Breakdown of SNP pre-filtering results:
  2431 SNPs to include in model (i.e., GRM)
  3044 additional non-GRM SNPs loaded
  48576 excluded SNPs
Allocating 2431 x 376/4 bytes to store genotypes
Reading genotypes and performing QC filtering on snps and indivs...
Reading bed file #1: EUR_subset.bed
    Expecting 5134845 (+3) bytes for 379 indivs, 54051 snps
Total indivs after QC: 373
Total post-QC SNPs: M = 2431
  Variance component 1: 2431 post-QC SNPs (name: 'modelSnps')
Time for SnpData setup = 1.01698 sec

=== Reading phenotype and covariate data ===

Read data for 373 indivs (ignored 0 without genotypes) from:
  EUR_subset.pheno2.covars
Read data for 373 indivs (ignored 0 without genotypes) from:
  EUR_subset.pheno2.covars
Number of indivs with no missing phenotype(s) to use: 369
NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
WARNING: 3 of 369 samples passing previous QC have missing covariates
  --covarUseMissingIndic is not set, so these samples will be removed
Number of individuals used in analysis: Nused = 366
Singular values of covariate matrix:
    S[0] = 39.4151
    S[1] = 13.5249
    S[2] = 6.56744
    S[3] = 4.65936
    S[4] = 6.61483e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           362.015344
Dimension of all-1s proj space (Nused-1): 365
Time for covariate data setup + Bolt initialization = 0.147562 sec

Phenotype 1:   N = 366   mean = 0.00450586   std = 1.0273

=== Computing linear regression (LINREG) stats ===

Time for computing LINREG stats = 0.00365305 sec

=== Estimating variance parameters ===

Using CGtol of 0.005 for this step
Using default number of random trials: 15 (for Nused = 366)

Estimating MC scaling f_REML at log(delta) = 1.09865, h2 = 0.25...
  Batch-solving 16 systems of equations using conjugate gradient iteration
  iter 1:  time=0.01  rNorms/orig: (0.1,0.1)  res2s: 767.193..199.099
  iter 2:  time=0.01  rNorms/orig: (0.01,0.03)  res2s: 791.087..208.371
  iter 3:  time=0.00  rNorms/orig: (0.002,0.004)  res2s: 791.958..209.121
  Converged at iter 3: rNorms/orig all < CGtol=0.005
  Time breakdown: dgemm = 40.9%, memory/overhead = 59.1%
  MCscaling: logDelta = 1.10, h2 = 0.250, f = 0.0583786

Estimating MC scaling f_REML at log(delta) = 4.23869e-05, h2 = 0.5...
  Batch-solving 16 systems of equations using conjugate gradient iteration
  iter 1:  time=0.00  rNorms/orig: (0.2,0.3)  res2s: 157.403..82.5002
  iter 2:  time=0.00  rNorms/orig: (0.04,0.1)  res2s: 176.427..94.685
  iter 3:  time=0.00  rNorms/orig: (0.01,0.02)  res2s: 178.429..97.6069
  iter 4:  time=0.00  rNorms/orig: (0.004,0.005)  res2s: 178.791..97.8407
  Converged at iter 4: rNorms/orig all < CGtol=0.005
  Time breakdown: dgemm = 41.0%, memory/overhead = 59.0%
  MCscaling: logDelta = 0.00, h2 = 0.500, f = 0.00362986

Estimating MC scaling f_REML at log(delta) = -0.0727959, h2 = 0.518202...
  Batch-solving 16 systems of equations using conjugate gradient iteration
  iter 1:  time=0.00  rNorms/orig: (0.2,0.3)  res2s: 140.004..76.2204
  iter 2:  time=0.00  rNorms/orig: (0.04,0.1)  res2s: 158.154..88.1446
  iter 3:  time=0.00  rNorms/orig: (0.01,0.03)  res2s: 160.162..91.1652
  iter 4:  time=0.00  rNorms/orig: (0.004,0.006)  res2s: 160.548..91.4234
  iter 5:  time=0.00  rNorms/orig: (0.0008,0.001)  res2s: 160.575..91.4401
  Converged at iter 5: rNorms/orig all < CGtol=0.005
  Time breakdown: dgemm = 41.0%, memory/overhead = 59.0%
  MCscaling: logDelta = -0.07, h2 = 0.518, f = -0.000114364

Secant iteration for h2 estimation converged in 1 steps
Estimated (pseudo-)heritability: h2g = 0.518
To more precisely estimate variance parameters and estimate s.e., use --reml
Variance params: sigma^2_K = 0.539611, logDelta = -0.072796, f = -0.000114364

Time for fitting variance components = 0.0655649 sec

=== Computing mixed model assoc stats (inf. model) ===

Selected 30 SNPs for computation of prospective stat
Tried 30; threw out 0 with GRAMMAR chisq > 5
Assigning SNPs to 6 chunks for leave-out analysis
Each chunk is excluded when testing SNPs belonging to the chunk
  Batch-solving 36 systems of equations using conjugate gradient iteration
  iter 1:  time=0.01  rNorms/orig: (0.2,0.3)  res2s: 77.2766..87.3902
  iter 2:  time=0.01  rNorms/orig: (0.05,0.1)  res2s: 91.4012..100.112
  iter 3:  time=0.01  rNorms/orig: (0.01,0.03)  res2s: 94.9553..101.227
  iter 4:  time=0.01  rNorms/orig: (0.003,0.008)  res2s: 95.3511..101.387
  iter 5:  time=0.01  rNorms/orig: (0.0008,0.002)  res2s: 95.3793..101.413
  iter 6:  time=0.01  rNorms/orig: (0.0003,0.0004)  res2s: 95.381..101.415
  Converged at iter 6: rNorms/orig all < CGtol=0.0005
  Time breakdown: dgemm = 59.2%, memory/overhead = 40.8%

AvgPro: 1.016   AvgRetro: 0.998   Calibration: 1.018 (0.008)   (30 SNPs)
Ratio of medians: 1.020   Median of ratios: 1.015

Time for computing infinitesimal model assoc stats = 0.038274 sec

=== Estimating chip LD Scores using 400 indivs ===

WARNING: Only 373 indivs available; using all
Reducing sample size to 368 for memory alignment

Time for estimating chip LD Scores = 0.00525188 sec

=== Reading LD Scores for calibration of Bayesian assoc stats ===

Looking up LD Scores...
  Looking for column header 'SNP': column number = 1
  Looking for column header 'LDSCORE': column number = 5
Found LD Scores for 2431/2431 SNPs

Estimating inflation of LINREG chisq stats using MLMe as reference...
Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01
# of SNPs passing filters before outlier removal: 2427/2431
Masking windows around outlier snps (chisq > 20.0)
# of SNPs remaining after outlier window removal: 2409/2427
Intercept of LD Score regression for ref stats:   1.042 (0.048)
Estimated attenuation: 0.428 (0.415)
Intercept of LD Score regression for cur stats: 1.094 (0.048)
Calibration factor (ref/cur) to multiply by:      0.952 (0.018)
LINREG intercept inflation = 1.05058

=== Estimating mixture parameters by cross-validation ===

Setting maximum number of iterations to 250 for this step
Max CV folds to compute = 5 (to have > 10000 samples)

====> Starting CV fold 1 <====

NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 292
Singular values of covariate matrix:
    S[0] = 35.2135
    S[1] = 12.0776
    S[2] = 5.84295
    S[3] = 4.11065
    S[4] = 1.02073e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           288.024349
Dimension of all-1s proj space (Nused-1): 291
  Beginning variational Bayes
  iter 1:  time=0.02 for 18 active reps
  iter 2:  time=0.01 for 18 active reps  approxLL diffs: (14.01,24.97)
  iter 3:  time=0.01 for 18 active reps  approxLL diffs: (0.54,2.37)
  iter 4:  time=0.01 for 18 active reps  approxLL diffs: (0.08,0.82)
  iter 5:  time=0.01 for 18 active reps  approxLL diffs: (0.01,0.62)
  iter 6:  time=0.01 for 11 active reps  approxLL diffs: (0.00,0.71)
  iter 7:  time=0.01 for  7 active reps  approxLL diffs: (0.00,0.59)
  iter 8:  time=0.00 for  6 active reps  approxLL diffs: (0.00,0.30)
  iter 9:  time=0.00 for  4 active reps  approxLL diffs: (0.01,0.17)
  iter 10:  time=0.00 for  3 active reps  approxLL diffs: (0.00,0.09)
  iter 11:  time=0.00 for  2 active reps  approxLL diffs: (0.02,0.04)
  iter 12:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.02)
  iter 13:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  iter 14:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  Converged at iter 14: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 27.0%, memory/overhead = 73.0%
Computing predictions on left-out cross-validation fold
Time for computing predictions = 0.00561309 sec

Average PVEs obtained by param pairs tested (high to low):
 f2=0.3, p=0.01: 0.126476
 f2=0.5, p=0.01: 0.115832
 f2=0.3, p=0.02: 0.114885
            ...
 f2=0.1, p=0.01: 0.061449

Detailed CV fold results:
  Absolute prediction MSE baseline (covariates only): 1.19142
  Absolute prediction MSE using standard LMM:         1.11233
  Absolute prediction MSE, fold-best f2=0.3, p=0.01:  1.04074
    Absolute pred MSE using   f2=0.5, p=0.5: 1.112334
    Absolute pred MSE using   f2=0.5, p=0.2: 1.110853
    Absolute pred MSE using   f2=0.5, p=0.1: 1.107629
    Absolute pred MSE using  f2=0.5, p=0.05: 1.100541
    Absolute pred MSE using  f2=0.5, p=0.02: 1.061305
    Absolute pred MSE using  f2=0.5, p=0.01: 1.053418
    Absolute pred MSE using   f2=0.3, p=0.5: 1.111904
    Absolute pred MSE using   f2=0.3, p=0.2: 1.107901
    Absolute pred MSE using   f2=0.3, p=0.1: 1.099884
    Absolute pred MSE using  f2=0.3, p=0.05: 1.078278
    Absolute pred MSE using  f2=0.3, p=0.02: 1.054545
    Absolute pred MSE using  f2=0.3, p=0.01: 1.040736
    Absolute pred MSE using   f2=0.1, p=0.5: 1.110527
    Absolute pred MSE using   f2=0.1, p=0.2: 1.102852
    Absolute pred MSE using   f2=0.1, p=0.1: 1.087635
    Absolute pred MSE using  f2=0.1, p=0.05: 1.068055
    Absolute pred MSE using  f2=0.1, p=0.02: 1.077632
    Absolute pred MSE using  f2=0.1, p=0.01: 1.118211

====> End CV fold 1: 18 remaining param pair(s) <====

Estimated proportion of variance explained using inf model: 0.066
Relative improvement in prediction MSE using non-inf model: 0.064

====> Starting CV fold 2 <====

NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 293
Singular values of covariate matrix:
    S[0] = 35.5041
    S[1] = 12.0959
    S[2] = 5.91229
    S[3] = 4.11948
    S[4] = 2.68583e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           289.038063
Dimension of all-1s proj space (Nused-1): 292
  Beginning variational Bayes
  iter 1:  time=0.01 for 18 active reps
  iter 2:  time=0.01 for 18 active reps  approxLL diffs: (13.23,27.41)
  iter 3:  time=0.01 for 18 active reps  approxLL diffs: (0.70,2.19)
  iter 4:  time=0.01 for 18 active reps  approxLL diffs: (0.10,0.68)
  iter 5:  time=0.01 for 18 active reps  approxLL diffs: (0.01,0.31)
  iter 6:  time=0.01 for 16 active reps  approxLL diffs: (0.00,0.16)
  iter 7:  time=0.00 for  5 active reps  approxLL diffs: (0.00,0.10)
  iter 8:  time=0.00 for  3 active reps  approxLL diffs: (0.03,0.10)
  iter 9:  time=0.00 for  3 active reps  approxLL diffs: (0.02,0.09)
  iter 10:  time=0.00 for  3 active reps  approxLL diffs: (0.01,0.07)
  iter 11:  time=0.00 for  3 active reps  approxLL diffs: (0.01,0.05)
  iter 12:  time=0.00 for  2 active reps  approxLL diffs: (0.02,0.04)
  iter 13:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.03)
  iter 14:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.03)
  iter 15:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.05)
  iter 16:  time=0.00 for  1 active reps  approxLL diffs: (0.06,0.06)
  iter 17:  time=0.00 for  1 active reps  approxLL diffs: (0.09,0.09)
  iter 18:  time=0.00 for  1 active reps  approxLL diffs: (0.12,0.12)
  iter 19:  time=0.00 for  1 active reps  approxLL diffs: (0.13,0.13)
  iter 20:  time=0.00 for  1 active reps  approxLL diffs: (0.10,0.10)
  iter 21:  time=0.00 for  1 active reps  approxLL diffs: (0.05,0.05)
  iter 22:  time=0.00 for  1 active reps  approxLL diffs: (0.02,0.02)
  iter 23:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  Converged at iter 23: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 23.4%, memory/overhead = 76.6%
Computing predictions on left-out cross-validation fold
Time for computing predictions = 0.00558901 sec

Average PVEs obtained by param pairs tested (high to low):
 f2=0.3, p=0.01: 0.110938
 f2=0.3, p=0.02: 0.099200
 f2=0.5, p=0.01: 0.094056
            ...
 f2=0.1, p=0.01: 0.033146

Detailed CV fold results:
  Absolute prediction MSE baseline (covariates only): 1.01771
  Absolute prediction MSE using standard LMM:         0.996793
  Absolute prediction MSE, fold-best f2=0.3, p=0.01:  0.920624
    Absolute pred MSE using   f2=0.5, p=0.5: 0.996793
    Absolute pred MSE using   f2=0.5, p=0.2: 0.994311
    Absolute pred MSE using   f2=0.5, p=0.1: 0.987578
    Absolute pred MSE using  f2=0.5, p=0.05: 0.966183
    Absolute pred MSE using  f2=0.5, p=0.02: 0.946617
    Absolute pred MSE using  f2=0.5, p=0.01: 0.944153
    Absolute pred MSE using   f2=0.3, p=0.5: 0.996126
    Absolute pred MSE using   f2=0.3, p=0.2: 0.989325
    Absolute pred MSE using   f2=0.3, p=0.1: 0.972973
    Absolute pred MSE using  f2=0.3, p=0.05: 0.951707
    Absolute pred MSE using  f2=0.3, p=0.02: 0.932719
    Absolute pred MSE using  f2=0.3, p=0.01: 0.920624
    Absolute pred MSE using   f2=0.1, p=0.5: 0.994079
    Absolute pred MSE using   f2=0.1, p=0.2: 0.981505
    Absolute pred MSE using   f2=0.1, p=0.1: 0.961207
    Absolute pred MSE using  f2=0.1, p=0.05: 0.940671
    Absolute pred MSE using  f2=0.1, p=0.02: 0.935037
    Absolute pred MSE using  f2=0.1, p=0.01: 1.012784

====> End CV fold 2: 3 remaining param pair(s) <====

====> Starting CV fold 3 <====

NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 293
Singular values of covariate matrix:
    S[0] = 35.1358
    S[1] = 12.1017
    S[2] = 5.88329
    S[3] = 4.16419
    S[4] = 4.06329e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           288.977885
Dimension of all-1s proj space (Nused-1): 292
  Beginning variational Bayes
  iter 1:  time=0.01 for  3 active reps
  iter 2:  time=0.00 for  3 active reps  approxLL diffs: (16.59,19.92)
  iter 3:  time=0.00 for  3 active reps  approxLL diffs: (1.28,3.38)
  iter 4:  time=0.00 for  3 active reps  approxLL diffs: (0.21,1.69)
  iter 5:  time=0.00 for  3 active reps  approxLL diffs: (0.02,0.54)
  iter 6:  time=0.00 for  3 active reps  approxLL diffs: (0.00,0.22)
  iter 7:  time=0.00 for  2 active reps  approxLL diffs: (0.02,0.14)
  iter 8:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.07)
  iter 9:  time=0.00 for  1 active reps  approxLL diffs: (0.03,0.03)
  iter 10:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  Converged at iter 10: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 30.5%, memory/overhead = 69.5%
Computing predictions on left-out cross-validation fold
Time for computing predictions = 0.00165081 sec

Average PVEs obtained by param pairs tested (high to low):
 f2=0.5, p=0.01: 0.090904
 f2=0.3, p=0.01: 0.065602
 f2=0.1, p=0.02: 0.049509

Detailed CV fold results:
  Absolute prediction MSE baseline (covariates only): 1.13673
  Absolute prediction MSE, fold-best f2=0.5, p=0.01:  1.04056
    Absolute pred MSE using  f2=0.5, p=0.01: 1.040557
    Absolute pred MSE using  f2=0.3, p=0.01: 1.165222
    Absolute pred MSE using  f2=0.1, p=0.02: 1.168803

====> End CV fold 3: 3 remaining param pair(s) <====

====> Starting CV fold 4 <====

NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 293
Singular values of covariate matrix:
    S[0] = 35.366
    S[1] = 12.1033
    S[2] = 5.89805
    S[3] = 4.20734
    S[4] = 2.03806e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           289.016478
Dimension of all-1s proj space (Nused-1): 292
  Beginning variational Bayes
  iter 1:  time=0.01 for  3 active reps
  iter 2:  time=0.00 for  3 active reps  approxLL diffs: (19.58,23.11)
  iter 3:  time=0.00 for  3 active reps  approxLL diffs: (3.37,4.20)
  iter 4:  time=0.00 for  3 active reps  approxLL diffs: (1.00,1.95)
  iter 5:  time=0.00 for  3 active reps  approxLL diffs: (0.58,0.94)
  iter 6:  time=0.00 for  3 active reps  approxLL diffs: (0.28,0.59)
  iter 7:  time=0.00 for  3 active reps  approxLL diffs: (0.30,0.44)
  iter 8:  time=0.00 for  3 active reps  approxLL diffs: (0.19,0.25)
  iter 9:  time=0.00 for  3 active reps  approxLL diffs: (0.03,0.29)
  iter 10:  time=0.00 for  3 active reps  approxLL diffs: (0.00,0.38)
  iter 11:  time=0.00 for  2 active reps  approxLL diffs: (0.06,0.38)
  iter 12:  time=0.00 for  2 active reps  approxLL diffs: (0.05,0.26)
  iter 13:  time=0.00 for  2 active reps  approxLL diffs: (0.05,0.14)
  iter 14:  time=0.00 for  2 active reps  approxLL diffs: (0.05,0.08)
  iter 15:  time=0.00 for  2 active reps  approxLL diffs: (0.03,0.05)
  iter 16:  time=0.00 for  2 active reps  approxLL diffs: (0.02,0.04)
  iter 17:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.04)
  iter 18:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.04)
  iter 19:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.06)
  iter 20:  time=0.00 for  2 active reps  approxLL diffs: (0.02,0.08)
  iter 21:  time=0.00 for  2 active reps  approxLL diffs: (0.02,0.11)
  iter 22:  time=0.00 for  2 active reps  approxLL diffs: (0.03,0.14)
  iter 23:  time=0.00 for  2 active reps  approxLL diffs: (0.03,0.15)
  iter 24:  time=0.00 for  2 active reps  approxLL diffs: (0.03,0.14)
  iter 25:  time=0.00 for  2 active reps  approxLL diffs: (0.02,0.11)
  iter 26:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.07)
  iter 27:  time=0.00 for  2 active reps  approxLL diffs: (0.01,0.04)
  iter 28:  time=0.00 for  1 active reps  approxLL diffs: (0.03,0.03)
  iter 29:  time=0.00 for  1 active reps  approxLL diffs: (0.02,0.02)
  iter 30:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  iter 31:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  Converged at iter 31: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 27.1%, memory/overhead = 72.9%
Computing predictions on left-out cross-validation fold
Time for computing predictions = 0.0018301 sec

Average PVEs obtained by param pairs tested (high to low):
 f2=0.5, p=0.01: 0.087902
 f2=0.3, p=0.01: 0.050466
 f2=0.1, p=0.02: 0.023887

Detailed CV fold results:
  Absolute prediction MSE baseline (covariates only): 0.941491
  Absolute prediction MSE, fold-best f2=0.5, p=0.01:  0.867212
    Absolute pred MSE using  f2=0.5, p=0.01: 0.867212
    Absolute pred MSE using  f2=0.3, p=0.01: 0.936730
    Absolute pred MSE using  f2=0.1, p=0.02: 0.991367

====> End CV fold 4: 3 remaining param pair(s) <====

====> Starting CV fold 5 <====

NOTE: Using all-1s vector (constant term) in addition to specified covariates
    Using categorical covariate: CAT_COV (adding level A)
    Using categorical covariate: CAT_COV (adding level B)
    Using quantitative covariate: QCOV1
    Using quantitative covariate: QCOV2
    Using quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 293
Singular values of covariate matrix:
    S[0] = 35.0554
    S[1] = 12.1063
    S[2] = 5.808
    S[3] = 4.21359
    S[4] = 1.41518e-15
Total covariate vectors: C = 5
Total independent covariate vectors: Cindep = 4

=== Initializing Bolt object: projecting and normalizing SNPs ===

Number of chroms with >= 1 good SNP: 6
Average norm of projected SNPs:           288.978200
Dimension of all-1s proj space (Nused-1): 292
  Beginning variational Bayes
  iter 1:  time=0.01 for  3 active reps
  iter 2:  time=0.00 for  3 active reps  approxLL diffs: (25.07,26.60)
  iter 3:  time=0.00 for  3 active reps  approxLL diffs: (3.20,5.69)
  iter 4:  time=0.00 for  3 active reps  approxLL diffs: (0.73,1.08)
  iter 5:  time=0.00 for  3 active reps  approxLL diffs: (0.10,0.20)
  iter 6:  time=0.00 for  3 active reps  approxLL diffs: (0.01,0.11)
  iter 7:  time=0.00 for  3 active reps  approxLL diffs: (0.00,0.06)
  iter 8:  time=0.00 for  1 active reps  approxLL diffs: (0.02,0.02)
  iter 9:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  Converged at iter 9: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 31.0%, memory/overhead = 69.0%
Computing predictions on left-out cross-validation fold
Time for computing predictions = 0.00177622 sec

Average PVEs obtained by param pairs tested (high to low):
 f2=0.5, p=0.01: 0.056417
 f2=0.3, p=0.01: 0.014181
 f2=0.1, p=0.02: -0.003485

Detailed CV fold results:
  Absolute prediction MSE baseline (covariates only): 0.99199
  Absolute prediction MSE, fold-best f2=0.5, p=0.01:  1.06096
    Absolute pred MSE using  f2=0.5, p=0.01: 1.060956
    Absolute pred MSE using  f2=0.3, p=0.01: 1.121899
    Absolute pred MSE using  f2=0.1, p=0.02: 1.104061

====> End CV fold 5: 3 remaining param pair(s) <====

Optimal mixture parameters according to CV: f2 = 0.5, p = 0.01

Time for estimating mixture parameters = 21.9299 sec

=== Computing Bayesian mixed model assoc stats with mixture prior ===

Assigning SNPs to 6 chunks for leave-out analysis
Each chunk is excluded when testing SNPs belonging to the chunk
  Beginning variational Bayes
  iter 1:  time=0.01 for  6 active reps
  iter 2:  time=0.00 for  6 active reps  approxLL diffs: (22.70,28.54)
  iter 3:  time=0.00 for  6 active reps  approxLL diffs: (1.57,2.82)
  iter 4:  time=0.00 for  6 active reps  approxLL diffs: (0.18,0.58)
  iter 5:  time=0.00 for  6 active reps  approxLL diffs: (0.01,0.18)
  iter 6:  time=0.00 for  5 active reps  approxLL diffs: (0.02,0.06)
  iter 7:  time=0.00 for  5 active reps  approxLL diffs: (0.00,0.05)
  iter 8:  time=0.00 for  1 active reps  approxLL diffs: (0.06,0.06)
  iter 9:  time=0.00 for  1 active reps  approxLL diffs: (0.07,0.07)
  iter 10:  time=0.00 for  1 active reps  approxLL diffs: (0.07,0.07)
  iter 11:  time=0.00 for  1 active reps  approxLL diffs: (0.05,0.05)
  iter 12:  time=0.00 for  1 active reps  approxLL diffs: (0.02,0.02)
  iter 13:  time=0.00 for  1 active reps  approxLL diffs: (0.01,0.01)
  Converged at iter 13: approxLL diffs each have been < LLtol=0.01
  Time breakdown: dgemm = 27.3%, memory/overhead = 72.7%
Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01
# of SNPs passing filters before outlier removal: 2427/2431
Masking windows around outlier snps (chisq > 20.0)
# of SNPs remaining after outlier window removal: 2409/2427
Intercept of LD Score regression for ref stats:   1.042 (0.048)
Estimated attenuation: 0.428 (0.415)
Intercept of LD Score regression for cur stats: 1.038 (0.044)
Calibration factor (ref/cur) to multiply by:      1.003 (0.015)

Time for computing Bayesian mixed model assoc stats = 0.051573 sec

Calibration stats: mean and lambdaGC (over SNPs used in GRM)
  (note that both should be >1 because of polygenicity)
Mean BOLT_LMM_INF: 1.09877 (2431 good SNPs)   lambdaGC: 1.10376
Mean BOLT_LMM: 1.0957 (2431 good SNPs)   lambdaGC: 1.06946

=== Streaming genotypes to compute and write assoc stats at all SNPs ===

Time for streaming genotypes and writing output = 0.219872 sec


=== Streaming genotypes to compute and write assoc stats at dosage SNPs ===

Time for streaming dosage genotypes and writing output = 0.055135 sec


=== Streaming genotypes to compute and write assoc stats at IMPUTE2 SNPs ===

Read 379 indivs; using 373 in filtered PLINK data

Time for streaming IMPUTE2 genotypes and writing output = 0.095871 sec


=== Streaming genotypes to compute and write assoc stats at dosage2 SNPs ===

Time for streaming dosage2 genotypes and writing output = 0.0834239 sec

Total elapsed time for analysis = 23.7132 sec