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|
+-----------------------------+
| ___ |
| BOLT-LMM, v2.4.1 /_ / |
| November 16, 2022 /_/ |
| Po-Ru Loh // |
| / |
+-----------------------------+
Copyright (C) 2014-2022 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 \
--phenoFile=EUR_subset.pheno2.covars \
--exclude=EUR_subset.exclude2 \
--phenoCol=PHENO \
--phenoCol=QCOV1 \
--modelSnps=EUR_subset.modelSnps2 \
--reml \
--numThreads=2
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
Total indivs stored in memory: N = 379
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.exclude2
Excluded 47959 SNP(s)
Reading list of SNPs to include in model (i.e., GRM): EUR_subset.modelSnps2
WARNING: SNP has been excluded: rs2176153
WARNING: SNP has been excluded: rs77036651
WARNING: SNP has been excluded: rs189917831
WARNING: SNP has been excluded: rs76452819
WARNING: SNP has been excluded: rs77203822
Included 1331 SNP(s) in model in 2 variance component(s)
WARNING: 10420 SNP(s) had been excluded
Breakdown of SNP pre-filtering results:
1331 SNPs to include in model (i.e., GRM)
0 additional non-GRM SNPs loaded
52720 excluded SNPs
Allocating 1331 x 380/4 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: 379
Total post-QC SNPs: M = 1331
Variance component 1: 660 post-QC SNPs (name: 'chr21')
Variance component 2: 671 post-QC SNPs (name: 'chr22')
Time for SnpData setup = 0.330156 sec
=== Reading phenotype and covariate data ===
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 quantitative covariate: CONST_ALL_ONES
Number of individuals used in analysis: Nused = 369
Singular values of covariate matrix:
S[0] = 19.2094
Total covariate vectors: C = 1
Total independent covariate vectors: Cindep = 1
=== Initializing Bolt object: projecting and normalizing SNPs ===
Number of chroms with >= 1 good SNP: 2
Average norm of projected SNPs: 368.000000
Dimension of all-1s proj space (Nused-1): 368
Time for covariate data setup + Bolt initialization = 0.0118818 sec
Phenotype 1: N = 369 mean = -0.000706532 std = 1.02606
Phenotype 2: N = 369 mean = 1.53117 std = 0.499705
=== Estimating variance parameters ===
=== Making initial guesses for phenotype 1 ===
Using 3 random trials
Estimating MC scaling f_REML at log(delta) = 1.09861, h2 = 0.25...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.1,0.2) res2s: 820.154..209.284
iter 2: time=0.00 rNorms/orig: (0.02,0.05) res2s: 871.566..224.032
iter 3: time=0.00 rNorms/orig: (0.006,0.009) res2s: 874.21..225.515
iter 4: time=0.00 rNorms/orig: (0.001,0.001) res2s: 874.469..225.607
iter 5: time=0.00 rNorms/orig: (0.0002,0.0002) res2s: 874.479..225.61
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 27.4%, memory/overhead = 72.6%
MCscaling: logDelta = 1.10, h2 = 0.250, f = -0.0414761
Estimating MC scaling f_REML at log(delta) = 1.94591, h2 = 0.125...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.07,0.09) res2s: 2311.27..290.653
iter 2: time=0.00 rNorms/orig: (0.006,0.01) res2s: 2349.46..296.495
iter 3: time=0.00 rNorms/orig: (0.0008,0.001) res2s: 2350.09..296.683
iter 4: time=0.00 rNorms/orig: (9e-05,0.0001) res2s: 2350.11..296.687
Converged at iter 4: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 26.9%, memory/overhead = 73.1%
MCscaling: logDelta = 1.95, h2 = 0.125, f = 0.012255
Estimating MC scaling f_REML at log(delta) = 1.75266, h2 = 0.147712...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.08,0.1) res2s: 1845.27..274.749
iter 2: time=0.00 rNorms/orig: (0.008,0.02) res2s: 1887.36..282.258
iter 3: time=0.00 rNorms/orig: (0.001,0.002) res2s: 1888.28..282.578
iter 4: time=0.00 rNorms/orig: (0.0002,0.0002) res2s: 1888.31..282.586
Converged at iter 4: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 27.0%, memory/overhead = 73.0%
MCscaling: logDelta = 1.75, h2 = 0.148, f = 0.00181293
Estimating MC scaling f_REML at log(delta) = 1.71911, h2 = 0.151986...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.09,0.1) res2s: 1773.51..271.812
iter 2: time=0.00 rNorms/orig: (0.009,0.02) res2s: 1816.25..279.639
iter 3: time=0.00 rNorms/orig: (0.001,0.002) res2s: 1817.23..279.989
iter 4: time=0.00 rNorms/orig: (0.0002,0.0002) res2s: 1817.27..279.999
Converged at iter 4: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 26.8%, memory/overhead = 73.2%
MCscaling: logDelta = 1.72, h2 = 0.152, f = -0.000107663
Secant iteration for h2 estimation converged in 2 steps
Estimated (pseudo-)heritability: h2g = 0.152
To more precisely estimate variance parameters and estimate s.e., use --reml
Variance params: sigma^2_K = 0.159672, logDelta = 1.719106, f = -0.000107663
h2 with all VCs: 0.151986
=== Re-estimating variance parameters for 2 leave-out reps ===
Using 3 random trials
Estimating MC scaling f_REML at log(delta) = 1.71911, h2 = 0.151986...
Batch-solving 8 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.1,0.2) res2s: 1731.97..280.462
iter 2: time=0.00 rNorms/orig: (0.02,0.03) res2s: 1807.5..292.698
iter 3: time=0.00 rNorms/orig: (0.003,0.004) res2s: 1809.69..293.26
iter 4: time=0.00 rNorms/orig: (0.0004,0.0007) res2s: 1809.76..293.283
iter 5: time=0.00 rNorms/orig: (6e-05,9e-05) res2s: 1809.76..293.283
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 33.0%, memory/overhead = 67.0%
Estimating MC scaling f_REML at log(delta) = 2.71911, h2 = 0.0618553...
Batch-solving 8 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.05,0.06) res2s: 5411.29..341.902
iter 2: time=0.00 rNorms/orig: (0.003,0.005) res2s: 5452.11..344.669
iter 3: time=0.00 rNorms/orig: (0.0002,0.0004) res2s: 5452.34..344.696
Converged at iter 3: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 32.7%, memory/overhead = 67.3%
WARNING: Estimated h2 on leave-out batch 0 exceeds all-SNPs h2
Replacing 0.265571 with 0.151986
MCscaling: logDelta[0] = 1.719106, h2 = 0.152, Mused = 671 (50.4%)
MCscaling: logDelta[1] = 4.635315, h2 = 0.010, Mused = 660 (49.6%)
h2 leaving out VC 1: 0.151986
h2 leaving out VC 2: 0.00960981
guess h2 for VC 1: 0.00903833
guess h2 for VC 2: 0.142948
=== Making initial guesses for phenotype 2 ===
Using 3 random trials
Estimating MC scaling f_REML at log(delta) = 1.09861, h2 = 0.25...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.1,0.2) res2s: 820.154..51.8247
iter 2: time=0.00 rNorms/orig: (0.02,0.04) res2s: 871.566..54.9262
iter 3: time=0.00 rNorms/orig: (0.006,0.008) res2s: 874.21..55.1839
iter 4: time=0.00 rNorms/orig: (0.001,0.001) res2s: 874.469..55.2016
iter 5: time=0.00 rNorms/orig: (0.0002,0.0002) res2s: 874.479..55.2022
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 27.2%, memory/overhead = 72.8%
MCscaling: logDelta = 1.10, h2 = 0.250, f = -0.103553
Estimating MC scaling f_REML at log(delta) = 1.94591, h2 = 0.125...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.07,0.08) res2s: 2311.27..70.4467
iter 2: time=0.00 rNorms/orig: (0.006,0.01) res2s: 2349.46..71.6106
iter 3: time=0.00 rNorms/orig: (0.0008,0.001) res2s: 2350.09..71.6417
iter 4: time=0.00 rNorms/orig: (9e-05,0.0001) res2s: 2350.11..71.6423
Converged at iter 4: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 27.5%, memory/overhead = 72.5%
MCscaling: logDelta = 1.95, h2 = 0.125, f = -0.0587766
Estimating MC scaling f_REML at log(delta) = 3.05814, h2 = 0.0448672...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.02,0.03) res2s: 7823.07..83.8702
iter 2: time=0.00 rNorms/orig: (0.0007,0.001) res2s: 7840.59..84.0634
iter 3: time=0.00 rNorms/orig: (4e-05,7e-05) res2s: 7840.64..84.0642
Converged at iter 3: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 26.9%, memory/overhead = 73.1%
MCscaling: logDelta = 3.06, h2 = 0.045, f = -0.0246466
Estimating MC scaling f_REML at log(delta) = 3.86133, h2 = 0.0206065...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.01,0.01) res2s: 18037.5..88.1625
iter 2: time=0.00 rNorms/orig: (0.0001,0.0003) res2s: 18046.2..88.2065
Converged at iter 2: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 27.5%, memory/overhead = 72.5%
MCscaling: logDelta = 3.86, h2 = 0.021, f = -0.0122715
Estimating MC scaling f_REML at log(delta) = 4.65779, h2 = 0.00939822...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.005,0.006) res2s: 40636.6..90.1814
iter 2: time=0.00 rNorms/orig: (3e-05,7e-05) res2s: 40640.7..90.1908
Converged at iter 2: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 27.1%, memory/overhead = 72.9%
MCscaling: logDelta = 4.66, h2 = 0.009, f = -0.0057282
Estimating MC scaling f_REML at log(delta) = 5.35504, h2 = 0.00470207...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.003,0.003) res2s: 82199.2..91.034
iter 2: time=0.00 rNorms/orig: (7e-06,2e-05) res2s: 82201.3..91.0364
Converged at iter 2: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 27.3%, memory/overhead = 72.7%
MCscaling: logDelta = 5.36, h2 = 0.005, f = -0.00257581
Estimating MC scaling f_REML at log(delta) = 5.92476, h2 = 0.00266533...
Batch-solving 4 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.001,0.002) res2s: 145816..91.405
iter 2: time=0.00 rNorms/orig: (2e-06,5e-06) res2s: 145817..91.4057
Converged at iter 2: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 27.3%, memory/overhead = 72.7%
MCscaling: logDelta = 5.92, h2 = 0.003, f = -0.00101218
WARNING: Secant iteration for h2 estimation may not have converged
Estimated (pseudo-)heritability: h2g = 0.003
To more precisely estimate variance parameters and estimate s.e., use --reml
Variance params: sigma^2_K = 0.000666, logDelta = 5.924759, f = -0.00101218
h2 with all VCs: 0.00266533
=== Re-estimating variance parameters for 2 leave-out reps ===
Using 3 random trials
Estimating MC scaling f_REML at log(delta) = 5.92476, h2 = 0.00266533...
Batch-solving 8 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.002,0.003) res2s: 145671..91.3763
iter 2: time=0.00 rNorms/orig: (5e-06,9e-06) res2s: 145674..91.3781
Converged at iter 2: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 32.7%, memory/overhead = 67.3%
Estimating MC scaling f_REML at log(delta) = 6.92476, h2 = 0.000982175...
Batch-solving 8 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.0008,0.001) res2s: 397458..91.7015
iter 2: time=0.00 rNorms/orig: (7e-07,1e-06) res2s: 397459..91.7018
Converged at iter 2: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 32.7%, memory/overhead = 67.3%
MCscaling: logDelta[0] = 60.456787, h2 = 0.000, Mused = 671 (50.4%)
WARNING: Estimated h2 on leave-out batch 1 exceeds all-SNPs h2
Replacing 0.10913 with 0.00266533
MCscaling: logDelta[1] = 5.924759, h2 = 0.003, Mused = 660 (49.6%)
h2 leaving out VC 1: 1e-09
h2 leaving out VC 2: 0.00266533
guess h2 for VC 1: 0.00266533
guess h2 for VC 2: 1e-09 (setting to 1e-09)
===============================================================================
Stochastic REML optimization with MCtrials = 15
phenoNormsCorrs[2,2]((1.02606,0.0479548),(0.0479548,0.499705))
Initial variance parameter guesses:
Vegs[0][2,2]((0.848014,0.0441016),(0.0441016,0.997335))
Vegs[1][2,2]((0.00903833,0.00023537),(0.00023537,0.00266533))
Vegs[2][2,2]((0.142948,5.73352e-07),(5.73352e-07,1e-09))
Performing initial gradient evaluation
Batch-solving 16 systems of equations using conjugate gradient iteration
iter 1: time=0.01 rNorms/orig: (0.09,0.1) res2s: 757.838..714.073
iter 2: time=0.00 rNorms/orig: (0.01,0.02) res2s: 775.25..735.608
iter 3: time=0.00 rNorms/orig: (0.002,0.004) res2s: 775.947..736.998
iter 4: time=0.00 rNorms/orig: (0.0002,0.0005) res2s: 775.968..737.057
iter 5: time=0.00 rNorms/orig: (4e-05,7e-05) res2s: 775.969..737.058
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 30.4%, memory/overhead = 69.6%
grad[9](3.09407,-6.72961,-1.13849,-3.04818,7.07062,1.17961,13.4749,-2.86227,-7.58339)
-------------------------------------------------------------------------------
Start ITER 1: computing AI matrix
Multiplying solutions by variance components... time=0.00
Batch-solving 9 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.04,0.2) res2s: 374.428..573.744
iter 2: time=0.00 rNorms/orig: (0.005,0.03) res2s: 391.131..576.513
iter 3: time=0.00 rNorms/orig: (0.0009,0.004) res2s: 391.905..576.576
iter 4: time=0.00 rNorms/orig: (0.0001,0.0006) res2s: 391.934..576.579
iter 5: time=0.00 rNorms/orig: (2e-05,8e-05) res2s: 391.935..576.579
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 32.8%, memory/overhead = 67.2%
Reducing off-diagonals by a factor of 4.47035e-08 to make matrix positive definite
Reducing off-diagonals by a factor of 1.86265e-09 to make matrix positive definite
Constrained Newton-Raphson optimized variance parameters:
optVegs[0][2,2]((0.786598,5.10255e-05),(5.10255e-05,0.961774))
optVegs[1][2,2]((0.00962446,0.0165025),(0.0165025,0.0282958))
optVegs[2][2,2]((0.231128,0.0112622),(0.0112622,0.000548774))
Predicted change in log likelihood: 0.753554
Computing actual (approximate) change in log likelihood
Batch-solving 16 systems of equations using conjugate gradient iteration
iter 1: time=0.01 rNorms/orig: (0.1,0.2) res2s: 721.951..689.606
iter 2: time=0.01 rNorms/orig: (0.03,0.04) res2s: 757.291..731.137
iter 3: time=0.01 rNorms/orig: (0.006,0.01) res2s: 759.881..735.584
iter 4: time=0.01 rNorms/orig: (0.001,0.002) res2s: 760.12..736.003
iter 5: time=0.01 rNorms/orig: (0.0003,0.0004) res2s: 760.136..736.026
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 44.7%, memory/overhead = 55.3%
grad[9](1.70519,0.912804,1.26463,-4.85099,7.69901,-1.24528,1.75122,0.944023,-5.57033)
Approximate change in log likelihood: 0.752385 (attempt 1)
rho (approximate / predicted change in LL) = 0.998449
Old trust region radius: 1e+100
New trust region radius: 1e+100
Accepted step
End ITER 1
Vegs[0][2,2]((0.786598,5.10255e-05),(5.10255e-05,0.961774))
Vegs[1][2,2]((0.00962446,0.0165025),(0.0165025,0.0282958))
Vegs[2][2,2]((0.231128,0.0112622),(0.0112622,0.000548774))
-------------------------------------------------------------------------------
Start ITER 2: computing AI matrix
Multiplying solutions by variance components... time=0.00
Batch-solving 9 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.04,0.2) res2s: 368.021..588.613
iter 2: time=0.00 rNorms/orig: (0.008,0.06) res2s: 397.595..592.278
iter 3: time=0.00 rNorms/orig: (0.002,0.01) res2s: 400.094..592.546
iter 4: time=0.00 rNorms/orig: (0.0005,0.002) res2s: 400.293..592.563
iter 5: time=0.00 rNorms/orig: (9e-05,0.0005) res2s: 400.305..592.564
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 42.1%, memory/overhead = 57.9%
Reducing off-diagonals by a factor of 6.51926e-09 to make matrix positive definite
Reducing off-diagonals by a factor of 2.8871e-07 to make matrix positive definite
Reducing off-diagonals by a factor of 8.3819e-09 to make matrix positive definite
Reducing off-diagonals by a factor of 8.10251e-08 to make matrix positive definite
Constrained Newton-Raphson optimized variance parameters:
optVegs[0][2,2]((0.792508,0.00513227),(0.00513227,0.970811))
optVegs[1][2,2]((0.0085912,0.0149489),(0.0149489,0.0260115))
optVegs[2][2,2]((0.236851,0.010186),(0.010186,0.000438061))
Predicted change in log likelihood: 0.015863
Computing actual (approximate) change in log likelihood
Batch-solving 16 systems of equations using conjugate gradient iteration
iter 1: time=0.01 rNorms/orig: (0.1,0.2) res2s: 715.749..677.576
iter 2: time=0.01 rNorms/orig: (0.03,0.05) res2s: 751.609..719.352
iter 3: time=0.00 rNorms/orig: (0.006,0.01) res2s: 754.272..723.865
iter 4: time=0.00 rNorms/orig: (0.002,0.002) res2s: 754.521..724.302
iter 5: time=0.00 rNorms/orig: (0.0003,0.0004) res2s: 754.538..724.327
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 38.9%, memory/overhead = 61.1%
grad[9](0.0579094,-0.0410736,-0.0340175,-6.19482,7.16577,-2.04715,0.0273764,0.616035,-6.7186)
Approximate change in log likelihood: 0.0158352 (attempt 1)
rho (approximate / predicted change in LL) = 0.998249
Old trust region radius: 1e+100
New trust region radius: 1e+100
Accepted step
End ITER 2
Vegs[0][2,2]((0.792508,0.00513227),(0.00513227,0.970811))
Vegs[1][2,2]((0.0085912,0.0149489),(0.0149489,0.0260115))
Vegs[2][2,2]((0.236851,0.010186),(0.010186,0.000438061))
-------------------------------------------------------------------------------
Start ITER 3: computing AI matrix
Multiplying solutions by variance components... time=0.00
Batch-solving 9 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.04,0.2) res2s: 354.746..573.629
iter 2: time=0.00 rNorms/orig: (0.009,0.06) res2s: 383.972..577.01
iter 3: time=0.00 rNorms/orig: (0.002,0.01) res2s: 386.49..577.253
iter 4: time=0.00 rNorms/orig: (0.0005,0.002) res2s: 386.694..577.269
iter 5: time=0.00 rNorms/orig: (9e-05,0.0005) res2s: 386.707..577.27
iter 6: time=0.00 rNorms/orig: (2e-05,9e-05) res2s: 386.707..577.27
Converged at iter 6: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 38.2%, memory/overhead = 61.8%
Reducing off-diagonals by a factor of 1.86265e-09 to make matrix positive definite
Constrained Newton-Raphson optimized variance parameters:
optVegs[0][2,2]((0.792791,0.004696),(0.004696,0.970364))
optVegs[1][2,2]((0.00863587,0.0150598),(0.0150598,0.0262622))
optVegs[2][2,2]((0.236845,0.0104505),(0.0104505,0.000461119))
Predicted change in log likelihood: 3.20465e-05
AI iteration converged: predicted change in log likelihood < tol = 0.01
===============================================================================
Refining REML optimization with MCtrials = 100
phenoNormsCorrs[2,2]((1.02606,0.0479548),(0.0479548,0.499705))
Initial variance parameter guesses:
Vegs[0][2,2]((0.792791,0.004696),(0.004696,0.970364))
Vegs[1][2,2]((0.00863587,0.0150598),(0.0150598,0.0262622))
Vegs[2][2,2]((0.236845,0.0104505),(0.0104505,0.000461119))
Performing initial gradient evaluation
Batch-solving 101 systems of equations using conjugate gradient iteration
iter 1: time=0.02 rNorms/orig: (0.1,0.2) res2s: 649.894..677.471
iter 2: time=0.02 rNorms/orig: (0.03,0.05) res2s: 690.393..719.267
iter 3: time=0.02 rNorms/orig: (0.006,0.01) res2s: 692.677..723.771
iter 4: time=0.02 rNorms/orig: (0.001,0.002) res2s: 692.852..724.206
iter 5: time=0.02 rNorms/orig: (0.0002,0.0005) res2s: 692.865..724.231
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 33.7%, memory/overhead = 66.3%
grad[9](-6.9566,10.7443,-1.85918,-15.0527,20.8568,0.457434,-9.31692,3.14494,-11.8159)
-------------------------------------------------------------------------------
Start ITER 1: computing AI matrix
Multiplying solutions by variance components... time=0.00
Batch-solving 9 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.04,0.2) res2s: 354.271..573.97
iter 2: time=0.00 rNorms/orig: (0.009,0.06) res2s: 383.438..577.399
iter 3: time=0.00 rNorms/orig: (0.002,0.01) res2s: 385.949..577.646
iter 4: time=0.00 rNorms/orig: (0.0005,0.002) res2s: 386.153..577.663
iter 5: time=0.00 rNorms/orig: (9e-05,0.0005) res2s: 386.165..577.663
iter 6: time=0.00 rNorms/orig: (2e-05,9e-05) res2s: 386.166..577.663
Converged at iter 6: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 35.2%, memory/overhead = 64.8%
Reducing off-diagonals by a factor of 3.72529e-09 to make matrix positive definite
Reducing off-diagonals by a factor of 1.11759e-08 to make matrix positive definite
Reducing off-diagonals by a factor of 2.70084e-08 to make matrix positive definite
Reducing off-diagonals by a factor of 9.31323e-10 to make matrix positive definite
Constrained Newton-Raphson optimized variance parameters:
optVegs[0][2,2]((0.786659,0.0436024),(0.0436024,0.933363))
optVegs[1][2,2]((0.0109177,0.0241875),(0.0241875,0.053586))
optVegs[2][2,2]((0.189789,-0.0133283),(-0.0133283,0.000936012))
Predicted change in log likelihood: 0.545289
Computing actual (approximate) change in log likelihood
Batch-solving 101 systems of equations using conjugate gradient iteration
iter 1: time=0.02 rNorms/orig: (0.1,0.2) res2s: 672.112..720.682
iter 2: time=0.02 rNorms/orig: (0.02,0.05) res2s: 709.603..761.233
iter 3: time=0.02 rNorms/orig: (0.004,0.009) res2s: 711.555..765.362
iter 4: time=0.02 rNorms/orig: (0.0006,0.002) res2s: 711.638..765.655
iter 5: time=0.02 rNorms/orig: (0.0001,0.0003) res2s: 711.643..765.667
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 33.6%, memory/overhead = 66.4%
grad[9](0.593026,-0.656444,0.398117,-8.84248,7.66129,-1.54565,0.982601,-1.77881,-9.57942)
Approximate change in log likelihood: 0.505571 (attempt 1)
rho (approximate / predicted change in LL) = 0.927162
Old trust region radius: 1e+100
New trust region radius: 1e+100
Accepted step
End ITER 1
Vegs[0][2,2]((0.786659,0.0436024),(0.0436024,0.933363))
Vegs[1][2,2]((0.0109177,0.0241875),(0.0241875,0.053586))
Vegs[2][2,2]((0.189789,-0.0133283),(-0.0133283,0.000936012))
-------------------------------------------------------------------------------
Start ITER 2: computing AI matrix
Multiplying solutions by variance components... time=0.00
Batch-solving 9 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.07,0.2) res2s: 415.655..595.98
iter 2: time=0.00 rNorms/orig: (0.01,0.05) res2s: 445.96..603.593
iter 3: time=0.00 rNorms/orig: (0.002,0.009) res2s: 448.151..603.809
iter 4: time=0.00 rNorms/orig: (0.0004,0.002) res2s: 448.291..603.825
iter 5: time=0.00 rNorms/orig: (7e-05,0.0003) res2s: 448.297..603.826
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 35.3%, memory/overhead = 64.7%
Reducing off-diagonals by a factor of 1.55531e-07 to make matrix positive definite
Reducing off-diagonals by a factor of 2.23517e-08 to make matrix positive definite
Constrained Newton-Raphson optimized variance parameters:
optVegs[0][2,2]((0.785709,0.0421649),(0.0421649,0.935688))
optVegs[1][2,2]((0.0106038,0.0237489),(0.0237489,0.0531895))
optVegs[2][2,2]((0.194902,-0.0127997),(-0.0127997,0.000840582))
Predicted change in log likelihood: 0.00319152
Computing actual (approximate) change in log likelihood
Batch-solving 101 systems of equations using conjugate gradient iteration
iter 1: time=0.02 rNorms/orig: (0.1,0.2) res2s: 669.569..716.126
iter 2: time=0.02 rNorms/orig: (0.02,0.05) res2s: 707.616..757.118
iter 3: time=0.02 rNorms/orig: (0.004,0.009) res2s: 709.676..761.41
iter 4: time=0.02 rNorms/orig: (0.0007,0.002) res2s: 709.767..761.724
iter 5: time=0.02 rNorms/orig: (0.0001,0.0003) res2s: 709.773..761.738
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 33.6%, memory/overhead = 66.4%
grad[9](0.0132718,0.00120605,-0.000469693,-9.30953,8.31106,-1.85435,-0.0542454,-1.31603,-9.91664)
Approximate change in log likelihood: 0.00315098 (attempt 1)
rho (approximate / predicted change in LL) = 0.987298
Old trust region radius: 1e+100
New trust region radius: 1e+100
Accepted step
End ITER 2
Vegs[0][2,2]((0.785709,0.0421649),(0.0421649,0.935688))
Vegs[1][2,2]((0.0106038,0.0237489),(0.0237489,0.0531895))
Vegs[2][2,2]((0.194902,-0.0127997),(-0.0127997,0.000840582))
-------------------------------------------------------------------------------
Start ITER 3: computing AI matrix
Multiplying solutions by variance components... time=0.00
Batch-solving 9 systems of equations using conjugate gradient iteration
iter 1: time=0.00 rNorms/orig: (0.07,0.2) res2s: 411.202..591.681
iter 2: time=0.00 rNorms/orig: (0.01,0.05) res2s: 441.841..599.021
iter 3: time=0.00 rNorms/orig: (0.002,0.01) res2s: 444.122..599.234
iter 4: time=0.00 rNorms/orig: (0.0004,0.002) res2s: 444.271..599.25
iter 5: time=0.00 rNorms/orig: (7e-05,0.0003) res2s: 444.279..599.251
Converged at iter 5: rNorms/orig all < CGtol=0.0005
Time breakdown: dgemm = 35.5%, memory/overhead = 64.5%
Constrained Newton-Raphson optimized variance parameters:
optVegs[0][2,2]((0.785936,0.0422243),(0.0422243,0.93567))
optVegs[1][2,2]((0.0105889,0.0237346),(0.0237346,0.0531999))
optVegs[2][2,2]((0.194709,-0.012845),(-0.012845,0.000847386))
Predicted change in log likelihood: 2.98539e-06
AI iteration converged: predicted change in log likelihood < tol = 0.0001
AIinv[9,9]((0.0166025,0.000365568,3.94078e-05,-0.006988,0.000181563,-2.49421e-05,-0.00674973,-0.000305458,-9.39318e-06),(0.000365568,0.00878615,0.00027276,2.19493e-05,-0.00339651,0.000239245,-0.000119058,-0.0035982,-0.000239038),(3.94078e-05,0.00027276,0.0184783,-3.17674e-05,2.15426e-05,-0.00654309,3.14413e-06,2.75145e-05,-0.00770363),(-0.006988,2.19493e-05,-3.17674e-05,0.00761882,0.000105566,5.51108e-05,-0.000142709,-7.93293e-05,-8.85897e-06),(0.000181563,-0.00339651,2.15426e-05,0.000105566,0.00359947,0.000158943,-0.000275534,5.20063e-05,-0.00011476),(-2.49421e-05,0.000239245,-0.00654309,5.51108e-05,0.000158943,0.00686656,-1.87299e-05,-0.000384708,0.000244659),(-0.00674973,-0.000119058,3.14413e-06,-0.000142709,-0.000275534,-1.87299e-05,0.00900681,0.000397722,1.58436e-05),(-0.000305458,-0.0035982,2.75145e-05,-7.93293e-05,5.20063e-05,-0.000384708,0.000397722,0.00417854,0.000318505),(-9.39318e-06,-0.000239038,-0.00770363,-8.85897e-06,-0.00011476,0.000244659,1.58436e-05,0.000318505,0.00797437))
Variance component 0: [2,2]((0.785936,0.0422243),(0.0422243,0.93567))
entry (1,1): 0.785936 (0.128851)
entry (1,2): 0.042224 (0.093734) corr (1,2): 0.049239
entry (2,2): 0.935670 (0.135935)
Variance component 1: [2,2]((0.0105889,0.0237346),(0.0237346,0.0531999))
entry (1,1): 0.010589 (0.087286)
entry (1,2): 0.023735 (0.059996) corr (1,2): 1.000000
entry (2,2): 0.053200 (0.082865)
Variance component 2: [2,2]((0.194709,-0.012845),(-0.012845,0.000847386))
entry (1,1): 0.194709 (0.094904)
entry (1,2): -0.012845 (0.064642) corr (1,2): -0.999999
entry (2,2): 0.000847 (0.089299)
Phenotype 1 variance sigma2: 1.043569 (0.077845)
Phenotype 2 variance sigma2: 0.247138 (0.027278)
Variance component 0: (environment/noise)
h2e (1,1): 0.792886 (0.126213)
resid corr (1,2): 0.049239 (0.109785)
h2e (2,2): 0.945391 (0.125305)
Variance component 1: "chr21"
h2g (1,1): 0.010683 (0.088440)
gen corr (1,2): 1.000000 (4.870449)
h2g (2,2): 0.053753 (0.083863)
Variance component 2: "chr22"
h2g (1,1): 0.196431 (0.092852)
gen corr (1,2): -0.999999 (53.474367)
h2g (2,2): 0.000856 (0.090672)
Total elapsed time for analysis = 2.18387 sec
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