+-----------------------------+ | ___ | | BOLT-LMM, v2.3.1 /_ / | | December 19, 2017 /_/ | | Po-Ru Loh // | | / | +-----------------------------+ Copyright (C) 2014-2017 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: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3.1/bolt \ --bed=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bed \ --bim=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bim \ --fam=/n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam \ --allowX \ --remove=/n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt \ --remove=/n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe1000.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt \ --maxMissingPerSnp=0.15 \ --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v3.061518.tab \ --phenoCol=body_HEIGHTz \ --covarFile=/n/groups/price/UKBiobank/app10438assoc/ukb4777.processed_and_post.plinkPCs.tab.gz \ --covarCol=cov_ASSESS_CENTER \ --covarCol=cov_GENO_ARRAY \ --covarCol=cov_SEX \ --covarMaxLevels=30 \ --qCovarCol=cov_AGE \ --qCovarCol=cov_AGE_SQ \ --qCovarCol=PC{1:20} \ --LDscoresFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/LDSCORE.1000G_EUR.tab.gz \ --geneticMapFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz \ --lmmForceNonInf \ --numThreads=8 \ --predBetasFile=bolt_460K_selfRepWhite.body_HEIGHTz.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.body_HEIGHTz.stats.gz \ --verboseStats Setting number of threads to 8 fam: /n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam bim(s): /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bim bed(s): /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bed === Reading genotype data === Total indivs in PLINK data: Nbed = 488377 Reading remove file (indivs to remove): /n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt Removed 976 individual(s) Reading remove file (indivs to remove): /n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt WARNING: Unable to find individual to remove: FID IID WARNING: Unable to find individual to remove: 6023494 6023494 WARNING: Unable to find individual to remove: 6022857 6022857 WARNING: Unable to find individual to remove: 6020026 6020026 WARNING: Unable to find individual to remove: 6017119 6017119 Removed 28074 individual(s) WARNING: 1684 individual(s) not found in data set Total indivs stored in memory: N = 459327 Reading bim file #1: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bim Read 63487 snps Reading bim file #2: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bim Read 61966 snps Reading bim file #3: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bim Read 52300 snps Reading bim file #4: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bim Read 47443 snps Reading bim file #5: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bim Read 46314 snps Reading bim file #6: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bim Read 53695 snps Reading bim file #7: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bim Read 42722 snps Reading bim file #8: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bim Read 38591 snps Reading bim file #9: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bim Read 34310 snps Reading bim file #10: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bim Read 38308 snps Reading bim file #11: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bim Read 40824 snps Reading bim file #12: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bim Read 37302 snps Reading bim file #13: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bim Read 26806 snps Reading bim file #14: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bim Read 25509 snps Reading bim file #15: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bim Read 24467 snps Reading bim file #16: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bim Read 28960 snps Reading bim file #17: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bim Read 28835 snps Reading bim file #18: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bim Read 21962 snps Reading bim file #19: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bim Read 26186 snps Reading bim file #20: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bim Read 19959 snps Reading bim file #21: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bim Read 11342 snps Reading bim file #22: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bim Read 12968 snps Reading bim file #23: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bim Read 20214 snps Total snps in PLINK data: Mbed = 804470 Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe1000.txt Excluded 69333 SNP(s) Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt WARNING: Unable to find SNP to exclude: rs1800865 WARNING: Unable to find SNP to exclude: Affx-89009265 WARNING: Unable to find SNP to exclude: Affx-89005343 WARNING: Unable to find SNP to exclude: Affx-89020964 WARNING: Unable to find SNP to exclude: Affx-89013736 Excluded 16710 SNP(s) Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt WARNING: 342 SNP(s) not found in data set WARNING: Unable to find SNP to exclude: rs1800865 WARNING: Unable to find SNP to exclude: Affx-89022776 WARNING: Unable to find SNP to exclude: Affx-89017694 WARNING: Unable to find SNP to exclude: Affx-89018603 WARNING: Unable to find SNP to exclude: Affx-79443721 Excluded 8428 SNP(s) WARNING: 112 SNP(s) not found in data set Breakdown of SNP pre-filtering results: 709999 SNPs to include in model (i.e., GRM) 0 additional non-GRM SNPs loaded 94471 excluded SNPs Filling in genetic map coordinates using reference file: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz Allocating 709999 x 459328/4 bytes to store genotypes Reading genotypes and performing QC filtering on snps and indivs... Reading bed file #1: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bed Expecting 7751445265 (+3) bytes for 488377 indivs, 63487 snps Reading bed file #2: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bed Expecting 7565738770 (+3) bytes for 488377 indivs, 61966 snps Reading bed file #3: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bed Expecting 6385568500 (+3) bytes for 488377 indivs, 52300 snps Reading bed file #4: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bed Expecting 5792553085 (+3) bytes for 488377 indivs, 47443 snps Reading bed file #5: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bed Expecting 5654707830 (+3) bytes for 488377 indivs, 46314 snps Reading bed file #6: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bed Expecting 6555891025 (+3) bytes for 488377 indivs, 53695 snps Reading bed file #7: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bed Expecting 5216142590 (+3) bytes for 488377 indivs, 42722 snps Reading bed file #8: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bed Expecting 4711768145 (+3) bytes for 488377 indivs, 38591 snps Reading bed file #9: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bed Expecting 4189079450 (+3) bytes for 488377 indivs, 34310 snps Reading bed file #10: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bed Expecting 4677215260 (+3) bytes for 488377 indivs, 38308 snps Reading bed file #11: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bed Expecting 4984406280 (+3) bytes for 488377 indivs, 40824 snps Reading bed file #12: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bed Expecting 4554387690 (+3) bytes for 488377 indivs, 37302 snps Reading bed file #13: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bed Expecting 3272878570 (+3) bytes for 488377 indivs, 26806 snps Reading bed file #14: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bed Expecting 3114521355 (+3) bytes for 488377 indivs, 25509 snps Reading bed file #15: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bed Expecting 2987298365 (+3) bytes for 488377 indivs, 24467 snps Reading bed file #16: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bed Expecting 3535871200 (+3) bytes for 488377 indivs, 28960 snps Reading bed file #17: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bed Expecting 3520609325 (+3) bytes for 488377 indivs, 28835 snps Reading bed file #18: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bed Expecting 2681450390 (+3) bytes for 488377 indivs, 21962 snps Reading bed file #19: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bed Expecting 3197179670 (+3) bytes for 488377 indivs, 26186 snps Reading bed file #20: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bed Expecting 2436894105 (+3) bytes for 488377 indivs, 19959 snps Reading bed file #21: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bed Expecting 1384801490 (+3) bytes for 488377 indivs, 11342 snps Reading bed file #22: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bed Expecting 1583327960 (+3) bytes for 488377 indivs, 12968 snps Reading bed file #23: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bed Expecting 2468028330 (+3) bytes for 488377 indivs, 20214 snps Total indivs after QC: 459327 Total post-QC SNPs: M = 709999 Variance component 1: 709999 post-QC SNPs (name: 'modelSnps') Time for SnpData setup = 3086.89 sec === Reading phenotype and covariate data === WARNING: Ignoring indiv not in genotype data: FID=6018780, IID=6018780 WARNING: Ignoring indiv not in genotype data: FID=6012488, IID=6012488 WARNING: Ignoring indiv not in genotype data: FID=5998913, IID=5998913 WARNING: Ignoring indiv not in genotype data: FID=5989416, IID=5989416 WARNING: Ignoring indiv not in genotype data: FID=5985954, IID=5985954 Read data for 460238 indivs (ignored 914 without genotypes) from: /n/groups/price/UKBiobank/app10438assoc/ukb4777.processed_and_post.plinkPCs.tab.gz WARNING: Ignoring indiv not in genotype data: FID=1000129, IID=1000129 WARNING: Ignoring indiv not in genotype data: FID=1000170, IID=1000170 WARNING: Ignoring indiv not in genotype data: FID=1000224, IID=1000224 WARNING: Ignoring indiv not in genotype data: FID=1000362, IID=1000362 WARNING: Ignoring indiv not in genotype data: FID=1000379, IID=1000379 Read data for 502655 indivs (ignored 43331 without genotypes) from: /n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v3.061518.tab Number of indivs with no missing phenotype(s) to use: 458303 NOTE: Using all-1s vector (constant term) in addition to specified covariates Using categorical covariate: cov_ASSESS_CENTER (adding level 10003) Using categorical covariate: cov_ASSESS_CENTER (adding level 11001) Using categorical covariate: cov_ASSESS_CENTER (adding level 11002) Using categorical covariate: cov_ASSESS_CENTER (adding level 11003) Using categorical covariate: cov_ASSESS_CENTER (adding level 11004) Using categorical covariate: cov_ASSESS_CENTER (adding level 11005) Using categorical covariate: cov_ASSESS_CENTER (adding level 11006) Using categorical covariate: cov_ASSESS_CENTER (adding level 11007) Using categorical covariate: cov_ASSESS_CENTER (adding level 11008) Using categorical covariate: cov_ASSESS_CENTER (adding level 11009) Using categorical covariate: cov_ASSESS_CENTER (adding level 11010) Using categorical covariate: cov_ASSESS_CENTER (adding level 11011) Using categorical covariate: cov_ASSESS_CENTER (adding level 11012) Using categorical covariate: cov_ASSESS_CENTER (adding level 11013) Using categorical covariate: cov_ASSESS_CENTER (adding level 11014) Using categorical covariate: cov_ASSESS_CENTER (adding level 11016) Using categorical covariate: cov_ASSESS_CENTER (adding level 11017) Using categorical covariate: cov_ASSESS_CENTER (adding level 11018) Using categorical covariate: cov_ASSESS_CENTER (adding level 11020) Using categorical covariate: cov_ASSESS_CENTER (adding level 11021) Using categorical covariate: cov_ASSESS_CENTER (adding level 11022) Using categorical covariate: cov_ASSESS_CENTER (adding level 11023) WARNING: Covariate cov_ASSESS_CENTER has a large number of distinct values (23) Should this covariate be quantitative rather than categorical? Using categorical covariate: cov_GENO_ARRAY (adding level 1) Using categorical covariate: cov_GENO_ARRAY (adding level 2) Using categorical covariate: cov_SEX (adding level 0) Using categorical covariate: cov_SEX (adding level 1) Using quantitative covariate: cov_AGE Using quantitative covariate: cov_AGE_SQ Using quantitative covariate: PC1 Using quantitative covariate: PC2 Using quantitative covariate: PC3 Using quantitative covariate: PC4 Using quantitative covariate: PC5 Using quantitative covariate: PC6 Using quantitative covariate: PC7 Using quantitative covariate: PC8 Using quantitative covariate: PC9 Using quantitative covariate: PC10 Using quantitative covariate: PC11 Using quantitative covariate: PC12 Using quantitative covariate: PC13 Using quantitative covariate: PC14 Using quantitative covariate: PC15 Using quantitative covariate: PC16 Using quantitative covariate: PC17 Using quantitative covariate: PC18 Using quantitative covariate: PC19 Using quantitative covariate: PC20 Using quantitative covariate: CONST_ALL_ONES Number of individuals used in analysis: Nused = 458303 Singular values of covariate matrix: S[0] = 2.30532e+06 S[1] = 4838.24 S[2] = 476.843 S[3] = 298.247 S[4] = 203.156 S[5] = 197.287 S[6] = 185.733 S[7] = 177.953 S[8] = 173.259 S[9] = 167.851 S[10] = 164.748 S[11] = 155.951 S[12] = 147.888 S[13] = 145.662 S[14] = 143.533 S[15] = 138.678 S[16] = 134.846 S[17] = 131.674 S[18] = 128.617 S[19] = 117.358 S[20] = 113.224 S[21] = 101.276 S[22] = 45.844 S[23] = 24.9911 S[24] = 21.1198 S[25] = 19.5471 S[26] = 0.99824 S[27] = 0.998203 S[28] = 0.998165 S[29] = 0.998109 S[30] = 0.998079 S[31] = 0.998045 S[32] = 0.997999 S[33] = 0.997987 S[34] = 0.997946 S[35] = 0.997877 S[36] = 0.997866 S[37] = 0.997808 S[38] = 0.997753 S[39] = 0.997709 S[40] = 0.997642 S[41] = 0.997589 S[42] = 0.997481 S[43] = 0.997254 S[44] = 0.981176 S[45] = 0.901368 S[46] = 7.02326e-12 S[47] = 4.95318e-13 S[48] = 3.13816e-13 Total covariate vectors: C = 49 Total independent covariate vectors: Cindep = 46 === Initializing Bolt object: projecting and normalizing SNPs === Number of chroms with >= 1 good SNP: 23 Average norm of projected SNPs: 455624.212002 Dimension of all-1s proj space (Nused-1): 458302 Time for covariate data setup + Bolt initialization = 4052.47 sec Phenotype 1: N = 458303 mean = 0.0361722 std = 0.987171 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 447.593 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 458303) Estimating MC scaling f_REML at log(delta) = 1.09285, h2 = 0.25... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=340.58 rNorms/orig: (0.6,0.7) res2s: 742867..86304 iter 2: time=337.35 rNorms/orig: (0.6,0.7) res2s: 876990..121635 iter 3: time=341.16 rNorms/orig: (0.3,0.4) res2s: 1.01494e+06..154918 iter 4: time=333.04 rNorms/orig: (0.2,0.3) res2s: 1.0699e+06..171784 iter 5: time=342.27 rNorms/orig: (0.1,0.2) res2s: 1.09559e+06..180201 iter 6: time=358.19 rNorms/orig: (0.09,0.1) res2s: 1.11037e+06..184784 iter 7: time=357.43 rNorms/orig: (0.06,0.08) res2s: 1.11775e+06..187062 iter 8: time=361.54 rNorms/orig: (0.04,0.05) res2s: 1.12074e+06..188107 iter 9: time=360.79 rNorms/orig: (0.02,0.03) res2s: 1.12236e+06..188676 iter 10: time=378.54 rNorms/orig: (0.02,0.02) res2s: 1.12313e+06..188913 iter 11: time=358.50 rNorms/orig: (0.01,0.01) res2s: 1.12345e+06..189040 iter 12: time=365.34 rNorms/orig: (0.007,0.009) res2s: 1.12361e+06..189093 iter 13: time=367.79 rNorms/orig: (0.005,0.006) res2s: 1.12367e+06..189117 iter 14: time=367.83 rNorms/orig: (0.003,0.004) res2s: 1.12371e+06..189129 Converged at iter 14: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 46.0%, memory/overhead = 54.0% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.544942 Estimating MC scaling f_REML at log(delta) = -0.00576179, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=349.62 rNorms/orig: (1,1) res2s: 78456.7..19425.2 iter 2: time=353.14 rNorms/orig: (1,1) res2s: 110417..33338.1 iter 3: time=362.51 rNorms/orig: (0.8,0.9) res2s: 167518..54373.6 iter 4: time=351.32 rNorms/orig: (0.6,0.7) res2s: 207813..71671.7 iter 5: time=374.56 rNorms/orig: (0.5,0.6) res2s: 235069..84664.1 iter 6: time=379.69 rNorms/orig: (0.4,0.4) res2s: 257989..95042.3 iter 7: time=394.72 rNorms/orig: (0.3,0.3) res2s: 275283..102570 iter 8: time=377.99 rNorms/orig: (0.3,0.3) res2s: 285147..107488 iter 9: time=372.87 rNorms/orig: (0.2,0.2) res2s: 292744..111331 iter 10: time=382.68 rNorms/orig: (0.2,0.2) res2s: 298100..113612 iter 11: time=386.02 rNorms/orig: (0.1,0.1) res2s: 301213..115366 iter 12: time=366.48 rNorms/orig: (0.09,0.1) res2s: 303510..116416 iter 13: time=360.70 rNorms/orig: (0.07,0.08) res2s: 304809..117081 iter 14: time=361.37 rNorms/orig: (0.06,0.06) res2s: 305819..117570 iter 15: time=362.61 rNorms/orig: (0.04,0.05) res2s: 306378..117864 iter 16: time=370.74 rNorms/orig: (0.03,0.04) res2s: 306756..118049 iter 17: time=354.78 rNorms/orig: (0.03,0.03) res2s: 307013..118174 iter 18: time=353.99 rNorms/orig: (0.02,0.02) res2s: 307174..118254 iter 19: time=351.29 rNorms/orig: (0.02,0.02) res2s: 307275..118307 iter 20: time=369.04 rNorms/orig: (0.01,0.01) res2s: 307331..118337 iter 21: time=381.01 rNorms/orig: (0.01,0.01) res2s: 307372..118357 iter 22: time=384.67 rNorms/orig: (0.007,0.008) res2s: 307397..118369 iter 23: time=387.74 rNorms/orig: (0.006,0.006) res2s: 307413..118377 iter 24: time=389.03 rNorms/orig: (0.004,0.005) res2s: 307423..118382 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 46.7%, memory/overhead = 53.3% MCscaling: logDelta = -0.01, h2 = 0.500, f = 0.133412 Estimating MC scaling f_REML at log(delta) = -0.361915, h2 = 0.588109... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=386.83 rNorms/orig: (1,1) res2s: 33691.4..10862.2 iter 2: time=395.38 rNorms/orig: (1,1) res2s: 49177.2..19556.6 iter 3: time=383.74 rNorms/orig: (1,1) res2s: 80363.7..34243.2 iter 4: time=382.35 rNorms/orig: (0.8,0.9) res2s: 106078..47892.2 iter 5: time=382.58 rNorms/orig: (0.6,0.7) res2s: 125482..59313.9 iter 6: time=389.94 rNorms/orig: (0.5,0.6) res2s: 143665..69410.8 iter 7: time=372.61 rNorms/orig: (0.4,0.5) res2s: 159072..77491.4 iter 8: time=373.89 rNorms/orig: (0.4,0.4) res2s: 168762..83267.9 iter 9: time=385.65 rNorms/orig: (0.3,0.3) res2s: 176917..88201.3 iter 10: time=360.60 rNorms/orig: (0.2,0.3) res2s: 183281..91391 iter 11: time=352.65 rNorms/orig: (0.2,0.2) res2s: 187293..94060.1 iter 12: time=361.90 rNorms/orig: (0.2,0.2) res2s: 190550..95801 iter 13: time=359.52 rNorms/orig: (0.1,0.1) res2s: 192548..96993.1 iter 14: time=364.12 rNorms/orig: (0.1,0.1) res2s: 194227..97943.1 iter 15: time=371.95 rNorms/orig: (0.09,0.09) res2s: 195238..98565 iter 16: time=361.93 rNorms/orig: (0.07,0.08) res2s: 195984..98988.5 iter 17: time=350.07 rNorms/orig: (0.06,0.06) res2s: 196529..99297.2 iter 18: time=357.94 rNorms/orig: (0.04,0.05) res2s: 196903..99512.5 iter 19: time=363.46 rNorms/orig: (0.04,0.04) res2s: 197157..99667.9 iter 20: time=360.78 rNorms/orig: (0.03,0.03) res2s: 197310..99761.4 iter 21: time=363.46 rNorms/orig: (0.02,0.03) res2s: 197434..99830.3 iter 22: time=364.13 rNorms/orig: (0.02,0.02) res2s: 197513..99876.7 iter 23: time=367.06 rNorms/orig: (0.02,0.02) res2s: 197567..99909.6 iter 24: time=380.50 rNorms/orig: (0.01,0.01) res2s: 197605..99930.9 iter 25: time=358.32 rNorms/orig: (0.01,0.01) res2s: 197629..99945.4 iter 26: time=355.75 rNorms/orig: (0.008,0.008) res2s: 197646..99955.7 iter 27: time=363.34 rNorms/orig: (0.007,0.007) res2s: 197657..99962.1 iter 28: time=398.93 rNorms/orig: (0.005,0.006) res2s: 197664..99966.5 iter 29: time=397.07 rNorms/orig: (0.004,0.005) res2s: 197669..99969.3 Converged at iter 29: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 46.4%, memory/overhead = 53.6% MCscaling: logDelta = -0.36, h2 = 0.588, f = -0.00575832 Estimating MC scaling f_REML at log(delta) = -0.347179, h2 = 0.584535... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=370.04 rNorms/orig: (1,1) res2s: 34906.2..11134.8 iter 2: time=365.55 rNorms/orig: (1,1) res2s: 50889.1..20011.4 iter 3: time=359.09 rNorms/orig: (1,1) res2s: 82943.5..34943.6 iter 4: time=349.83 rNorms/orig: (0.8,0.9) res2s: 109221..48756.1 iter 5: time=337.01 rNorms/orig: (0.6,0.7) res2s: 128966..60266 iter 6: time=352.13 rNorms/orig: (0.5,0.6) res2s: 147390..70400.5 iter 7: time=365.43 rNorms/orig: (0.4,0.5) res2s: 162930..78479.6 iter 8: time=358.31 rNorms/orig: (0.4,0.4) res2s: 172664..84234.7 iter 9: time=359.75 rNorms/orig: (0.3,0.3) res2s: 180827..89132.7 iter 10: time=359.53 rNorms/orig: (0.2,0.3) res2s: 187172..92288.7 iter 11: time=343.95 rNorms/orig: (0.2,0.2) res2s: 191159..94921.2 iter 12: time=344.42 rNorms/orig: (0.2,0.2) res2s: 194384..96632.4 iter 13: time=355.13 rNorms/orig: (0.1,0.1) res2s: 196355..97800.7 iter 14: time=360.83 rNorms/orig: (0.1,0.1) res2s: 198007..98728.8 iter 15: time=355.31 rNorms/orig: (0.08,0.09) res2s: 198999..99334.5 iter 16: time=331.29 rNorms/orig: (0.07,0.08) res2s: 199728..99745.6 iter 17: time=342.37 rNorms/orig: (0.05,0.06) res2s: 200259..100044 iter 18: time=342.75 rNorms/orig: (0.04,0.05) res2s: 200623..100252 iter 19: time=341.69 rNorms/orig: (0.04,0.04) res2s: 200868..100402 iter 20: time=339.56 rNorms/orig: (0.03,0.03) res2s: 201016..100491 iter 21: time=347.33 rNorms/orig: (0.02,0.02) res2s: 201135..100557 iter 22: time=362.27 rNorms/orig: (0.02,0.02) res2s: 201212..100601 iter 23: time=367.42 rNorms/orig: (0.01,0.02) res2s: 201263..100633 iter 24: time=376.04 rNorms/orig: (0.01,0.01) res2s: 201299..100653 iter 25: time=377.03 rNorms/orig: (0.01,0.01) res2s: 201322..100667 iter 26: time=364.47 rNorms/orig: (0.008,0.008) res2s: 201338..100676 iter 27: time=357.83 rNorms/orig: (0.006,0.007) res2s: 201348..100682 iter 28: time=369.18 rNorms/orig: (0.005,0.006) res2s: 201355..100686 iter 29: time=378.10 rNorms/orig: (0.004,0.004) res2s: 201360..100689 Converged at iter 29: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 46.7%, memory/overhead = 53.3% MCscaling: logDelta = -0.35, h2 = 0.585, f = -6.72083e-06 Secant iteration for h2 estimation converged in 2 steps Estimated (pseudo-)heritability: h2g = 0.585 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.501267, logDelta = -0.347179, f = -6.72083e-06 Time for fitting variance components = 35838 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 23 chunks for leave-out analysis Each chunk is excluded when testing SNPs belonging to the chunk Batch-solving 53 systems of equations using conjugate gradient iteration iter 1: time=696.61 rNorms/orig: (1,2) res2s: 11076.8..70755.7 iter 2: time=682.38 rNorms/orig: (1,1) res2s: 20315..90764.4 iter 3: time=700.62 rNorms/orig: (0.7,1) res2s: 36067.7..129743 iter 4: time=672.13 rNorms/orig: (0.7,1) res2s: 51187.1..145474 iter 5: time=682.60 rNorms/orig: (0.6,0.8) res2s: 63780.4..163100 iter 6: time=700.08 rNorms/orig: (0.4,0.7) res2s: 74928.2..174492 iter 7: time=677.00 rNorms/orig: (0.4,0.5) res2s: 84025.9..184405 iter 8: time=691.60 rNorms/orig: (0.3,0.4) res2s: 90559.6..191292 iter 9: time=686.53 rNorms/orig: (0.2,0.4) res2s: 96067.4..195847 iter 10: time=661.30 rNorms/orig: (0.2,0.3) res2s: 99925.9..199520 iter 11: time=673.77 rNorms/orig: (0.2,0.2) res2s: 102877..202102 iter 12: time=665.21 rNorms/orig: (0.1,0.2) res2s: 105040..203855 iter 13: time=660.78 rNorms/orig: (0.1,0.2) res2s: 106445..205012 iter 14: time=677.40 rNorms/orig: (0.08,0.1) res2s: 107606..205908 iter 15: time=671.51 rNorms/orig: (0.06,0.1) res2s: 108388..206510 iter 16: time=641.22 rNorms/orig: (0.05,0.09) res2s: 108912..206943 iter 17: time=617.59 rNorms/orig: (0.04,0.07) res2s: 109292..207230 iter 18: time=621.97 rNorms/orig: (0.03,0.05) res2s: 109581..207431 iter 19: time=615.93 rNorms/orig: (0.02,0.05) res2s: 109776..207597 iter 20: time=623.09 rNorms/orig: (0.02,0.04) res2s: 109900..207689 iter 21: time=661.73 rNorms/orig: (0.01,0.03) res2s: 109991..207753 iter 22: time=661.18 rNorms/orig: (0.01,0.02) res2s: 110055..207795 iter 23: time=661.10 rNorms/orig: (0.008,0.02) res2s: 110099..207827 iter 24: time=650.62 rNorms/orig: (0.007,0.02) res2s: 110129..207845 iter 25: time=650.78 rNorms/orig: (0.005,0.01) res2s: 110150..207858 iter 26: time=660.54 rNorms/orig: (0.004,0.01) res2s: 110164..207868 iter 27: time=654.24 rNorms/orig: (0.003,0.008) res2s: 110174..207874 iter 28: time=678.49 rNorms/orig: (0.002,0.008) res2s: 110180..207878 iter 29: time=708.00 rNorms/orig: (0.002,0.006) res2s: 110185..207881 iter 30: time=708.48 rNorms/orig: (0.001,0.005) res2s: 110188..207883 iter 31: time=692.91 rNorms/orig: (0.001,0.004) res2s: 110190..207884 iter 32: time=687.32 rNorms/orig: (0.0008,0.003) res2s: 110192..207885 iter 33: time=685.55 rNorms/orig: (0.0006,0.003) res2s: 110192..207885 iter 34: time=698.97 rNorms/orig: (0.0004,0.002) res2s: 110193..207886 iter 35: time=697.06 rNorms/orig: (0.0003,0.002) res2s: 110194..207886 iter 36: time=669.82 rNorms/orig: (0.0003,0.001) res2s: 110194..207886 iter 37: time=682.76 rNorms/orig: (0.0003,0.001) res2s: 110194..207886 iter 38: time=685.17 rNorms/orig: (0.0002,0.0009) res2s: 110194..207886 iter 39: time=693.75 rNorms/orig: (0.0001,0.0007) res2s: 110194..207886 iter 40: time=700.22 rNorms/orig: (0.0001,0.0006) res2s: 110194..207886 iter 41: time=684.33 rNorms/orig: (8e-05,0.0004) res2s: 110195..207887 Converged at iter 41: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 70.4%, memory/overhead = 29.6% AvgPro: 2.806 AvgRetro: 2.711 Calibration: 1.035 (0.003) (30 SNPs) Ratio of medians: 1.033 Median of ratios: 1.031 Time for computing infinitesimal model assoc stats = 28022.9 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 15.9293 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 605211/709999 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: 583483/709999 Masking windows around outlier snps (chisq > 458.3) # of SNPs remaining after outlier window removal: 562181/583483 Intercept of LD Score regression for ref stats: 1.498 (0.037) Estimated attenuation: 0.164 (0.011) Intercept of LD Score regression for cur stats: 1.457 (0.026) Calibration factor (ref/cur) to multiply by: 1.028 (0.009) LINREG intercept inflation = 0.972573 NOTE: LINREG stats in output are NOT corrected for estimated inflation === Estimating mixture parameters by cross-validation === Setting maximum number of iterations to 250 for this step Max CV folds to compute = 1 (to have > 10000 samples) ====> Starting CV fold 1 <==== NOTE: Using all-1s vector (constant term) in addition to specified covariates Using categorical covariate: cov_ASSESS_CENTER (adding level 10003) Using categorical covariate: cov_ASSESS_CENTER (adding level 11001) Using categorical covariate: cov_ASSESS_CENTER (adding level 11002) Using categorical covariate: cov_ASSESS_CENTER (adding level 11003) Using categorical covariate: cov_ASSESS_CENTER (adding level 11004) Using categorical covariate: cov_ASSESS_CENTER (adding level 11005) Using categorical covariate: cov_ASSESS_CENTER (adding level 11006) Using categorical covariate: cov_ASSESS_CENTER (adding level 11007) Using categorical covariate: cov_ASSESS_CENTER (adding level 11008) Using categorical covariate: cov_ASSESS_CENTER (adding level 11009) Using categorical covariate: cov_ASSESS_CENTER (adding level 11010) Using categorical covariate: cov_ASSESS_CENTER (adding level 11011) Using categorical covariate: cov_ASSESS_CENTER (adding level 11012) Using categorical covariate: cov_ASSESS_CENTER (adding level 11013) Using categorical covariate: cov_ASSESS_CENTER (adding level 11014) Using categorical covariate: cov_ASSESS_CENTER (adding level 11016) Using categorical covariate: cov_ASSESS_CENTER (adding level 11017) Using categorical covariate: cov_ASSESS_CENTER (adding level 11018) Using categorical covariate: cov_ASSESS_CENTER (adding level 11020) Using categorical covariate: cov_ASSESS_CENTER (adding level 11021) Using categorical covariate: cov_ASSESS_CENTER (adding level 11022) Using categorical covariate: cov_ASSESS_CENTER (adding level 11023) WARNING: Covariate cov_ASSESS_CENTER has a large number of distinct values (23) Should this covariate be quantitative rather than categorical? Using categorical covariate: cov_GENO_ARRAY (adding level 1) Using categorical covariate: cov_GENO_ARRAY (adding level 2) Using categorical covariate: cov_SEX (adding level 0) Using categorical covariate: cov_SEX (adding level 1) Using quantitative covariate: cov_AGE Using quantitative covariate: cov_AGE_SQ Using quantitative covariate: PC1 Using quantitative covariate: PC2 Using quantitative covariate: PC3 Using quantitative covariate: PC4 Using quantitative covariate: PC5 Using quantitative covariate: PC6 Using quantitative covariate: PC7 Using quantitative covariate: PC8 Using quantitative covariate: PC9 Using quantitative covariate: PC10 Using quantitative covariate: PC11 Using quantitative covariate: PC12 Using quantitative covariate: PC13 Using quantitative covariate: PC14 Using quantitative covariate: PC15 Using quantitative covariate: PC16 Using quantitative covariate: PC17 Using quantitative covariate: PC18 Using quantitative covariate: PC19 Using quantitative covariate: PC20 Using quantitative covariate: CONST_ALL_ONES Number of individuals used in analysis: Nused = 366642 Singular values of covariate matrix: S[0] = 2.06236e+06 S[1] = 4327.18 S[2] = 426.479 S[3] = 266.739 S[4] = 181.783 S[5] = 176.51 S[6] = 166.173 S[7] = 159.094 S[8] = 154.973 S[9] = 150.28 S[10] = 147.405 S[11] = 139.479 S[12] = 132.272 S[13] = 130.038 S[14] = 128.212 S[15] = 123.912 S[16] = 120.574 S[17] = 117.56 S[18] = 115.001 S[19] = 105.129 S[20] = 101.501 S[21] = 90.7547 S[22] = 41.1019 S[23] = 22.2882 S[24] = 18.6884 S[25] = 17.473 S[26] = 0.895383 S[27] = 0.894579 S[28] = 0.894252 S[29] = 0.894042 S[30] = 0.893581 S[31] = 0.893551 S[32] = 0.893158 S[33] = 0.892976 S[34] = 0.892732 S[35] = 0.89267 S[36] = 0.892265 S[37] = 0.891881 S[38] = 0.891853 S[39] = 0.891401 S[40] = 0.890978 S[41] = 0.890825 S[42] = 0.890573 S[43] = 0.89014 S[44] = 0.874578 S[45] = 0.805784 S[46] = 4.35343e-12 S[47] = 4.44166e-13 S[48] = 3.34563e-14 Total covariate vectors: C = 49 Total independent covariate vectors: Cindep = 46 === Initializing Bolt object: projecting and normalizing SNPs === Number of chroms with >= 1 good SNP: 23 Average norm of projected SNPs: 364489.949130 Dimension of all-1s proj space (Nused-1): 366641 Beginning variational Bayes iter 1: time=672.30 for 18 active reps iter 2: time=445.68 for 18 active reps approxLL diffs: (64533.58,70965.62) iter 3: time=486.92 for 18 active reps approxLL diffs: (11943.25,15119.52) iter 4: time=449.61 for 18 active reps approxLL diffs: (3551.52,5438.01) iter 5: time=447.75 for 18 active reps approxLL diffs: (1417.81,2652.67) iter 6: time=440.44 for 18 active reps approxLL diffs: (674.21,1586.36) iter 7: time=464.82 for 18 active reps approxLL diffs: (356.53,997.30) iter 8: time=454.72 for 18 active reps approxLL diffs: (203.10,678.07) iter 9: time=452.41 for 18 active reps approxLL diffs: (122.90,504.24) iter 10: time=448.90 for 18 active reps approxLL diffs: (78.41,392.27) iter 11: time=466.28 for 18 active reps approxLL diffs: (52.39,293.22) iter 12: time=452.89 for 18 active reps approxLL diffs: (36.36,242.70) iter 13: time=447.22 for 18 active reps approxLL diffs: (26.06,183.50) iter 14: time=443.00 for 18 active reps approxLL diffs: (19.24,148.38) iter 15: time=455.81 for 18 active reps approxLL diffs: (14.60,147.58) iter 16: time=439.60 for 18 active reps approxLL diffs: (11.23,111.23) iter 17: time=451.21 for 18 active reps approxLL diffs: (8.71,86.82) iter 18: time=452.06 for 18 active reps approxLL diffs: (6.87,72.68) iter 19: time=451.60 for 18 active reps approxLL diffs: (5.52,61.07) iter 20: time=451.54 for 18 active reps approxLL diffs: (4.50,58.86) iter 21: time=439.18 for 18 active reps approxLL diffs: (3.72,49.60) iter 22: time=439.98 for 18 active reps approxLL diffs: (3.11,45.59) iter 23: time=440.38 for 18 active reps approxLL diffs: (2.62,39.58) iter 24: time=438.74 for 18 active reps approxLL diffs: (2.22,40.82) iter 25: time=441.34 for 18 active reps approxLL diffs: (1.89,28.22) iter 26: time=438.19 for 18 active reps approxLL diffs: (1.62,26.15) iter 27: time=448.04 for 18 active reps approxLL diffs: (1.41,25.31) iter 28: time=434.69 for 18 active reps approxLL diffs: (1.23,26.34) iter 29: time=441.35 for 18 active reps approxLL diffs: (1.09,22.42) iter 30: time=444.32 for 18 active reps approxLL diffs: (0.97,19.69) iter 31: time=434.99 for 18 active reps approxLL diffs: (0.87,16.65) iter 32: time=444.51 for 18 active reps approxLL diffs: (0.78,23.67) iter 33: time=436.92 for 18 active reps approxLL diffs: (0.71,21.32) iter 34: time=443.54 for 18 active reps approxLL diffs: (0.64,18.86) iter 35: time=435.32 for 18 active reps approxLL diffs: (0.58,11.80) iter 36: time=440.18 for 18 active reps approxLL diffs: (0.53,9.14) iter 37: time=431.46 for 18 active reps approxLL diffs: (0.48,11.06) iter 38: time=437.38 for 18 active reps approxLL diffs: (0.44,15.10) iter 39: time=436.63 for 18 active reps approxLL diffs: (0.41,12.07) iter 40: time=443.49 for 18 active reps approxLL diffs: (0.37,10.51) iter 41: time=436.49 for 18 active reps approxLL diffs: (0.34,13.67) iter 42: time=444.37 for 18 active reps approxLL diffs: (0.32,11.90) iter 43: time=448.24 for 18 active reps approxLL diffs: (0.29,9.29) iter 44: time=450.77 for 18 active reps approxLL diffs: (0.27,10.67) iter 45: time=449.24 for 18 active reps approxLL diffs: (0.25,9.85) iter 46: time=445.66 for 18 active reps approxLL diffs: (0.23,5.34) iter 47: time=457.33 for 18 active reps approxLL diffs: (0.22,4.24) iter 48: time=443.45 for 18 active reps approxLL diffs: (0.21,4.27) iter 49: time=433.02 for 18 active reps approxLL diffs: (0.19,5.00) iter 50: time=434.97 for 18 active reps approxLL diffs: (0.18,8.92) iter 51: time=439.33 for 18 active reps approxLL diffs: (0.17,5.46) iter 52: time=443.16 for 18 active reps approxLL diffs: (0.16,5.76) iter 53: time=434.30 for 18 active reps approxLL diffs: (0.15,5.56) iter 54: time=435.48 for 18 active reps approxLL diffs: (0.14,6.66) iter 55: time=439.12 for 18 active reps approxLL diffs: (0.13,4.26) iter 56: time=437.85 for 18 active reps approxLL diffs: (0.12,3.63) iter 57: time=431.32 for 18 active reps approxLL diffs: (0.07,3.65) iter 58: time=436.59 for 18 active reps approxLL diffs: (0.05,4.85) iter 59: time=434.96 for 18 active reps approxLL diffs: (0.04,2.60) iter 60: time=441.71 for 18 active reps approxLL diffs: (0.04,2.76) iter 61: time=434.91 for 18 active reps approxLL diffs: (0.04,3.80) iter 62: time=433.49 for 18 active reps approxLL diffs: (0.04,5.43) iter 63: time=434.29 for 18 active reps approxLL diffs: (0.04,2.88) iter 64: time=437.13 for 18 active reps approxLL diffs: (0.04,2.50) iter 65: time=430.54 for 18 active reps approxLL diffs: (0.04,4.12) iter 66: time=429.51 for 18 active reps approxLL diffs: (0.05,3.76) iter 67: time=429.40 for 18 active reps approxLL diffs: (0.05,2.00) iter 68: time=438.28 for 18 active reps approxLL diffs: (0.04,3.27) iter 69: time=428.51 for 18 active reps approxLL diffs: (0.04,2.73) iter 70: time=433.03 for 18 active reps approxLL diffs: (0.06,3.32) iter 71: time=420.09 for 18 active reps approxLL diffs: (0.06,4.86) iter 72: time=417.57 for 18 active reps approxLL diffs: (0.06,2.45) iter 73: time=414.70 for 18 active reps approxLL diffs: (0.05,3.45) iter 74: time=415.33 for 18 active reps approxLL diffs: (0.05,2.04) iter 75: time=416.57 for 18 active reps approxLL diffs: (0.05,4.87) iter 76: time=414.34 for 18 active reps approxLL diffs: (0.05,4.89) iter 77: time=416.32 for 18 active reps approxLL diffs: (0.04,2.19) iter 78: time=417.96 for 18 active reps approxLL diffs: (0.04,2.39) iter 79: time=419.89 for 18 active reps approxLL diffs: (0.03,1.46) iter 80: time=423.82 for 18 active reps approxLL diffs: (0.03,1.70) iter 81: time=417.13 for 18 active reps approxLL diffs: (0.03,2.18) iter 82: time=417.37 for 18 active reps approxLL diffs: (0.03,1.02) iter 83: time=416.06 for 18 active reps approxLL diffs: (0.03,2.27) iter 84: time=414.06 for 18 active reps approxLL diffs: (0.03,2.00) iter 85: time=414.94 for 18 active reps approxLL diffs: (0.02,1.64) iter 86: time=415.38 for 18 active reps approxLL diffs: (0.02,0.56) iter 87: time=413.65 for 18 active reps approxLL diffs: (0.02,0.52) iter 88: time=412.83 for 18 active reps approxLL diffs: (0.02,0.67) iter 89: time=416.16 for 18 active reps approxLL diffs: (0.02,0.91) iter 90: time=411.74 for 18 active reps approxLL diffs: (0.01,1.11) iter 91: time=413.35 for 18 active reps approxLL diffs: (0.01,1.15) iter 92: time=396.64 for 17 active reps approxLL diffs: (0.01,1.62) iter 93: time=371.65 for 16 active reps approxLL diffs: (0.01,1.01) iter 94: time=379.11 for 16 active reps approxLL diffs: (0.01,0.82) iter 95: time=378.58 for 16 active reps approxLL diffs: (0.01,1.45) iter 96: time=379.28 for 16 active reps approxLL diffs: (0.01,0.69) iter 97: time=377.38 for 16 active reps approxLL diffs: (0.01,1.29) iter 98: time=379.24 for 16 active reps approxLL diffs: (0.01,1.37) iter 99: time=392.64 for 15 active reps approxLL diffs: (0.01,0.89) iter 100: time=395.65 for 15 active reps approxLL diffs: (0.01,1.35) iter 101: time=364.89 for 14 active reps approxLL diffs: (0.02,0.90) iter 102: time=367.06 for 14 active reps approxLL diffs: (0.02,0.48) iter 103: time=366.94 for 14 active reps approxLL diffs: (0.01,0.61) iter 104: time=365.93 for 14 active reps approxLL diffs: (0.01,1.20) iter 105: time=355.30 for 13 active reps approxLL diffs: (0.01,1.92) iter 106: time=356.72 for 13 active reps approxLL diffs: (0.01,1.59) iter 107: time=357.62 for 13 active reps approxLL diffs: (0.01,2.24) iter 108: time=354.88 for 13 active reps approxLL diffs: (0.01,1.71) iter 109: time=361.29 for 13 active reps approxLL diffs: (0.01,1.60) iter 110: time=358.59 for 13 active reps approxLL diffs: (0.01,0.94) iter 111: time=358.72 for 13 active reps approxLL diffs: (0.01,1.13) iter 112: time=366.93 for 13 active reps approxLL diffs: (0.01,1.24) iter 113: time=364.41 for 11 active reps approxLL diffs: (0.01,1.29) iter 114: time=387.26 for 11 active reps approxLL diffs: (0.01,3.14) iter 115: time=372.95 for 11 active reps approxLL diffs: (0.01,3.46) iter 116: time=387.58 for 11 active reps approxLL diffs: (0.01,1.76) iter 117: time=360.43 for 11 active reps approxLL diffs: (0.01,1.29) iter 118: time=354.27 for 11 active reps approxLL diffs: (0.01,1.65) iter 119: time=329.32 for 10 active reps approxLL diffs: (0.01,1.82) iter 120: time=342.85 for 10 active reps approxLL diffs: (0.01,1.32) iter 121: time=335.82 for 10 active reps approxLL diffs: (0.01,0.92) iter 122: time=341.65 for 10 active reps approxLL diffs: (0.01,0.74) iter 123: time=332.38 for 10 active reps approxLL diffs: (0.01,0.50) iter 124: time=336.22 for 10 active reps approxLL diffs: (0.01,0.37) iter 125: time=316.53 for 9 active reps approxLL diffs: (0.01,0.48) iter 126: time=314.04 for 9 active reps approxLL diffs: (0.01,0.75) iter 127: time=312.12 for 9 active reps approxLL diffs: (0.01,1.13) iter 128: time=309.94 for 9 active reps approxLL diffs: (0.01,1.31) iter 129: time=319.58 for 9 active reps approxLL diffs: (0.01,0.65) iter 130: time=322.95 for 9 active reps approxLL diffs: (0.01,0.75) iter 131: time=333.01 for 9 active reps approxLL diffs: (0.01,1.51) iter 132: time=329.10 for 9 active reps approxLL diffs: (0.01,2.26) iter 133: time=293.70 for 8 active reps approxLL diffs: (0.01,1.10) iter 134: time=286.71 for 8 active reps approxLL diffs: (0.01,0.54) iter 135: time=304.39 for 8 active reps approxLL diffs: (0.01,3.23) iter 136: time=298.38 for 8 active reps approxLL diffs: (0.01,1.85) iter 137: time=287.64 for 8 active reps approxLL diffs: (0.01,0.94) iter 138: time=287.89 for 8 active reps approxLL diffs: (0.01,1.06) iter 139: time=289.46 for 8 active reps approxLL diffs: (0.01,1.24) iter 140: time=289.01 for 8 active reps approxLL diffs: (0.01,0.66) iter 141: time=286.50 for 8 active reps approxLL diffs: (0.01,0.16) iter 142: time=292.23 for 8 active reps approxLL diffs: (0.01,0.23) iter 143: time=286.44 for 8 active reps approxLL diffs: (0.01,0.61) iter 144: time=278.77 for 8 active reps approxLL diffs: (0.01,1.15) iter 145: time=277.27 for 8 active reps approxLL diffs: (0.01,0.67) iter 146: time=278.74 for 8 active reps approxLL diffs: (0.01,0.18) iter 147: time=275.19 for 8 active reps approxLL diffs: (0.01,0.39) iter 148: time=297.73 for 7 active reps approxLL diffs: (0.01,0.49) iter 149: time=294.23 for 7 active reps approxLL diffs: (0.01,0.54) iter 150: time=306.81 for 7 active reps approxLL diffs: (0.01,0.73) iter 151: time=296.33 for 7 active reps approxLL diffs: (0.01,1.03) iter 152: time=294.64 for 7 active reps approxLL diffs: (0.01,0.85) iter 153: time=296.47 for 7 active reps approxLL diffs: (0.01,0.41) iter 154: time=269.05 for 6 active reps approxLL diffs: (0.01,0.64) iter 155: time=271.20 for 6 active reps approxLL diffs: (0.01,0.85) iter 156: time=275.13 for 6 active reps approxLL diffs: (0.01,0.43) iter 157: time=259.09 for 5 active reps approxLL diffs: (0.02,0.45) iter 158: time=262.78 for 5 active reps approxLL diffs: (0.01,0.75) iter 159: time=262.92 for 5 active reps approxLL diffs: (0.01,0.98) iter 160: time=259.89 for 5 active reps approxLL diffs: (0.01,1.51) iter 161: time=244.82 for 4 active reps approxLL diffs: (0.02,2.38) iter 162: time=245.71 for 4 active reps approxLL diffs: (0.01,0.68) iter 163: time=249.89 for 3 active reps approxLL diffs: (0.03,0.21) iter 164: time=257.96 for 3 active reps approxLL diffs: (0.02,0.52) iter 165: time=257.06 for 3 active reps approxLL diffs: (0.02,1.05) iter 166: time=258.87 for 3 active reps approxLL diffs: (0.03,0.70) iter 167: time=250.70 for 3 active reps approxLL diffs: (0.03,0.24) iter 168: time=244.52 for 3 active reps approxLL diffs: (0.05,0.11) iter 169: time=243.65 for 3 active reps approxLL diffs: (0.03,0.18) iter 170: time=243.15 for 3 active reps approxLL diffs: (0.02,0.16) iter 171: time=252.82 for 3 active reps approxLL diffs: (0.02,0.07) iter 172: time=258.60 for 3 active reps approxLL diffs: (0.02,0.07) iter 173: time=246.56 for 3 active reps approxLL diffs: (0.02,0.13) iter 174: time=247.72 for 3 active reps approxLL diffs: (0.03,0.22) iter 175: time=249.87 for 3 active reps approxLL diffs: (0.03,0.23) iter 176: time=258.71 for 3 active reps approxLL diffs: (0.04,0.24) iter 177: time=253.75 for 3 active reps approxLL diffs: (0.05,0.30) iter 178: time=244.47 for 3 active reps approxLL diffs: (0.08,0.14) iter 179: time=245.36 for 3 active reps approxLL diffs: (0.04,0.14) iter 180: time=250.58 for 3 active reps approxLL diffs: (0.02,0.29) iter 181: time=244.92 for 3 active reps approxLL diffs: (0.01,0.75) iter 182: time=242.93 for 3 active reps approxLL diffs: (0.01,1.38) iter 183: time=244.71 for 3 active reps approxLL diffs: (0.02,1.23) iter 184: time=248.22 for 3 active reps approxLL diffs: (0.02,0.39) iter 185: time=249.11 for 3 active reps approxLL diffs: (0.02,0.47) iter 186: time=245.54 for 3 active reps approxLL diffs: (0.02,0.53) iter 187: time=248.98 for 3 active reps approxLL diffs: (0.01,0.62) iter 188: time=229.29 for 2 active reps approxLL diffs: (0.01,0.59) iter 189: time=241.63 for 2 active reps approxLL diffs: (0.01,0.41) iter 190: time=207.20 for 1 active reps approxLL diffs: (0.23,0.23) iter 191: time=217.69 for 1 active reps approxLL diffs: (0.11,0.11) iter 192: time=223.12 for 1 active reps approxLL diffs: (0.05,0.05) iter 193: time=248.64 for 1 active reps approxLL diffs: (0.02,0.02) iter 194: time=274.73 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 194: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 75.3%, memory/overhead = 24.7% Computing predictions on left-out cross-validation fold Time for computing predictions = 8670.92 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.1: 0.437311 f2=0.3, p=0.05: 0.435357 f2=0.5, p=0.05: 0.434663 ... f2=0.5, p=0.5: 0.377858 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.949913 Absolute prediction MSE using standard LMM: 0.590981 Absolute prediction MSE, fold-best f2=0.3, p=0.1: 0.534505 Absolute pred MSE using f2=0.5, p=0.5: 0.590981 Absolute pred MSE using f2=0.5, p=0.2: 0.561486 Absolute pred MSE using f2=0.5, p=0.1: 0.542926 Absolute pred MSE using f2=0.5, p=0.05: 0.537021 Absolute pred MSE using f2=0.5, p=0.02: 0.539089 Absolute pred MSE using f2=0.5, p=0.01: 0.542404 Absolute pred MSE using f2=0.3, p=0.5: 0.579440 Absolute pred MSE using f2=0.3, p=0.2: 0.543939 Absolute pred MSE using f2=0.3, p=0.1: 0.534505 Absolute pred MSE using f2=0.3, p=0.05: 0.536362 Absolute pred MSE using f2=0.3, p=0.02: 0.541397 Absolute pred MSE using f2=0.3, p=0.01: 0.544558 Absolute pred MSE using f2=0.1, p=0.5: 0.561517 Absolute pred MSE using f2=0.1, p=0.2: 0.537118 Absolute pred MSE using f2=0.1, p=0.1: 0.542608 Absolute pred MSE using f2=0.1, p=0.05: 0.552612 Absolute pred MSE using f2=0.1, p=0.02: 0.561815 Absolute pred MSE using f2=0.1, p=0.01: 0.564124 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.378 Relative improvement in prediction MSE using non-inf model: 0.096 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.1 Time for estimating mixture parameters = 84297.6 sec === Computing Bayesian mixed model assoc stats with mixture prior === Assigning SNPs to 23 chunks for leave-out analysis Each chunk is excluded when testing SNPs belonging to the chunk Beginning variational Bayes iter 1: time=722.57 for 23 active reps iter 2: time=526.58 for 23 active reps approxLL diffs: (82019.32,87238.37) iter 3: time=523.57 for 23 active reps approxLL diffs: (17838.92,18805.36) iter 4: time=491.00 for 23 active reps approxLL diffs: (6255.21,6627.67) iter 5: time=490.52 for 23 active reps approxLL diffs: (2912.08,3086.47) iter 6: time=479.67 for 23 active reps approxLL diffs: (1594.27,1692.86) iter 7: time=474.01 for 23 active reps approxLL diffs: (946.73,1011.13) iter 8: time=465.17 for 23 active reps approxLL diffs: (598.27,648.92) iter 9: time=477.39 for 23 active reps approxLL diffs: (402.29,438.37) iter 10: time=472.03 for 23 active reps approxLL diffs: (276.73,308.27) iter 11: time=464.38 for 23 active reps approxLL diffs: (201.01,225.00) iter 12: time=487.63 for 23 active reps approxLL diffs: (151.93,172.42) iter 13: time=478.04 for 23 active reps approxLL diffs: (111.53,131.79) iter 14: time=481.58 for 23 active reps approxLL diffs: (83.07,102.98) iter 15: time=484.25 for 23 active reps approxLL diffs: (66.01,84.91) iter 16: time=487.29 for 23 active reps approxLL diffs: (54.63,70.03) iter 17: time=497.14 for 23 active reps approxLL diffs: (45.83,58.91) iter 18: time=477.32 for 23 active reps approxLL diffs: (38.63,48.17) iter 19: time=477.72 for 23 active reps approxLL diffs: (33.74,43.03) iter 20: time=482.95 for 23 active reps approxLL diffs: (29.37,37.50) iter 21: time=483.65 for 23 active reps approxLL diffs: (25.74,31.69) iter 22: time=485.40 for 23 active reps approxLL diffs: (22.83,29.04) iter 23: time=483.40 for 23 active reps approxLL diffs: (19.99,26.34) iter 24: time=483.20 for 23 active reps approxLL diffs: (16.88,21.66) iter 25: time=483.96 for 23 active reps approxLL diffs: (15.03,19.61) iter 26: time=506.85 for 23 active reps approxLL diffs: (12.79,16.67) iter 27: time=522.00 for 23 active reps approxLL diffs: (9.80,15.92) iter 28: time=540.71 for 23 active reps approxLL diffs: (7.85,14.66) iter 29: time=493.12 for 23 active reps approxLL diffs: (6.96,11.44) iter 30: time=484.25 for 23 active reps approxLL diffs: (6.54,9.58) iter 31: time=516.77 for 23 active reps approxLL diffs: (4.95,9.17) iter 32: time=480.57 for 23 active reps approxLL diffs: (4.19,8.53) iter 33: time=474.93 for 23 active reps approxLL diffs: (3.93,7.59) iter 34: time=491.61 for 23 active reps approxLL diffs: (3.57,6.88) iter 35: time=479.41 for 23 active reps approxLL diffs: (2.96,6.00) iter 36: time=468.33 for 23 active reps approxLL diffs: (2.36,5.37) iter 37: time=480.84 for 23 active reps approxLL diffs: (2.19,5.21) iter 38: time=485.45 for 23 active reps approxLL diffs: (2.16,4.49) iter 39: time=473.05 for 23 active reps approxLL diffs: (2.26,4.56) iter 40: time=473.97 for 23 active reps approxLL diffs: (1.93,4.85) iter 41: time=495.46 for 23 active reps approxLL diffs: (1.55,4.30) iter 42: time=482.49 for 23 active reps approxLL diffs: (1.36,4.37) iter 43: time=488.33 for 23 active reps approxLL diffs: (1.20,3.45) iter 44: time=481.08 for 23 active reps approxLL diffs: (1.02,2.87) iter 45: time=479.89 for 23 active reps approxLL diffs: (0.93,2.97) iter 46: time=492.23 for 23 active reps approxLL diffs: (0.99,3.09) iter 47: time=475.73 for 23 active reps approxLL diffs: (0.97,2.94) iter 48: time=466.18 for 23 active reps approxLL diffs: (0.77,2.61) iter 49: time=474.25 for 23 active reps approxLL diffs: (0.70,2.77) iter 50: time=477.04 for 23 active reps approxLL diffs: (0.65,2.96) iter 51: time=480.30 for 23 active reps approxLL diffs: (0.52,2.34) iter 52: time=473.05 for 23 active reps approxLL diffs: (0.40,2.27) iter 53: time=486.15 for 23 active reps approxLL diffs: (0.37,2.18) iter 54: time=488.54 for 23 active reps approxLL diffs: (0.35,1.75) iter 55: time=474.79 for 23 active reps approxLL diffs: (0.29,1.39) iter 56: time=475.73 for 23 active reps approxLL diffs: (0.26,1.34) iter 57: time=479.81 for 23 active reps approxLL diffs: (0.28,1.27) iter 58: time=462.48 for 23 active reps approxLL diffs: (0.32,1.42) iter 59: time=458.54 for 23 active reps approxLL diffs: (0.35,1.59) iter 60: time=454.53 for 23 active reps approxLL diffs: (0.29,1.54) iter 61: time=465.36 for 23 active reps approxLL diffs: (0.23,1.35) iter 62: time=477.56 for 23 active reps approxLL diffs: (0.19,1.24) iter 63: time=486.39 for 23 active reps approxLL diffs: (0.17,1.56) iter 64: time=484.23 for 23 active reps approxLL diffs: (0.15,1.91) iter 65: time=479.64 for 23 active reps approxLL diffs: (0.12,2.10) iter 66: time=477.73 for 23 active reps approxLL diffs: (0.09,1.97) iter 67: time=476.97 for 23 active reps approxLL diffs: (0.07,1.62) iter 68: time=475.74 for 23 active reps approxLL diffs: (0.07,1.38) iter 69: time=476.79 for 23 active reps approxLL diffs: (0.06,1.28) iter 70: time=485.43 for 23 active reps approxLL diffs: (0.06,1.41) iter 71: time=477.32 for 23 active reps approxLL diffs: (0.06,1.61) iter 72: time=467.11 for 23 active reps approxLL diffs: (0.06,0.86) iter 73: time=471.43 for 23 active reps approxLL diffs: (0.06,0.79) iter 74: time=478.46 for 23 active reps approxLL diffs: (0.06,0.64) iter 75: time=478.43 for 23 active reps approxLL diffs: (0.06,0.70) iter 76: time=472.31 for 23 active reps approxLL diffs: (0.06,1.08) iter 77: time=485.31 for 23 active reps approxLL diffs: (0.06,0.84) iter 78: time=512.53 for 23 active reps approxLL diffs: (0.06,0.97) iter 79: time=516.87 for 23 active reps approxLL diffs: (0.07,0.91) iter 80: time=500.19 for 23 active reps approxLL diffs: (0.05,0.86) iter 81: time=493.47 for 23 active reps approxLL diffs: (0.04,1.45) iter 82: time=503.43 for 23 active reps approxLL diffs: (0.04,0.77) iter 83: time=515.29 for 23 active reps approxLL diffs: (0.04,0.62) iter 84: time=485.94 for 23 active reps approxLL diffs: (0.05,0.48) iter 85: time=459.96 for 23 active reps approxLL diffs: (0.04,0.36) iter 86: time=477.92 for 23 active reps approxLL diffs: (0.03,0.39) iter 87: time=472.29 for 23 active reps approxLL diffs: (0.03,0.42) iter 88: time=480.28 for 23 active reps approxLL diffs: (0.03,0.53) iter 89: time=504.51 for 23 active reps approxLL diffs: (0.03,0.48) iter 90: time=500.21 for 23 active reps approxLL diffs: (0.03,0.42) iter 91: time=490.42 for 23 active reps approxLL diffs: (0.02,0.46) iter 92: time=486.50 for 23 active reps approxLL diffs: (0.02,0.53) iter 93: time=466.37 for 23 active reps approxLL diffs: (0.01,0.67) iter 94: time=469.64 for 23 active reps approxLL diffs: (0.01,1.02) iter 95: time=475.87 for 23 active reps approxLL diffs: (0.01,1.08) iter 96: time=477.57 for 23 active reps approxLL diffs: (0.01,0.77) iter 97: time=479.06 for 23 active reps approxLL diffs: (0.01,0.54) iter 98: time=473.40 for 23 active reps approxLL diffs: (0.01,0.66) iter 99: time=469.58 for 23 active reps approxLL diffs: (0.01,0.60) iter 100: time=465.81 for 23 active reps approxLL diffs: (0.01,0.43) iter 101: time=457.85 for 23 active reps approxLL diffs: (0.01,0.33) iter 102: time=460.41 for 23 active reps approxLL diffs: (0.01,0.30) iter 103: time=484.62 for 23 active reps approxLL diffs: (0.01,0.44) iter 104: time=484.66 for 23 active reps approxLL diffs: (0.01,0.33) iter 105: time=478.10 for 23 active reps approxLL diffs: (0.01,0.28) iter 106: time=456.79 for 22 active reps approxLL diffs: (0.01,0.36) iter 107: time=441.58 for 21 active reps approxLL diffs: (0.01,0.47) iter 108: time=414.35 for 20 active reps approxLL diffs: (0.01,0.48) iter 109: time=415.49 for 20 active reps approxLL diffs: (0.01,0.35) iter 110: time=419.15 for 20 active reps approxLL diffs: (0.01,0.39) iter 111: time=405.06 for 20 active reps approxLL diffs: (0.01,0.59) iter 112: time=436.77 for 19 active reps approxLL diffs: (0.01,0.57) iter 113: time=435.32 for 19 active reps approxLL diffs: (0.01,0.48) iter 114: time=425.93 for 18 active reps approxLL diffs: (0.02,0.59) iter 115: time=427.44 for 18 active reps approxLL diffs: (0.01,0.48) iter 116: time=425.44 for 18 active reps approxLL diffs: (0.01,0.48) iter 117: time=408.84 for 17 active reps approxLL diffs: (0.01,0.67) iter 118: time=413.36 for 17 active reps approxLL diffs: (0.01,0.60) iter 119: time=412.60 for 17 active reps approxLL diffs: (0.01,0.68) iter 120: time=410.05 for 17 active reps approxLL diffs: (0.01,0.52) iter 121: time=406.87 for 15 active reps approxLL diffs: (0.01,0.40) iter 122: time=379.42 for 14 active reps approxLL diffs: (0.01,0.37) iter 123: time=374.64 for 14 active reps approxLL diffs: (0.01,0.44) iter 124: time=357.42 for 13 active reps approxLL diffs: (0.01,0.38) iter 125: time=350.80 for 13 active reps approxLL diffs: (0.01,0.19) iter 126: time=355.11 for 11 active reps approxLL diffs: (0.02,0.25) iter 127: time=341.73 for 11 active reps approxLL diffs: (0.02,0.32) iter 128: time=340.10 for 11 active reps approxLL diffs: (0.01,0.53) iter 129: time=320.24 for 10 active reps approxLL diffs: (0.01,0.67) iter 130: time=322.90 for 10 active reps approxLL diffs: (0.01,0.48) iter 131: time=340.79 for 10 active reps approxLL diffs: (0.01,0.64) iter 132: time=327.40 for 10 active reps approxLL diffs: (0.01,0.49) iter 133: time=323.43 for 10 active reps approxLL diffs: (0.01,0.31) iter 134: time=326.77 for 10 active reps approxLL diffs: (0.01,0.28) iter 135: time=330.30 for 10 active reps approxLL diffs: (0.01,0.26) iter 136: time=322.89 for 9 active reps approxLL diffs: (0.01,0.21) iter 137: time=328.27 for 9 active reps approxLL diffs: (0.01,0.15) iter 138: time=327.29 for 7 active reps approxLL diffs: (0.03,0.11) iter 139: time=297.85 for 7 active reps approxLL diffs: (0.02,0.07) iter 140: time=291.04 for 7 active reps approxLL diffs: (0.02,0.07) iter 141: time=292.00 for 7 active reps approxLL diffs: (0.01,0.08) iter 142: time=296.23 for 7 active reps approxLL diffs: (0.01,0.09) iter 143: time=248.80 for 5 active reps approxLL diffs: (0.03,0.14) iter 144: time=252.87 for 5 active reps approxLL diffs: (0.04,0.21) iter 145: time=250.34 for 5 active reps approxLL diffs: (0.04,0.24) iter 146: time=253.40 for 5 active reps approxLL diffs: (0.03,0.21) iter 147: time=253.57 for 5 active reps approxLL diffs: (0.02,0.38) iter 148: time=268.21 for 5 active reps approxLL diffs: (0.01,0.52) iter 149: time=269.59 for 5 active reps approxLL diffs: (0.01,0.37) iter 150: time=244.10 for 4 active reps approxLL diffs: (0.07,0.15) iter 151: time=242.22 for 4 active reps approxLL diffs: (0.04,0.16) iter 152: time=241.75 for 4 active reps approxLL diffs: (0.02,0.24) iter 153: time=251.36 for 4 active reps approxLL diffs: (0.01,0.46) iter 154: time=256.46 for 4 active reps approxLL diffs: (0.01,0.60) iter 155: time=243.05 for 4 active reps approxLL diffs: (0.02,0.24) iter 156: time=238.79 for 4 active reps approxLL diffs: (0.02,0.21) iter 157: time=237.25 for 4 active reps approxLL diffs: (0.01,0.09) iter 158: time=259.22 for 3 active reps approxLL diffs: (0.02,0.05) iter 159: time=264.04 for 3 active reps approxLL diffs: (0.01,0.09) iter 160: time=251.92 for 3 active reps approxLL diffs: (0.01,0.12) iter 161: time=248.29 for 3 active reps approxLL diffs: (0.01,0.12) iter 162: time=252.12 for 3 active reps approxLL diffs: (0.01,0.09) iter 163: time=260.51 for 3 active reps approxLL diffs: (0.01,0.06) iter 164: time=270.78 for 3 active reps approxLL diffs: (0.02,0.04) iter 165: time=250.60 for 3 active reps approxLL diffs: (0.02,0.04) iter 166: time=255.18 for 3 active reps approxLL diffs: (0.01,0.07) iter 167: time=251.73 for 3 active reps approxLL diffs: (0.01,0.11) iter 168: time=242.98 for 2 active reps approxLL diffs: (0.12,0.14) iter 169: time=245.92 for 2 active reps approxLL diffs: (0.11,0.12) iter 170: time=231.91 for 2 active reps approxLL diffs: (0.05,0.10) iter 171: time=233.56 for 2 active reps approxLL diffs: (0.02,0.07) iter 172: time=236.75 for 2 active reps approxLL diffs: (0.01,0.06) iter 173: time=240.96 for 2 active reps approxLL diffs: (0.01,0.05) iter 174: time=249.37 for 2 active reps approxLL diffs: (0.02,0.04) iter 175: time=233.67 for 2 active reps approxLL diffs: (0.02,0.03) iter 176: time=228.48 for 2 active reps approxLL diffs: (0.01,0.04) iter 177: time=228.16 for 2 active reps approxLL diffs: (0.01,0.05) iter 178: time=193.22 for 1 active reps approxLL diffs: (0.05,0.05) iter 179: time=195.97 for 1 active reps approxLL diffs: (0.05,0.05) iter 180: time=199.71 for 1 active reps approxLL diffs: (0.04,0.04) iter 181: time=189.84 for 1 active reps approxLL diffs: (0.05,0.05) iter 182: time=203.78 for 1 active reps approxLL diffs: (0.06,0.06) iter 183: time=212.42 for 1 active reps approxLL diffs: (0.06,0.06) iter 184: time=217.07 for 1 active reps approxLL diffs: (0.05,0.05) iter 185: time=208.61 for 1 active reps approxLL diffs: (0.03,0.03) iter 186: time=226.13 for 1 active reps approxLL diffs: (0.02,0.02) iter 187: time=245.85 for 1 active reps approxLL diffs: (0.01,0.01) iter 188: time=233.96 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 188: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 77.6%, memory/overhead = 22.4% Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 583483/709999 Masking windows around outlier snps (chisq > 458.3) # of SNPs remaining after outlier window removal: 562181/583483 Intercept of LD Score regression for ref stats: 1.498 (0.037) Estimated attenuation: 0.164 (0.011) Intercept of LD Score regression for cur stats: 1.491 (0.038) Calibration factor (ref/cur) to multiply by: 1.005 (0.003) Time for computing Bayesian mixed model assoc stats = 76089.1 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=436.61 for 1 active reps iter 2: time=233.12 for 1 active reps approxLL diffs: (87685.50,87685.50) iter 3: time=238.30 for 1 active reps approxLL diffs: (18853.22,18853.22) iter 4: time=234.96 for 1 active reps approxLL diffs: (6628.46,6628.46) iter 5: time=232.53 for 1 active reps approxLL diffs: (3083.80,3083.80) iter 6: time=229.00 for 1 active reps approxLL diffs: (1697.41,1697.41) iter 7: time=221.81 for 1 active reps approxLL diffs: (1005.84,1005.84) iter 8: time=223.10 for 1 active reps approxLL diffs: (642.12,642.12) iter 9: time=228.54 for 1 active reps approxLL diffs: (429.55,429.55) iter 10: time=238.74 for 1 active reps approxLL diffs: (294.12,294.12) iter 11: time=232.50 for 1 active reps approxLL diffs: (209.54,209.54) iter 12: time=230.86 for 1 active reps approxLL diffs: (158.80,158.80) iter 13: time=236.12 for 1 active reps approxLL diffs: (122.83,122.83) iter 14: time=242.31 for 1 active reps approxLL diffs: (96.56,96.56) iter 15: time=238.91 for 1 active reps approxLL diffs: (79.76,79.76) iter 16: time=233.66 for 1 active reps approxLL diffs: (69.24,69.24) iter 17: time=229.38 for 1 active reps approxLL diffs: (58.26,58.26) iter 18: time=235.07 for 1 active reps approxLL diffs: (50.25,50.25) iter 19: time=238.03 for 1 active reps approxLL diffs: (43.34,43.34) iter 20: time=230.98 for 1 active reps approxLL diffs: (37.21,37.21) iter 21: time=221.56 for 1 active reps approxLL diffs: (31.19,31.19) iter 22: time=220.03 for 1 active reps approxLL diffs: (26.62,26.62) iter 23: time=212.48 for 1 active reps approxLL diffs: (23.00,23.00) iter 24: time=231.72 for 1 active reps approxLL diffs: (19.77,19.77) iter 25: time=227.53 for 1 active reps approxLL diffs: (16.88,16.88) iter 26: time=223.97 for 1 active reps approxLL diffs: (14.18,14.18) iter 27: time=218.12 for 1 active reps approxLL diffs: (11.89,11.89) iter 28: time=224.95 for 1 active reps approxLL diffs: (10.68,10.68) iter 29: time=244.98 for 1 active reps approxLL diffs: (9.44,9.44) iter 30: time=234.17 for 1 active reps approxLL diffs: (8.60,8.60) iter 31: time=227.82 for 1 active reps approxLL diffs: (7.78,7.78) iter 32: time=224.04 for 1 active reps approxLL diffs: (6.33,6.33) iter 33: time=227.38 for 1 active reps approxLL diffs: (5.59,5.59) iter 34: time=241.89 for 1 active reps approxLL diffs: (5.41,5.41) iter 35: time=249.94 for 1 active reps approxLL diffs: (4.83,4.83) iter 36: time=237.06 for 1 active reps approxLL diffs: (4.08,4.08) iter 37: time=223.92 for 1 active reps approxLL diffs: (3.89,3.89) iter 38: time=236.00 for 1 active reps approxLL diffs: (3.84,3.84) iter 39: time=234.80 for 1 active reps approxLL diffs: (3.74,3.74) iter 40: time=234.63 for 1 active reps approxLL diffs: (3.86,3.86) iter 41: time=242.97 for 1 active reps approxLL diffs: (3.84,3.84) iter 42: time=238.71 for 1 active reps approxLL diffs: (2.85,2.85) iter 43: time=233.56 for 1 active reps approxLL diffs: (2.20,2.20) iter 44: time=229.49 for 1 active reps approxLL diffs: (1.93,1.93) iter 45: time=223.44 for 1 active reps approxLL diffs: (1.47,1.47) iter 46: time=235.49 for 1 active reps approxLL diffs: (1.24,1.24) iter 47: time=239.14 for 1 active reps approxLL diffs: (1.22,1.22) iter 48: time=240.55 for 1 active reps approxLL diffs: (0.97,0.97) iter 49: time=224.52 for 1 active reps approxLL diffs: (0.56,0.56) iter 50: time=223.65 for 1 active reps approxLL diffs: (0.38,0.38) iter 51: time=223.70 for 1 active reps approxLL diffs: (0.33,0.33) iter 52: time=233.34 for 1 active reps approxLL diffs: (0.31,0.31) iter 53: time=240.66 for 1 active reps approxLL diffs: (0.29,0.29) iter 54: time=244.43 for 1 active reps approxLL diffs: (0.26,0.26) iter 55: time=237.26 for 1 active reps approxLL diffs: (0.24,0.24) iter 56: time=233.73 for 1 active reps approxLL diffs: (0.25,0.25) iter 57: time=227.06 for 1 active reps approxLL diffs: (0.28,0.28) iter 58: time=235.73 for 1 active reps approxLL diffs: (0.35,0.35) iter 59: time=227.15 for 1 active reps approxLL diffs: (0.41,0.41) iter 60: time=227.80 for 1 active reps approxLL diffs: (0.43,0.43) iter 61: time=223.65 for 1 active reps approxLL diffs: (0.37,0.37) iter 62: time=225.59 for 1 active reps approxLL diffs: (0.29,0.29) iter 63: time=228.88 for 1 active reps approxLL diffs: (0.21,0.21) iter 64: time=224.89 for 1 active reps approxLL diffs: (0.16,0.16) iter 65: time=230.20 for 1 active reps approxLL diffs: (0.15,0.15) iter 66: time=228.04 for 1 active reps approxLL diffs: (0.19,0.19) iter 67: time=229.92 for 1 active reps approxLL diffs: (0.31,0.31) iter 68: time=238.02 for 1 active reps approxLL diffs: (0.45,0.45) iter 69: time=238.83 for 1 active reps approxLL diffs: (0.45,0.45) iter 70: time=234.49 for 1 active reps approxLL diffs: (0.45,0.45) iter 71: time=236.24 for 1 active reps approxLL diffs: (0.49,0.49) iter 72: time=229.12 for 1 active reps approxLL diffs: (0.46,0.46) iter 73: time=229.72 for 1 active reps approxLL diffs: (0.39,0.39) iter 74: time=227.35 for 1 active reps approxLL diffs: (0.34,0.34) iter 75: time=223.97 for 1 active reps approxLL diffs: (0.33,0.33) iter 76: time=229.44 for 1 active reps approxLL diffs: (0.39,0.39) iter 77: time=228.11 for 1 active reps approxLL diffs: (0.54,0.54) iter 78: time=224.78 for 1 active reps approxLL diffs: (0.67,0.67) iter 79: time=197.98 for 1 active reps approxLL diffs: (0.52,0.52) iter 80: time=200.72 for 1 active reps approxLL diffs: (0.26,0.26) iter 81: time=225.37 for 1 active reps approxLL diffs: (0.13,0.13) iter 82: time=239.98 for 1 active reps approxLL diffs: (0.08,0.08) iter 83: time=241.46 for 1 active reps approxLL diffs: (0.05,0.05) iter 84: time=234.34 for 1 active reps approxLL diffs: (0.04,0.04) iter 85: time=212.23 for 1 active reps approxLL diffs: (0.04,0.04) iter 86: time=219.33 for 1 active reps approxLL diffs: (0.06,0.06) iter 87: time=214.07 for 1 active reps approxLL diffs: (0.11,0.11) iter 88: time=212.86 for 1 active reps approxLL diffs: (0.30,0.30) iter 89: time=207.82 for 1 active reps approxLL diffs: (0.66,0.66) iter 90: time=230.86 for 1 active reps approxLL diffs: (0.61,0.61) iter 91: time=215.42 for 1 active reps approxLL diffs: (0.31,0.31) iter 92: time=210.61 for 1 active reps approxLL diffs: (0.19,0.19) iter 93: time=219.42 for 1 active reps approxLL diffs: (0.19,0.19) iter 94: time=223.19 for 1 active reps approxLL diffs: (0.17,0.17) iter 95: time=234.28 for 1 active reps approxLL diffs: (0.11,0.11) iter 96: time=273.41 for 1 active reps approxLL diffs: (0.07,0.07) iter 97: time=224.89 for 1 active reps approxLL diffs: (0.06,0.06) iter 98: time=212.13 for 1 active reps approxLL diffs: (0.06,0.06) iter 99: time=204.92 for 1 active reps approxLL diffs: (0.05,0.05) iter 100: time=208.62 for 1 active reps approxLL diffs: (0.04,0.04) iter 101: time=201.34 for 1 active reps approxLL diffs: (0.04,0.04) iter 102: time=211.86 for 1 active reps approxLL diffs: (0.04,0.04) iter 103: time=206.29 for 1 active reps approxLL diffs: (0.05,0.05) iter 104: time=205.59 for 1 active reps approxLL diffs: (0.06,0.06) iter 105: time=204.18 for 1 active reps approxLL diffs: (0.06,0.06) iter 106: time=206.83 for 1 active reps approxLL diffs: (0.04,0.04) iter 107: time=206.71 for 1 active reps approxLL diffs: (0.03,0.03) iter 108: time=210.70 for 1 active reps approxLL diffs: (0.02,0.02) iter 109: time=224.18 for 1 active reps approxLL diffs: (0.03,0.03) iter 110: time=213.46 for 1 active reps approxLL diffs: (0.05,0.05) iter 111: time=215.12 for 1 active reps approxLL diffs: (0.10,0.10) iter 112: time=211.73 for 1 active reps approxLL diffs: (0.16,0.16) iter 113: time=219.16 for 1 active reps approxLL diffs: (0.19,0.19) iter 114: time=209.09 for 1 active reps approxLL diffs: (0.16,0.16) iter 115: time=221.59 for 1 active reps approxLL diffs: (0.11,0.11) iter 116: time=237.74 for 1 active reps approxLL diffs: (0.07,0.07) iter 117: time=232.81 for 1 active reps approxLL diffs: (0.04,0.04) iter 118: time=216.27 for 1 active reps approxLL diffs: (0.03,0.03) iter 119: time=215.65 for 1 active reps approxLL diffs: (0.02,0.02) iter 120: time=215.89 for 1 active reps approxLL diffs: (0.01,0.01) iter 121: time=214.78 for 1 active reps approxLL diffs: (0.01,0.01) iter 122: time=242.13 for 1 active reps approxLL diffs: (0.01,0.01) iter 123: time=211.51 for 1 active reps approxLL diffs: (0.02,0.02) iter 124: time=208.65 for 1 active reps approxLL diffs: (0.04,0.04) iter 125: time=209.26 for 1 active reps approxLL diffs: (0.10,0.10) iter 126: time=206.86 for 1 active reps approxLL diffs: (0.23,0.23) iter 127: time=210.05 for 1 active reps approxLL diffs: (0.39,0.39) iter 128: time=218.49 for 1 active reps approxLL diffs: (0.35,0.35) iter 129: time=262.30 for 1 active reps approxLL diffs: (0.19,0.19) iter 130: time=214.06 for 1 active reps approxLL diffs: (0.08,0.08) iter 131: time=210.14 for 1 active reps approxLL diffs: (0.03,0.03) iter 132: time=217.56 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 132: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 54.4%, memory/overhead = 45.6% Time for computing and writing betas = 30037 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 3.77507 (709970 good SNPs) lambdaGC: 2.08701 Mean BOLT_LMM_INF: 4.58967 (709970 good SNPs) lambdaGC: 2.20064 Mean BOLT_LMM: 4.75375 (709970 good SNPs) lambdaGC: 2.2119 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 6973.01 sec Total elapsed time for analysis = 268861 sec Command being timed: "/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3.1/bolt --bed=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bed --bim=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bim --fam=/n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam --allowX --remove=/n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt --remove=/n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt --exclude=/n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe1000.txt --exclude=/n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt --exclude=/n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt --maxMissingPerSnp=0.15 --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v3.061518.tab --phenoCol=body_HEIGHTz --covarFile=/n/groups/price/UKBiobank/app10438assoc/ukb4777.processed_and_post.plinkPCs.tab.gz --covarCol=cov_ASSESS_CENTER --covarCol=cov_GENO_ARRAY --covarCol=cov_SEX --covarMaxLevels=30 --qCovarCol=cov_AGE --qCovarCol=cov_AGE_SQ --qCovarCol=PC{1:20} --LDscoresFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/LDSCORE.1000G_EUR.tab.gz --geneticMapFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz --lmmForceNonInf --numThreads=8 --predBetasFile=bolt_460K_selfRepWhite.body_HEIGHTz.predbetas.txt.gz --statsFile=bolt_460K_selfRepWhite.body_HEIGHTz.stats.gz --verboseStats" User time (seconds): 2028084.26 System time (seconds): 27666.51 Percent of CPU this job got: 764% Elapsed (wall clock) time (h:mm:ss or m:ss): 74:41:45 Average shared text size (kbytes): 0 Average unshared data size (kbytes): 0 Average stack size (kbytes): 0 Average total size (kbytes): 0 Maximum resident set size (kbytes): 86957944 Average resident set size (kbytes): 0 Major (requiring I/O) page faults: 89844 Minor (reclaiming a frame) page faults: 4672144066 Voluntary context switches: 3864052 Involuntary context switches: 24589075 Swaps: 0 File system inputs: 385479736 File system outputs: 93616 Socket messages sent: 0 Socket messages received: 0 Signals delivered: 0 Page size (bytes): 4096 Exit status: 0