+-----------------------------+ | ___ | | 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.hwe200.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 \ --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v2.092517.tab \ --phenoCol=blood_WHITE_COUNT \ --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.blood_WHITE_COUNT.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_WHITE_COUNT.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.hwe200.txt Excluded 73451 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) WARNING: 342 SNP(s) not found in data set Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt 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: 705881 SNPs to include in model (i.e., GRM) 0 additional non-GRM SNPs loaded 98589 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 705881 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 = 705881 Variance component 1: 705881 post-QC SNPs (name: 'modelSnps') Time for SnpData setup = 4147.57 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_v2.092517.tab Number of indivs with no missing phenotype(s) to use: 444502 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 = 444502 Singular values of covariate matrix: S[0] = 2.27036e+06 S[1] = 4763.71 S[2] = 469.698 S[3] = 293.435 S[4] = 199.857 S[5] = 194.378 S[6] = 183.58 S[7] = 175.073 S[8] = 170.781 S[9] = 165.485 S[10] = 162.747 S[11] = 153.74 S[12] = 145.747 S[13] = 143.687 S[14] = 140.963 S[15] = 135.802 S[16] = 132.442 S[17] = 130.163 S[18] = 126.136 S[19] = 116.282 S[20] = 112.109 S[21] = 99.8137 S[22] = 44.8379 S[23] = 23.9597 S[24] = 19.3122 S[25] = 0.983818 S[26] = 0.983571 S[27] = 0.983559 S[28] = 0.983404 S[29] = 0.983193 S[30] = 0.983031 S[31] = 0.982932 S[32] = 0.982825 S[33] = 0.982802 S[34] = 0.982593 S[35] = 0.982536 S[36] = 0.98242 S[37] = 0.982249 S[38] = 0.982187 S[39] = 0.981892 S[40] = 0.98179 S[41] = 0.981687 S[42] = 0.981473 S[43] = 0.964586 S[44] = 0.888073 S[45] = 4.50617e-12 S[46] = 4.64614e-13 S[47] = 1.1724e-13 S[48] = -0 Total covariate vectors: C = 49 Total independent covariate vectors: Cindep = 45 === Initializing Bolt object: projecting and normalizing SNPs === Number of chroms with >= 1 good SNP: 23 Average norm of projected SNPs: 441912.656512 Dimension of all-1s proj space (Nused-1): 444501 Time for covariate data setup + Bolt initialization = 4038.87 sec Phenotype 1: N = 444502 mean = 0.00704836 std = 0.993455 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 435.544 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 444502) Estimating MC scaling f_REML at log(delta) = 1.09287, h2 = 0.25... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=326.83 rNorms/orig: (0.6,0.7) res2s: 719522..151219 iter 2: time=366.83 rNorms/orig: (0.5,0.6) res2s: 853438..194189 iter 3: time=351.93 rNorms/orig: (0.3,0.4) res2s: 986454..228003 iter 4: time=364.78 rNorms/orig: (0.2,0.2) res2s: 1.03741e+06..243448 iter 5: time=344.47 rNorms/orig: (0.1,0.1) res2s: 1.06369e+06..250953 iter 6: time=343.69 rNorms/orig: (0.09,0.1) res2s: 1.07597e+06..254407 iter 7: time=334.56 rNorms/orig: (0.06,0.06) res2s: 1.08204e+06..256341 iter 8: time=306.72 rNorms/orig: (0.04,0.04) res2s: 1.08496e+06..257190 iter 9: time=309.71 rNorms/orig: (0.02,0.03) res2s: 1.08636e+06..257536 iter 10: time=308.14 rNorms/orig: (0.02,0.02) res2s: 1.08704e+06..257711 iter 11: time=292.95 rNorms/orig: (0.01,0.01) res2s: 1.08735e+06..257780 iter 12: time=309.63 rNorms/orig: (0.007,0.007) res2s: 1.08749e+06..257817 iter 13: time=332.17 rNorms/orig: (0.004,0.005) res2s: 1.08755e+06..257830 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 49.8%, memory/overhead = 50.2% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.0375648 Estimating MC scaling f_REML at log(delta) = -0.00574106, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=306.19 rNorms/orig: (1,1) res2s: 76054.5..45651.7 iter 2: time=325.40 rNorms/orig: (1,1) res2s: 109193..72059.3 iter 3: time=303.00 rNorms/orig: (0.8,0.9) res2s: 164239..104765 iter 4: time=312.94 rNorms/orig: (0.6,0.7) res2s: 202752..128019 iter 5: time=310.93 rNorms/orig: (0.5,0.5) res2s: 231307..144543 iter 6: time=296.78 rNorms/orig: (0.4,0.4) res2s: 251564..155377 iter 7: time=314.97 rNorms/orig: (0.3,0.3) res2s: 266027..164116 iter 8: time=290.44 rNorms/orig: (0.2,0.3) res2s: 276026..169755 iter 9: time=302.06 rNorms/orig: (0.2,0.2) res2s: 282867..173073 iter 10: time=304.83 rNorms/orig: (0.1,0.2) res2s: 287739..175497 iter 11: time=307.15 rNorms/orig: (0.1,0.1) res2s: 290868..176868 iter 12: time=295.05 rNorms/orig: (0.09,0.09) res2s: 292992..177907 iter 13: time=297.23 rNorms/orig: (0.07,0.07) res2s: 294190..178452 iter 14: time=293.45 rNorms/orig: (0.05,0.06) res2s: 295077..178863 iter 15: time=293.96 rNorms/orig: (0.04,0.04) res2s: 295617..179129 iter 16: time=295.89 rNorms/orig: (0.03,0.03) res2s: 295960..179287 iter 17: time=295.20 rNorms/orig: (0.02,0.03) res2s: 296151..179409 iter 18: time=293.27 rNorms/orig: (0.02,0.02) res2s: 296296..179477 iter 19: time=291.98 rNorms/orig: (0.01,0.02) res2s: 296390..179520 iter 20: time=287.68 rNorms/orig: (0.01,0.01) res2s: 296443..179549 iter 21: time=287.40 rNorms/orig: (0.009,0.009) res2s: 296476..179566 iter 22: time=287.97 rNorms/orig: (0.007,0.007) res2s: 296498..179577 iter 23: time=289.02 rNorms/orig: (0.005,0.006) res2s: 296512..179584 iter 24: time=285.53 rNorms/orig: (0.004,0.004) res2s: 296520..179589 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 51.4%, memory/overhead = 48.6% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.276152 Estimating MC scaling f_REML at log(delta) = 0.961322, h2 = 0.275466... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=291.19 rNorms/orig: (0.7,0.8) res2s: 572010..135184 iter 2: time=288.52 rNorms/orig: (0.6,0.7) res2s: 693952..177778 iter 3: time=287.14 rNorms/orig: (0.4,0.4) res2s: 823812..213358 iter 4: time=289.70 rNorms/orig: (0.2,0.3) res2s: 877422..230591 iter 5: time=291.11 rNorms/orig: (0.2,0.2) res2s: 906305..239414 iter 6: time=292.92 rNorms/orig: (0.1,0.1) res2s: 920608..243685 iter 7: time=290.97 rNorms/orig: (0.07,0.08) res2s: 928043..246206 iter 8: time=296.20 rNorms/orig: (0.05,0.05) res2s: 931802..247379 iter 9: time=291.44 rNorms/orig: (0.03,0.04) res2s: 933702..247882 iter 10: time=287.97 rNorms/orig: (0.02,0.02) res2s: 934683..248152 iter 11: time=286.46 rNorms/orig: (0.01,0.02) res2s: 935146..248265 iter 12: time=297.21 rNorms/orig: (0.01,0.01) res2s: 935380..248328 iter 13: time=292.78 rNorms/orig: (0.007,0.007) res2s: 935474..248352 iter 14: time=290.57 rNorms/orig: (0.004,0.005) res2s: 935527..248366 Converged at iter 14: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 51.3%, memory/overhead = 48.7% MCscaling: logDelta = 0.96, h2 = 0.275, f = 0.000906497 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.275 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.266049, logDelta = 0.961322, f = 0.000906497 Time for fitting variance components = 16130.2 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=642.49 rNorms/orig: (0.5,0.8) res2s: 135100..227922 iter 2: time=648.76 rNorms/orig: (0.5,0.7) res2s: 178609..265668 iter 3: time=654.13 rNorms/orig: (0.2,0.4) res2s: 215471..283987 iter 4: time=656.07 rNorms/orig: (0.2,0.3) res2s: 234166..291744 iter 5: time=640.42 rNorms/orig: (0.1,0.2) res2s: 243982..296645 iter 6: time=643.22 rNorms/orig: (0.07,0.1) res2s: 248777..298968 iter 7: time=640.24 rNorms/orig: (0.05,0.09) res2s: 251725..300207 iter 8: time=643.61 rNorms/orig: (0.04,0.06) res2s: 253146..300870 iter 9: time=642.39 rNorms/orig: (0.02,0.04) res2s: 253767..301170 iter 10: time=645.85 rNorms/orig: (0.01,0.03) res2s: 254118..301327 iter 11: time=643.53 rNorms/orig: (0.008,0.02) res2s: 254266..301402 iter 12: time=645.87 rNorms/orig: (0.005,0.01) res2s: 254354..301439 iter 13: time=643.89 rNorms/orig: (0.003,0.009) res2s: 254392..301456 iter 14: time=634.17 rNorms/orig: (0.002,0.006) res2s: 254414..301465 iter 15: time=645.29 rNorms/orig: (0.001,0.004) res2s: 254424..301469 iter 16: time=636.07 rNorms/orig: (0.001,0.003) res2s: 254429..301471 iter 17: time=637.55 rNorms/orig: (0.0006,0.002) res2s: 254431..301472 iter 18: time=636.41 rNorms/orig: (0.0003,0.001) res2s: 254432..301472 iter 19: time=641.87 rNorms/orig: (0.0002,0.0008) res2s: 254433..301472 iter 20: time=646.12 rNorms/orig: (0.0001,0.0006) res2s: 254433..301472 iter 21: time=639.85 rNorms/orig: (9e-05,0.0004) res2s: 254433..301472 Converged at iter 21: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 76.3%, memory/overhead = 23.7% AvgPro: 1.454 AvgRetro: 1.438 Calibration: 1.011 (0.001) (30 SNPs) Ratio of medians: 1.010 Median of ratios: 1.011 Time for computing infinitesimal model assoc stats = 13934.2 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 26.7535 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 601289/705881 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: 579570/705881 Masking windows around outlier snps (chisq > 444.5) # of SNPs remaining after outlier window removal: 577204/579570 Intercept of LD Score regression for ref stats: 1.175 (0.017) Estimated attenuation: 0.152 (0.015) Intercept of LD Score regression for cur stats: 1.173 (0.016) Calibration factor (ref/cur) to multiply by: 1.002 (0.002) LINREG intercept inflation = 0.997973 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 = 355601 Singular values of covariate matrix: S[0] = 2.03082e+06 S[1] = 4261.92 S[2] = 420.082 S[3] = 262.455 S[4] = 178.909 S[5] = 173.978 S[6] = 164.278 S[7] = 156.511 S[8] = 152.705 S[9] = 148.003 S[10] = 145.568 S[11] = 137.466 S[12] = 130.249 S[13] = 128.541 S[14] = 125.971 S[15] = 121.418 S[16] = 118.401 S[17] = 116.426 S[18] = 112.822 S[19] = 103.913 S[20] = 100.387 S[21] = 89.2818 S[22] = 39.9178 S[23] = 21.5615 S[24] = 17.2847 S[25] = 0.881845 S[26] = 0.881301 S[27] = 0.880955 S[28] = 0.880455 S[29] = 0.880312 S[30] = 0.879855 S[31] = 0.879455 S[32] = 0.879284 S[33] = 0.878852 S[34] = 0.878288 S[35] = 0.878208 S[36] = 0.877826 S[37] = 0.877746 S[38] = 0.877523 S[39] = 0.877253 S[40] = 0.876626 S[41] = 0.876412 S[42] = 0.876219 S[43] = 0.861188 S[44] = 0.794266 S[45] = 6.86576e-12 S[46] = 3.73984e-13 S[47] = 1.84431e-13 S[48] = 0 Total covariate vectors: C = 49 Total independent covariate vectors: Cindep = 45 === Initializing Bolt object: projecting and normalizing SNPs === Number of chroms with >= 1 good SNP: 23 Average norm of projected SNPs: 353520.791008 Dimension of all-1s proj space (Nused-1): 355600 Beginning variational Bayes iter 1: time=657.83 for 18 active reps iter 2: time=420.97 for 18 active reps approxLL diffs: (8874.60,10856.08) iter 3: time=421.42 for 18 active reps approxLL diffs: (837.53,1691.55) iter 4: time=419.75 for 18 active reps approxLL diffs: (155.45,593.85) iter 5: time=419.78 for 18 active reps approxLL diffs: (45.18,301.25) iter 6: time=421.48 for 18 active reps approxLL diffs: (18.48,183.87) iter 7: time=421.51 for 18 active reps approxLL diffs: (9.21,111.17) iter 8: time=419.38 for 18 active reps approxLL diffs: (5.32,65.61) iter 9: time=423.82 for 18 active reps approxLL diffs: (3.42,46.19) iter 10: time=421.30 for 18 active reps approxLL diffs: (2.33,36.72) iter 11: time=424.15 for 18 active reps approxLL diffs: (1.67,25.86) iter 12: time=425.74 for 18 active reps approxLL diffs: (1.26,22.53) iter 13: time=421.25 for 18 active reps approxLL diffs: (0.99,19.30) iter 14: time=419.98 for 18 active reps approxLL diffs: (0.78,14.79) iter 15: time=420.36 for 18 active reps approxLL diffs: (0.56,10.46) iter 16: time=420.95 for 18 active reps approxLL diffs: (0.40,7.50) iter 17: time=419.25 for 18 active reps approxLL diffs: (0.31,7.42) iter 18: time=418.68 for 18 active reps approxLL diffs: (0.24,6.41) iter 19: time=420.35 for 18 active reps approxLL diffs: (0.18,5.31) iter 20: time=420.40 for 18 active reps approxLL diffs: (0.14,5.78) iter 21: time=417.13 for 18 active reps approxLL diffs: (0.11,3.37) iter 22: time=413.91 for 18 active reps approxLL diffs: (0.08,3.20) iter 23: time=412.18 for 18 active reps approxLL diffs: (0.06,6.08) iter 24: time=417.88 for 18 active reps approxLL diffs: (0.05,4.30) iter 25: time=420.09 for 18 active reps approxLL diffs: (0.04,3.46) iter 26: time=423.62 for 18 active reps approxLL diffs: (0.03,4.73) iter 27: time=426.53 for 18 active reps approxLL diffs: (0.03,3.61) iter 28: time=417.05 for 18 active reps approxLL diffs: (0.02,1.71) iter 29: time=411.53 for 18 active reps approxLL diffs: (0.02,1.17) iter 30: time=428.20 for 18 active reps approxLL diffs: (0.01,1.32) iter 31: time=422.99 for 18 active reps approxLL diffs: (0.01,2.23) iter 32: time=434.95 for 18 active reps approxLL diffs: (0.01,3.67) iter 33: time=389.79 for 17 active reps approxLL diffs: (0.01,4.57) iter 34: time=402.42 for 17 active reps approxLL diffs: (0.01,2.14) iter 35: time=402.05 for 17 active reps approxLL diffs: (0.01,4.06) iter 36: time=398.95 for 17 active reps approxLL diffs: (0.01,2.79) iter 37: time=402.30 for 15 active reps approxLL diffs: (0.01,0.72) iter 38: time=395.20 for 15 active reps approxLL diffs: (0.01,0.58) iter 39: time=368.74 for 14 active reps approxLL diffs: (0.01,0.60) iter 40: time=356.58 for 13 active reps approxLL diffs: (0.01,0.46) iter 41: time=345.95 for 11 active reps approxLL diffs: (0.01,0.95) iter 42: time=332.33 for 9 active reps approxLL diffs: (0.02,0.86) iter 43: time=308.91 for 9 active reps approxLL diffs: (0.03,0.64) iter 44: time=305.55 for 9 active reps approxLL diffs: (0.03,0.77) iter 45: time=297.50 for 9 active reps approxLL diffs: (0.03,0.41) iter 46: time=304.33 for 9 active reps approxLL diffs: (0.02,0.63) iter 47: time=301.63 for 9 active reps approxLL diffs: (0.01,0.50) iter 48: time=298.74 for 9 active reps approxLL diffs: (0.01,0.43) iter 49: time=259.70 for 8 active reps approxLL diffs: (0.02,0.40) iter 50: time=257.13 for 8 active reps approxLL diffs: (0.01,0.20) iter 51: time=259.02 for 8 active reps approxLL diffs: (0.01,0.40) iter 52: time=284.59 for 7 active reps approxLL diffs: (0.01,1.01) iter 53: time=258.13 for 6 active reps approxLL diffs: (0.02,1.16) iter 54: time=258.52 for 6 active reps approxLL diffs: (0.01,0.77) iter 55: time=238.40 for 5 active reps approxLL diffs: (0.01,0.23) iter 56: time=219.81 for 4 active reps approxLL diffs: (0.01,0.14) iter 57: time=221.15 for 4 active reps approxLL diffs: (0.01,0.09) iter 58: time=232.63 for 3 active reps approxLL diffs: (0.01,0.10) iter 59: time=213.01 for 2 active reps approxLL diffs: (0.03,0.18) iter 60: time=213.82 for 2 active reps approxLL diffs: (0.04,0.21) iter 61: time=211.76 for 2 active reps approxLL diffs: (0.07,0.15) iter 62: time=213.86 for 2 active reps approxLL diffs: (0.09,0.12) iter 63: time=212.49 for 2 active reps approxLL diffs: (0.06,0.14) iter 64: time=210.69 for 2 active reps approxLL diffs: (0.04,0.08) iter 65: time=210.77 for 2 active reps approxLL diffs: (0.03,0.04) iter 66: time=215.23 for 2 active reps approxLL diffs: (0.02,0.04) iter 67: time=218.95 for 2 active reps approxLL diffs: (0.01,0.07) iter 68: time=213.04 for 2 active reps approxLL diffs: (0.01,0.11) iter 69: time=202.92 for 1 active reps approxLL diffs: (0.13,0.13) iter 70: time=234.86 for 1 active reps approxLL diffs: (0.11,0.11) iter 71: time=232.96 for 1 active reps approxLL diffs: (0.06,0.06) iter 72: time=242.60 for 1 active reps approxLL diffs: (0.03,0.03) iter 73: time=243.17 for 1 active reps approxLL diffs: (0.01,0.01) iter 74: time=236.85 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 74: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 77.6%, memory/overhead = 22.4% Computing predictions on left-out cross-validation fold Time for computing predictions = 8433.9 sec Average PVEs obtained by param pairs tested (high to low): f2=0.5, p=0.02: 0.138001 f2=0.3, p=0.05: 0.137870 f2=0.5, p=0.01: 0.137446 ... f2=0.5, p=0.5: 0.105238 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.971015 Absolute prediction MSE using standard LMM: 0.868827 Absolute prediction MSE, fold-best f2=0.5, p=0.02: 0.837014 Absolute pred MSE using f2=0.5, p=0.5: 0.868827 Absolute pred MSE using f2=0.5, p=0.2: 0.859250 Absolute pred MSE using f2=0.5, p=0.1: 0.849085 Absolute pred MSE using f2=0.5, p=0.05: 0.840613 Absolute pred MSE using f2=0.5, p=0.02: 0.837014 Absolute pred MSE using f2=0.5, p=0.01: 0.837553 Absolute pred MSE using f2=0.3, p=0.5: 0.865757 Absolute pred MSE using f2=0.3, p=0.2: 0.852009 Absolute pred MSE using f2=0.3, p=0.1: 0.842175 Absolute pred MSE using f2=0.3, p=0.05: 0.837142 Absolute pred MSE using f2=0.3, p=0.02: 0.838380 Absolute pred MSE using f2=0.3, p=0.01: 0.840896 Absolute pred MSE using f2=0.1, p=0.5: 0.860759 Absolute pred MSE using f2=0.1, p=0.2: 0.846129 Absolute pred MSE using f2=0.1, p=0.1: 0.838985 Absolute pred MSE using f2=0.1, p=0.05: 0.839275 Absolute pred MSE using f2=0.1, p=0.02: 0.847049 Absolute pred MSE using f2=0.1, p=0.01: 0.850943 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.105 Relative improvement in prediction MSE using non-inf model: 0.037 Optimal mixture parameters according to CV: f2 = 0.5, p = 0.02 Time for estimating mixture parameters = 37867.3 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=722.30 for 23 active reps iter 2: time=503.18 for 23 active reps approxLL diffs: (13005.06,13929.26) iter 3: time=492.91 for 23 active reps approxLL diffs: (1994.22,2230.71) iter 4: time=480.03 for 23 active reps approxLL diffs: (604.14,688.88) iter 5: time=484.68 for 23 active reps approxLL diffs: (255.05,310.75) iter 6: time=482.05 for 23 active reps approxLL diffs: (130.18,162.18) iter 7: time=478.78 for 23 active reps approxLL diffs: (81.69,99.00) iter 8: time=487.33 for 23 active reps approxLL diffs: (51.96,65.17) iter 9: time=529.15 for 23 active reps approxLL diffs: (37.85,51.07) iter 10: time=483.51 for 23 active reps approxLL diffs: (26.46,39.88) iter 11: time=470.77 for 23 active reps approxLL diffs: (16.49,28.75) iter 12: time=495.65 for 23 active reps approxLL diffs: (11.08,20.76) iter 13: time=496.28 for 23 active reps approxLL diffs: (10.18,16.69) iter 14: time=499.00 for 23 active reps approxLL diffs: (7.12,13.99) iter 15: time=481.74 for 23 active reps approxLL diffs: (5.22,10.74) iter 16: time=492.72 for 23 active reps approxLL diffs: (3.41,10.11) iter 17: time=485.76 for 23 active reps approxLL diffs: (2.43,8.60) iter 18: time=476.56 for 23 active reps approxLL diffs: (2.29,6.18) iter 19: time=494.98 for 23 active reps approxLL diffs: (1.83,8.22) iter 20: time=505.30 for 23 active reps approxLL diffs: (1.28,6.38) iter 21: time=510.33 for 23 active reps approxLL diffs: (1.12,4.07) iter 22: time=494.48 for 23 active reps approxLL diffs: (0.98,4.39) iter 23: time=474.03 for 23 active reps approxLL diffs: (0.60,3.29) iter 24: time=483.80 for 23 active reps approxLL diffs: (0.36,4.03) iter 25: time=471.42 for 23 active reps approxLL diffs: (0.24,3.12) iter 26: time=476.11 for 23 active reps approxLL diffs: (0.21,4.10) iter 27: time=505.98 for 23 active reps approxLL diffs: (0.16,2.75) iter 28: time=503.21 for 23 active reps approxLL diffs: (0.14,1.93) iter 29: time=508.23 for 23 active reps approxLL diffs: (0.16,1.45) iter 30: time=526.87 for 23 active reps approxLL diffs: (0.16,1.79) iter 31: time=496.73 for 23 active reps approxLL diffs: (0.08,1.60) iter 32: time=487.43 for 23 active reps approxLL diffs: (0.06,1.82) iter 33: time=466.18 for 23 active reps approxLL diffs: (0.03,1.72) iter 34: time=463.60 for 23 active reps approxLL diffs: (0.02,2.51) iter 35: time=517.90 for 23 active reps approxLL diffs: (0.02,1.36) iter 36: time=459.12 for 23 active reps approxLL diffs: (0.01,0.94) iter 37: time=484.13 for 23 active reps approxLL diffs: (0.01,0.89) iter 38: time=479.71 for 23 active reps approxLL diffs: (0.01,0.53) iter 39: time=451.11 for 22 active reps approxLL diffs: (0.02,1.42) iter 40: time=440.04 for 22 active reps approxLL diffs: (0.02,0.68) iter 41: time=464.51 for 22 active reps approxLL diffs: (0.01,0.27) iter 42: time=460.18 for 22 active reps approxLL diffs: (0.01,1.43) iter 43: time=441.25 for 22 active reps approxLL diffs: (0.01,1.42) iter 44: time=418.92 for 21 active reps approxLL diffs: (0.01,1.32) iter 45: time=479.37 for 21 active reps approxLL diffs: (0.01,2.75) iter 46: time=393.73 for 20 active reps approxLL diffs: (0.01,1.78) iter 47: time=398.41 for 18 active reps approxLL diffs: (0.01,0.64) iter 48: time=408.94 for 18 active reps approxLL diffs: (0.01,0.59) iter 49: time=426.84 for 18 active reps approxLL diffs: (0.01,0.99) iter 50: time=405.69 for 17 active reps approxLL diffs: (0.01,0.70) iter 51: time=356.12 for 14 active reps approxLL diffs: (0.01,2.13) iter 52: time=359.61 for 13 active reps approxLL diffs: (0.01,0.89) iter 53: time=322.53 for 12 active reps approxLL diffs: (0.01,0.27) iter 54: time=354.44 for 11 active reps approxLL diffs: (0.00,0.18) iter 55: time=258.55 for 6 active reps approxLL diffs: (0.01,0.16) iter 56: time=242.93 for 5 active reps approxLL diffs: (0.03,0.26) iter 57: time=235.92 for 5 active reps approxLL diffs: (0.03,0.77) iter 58: time=245.88 for 5 active reps approxLL diffs: (0.03,2.63) iter 59: time=237.91 for 5 active reps approxLL diffs: (0.02,1.61) iter 60: time=242.66 for 5 active reps approxLL diffs: (0.01,1.31) iter 61: time=245.28 for 5 active reps approxLL diffs: (0.01,0.88) iter 62: time=240.83 for 5 active reps approxLL diffs: (0.01,0.31) iter 63: time=242.70 for 5 active reps approxLL diffs: (0.01,0.13) iter 64: time=242.88 for 5 active reps approxLL diffs: (0.01,0.09) iter 65: time=242.29 for 5 active reps approxLL diffs: (0.02,0.07) iter 66: time=241.75 for 5 active reps approxLL diffs: (0.02,0.07) iter 67: time=242.03 for 5 active reps approxLL diffs: (0.01,0.20) iter 68: time=247.60 for 5 active reps approxLL diffs: (0.01,0.49) iter 69: time=222.66 for 4 active reps approxLL diffs: (0.02,0.51) iter 70: time=220.61 for 4 active reps approxLL diffs: (0.01,0.30) iter 71: time=227.34 for 4 active reps approxLL diffs: (0.01,0.18) iter 72: time=241.83 for 3 active reps approxLL diffs: (0.02,0.19) iter 73: time=246.17 for 3 active reps approxLL diffs: (0.01,0.28) iter 74: time=249.28 for 3 active reps approxLL diffs: (0.01,0.17) iter 75: time=251.03 for 3 active reps approxLL diffs: (0.01,0.13) iter 76: time=235.58 for 2 active reps approxLL diffs: (0.01,0.16) iter 77: time=205.31 for 1 active reps approxLL diffs: (0.13,0.13) iter 78: time=201.77 for 1 active reps approxLL diffs: (0.06,0.06) iter 79: time=201.84 for 1 active reps approxLL diffs: (0.03,0.03) iter 80: time=199.29 for 1 active reps approxLL diffs: (0.04,0.04) iter 81: time=195.80 for 1 active reps approxLL diffs: (0.04,0.04) iter 82: time=247.23 for 1 active reps approxLL diffs: (0.03,0.03) iter 83: time=271.39 for 1 active reps approxLL diffs: (0.01,0.01) iter 84: time=275.40 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 84: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 80.1%, memory/overhead = 19.9% Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 579570/705881 Masking windows around outlier snps (chisq > 444.5) # of SNPs remaining after outlier window removal: 577204/579570 Intercept of LD Score regression for ref stats: 1.175 (0.017) Estimated attenuation: 0.152 (0.015) Intercept of LD Score regression for cur stats: 1.170 (0.017) Calibration factor (ref/cur) to multiply by: 1.004 (0.001) Time for computing Bayesian mixed model assoc stats = 32949.6 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=504.16 for 1 active reps iter 2: time=278.45 for 1 active reps approxLL diffs: (13981.50,13981.50) iter 3: time=275.84 for 1 active reps approxLL diffs: (2223.36,2223.36) iter 4: time=269.20 for 1 active reps approxLL diffs: (683.56,683.56) iter 5: time=268.52 for 1 active reps approxLL diffs: (307.15,307.15) iter 6: time=259.94 for 1 active reps approxLL diffs: (154.88,154.88) iter 7: time=259.07 for 1 active reps approxLL diffs: (95.09,95.09) iter 8: time=257.86 for 1 active reps approxLL diffs: (63.20,63.20) iter 9: time=256.17 for 1 active reps approxLL diffs: (46.73,46.73) iter 10: time=252.15 for 1 active reps approxLL diffs: (34.57,34.57) iter 11: time=253.42 for 1 active reps approxLL diffs: (22.35,22.35) iter 12: time=270.08 for 1 active reps approxLL diffs: (15.18,15.18) iter 13: time=270.38 for 1 active reps approxLL diffs: (10.05,10.05) iter 14: time=269.39 for 1 active reps approxLL diffs: (7.95,7.95) iter 15: time=268.68 for 1 active reps approxLL diffs: (6.88,6.88) iter 16: time=267.51 for 1 active reps approxLL diffs: (7.05,7.05) iter 17: time=268.94 for 1 active reps approxLL diffs: (7.60,7.60) iter 18: time=273.25 for 1 active reps approxLL diffs: (7.15,7.15) iter 19: time=279.24 for 1 active reps approxLL diffs: (5.39,5.39) iter 20: time=277.73 for 1 active reps approxLL diffs: (3.41,3.41) iter 21: time=270.78 for 1 active reps approxLL diffs: (2.09,2.09) iter 22: time=265.07 for 1 active reps approxLL diffs: (1.90,1.90) iter 23: time=227.77 for 1 active reps approxLL diffs: (1.96,1.96) iter 24: time=215.56 for 1 active reps approxLL diffs: (1.40,1.40) iter 25: time=234.69 for 1 active reps approxLL diffs: (0.66,0.66) iter 26: time=240.65 for 1 active reps approxLL diffs: (0.26,0.26) iter 27: time=231.87 for 1 active reps approxLL diffs: (0.15,0.15) iter 28: time=244.79 for 1 active reps approxLL diffs: (0.11,0.11) iter 29: time=259.97 for 1 active reps approxLL diffs: (0.08,0.08) iter 30: time=260.08 for 1 active reps approxLL diffs: (0.06,0.06) iter 31: time=268.08 for 1 active reps approxLL diffs: (0.06,0.06) iter 32: time=295.66 for 1 active reps approxLL diffs: (0.07,0.07) iter 33: time=309.58 for 1 active reps approxLL diffs: (0.12,0.12) iter 34: time=304.40 for 1 active reps approxLL diffs: (0.21,0.21) iter 35: time=263.46 for 1 active reps approxLL diffs: (0.26,0.26) iter 36: time=282.66 for 1 active reps approxLL diffs: (0.27,0.27) iter 37: time=291.27 for 1 active reps approxLL diffs: (0.32,0.32) iter 38: time=295.73 for 1 active reps approxLL diffs: (0.16,0.16) iter 39: time=287.02 for 1 active reps approxLL diffs: (0.12,0.12) iter 40: time=287.93 for 1 active reps approxLL diffs: (0.29,0.29) iter 41: time=290.82 for 1 active reps approxLL diffs: (0.39,0.39) iter 42: time=289.92 for 1 active reps approxLL diffs: (0.15,0.15) iter 43: time=299.00 for 1 active reps approxLL diffs: (0.08,0.08) iter 44: time=297.63 for 1 active reps approxLL diffs: (0.11,0.11) iter 45: time=298.41 for 1 active reps approxLL diffs: (0.19,0.19) iter 46: time=297.30 for 1 active reps approxLL diffs: (0.78,0.78) iter 47: time=270.27 for 1 active reps approxLL diffs: (1.55,1.55) iter 48: time=311.18 for 1 active reps approxLL diffs: (0.30,0.30) iter 49: time=309.60 for 1 active reps approxLL diffs: (0.03,0.03) iter 50: time=303.87 for 1 active reps approxLL diffs: (0.02,0.02) iter 51: time=299.15 for 1 active reps approxLL diffs: (0.02,0.02) iter 52: time=301.22 for 1 active reps approxLL diffs: (0.02,0.02) iter 53: time=306.76 for 1 active reps approxLL diffs: (0.02,0.02) iter 54: time=313.39 for 1 active reps approxLL diffs: (0.01,0.01) iter 55: time=319.84 for 1 active reps approxLL diffs: (0.01,0.01) iter 56: time=306.83 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 56: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 58.0%, memory/overhead = 42.0% Time for computing and writing betas = 15736.8 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.15133 (705851 good SNPs) lambdaGC: 1.50533 Mean BOLT_LMM_INF: 2.23721 (705851 good SNPs) lambdaGC: 1.51578 Mean BOLT_LMM: 2.26065 (705851 good SNPs) lambdaGC: 1.51711 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 8098.01 sec Total elapsed time for analysis = 133365 sec