+-----------------------------+ | ___ | | 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_MONOCYTE_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_MONOCYTE_COUNT.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_MONOCYTE_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 = 2827.33 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: 442620 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 = 442620 Singular values of covariate matrix: S[0] = 2.26491e+06 S[1] = 4754.32 S[2] = 468.642 S[3] = 292.711 S[4] = 199.658 S[5] = 194.17 S[6] = 183.408 S[7] = 174.699 S[8] = 170.272 S[9] = 164.924 S[10] = 162.531 S[11] = 153.276 S[12] = 145.149 S[13] = 143.078 S[14] = 140.163 S[15] = 135.459 S[16] = 132.286 S[17] = 130.021 S[18] = 126.086 S[19] = 116.148 S[20] = 111.973 S[21] = 99.7234 S[22] = 44.6627 S[23] = 23.8508 S[24] = 19.2727 S[25] = 0.981861 S[26] = 0.981564 S[27] = 0.981402 S[28] = 0.981216 S[29] = 0.98111 S[30] = 0.981028 S[31] = 0.980935 S[32] = 0.98081 S[33] = 0.980697 S[34] = 0.980529 S[35] = 0.980435 S[36] = 0.980392 S[37] = 0.980178 S[38] = 0.980103 S[39] = 0.979816 S[40] = 0.979633 S[41] = 0.979493 S[42] = 0.979373 S[43] = 0.962669 S[44] = 0.886015 S[45] = 6.33599e-12 S[46] = 9.69057e-13 S[47] = 2.35348e-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: 440041.356889 Dimension of all-1s proj space (Nused-1): 442619 Time for covariate data setup + Bolt initialization = 4043.24 sec Phenotype 1: N = 442620 mean = 0.0132674 std = 0.995885 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 439.314 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 442620) 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=319.38 rNorms/orig: (0.6,0.7) res2s: 722799..149786 iter 2: time=290.37 rNorms/orig: (0.5,0.6) res2s: 851699..185905 iter 3: time=295.19 rNorms/orig: (0.3,0.3) res2s: 986314..223712 iter 4: time=299.68 rNorms/orig: (0.2,0.2) res2s: 1.03493e+06..238641 iter 5: time=307.47 rNorms/orig: (0.1,0.1) res2s: 1.05994e+06..245446 iter 6: time=313.13 rNorms/orig: (0.09,0.1) res2s: 1.07274e+06..249225 iter 7: time=297.51 rNorms/orig: (0.06,0.06) res2s: 1.0784e+06..250982 iter 8: time=296.24 rNorms/orig: (0.04,0.04) res2s: 1.08138e+06..251722 iter 9: time=295.60 rNorms/orig: (0.02,0.03) res2s: 1.08281e+06..252079 iter 10: time=296.94 rNorms/orig: (0.02,0.02) res2s: 1.08343e+06..252235 iter 11: time=292.58 rNorms/orig: (0.01,0.01) res2s: 1.08375e+06..252316 iter 12: time=286.80 rNorms/orig: (0.006,0.007) res2s: 1.08388e+06..252352 iter 13: time=288.91 rNorms/orig: (0.004,0.004) res2s: 1.08394e+06..252369 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.5%, memory/overhead = 49.5% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.101236 Estimating MC scaling f_REML at log(delta) = -0.00574123, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=297.07 rNorms/orig: (1,1) res2s: 77157.2..44760.5 iter 2: time=293.34 rNorms/orig: (1,1) res2s: 108506..65960.7 iter 3: time=293.72 rNorms/orig: (0.8,0.9) res2s: 166413..101159 iter 4: time=295.91 rNorms/orig: (0.6,0.7) res2s: 203347..123555 iter 5: time=294.51 rNorms/orig: (0.5,0.5) res2s: 230712..138405 iter 6: time=295.07 rNorms/orig: (0.4,0.4) res2s: 251746..150288 iter 7: time=289.97 rNorms/orig: (0.3,0.3) res2s: 265421..158370 iter 8: time=295.49 rNorms/orig: (0.2,0.3) res2s: 275379..163293 iter 9: time=316.14 rNorms/orig: (0.2,0.2) res2s: 282426..166685 iter 10: time=363.92 rNorms/orig: (0.1,0.2) res2s: 286857..168777 iter 11: time=377.42 rNorms/orig: (0.1,0.1) res2s: 290036..170304 iter 12: time=365.07 rNorms/orig: (0.08,0.09) res2s: 291997..171274 iter 13: time=409.04 rNorms/orig: (0.07,0.07) res2s: 293236..171908 iter 14: time=331.66 rNorms/orig: (0.05,0.06) res2s: 294114..172337 iter 15: time=361.09 rNorms/orig: (0.04,0.04) res2s: 294665..172592 iter 16: time=314.08 rNorms/orig: (0.03,0.03) res2s: 294991..172768 iter 17: time=312.04 rNorms/orig: (0.02,0.03) res2s: 295217..172881 iter 18: time=312.15 rNorms/orig: (0.02,0.02) res2s: 295357..172953 iter 19: time=303.26 rNorms/orig: (0.01,0.02) res2s: 295445..172995 iter 20: time=293.72 rNorms/orig: (0.01,0.01) res2s: 295498..173021 iter 21: time=299.01 rNorms/orig: (0.009,0.009) res2s: 295531..173039 iter 22: time=315.93 rNorms/orig: (0.007,0.007) res2s: 295553..173048 iter 23: time=314.06 rNorms/orig: (0.005,0.006) res2s: 295566..173054 iter 24: time=322.30 rNorms/orig: (0.004,0.004) res2s: 295574..173058 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.0%, memory/overhead = 50.0% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.220747 Estimating MC scaling f_REML at log(delta) = 0.747453, h2 = 0.320126... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=353.45 rNorms/orig: (0.8,0.8) res2s: 387341..109219 iter 2: time=338.35 rNorms/orig: (0.7,0.8) res2s: 483461..143027 iter 3: time=336.59 rNorms/orig: (0.5,0.5) res2s: 605763..184434 iter 4: time=318.43 rNorms/orig: (0.3,0.3) res2s: 658706..203666 iter 5: time=304.50 rNorms/orig: (0.2,0.2) res2s: 689332..213655 iter 6: time=292.72 rNorms/orig: (0.2,0.2) res2s: 707358..219976 iter 7: time=290.78 rNorms/orig: (0.1,0.1) res2s: 716457..223350 iter 8: time=293.74 rNorms/orig: (0.07,0.08) res2s: 721852..224979 iter 9: time=293.96 rNorms/orig: (0.05,0.06) res2s: 724830..225875 iter 10: time=296.20 rNorms/orig: (0.04,0.04) res2s: 726310..226322 iter 11: time=293.50 rNorms/orig: (0.03,0.03) res2s: 727164..226584 iter 12: time=299.64 rNorms/orig: (0.02,0.02) res2s: 727576..226718 iter 13: time=297.82 rNorms/orig: (0.01,0.01) res2s: 727784..226788 iter 14: time=293.34 rNorms/orig: (0.008,0.009) res2s: 727904..226826 iter 15: time=291.41 rNorms/orig: (0.006,0.006) res2s: 727963..226844 iter 16: time=292.33 rNorms/orig: (0.004,0.004) res2s: 727991..226854 Converged at iter 16: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.3%, memory/overhead = 49.7% MCscaling: logDelta = 0.75, h2 = 0.320, f = 0.0024814 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.320 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.312721, logDelta = 0.747453, f = 0.0024814 Time for fitting variance components = 17036.4 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=640.33 rNorms/orig: (0.5,0.9) res2s: 109991..199136 iter 2: time=644.16 rNorms/orig: (0.6,0.9) res2s: 143730..239430 iter 3: time=643.88 rNorms/orig: (0.3,0.5) res2s: 186503..260611 iter 4: time=645.49 rNorms/orig: (0.2,0.4) res2s: 207657..270377 iter 5: time=646.16 rNorms/orig: (0.2,0.3) res2s: 218404..276934 iter 6: time=645.13 rNorms/orig: (0.09,0.2) res2s: 225368..280220 iter 7: time=644.63 rNorms/orig: (0.08,0.1) res2s: 229376..282123 iter 8: time=652.85 rNorms/orig: (0.06,0.09) res2s: 231289..283224 iter 9: time=649.06 rNorms/orig: (0.03,0.06) res2s: 232356..283763 iter 10: time=651.73 rNorms/orig: (0.02,0.04) res2s: 232919..284069 iter 11: time=660.50 rNorms/orig: (0.01,0.03) res2s: 233247..284227 iter 12: time=654.79 rNorms/orig: (0.01,0.02) res2s: 233407..284312 iter 13: time=661.97 rNorms/orig: (0.006,0.01) res2s: 233502..284353 iter 14: time=650.13 rNorms/orig: (0.005,0.01) res2s: 233553..284377 iter 15: time=634.93 rNorms/orig: (0.003,0.008) res2s: 233577..284387 iter 16: time=639.69 rNorms/orig: (0.002,0.005) res2s: 233592..284393 iter 17: time=661.81 rNorms/orig: (0.001,0.004) res2s: 233600..284396 iter 18: time=656.65 rNorms/orig: (0.0008,0.003) res2s: 233603..284398 iter 19: time=663.51 rNorms/orig: (0.0005,0.002) res2s: 233605..284398 iter 20: time=655.79 rNorms/orig: (0.0004,0.001) res2s: 233606..284399 iter 21: time=643.54 rNorms/orig: (0.0002,0.0009) res2s: 233607..284399 iter 22: time=647.88 rNorms/orig: (0.0001,0.0006) res2s: 233607..284399 iter 23: time=675.54 rNorms/orig: (0.0001,0.0004) res2s: 233607..284399 Converged at iter 23: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 75.9%, memory/overhead = 24.1% AvgPro: 1.470 AvgRetro: 1.451 Calibration: 1.013 (0.001) (30 SNPs) Ratio of medians: 1.012 Median of ratios: 1.012 Time for computing infinitesimal model assoc stats = 15396.8 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 25.3273 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 > 442.6) # of SNPs remaining after outlier window removal: 571058/579570 Intercept of LD Score regression for ref stats: 1.203 (0.023) Estimated attenuation: 0.191 (0.027) Intercept of LD Score regression for cur stats: 1.184 (0.021) Calibration factor (ref/cur) to multiply by: 1.016 (0.003) LINREG intercept inflation = 0.984501 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 = 354096 Singular values of covariate matrix: S[0] = 2.02545e+06 S[1] = 4251.51 S[2] = 419.167 S[3] = 261.806 S[4] = 178.557 S[5] = 173.628 S[6] = 163.936 S[7] = 156.295 S[8] = 152.309 S[9] = 147.552 S[10] = 145.49 S[11] = 137.121 S[12] = 129.819 S[13] = 127.908 S[14] = 125.343 S[15] = 121.204 S[16] = 118.443 S[17] = 116.231 S[18] = 112.608 S[19] = 103.95 S[20] = 100.11 S[21] = 89.2979 S[22] = 39.9564 S[23] = 21.3934 S[24] = 17.2371 S[25] = 0.879613 S[26] = 0.879156 S[27] = 0.878435 S[28] = 0.878262 S[29] = 0.878086 S[30] = 0.877918 S[31] = 0.877646 S[32] = 0.877546 S[33] = 0.877211 S[34] = 0.876876 S[35] = 0.876664 S[36] = 0.876203 S[37] = 0.87593 S[38] = 0.87564 S[39] = 0.875506 S[40] = 0.8751 S[41] = 0.874873 S[42] = 0.874273 S[43] = 0.861927 S[44] = 0.792067 S[45] = 6.3715e-12 S[46] = 6.7147e-13 S[47] = 2.13988e-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: 352023.629846 Dimension of all-1s proj space (Nused-1): 354095 Beginning variational Bayes iter 1: time=704.95 for 18 active reps iter 2: time=466.97 for 18 active reps approxLL diffs: (12139.03,16012.01) iter 3: time=464.78 for 18 active reps approxLL diffs: (1176.45,2974.28) iter 4: time=467.46 for 18 active reps approxLL diffs: (216.72,1032.99) iter 5: time=465.99 for 18 active reps approxLL diffs: (61.14,509.96) iter 6: time=459.04 for 18 active reps approxLL diffs: (23.60,281.31) iter 7: time=435.00 for 18 active reps approxLL diffs: (11.43,172.80) iter 8: time=415.54 for 18 active reps approxLL diffs: (6.52,118.87) iter 9: time=414.10 for 18 active reps approxLL diffs: (4.21,70.94) iter 10: time=417.64 for 18 active reps approxLL diffs: (2.96,47.62) iter 11: time=411.27 for 18 active reps approxLL diffs: (2.20,28.90) iter 12: time=411.46 for 18 active reps approxLL diffs: (1.70,24.62) iter 13: time=409.81 for 18 active reps approxLL diffs: (1.32,24.55) iter 14: time=407.79 for 18 active reps approxLL diffs: (1.01,24.05) iter 15: time=407.62 for 18 active reps approxLL diffs: (0.79,11.56) iter 16: time=409.10 for 18 active reps approxLL diffs: (0.61,14.41) iter 17: time=410.01 for 18 active reps approxLL diffs: (0.47,15.07) iter 18: time=414.41 for 18 active reps approxLL diffs: (0.36,8.43) iter 19: time=416.23 for 18 active reps approxLL diffs: (0.30,5.18) iter 20: time=415.03 for 18 active reps approxLL diffs: (0.25,3.65) iter 21: time=411.39 for 18 active reps approxLL diffs: (0.21,4.37) iter 22: time=409.30 for 18 active reps approxLL diffs: (0.17,5.17) iter 23: time=411.75 for 18 active reps approxLL diffs: (0.13,5.76) iter 24: time=413.12 for 18 active reps approxLL diffs: (0.11,7.42) iter 25: time=409.65 for 18 active reps approxLL diffs: (0.09,4.82) iter 26: time=409.05 for 18 active reps approxLL diffs: (0.07,4.61) iter 27: time=410.06 for 18 active reps approxLL diffs: (0.06,4.51) iter 28: time=407.12 for 18 active reps approxLL diffs: (0.05,2.99) iter 29: time=409.77 for 18 active reps approxLL diffs: (0.04,2.09) iter 30: time=406.89 for 18 active reps approxLL diffs: (0.04,2.45) iter 31: time=406.84 for 18 active reps approxLL diffs: (0.03,2.00) iter 32: time=406.84 for 18 active reps approxLL diffs: (0.03,3.07) iter 33: time=408.32 for 18 active reps approxLL diffs: (0.02,2.16) iter 34: time=415.27 for 18 active reps approxLL diffs: (0.02,1.36) iter 35: time=409.69 for 18 active reps approxLL diffs: (0.02,1.35) iter 36: time=408.84 for 18 active reps approxLL diffs: (0.01,1.21) iter 37: time=408.95 for 18 active reps approxLL diffs: (0.01,1.07) iter 38: time=410.86 for 18 active reps approxLL diffs: (0.01,1.46) iter 39: time=395.01 for 17 active reps approxLL diffs: (0.02,0.48) iter 40: time=396.10 for 17 active reps approxLL diffs: (0.02,0.93) iter 41: time=396.60 for 17 active reps approxLL diffs: (0.01,2.18) iter 42: time=395.29 for 17 active reps approxLL diffs: (0.01,1.22) iter 43: time=389.86 for 17 active reps approxLL diffs: (0.01,1.03) iter 44: time=395.67 for 17 active reps approxLL diffs: (0.01,0.76) iter 45: time=388.33 for 15 active reps approxLL diffs: (0.01,0.39) iter 46: time=388.44 for 15 active reps approxLL diffs: (0.01,0.96) iter 47: time=346.93 for 13 active reps approxLL diffs: (0.01,4.14) iter 48: time=347.25 for 13 active reps approxLL diffs: (0.00,2.72) iter 49: time=341.36 for 11 active reps approxLL diffs: (0.01,1.08) iter 50: time=316.48 for 10 active reps approxLL diffs: (0.01,0.68) iter 51: time=316.12 for 10 active reps approxLL diffs: (0.01,1.21) iter 52: time=258.17 for 8 active reps approxLL diffs: (0.01,0.77) iter 53: time=282.28 for 7 active reps approxLL diffs: (0.02,0.98) iter 54: time=282.62 for 7 active reps approxLL diffs: (0.01,2.19) iter 55: time=261.82 for 6 active reps approxLL diffs: (0.03,3.23) iter 56: time=260.54 for 6 active reps approxLL diffs: (0.01,0.54) iter 57: time=261.86 for 6 active reps approxLL diffs: (0.00,0.70) iter 58: time=223.09 for 4 active reps approxLL diffs: (0.02,2.30) iter 59: time=222.30 for 4 active reps approxLL diffs: (0.02,2.00) iter 60: time=221.93 for 4 active reps approxLL diffs: (0.02,0.80) iter 61: time=222.81 for 4 active reps approxLL diffs: (0.02,1.03) iter 62: time=223.19 for 4 active reps approxLL diffs: (0.02,0.95) iter 63: time=221.91 for 4 active reps approxLL diffs: (0.01,0.50) iter 64: time=232.52 for 3 active reps approxLL diffs: (0.06,0.18) iter 65: time=231.85 for 3 active reps approxLL diffs: (0.02,0.08) iter 66: time=232.44 for 3 active reps approxLL diffs: (0.01,0.05) iter 67: time=213.50 for 2 active reps approxLL diffs: (0.01,0.05) iter 68: time=212.55 for 2 active reps approxLL diffs: (0.00,0.06) iter 69: time=183.92 for 1 active reps approxLL diffs: (0.05,0.05) iter 70: time=185.51 for 1 active reps approxLL diffs: (0.05,0.05) iter 71: time=185.26 for 1 active reps approxLL diffs: (0.03,0.03) iter 72: time=184.23 for 1 active reps approxLL diffs: (0.02,0.02) iter 73: time=184.95 for 1 active reps approxLL diffs: (0.02,0.02) iter 74: time=184.64 for 1 active reps approxLL diffs: (0.02,0.02) iter 75: time=185.76 for 1 active reps approxLL diffs: (0.03,0.03) iter 76: time=186.29 for 1 active reps approxLL diffs: (0.06,0.06) iter 77: time=187.56 for 1 active reps approxLL diffs: (0.13,0.13) iter 78: time=187.92 for 1 active reps approxLL diffs: (0.24,0.24) iter 79: time=187.78 for 1 active reps approxLL diffs: (0.26,0.26) iter 80: time=187.45 for 1 active reps approxLL diffs: (0.17,0.17) iter 81: time=187.86 for 1 active reps approxLL diffs: (0.07,0.07) iter 82: time=188.39 for 1 active reps approxLL diffs: (0.04,0.04) iter 83: time=186.88 for 1 active reps approxLL diffs: (0.06,0.06) iter 84: time=207.78 for 1 active reps approxLL diffs: (0.16,0.16) iter 85: time=208.75 for 1 active reps approxLL diffs: (0.46,0.46) iter 86: time=210.37 for 1 active reps approxLL diffs: (0.77,0.77) iter 87: time=215.45 for 1 active reps approxLL diffs: (1.28,1.28) iter 88: time=216.03 for 1 active reps approxLL diffs: (2.26,2.26) iter 89: time=221.18 for 1 active reps approxLL diffs: (0.46,0.46) iter 90: time=224.84 for 1 active reps approxLL diffs: (0.03,0.03) iter 91: time=232.74 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 91: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 77.0%, memory/overhead = 23.0% Computing predictions on left-out cross-validation fold Time for computing predictions = 9085.59 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.02: 0.199494 f2=0.3, p=0.01: 0.198989 f2=0.5, p=0.01: 0.198039 ... f2=0.5, p=0.5: 0.127569 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.991145 Absolute prediction MSE using standard LMM: 0.864706 Absolute prediction MSE, fold-best f2=0.3, p=0.02: 0.793418 Absolute pred MSE using f2=0.5, p=0.5: 0.864706 Absolute pred MSE using f2=0.5, p=0.2: 0.839557 Absolute pred MSE using f2=0.5, p=0.1: 0.819303 Absolute pred MSE using f2=0.5, p=0.05: 0.804096 Absolute pred MSE using f2=0.5, p=0.02: 0.795732 Absolute pred MSE using f2=0.5, p=0.01: 0.794860 Absolute pred MSE using f2=0.3, p=0.5: 0.855805 Absolute pred MSE using f2=0.3, p=0.2: 0.825650 Absolute pred MSE using f2=0.3, p=0.1: 0.807008 Absolute pred MSE using f2=0.3, p=0.05: 0.796689 Absolute pred MSE using f2=0.3, p=0.02: 0.793418 Absolute pred MSE using f2=0.3, p=0.01: 0.793918 Absolute pred MSE using f2=0.1, p=0.5: 0.844976 Absolute pred MSE using f2=0.1, p=0.2: 0.814705 Absolute pred MSE using f2=0.1, p=0.1: 0.800034 Absolute pred MSE using f2=0.1, p=0.05: 0.796067 Absolute pred MSE using f2=0.1, p=0.02: 0.797404 Absolute pred MSE using f2=0.1, p=0.01: 0.799621 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.128 Relative improvement in prediction MSE using non-inf model: 0.082 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.02 Time for estimating mixture parameters = 44143 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=737.93 for 23 active reps iter 2: time=512.77 for 23 active reps approxLL diffs: (18134.29,19568.98) iter 3: time=511.71 for 23 active reps approxLL diffs: (3540.59,3873.21) iter 4: time=512.12 for 23 active reps approxLL diffs: (1156.11,1296.41) iter 5: time=513.08 for 23 active reps approxLL diffs: (521.37,593.08) iter 6: time=512.71 for 23 active reps approxLL diffs: (284.55,324.23) iter 7: time=514.48 for 23 active reps approxLL diffs: (175.73,211.88) iter 8: time=515.20 for 23 active reps approxLL diffs: (113.84,143.54) iter 9: time=513.79 for 23 active reps approxLL diffs: (70.03,88.30) iter 10: time=512.83 for 23 active reps approxLL diffs: (40.43,59.14) iter 11: time=513.05 for 23 active reps approxLL diffs: (24.71,39.75) iter 12: time=514.82 for 23 active reps approxLL diffs: (18.49,27.52) iter 13: time=514.27 for 23 active reps approxLL diffs: (11.63,19.68) iter 14: time=513.40 for 23 active reps approxLL diffs: (9.28,17.71) iter 15: time=514.13 for 23 active reps approxLL diffs: (5.41,14.78) iter 16: time=513.55 for 23 active reps approxLL diffs: (4.56,11.18) iter 17: time=512.43 for 23 active reps approxLL diffs: (4.15,10.82) iter 18: time=513.34 for 23 active reps approxLL diffs: (2.54,9.24) iter 19: time=513.08 for 23 active reps approxLL diffs: (1.82,8.46) iter 20: time=515.50 for 23 active reps approxLL diffs: (1.14,7.14) iter 21: time=514.55 for 23 active reps approxLL diffs: (0.99,6.28) iter 22: time=512.75 for 23 active reps approxLL diffs: (1.14,7.36) iter 23: time=513.41 for 23 active reps approxLL diffs: (1.03,4.52) iter 24: time=511.66 for 23 active reps approxLL diffs: (1.00,3.74) iter 25: time=511.96 for 23 active reps approxLL diffs: (1.13,4.55) iter 26: time=512.20 for 23 active reps approxLL diffs: (0.67,4.64) iter 27: time=514.79 for 23 active reps approxLL diffs: (0.55,5.13) iter 28: time=512.41 for 23 active reps approxLL diffs: (0.47,3.63) iter 29: time=514.48 for 23 active reps approxLL diffs: (0.39,4.89) iter 30: time=513.57 for 23 active reps approxLL diffs: (0.30,5.30) iter 31: time=512.50 for 23 active reps approxLL diffs: (0.13,3.19) iter 32: time=514.42 for 23 active reps approxLL diffs: (0.09,3.01) iter 33: time=514.57 for 23 active reps approxLL diffs: (0.10,3.72) iter 34: time=513.93 for 23 active reps approxLL diffs: (0.09,3.64) iter 35: time=517.63 for 23 active reps approxLL diffs: (0.12,3.43) iter 36: time=487.66 for 23 active reps approxLL diffs: (0.17,4.48) iter 37: time=478.45 for 23 active reps approxLL diffs: (0.18,3.25) iter 38: time=477.81 for 23 active reps approxLL diffs: (0.19,2.78) iter 39: time=476.79 for 23 active reps approxLL diffs: (0.14,2.48) iter 40: time=476.53 for 23 active reps approxLL diffs: (0.12,2.88) iter 41: time=477.93 for 23 active reps approxLL diffs: (0.07,3.33) iter 42: time=477.54 for 23 active reps approxLL diffs: (0.04,2.53) iter 43: time=476.78 for 23 active reps approxLL diffs: (0.04,1.50) iter 44: time=477.49 for 23 active reps approxLL diffs: (0.01,2.15) iter 45: time=478.01 for 23 active reps approxLL diffs: (0.01,2.82) iter 46: time=459.22 for 22 active reps approxLL diffs: (0.01,2.38) iter 47: time=439.63 for 21 active reps approxLL diffs: (0.01,1.02) iter 48: time=437.00 for 21 active reps approxLL diffs: (0.01,1.53) iter 49: time=445.28 for 19 active reps approxLL diffs: (0.01,0.53) iter 50: time=422.04 for 18 active reps approxLL diffs: (0.01,0.91) iter 51: time=421.58 for 18 active reps approxLL diffs: (0.02,0.81) iter 52: time=422.39 for 18 active reps approxLL diffs: (0.02,0.81) iter 53: time=424.19 for 18 active reps approxLL diffs: (0.02,0.73) iter 54: time=421.76 for 18 active reps approxLL diffs: (0.01,1.20) iter 55: time=404.03 for 17 active reps approxLL diffs: (0.01,1.63) iter 56: time=380.87 for 16 active reps approxLL diffs: (0.01,2.44) iter 57: time=379.10 for 16 active reps approxLL diffs: (0.01,1.76) iter 58: time=393.25 for 15 active reps approxLL diffs: (0.01,2.94) iter 59: time=391.24 for 15 active reps approxLL diffs: (0.01,1.84) iter 60: time=393.77 for 15 active reps approxLL diffs: (0.01,0.79) iter 61: time=370.09 for 14 active reps approxLL diffs: (0.01,1.18) iter 62: time=354.30 for 13 active reps approxLL diffs: (0.01,1.96) iter 63: time=336.21 for 12 active reps approxLL diffs: (0.01,1.37) iter 64: time=337.84 for 12 active reps approxLL diffs: (0.01,1.90) iter 65: time=324.65 for 10 active reps approxLL diffs: (0.02,1.00) iter 66: time=324.73 for 10 active reps approxLL diffs: (0.01,0.48) iter 67: time=324.73 for 10 active reps approxLL diffs: (0.01,0.48) iter 68: time=325.86 for 10 active reps approxLL diffs: (0.01,0.49) iter 69: time=324.71 for 10 active reps approxLL diffs: (0.01,0.43) iter 70: time=305.58 for 9 active reps approxLL diffs: (0.01,0.30) iter 71: time=268.38 for 8 active reps approxLL diffs: (0.02,0.78) iter 72: time=266.58 for 8 active reps approxLL diffs: (0.00,0.82) iter 73: time=290.66 for 7 active reps approxLL diffs: (0.00,0.48) iter 74: time=252.07 for 5 active reps approxLL diffs: (0.02,0.22) iter 75: time=251.30 for 5 active reps approxLL diffs: (0.01,0.09) iter 76: time=233.03 for 4 active reps approxLL diffs: (0.01,0.12) iter 77: time=234.06 for 4 active reps approxLL diffs: (0.01,0.12) iter 78: time=242.55 for 3 active reps approxLL diffs: (0.02,0.06) iter 79: time=242.46 for 3 active reps approxLL diffs: (0.01,0.02) iter 80: time=242.57 for 3 active reps approxLL diffs: (0.00,0.02) iter 81: time=194.97 for 1 active reps approxLL diffs: (0.03,0.03) iter 82: time=196.14 for 1 active reps approxLL diffs: (0.04,0.04) iter 83: time=196.98 for 1 active reps approxLL diffs: (0.06,0.06) iter 84: time=195.98 for 1 active reps approxLL diffs: (0.08,0.08) iter 85: time=195.54 for 1 active reps approxLL diffs: (0.06,0.06) iter 86: time=194.64 for 1 active reps approxLL diffs: (0.03,0.03) iter 87: time=195.30 for 1 active reps approxLL diffs: (0.01,0.01) iter 88: time=195.31 for 1 active reps approxLL diffs: (0.00,0.00) Converged at iter 88: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 79.7%, memory/overhead = 20.3% 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 > 442.6) # of SNPs remaining after outlier window removal: 571058/579570 Intercept of LD Score regression for ref stats: 1.203 (0.023) Estimated attenuation: 0.191 (0.027) Intercept of LD Score regression for cur stats: 1.208 (0.024) Calibration factor (ref/cur) to multiply by: 0.995 (0.002) Time for computing Bayesian mixed model assoc stats = 37076.8 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=431.49 for 1 active reps iter 2: time=199.64 for 1 active reps approxLL diffs: (19634.05,19634.05) iter 3: time=200.93 for 1 active reps approxLL diffs: (3866.80,3866.80) iter 4: time=199.94 for 1 active reps approxLL diffs: (1288.79,1288.79) iter 5: time=199.16 for 1 active reps approxLL diffs: (590.26,590.26) iter 6: time=199.45 for 1 active reps approxLL diffs: (314.10,314.10) iter 7: time=199.99 for 1 active reps approxLL diffs: (195.01,195.01) iter 8: time=199.56 for 1 active reps approxLL diffs: (125.46,125.46) iter 9: time=199.72 for 1 active reps approxLL diffs: (84.62,84.62) iter 10: time=199.72 for 1 active reps approxLL diffs: (55.50,55.50) iter 11: time=199.73 for 1 active reps approxLL diffs: (32.78,32.78) iter 12: time=199.93 for 1 active reps approxLL diffs: (19.13,19.13) iter 13: time=200.01 for 1 active reps approxLL diffs: (13.13,13.13) iter 14: time=199.81 for 1 active reps approxLL diffs: (10.28,10.28) iter 15: time=199.57 for 1 active reps approxLL diffs: (9.55,9.55) iter 16: time=199.80 for 1 active reps approxLL diffs: (9.57,9.57) iter 17: time=199.91 for 1 active reps approxLL diffs: (5.02,5.02) iter 18: time=198.69 for 1 active reps approxLL diffs: (3.60,3.60) iter 19: time=199.85 for 1 active reps approxLL diffs: (4.69,4.69) iter 20: time=200.09 for 1 active reps approxLL diffs: (5.54,5.54) iter 21: time=205.75 for 1 active reps approxLL diffs: (3.89,3.89) iter 22: time=207.32 for 1 active reps approxLL diffs: (2.29,2.29) iter 23: time=206.87 for 1 active reps approxLL diffs: (3.05,3.05) iter 24: time=206.99 for 1 active reps approxLL diffs: (3.73,3.73) iter 25: time=207.45 for 1 active reps approxLL diffs: (4.81,4.81) iter 26: time=207.21 for 1 active reps approxLL diffs: (6.36,6.36) iter 27: time=207.70 for 1 active reps approxLL diffs: (4.39,4.39) iter 28: time=207.60 for 1 active reps approxLL diffs: (2.64,2.64) iter 29: time=207.51 for 1 active reps approxLL diffs: (1.94,1.94) iter 30: time=207.72 for 1 active reps approxLL diffs: (1.71,1.71) iter 31: time=207.56 for 1 active reps approxLL diffs: (1.44,1.44) iter 32: time=207.09 for 1 active reps approxLL diffs: (0.97,0.97) iter 33: time=207.18 for 1 active reps approxLL diffs: (0.53,0.53) iter 34: time=207.27 for 1 active reps approxLL diffs: (0.28,0.28) iter 35: time=207.19 for 1 active reps approxLL diffs: (0.17,0.17) iter 36: time=207.07 for 1 active reps approxLL diffs: (0.12,0.12) iter 37: time=207.17 for 1 active reps approxLL diffs: (0.10,0.10) iter 38: time=207.40 for 1 active reps approxLL diffs: (0.08,0.08) iter 39: time=206.19 for 1 active reps approxLL diffs: (0.10,0.10) iter 40: time=206.20 for 1 active reps approxLL diffs: (0.14,0.14) iter 41: time=206.97 for 1 active reps approxLL diffs: (0.18,0.18) iter 42: time=207.03 for 1 active reps approxLL diffs: (0.17,0.17) iter 43: time=208.08 for 1 active reps approxLL diffs: (0.11,0.11) iter 44: time=207.67 for 1 active reps approxLL diffs: (0.10,0.10) iter 45: time=207.97 for 1 active reps approxLL diffs: (0.13,0.13) iter 46: time=207.85 for 1 active reps approxLL diffs: (0.23,0.23) iter 47: time=207.46 for 1 active reps approxLL diffs: (0.43,0.43) iter 48: time=207.19 for 1 active reps approxLL diffs: (0.64,0.64) iter 49: time=206.81 for 1 active reps approxLL diffs: (0.95,0.95) iter 50: time=206.50 for 1 active reps approxLL diffs: (0.62,0.62) iter 51: time=207.65 for 1 active reps approxLL diffs: (0.22,0.22) iter 52: time=207.32 for 1 active reps approxLL diffs: (0.07,0.07) iter 53: time=207.40 for 1 active reps approxLL diffs: (0.02,0.02) iter 54: time=207.53 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 54: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 58.1%, memory/overhead = 41.9% Time for computing and writing betas = 11277.9 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.17747 (705849 good SNPs) lambdaGC: 1.41919 Mean BOLT_LMM_INF: 2.33524 (705849 good SNPs) lambdaGC: 1.4483 Mean BOLT_LMM: 2.41487 (705849 good SNPs) lambdaGC: 1.45657 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 6959.94 sec Total elapsed time for analysis = 139226 sec